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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Zhao, Xue | Li, Qiaoyan | Xing, Zhiwei | Dai, Xuezhen
Article Type: Research Article
Abstract: Traditional multi-label feature selection is usually performed under the condition of given label information, but nowadays, labeling multi-label data is a huge project, which is both time-consuming and labor-intensive, but if there is no label information condition, it will lead to poor feature selection, in order to solve this problem, this paper proposes a new semi-supervised multi-label feature selection method, i.e., semi-supervised multi-label feature selection algorithm based on dual dynamic graph. In this paper, a semi-supervised multi-label feature selection algorithm is proposed by constructing a dual dynamic graph. First, the method selects the most discriminative features for dimensionality reduction through …the feature selection method of least squares regression, combined with the redundancy penalty of highly correlated features. Second, the label information is added to the construction of sample matrix similarity to learn the similarity. A semi-supervised multi-label feature selection framework is constructed by designing iterative updates of dual dynamic graphs to learn more accurate pseudo-label matrices to guide feature selection. Finally, the paper validates the above model using the alternating iteration optimization algorithm and verifies the effectiveness of the algorithm through experiments. Show more
Keywords: Multi-label learning, semi-supervised, feature selection, dual dynamic graph, redundant regular terms
DOI: 10.3233/JIFS-237146
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Shrivastava, Ankit | Poonkuntran, S.
Article Type: Research Article
Abstract: Ensuring real-time performance while leveraging pedestrian detection is a crucial prerequisite for intelligent driving technology. The development of lightweight models with good detection accuracy is also crucial. This work proposes a novel method, the Attention Digital Filter with Anchor-Free Feature Pyramid Learning Model (ADFAFPLM), to meet these needs. The suggested method consists of combining two networks: one is a digital filter based on an attention network that eliminates noise and other picture distortions. The attention-based residual network digital filters are chosen for their enhanced filtering performance, adaptability, efficient learning through residual connections, noise suppression, interpretability, and generalization capabilities. Next, from …the input crowded and occluded photos, the pedestrian is identified using an anchor-free feature pyramid network. The Eurocity person dataset was used to train the model, and it was also tested on other datasets like CityPersons, INRIA, PennFudan, and Eurocity. The investigation was expanded to include images in hazy, noisy, and occlusion environments, among other environmental conditions. The image resolutions were also considered for analysis and it was observed that with increasing image resolution, the mAP increases. Based on the ablation study, the ADF-AFPLM adopted YOLOv8n with batch size 16, and image size 640 is considered for efficient result with different testing datasets. The model achieved a mean average precision (mAP) of approx. 87% and shows its efficacy over state-of-art models. Show more
Keywords: Object detection, pedestrian, deep learning, feature pyramid network, YOLO
DOI: 10.3233/JIFS-237639
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Wang, Yu-Lin | Wang, Jin-Heng
Article Type: Research Article
Abstract: Virtual machine (VM) consolidation and migration that only consider current workload can result in excessive unnecessary migrations. To address this issue, a VM consolidation algorithm based on resource utilization prediction is proposed. An improved K-nearest neighbor (KNN) classification algorithm weighted by attribute inconsistency is proposed to predict the workload of both the host and the VMs. Firstly, two distributions are partitioned according to the neighboring relationship for comparing consistency. Then, an inconsistency evaluation function based on earth mover’s distance (EMD) is designed to measure the inconsistency between the neighboring sample set of each sample under each attribute and the equivalent …partition refined by the decision attribute. Finally, the inconsistency level of the neighboring samples is transformed into the importance of the corresponding attribute to implement the attribute weighting KNN classifier. When selecting the source host and target host for VM migration, both current and predicted overloads are considered to avoid unnecessary VM migrations. Simulation tests were performed with random and realistic workloads, and the results show that the proposed method can reduce the overall energy consumption of the host, while also reducing service level agreement (SLA) violations and VM migration. Show more
Keywords: Cloud computing, virtual machine consolidation, improved K-nearest neighbor regression, earth mover’s distance, attribute weighting
DOI: 10.3233/JIFS-239851
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Sivaranjani, N. | Senthil Ragavan, V.K. | Jawaherlalnehru, G.
Article Type: Research Article
Abstract: Industry experts are motivated to collect, collate, and analyse historical data in the legal sector in attempt to predict court case outcomes as the amount of historical data available in this field has increased over time. But using judicial data to predict and defend court judgements is no simple undertaking. Using Machine Learning (ML) models and traditional approaches for categorical feature encoding, previous research on predicting court outcomes using limited experimental datasets produced a number of unexpected predictions. The paper proposes an ensemble model combining Convolutional Neural Network (CNN), attention mechanism and eXtreme Gradient Boosting (XGB) algorithm. This model is …primarily based on a self-attention network, which could simultaneously capture linguistic relationships over lengthy sequences like RNN (Recurrent Neural Network) and is nevertheless speedy to train like CNN. C-XGB can obtain accuracy that surpasses the state-of-art model on numerous classification/prediction tasks simultaneously as being twice as speedy to train. The proposed C-XGB model is designed to process the documents hierarchically and calculates the attention weights. Two convolutional layers are used to calculate the attention weights, one at the word level and another at the sentence level. And finally, at the last layer, the XGB algorithm predicts the input case file’s outcome. The experimental results shows that the proposed model outperforms the existing model with 4.67% improvement in accuracy value. Show more
Keywords: Neural Networks, machine learning, legal judgment prediction, Indian Supreme Court
DOI: 10.3233/JIFS-235936
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wang, Qian
Article Type: Research Article
Abstract: Neuroimaging technology is considered a non-invasive method research the structure and function of the brain which have been widely used in neuroscience, psychiatry, psychology, and other fields. The development of Deep Learning Neural Network (DLNN), based on the deep learning algorithms of neural imaging techniques in brain disease diagnosis plays a more and more important role. In this paper, a deep neural network imaging technology based on Stack Auto-Encoder (SAE) feature extraction is constructed, and then Support Vector Machine (SVM) was used to solve binary classification problems (Alzheimer’s disease [AD] and Mild Cognitive Impairment [MCI]). Four sets of experimental data …were employed to perform the training and testing stages of DLNN. The number of neurons in each of the DLNNs was determined using the grid search technique. Overall, the results of DLNNs performance indicated that the SAE feature extraction was superior over (Accuracy Rate [AR] = 74.9% with structure of 93-171-49-22-93) shallow layer features extraction (AR = 70.8% with structure of 93-22-93) and primary features extraction (AR = 69.2%). Show more
Keywords: Deep learning neural network, neuroimaging technology, brain diseases, disease diagnosis, feature extraction
DOI: 10.3233/JIFS-237979
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Nagamani, T. | Logeswari, S.
Article Type: Research Article
Abstract: A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations …for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19% . This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy. Show more
Keywords: Adam optimization, coronary artery disease, grid search, one hot encoding, random search, XGBoost
DOI: 10.3233/JIFS-233804
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Yang, Xingyao | Chang, Mengxue | Yu, Jiong | Wang, Dongxiao | Dang, Zibo
Article Type: Research Article
Abstract: Social recommendations enhance the quality of recommendations by integrating social network information. Existing methods predominantly rely on pairwise relationships to uncover potential user preferences. However, they usually overlook the exploration of higher-order user relations. Moreover, because social relation graphs often exhibit scale-free graph structures, directly embedding them in Euclidean space will lead to significant distortion. To this end, we propose a novel graph neural network framework with hypergraph and hyperbolic embedding learning, namely HMGCN. Specifically, we first construct hypergraphs over user-item interactions and social networks, and then perform graph convolution on the hypergraphs. At the same time, a multi-channel setting …is employed in the convolutional network, with each channel encoding its corresponding hypergraph to capture different high-order user relation patterns. In addition, we feed the item embeddings and the obtained high-order user embeddings into a hyperbolic graph convolutional network to extract user and item representations, enabling the model to better capture the hierarchical structure of their complex relationships. Experimental results on three public datasets, namely FilmTrust, LastFM, and Yelp, demonstrate that the model achieves more comprehensive user and item representations, more accurate fitting and processing of graph data, and effectively addresses the issues of insufficient user relationship extraction and data embedding distortion in social recommendation models. Show more
Keywords: Social recommendation, hypergraph learning, hyperbolic embedding, graph convolutional network, data mining
DOI: 10.3233/JIFS-235266
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Peng, Jun long | Liu, Xiao
Article Type: Research Article
Abstract: This study explores the impact of public health events, multi-modal projects, multi-project environments, and multi-capacity resource constraints on project scheduling. It describes the comprehensive resource-constrained project scheduling problem (MCMRCMPSP) specifically for public health events, and proposes two approaches for modelling and solving the problem. The objective is to enhance the practical relevance of project scheduling and enrich the problem itself. To improve efficiency and the algorithm for scheduling problems, an enhanced quantum algorithm based on the quantum particle swarm algorithm (QPSO) is proposed. The enhancements include Gaussian variation and a tournament selection strategy. Furthermore, the article integrates multiple heuristic rules …with the algorithm to minimize illogical computations, improve computational efficiency, and enhance solution quality. The proposed algorithm’s effectiveness is validated through performance tests and practical application experiments. The results show that the algorithm has superior convergence performance and solution accuracy compared with the traditional QPSO, particle swarm algorithm (PSO), genetic algorithm, ant colony algorithm, and cuckoo algorithm. Thus, the algorithm provides a targeted resource scheduling plan for real-world cases. This research contributes to the field of project scheduling problems and proposes a new solution. Show more
Keywords: Public health events, improved quantum algorithm, multi-mode, multi-project, multi-capability resource-constrained project scheduling
DOI: 10.3233/JIFS-236757
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Li, Zhigang | Nian, Wenhao | Sun, Xiaochuan | Li, Shujie
Article Type: Research Article
Abstract: Military object military object detection technology serves as the foundation and critical component for reconnaissance and command decision-making, playing a significant role in information-based and intelligent warfare. However, many existing military object detection models focus on exploring deeper and more complex architectures, which results in models with a large number of parameters. This makes them unsuitable for inference on mobile or resource-constrained combat equipment, such as combat helmets and reconnaissance Unmanned Aerial Vehicles (UAVs). To tackle this problem, this paper proposes a lightweight detection framework. A CSP-GhostnetV2 module is proposed in our method to make the feature extraction network more …lightweight while extracting more effective information. Furthermore, to fuse multiscale information in low-computational scenarios, GSConv and the proposed CSP-RepGhost are used to form a lightweight feature aggregation network. The experimental results demonstrate that our proposed lightweight model has significant advantages in detection accuracy and efficiency compared to other detection algorithms. Show more
Keywords: Deep learning, convolutional neural network, lightweight network, military object detection
DOI: 10.3233/JIFS-234127
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Chang, Chih-Yung | Yang, Yu-Ting | Zhang, Qiaoyun | Lin, Yi-Ti | Roy, Diptendu Sinha
Article Type: Research Article
Abstract: With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent Citation Recommendation Mechanism), designed to automate the process of suggesting the appropriate number of citations for individual brackets within a document. The proposed ICRM comprises three phases: Coarse-grained Weighted Bag of Word (WCBW), Fine-grained SciBERT (FSB) and Citation Adjustment phases. Firstly, the WCBW phase employs TF-IDF to extract keywords from both target and candidate documents, forming vectors that capture word significance along with metadata like authorship, keywords, and titles. It aims to …identify relevant papers from a database, serving as initial candidates for each bracket. Secondly, the FSB phase employs the SciBERT model to assess the similarity between candidate documents and the local context around brackets, enhancing the precision of recommendations. It refines this selection by analyzing candidate-document relationships within the proximity of the brackets. Lastly, the Citation Adjustment phase tackles overlapping citations and ensures that recommended citation numbers align with user-defined criteria, resolving issues of imbalance. The simulation results demonstrate that the proposed ICRM outperforms existing models significantly in terms of precision, recall and F1-score. Show more
Keywords: Citation recommendation, TF-IDF, weighted bag of word, BERT
DOI: 10.3233/JIFS-237975
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Ping, Yang
Article Type: Research Article
Abstract: This study delves into a novel approach for energy conservation and environmental pollution reduction through modern environmental art design, guided by the ecological civilization concept and powered by artificial intelligence (AI) technology. The environmental art framework, aligning with the ecological civilization paradigm, is intricately designed. The data acquisition layer employs diverse sensors to gather equipment status, environmental, and pollution data, transmitting it to the executive controller layer via internal WIFI connectivity. The collected data undergoes meticulous analysis and processing within the data layer before reaching the actuator control layer. Leveraging support vector machines in artificial intelligence, the executive controller layer …amalgamates the analyzed equipment and environmental data to devise energy-saving equipment and environmental pollution control schemes. Real-time visualization of these outcomes is achieved through the display operation layer. Findings affirm the effectiveness of this method in acquiring pertinent data for modern environmental art design and managing equipment states. Implementation of this approach successfully diminishes power consumption, dust concentration, and formaldehyde levels in the modern environmental art design zone, showcasing its prowess in energy conservation and pollution control. The integration of AI within the ecological civilization framework highlights its potential in fostering sustainable and environmentally conscious practices in modern art creation. Show more
Keywords: Artificial intelligence technology, ecological civilization concept, modern environmental art, support vector machine, energy saving control, environmental pollution
DOI: 10.3233/JIFS-239687
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Qiao, Gongzhe | Zhuang, Yi | Ye, Tong | Qiao, Yuan
Article Type: Research Article
Abstract: The intelligent network information systems, such as smart grid systems, face many security problems in the aspects of sensing, communication and computing. Information security risk assessment is an important way to assess the threats faced by information systems before risk events occur and ensure the security of assets. However, a comprehensive risk assessment of the system is a very resource-consuming process. Many existing risk assessment methods rely on a large number of experts and computing resources. Their assessment results are vulnerable to the differences in experts’ subjective judgments. Therefore, we propose FRAMB, a novel man-machine collaborative risk assessment method based …on fitting upper and lower bounds. Firstly, we present a risk assessment criterion including four categories and sixteen risk factors following the ISO/IEC 27005:2018 standard. On this basis, we present the DFAHP and CM-NN assessment models to obtain the upper and lower bounds of the risk assessment value, which provides a reference for expert assessment. FRAMB integrates the experts’ assessment value and the values of upper and lower bounds, and adjusts the weights of these values to give the final risk assessment value. We introduce the risk assessment process of FRAMB in detail through a case study of the smart grid system risk assessment. We evaluate the effectiveness and accuracy of FRAMB through experiments. The experimental results show that FRAMB can effectively and accurately assess the security risks of the intelligent network information systems. Show more
Keywords: Risk assessment, information systems, neural network, analytic hierarchy process, expert evaluation
DOI: 10.3233/JIFS-231880
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Zong, Xinlu | Li, Hejing | Liu, Aiping | Xu, Hui
Article Type: Research Article
