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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: Liu, Jianping | Chu, Xintao | Wang, Jian | Wang, Meng | Wang, Yingfei
Article Type: Research Article
Abstract: Due to the polysemy and complexity of the Chinese language, Chinese machine reading comprehension has always been a challenging task. To improve the semantic understanding and robustness of Chinese machine reading comprehension models, we propose a model that utilizes adversarial training algorithms and Permuted Language Model (PERT). Firstly, we employ the PERT pre-training model to embed paragraphs and questions into vector space to obtain corresponding sequential representations. Secondly, we use a multi-head self-attention mechanism to extract key textual information from the sequence and employ a Bi-GRU network to semantically fuse the output feature vectors, aiming to learn deep semantic representations …in the text. Finally, we introduce perturbations into the model training process. We achieve this by utilizing adversarial training algorithms such as Fast Gradient Method (FGM) and Projected Gradient Descent (PGD). These algorithms generate adversarial samples to enhance the model’s robustness and stability when facing diverse inputs. We conducted comparative experiments on the publicly available Chinese reading comprehension datasets CMRC2018 and DRCD. The experimental results show that our proposed model has achieved significant improvements in both EM and F1-Score compared to the baseline model. To validate the model’s generalization and robustness, we utilized ChatGPT to construct a scientific dataset that includes a large number of domain-specific terms, sentences with mixed Chinese and English, and complex comprehension tasks. Our model also performed remarkably well on the self-built dataset. In conclusion, the proposed model not only effectively enhances the understanding of semantic information in Chinese text but also demonstrates a certain level of generalization capability. Show more
Keywords: Machine reading comprehension, pre-trained model, adversarial training, Bi-GRU, multi-head self-attention mechanism
DOI: 10.3233/JIFS-234417
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10059-10073, 2024
Authors: Zhu, Meng-Meng | Mao, Jun-Jun | Xu, Wei
Article Type: Research Article
Abstract: Linguistic preference relations with self-confidence (LPRs-SC) are the preference relation that can reflect the decision maker’s (DM) confidence psychology and has received widespread attention for their simple form and multiple information. Currently, arithmetic studies of LPRs-SC are conducted separately for preference relations and self-confidence. In addition, personalized individual semantics (PIS) is an important tool in large-scale decision-making to reflect the differences in the semantic understanding of DMs. However, the confidence level in LPRs-SC limits the preference relation to a certain extent and the linguistic representations of these two components are usually different. This means that it is not only necessary …to propose an arithmetic rule that can express the restrictive relationship between the two but also to construct a model that can extract the PIS of preference relation and confidence respectively. Besides, we constructed a two-stage consensus reaching process (CRP) based on the specificity of the LPRs-SC structure when enhancing group harmony. The process takes self-confidence as an independent source of information, delineates the adjusted categories in detail, and builds an adjustment model accordingly. Finally, the example and comparative analyses verify the merits of the proposed PIS in terms of consistency enhancement and CRP in terms of speed and accuracy harmonization. Show more
Keywords: Personalized individual semantics, linguistic preference relations with self-confidence, consensus reaching process, large scale decision making
DOI: 10.3233/JIFS-236552
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10075-10093, 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. 46, no. 4, pp. 10095-10114, 2024
Authors: Kahraman, Cengiz
Article Type: Research Article
Abstract: Intuitionistic fuzzy sets aims at taking the hesitancy of an expert into account in assigning a membership degree or a non-membership degree. The direct assignment of decimal numbers for membership and non-membership degrees of an element in intuitionistic fuzzy sets is not practical. Besides, the assigned degrees are generally composed of one digit or at most two digits after dot. This problem has not been addressed as much as it deserves in the literature. The hypothesis of the paper is that the determination of proportional relationships between membership and non-membership degrees is more appropriate than the direct assignment to obtain …the degrees. Proportional intuitionistic fuzzy (PIF) sets require only the proportion relations between an intuitionistic fuzzy set’s parameters. The accuracy of the results obtained with multi-criteria decision-making models definitely depends on how accurately the membership degrees are determined. In this paper, we extend Combinative distance-based assessment (CODAS) method by using single-valued proportional intuitionistic fuzzy sets. We compare the proposed PIF CODAS method with ordinary fuzzy CODAS method. A cloud service provider selection problem is handled to show the validity of the proposed PIF CODAS method. Additionally, a comparative analysis and a sensitivity analysis together with a discussion are presented. Show more
Keywords: Proportional intuitionistic fuzzy sets, aggregation operators, multi-criteria decision making, CODAS, Cloud service provider selection
DOI: 10.3233/JIFS-237389
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10115-10133, 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. 46, no. 4, pp. 10135-10150, 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. 46, no. 4, pp. 10151-10165, 2024
Authors: Du, Xin-Feng | Wang, Jie-Sheng | Sun, Wei-Zhen | Zhang, Zhi-Hao | Zhang, Yun-Hao
Article Type: Research Article