Abstract: Emotion is a crucial factor which influences evacuation effects. However, the studies and quantitative analysis of evacuation emotions, including the emotion generated by external factors and internal personality or cognition levels, emotional contagion evolution, and the regulation mechanism of pedestrians to negative emotion, are still rare. In this paper, an evacuation model based on emotional cognition and contagion (EMECC) is presented. Firstly, individual’s emotion is generated and quantified based on Lazarus’s cognitive theory. Secondly, the emotional contagion between individuals is simulated by SIS (Susceptible Infected Susceptible) infectious disease model. Combining with cellular automata model, an emotion-driven moving rule is proposed …to guide pedestrians move towards the directions with more positive individuals so that positive emotions can be spread effectively. Various experiments on model parameters, obstacles, and emotional contagion process are implemented to verify the effectiveness of the EMECC model. The simulation and experimental results show that emotional regulation mechanism can improve pedestrian’s decision-making ability and contagion of positive emotion can accelerate evacuation process. The EMECC model can simulate emotional changes dynamically and guide pedestrians efficiently and reasonably in emergency evacuation. Show more
Keywords: Emergency evacuation, crowd simulation, emotion, emotional contagion
DOI: 10.3233/JIFS-237147
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Faheem Nikhat, H. | Sait, Saad Yunus
Article Type: Research Article
Abstract: To ensure a safe and pleasant user experience while watching content on YouTube, it is necessary to identify and classify inappropriate content, especially content that is inappropriate for children. In this work, we have concentrated on establishing an efficient system for detecting inappropriate content on YouTube. Most of the work focuses on manual pre-processing; however, it takes too much time, requires manpower support, and is not ideal for solving real-time problems. To address this challenge, we have proposed an automatic preprocessing scheme for selecting appropriate frames and removing unwanted frames such as noise and duplicate frames. For this purpose, we …have utilized the proposed novel auto-determined k-means (PADK-means) algorithm. Our PADK-means algorithm automatically determines the optimal cluster count instead of manual specifications. By doing this, we have solved the manual cluster count specification problem in the traditional k-means clustering algorithm. On the other hand, to improve the system’s performance, we utilized the Proposed Feature Extraction (PFE) method, which includes two pre-trained models DenseNet121 and Inception V3 are utilized to extract local and global features from the frame. Finally, we employ a proposed double-branch recurrent network (PDBRNN) architecture, which includes bi-LSTM and GRU, to classify the video as appropriate or inappropriate. Our proposed automatic preprocessing mechanism, proposed feature extraction method, and proposed double-branch RNN classifier yielded an impressive accuracy of 97.9% . Show more
Keywords: DenseNet121, inappropriate YouTube content detection, InceptionV3, PADK-means, PFE, PDBRNN
DOI: 10.3233/JIFS-236871
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Mahapatra, Rupkumar | Samanta, Sovan | Pal, Madhumangal
Article Type: Research Article
Abstract: The most critical task of a social network is to identify a central node. Numerous methods for determining centrality are documented in the literature. It contributes to online commerce by disseminating news, advertisements and other content via central nodes. Existing methods capture the node’s direct reachability. This study introduces a novel method for quantifying centrality in a fuzzy environment. This measurement takes into account the reachability of nodes and their direct connections. Several critical properties have been demonstrated. A small Facebook network is used to illustrate the issue. Additionally, appropriate tables and graphs present a comparative study with existing methods …for centrality measurement. Show more
Keywords: Fuzzy graph, social network, centrality measure
DOI: 10.3233/JIFS-232602
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ge, Pengqiang | Chen, Yiyang | Wang, Guina | Weng, Guirong | Chen, Hongtian
Article Type: Research Article
Abstract: Active contour model (ACM) is considered as one of the most frequently employed models in image segmentation due to its effectiveness and efficiency. However, the segmentation results of images with intensity non-uniformity processed by the majority of existing ACMs are possibly inaccurate or even wrong in the forms of edge leakage, long convergence time and poor robustness. In addition, they usually become unstable with the existence of different initial contours and unevenly distributed intensity. To better solve these problems and improve segmentation results, this paper puts forward an ACM approach using adaptive local pre-fitting energy (ALPF) for image segmentation with …intensity non-uniformity. Firstly, the pre-fitting functions generate fitted images inside and outside contour line ahead of iteration, which significantly reduces convergence time of level set function. Next, an adaptive regularization function is designed to normalize the energy range of data-driven term, which improves robustness and stability to different initial contours and intensity non-uniformity. Lastly, an improved length constraint term is utilized to continuously smooth and shorten zero level set, which reduces the chance of edge leakage and filters out irrelevant background noise. In contrast with newly constructed ACMs, ALPF model not only improves segmentation accuracy (Intersection over union(IOU)), but also significantly reduces computation cost (CPU operating time T ), while handling three types of images. Experiments also indicate that it is not only more robust to different initial contours as well as different noise, but also more competent to process images with intensity non-uniformity. Show more
Keywords: Image segmentation, partial derivative, intensity non-uniformity, optimization
DOI: 10.3233/JIFS-237629
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Bin, Chenzhong | Liu, Wenqiang | Ding, Hantao | Wen, Yimin
Article Type: Research Article
Abstract: Existing POI recommendation methods often fail to capture the fine-grained preferences of users and face the challenge of modeling multiple relationships. Moreover, knowledge graph-based recommendation methods are limited in storing dynamic user trajectories, making them unsuitable for POI recommendation scenarios. In this paper, we propose a Multi-View Heterogeneous Knowledge learning model that utilizes techniques for heterogeneous knowledge representation learning and multi-view context modeling. Our model comprehensively models user preferences and the relationships between users and POIs by utilizing information from users’ visiting sequences and POI attributes knowledge graph. Specifically, we design a heterogeneous knowledge embedding method to learn the representation …of users and POIs using POI attribute knowledge and users’ visiting sequences. Additionally, we constructed a user trajectory similarity graph and a POI attribute similarity graph to explore potential relations between users and between POIs. The former measures the similarity of user behaviors based on user visit sequences, and the latter quantifies the similarity between different POIs through a novel feature mapping method. Finally, we propose a multi-view hybrid learning method that combines unsupervised and supervised learning paradigms to model complex relationships, improving the overall recommendation performance. Extensive experiments on real-world datasets validate the effectiveness of our method. Show more
Keywords: POI recommendations, heterogeneous knowledge learning, multi-view learning, multiple context modeling, knowledge graph
DOI: 10.3233/JIFS-232792
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhang, Xiwen | Xiao, Hui
Article Type: Research Article
Abstract: Non-speech emotion recognition involves identifying emotions conveyed through non-verbal vocalizations such as laughter, crying, and other sound signals, which play a crucial role in emotional expression and transmission. This paper employs a nine-category discrete emotion model encompassing happy, sad, angry, peaceful, fearful, loving, hateful, brave, and neutral. A proprietary non-speech dataset comprising 2337 instances was utilized, with 384-dimensional feature vectors extracted. The traditional Backpropagation Neural Network (BPNN) algorithm achieved a recognition rate of 87.7% on the non-speech dataset. In contrast, the proposed Whale Optimization Algorithm - Backpropagation Neural Network (WOA-BPNN) algorithm, applied to a self-made non-speech dataset, demonstrated a remarkable …accuracy of 98.6% . Notably, even without facial emotional cues, non-speech sounds effectively convey dynamic information, and the proposed algorithm excels in their recognition. The study underscores the importance of non-speech emotional signals in communication, especially with the continuous advancement of artificial intelligence technology. The abstract thus encapsulates the paper’s focus on leveraging AI algorithms for high-precision non-speech emotion recognition. Show more
Keywords: Non-speech, emotion recognition, emotion classification, self-made data set, WOA-BPNN
DOI: 10.3233/JIFS-238700
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Li, Junwei | Lian, Mengmeng | Jin, Yong | Xia, Miaomiao | Hou, Huaibin
Article Type: Research Article
Abstract: To address the issue of unknown expert and attribute weights in the comprehensive assessment of hospitals, as well as the potential challenges posed by distance measures, this paper presents a probabilistic language multi-attribute group decision-making (MAGDM) approach that utilizes correlation coefficients and improved entropy. First, the correlation function, called the probabilistic linguistic correlation coefficient, is introduced into the probabilistic linguistic term set(PLTS) to measure the consistency among experts, so as to obtain the weights of experts. Next, based on Shannon entropy, an improved probabilistic linguistic entropy is proposed to measure the uncertainty of PLTS considering the number of alternatives and …information quantity. Then, based on the correlation coefficient and improved entropy, the attribute weights are obtained. In addition, in order to overcome the counter-intuitive problem of existing distance measurement, this paper proposes a probabilistic language distance measurement method based on the Bray-Curtis distance to measure the differences between PLTSs. On this basis, by applying the technique for order preference by similarity to ideal solution (TOPSIS) method and using PLTSs to construct the MAGDM method, the ranking of alternative schemes is generated. Finally, the improved MAGDM method is applied to an example of the comprehensive evaluation of the smart medical hospitals. The results show that compared with the existing methods, this method can determine the weight information more reasonably, and the decision-making results are not counter-intuitive, so it can evaluate the hospital more objectively. Show more
Keywords: Probabilistic linguistic term set (PLTS), multi-attribute group decision-making (MAGDM), expert weights, attribute weights, correlation coefficient
DOI: 10.3233/JIFS-235593
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Sujeeth, T. | Ramesh, C. | Palwe, Sushila | Ramu, Gandikota | Basha, Shaik Johny | Upadhyay, Deepak | Chanthirasekaran, K. | Sivasankari, K. | Rajaram, A.
Article Type: Research Article
Abstract: Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other …machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26% . The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. Show more
Keywords: Solar power generation, forecasting, artificial neural network, machine learning, renewable energy, grid management
DOI: 10.3233/JIFS-235612
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Guan, Hao | Sadati, Seyed Hossein | Talebi, Ali Asghar | Shafi, Jana | Khan, Aysha
Article Type: Research Article
Abstract: A cubic fuzzy graph is a type of fuzzy graph that simultaneously supports two different fuzzy memberships. The study of connectivity in cubic fuzzy graph is an interesting and challenging topic. This research generalized the neighborhood connectivity index in a cubic fuzzy graph with the aim of investigating the connection status of nodes with respect to adjacent vertices. In this survey, the neighborhood connectivity index was introduced in the form of two numerical and distance values. Some characteristics of the neighborhood connectivity index were investigated in cubic fuzzy cycles, saturated cubic fuzzy cycle, complete cubic fuzzy graph and complementary cubic …fuzzy graph. The method of constructing a cubic fuzzy graph with arbitrary neighborhood connectivity index was the other point in this research. The results showed that the neighborhood connectivity index depends on the potential of nodes and the number of neighboring nodes. This research was conducted on the Central Bank’s data regarding inter-bank relations and its results were compared in terms of neighborhood connectivity index. Show more
Keywords: Cubic fuzzy graph, neighborhood connectivity index, saturated cubic fuzzy cycle, complement cubic fuzzy graph
DOI: 10.3233/JIFS-238021
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Wu, Guangli | Yang, Zhijun | Zhang, Jing
Article Type: Research Article
Abstract: Temporal sentence grounding in videos (TSGV), which aims to retrieve video segments from an untrimmed videos that semantically match a given query. Most previous methods focused on learning either local or global query features and then performed cross-modal interaction, but ignore the complementarity between local and global features. In this paper, we propose a novel Multi-Level Interaction Network for Temporal Sentence Grounding in Videos. This network explores the semantics of queries at both phrase and sentence levels, interacting phrase-level features with video features to highlight video segments relevant to the query phrase and sentence-level features with video features to learn …more about global localization information. A stacked fusion gate module is designed, which effectively captures the temporal relationships and semantic information among video segments. This module also introduces a gating mechanism to enable the model to adaptively regulate the fusion degree of video features and query features, further improving the accuracy of predicting the target segments. Extensive experiments on the ActivityNet Captions and Charades-STA benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art methods. Show more
Keywords: Temporal sentence grounding in videos, Multi-level cross-model interactions, Multi-level text representation
DOI: 10.3233/JIFS-234800
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Xie, Mengtong | Chai, Huaqi
Article Type: Research Article
Abstract: A human resources management plan is presently recognised as one of the most important components of a corporate technique. This is due to the fact that its major purpose is to interact with people, who are the most precious asset that an organisation has. It is impossible for an organisation to achieve its objectives without the participation of individuals. An organisation may effectively plan as well as manage individual processes to support the organization’s objectives and adapt nimbly to any change if it has well-prepared HR techniques and an action plan for its execution. This investigation puts up a fresh …way for the board of directors of a private firm to increase their assets and advance their growth by using cloud programming that is characterised by networks. The small company resource has been improved by strengthening human resource management techniques, and the cloud SDN network is used for job scheduling using Q-convolutional reinforcement recurrent learning. The proposed technique attained Quadratic normalized square error of 60%, existing SDN attained 55%, HRM attained 58% for Synthetic dataset; for Human resources dataset propsed technique attained Quadratic normalized square error of 62%, existing SDN attained 56%, HRM attained 59% ; proposed technique attained Quadratic normalized square error of 64%, existing SDN attained 58%, HRM attained 59% for SyriaTel dataset. Show more
Keywords: Small business management, cloud software defined networks, human resource management, task scheduling, recurrent learning
DOI: 10.3233/JIFS-235379
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: He, Liu | Zhu, Yuanguo | Ye, Tingqing
Article Type: Research Article
Abstract: In recent years, uncertain fractional differential equations was proposed for the description of complex uncertain dynamic systems with historical characteristics. For wider applications of uncertain fractional differential equations, researches on parameter estimation for uncertain fractional differential equations are of great importance. In this paper, based on the thought of least squares estimation and uncertain hypothesis test, an algorithm of parameter estimation for uncertain fractional differential equations is discussed. Finally, we consider the application of uncertain fractional differential equations based model to predict the forecasting stock price of three major indexes of U.S. stocks and make a comparison between uncertain fractional …differential equations, uncertain differential equations and stochastic differential equations. Show more
Keywords: Uncertainty theory, Uncertain fractional differential equations, Parameter estimation, Least squares estimation, Uncertain stock price model
DOI: 10.3233/JIFS-237977
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Wang, Yu
Article Type: Research Article
Abstract: Traditional psychological awareness relating to vocal musical instruction often disregards the impact of earlier experiences on music learning could result in a gap in meeting the needs of individual students. Conventional learning techniques of music related to psychological awareness for each individual has been focused on and addressed in this research. Technological upgrades in Fuzzy Logic (FL) and Big Data (BD) related to Artificial Intelligence (AI) are provided as a solution for the existing challenges and provide enhancement in personalized music education. The combined approach of BD-assisted Radial Basis Function is added with the Takagi Sugeno (RBF-TS) inference system, able …to give personalized vocal music instruction recommendations and indulge psychological awareness among students. Applying Mel-Frequency Cepstral Coefficients (MFCC) is beneficial in capturing variant vocal characteristics as a feature extraction technique. The BD-assisted RBF can identify the accuracy of pitch differences and quality of tone, understand choices from students, and stimulate psychological awareness. The uncertainties are addressed by using the TS fuzzy inference system and delivering personalized vocal training depending on different student preference factors. With the use of multimodal data, the proposed RBF-TS approach can establish a fuzzy rule base in accordance with the personalized emotional elements, enhancing self-awareness and psychological well-being. Validation of the proposed approach using an Instruction Resource Utilization Rate (IRUR) gives significant improvements in engaging students, analyzing the pitching accuracy, frequency distribution of vocal music instruction, and loss function called Mean Square Error(MSE). The proposed research algorithm pioneers a novel solution using advanced AI algorithms addressing the research challenges in existing personalized vocal music education. It promises better student outcomes in the field of music education. Show more
Keywords: Big data, Mel-Frequency Cepstral Coefficients, takagi-sugeno inference system, radial basis function, pitch accurateness, vocal music instruction
DOI: 10.3233/JIFS-236248
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Dhivya, S. | Rajeswari, A.
Article Type: Research Article
Abstract: The utilization of the spectrum is optimized through which primary users of modern wireless communication technologies might obtain a higher chance of detection. The research aims to study how the NI-USRP hardware platform can be used to set up greedy cooperative spectrum sensing for cognitive radio networks. Research primarily deals with energy detection and eigenvalue-based detection approaches, both of which are highly recognized for their capacity to sense the spectrum without having prior knowledge of the primary user signals. In the hardware arrangement, there is one transmitter and two cognitive radio receivers. LABVIEW makes it simple to deploy and maximizes …the detection probability across a large sample. Here, it was demonstrated that cooperative spectrum sensing is superior to non-cooperative spectrum sensing, which results in a reduction in the risk of errors occurring during detection. The research discovered that the OR combination rule has a higher detection probability than the AND rule at the same time. The research emphasizes the significance of expanding cooperative spectrum sensing to improve overall detection capabilities. SNRs that are more than 10 dB allow the energy detector to operate, and the eigenvalue detector continues to work when the SNR drops to –9 dB. Show more
Keywords: Cognitive radio, cooperative spectrum sensing, NI-USRP hardware implementation, energy detection, eigenvalue-based detection
DOI: 10.3233/JIFS-239871
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zhang, Yu | Wang, Zilong | Zhu, Yongjian | Li, Jianxin
Article Type: Research Article
Abstract: Point cloud object detection is gradually playing a key role in autonomous driving tasks. To address the issue of insensitivity to sparse objects in point cloud object detection, we have made improvements to the voxel encoding and 3D backbone network of the PVRCNN++. We have introduced adaptive pooling operations during voxel feature encoding to expand the point cloud information within each voxel, followed by the utilization of multi-layer perceptrons to extract richer point cloud features. On the 3D backbone network, we have employed adaptive sparse convolution operations to make the backbone network’s channel count more flexible, allowing it to accommodate …a wider range of input data types. Furthermore, we have integrated Focal Loss to tackle the issue of class imbalance in detection tasks. Experimental results on the public KITTI dataset demonstrate significant improvements over the PVRCNN++, particularly in pedestrian and bicycle detection tasks. Specifically, we have observed 1% increase in detection accuracy for pedestrians and 2.1% improvement for bicycles. Our detection performance also surpasses that of other comparative detection algorithms. Show more
Keywords: 3D point cloud object detection, adaptive pooling, sparse convolution, focal loss
DOI: 10.3233/JIFS-238176
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Adar-Yazar, Elanur | Karatop, Buket | Karatop, Selim Gökcan
Article Type: Research Article
Abstract: Many factors such as population growth, development of industry/technology, and increase in production-consumption disrupt the ecological balance and cause climate change, which is a global problem. Determining the criteria that cause climate change is very important in finding effective solutions to the problem. In the study, the criteria were determined, weighted with a new method, Step-wise Weight Assessment Ratio Analysis (SWARA), and ranked according to their priorities with two-layer fuzzy logic model. The Fuzzy SWARA method allows the evaluation process, which becomes complicated due to the difficulties and factors experienced in decision-making, to be carried out more effectively and realistically. …The risk and effect of climate change in Turkiye were evaluated regionally. However, the developed model also has a wide application area. Research findings revealed that the highest risk/effect of climate change have the Marmara and Central Anatolia regions. The lowest risk region is the Eastern Anatolia. Air pollution, population growth and deforestation have the highest weights. Important suggestions have presented especially for priority criteria. In this way, the factors that should be prioritized in climate change environmental problem solutions have been revealed and will make it easier for researchers and managers to provide more effective management. Show more
Keywords: Climate change, two-layer, fuzzy SWARA, Turkiye, risk
DOI: 10.3233/JIFS-236298
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Huang, Cheng | Hou, Shuyu
Article Type: Research Article
Abstract: To address the issue of target detection in the planar grasping task, a position and attitude estimation method based on YOLO-Pose is proposed. The aim is to detect the three-dimensional position of the spacecraft’s center point and the planar two-dimensional attitude in real time. First, the weight is trained through transfer learning, and the number of key points is optimized by analyzing the shape characteristics of the spacecraft to improve the representation of pose information. Second, the CBAM dual-channel attention mechanism is integrated into the C3 module of the backbone network to improve the accuracy of pose estimation. Furthermore, the …Wing Loss function is used to mitigate the problem of random offset in key points. The incorporation of the bi-directional feature pyramid network (BiFPN) structure into the neck network further improves the accuracy of target detection. The experimental results show that the average accuracy value of the optimized algorithm has increased. The average detection speed can meet the speed and accuracy requirements of the actual capture task and has practical application value. Show more
Keywords: Pose estimation, planar grasp, convolutional neural network, attention mechanism, feature fusion
DOI: 10.3233/JIFS-234351
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Hajiloei, Mehdi | Jahromi, Alireza Fakharzadeh | Zolmani, Somayeh
Article Type: Research Article
Abstract: Density based methods are significant approaches in outlier detection for high dimensional datasets and Local correlation integral (LOCI) is one of the best of them. To extend LOCI for fuzzy datasets, we should employ suitable metrics to measure the distance between two fuzzy numbers. Euclidean distance measure is a classic one in metric learning, but to overcome curse of dimensionality, we apply fractional distance metric too. Then, after introducing the FLOCI outlier detection algorithm for identifying the fuzzy outliers, we study the efficiency of the proposed method by doing some numerical experiments, in which the obtained results were completely successfull. …We also compared the results with Fuzzy versions of Distance based ABOD and SOD methods to prove robustness of this approache. More than the above, one of the main advantages of the new approach is the determination of outlierness factor for each data which is not presented in classical LOCI method. Show more
Keywords: Outlier data, Multi-granularity deviation factor, Triangular fuzzy number, LOCI method, Fractional distance metric
DOI: 10.3233/JIFS-234448
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Parisae, Veeraswamy | Bhavanam, S. Nagakishore
Article Type: Research Article
Abstract: The goal of speech enhancement is to restore clean speech in noisy environments. Acoustic scenarios with low signal-to-noise ratios (SNR) make it quite challenging to extract the target speech from its noise. In the current study, to enhance noisy speech, we propose a feature recalibration based multi-scale convolutional encoder-decoder architecture with squeeze temporal convolutional networks (S-TCN) bottleneck. Each multi-scale convolutional layer in encoder and decoder is followed by time-frequency attention module (TFA). The recalibration based multi-scale 2D convolution layers are used to extract local and contextual information. Additionally, the recalibration network is equipped with a gating mechanism to control the …flow of information among the layers, enabling weighting of the scaled features for noise suppression and speech retention. The fully connected layer (FC) in the bottleneck part of encoder-decoder contains a few neurons, which capture the global information from the multi-scale 2D convolution layer and reduce parameters. A S-TCN, inspired by the popular temporal convolutional neural network (TCNN), is inserted between the encoder and the decoder to model long-term dependencies in speech. The TFA is a highly efficient network component, that operates through two simultaneous attentions, one focused on time frames, and the other on frequency channels. These attentions work together to explicitly exploit positional information to create a two-dimensional attention map to effectively capture the significant time-frequency distribution of speech. Utilizing the common voice dataset, our proposed model consistently enhances results compared to the current benchmarks, as demonstrated by two extensively utilized objective measures PESQ and STOI. The proposed model shows significant improvements, with average PESQ and STOI scores increasing by 45.7% and 23.8% respectively for seen background noises, and by 43.5% and 21.4% for unseen background noises, when compared to the quality of noisy speech. Tests validate that the proposed approach outperforms numerous cutting-edge algorithms. Show more
Keywords: TFA - time-frequency attention, S-TCN - squeeze temporal convolutional networks, MSCL - multi scale convolutional layer, FR - feature recalibration, FRMSC - feature recalibration based multi scale convolution
DOI: 10.3233/JIFS-233312
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Yu, Jiamao | Yu, Ying | Qian, Jin | Han, Xing | Zhu, Feng | Zhu, Zhiliang
Article Type: Research Article
Abstract: Efficient feature representation is the key to improving crowd counting performance. CNN and Transformer are the two commonly used feature extraction frameworks in the field of crowd counting. CNN excels at hierarchically extracting local features to obtain a multi-scale feature representation of the image, but it struggles with capturing global features. Transformer, on the other hand, could capture global feature representation by utilizing cascaded self-attention to capture remote dependency relationships, but it often overlooks local detail information. Therefore, relying solely on CNN or Transformer for crowd counting has certain limitations. In this paper, we propose the TCHNet crowd counting model …by combining the CNN and Transformer frameworks. The model employs the CMT (CNNs Meet Vision Transformers) backbone network as the Feature Extraction Module (FEM) to hierarchically extract local and global features of the crowd using a combination of convolution and self-attention mechanisms. To obtain more comprehensive spatial local information, an improved Progressive Multi-scale Learning Process (PMLP) is introduced into the FEM, guiding the network to learn at different granularity levels. The features from these three different granularity levels are then fed into the Multi-scale Feature Aggregation Module (MFAM) for fusion. Finally, a Multi-Scale Regression Module (MSRM) is designed to handle the multi-scale fused features, resulting in crowd features rich in high-level semantics and low-level detail. Experimental results on five benchmark datasets demonstrate that TCHNet achieves highly competitive performance compared to some popular crowd counting methods. Show more
Keywords: Crowd counting, Transformer, CNN, multi-granularity, progressive learning
DOI: 10.3233/JIFS-236370
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Ramkumar, N. | Karthika Renuka, D.
Article Type: Research Article
Abstract: In BCI (brain-computer interface) applications, it is difficult to obtain enough well-labeled EEG data because of the expensive annotation and time-consuming data capture procedure. Conventional classification techniques that repurpose EEG data across domains and subjects lead to significant decreases in silent speech recognition classification accuracy. This research provides a supervised domain adaptation using Convolutional Neural Network framework (SDA-CNN) to tackle this problem. The objective is to provide a solution for the distribution divergence issue in the categorization of speech recognition across domains. The suggested framework involves taking raw EEG data and deriving deep features from it and the proposed feature …selection method also retrieves the statistical features from the corresponding channels. Moreover, it attempts to minimize the distribution divergence caused by variations in people and settings by aligning the correlation of both the source and destination EEG characteristic dissemination. In order to obtain minimal feature distribution divergence and discriminative classification performance, the last stage entails simultaneously optimizing the loss of classification and adaption loss. The usefulness of the suggested strategy in reducing distributed divergence among the source and target Electroencephalography (EEG) data is demonstrated by extensive experiments carried out on KaraOne datasets. The suggested method achieves an average accuracy for classification of 87.4% for single-subject classification and a noteworthy average class accuracy of 88.6% for cross-subject situations, which shows that it surpasses existing cutting-edge techniques in thinking tasks. Regarding the speaking task, the model’s median classification accuracy for single-subject categorization is 86.8%, while its average classification accuracy for cross-subject classification is 87.8% . These results underscore the innovative approach of SDA-CNN to mitigating distribution discrepancies while optimizing classification performance, offering a promising avenue to enhance accuracy and adaptability in brain-computer interface applications. Show more
Keywords: Brain-computer interface, supervised domain adaptation, Convolutional Neural Network, Electroencephalography, distribution divergence
DOI: 10.3233/JIFS-237890
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Mohana, M. | Subashini, P. | Shukla, Diksha
Article Type: Research Article
Abstract: In recent years, face detection has emerged as a prominent research field within Computer Vision (CV) and Deep Learning. Detecting faces in images and video sequences remains a challenging task due to various factors such as pose variation, varying illumination, occlusion, and scale differences. Despite the development of numerous face detection algorithms in deep learning, the Viola-Jones algorithm, with its simple yet effective approach, continues to be widely used in real-time camera applications. The conventional Viola-Jones algorithm employs AdaBoost for classifying faces in images and videos. The challenge lies in working with cluttered real-time facial images. AdaBoost needs to search …through all possible thresholds for all samples to find the minimum training error when receiving features from Haar-like detectors. Therefore, this exhaustive search consumes significant time to discover the best threshold values and optimize feature selection to build an efficient classifier for face detection. In this paper, we propose enhancing the conventional Viola-Jones algorithm by incorporating Particle Swarm Optimization (PSO) to improve its predictive accuracy, particularly in complex face images. We leverage PSO in two key areas within the Viola-Jones framework. Firstly, PSO is employed to dynamically select optimal threshold values for feature selection, thereby improving computational efficiency. Secondly, we adapt the feature selection process using AdaBoost within the Viola-Jones algorithm, integrating PSO to identify the most discriminative features for constructing a robust classifier. Our approach significantly reduces the feature selection process time and search complexity compared to the traditional algorithm, particularly in challenging environments. We evaluated our proposed method on a comprehensive face detection benchmark dataset, achieving impressive results, including an average true positive rate of 98.73% and a 2.1% higher average prediction accuracy when compared against both the conventional Viola-Jones approach and contemporary state-of-the-art methods. Show more
Keywords: AdaBoost, Computer Vision (CV), face detection algorithm, particle swarm optimization, Viola-Jones
DOI: 10.3233/JIFS-238947
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ding, Xiaoting | Jiang, Jiuchuan | Wei, Mengting | Leng, Yue | Wang, Haixian
Article Type: Research Article
Abstract: Analyzing physiological signals in the brain under outdoor conditions, like observing animal behavior, forms the normative basis for the outdoor task and provides new insights into the cognitive neuronal mechanisms of children’s functional brain systems. Here we investigated EEG data from a cohort of seventeen children (6–7 years old, 30-channel EEG) in the resting state and animal-observation state, using the microstate method combined with source-localization analysis to identify the changes in network-level functional interactions. Our study suggested that: while observing animal behavior, the parameters (global explained variance, occurrence, coverage, and duration) of microstates showed a regular trend, and the dynamic …reorganization patterns of children’s brains were associated with verbal input networks and higher-order cognitive networks; the activity of the brain network in the frontal and temporal lobes of children increased, while the activity of the insula brain area decreased after observing the behavioral activities of animals. This study may be essential to understand the effects of animal behavior on changes in healthy children’s emotions and have important implications for education. Show more
Keywords: Naturalistic observation task, healthy children, EEG microstates, brain development
DOI: 10.3233/JIFS-235533
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Sun, Ling | Jiang, Rong | Wan, Wenbing
Article Type: Research Article
Abstract: In the era of digital intelligence, this paper studies the task allocation algorithm of distributed large data stream group computing, and reasonably allocates the task of group computing to meet the needs of massive computing and analysis of distributed large data stream. According to the idea of swarm intelligence perception and crowdsourcing platform, the task allocation model of distributed large data stream group computing is constructed to realize the task allocation of group computing. A distributed large data stream group computing task model and a user model are constructed, user attributes are initialized by using the accuracy of the answers …submitted by users, the possibility that users can participate in the group computing task is predicted by a logistic regression algorithm, so that user candidate sequences participating in the computing task can be obtained, and the accuracy of the user’s real topics and corresponding topics can be grasped by capturing the candidate users’ real topics and evaluating the accuracy algorithm. Select the users who meet the subject area, update the candidate user sequence, and filter the users again on the basis of fully considering the factors such as information gain, user integrity and cost, so as to get the final user sequence and complete the task allocation of group computing. Experiments show that this method can solve the problem of distributed large data flow group computing task allocation, achieve high accuracy, reduce the cost, and effectively improve the information gain. Show more
Keywords: Age of mathematical intelligence, distributed data flow, calculate task assignment, crowd intelligence perception, crowdsourcing mode, user accuracy
DOI: 10.3233/JIFS-238427
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Sundara Kumar, M.R. | Mohan, H.S.