Abstract: Accurate blood vessel segmentation on retinal blood vessel images is helpful for the early detection of ophthalmic diseases such as diabetes, hypertension, cardiovascular and cerebrovascular diseases, and inhibits the deterioration of the disease. In current research within the field of retinal blood vessel segmentation, significant challenges exist in accurately segmenting small blood vessels and maintaining blood vessel continuity. The segmentation algorithm proposed in this article offers substantial improvements to address these issues. To enhance the segmentation performance of retinal blood vessels and facilitate more accurate diagnosis of fundus diseases by ophthalmologists, this paper introduces a novel bidirectional convolutional long short-term …memory (LSTM) residual U-Net segmentation algorithm, incorporating improvements to the Focal loss function. Firstly, in the encoding part of U-Net, the multi-scale convolution kernels and Bi-ConvLSTM were adopted to improve the residual structure, obtain richer blood vessel features and enhance the detection ability of micro vessels and the continuity of blood vessel characteristics. At the same time, the class balanced cross entropy loss function was improved and the proportional modulation factor is introduced to enhance the learning ability of the network for difficult samples. By adding the Bi-ConvLSTM to the residual structure and introducing the proportional modulation coefficient to the loss function, the network structure realizes better feature information detection and greatly enhances the detection ability of small blood vessels. The experimental analysis on the DRIVE and CHASE_DB1 data sets showed that the sensitivity, specificity, accuracy and AUC reached 0.7961, 0.9796, 0.9563, 0.9792; 0.8344, 0.9665, 0.9547, 0.9758, respectively. The experimental results fully show that the Bi-ConvLSTM residual U-Net segmentation algorithm based on the improved Focal loss function enhances the detection ability of small blood vessel features, improves the continuity of blood vessel features and the network segmentation performance, and is superior to U-Net algorithm and some current mainstream retinal blood vessel segmentation algorithms. Show more
Keywords: Retinal blood vessel segmentation, bi-directional convolution long and short time memory network, residual block, Multi-scaleconvolution, U-Net, proportional modulation coefficient
DOI: 10.3233/JIFS-236702
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10167-10186, 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. 46, no. 4, pp. 10187-10200, 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. 46, no. 4, pp. 10201-10212, 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. 46, no. 4, pp. 10213-10229, 2024
Authors: Zhan, Qiuyan | Saeid, A. Borumand | Davvaz, Bijan
Article Type: Research Article
Abstract: The aim of this paper is to investigate several operators on L -algebras. At first, closure (interior) operators on L -algebras are defined and some properties of them are obtained. Then, existential operators and universal operators on L -algebras are studied, a one-to-one correspondence between the set of all quantifier operators and the set of all relative complete subalgebras of CKL -algebras is constructed. Furthermore, very true operators on L -algebras are investigated and by giving a very true ideal of a very true L -algebra, quotient structures on very true L -algebras are established.
Keywords: L-algebra, closure (interior) operator, existential (universal) operator, very true operator
DOI: 10.3233/JIFS-234370
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10231-10241, 2024
Authors: Ajitha Gladis, K.P. | Srinivasan, R. | Sugashini, T. | Ananda Raj, S.P.
Article Type: Research Article
Abstract: Visual impairment people have many difficulties in everyday life, including communicating and getting information, as well as navigating independently and safely. Using auditory alerts, our study hopes to improve the lives of visually impaired individuals by alerting them to items in their path. In this research, a Video-based Smart object detection model named Smart YOLO Glass has been proposed for visually impaired persons. A Paddling - Paddling Squeeze and Attention YOLO Network model is trained with multiple images to detect outdoor objects to assist visually impaired people. In order to calculate the distance between a blind person and obstacles when …moving from one location to another, the proposed method additionally included a distance-measuring sensor. The visually impaired will benefit from this system’s information about around objects and assistance with independent navigation. Recall, accuracy, specificity, precision, and F-measure were among the metrics used to evaluate the proposed strategy. Because there is less time complexity, the user can see the surrounding environment in real time. When comparing the proposed technique to Med glasses, DL smart glass, and DL-FDS, the total accuracy is improved by 7.6%, 4.8%, and 3.1%, respectively. Show more
Keywords: Visual impairment, deep learning, outdoor object detection, wearable system, YOLO network
DOI: 10.3233/JIFS-234453
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10243-10256, 2024
Authors: Yu, Dan | Wu, Jun | He, Yongling
Article Type: Research Article
Abstract: The distributed robust optimal allocation method for multi-microgrid interconnected systems usually involves a large number of variables and constraints, and the computational complexity is high in practical applications, which makes it difficult to solve the problem. Therefore, a distributed robust optimal allocation method for multi-microgrid interconnection systems based on multi-objective swarm algorithm is proposed. A distributed robust optimization configuration constraint index model for multi-microgrid interconnection system is established. Considering the influence of energy storage technology operation characteristics on its service life, a micro-grid hybrid energy storage capacity optimization configuration model with the minimum annual comprehensive energy storage cost as the …objective function is established with charge and discharge power and residual power as the constraint conditions. The multi-objective swarm algorithm is used to realize the optimization model of distributed robust configuration microgrid interconnection system. By determining the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points, the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points are determined. The hybrid energy storage configuration model of multi-microgrid interconnection system is established with the minimum alternative operating cost as the objective function, so as to realize the distributed robust optimal configuration of multi-microgrid interconnection system. The simulation results show that the distributed configuration of multi-microgrid interconnection system with the proposed method has good robustness and strong optimization control ability. Show more
Keywords: Multi-objective bee colony algorithm, multi-microgrid, interconnection system, robust allocation
DOI: 10.3233/JIFS-235092
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10257-10268, 2024
Authors: Mohan, Prakash | Aishwarya, S.