Article Type: Research Article
Abstract: Big Data Analytics (BDA) is an unavoidable technique in today’s digital world for dealing with massive amounts of digital data generated by online and internet sources. It is kept in repositories for data processing via cluster nodes that are distributed throughout the wider network. Because of its magnitude and real-time creation, big data processing faces challenges with latency and throughput. Modern systems such as Hadoop and SPARK manage large amounts of data with their HDFS, Map Reduce, and In-Memory analytics approaches, but the migration cost is higher than usual. With Genetic Algorithm-based Optimization (GABO), Map Reduce Scheduling (MRS) and Data …Replication have provided answers to this challenge. With multi objective solutions provided by Genetic Algorithm, resource utilization and node availability improve processing performance in large data environments. This work develops a novel creative strategy for enhancing data processing performance in big data analytics called Map Reduce Scheduling Based Non-Dominated Sorting Genetic Algorithm (MRSNSGA). The Hadoop-Map Reduce paradigm handles the placement of data in distributed blocks as a chunk and their scheduling among the cluster nodes in a wider network. Best fit solutions with high latency and low accessing time are extracted from the findings of various objective solutions. Experiments were carried out as a simulation with several inputs of varied location node data and cluster racks. Finally, the results show that the speed of data processing in big data analytics was enhanced by 30–35% over previous methodologies. Optimization approaches developed to locate the best solutions from multi-objective solutions at a rate of 24–30% among cluster nodes. Show more
Keywords: Big data analytics, hadoop distributed file system, non-dominated sorting genetic algorithm, map reduce scheduling based non-dominated sorting genetic algorithm, map reduce scheduling, genetic algorithm-based optimization
DOI: 10.3233/JIFS-240069
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Chiadamrong, Navee | Suthamanondh, Pisacha
Article Type: Research Article
Abstract: Competitiveness in the global market is getting more intense. Due to resource and budget constraints, firms need to achieve their expected goals and satisfy all investment constraints under uncertainty. Selecting the set of projects among other candidates to get the most efficient portfolio requires a lot of attention from the Decision Makers (DMs) as this consideration no longer relies purely on the financial term. This problem becomes a multi-objective problem under uncertainty where the financial return and risk from uncertainty are required into the trading off consideration. Due to the financial uncertainty, the chance-constrained programming has been employed in this …study for defuzzifying and solving uncertain optimization problems at a specified confidence level that is defined by the DMs. Then, various kinds of investment or financial risk measures, Lower-Semi Variance Index (LSVI), the absolute deviation with the expected FNPV, and the absolute mean-Conditional Value at Risk (CVaR) gap are provided in the selection of such risk measures to show their differences in characteristics and performances in the obtained results. Since, such problems can consist of many project candidates and complex constraints, which may grow beyond the application of the exact optimization approach, a meta-heuristic method, Genetic Algorithm (GA), is introduced to optimize this problem through designing and constructing a decision support tool for the investment portfolio selection and optimization. The applicability of the proposed comparative approach and the constructed tool are illustrated through examples. Show more
Keywords: Multi-objective portfolio selection and optimization, risk of uncertainty, absolute mean-conditional value at risk, Lower Semi-Variance Index (LSVI), absolute deviation with the expected FNPV
DOI: 10.3233/JIFS-233036
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2024
Authors: Liang, Yonghong | Ge, Xianlong | Jin, Yuanzhi | Zheng, Zhong | Zhang, Yating | Jiang, Yunyun
Article Type: Research Article
Abstract: The rapid development of modern cold chain logistics technology has greatly expanded the sales market of agricultural products in rural areas. However, due to the uncertainty of agricultural product harvesting, relying on the experience values provided by farmers for vehicle scheduling can easily lead to low utilization of vehicle capacity during the pickup process and generate more transportation cost. Therefore, this article adopts a non-linear improved grey prediction method based on data transformation to estimate the pickup demand of fresh agricultural products, and then establishes a mathematical model that considers the fixed vehicle usage cost, the damage cost caused by …non-linear fresh fruit and vegetable transportation damage and decay rate, the cooling cost generated by refrigerated transportation, and the time window penalty cost. In order to solve the model, a hybrid simulated annealing algorithm integrating genetic operators was designed to solve this problem. This hybrid algorithm combines local search strategies such as the selection operator without repeated strings and the crossover operator that preserves the best substring to improve the algorithm’s solving performance. Numerical experiments were conducted through a set of benchmark examples, and the results showed that the proposed algorithm can adapt to problem instances of different scales. In 50 customer examples, the difference between the algorithm and the standard value in this paper is 2.30%, which is 7.29% higher than C&S. Finally, the effectiveness of the grey prediction freight path optimization model was verified through a practical case simulation analysis, achieving a logistics cost savings of 9.73% . Show more
Keywords: Pick-up routing problems, fresh logistics, gray prediction, hybrid simulated annealing
DOI: 10.3233/JIFS-235260
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Wang, Zhiwen | Zhao, Yibin | Shi, Yaoke | Ling, Guobi
Article Type: Research Article
Abstract: Due to the complexity of the factors influencing membrane fouling in membrane bioreactors (MBR), it is difficult to accurately predict membrane fouling. This paper proposes a multi-strategy of integration aquila optimizer deep belief network (MAO-DBN) based membrane fouling prediction method. The method is developed to improve the accuracy and efficiency of membrane fouling prediction. Firstly, partial least squares (PLS) are used to reduce the dimensionality of many membrane fouling factors to improve the algorithm’s generalization ability. Secondly, considering the drawbacks of deep belief network (DBN) such as long training time and easy overfitting, piecewise mapping is introduced in aquila optimizer …(AO) to improve the uniformity of population distribution, while adaptive weighting is used to improve the convergence speed and prevent falling into local optimum. Finally, the prediction of membrane fouling is carried out by utilizing membrane fouling data as the research object. The experimental results show that the method proposed in this paper can achieve accurate prediction of membrane fluxes, with an 88.45% reduction in RMSE and 87.53% reduction in MAE compared with the DBN model before improvement. The experimental results show that the model proposed in this paper achieves a prediction accuracy of 98.61%, both higher than other comparative models, which can provide a theoretical basis for membrane fouling prediction in the practical operation of membrane water treatment. Show more
Keywords: Membrane bioreactors (MBR), membrane fouling prediction, deep belief network (DBN), aquila optimizer (AO)
DOI: 10.3233/JIFS-233655
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Limei, Nong | Dongfan, Wu | Bo, Zhang
Article Type: Research Article
Abstract: Garden landscape is the combination of nature and humanity, with high aesthetic value, ecological value and cultural value, has become an important part of people’s life. Modern people have a higher pursuit for the spiritual food such as garden landscape after the material life is satisfied, which brings new challenges to the construction of urban garden landscape. As an advanced type of machine learning, deep learning applied to landscape image recognition can solve the problem of low quality and low efficiency of manual recognition. Based on this, this paper proposes a garden landscape image recognition algorithm based on SSD (Single …Shot Multibox Detector), which realizes accurate extraction and recognition of image features by positioning the target, and can effectively improve the quality and efficiency of landscape image recognition. In order to test the feasibility of the algorithm proposed in this paper, experimental analysis was carried out in the CVPR 2023 landscape data set. The experimental results show that the algorithm has a high recognition accuracy for landscape images, and has excellent performance compared with traditional image recognition algorithms. Show more
Keywords: Deep learning, garden landscape, image recognition, target detection; image analysis
DOI: 10.3233/JIFS-239654
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ramkumar, N. | Karthika Renuka, D.
Article Type: Research Article
Abstract: In recent times, the rapid advancement of deep learning has led to increased interest in utilizing Electroencephalogram (EEG) signals for automatic speech recognition. However, due to the significant variation observed in EEG signals from different individuals, the field of EEG-based speech recognition faces challenges related to individual differences across subjects, which ultimately impact recognition performance. In this investigation, a novel approach is proposed for EEG-based speech recognition that combines the capabilities of Long Short Term Memory (LSTM) and Graph Attention Network (GAT). The LSTM component of the model is designed to process sequential patterns within the data, enabling it to …capture temporal dependencies and extract pertinent features. On the other hand, the GAT component exploits the interconnections among data points, which may represent channels, nodes, or features, in the form of a graph. This innovative model not only delves deeper into the connection between connectivity features and thinking as well as speaking states, but also addresses the challenge of individual disparities across subjects. The experimental results showcase the effectiveness of the proposed approach. When considering the thinking state, the average accuracy for single subjects and cross-subject are 65.7% and 67.3% respectively. Similarly, for the speaking state, the average accuracies were 65.4% for single subjects and 67.4% for cross-subject conditions, all based on the KaraOne dataset. These outcomes highlight the model’s positive impact on the task of cross-subject EEG speech recognition. The motivations for conducting cross subject are real world applicability, Generalization, Adaptation and personalization and performance evaluation. Show more
Keywords: Electroencephalography, recurrent neural network, long short term memory, gated recurrent unit, graph convolution network and graph attention network
DOI: 10.3233/JIFS-233143
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Peng, Weishi | Fang, Yangwang | Ma, Yongzhong
Article Type: Research Article
Abstract: Although many scholars say that their algorithms are better than others in the state estimation problem, only a fewer convincing algorithms were applied to engineering practices. The reason is that their algorithms outperform others only in some aspects such as the estimation accuracy or the computation load. To solve the problem of performance evaluation of state estimation algorithms, in this paper, the comprehensive evaluation measures (CEM) for evaluating the nonlinear estimation algorithm (NEA) is proposed, which can comprehensively reflect the performance of the NEAs. First, we introduce three types of the NEAs. Second, the CEM combining the flatness, estimation accuracy …and computation time of the NEAs, is designed to evaluate the above NEAs. Finally, the superiority of the CEM is verified by a numerical example, which helps decision makers of nonlinear estimation algorithms theoretically and technically. Show more
Keywords: Performance evaluation, nonlinear estimation algorithm, comprehensive metrics, error spectrum, EKF, UKF, PF
DOI: 10.3233/JIFS-231376
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Valdez-Rodríguez, José E. | Rangel, Nahum | Moreno-Armendáriz, Marco A.
Article Type: Research Article
Abstract: Visual detection of fingering on the trumpet is an increasingly interesting topic in music research. The ability to recognize and track the movements of the trumpet player’s fingers during the performance of a musical piece can provide valuable information for analyzing and improving instrument technique. However, this is a largely unexplored task, as most works focus on audio quality rather than instrument fingering techniques. Developing techniques for identifying essential finger positions on a musical instrument is crucial, as poor fingering techniques can harm instrument performance. In this work, we propose the visual detection of this fingering using convolutional neural networks …with a proprietary dataset created for this purpose. Additionally, to improve the results and focus on the essential parts of the instrument, we use self-attention mechanisms by extracting these features automatically. Show more
Keywords: Fingering detection, Convolutional Neural Networks, Self-attention mechanisms, Visual detection, Trumpet
DOI: 10.3233/JIFS-219342
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Ganesh, M.A. | Saravana Perumaal, S. | Gomathi Sankar, S.M.
Article Type: Research Article
Abstract: The current framework for detecting Fake License Plates (FLP) in real-time is not robust enough for patrol teams. The objective of this paper is to develop a robust license plate authentication framework, based on the Vehicle Make and Model Recognition (VMMR) and the License Plate Recognition (LPR) algorithms that is implementable at the edge devices. The contributions of this paper are (i) Development of license plate database for 547 Indian cars, (ii) Development of an image dataset with 3173 images of 547 Indian cars in 8 classes, (iii) Development of an ensemble model to recognize vehicle make and model from …frontal, rear, and side images, and (iv) Development of a framework to authenticate the license plates with frontal, rear, and side images. The proposed ensemble model is compared with the state-of-the-art networks from the literature. Among the implemented networks for VMMR, the Ensembling model with a size of 303.2 MB achieves the best accuracy of 89% . Due to the limited memory size, Easy OCR is chosen to recognize license plate. The total size of the authentication framework is 308 MB. The performance of the proposed framework is compared with the literature. According to the results, the proposed framework enhances FLP recognition due to the recognition of vehicles from side images. The dataset is made public at https://www.kaggle.com/ganeshmailecture/datasets . Show more
Keywords: Vehicle make and model recognition, fake license plate detection, license plate authentication, intelligent transportation system
DOI: 10.3233/JIFS-230607
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2024
Authors: Yin, Songyi | Wang, Yu | Fu, Yelin
Article Type: Research Article
Abstract: The environmental, social, and governance (ESG) rating method is a powerful tool that can help investors to judge the investment value of companies based on the information disclosure. However, mainstream ESG rating methods ignore the distinction between companies with incomplete information disclosure and companies without information disclosure, which decreases the initiative and enthusiasm of companies to disclose information. In this study, a self-disclosure ESG (SDESG) rating method is proposed to evaluate companies’ ESG performance capabilities. First, based on the fuzzy set, fuzzy data is defined and applied to the SDESG rating method. Second, analogous to the academic reward system of …a university, a reward mechanism of disclosure is used in the SDESG rating method. Finally, the effectiveness and reliability of the SDESG rating method are demonstrated through Refinitiv’s case. The results show that the SDESG rating method can distinguish companies with incomplete information disclosure from companies without information disclosure and allow companies that proactively disclose information to obtain better ESG scores under each industry. The implications of the study would increase companies’ enthusiasm to disclose information and maintain transparency within a company. Show more
Keywords: ESG rating method, information disclosure, fuzzy set, reward mechanism
DOI: 10.3233/JIFS-230777
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wang, Dan | Yao, Jingfa | Zhang, Yanmin
Article Type: Research Article
Abstract: Nowadays, automatic human activity recognition from video images is necessary for monitoring applications and caring for disabled people. The use of surveillance cameras and the processing of the obtained images leads to the achievement of a smart, accurate system for the recognition of human behavior. Since human detection in different scenes is associated with many challenges, several approaches have been implemented to detect human activity from video image processing. Due to the complexity of human activities, background noises and other factors affect the detection. For the solution of these problems, two deep learning-based algorithms have been described in the current …article. According to the convolutional neural networks, the LSTM + CNN method and the 3D CNN method have been used to recognize the human activities in the images of the video. Each algorithm is explained and analyzed in detail. The experiments designed in this paper are performed by two datasets: the HMDB-51 dataset and the UCF101 dataset. In the HMDB-51 dataset, the highest obtained accuracy for CNN + LSTM method was equal to 70.2 and for method 3D CNN equal to 54.4. In the UCF101 dataset, the highest obtained accuracy for CNN + LSTM method was equal to 95.1 and for method 3D CNN equal to 90.8. Show more
Keywords: Long short-term memory (LSTM), video processing, deep learning, human activity recognition, convolutional neural network (CNN)
DOI: 10.3233/JIFS-236068
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Viet, Hoang Huu | Uyen, Nguyen Thi | Cao, Son Thanh | Nguyen, Long Giang
Article Type: Research Article
Abstract: The Student-Project Allocation with preferences over Projects problem is a many-to-one stable matching problem that aims to assign students to projects in project-based courses so that students and lecturers meet their preference and capacity constraints. In this paper, we propose an efficient two-heuristic algorithm to solve this problem. Our algorithm starts from an empty matching and iteratively constructs a maximum stable matching of students to projects. At each iteration, our algorithm finds an unassigned student and assigns her/his most preferred project to her/him to form a student-project pair in the matching. If the project or the lecturer who offered the …project is over-subscribed, our algorithm uses two heuristic functions, one for the over-subscribed project and the other for the over-subscribed lecturer, to remove a student-project pair in the matching. To reach a stable matching of a maximum size, our two heuristics are designed such that the removed student has the most opportunities to be assigned to some project in the next iterations. Experimental results show that our algorithm is efficient in execution time and solution quality for solving the problem. Show more
Keywords: Approximation algorithm, heuristic search, matching problem, student-project allocation problem
DOI: 10.3233/JIFS-236300
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Huang, Jinsong | Hou, Hecheng | Li, Xiaoying | Zhang, Ziyi | Jia, Qi
Article Type: Research Article
Abstract: In the context of the digital era, the factors influencing the cognitive load of the full ecological smart home on the elderly are mostly interconnected. Most existing studies have conducted single correlation analyses, ignoring the fact that cognitive load is the result among multiple interactions of multiple factors. Furthermore, the color, material and Finishing of the product design can also impact on the user’s perceptual needs. Therefore, exploring the grouping dynamics of cognitive load and users’ perceptual needs for color (C), material (M), and Finishing (F) of smart products can provide insights for inclusive design of smart homes. The article …analyzes the asymmetric multiple concurrent causal effects of full ecological smart homes on the cognitive load of the elderly from a histological perspective using fuzzy set Qualitative Comparative Analysis (fsQCA) based on the four elements of Innovation Diffusion Theory. At the same time, principal component analysis and quantitative theory I class method are used to explore the quantitative relationship between color, material, Finishing and users’ perceptual imagery of the product. The results of the study showed that there were no necessary conditions leading to high or low cognitive load in the fsQCA analysis, indicating that the problem was the result of the interaction of multiple conditions, and the final analysis yielded three histological pathways leading to low cognitive load and one pathway leading to high load in older adults. Moreover, the study identifies the combination of colors, materials, and finishes that best represent user preferences. This study establishes a dialogue between theory, results, and cases in analyzing of the group dynamics of the impact of full ecological smart homes on the cognitive load of the elderly. It provides a theoretical basis for the development of digital inclusion enhancement strategies. Show more
Keywords: Smart home, cognitive load, diffusion of innovation, qualitative comparative analysis (QCA), human-computer interaction
DOI: 10.3233/JIFS-237212
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Jansi Sophia Mary, C. | Mahalakshmi, K.