Article Type: Research Article
Abstract: Price changes in construction materials have a significant impact on building construction projects. Such price variations occur at random and at varying rates over time. A system that can estimate the magnitude and quantity of the change in material prices with reasonable accuracy is required. The primary goal is to create a machine-learning model that can predict the type of building material chosen based on environmental factors. The compressive strength of concrete is critical in defining its mechanical qualities. Long laboratory testing is needed to determine the compressive strength of concrete. The capacity of powerful machine learning algorithms to forecast …concrete compressive strength speeds up these lengthy experimental methods while also lowering expenses. This study provides abilities to precisely anticipate and categorize numerous qualities and traits of distinct materials. The framework includes a broad dataset that details materials, composition, and performance characteristics. Machine learning algorithms such as logistic regression (LR), decision trees (DT), and random forests (RF) train models on the training data. The models are hyper-parameter tweaked and feature developed to achieve the most outstanding performance. The k-fold method is used throughout the training and assessment phase to guarantee robustness and reduce bias. The F1 score and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) curve are two performance measures used to measure how accurate and predictive the trained models are. The study findings provide insights into the qualities of the materials, facilitating improved material selection, quality assurance, and decision-making in the building sector. In the analyses, the best accuracy value was 99.92%, and the precision value was 88.83% using the LR algorithm. As a result, it was determined that the LR algorithm had the least execution 57.826 ms, and is thus the most suitable for use in concrete compressive strength estimation. Show more
Keywords: Building materials, machine learning algorithms, feature selection, model training, K-fold, performance evaluation
DOI: 10.3233/JIFS-236111
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10269-10285, 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. 46, no. 4, pp. 10287-10303, 2024
Authors: Tang, Sicong | Wang, Hailong
Article Type: Research Article
Abstract: With the continuous deepening of the urbanization process and the progress of science and technology, people transform nature and develop nature on a larger and larger scale, among which the most iconic transformation is a variety of building structures built by people. And with the passage of time, the building structure in the perennial wind and sun, there will be signs of “illness”, if not timely treatment, it will have a huge impact on the stability and safety of the building structure. Based on this, in this paper, according to the characteristics of crack identification on the surface of concrete …structure, background subtraction algorithm is selected for image noise reduction processing. Through three steps of digital image noise reduction, crack extraction and crack parameter identification, the quantitative recognition of cracks is completed and a complete system of crack parameter identification is formed. The experimental results show that the machine learning model of building structure health monitoring and damage recognition algorithm proposed in this paper has excellent statistical performance, and the relative error accuracy of recognition can be controlled within 10%. Show more
Keywords: Image processing, building structure, health monitoring, damage identification, crack identification
DOI: 10.3233/JIFS-239655
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10305-10314, 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. 46, no. 4, pp. 10315-10328, 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. 46, no. 4, pp. 10329-10343, 2024
Authors: Saichand, N. Venkata | Naik, S. Gopiya
Article Type: Research Article
Abstract: Epilepsy is considered a most general neurological disorder related to brain activity disruption. In epileptic seizures detection and classification, EEG (Electroencephalogram) measurements that record the brain’s electrical activities are used frequently. Generally, physicians investigate the abnormalities in the brain. However, this technique is time-consuming, faced complexity in seizure detection, and poor consistency because of data imbalance. To overcome these difficulties, Improved Empirical Mode Decomposition for feature extraction and Improved Weight Updated KNN (K-Nearest Neighbor) algorithm for classification are proposed. In the case of pre-processing, a rule-based filter, namely a wiener scalar filter with integer wavelet transform is used for multiple …channels conversion and further signal to noise ratio is increased. Further in feature extraction, better features are extracted using an improved empirical mode decomposition-based bandpass filter. By using the Improved Weight updated KNN, feature extracted samples are classified incorrect manner, avoiding data imbalance issues. Feature vectors’ effective classification is performed attains higher computational speed and sensitivity. The EEG input signal of the proposed study utilizing the BONN dataset and different performance metrics such as accuracy, sensitivity, specificity, recall, f-score, and error values were performed and compared with various existing studies. From the results, it is clear that the proposed method provides effective detection for seizure and non-seizure patients compared with existing studies. Show more
Keywords: Seizure detection, bandpass filter, rule-based filter, improved empirical mode decomposition, improved weight updated KNN
DOI: 10.3233/JIFS-222960
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10345-10358, 2024
Authors: Li, Zheming | Chen, Yidan | Yang, Bo | Li, Chenwei | Zhang, Shihua | Li, Wei | Zhang, Hengwei
Article Type: Research Article
Abstract: Abstract Adversarial examples are often used to test and evaluate the security and robustness of image classification models. Though adversarial attacks under white-box setting can achieve a high attack success rate, due to overfitting, the success rate of black-box attacks is relatively low. To this end, this paper proposes diversified input strategies to improve the transferability of adversarial examples. In this method, various transformation methods are applied to randomly transform the original image multiple times, thereby generating a batch of transformed images. Then, in the process of back-propagation, the loss function gradient of the transformed images is calculated, and a weighted …average of the obtained gradient values is performed to generate adversarial perturbation, which is iteratively added to the original image to generate adversarial examples. Meanwhile, by increasing the variety of data augmentation transformation types and the number of input images, the proposed method effectively alleviates overfitting and improves the transferability of adversarial examples. Extensive experiments on the ImageNet dataset indicate that the proposed method can perform black-box attacks better than benchmark methods, with an average of 97.2% success rate attacking multiple models simultaneously. Show more
Keywords: Deep neural network, image classification, adversarial examples, black-box attacks, diversified input
DOI: 10.3233/JIFS-223584
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10359-10373, 2024
Authors: Duan, Chunyan | Zhu, Mengshan | Wang, Kangfan
Article Type: Research Article
Abstract: Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an …improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems. Show more
Keywords: Failure mode and effects analysis, reliability analysis, intelligent manufacturing systems, machine learning
DOI: 10.3233/JIFS-232712
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10375-10392, 2024
Authors: Ren, Yonghui | Shi, Yan | Li, Chenglin | Jin, Yanxu
Article Type: Research Article
Abstract: Robots can help people complete repetitive and high-risk tasks, such as industrial production, medical care, environmental monitoring, etc. The control system of robots is the key to their ability to complete tasks, and studying robot control systems is of great significance. This article used Convolutional Neural Network (CNN) and Robotic Process Automation (RPA) technologies to optimize and train the robot control system and constructed a robot control system. This article conducts perception and decision-making experiments and execution experiments in the experimental section. According to the experimental results, it can be concluded that the average image recognition accuracy of the robot …control system in perception and decision-making experiments was 94.62%. The average decision accuracy was 87.5%, and the average time efficiency was 176 seconds. During the execution of the experiment, the deviation of the motion trajectory shall not exceed 5 cm, and the oscillation amplitude shall not exceed 6°; the distance from the obstacle shall not exceed 20 cm, and the movement speed shall not exceed 0.6 m/s; the operating time shall not exceed 25 hours, and the number of faults shall not exceed 0.2 times per hour, all within the normal range. The robot control system based on Deep Learning (DL) and RPA has broad application prospects and research value, which would bring new opportunities and challenges to the development and application of robot technology. Show more
Keywords: Robot control system, Robotic Process Automation (RPA), Convolutional Neural Network (CNN), Deep Learning (DL)
DOI: 10.3233/JIFS-233056
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10393-10403, 2024
Authors: Maddali, Deepika
Article Type: Research Article
Abstract: A rising number of edge devices, like controllers, sensors, and robots, are crucial for Industrial Internet of Things (IIoT) networks for collecting data for communication, storage, and processing. The security of the IIoT could be compromised by any malicious or unusual behavior on the part of any of these devices. They may also make it possible for malicious software placed on end nodes to enter the network and perform unauthorized activities. Existing anomaly detection techniques are less effective due to the increasing diversity of the network and the complexity of cyberattacks. In addition, most strategies are ineffective for devices with …limited resources. Therefore, this work presents an effective deep learning based Malware Detection framework to make the edge based IIoT network more secure. This multi-stage system begins with the Deep Convolutional Generative Adversarial Networks (DCGAN) based data augmentation method to overcome the issue of data imbalance. Next, a ConvNeXt-based method extracts the features from the input data. Finally, an optimized Enhanced Elman Spike Neural Network (EESNN) based deep learning is utilized for malware recognition and classification. Using two distinct datasets— MaleVis and Malimg— the generalizability of the suggested model is clearly demonstrated. With an accuracy of 99.24% for MaleVis and 99.31% for the Malimg dataset, the suggested strategy demonstrated excellent results and surpassed all other existing methods. It illustrates how the suggested strategy outperforms alternative models and offers numerous benefits. Show more
Keywords: IIoT, deep learning, ConvNeXt, Malimg, EESNN, DCGAN, MaleVis
DOI: 10.3233/JIFS-234897
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10405-10421, 2024
Authors: Yi, Lingzhi | Peng, Xinlong | Fan, Chaodong | Wang, Yahui | Li, Yunfan | Liu, Jiangyong
Article Type: Research Article
Abstract: Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in …this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting. Show more
Keywords: Short-term residential load forecasting, gate recurrent unit, multi-objective optimization algorithm, deep learning
DOI: 10.3233/JIFS-237189
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10423-10440, 2024
Authors: Yang, Jingling | Chen, Liren | Chen, Huayou | Liu, Jinpei | Han, Bing
Article Type: Research Article