Article Type: Research Article
Abstract: Intrusion Detection (ID) in cloud environments is vital to maintain the safety and integrity of data and resources. However, the presence of class imbalance, where normal samples significantly outweigh intrusive instances, poses a challenge in constructing a potential ID system. Deep Learning (DL) methods, with their capability to automatically study complex patterns and features, present a promising solution in various ID tasks. Such methods can automatically learn intricate features and patterns from the input dataset, making them suitable for detecting anomalies and finding intrusions in cloud environments. Therefore, this study proposes a Class Imbalance Data Handling with an Optimal Deep …Learning-Based Intrusion Detection System (CIDH-ODLIDS) in a cloud computing atmosphere. The CIDH-ODLIDS technique leverages optimal DL-based classification and addresses class imbalance. Primarily, the CIDH-ODLIDS technique preprocesses the input data using a Z-score normalization approach to ensure data quality and consistency. To handle class imbalance, the CIDH-ODLIDS technique employs oversampling techniques, particularly focused on synthetic minority oversampling techniques such as Adaptive Synthetic (ADASYN) sampling. ADASYN generates synthetic instances for the minority class depending on the available data instances, effectively balancing the class distribution and mitigating the impact of class imbalance. For the ID process, the CIDH-ODLIDS technique utilizes a Fuzzy Deep Neural Network (FDNN) model, and its tuning procedure is performed using the Chaotic Tunicate Swarm Algorithm (CTSA). CTSA is employed to choose the learning rate of the FDNN methods optimally. The experimental assessment of the CIDH-ODLIDS method is extensively conducted on three IDS datasets. The comprehensive comparison results confirm the superiority of the CIDH-ODLIDS algorithm over existing techniques. Show more
Keywords: Cloud computing, security, deep learning, intrusion detection system, tunicate swarm algorithm, class imbalance data
DOI: 10.3233/JIFS-237900
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Lomas-Barrie, Victor | Reyes-Camacho, Michelle | Neme, Antonio
Article Type: Research Article
Abstract: Horizontal gene transference is a biological process that involves the donation of DNA or RNA from an organism to a second, unrelated organism. This process is different from the more common one, vertical transference, which is present whenever an organism or pair of organisms reproduce and transmit their genetic material to the descendants. The identification of segments of genetic material that are the result of horizontal transference is relevant to construct accurate phylogenetic trees, on one hand, and to detect possible drug-resistance mechanisms, on the other, since this movement of genetic material is the main cause behind antibiotic resistance in …bacteria. Here, we describe a novel algorithm able to detect sequences of foreign origin, and thus, possible acquired via horizontal transference. The general idea of our method is that within the genome of an organism, there might be sequences that are different from the vast majority of the remaining sequences from the same organism. The former are candidate anomalies, and thus, their origin may be explained by horizontal transference. This approach is equivalent to a particular instance of the authorship attribution problem, that in which from a set of texts or paragraphs, almost all of them were written by the same author, whereas a minority has a different authorship. The constraint is that the author of each text is not known, so the algorithm has to attribute the authorship of each one of the texts. The texts detected to be written by a different author are the equivalent of the sequences of foreign origin for the case of genetic material. We describe here a novel method to detect anomalous sequences, based on interpretable embeddings derived from a common attention mechanism in humans, that of identifying novel tokens within a given sequence. Our proposal achieves novel and consistent results over the genome of a well known organism. Show more
Keywords: Horizontal gene transference, anomaly detection, embeddings, natural language processing, genomics
DOI: 10.3233/JIFS-219337
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Weng, Zhi | Bai, Rongfei | Zheng, Zhiqiang
Article Type: Research Article
Abstract: Cattle detection and counting is one of the most important topics in the development of modern agriculture and animal husbandry. The traditional manual monitoring methods are inefficient and constrained by factors such as site. To solve the above problems, a SCS-YOLOv5 cattle detection and counting model for complex breeding scenarios is proposed. The original SPPF module is replaced in the YOLOv5 backbone network with a CSP structured SPPFCSPC. A CA (Coordinate Attention) mechanism is added to the neck network, as well as the SC (Standard Convolution) of the Neck network is replaced with a light convolution GSConv and Slim Neck …is introduced, and training strategies such as multi-scale training are also employed. The experimental results show that the proposed method enhances the feature extraction ability and feature fusion ability, balances the localization accuracy and detection speed, and improves the use effect in real farming scenarios. The Precision of the improved network model is improved from 93.2% to 95.5%, [email protected] is improved from 94.5% to 95.2%, the RMSE is reduced by about 0.03, and the FPS reaches 88. Compared with other mainstream algorithms, the comprehensive performance of SCS-YOLOv5 s is in a leading position, with fewer missed and false detections, and the strong robustness and generalization ability of this model are proved on multi-category public datasets. Applying the improvement ideas in this paper to YOLOv8 s also yields an increase in accuracy. The improved method in this study can greatly improve the accuracy of cattle detection and counting in complex environments, and has good real-time performance, so as to provide technical support for large-scale cattle breeding. Show more
Keywords: Cattle detection, counting, attention mechanism, occlusion, complex environments
DOI: 10.3233/JIFS-237231
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Lugo-Torres, Gerardo | Valdez-Rodríguez, José E. | Peralta-Rodríguez, Diego A.
Article Type: Research Article
Abstract: The use of generative models in image synthesis has become increasingly prevalent. Synthetic medical imaging data is of paramount importance, primarily because medical imaging data is scarce, costly, and encumbered by legal considerations pertaining to patient confidentiality. Synthetic medical images offer a potential answer to these issues. The predominant approaches primarily assess the quality of images and the degree of resemblance between these images and the original ones employed for their generation.The central idea of the work can be summarized in the question: Do the performance metrics of Frechet Inception Distance(FID) and Inception Score(IS) in the Cycle-consistent Generative Adversarial Networks …(CycleGAN) model are adequate to determine how real a generated chest x-ray pneumonia image is? In this study, a CycleGAN model was employed to produce artificial images depicting 3 classes of chest x-ray pneumonia images: general(any type), bacterial, and viral pneumonia. The quality of the images were evaluated assessing and contrasting 3 criteria: performance metric of CycleGAN model, clinical assessment of respiratory experts and the results of classification of a visual transformer(ViT). The overall results showed that the evaluation metrics of the CycleGAN are insufficient to establish realism in generated medical images. Show more
Keywords: Synthetic chest x-ray, cycle generative adversarial network, pneumonia, image-to-image translation, visual transformer
DOI: 10.3233/JIFS-219373
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ramírez-Martínez, Angel | Chong-Quero, J. Enrique | Cervantes-Culebro, Héctor | Cruz-Villar, Carlos A.
Article Type: Research Article
Abstract: This paper presents a data-driven control approach for a five-bar robot with compliant joints. The robot consists of a parallel mechanism with compliant elements that introduce uncertainties in modeling and control. To address this fact, it is implemented a model-less data-driven controller based on a Feedforward Neural Network Module (FNNM) that identifies the inverse dynamics of the robot. The FNNM is incorporated into a coordination of Feedforward Control Method (CFCM) to achieve precise trajectory tracking. Experiments compare the compliant joints robot to a bearing-joint robot performing pick-and-place tasks from 0.15 to 3.15 Hz. Results show the compliant robot maintaining trajectory tracking …up to 1.25 Hz with a Root Mean Square Error (RMSE) of 9.02 mm. Show more
Keywords: Data-driven, five-bar robot, compliant joints, vision-based
DOI: 10.3233/JIFS-219364
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Chen, Kang | Song, Changming | Cheng, Dongxu | Li, Hao
Article Type: Research Article
Abstract: Video anomaly detection (VAD) has garnered substantial attention from researchers due to its broad applications, including fire detection, drop detection, and vibration detection. In the current context of VAD, existing methods prioritize detection efficiency but overlook the impact of motion and appearance information. Additionally, achieving accurate predictions while retaining motion and appearance information poses a significant challenge. This paper proposes a novel semi-supervised method for VAD based on Generative Adversarial Network (GAN) structures with dual generators and dual discriminators, namely Dual-GAN. The future frame generator utilizes an improved encoder-decoder network to preserve more spatial information. Motion information for the future …flow generator is obtained by estimating optical flow between reconstruction frames, complementing the optical flow between prediction frames. The introduction of a frame discriminator and a motion discriminator against the frame generator enhances the realism of prediction frames, which facilitates the identification of unexpected abnormal events. This method significantly outperforms comparative approaches in synthesizing video frames and predicting future flows, showcasing its effectiveness in handling diverse video data. Extensive experiments are performed on four publicly available datasets to ensure a comprehensive evaluation of the model performance. Further exploration could include refining the model architecture, exploring additional datasets, and adapting the methodology to specific application domains. Show more
Keywords: Anomaly detection, generative adversarial network, dual discriminators, future flow
DOI: 10.3233/JIFS-237831
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Karthikeyan, M. | Colak, Ilhami | Sagar Imambi, S. | Joselin Jeya Sheela, J. | Nair, Sruthi | Umarani, B. | Alagusabai, Andril | Suriyakrishnaan, K. | Rajaram, A.
Article Type: Research Article
Abstract: This research paper introduces a cutting-edge approach to electric demand forecasting by incorporating the Temporal Fusion Transformer (TFT). As the landscape of demand forecasting becomes increasingly intricate, precise predictions are vital for effective energy management. To tackle this challenge, we leverage the sequential and temporal patterns in an extensive electric demand dataset spanning from 2003 to 2014. Our proposed Temporal Fusion Transformer model combines attention mechanisms with the transformer architecture, enabling it to adeptly capture intricate temporal dependencies. Thorough data preprocessing, including temporal embedding and external features, enhances prediction accuracy. Through rigorous evaluation, the TFT model surpasses existing forecasting techniques, …showcasing its capacity for accurate, resilient, and adaptive predictions. This research contributes to the advancement of electric demand forecasting, harnessing the TFT’s capabilities to excel in capturing diverse temporal patterns. The findings hold the potential to enhance energy management and support decision-making in the energy sector, bridging the gap between innovation and practical utility. Show more
Keywords: Electric demand forecasting, temporal fusion transformer, energy management, time-series analysis, transformer architecture
DOI: 10.3233/JIFS-236036
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Arenas Muñiz, Andrés Antonio | Mújica-Vargas, Dante | Rendón Castro, Arturo | Luna-Álvarez, Antonio | Vela-Rincón, Virna V.
Article Type: Research Article
Abstract: The selection of an appropriate trajectory for self-driving vehicles involves the analysis of several criteria that describe the generated trajectories. This problem evolves into an optimization problem when it is desired to increase or decrease the values for a specific criterion. The contribution of this thesis is to explore the use and optimization of another technique for decision-making, such as TOPSIS, with a sufficiently robust method that allows the inclusion of multiple parameters and their proper optimization, incorporating human experience. The proposed approach showed significantly higher safety and comfort performance, with about 20% better efficiency and 80% fewer safety violations …compared to other state-of-the-art methods, and in some cases outperforming in comfort by about 30.43%. Show more
Keywords: Decision-making, human experience, trajectory selection, self-driving
DOI: 10.3233/JIFS-219365
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Téllez-Velázquez, Arturo | Delice, Pierre A. | Salgado-Leyva, Rafael | Cruz-Barbosa, Raúl
Article Type: Research Article
Abstract: This paper performs an analysis comparing two evolutionary explainable fuzzy models that make inferences in a pipeline with a blood test data set for COVID-19 classification. Firstly, data is preprocessed by the following stages: cleaning, imputation and ranking feature selection. Later, we perform a comparative analysis between several clustering methods used in an Evolutionary Clustering-Structured Fuzzy Classifier (ECSFC) to solve this classification problem using the Differential Evolution (DE) algorithm. Complementarily, we find that the Fuzzy Decision Tree model produces similar performance when is tuned with the DE algorithm (EFDT). The obtained results show that, simpler models are easier to explain …qualitatively, i.e., increasing the number of clusters in ECSFC model or the maximum depth of the tree in EFDT model, does not necessarily help to obtain simplified and accurate models. In addition, although the EFDT model is by itself an intuitively explainable model, the ECSFC, with the help of the proposed Weighted Stacked Features Plot, generates more intuitive models that allow not only highlighting the features and the linguistic terms that defines a patient with COVID-19, but also allows users to visualize in a single graph and in specific colors the analyzed classes. Show more
Keywords: COVID-19, blood tests, fuzzy classifier, fuzzy decision tree, clustering, differential evolution
DOI: 10.3233/JIFS-219372
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Yao, Ziyang
Article Type: Research Article
Abstract: The traditional multi-task Takagi-Sugeno-Kang (TSK) fuzzy system modeling methods pay more attention to utilizing the inter-task correlation to learn the consequent parameters but ignore the importance of the antecedent parameters of the model. To this end, we propose a novel multi-task TSK fuzzy system modeling method based on multi-task fuzzy clustering. This method first proposes a novel multi-task fuzzy c-means clustering method that learns multiple specific clustering centers for each task and some common clustering centers for all tasks. Secondly, for the consequent parameters of the fuzzy system, the novel low-rank and row-sparse constraints are proposed to better implement multi-task …learning. The experimental results demonstrate that the proposed model shows better performance compared with other existing methods. Show more
Keywords: Multi-task fuzzy clustering, TSK fuzzy system, low-rank, row-sparsity, joint learning
DOI: 10.3233/JIFS-232312
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Gao, Lijun | Liu, Kai | Liu, Wenjun | Wu, Jiehong | Jin, Xiao
Article Type: Research Article
Abstract: As machine learning models become increasingly integrated into practical applications and are made accessible via public APIs, the risk of model extraction attacks has gained prominence. This study presents an innovative and efficient approach to model extraction attacks, aimed at reducing query costs and enhancing attack effectiveness. The method begins by leveraging a pre-trained model to identify high-confidence samples from unlabeled datasets. It then employs unsupervised contrastive learning to thoroughly dissect the structural nuances of these samples, constructing a dataset of high quality that precisely mirrors a variety of features. A mixed information confidence strategy is employed to refine the …query set, effectively probing the decision boundaries of the target model. By integrating consistency regularization and pseudo-labeling techniques, reliance on authentic labels is minimized, thus improving the feature extraction capabilities and predictive precision of the surrogate models. Evaluation on four major datasets reveals that the models crafted through this method bear a close functional resemblance to the original models, with a real-world API test success rate of 62.35%, which vouches for the method’s validity. Show more
Keywords: Model extraction, unsupervised learning, selection of strategies, active learning
DOI: 10.3233/JIFS-239504
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Yu, Xiaobing | Zhang, Yuexin | Wang, Xuming
Article Type: Research Article
Abstract: Sensors are often deployed in harsh environments, in which some threats may endanger the safety of sensors. In this paper, a sensor deployment model is developed in Wireless Sensor Networks (WSNs), in which the coverage rate and the threat risk are considered simultaneously. The model is established as an optimization problem. An adaptive ranking teaching learning-based optimization algorithm (ARTLBO) is proposed to solve the problem. Learners are divided into inferior and superior groups. The teacher phase is boosted by replacing the teacher with the top three learners, and the learner phase is improved by providing some guidance for inferior learners. …The experimental results show that the proposed ARTLBO algorithm can effectively optimize the model. The fitness values of the proposed model found by the proposed ARTLBO are 0.4894, 0.4886, which are better than its competitors. The algorithm can provide a higher coverage rate and lower threat risk. Show more
Keywords: WSNs, teaching-learning-based optimization, sensor deployment, coverage rate
DOI: 10.3233/JIFS-240215
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Zhang, Lixin | Yin, Hongtao | Li, Ang | Hu, Longbiao
Article Type: Research Article
Abstract: In large-scale scenes, how to quickly obtain paths while ensuring the shortest possible path length is a key issue. Rapidly-exploring Random Tree (RRT) have the characteristic of quickly exploring the state space, but it is often difficult to obtain a short path. To overcome this problem, this paper proposes an improved RRT algorithm based on equidistance retention strategy and A* local search(ERRRT-A*). First, RRT is used for large-step global fast exploration to obtain approximate paths. Then, an equidistance retention strategy is used to discard most of the points and retain a small number of key points. Finally, A* is used …to search between each segment to obtain a new path. The ERRRT-A* algorithm is compared with other commonly used algorithms on maps of different size in terms of path length and planning time. Simulation results indicate that compared with other algorithms, this algorithm achieves fast planning in large-scale scenes while obtaining short path length, which can effectively balance the path length and planning time. Show more
Keywords: Path planning, large-scale scenes, unmanned vehicles, RRT
DOI: 10.3233/JIFS-238695
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: López-Jasso, Edgar | Felipe-Riverón, Edgardo M. | Valdez-Rodríguez, José E.