Abstract: The conventional approaches to constructing Prediction Intervals (PIs) always follow the principle of ‘high coverage and narrow width’. However, the deviation information has been largely neglected, making the PIs unsatisfactory. For high-risk forecasting tasks, the cost of forecast failure may be prohibitive. To address this, this work introduces a multi-objective loss function that includes Prediction Interval Accumulation Deviation (PIAD) within the Lower Upper Bound Estimation (LUBE) framework. The proposed model can achieve the goal of ‘high coverage, narrow width, and small bias’ in PIs, thus minimizing costs even in cases of prediction failure. A salient feature of the LUBE framework …is its ability to discern uncertainty without explicit uncertainty labels, where the data uncertainty and model uncertainty are learned by Deep Neural Networks (DNN) and a model ensemble, respectively. The validity of the proposed method is demonstrated through its application to the prediction of carbon prices in China. Compared with conventional uncertainty quantification methods, the improved interval optimization method can achieve narrower PI widths. Show more
Keywords: Prediction interval, uncertainty prediction, deep neural networks, carbon price
DOI: 10.3233/JIFS-237524
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10441-10456, 2024
Authors: Zou, Yu | Fu, Deyu | Mo, Honghuai | Chen, Henglong | Wang, Deyin
Article Type: Research Article
Abstract: Foreign objects identification in the distribution network is an important link in the security of electric power, and is of great significance to the normal transportation of electric power. At present, a lot of equipment in the distribution network is in the open air environment, facing a large number of foreign interference. These foreign objects not only bring potential safety hazards to the distribution network, but also easily lead to short circuit, causing power supply difficulties within the region. Therefore, the research first constructs an optimized triplet feature learning model. On this basis, the HOG-SVM depth feature recognition model is …proposed. In HOG-SVM, AM is introduced to improve recognition accuracy. In addition, the research enhances the night vision ability of the model by standardizing the features in the image region block. The results show that the AP of the model is stable at more than 90.54%, the average FPR is 2.21%, and the average FNR is 3.17%. The performance of HOG-SVM is significantly better than that of traditional SVM. It verifies the contribution of this research in the field of foreign object recognition and application value in ensuring the security of distribution network. Show more
Keywords: Distribution network, foreign objects, depth characteristics, attention
DOI: 10.3233/JIFS-237868
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10457-10470, 2024
Authors: Wang, Lai-Wang | Hung, Chen-Chih
Article Type: Research Article
Abstract: In response to the low efficiency and poor quality of current seed optimization algorithms for multi-threshold image segmentation, this paper proposes the utilization of the normal distribution in the cluster distribution mathematical model, the Levy flight mechanism, and the differential evolution algorithm to address the deficiencies of the seed optimization algorithm. The main innovation lies in applying the BBO algorithm to image multi threshold segmentation, providing a new perspective and method for image segmentation tasks. The second significant progress is the combination of Levy flight dynamics and differential evolution algorithm (DEA) to improve the BBO algorithm, thereby enhancing its performance …and image segmentation quality. Therefore, a multi-threshold image segmentation model based on the optimized seed optimization algorithm is developed. The experimental results showed that on the function f1, the iteration of the improved seed optimization algorithm was 53, the Generational Distance value was 0.0020, the Inverted Generational Distance value was 0.098, and the Spacing value was 0.051. Compared with the other two algorithms, the improved seed optimization algorithm has better image segmentation performance and clearer image segmentation details. In summary, compared with existing multi-threshold image segmentation methods, the proposed multi-threshold image segmentation model based on the improved seed optimization algorithm has a better image segmentation effect and higher efficiency, can significantly improve the quality of image segmentation, has positive significance for the development of image processing technology, and also provides references for the improvement and application of optimization algorithms. Show more
Keywords: Seed optimization algorithm, differential evolution algorithm, image segmentation, levy flight mechanism
DOI: 10.3233/JIFS-237994
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10471-10492, 2024
Authors: Zhang, Bei | Cao, Yuan | Wang, Changqing | Wang, Meng
Article Type: Research Article
Abstract: To address the challenges of dense scenarios with densely distributed small-scale faces, severe occlusions, and unclear features leading to inaccurate detection and high miss rates, we propose a lightweight small-scale face detection algorithm based on YOLOv5. The aim is to enhance the accuracy and precision of target detection. Firstly, we introduce the Convolutional Block Attention Module (CBAM) into the existing backbone network, obtaining more detailed features by comprehensively considering both spatial and channel dimensions. Next, in the Neck network, we embed involution to enhance channel information and weight distribution. Finally, a new feature fusion layer is added to improve the …capture capability of feature information for smaller pixels and smaller targets in visible areas by integrating deep semantic information with shallow semantic information. The experimental results demonstrate that the improved model exhibits an increase in the average precision across all three subsets of the public WIDER FACE dataset, with improvements of 3.2%, 3.4%, and 2.6% respectively. The detection frame rate reaches 87 frames per second (FPS), significantly enhancing the detection performance of facial targets. This improvement meets the accuracy and real-time requirements for detecting small-scale facial targets in dense scenarios. Show more
Keywords: Dense scenarios, small-scale faces, CBAM, involution, feature fusion layer
DOI: 10.3233/JIFS-238575
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10493-10505, 2024
Authors: Kaur, Gaganpreet | Shobana, M. | Kavin, F. | Sellakumar, S. | Meenakshi, D. | Bharathiraja, N.
Article Type: Research Article
Abstract: The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, …this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture. Show more
Keywords: Intelligent Transport System, vulnerabilities, security, Big Data, business intelligence development
DOI: 10.3233/JIFS-230831
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10507-10521, 2024
Authors: Lan, Zhiqiang | Wu, Guoyao | Wu, Jiacheng | Li, Jiaqi | Pan, Fan
Article Type: Research Article
Abstract: In the application of new energy consumption system engineering, in order to evaluate the contribution of electric power industry expansion, an evaluation model of electric power industry expansion contribution considering the influencing factors of new energy consumption is constructed. In the process of power industry expansion, the growth of new energy installed capacity, power system regulation ability, power grid interconnection and electricity demand are the core factors that affect the change of power contribution to power industry expansion. Using the characteristic extraction method of power consumption behavior of users with industrial expansion, after extracting two characteristics, namely, the utilization hours …of user’s industrial expansion capacity and the proportion of new energy load put into operation under the change of four major factors, the monthly industrial expansion power consumption of typical users is predicted by the monthly industrial expansion power consumption forecasting method of users considering industrial expansion capacity, and then the growth curve of user’s industrial expansion power consumption is drawn. Based on the forecast method of monthly industry expansion electricity generated by industry expansion quantity, the industry expansion quantity of typical individual users is calculated, and the industry expansion quantity is input into RBF network model trained by particle swarm optimization algorithm to complete the forecast of monthly industry expansion electricity; Finally, the contribution ratio of each influencing factor is calculated, and the evaluation of power industry expansion contribution considering the influencing factors of new energy consumption is completed. After testing, this model can be used as an available model for evaluating the contribution of electric power industry under the condition of considering the influencing factors of new energy consumption. Show more
Keywords: New energy consumption, influencing factors, power industry expansion, contribute electricity, evaluation model, industry expansion capacity
DOI: 10.3233/JIFS-236907
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10523-10534, 2024
Authors: Zhang, Jianhua | Liu, Chan | Geng, Na | Zhang, Yixuan | Yang, Liqiang
Article Type: Research Article
Abstract: An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems. To this end, firstly, the search direction number is expanded from 4 or 8 into 32; Secondly, the distance heuristic information is replaced by an area heuristic function, which deviated from the traditional approach that only considers pheromone information between two points; Then, the influence of path angle and number of turns is taken into account in the local pheromone update. Additionally, a reward …and punishment mechanism is employed in the global pheromone update to adjust the pheromone concentrations of different paths; Furthermore, an adaptive update strategy for pheromone volatility factor adaptive is proposed to expand the search range of the algorithm. Finally, simulation experiments are conducted under various scenarios to verify the superiority and effectiveness of the proposed algorithm. Show more
Keywords: Ant colony optimization, mobile robot, path planning, search direction, area-inspired, grid map
DOI: 10.3233/JIFS-238095
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10535-10552, 2024
Authors: Özlü, Şerif | Al-Quran, Ashraf | Riaz, Muhammad
Article Type: Research Article
Abstract: This paper aims to present Bipolar valued probabilistic hesitant fuzzy sets (BVPHFSs) by combining bipolar fuzzy sets and probabilistic hesitant fuzzy sets (PHFSs). PHFSs are a strong version of hesitant fuzzy sets (HFSs) in terms of evaluated as probabilistic of each element. Probabilistic hesitant fuzzy sets (PHFSs) are a set structure that argues that each alternative should be evaluated probabilistically. In this framework, the proposed cluster allows probabilistic evaluation of decision- makers’ opinions as negative. Thus, this case proposes flexibility about selection of an element and aids to overcome with noise channels. Furthermore, some new aggregation operators are discussed called …bipolar valued probabilistic hesitant fuzzy weighted average operator (BVPHFWA), Generalized bipolar valued probabilistic hesitant fuzzy weighted average operator (GBVPHFWA), bipolar valued probabilistic hesitant fuzzy weighted geometric operator (BVPHFWG), Generalized bipolar valued probabilistic hesitant fuzzy weighted geometric operator (GBVPHFWG), bipolar valued probabilistic hesitant fuzzy hybrid weighted arithmetic and geometric operator (BVPHFHWAG) and Generalized bipolar valued probabilistic hesitant fuzzy hybrid weighted arithmetic and geometric (GBVPHFHWAG) and some basic properties are presented. A score function is defined ranking alternatives. Moreover, two different algorithms are put forward with helping to TOPSIS method and by using aggregation operators over BVPHFSs. The validity of proposed operators are analyzed with an example and results are compared in their own. Show more