Article Type: Research Article
Abstract: This study underscores the crucial role of image preprocessing in enhancing the outcomes of multimodal image registration tasks using scale-invariant feature selection. The primary focus is on registering two types of retinal images, assessing a methodology’s performance on a set of retinal image pairs, including those with and without microaneurysms. Each pair comprises a color optical image and a gray-level fluorescein image, presenting distinct characteristics and captured under varying conditions. The SIFT methodology, encompassing five stages, with preprocessing as the initial and pivotal stage, is employed for image registration. Out of 35 test retina image pairs, 33 (94.28%) were successfully …registered, with the inability to extract features hindering automatic registration in the remaining pairs. Among the registered pairs, 42.42% were retinal images without microaneurysms, and 57.57% had microaneurysms. Instead of simultaneous registration of all channels, independent registration of preprocessed images in each channel proved more effective. The study concludes with an analysis of the fifth registration’s resulting image to detect abnormalities or pathologies, highlighting the challenges encountered in registering blue channel images due to high intrinsic noise. Show more
Keywords: Image SIFT registration, microaneurysms counting, retina image analysis, multimodal registration, image processing
DOI: 10.3233/JIFS-219374
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Pradeepkumar, G. | Kavitha, S.
Article Type: Research Article
Abstract: To provide the best possible performance in precisely segmenting clinical images, several approaches are used. Convolutional neural networks are one method used in it to extract its features, which combine several models with several additional methods. It also improves the efficiency of generalisation between categorised and uncategorized image categories. The method proposed combines multi-style image fusion with two-dimensional fracture image representation. The photographs on this page have been updated with a variety of images to improve concentration sharing and achieve the desired visual look. The border detection algorithm is then used to extract the exact border of the image from …the contrast extended images. It will then be divided into basic and comprehensive layers. The fused image was then created using augmented end layers. Show more
Keywords: Segmenting, clinical images, extract features, categorized image, uncategorized image, multi style, border detection, image extraction
DOI: 10.3233/JIFS-239695
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Wei, Xiao | Lin, Yidian
Article Type: Research Article
Abstract: Legal judgment prediction(LJP) has achieved remarkable results. However, existing methods still face problems such as difficulties in obtaining key feature words for charges, which impose limitations on the improvement of prediction results. To this end, we propose a legal judgment prediction model with legal feature Word subgraph Label-Embedding and Dual-knowledge Distillation(WLEDD). Compared with traditional methods, our method has two contributions: (1) To mitigate the impact of overly sparse tail class data and high similarity text representations, we capture the critical features related to the charges by fusing LDA and legal feature word subgraphs. Then we encode them as label information …to obtain highly distinguished representations of legal documents. (2) To solve the problem of high difficulty in some subtasks in LJP, we perform subtask-oriented compression of models to construct a student model with lower complexity and higher accuracy through dual knowledge distillation. Moreover, we exploit the logical association between the subtasks to constrain the labels of articles by charge prediction results. It greatly reduces the difficulty of article prediction. Experimental results on four datasets show that our approach significantly outperforms the baseline models. Compared with the state-of-art method, the F1 value of WLEDD for charge prediction has increased by an average of 2.57% . For article prediction, the F1 value has increased by an average of 1.09% . In addition, we demonstrate its effectiveness through ablation experiments and analytical experiments. Show more
Keywords: Legal judgment prediction, knowledge distillation, label embedding, legal text mining
DOI: 10.3233/JIFS-237323
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Yang, Hong | Wang, Lina
Article Type: Research Article
Abstract: The paper focuses on how to improve the prediction accuracy of time series and the interpretability of prediction results. First, a novel Prophet model based on Gaussian linear fuzzy approximate representation (GF-Prophet) is proposed for long-term prediction, which uniformly predicts the data with consistent trend characteristics. By taking Gaussian linear fuzzy information granules as inputs and outputs, GF-Prophet predicts with significantly smaller cumulative error. Second, noticing that trend extraction affects prediction accuracy seriously, a novel granulation modification algorithm is proposed to merge adjacent information granules that do not have significant differences. This is the first attempt to establish Prophet based …on fuzzy information granules to predict trend characteristics. Experiments on public datasets show that the introduction of Gaussian linear fuzzy information granules significantly improves prediction performance of traditional Prophet model. Compared with other classical models, GF-Prophet has not only higher prediction accuracy, but also better interpretability, which can clearly give the change information, fluctuation amplitude and duration of a certain trend in the future that investors actually pay attention to. Show more
Keywords: Fuzzy number, gaussian linear fuzzy information granule, the prophet model, long-term prediction
DOI: 10.3233/JIFS-230313
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ning, Tao | Zhang, Tingting | Huang, Guowei
Article Type: Research Article
Abstract: Folk dance is an important intangible cultural heritage in China. In the environment where movement recognition technology is widely used, there is still no research field on the protection and inheritance of folk dance culture. In order to better protect and inherit the minority dance, screening the typical movements of 5 types of minority dance, through the dance video frame processing, obtain the key movements of 19 class dance sequence, build the national dance typical action data set, put forward a 3D CNN fusion Transformer national dance recognition network model (FCTNet), the recognition rate of 96.7% in the experiment. The …results show that the construction method of the folk dance data set is reasonable, the identification model has good performance for the classification of folk dance, and can effectively identify and record the folk dance movements, which also makes new contributions to the digital protection of folk dance. Show more
Keywords: Transformer, folk dance, cultural protection
DOI: 10.3233/JIFS-235302
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-09, 2024
Authors: Shao, Shuai | Li, Dongwei
Article Type: Research Article
Abstract: As technology evolves, the allocation and use of educational resources becomes increasingly complex. Due to the many factors involved in recommending and matching English education resources, traditional predictive control models are no longer adequate. Therefore, fuzzy predictive control models based on neural networks have emerged. To increase the effectiveness and efficiency of using English educational resources (EER), this research aims to create a neural network-based fuzzy predictive control model (T-S-BPNN) for resource suggestion and matching. The results of the study show that the T-S-BPNN model α proposed in the study starts from 0 and increases sequentially by 0.1 up to …1, observing the change in MAE values. The experiment’s findings demonstrate that the value of MAE is lowest at values around 0.5. The T-S-BPNN model, on the other hand, gradually plateaued in its adaptation rate up to 7 runs, reaching about 9.8%. The accuracy rate peaked at 0.843 when the number of recommendations reached 7. The recall rate also peaked at 0.647 when the number of recommended English courses reached 7. The R-value for each set hovered around 0.97, which is a good fit. And the R-value of the training set is 0.97024, which can indicate that the T-S-BPNN model model proposed in the study fits well. It indicates that the algorithm proposed in the study is highly practical. Show more
Keywords: Resource recommendation, english teaching, fuzzy predictive control, recommended evaluation, neural network
DOI: 10.3233/JIFS-233265
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Ammavasai, S.K.
Article Type: Research Article
Abstract: The rapid growth of the cloud computing landscape has created significant challenges in managing the escalating volume of data and diverse resources within the cloud environment, catering to a broad spectrum of users ranging from individuals to large corporations. Ineffectual resource allocation in cloud systems poses a threat to overall performance, necessitating the equitable distribution of resources among stakeholders to ensure profitability and customer satisfaction. This paper addresses the critical issue of resource management in cloud computing through the introduction of a Dynamic Task Scheduling with Virtual Machine allocation (DTS-VM) strategy, incorporating Edge-Cloud computing for the Internet of Things (IoT). …The proposed approach begins by employing a Recurrent Neural Network (RNN) algorithm to classify user tasks into Low Priority, Mid Priority, and High Priority categories. Tasks are then assigned to Edge nodes based on their priority, optimizing efficiency through the application of the Spotted Hyena Optimization (SHO) algorithm for selecting the most suitable edge node. To address potential overloads on the edge, a Fuzzy approach evaluates offloading decisions using multiple metrics. Finally, optimal Virtual Machine allocation is achieved through the application of the Stable Matching algorithm. The seamless integration of these components ensures a dynamic and efficient allocation of resources, preventing the prolonged withholding of customer requests due to the absence of essential resources. The proposed system aims to enhance overall cloud system performance and user satisfaction while maintaining organizational profitability. The effectiveness of the DTS-VM strategy is validated through comprehensive testing and evaluation, showcasing its potential to address the challenges posed by the diverse and expanding cloud computing landscape. Show more
Keywords: Task scheduling, priority, classification, edge computing, cloud, VM allocation, IoT
DOI: 10.3233/JIFS-236838
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Du, Baigang | Zhang, Fujiang | Guo, Jun | Sun, Xiang
Article Type: Research Article
Abstract: The actual operating environment of rotating mechanical device contains a large number of noisy interference sources, leading to complex components, strong coupling, and low signal to noise ratio for vibration. It becomes a big challenge for intelligent fault diagnosis from high-noise vibration signals. Thus, this paper proposes a new deep learning approach, namely decomposition-enhance Fourier residual network (DEFR-net), to achieve high noise immunity for vibration signal and learn effective features to discriminate between different types of rotational machine faults. In the proposed DEFR-net, a novel algorithm is proposed to explicitly model high-noise signals for noisy data filtering and effective feature …enhancement based on a hard threshold decomposition function and muti-channel self-attention mechanism. Furthermore, it deeply integrates complementary analysis based on fast Fourier transform in the time-frequency domain and extends the breadth of network. The performance of the proposed model is verified by comparison with five state-of-the-art algorithms on two public datasets. Moreover, the noise experimental results show that the fault diagnosis accuracy is still 85.91% when the signal-to-noise-ratio reaches extreme noise of –8 dB. The results demonstrate that the proposed method is a valuable study for intelligent fault diagnosis of rotating machines in high-noise environments. Show more
Keywords: Intelligent fault diagnosis, high noise immunity, fourier residual network, decompose-enhance algorithm, attention mechanism
DOI: 10.3233/JIFS-233190
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Shao, Changshun | Yu, Zhenglin | Tang, Jianyin | Li, Zheng | Zhou, Bin | Wu, Di | Duan, Jingsong
Article Type: Research Article
Abstract: The main focus of this paper is to solve the optimization problem of minimizing the maximum completion time in the flexible job-shop scheduling problem. In order to optimize this objective, random sampling is employed to extract a subset of states, and the mutation operator of the genetic algorithm is used to increase the diversity of sample chromosomes. Additionally, 5-tuple are defined as the state space, and a 4-tuple is designed as the action space. A suitable reward function is also developed. To solve the problem, four reinforcement learning algorithms (Double-Q-learning algorithm, Q-learning algorithm, SARS algorithm, and SARSA(λ ) algorithm) are …utilized. This approach effectively extracts states and avoids the curse of dimensionality problem that occurs when using reinforcement learning algorithms. Finally, experimental results using an international benchmark demonstrate the effectiveness of the proposed solution model. Show more
Keywords: Reinforcement learning, flexible job-shop scheduling, maximum completion time, Variation
DOI: 10.3233/JIFS-236981
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Lin, Zhiwei | Zhang, Songchuan | Zhou, Yiwei | Wang, Haoyu | Wang, Shilei
Article Type: Research Article
Abstract: Current mainstream deep learning optimization algorithms can be classified into two categories: non-adaptive optimization algorithms, such as Stochastic Gradient Descent with Momentum (SGDM), and adaptive optimization algorithms, like Adaptive Moment Estimation with Weight Decay (AdamW). Adaptive optimization algorithms for many deep neural network models typically enable faster initial training, whereas non-adaptive optimization algorithms often yield better final convergence. Our proposed Adaptive Learning Rate Burst (Adaburst) algorithm seeks to combine the strengths of both categories. The update mechanism of Adaburst incorporates elements from AdamW and SGDM, ensuring a seamless transition between the two. Adaburst modifies the learning rate of the SGDM …algorithm based on a cosine learning rate schedule, particularly when the algorithm encounters an update bottleneck, which is called learning rate burst. This approach helps the model to escape current local optima more effectively. The results of the Adaburst experiment underscore its enhanced performance in image classification and generation tasks when compared with alternative approaches, characterized by expedited convergence and elevated accuracy. Notably, on the MNIST, CIFAR-10, and CIFAR-100 datasets, Adaburst attained accuracies that matched or exceeded those achieved by SGDM. Furthermore, in training diffusion models on the DeepFashion dataset, Adaburst achieved convergence in fewer epochs than a meticulously calibrated AdamW optimizer while avoiding abrupt blurring or other training instabilities. Adaburst augmented the final training set accuracy on the MNIST, CIFAR-10, and CIFAR-100 datasets by 0.02%, 0.41%, and 4.18%, respectively. In addition, the generative model trained on the DeepFashion dataset demonstrated a 4.62-point improvement in the Frechet Inception Distance (FID) score, a metric for assessing generative model quality. Consequently, this evidence suggests that Adaburst introduces an innovative optimization algorithm that simultaneously updates AdamW and SGDM and incorporates a learning rate burst mechanism. This mechanism significantly enhances deep neural networks’ training speed and convergence accuracy. Show more
Keywords: Convolutional neural networks (CNNs), MNIST, CIFAR, deep learning, optimization algorithms, person image generation, diffusion models
DOI: 10.3233/JIFS-239157
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Gonzalez, Claudia I. | Torres, Cesar
Article Type: Research Article
Abstract: This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are …applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found. Show more
Keywords: Fuzzy-neural network, fuzzy CNN, fuzzy deep learning model, fuzzy data, fuzzy convolutional
DOI: 10.3233/JIFS-219369
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Sun, Haibin | Zhang, Wenbo
Article Type: Research Article
Abstract: The ability of deep learning based bearing fault diagnosis methods is developing rapidly. However, it is difficult to obtain sufficient and comprehensive fault data in industrial applications, and changes in vibration signals caused by machine operating conditions can also hinder the accuracy of the model. The problem of limited data and frequent changes in operating conditions can seriously affect the effectiveness of deep learning methods. To tackle these challenges, a novel transformer model named the Differential Window Transformer (Dwin Transformer), which employs a new differential window self-attention mechanism, is presented in this paper. Meanwhile, the model introduces a hierarchical structure …and a new patch merging to further improve performance. Furthermore, a new fault diagnosis model dealing with limited training data is proposed, which combines the Auxiliary Classifier Generative Adversarial Network with the Dwin Transformer(DT-ACGAN). The DT-ACGAN model can generate high-quality fake samples to facilitate training with limited data, significantly improving diagnostic capabilities. The proposed model can achieve excellent results under the dual challenges of limited data and variable working conditions by combining Dwin Transformer with GAN. The DT-ACGAN owns superior diagnostic accuracy and generalization performance under limited sample data and varying working environments when compared with other existing models. A comparative test about cross-domain ability is conducted on the Case Western Reserve University dataset and Jiang Nan University dataset. The results show that the proposed method achieves an average accuracy of 11.3% and 3.76% higher than other existing methods with limited data respectively. Show more
Keywords: Transformer, generative adversarial network, cross-domains, limited data, fault diagnosis
DOI: 10.3233/JIFS-236787
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Dai, Jinpeng | Zhang, Zhijie | Yang, Xiaoyuan | Wang, Qicai | He, Jie
Article Type: Research Article
Abstract: This study explores nine machine learning (ML) methods, including linear, non-linear and ensemble learning models, using nine concrete parameters as characteristic variables. Including the dosage of cement (C), fly ash (FA), Ground granulated blast furnace slag (GGBS), coarse aggregate (G), fine aggregate (S), water reducing agent (WRA) and water (W), initial gas content (GC) and number of freeze-thaw cycles (NFTC), To predict relative dynamic elastic modulus (RDEM) and mass loss rate (MLR). Based on the linear correlation analysis and the evaluation of four performance indicators of R2 , MSE, MAE and RMSE, it is found that the nonlinear model has …better performance. In the prediction of RDEM, the integrated learning GBDT model has the best prediction ability. The evaluation indexes were R2 = 0.78, MSE = 0.0041, MAE = 0.0345, RMSE = 0.0157, SI = 0.0177, BIAS = 0.0294. In the prediction of MLR, ensemble learning Catboost algorithm model has the best prediction ability, and the evaluation indexes are R2 = 0.84, MSE = 0.0036, RMSE = 0.0597, MAE = 0.0312, SI = 5.5298, BIAS = 0.1772. Then, Monte Carlo fine-tuning method is used to optimize the concrete mix ratio, so as to obtain the best mix ratio. Show more