Keywords: Probabilistic hesitant fuzzy sets, bipolar valued probabilistic hesitant fuzzy sets, generalized hybrid operators, decision-making
DOI: 10.3233/JIFS-238331
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10553-10572, 2024
Authors: Wang, Chishe | Li, Jun | Wang, Jie | Zhao, Weikang
Article Type: Research Article
Abstract: Rapid urbanization has made road construction and maintenance imperative, but detecting road diseases has been time-consuming with limited accuracy. To overcome these challenges, we propose an efficient YOLOv7 road disease detection model. Our approach involves integrating MobilieNetV3 as the backbone feature extraction network to reduce the network’s parameters and computational requirements. Additionally, we introduce the BRA attention module into the spatial pyramid pooling module to eliminate redundant information and enhance the network’s feature representation capability. Moreover, we utilize the F-ReLU activation function in the backbone network, expanding the convolutional layers’ receptive field range. To optimize the model’s boundary loss, we …employ the Wise-IoU loss function, which places more emphasis on the quality of ordinary samples and enhances the overall performance and generalization ability of the network. Experimental results demonstrate that our improved detection algorithm achieves a higher recall rate and mean average precision (mAP) on the public dataset (RDD) and the NJdata dataset in Nanjing’s urban area. Specifically, compared to YOLOv7, our model increases the recall rate and mAP on RDD by 3.3% and 2.6%, respectively. On the NJdata dataset, our model improves the recall rate and mAP by 1.9% and 1.3%, respectively. Furthermore, our model reduces parameter and computational requirements by 30% and 22.5%, respectively, striking a balance between detection accuracy and speed. In conclusion, our road disease detection model presents an effective solution to address the challenges associated with road disease detection in urban areas. It offers improved accuracy, efficiency, and generalization capabilities compared to existing models. Show more
Keywords: Yolov7, lightweight, MobilieNetV3, BRA, F-ReLU, Wise-IoU
DOI: 10.3233/JIFS-239289
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10573-10589, 2024
Authors: Premalatha, G. | Chandramani, Premanand V. | Panimalar, K.
Article Type: Research Article
Abstract: Gait analysis is a widely used technique for passive human identification and tracking, with potential applications in security and surveillance systems. However, existing gait recognition methods face challenges in handling changing angles and uncertain features. In this paper, we propose a novel gait recognition approach that leverages real-time spatio-temporal gait features, including step length, gait cycle, height, cadence, swing ratio, and foot length. We apply the Extreme Learning Machines (ELM) algorithm for classification, which has been shown to be effective in various applications due to its fast-learning speed and good generalization performance. To further enhance the recognition rate, we introduce …an evolutionary BAT-optimized ELM algorithm that addresses the instability issue in ELM. The proposed BAT-ELM algorithm can optimize the hidden nodes and weights of ELM, which leads to improved efficiency in recognizing gait from multiple view angles ranging from 0° to 180°. Our comprehensive analysis of the proposed approach indicates that it outperforms other reported algorithms in terms of recognition rate and efficiency. Our work demonstrates the effectiveness of combining real-time spatio-temporal gait features with the BAT-ELM algorithm for gait recognition. The proposed approach has potential applications in various fields, including security and surveillance systems, healthcare, and robotics. Our findings highlight the importance of leveraging evolutionary algorithms to optimize machine learning models and achieve better performance in complex recognition tasks. Show more
Keywords: Spatio-temporal feature, BAT, extreme learning machines, gait cycle
DOI: 10.3233/JIFS-210522
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10591-10605, 2024
Authors: Shakkeera, L. | Dhiyanesh, B. | Asha, A. | Kiruthiga, G.