Keywords: Machine learning, relative dynamic elastic modulus, mass loss rate, sensitivity analysis, optimization design of mix proportions
DOI: 10.3233/JIFS-236703
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-26, 2024
Authors: Yuan, Weihao | Yang, Mengdao | Gu, Hexu | Xu, Gaojian
Article Type: Research Article
Abstract: There is scope to enhance agricultural measurement and control systems user interactivity, which typically necessitates training for users to perform specific operations successfully. With the continuous development of natural language semantic processing technology, it has become essential to augment the user-friendliness of multifaceted control and query operations in the agricultural measurement and control sector, ultimately leading to reduced operation costs for users. The study aims to focus on command parsing. The proposed AMR-OPO semantic parsing framework is based on the natural language understanding method of Abstract Meaning Representation of Rooted Markup Graphs (AMR). It transforms the user’s natural language inputs …into structured ternary (OPO) statements (operation-place-object) and converts the corresponding parameters of the user’s input commands. The framework subsequently sends the transformed commands to the relevant devices via the IoT gateway. To tackle the intricate task of parsing instructions, we developed a BERT-BiLSTM-ATT-CRF-OPO entity recognition model. This model can detect and extract entities from agricultural instructions, and precisely populate them into OPO statements. Our model shows exceptional accuracy in instruction parsing, with precision, recall, and F-value all measuring at 92.13%, 93.12%, and 92.76%, correspondingly. The findings from our experiment reveal outstanding and precise performance of our approach. It is anticipated that our algorithm will enhance the user experience offered by agricultural measurement and control systems, while also making them more user-friendly. Show more
Keywords: Natural language processing, abstract meaning representation, entity recognition, natural language understanding, human-computer interaction
DOI: 10.3233/JIFS-237280
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Li, Yingjie | Sun, Rongrong | Huang, Guangrong | Deng, Yuanbin | Zhang, Haixuan | Zhang, Delong
Article Type: Research Article
Abstract: In response to a series of issues in the distribution network, such as inadequate and inflexible utilization of flexible loads, delayed response to demand participation, and the uncertainty of new energy source output, a differentiated objective-based method for optimizing distribution network operations is proposed. Firstly, flexible loads are categorized, and corresponding mathematical models are established. Secondly, by employing chance-constrained programming to account for the uncertainty in new energy source output, a multi-objective optimization model is developed to reduce distribution network economic costs, decrease network losses, and enhance power supply reliability. Subsequently, an improved NSGA-III algorithm is introduced to address the …multi-objective model. Finally, an 11-node distribution network is used as a case study, and three different algorithms are comprehensively compared. This confirms the rationality of the optimized scheduling scheme proposed in this paper. Show more
Keywords: Distribution network, flexible load, multi-objective optimization, chance-constrained programming
DOI: 10.3233/JIFS-238367
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Yan, HongJu
Article Type: Research Article
Abstract: To solve the problem of lack of practice in Japanese teaching, a design of a Japanese remote interactive practical teaching platform based on the modern edge computing-based wireless sensor network is proposed. In terms of hardware, it mainly refits interactive mobile edge computing, wireless sensor networks, microprocessors, and other equipment, and adjusts the interface circuit. The Japanese teaching data and relevant Japanese teaching resources generated in the process of Japanese Teaching of practical courses are stored in the corresponding database table according to a certain format, and the logical relationship between database tables is used to update the database. The …software function of the platform is designed with the support of a database and hardware equipment. It consists of multiple modules, including platform user roles, interactive practical teaching and management, practical resource management and distribution, practice project information release, practice investigation statistics, and platform operation safety. Through the above design, the operation of a Japanese remote interactive practical teaching platform is realized. The test results show that there is no significant difference in the function realization of the design platform, but when multiple users are online at the same time, the interaction performance of the design platform is stronger, that is, the operation performance of the platform has obvious advantages. Show more
Keywords: Mobile edge computing, wireless sensor network, Japanese teaching platform, remote interactive learning, microprocessor, platform user roles, practical teaching, database management, interaction performance
DOI: 10.3233/JIFS-238196
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Sheng, Wenshun | Shen, Jiahui | Huang, Qiming | Liu, Zhixuan | Ding, Zihao
Article Type: Research Article
Abstract: A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for multi-target tracking (S-YOFEO) is proposed with the aim of addressing the issue of target ID transformation and loss caused by the increase of practical background complexity. For the purpose of further enhancing the representation of small-scale features, a small target detection head is first introduced to the detection layer of YOLOv8 in this paper with the aim of collecting more detailed information by increasing the detection resolution of YOLOv8. Secondly, …the Omni-Scale Network (OSNet) feature extraction network is implemented to enable accurate and efficient fusion of the extracted complex and comparable feature information, taking into account the restricted computational power of DeepSORT’s original feature extraction network. Again, a novel adaptive forgetting Kalman filter algorithm (FSA) is devised to enhance the precision of model prediction and the effectiveness of parameter updates to adjust to the uncertain movement speed and trajectory of pedestrians in real scenarios. Following that, an accurate and stable association matching process is obtained by substituting Efficient-Intersection over Union (EIOU) for Complete-Intersection over Union (CIOU) in DeepSORT to boost the convergence speed and matching effect during association matching. Last but not least, One-Shot Aggregation (OSA) is presented as the trajectory feature extractor to deal with the various noise interferences in the complex scene. OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. According to the trial results, S-YOFEO has made some developments as its precision can reach 78.2% and its speed can reach 56.0 frames per second (FPS). Show more
Keywords: Pedestrian tracking, YOLOv8, DeepSORT, association matching
DOI: 10.3233/JIFS-237208
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ahani, Zahra | Shahiki Tash, Moein | Ledo Mezquita, Yoel | Angel, Jason
Article Type: Research Article
Abstract: Super-enhancers are a category of active super-enhancers densely occupied by transcription factors and chromatin regulators, controlling the expression of disease-related genes and cellular identity. Recent studies have demonstrated the formation of complex structures by various factors and super-enhancers, particularly in various cancers. However, our current knowledge of super-enhancers, such as their genomic locations, interaction with factors, functions, and distinction from other super-enhancers regions, remains limited. This research aims to employ deep learning techniques to detect and differentiate between super-enhancers and enhancers based on genomic and epigenomic features and compare the accuracy of the results with other machine learning methods In …this study, in addition to evaluating algorithms, we trained a set of genomic and epigenomic features using a deep learning algorithm and the Python-based cross-platform software to detect super-enhancers in DNA sequences. We successfully predicted the presence of super-enhancers in the sequences with higher accuracy and precision. Show more
Keywords: Super-enhancer, enhancer, genomic, epigenomic, deep learning
DOI: 10.3233/JIFS-219356
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Shahbazova, Shahnaz N. | Rzayev, Ab.G. | Asadova, R.Sh. | Jabiyev, K.M.
Article Type: Research Article
Abstract: The paper gives a systems analysis in the field of heat transfer and temperature distribution (TD) along the length of oil production wells (OPW). The analysis shows that the existing mathematical models are suitable only for determining TD along the length of casing string (CS) and are not suitable for determining TD along the length of the tubing run, since the existence of the interfacial (between the CS and the tubing) annulus of the fluid and gas layers with heat capacity and thermal conductivity that differ significantly from the heat capacity and thermal conductivity of rocks surrounding the CS. Given …the above, mathematical models taking into account the impact of the fluid and gas layers in the annulus on the heat transfer from the upward fluid flow to the tubing wall and from the wall to the interfacial annulus are developed. To ensure the technological effectiveness of the obtained model, formulas for quantitative estimation of the heat transfer of the fluid flow into the surrounding environment are given. Show more
Keywords: Heat exchange, heat transfer, heat dissipation, thermal conductivity, temperature distribution, oil production well.
DOI: 10.3233/JIFS-219366
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Bai, Yu | Hu, Qijun | Zhou, Zhenxiang | Cai, Qijie | He, Leping
Article Type: Research Article
Abstract: The interaction of several workers with intelligent construction machinery can lead to serious collisions. Typically, the safety distance is used as an indicator of the safety of worker–machine interactions (WMI). However, the degree of risk does not increase linearly with decreasing worker–machine distances. To further reveal the essence of WMI safety, this study proposes a new method for assessing the safety state of WMIs, namely, the construction safety potential field. It is used to describe the factors and patterns associated with the spatial overlap and decay of hazardous energy in WMI operations. The proposed method was tested in an earthworks …construction WMI operation and the results were valid. A preliminary discussion of the relevant parameters constituting the construction safety potential field model is presented. The contributions of the research is proposing a generic energy-based model, which provides a novel idea for the interpretation of safety issues in construction WMI operations and opens up a new foundation for the development of active safety control. Show more
Keywords: Construction site, worker–machine safety, safety field, potential function
DOI: 10.3233/JIFS-236423
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Zhang, Qian | Bai, Enrui | Shao, Mingwen | Liang, Hong
Article Type: Research Article
Abstract: Convolutional neural networks (CNNs) and Transformer architectures have traditionally been recognized as the preferred models for addressing computer vision tasks. However, there has been a recent surge in the popularity of networks based on multi-layer perceptron (MLP) structures that do not rely on convolution or attention mechanisms. These MLP architectures have demonstrated exceptional performance in image classification tasks, exhibiting lower time complexity while maintaining high accuracy. In contrast, video classification tasks involve larger amounts of data and necessitate more intricate feature extraction, resulting in increased time and resource consumption. To enhance computational efficiency and minimize resource utilization, we propose a …convolution-free and Transformer-free architecture for video classification called Video-MLP for video classification. Video-MLP utilizes a simple MLP structure to learn video features. Specifically, it comprises two types of layers: Spatial-Mixer and Temporal-Mixer, which respectively capture spatial and temporal information. The Spatial-Mixer extracts spatial information from each frame along the height and width dimensions, while the Temporal-Mixer models temporal information for the same spatial positions across frames. To improve the efficiency of spatial-temporal modeling in our network, we used a spatial-temporal information fusion approach to integrate information at different scales. Additionally, we grouped the input data along the time dimension and designed three different grouping schemes when extracting temporal information. The experimental results indicate that Video-MLP achieved accuracy rates of 87.2% on the Kinetics-400 dataset and 75.3% on the SomethingV2 dataset, outperforming models with equivalent computational complexity. Notably, Video-MLP achieved these results without using convolution and attention mechanisms, and without pre-training on large-scale image and video datasets. Show more
Keywords: MLP-based-model, video classification, computer vision, deep learning
DOI: 10.3233/JIFS-240310
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Yue, Lizhu | Lv, Yue
Article Type: Research Article
Abstract: The Vlsekriterijumska Optimizacija I Komprosmisno Resenie (VIKOR) method to some extent modifies the utility function to a value function that can consider different risk preferences. However, the weight and risk attitude parameters involved in the model are difficult to determine, which limits its application. To overcome this problem, a Poset-VIKOR model is proposed. A partial order set is a non-parametric decision-making method. Through the combination of partial order set and VIKOR model, the parameters can be “eliminated”, and a robust method that can run the model is obtained. This method uses the Hasse diagram to express the evaluation results, which …can not only directly display the hierarchical and clustering information, but also show the robustness characteristics of the alternative comparison. Show more
Keywords: VIKOR method, poset, weight, multiple attribute decision making
DOI: 10.3233/JIFS-230680
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Shao, Dangguo | Huang, Chunsheng | Liu, Cuiyin | Ma, Lei | Yi, Sanli
Article Type: Research Article
Abstract: The automatic segmentation of diabetic retinopathy (DR) holds significant importance for assisting physicians in diagnosis and treatment. Given the complexity, high inter-class similarity, and uncertainty of DR, it is crucial to integrate multiscale information between lesions and establish global correlations among them. To address these issues, a novel HRU-TNet (Hybrid Residual U-Transformer Network) algorithm for retinal lesion segmentation is proposed. In this framework, the network is augmented with lightweight self-attention residual U-modules (LSA-RSU) to capture high-frequency details of the lesions and global contextual information. The skip connections are then enhanced through interactive residual transformer fusion modules (IRTF) and channel-cross attention …(CCA), promoting dependencies among features at different scales and filtering out interfering information to guide feature fusion and eliminate ambiguity. Additionally, a novel retinal image enhancement technique is devised, employing local wavelet transformations to capture detailed components of the retinal images, thereby enhancing the representational capacity of the segmentation network. Data augmentation is also performed to ensure network adaptability to small datasets. Comprehensive experiments conducted on the publicly available IDRID and e_ophtha datasets yielded average AUC_PR values of 0.709 and 0.451, respectively. The proposed approach demonstrated superior generalization on the DDR dataset compared to other methods mentioned in the literature. These results demonstrate that our proposed method is better suited for small retinal datasets, exhibiting improved segmentation accuracy and generalization compared to existing approaches. Show more
Keywords: Lesion segmentation, fundus image enhancement, transformer, cross attention fusion, light self-attention residual
DOI: 10.3233/JIFS-240788
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: He, Xiaorong | Fang, Anran | Yu, Dejian
Article Type: Research Article
Abstract: Electronic commerce (EC) has become the most critical business activity in the world. China has become the world’s largest market for EC. Over the past three decades, numerous researches have examined the current status of the development of monolingual EC research in specific scenarios. However, the paradigm shift in EC development through the analysis of the dynamic evolution of semantic information has not yet been examined, and the distinctions and connections between multilingual EC studies have not yet been established. This study analyzed 16,207 English and 17,850 Chinese EC-related articles from the Web of Science database and CNKI by combining …the BERTopic topic model and SBERT sentence embedding-based similarity computations. The results reveal the distributions of global and local topics in the English and Chinese EC literature, analyze the semantic intricacies of topic convergence and evolution across continuous time, as well as the distinctions and connections between English and Chinese topics. Finally, the evolutionary patterns and life cycle of three crucial English and Chinese topics are explored respectively, including their emergence, development, maturity, and decline. Overall, this study provides a comprehensive overview of EC studies from a topic perspective. Show more
Keywords: Electronic commerce, BERTopic, topic modeling, topic evolution, sentence embedding
DOI: 10.3233/JIFS-232825
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Kazancı, O. | Hoskova-Mayerova, S. | Davvaz, B.
Article Type: Research Article
Abstract: In recent years, the m-polar fuzziness structure has attracted the attention of researchers and has been commonly applied in algebraic structures. In this article, we present the notion of multi-polar fuzzy hyperideals of ordered semihyperrings, which is a generalization of the concept of bi-polar fuzzy hyperideals of ordered semihyperrings. We investigate some of their associated properties. Furthermore, we characterized regular ordered semihyperring in terms of multi-polar fuzzy quasi-ideals and multi-polar fuzzy bi-ideals.
Keywords: Semihyperring, ordered semihyperring, m-polar fuzzy semihyperring, m-polar fuzzy hyperideals
DOI: 10.3233/JIFS-238654
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-09, 2024
Authors: Ameen, Zanyar A. | Mohammed, Ramadhan A. | Al-shami, Tareq M. | Asaad, Baravan A.