Article Type: Research Article
Abstract: To address this storage issue, we propose a Content-Aware Deduplication Clustering Analysis for Cloud Storage Optimization (CADC-FPRLE) based on a file partitioning running length encoder. At first, preprocessing was done by indexing, counting terms, cleansing, and tokenizing. Further multi-objective clustering points are analysed based on the bisecting divisible partition block, which divides a set of documents. The count terms are filtered from the divisible blocks and make up the count terms content block. Using Content-Aware Multi-Hash Ensemble Clustering (CAMH-EC) to group the similar blocks into clusters. This creates a high-dimensional Euclidean interval to create the number of clusters, and points …are performed randomly to set the initial collection. Then, the Magnitude Vector Space Rate (MVSR) estimates the similarity distance between the groups to select the highest scatter value content for indexing. Finally, the Running Block Parity Encoder (RBPE) generates similarity parity in order to reduce the content to a redundant, singularized file in order to optimise storage. This implementation proves a higher level of storage optimization compared to the previous system than other methods. Show more
Keywords: Data deduplication, semantic analysis, cloud storage, magnitude vector space, cluster analysis, running length encoder
DOI: 10.3233/JIFS-231223
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10607-10619, 2024
Authors: Fan, Lin | Wang, Wenli
Article Type: Research Article
Abstract: The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this …model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice. Show more
Keywords: Big data, English learning, fuzzy logic system, student ability
DOI: 10.3233/JIFS-232619
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10621-10636, 2024
Authors: Song, Can
Article Type: Research Article
Abstract: The development and utilization of network big data is also accompanied by data theft and destruction, so the monitoring of network security is particularly important. Based on this, the study applies the fuzzy C-mean clustering algorithm to the network security model, however, the algorithm has major defects in discrete data processing and the influence of feature weights. Therefore, the study introduces the concept of local density and optimizes the initial clustering center to solve its sensitive defects as well as empirical limitations; at the same time, the study introduces the adaptive methods of fuzzy indicators and feature weighting, and uses …the concepts such as fuzzy center-of-mass distribution to avoid problems such as the model converging too fast and not being able to handle discrete data. Finally, the study does a simulation analysis of the performance of each module, and the comparison of the overall algorithm with the rest of the models. The experimental results show that in the comparison of the overall algorithm, its false detection rate decreases by 8.57% in the IDS Dataset dataset, compared to the particle swarm algorithm. Therefore, the adaptive weighted fuzzy C-Means algorithm based on local density proposed in the study can effectively improve the network intrusion detection performance. Show more
Keywords: Local density, fuzzy clustering, adaptive, hybrid weighting, network security
DOI: 10.3233/JIFS-235082
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10637-10651, 2024
Authors: Ding, Yahui | Wang, Hongjuan | Liu, Nan | Li, Tong
Article Type: Research Article
Abstract: Traditional Chinese painting (TCP), culturally significant, reflects China’s rich history and aesthetics. In recent years, TCP classification has shown impressive performance, but obtaining accurate annotations for these tasks is time-consuming and expensive, involving professional art experts. To address this challenge, we present a semi-supervised learning (SSL) method for traditional painting classification, achieving exceptional results even with a limited number of labels. To improve global representation learning, we employ the self-attention-based MobileVit model as the backbone network. Furthermore, We present a data augmentation strategy, Random Brushwork Augment (RBA), which integrates brushwork to enhance the performance. Comparative experiments confirm the effectiveness of …TCP-RBA in Chinese painting classification, demonstrating outstanding accuracy of 88.27% on the test dataset, even with only 10 labels, each representing a single class. Show more
Keywords: Traditional chinese paintings, brushwork, semi-supervised learning, image classification
DOI: 10.3233/JIFS-236533
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10653-10663, 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. 46, no. 4, pp. 10665-10681, 2024
Authors: Cui, Hongzhen | Zhang, Longhao | Zhu, Xiaoyue | Guo, Xiuping | Peng, Yunfeng
Article Type: Research Article
Abstract: Extracting and digitizing drug attributes from medical literature is the first step to build a knowledge computing system for precision disease treatment. In order to build a cardiovascular drug knowledge base, this paper proposes a multi-label text classification method for cardiovascular drug attributes from the Chinese drug guideline. The drug attributes are characterized by a BERT pre-trained model, and a dual-feature extraction structure is proposed based on the BiGRU neural network to capture high-level semantic information. Label categorization of cardiovascular drug attributes, such as indications and mode of administration, is accomplished. The F1 score of 0.8431 was obtained using 5-fold …cross-validation. Comparing KNN and Naïve bayes, and conducting CNN and BiGRU control experiments on the basis of Word2Vec characterization of medication guidelines, the proposed multi-label text classification method is effective and the F1 value is significantly improved. Proved by analysis of ablation and crossover experiments, the proposed method can achieve a high accuracy rate averaged at 0.8339. Show more
Keywords: Multi-label text classification, cardiovascular drug attributes, BERT, BiGRU, dual feature extraction
DOI: 10.3233/JIFS-236115
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10683-10693, 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. 46, no. 4, pp. 10695-10711, 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. 46, no. 4, pp. 10713-10726, 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. 46, no. 4, pp. 10727-10741, 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. 46, no. 4, pp. 10743-10755, 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. 46, no. 4, pp. 10757-10771, 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. 46, no. 4, pp. 10773-10785, 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. 46, no. 4, pp. 10787-10803, 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. 46, no. 4, pp. 10805-10812, 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. 46, no. 4, pp. 10813-10832, 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. 46, no. 4, pp. 10833-10845, 2024
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