Article Type: Research Article
Abstract: This paper introduces a new fuzzy structure named “fuzzy primal.” Then, it studies the essential properties and discusses their basic operations. By applying the q-neighborhood system in a primal fuzzy topological space and the Łukasiewicz disjunction, we establish a fuzzy operator (·) ⋄ on the family of all fuzzy sets, followed by its core characterizations. Next, we use (·) ⋄ to investigate a further fuzzy operator denoted by Cl ⋄ . To determine a new fuzzy topology from the existing one, the earlier fuzzy operators are explored. Such a new fuzzy topology is called primal fuzzy topology. Various properties of …primal fuzzy topologies are found. Among others, the structure of a fuzzy base that generates a primal fuzzy topology. Furthermore, the concept of compatibility between fuzzy primals and fuzzy topologies is introduced, and some equivalent conditions to that concept are examined. It is shown that if a fuzzy primal is compatible with a fuzzy topology, then the fuzzy base that produces the primal fuzzy topology is itself a fuzzy topology. Show more
Keywords: Fuzzy primal, fuzzy grill, fuzzy ideal, primal fuzzy topology, fuzzy ideal topology
DOI: 10.3233/JIFS-238408
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Article Type: Research Article
Abstract: Background: Breast cancer diagnosis relies on accurate lesion segmentation in medical images. Automated computer-aided diagnosis reduces clinician workload and improves efficiency, but existing image segmentation methods face challenges in model performance and generalization. Objective: This study aims to develop a generative framework using a denoising diffusion model for efficient and accurate breast cancer lesion segmentation in medical images. Methods: We design a novel generative framework, PalScDiff, that leverages a denoising diffusion probabilistic model to reconstruct the label distribution for medical images, thereby enabling the sampling of diverse, plausible segmentation outcomes. Specifically, with the …condition of the corresponding image, PalScDiff learns to estimate the masses region probability through denoising step by step. Furthermore, we design a Progressive Augmentation Learning strategy to incrementally handle segmentation challenges of irregular and blurred tumors. Moreover, multi-round sampling is employed to achieve robust breast mass segmentation. Results: Our experimental results show that PalScDiff outperforms established models such as U-Net and transformer-based alternatives, achieving an accuracy of 95.15%, precision of 79.74%, Dice coefficient of 77.61%, and Intersection over Union (IOU) of 81.51% . Conclusion: The proposed model demonstrates promising capabilities for accurate and efficient computer-aided segmentation of breast cancer. Show more
Keywords: Diffusion model, consistent regularization, breast cancer, medical image segmentation, data augmentation
DOI: 10.3233/JIFS-239703
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Yang, Guang | Qi, Juntong | Wang, Mingming | Wu, Chong | Liu, Yansheng | Liu, Zhengjun | Ping, Yuan
Article Type: Research Article
Abstract: Target encirclement is widely used in the field of unmanned aerial vehicles(UAVs), which can effectively monitor and intercept external threats. However, the integration from target detection, localization to final tracking is difficult or costly. This article proposes a complete and inexpensive framework of the target encirclement for multiple quadrotors. The framework consists of three modules: object detection, target localization and formation tracking. Firstly, a one-stage object detector based on a convolutional neural network is used to achieve fast and accurate object detection. Then, combined with the position and attitude states of the quadrotor, a 3D target localization scheme to locate …the target position is proposed. Based on consensus theory, a time-varying formation tracking control protocol is proposed. Finally, a multiple quadrotor platform composed of one reconnaissance quadrotor and four hunter quadrotors is built with self-organizing network communication, which avoids the expensive cost of deploying object detection modules on each quadrotor platform. We deployed the framework on the multiple quadrotor platform and conducted static and dynamic localization and encirclement experiments with a minibus as the target. The result shows that the reconnaissance quadrotors can detect and accurately locate targets over 30 fps , and the average deviation of locating the target minibus could reach a minimum of 0.0712 m . The hunter quadrotors could track and encircle the dynamic moving target minibus in a time-varying formation. Experiments demonstrate the effectiveness and practicality of the proposed framework of the target encirclement for multiple quadrotors. Show more
Keywords: Multiple quadrotors, target encirclement, visual detection, target localization, time-varying formation tracking
DOI: 10.3233/JIFS-238335
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ou, Qiqi | Zhang, Xiaohong | Wang, Jingqian
Article Type: Research Article
Abstract: Fuzzy rough sets (FRSs) play a significant role in the field of data analysis, and one of the common methods for constructing FRSs is the use of the fuzzy logic operators. To further extend FRSs theory to more diverse information backgrounds, this article proposes a covering variable precision fuzzy rough set model based on overlap functions and fuzzy β-neighbourhood operators (OCVPFRS). Some necessary properties of OCVPFRS have also been studied in this work. Furthermore, multi-label classification is a prevalent task in the realm of machine learning. Each object (sample or instance) in multi-label data is associated with various labels (classes), …and there are numerous features or attributes that need to be taken into account within the attribute space. To enhance various performance metrics in the multi-label classification task, attribute reduction is an essential pre-processing step. Therefore, according to overlap functions and fuzzy rough sets’ excellent work on applications: such as image processing and multi-criteria decision-making, we establish an attribute reduction method suitable for multi-label data based on OCVPFRS. Through a series of experiments and comparative analysis with existing multi-label attribute reduction methods, the effectiveness and superiority of the proposed method have been verified. Show more
Keywords: Fuzzy rough sets, overlap functions, fuzzy β-neighbourhood operators, attribute reduction, multi-label classification
DOI: 10.3233/JIFS-238245
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Embriz-Islas, Cesar | Benavides-Alvarez, Cesar | Avilés-Cruz, Carlos | Zúñiga-López, Arturo | Ferreyra-Ramírez, Andrés | Rodríguez-Martínez, Eduardo
Article Type: Research Article
Abstract: Speech recognition with visual context is a technique that uses digital image processing to detect lip movements within the frames of a video to predict the words uttered by a speaker. Although models with excellent results already exist, most of them are focused on very controlled environments with few speaker interactions. In this work, a new implementation of a model based on Convolutional Neural Networks (CNN) is proposed, taking into account image frames and three models of audio usage throughout spectrograms. The results obtained are very encouraging in the field of automatic speech recognition.
Keywords: CNN, artificial intelligence, deep learning, speech recognition
DOI: 10.3233/JIFS-219346
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Zavala-Díaz, Jonathan | Olivares-Rojas, Juan C. | Gutiérrez-Gnecchi, José A. | Téllez-Anguiano, Adriana C. | Alcaraz-Chávez, J. Eduardo | Reyes-Archundia, Enrique
Article Type: Research Article
Abstract: Efficient medical information management is essential in today’s healthcare, significantly to automate diagnoses of chronic diseases. This study focuses on the automated identification of diabetic patients through a clinical note classification system. This innovative approach combines rules, information extraction, and machine learning algorithms to promise greater accuracy and adaptability. Initially, the four algorithms evaluated showed similar performance, with Gradient Boosting standing out with an accuracy of 0.999. They were tested on our clinical and oncology notes, where SVM excelled in correctly labeling non-oncology notes with a 0.99. Gradient Boosting had the best average with 0.966. The combination of rules, information …extraction, and Random Forest provided the best average performance, significantly improving the classification of clinical notes and reducing the margin of error in identifying diabetic patients. The principal contribution of this research lies in the pioneering integration of rule-based methods, information extraction techniques, and machine learning algorithms for enhanced accuracy in diabetic patient identification. For future work, we consider implementing these algorithms in natural clinical settings to evaluate their practical performance. Additionally, additional approaches will be explored to improve the accuracy and applicability of clinical note-grading systems in healthcare. Show more
Keywords: NLP, diabetes, machine learning, binary classification, word frequency analysis
DOI: 10.3233/JIFS-219375
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Valadez-Godínez, Sergio | Sossa, Humberto | Santiago-Montero, Raúl
Article Type: Research Article
Abstract: The Associative Pattern Classifier (APC) was designed as an associative memory, focusing particularly on pattern classification. This implies that the training memory is constructed in a single operation and pattern classification also occurs in a single process. It is important to note that the APC translates the input patterns through a translation vector, which represents the average of all input patterns. Until now, there is no theoretical framework to explain the inner workings of the APC. Its relevance is inferred from the fact that several studies have been conducted using it as a foundation. This paper seeks to provide a …theoretical comprehension of the APC’s operation to facilitate future enhancements. We found the APC creates a system in static equilibrium through concurrent vectors at the origin (translation vector), resulting in a balanced separation of patterns. However, the APC cannot achieve complete pattern separation because of the presence of a neutral region. The neutral region is defined by all the points that define the separation hyperplanes. The points over the hyperplanes cannot be classified by the APC. Additionally, we discovered that the APC is unable to accurately classify the translation vector, which could be included as part of the input patterns. Our previous research showed that the APC is unsuccessful in achieving the linear separation of the AND function. In this research, we broaden the examination of the AND function to illustrate that achieving linear separation is not feasible because the separation line represents a neutral region. The APC demonstrated exceptional performance when tested with artificial datasets where patterns were distributed over balanced regions, thus operating as an efficient multiclass and non-linear classifier. Nevertheless, the performance of the APC is lower when tested with real-world databases, making the APC inaccurate due to its restricted inner workings. Show more
Keywords: Classifier, pattern, associative memory, class, classification
DOI: 10.3233/JIFS-219347
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Martinez, German | Duta, Eduard-Andrei | Sanchez-Romero, Jose-Luis | Jimeno-Morenilla, Antonio | Mora-Mora, Higinio
Article Type: Research Article
Abstract: Within various industrial settings, such as shipping, aeronautics, woodworking, and footwear, there exists a significant challenge: optimizing the extraction of sections from material sheets, a process known as “nesting”, to minimize wasted surface area. This paper investigates efficient solutions to complex nesting problems, emphasizing rapid computation over ultimate precision. We introduce a dual-approach methodology that couples both a greedy technique and a genetic algorithm. The genetic algorithm is instrumental in determining the optimal sequence for placing sections, ensuring each is located in its current best position. A specialized representation system is devised for both the sections and the material sheet, …promoting streamlined computation and tangible results. By balancing speed and accuracy, this study offers robust solutions for real-world nesting challenges within a reduced computational timeframe. Show more
Keywords: Genetic algorithm, 2D nesting, irregular pattern, cutting, industrial automation
DOI: 10.3233/JIFS-219345
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ling, Lina | Wen, Mi | Wang, Haizhou | Zhu, Zhou | Meng, Xiangjie
Article Type: Research Article
Abstract: The detection of out-of-distribution (OoD) samples in semantic segmentation is crucial for autonomous driving, as deep learning models are typically trained under the assumption of a closed environment, whereas the real world presents an open and diverse set of scenarios. Existing methods employ uncertainty estimation, image reconstruction, and other techniques for OoD sample detection. We have observed that different classes may exhibit connections and associations in varying contexts. For example, objects encountered by autonomous vehicles differ in rural road scenes compared to urban environments, and the likelihood of encountering novel objects varies. This aspect is missing in current anomaly detection …methods and is vital for OoD sample detection. Existing approaches solely consider the relative significance of each prediction class, overlooking the inter-object correlation. Although prediction scores (e.g., max logits) obtained from the segmentation network are applicable for OoD sample detection, the same problem persists, particularly for OoD objects. To address this issue, we propose the utilization of the Mahalanobis distance of max logits to evaluate the final predicted score. By calculating the Mahalanobis distance, the paper aims to uncover correlations between different classes, thus enhancing the effectiveness of OoD detection. To this end, we also extend the state-of-the-art segmentation model, DeepLabV3+, to enable OoD sample detection in this paper. Specifically, this paper proposes a novel backbone network, SOD-ResNet101, for extracting contextual and multi-scale semantic information, leveraging the class correlation feature of the Mahalanobis distance to enhance the detection performance of out-of-distribution objects. Notably, our approach eliminates the need for external datasets or separate network training, making it highly applicable to existing pretraining segmentation models. Show more
Keywords: Semantic segmentation, deep learning, anomaly segmentation, automatic driving, out-of-distribution detection
DOI: 10.3233/JIFS-237799
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Kumar Sahu, Vinay | Pandey, Dhirendra | Singh, Priyanka | Haque Ansari, Md Shamsul | Khan, Asif | Varish, Naushad | Khan, Mohd Waris
Article Type: Research Article
Abstract: The Internet of Things (IoT) strategy enables physical objects to easily produce, receive, and exchange data. IoT devices are getting more common in our daily lives, with diverse applications ranging from consumer sector to industrial and commercial systems. The rapid expansion and widespread use of IoT devices highlight the critical significance of solid and effective cybersecurity standards across the device development life cycle. Therefore, if vulnerability is exploited directly affects the IoT device and the applications. In this paper we investigated and assessed the various real-world critical IoT attacks/vulnerabilities that have affected IoT deployed in the commercial, industrial and consumer …sectors since 2010. Subsequently, we evoke the vulnerabilities or type of attack, exploitation techniques, compromised security factors, intensity of vulnerability and impacts of the expounded real-world attacks/vulnerabilities. We first categorise how each attack affects information security parameters, and then we provide a taxonomy based on the security factors that are affected. Next, we perform a risk assessment of the security parameters that are encountered, using two well-known multi-criteria decision-making (MCDM) techniques namely Fuzzy-Analytic Hierarchy Process (F-AHP) and Fuzzy-Analytic Network Process (F-ANP) to determine the severity of severely impacted information security measures. Show more
Keywords: IoT attacks, fuzzy-ANP, fuzzy-AHP, MCDM, IoT vulnerabilities
DOI: 10.3233/JIFS-233759
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Chen, Jian | Cai, Zhiming | Peng, Sheng | Lu, Fei
Article Type: Research Article
Abstract: In the era of widespread connectivity, leveraging artificial intelligence models and analyzing the vast datasets generated by smart devices are central points in IoT research. While existing studies mainly focus on improving the decision-making prowess of central systems, the potential for local optimization remains largely unexplored. This paper presents an Ensemble Voting Scheme with Multilayer Dynamic Groups (EVMDS), which assigns decision weights to IoT devices based on their attribute data. By employing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, dynamic clusters among IoT devices can be identified, the application of ensemble voting rules at each stage of …group formation, enabling layered computations to ease backend burden and achieve hierarchical decision-making capability, facilitating regional-level decision-making that strikes a balance between local and global optimization. Through simulated decision-making scenarios in a small-scale IoT environment, our experiments demonstrate the superior accuracy and reliability of the proposed approach compared to existing models. Show more
Keywords: Local optimization, Internet-of-things, ensemble-voting, DBSCAN, dynamic grouping
DOI: 10.3233/JIFS-236899
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Bochkarev, Vladimir V. | Savinkov, Andrey V. | Shevlyakova, Anna V. | Solovyev, Valery D.
Article Type: Research Article
Abstract: This work considers implementation of a diachronic predictor of valence, arousal and dominance ratings of English words. The estimation of affective ratings is based on data on word co-occurrence statistics in the large diachronic Google Books Ngram corpus. Affective ratings from the NRC VAD dictionary are used as target values for training. When tested on synchronic data, the obtained Pearson‘s correlation coefficients between human affective ratings and their machine ratings are 0.843, 0.779 and 0.792 for valence, aroused and dominance, respectively. We also provide a detailed analysis of the accuracy of the predictor on diachronic data. The main result of …the work is creation of a diachronic affective dictionary of English words. Several examples are considered that illustrate jumps in the time series of affective ratings when a word gains a new meaning. This indicates that changes in affective ratings can serve as markers of lexical-semantic changes. Show more
Keywords: Affective words, affective norms, sentiment dictionary, word valence ratings, lexical semantic change
DOI: 10.3233/JIFS-219358
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zhang, Yingmin | Yi, Afa | Li, Shuo
Article Type: Research Article
Abstract: The constant development and application of new technologies, such as big data, artificial intelligence and the mobile Internet, have profoundly changed the personal and professional spheres. Despite these advances, finance professionals are still faced with a multitude of routine, repetitive and error-prone tasks. At the same time, they are challenged by the shift to management accounting, resulting in reduced productivity. This paper addresses these issues by introducing a financial statement filing robot developed using Robotic Process Automation (RPA) technology. The application of this robot has been shown to provide superior efficiency and accuracy, reduce the heavy burden of routine tasks, …and facilitate a smooth transition to management accounting practices. In addition, this research provides a valuable reference for the application and diffusion of RPA technology in the financial sector. Given the large amount of text data generated by financial processes, this paper proposes an automatic text categorization model. The effectiveness of the model is demonstrated as a response to address the challenges encountered in the consultation and archiving process. This contribution informs the development of text categorization robots tailored to the needs of finance professionals. Show more
Keywords: RPA technology, robot, financial statements, text classification, naive Bayes classifier model
DOI: 10.3233/JIFS-236716
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
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