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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: Sageengrana, S. | Selvakumar, S.
Article Type: Research Article
Abstract: Distraction and fatigue are serious issues in online learning, and they directly impact educational outcomes. To achieve excellent academic achievement, students need to focus on their studies without being distracted or fatigued. Learners frequently overlook crucial information, directions, and concepts while they are passive and sleepy. They tend to miss important content, instructions, and concepts. Iris Angle Position (IAP) and electroencephalography (EEG) were used in this model to identify the behaviour of learners. Specifically, a Deep Convolutional Neural Network (DCNN) is constructed to extract IAP in order to accurately capture the learner’s facial area. EEG signals are effectively handled and …sorted using deep reinforcement learning (DRL). The learners’ facial landmarks are retrieved from a frame using the dlib toolbox. Only eye landmark points from face landmarks alone are focused on in order to determine the learner’s behaviour. When the learners EEG signals and Iris positions are monitored simultaneously, it’s helpful to identify the learner’s fatigue state (LFS) and the learner’s distraction state (LDS). The Brain Vision Algorithm (BVA) uses iris position and minimal facial landmarks, along with brain activity, to properly identify the learner’s level of distraction and exhaustion. When a student is detected as being preoccupied or sleepy, an alert goes off automatically, and the educator gets performance feedback. Iris position data and brain-computer interface-based EEG signal values are utilised to identify distraction and sleepiness. Comparative tests have demonstrated that this innovative method offers fast and high-accuracy student activity detection in virtual learning settings. Applying the suggested approach to different existing classifiers yields an F-Score of 91.92%, a recall of 93.87%, and a precision of 92.37% . The results showed that the detection rates for both distracted and sleepy phases were higher than those attained with other currently used techniques. Show more
Keywords: Drowsiness, online learning, iris position, EEG signals, distraction, brain vision algorithm
DOI: 10.3233/JIFS-237016
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Canul-Chin, Miguel Angel | Moguel-Ordóñez, Yolanda Beatriz | Martin-Gonzalez, Anabel | Brito-Loeza, Carlos | Legarda-Saenz, Ricardo
Article Type: Research Article
Abstract: Yucatan has a variety of plant species of melliferous importance. The honey produced in Yucatan has several special properties that make it one of the most demanded internationally. Analyzing the pollen grains present in honey is essential to determine its quality and identify its plants of origin. This study is a time-consuming process that must be carried out by highly trained palynologists. In this work, we propose an improved model based on a fully convolutional neural network for the automatic detection of pollen grains in microscopic images of four plant species of Yucatan to contribute to the analysis of the …honey designation of origin. Show more
Keywords: Pollen analysis, object detection, palynology, deep learning
DOI: 10.3233/JIFS-219379
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Hashmi, Hina | Dwivedi, Rakesh | Kumar, Anil | Kumar, Aman
Article Type: Research Article
Abstract: The rapid advancements in satellite imaging technology have brought about an unprecedented influx of high-resolution satellite imagery. One of the critical tasks in this domain is the automated detection of buildings within satellite imagery. Building detection holds substantial significance for urban planning, disaster management, environmental monitoring, and various other applications. The challenges in this field are manifold, including variations in building sizes, shapes, orientations, and surrounding environments. Furthermore, satellite imagery often contains occlusions, shadows, and other artifacts that can hinder accurate building detection. The proposed method introduces a novel approach to improve the boundary detection of detected buildings in high-resolution …remote sensed images having shadows and irregular shapes. It aims to enhance the accuracy of building detection and classification. The proposed algorithm is compared with Customized Faster R-CNNs and Single-Shot Multibox Detectors to show the significance of the results. We have used different datasets for training and evaluating the algorithm. Experimental results show that SESLM for Building Detection in Satellite Imagery can detect 98.5% of false positives at a rate of 8.4%. In summary, SESLM showcases high accuracy and improved robustness in detecting buildings, particularly in the presence of shadows. Show more
Keywords: Object detection, image analysis, faster R-CNN, CNN, satellite imagery, object localization
DOI: 10.3233/JIFS-235150
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Huang, De Ling | Huang, Yi Fan | Yang, Yu Qiao
Article Type: Research Article
Abstract: Practical Byzantine Fault Tolerance (PBFT), the widest-used consensus algorithm in the alliance blockchain, suffers from high communications complexity and relatively low scalability, making it difficult to support large-scale networks. To overcome these limitations, we propose a secure and scalable consensus algorithm, Vague Sets-based Double Layer PBFT (VSDL-PBFT). Roles and tasks of consensus nodes are redesigned. Three-phase consensus process of the original PBFT is optimized. Through these approaches, the communication complexity of the algorithm is significantly reduced. In order to better fit the complexity of voting in the real world, we use a vague set to select primary nodes of consensus …groups. This can greatly reduce the likelihood of malicious nodes being selected as the primary nodes. The experimental results show that the VSDL-PBFT consensus algorithm improves the system’s fault tolerance, it also achieves better performance in algorithm security, communications complexity, and transaction throughput compared to the baseline consensus algorithms. Show more
Keywords: Blockchain, consensus algorithm, Byzantine fault tolerance, PBFT
DOI: 10.3233/JIFS-239745
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Rodriguez-Bazan, Horacio | Sidorov, Grigory | Escamilla-Ambrosio, Ponciano Jorge
Article Type: Research Article
Abstract: Recently, Android device usage has increased significantly, and malicious applications for the Android ecosystem have also increased. Security researchers have studied Android malware analysis as an emerging issue. The proposed methods employ a combination of static, dynamic, or hybrid analysis along with Machine Learning (ML) algorithms to detect and classify malware into families. These families often exhibit shared similarities among their members or with other families. This paper presents a new method that combines Fuzzy Hashing and Natural Language Processing (NLP) techniques to find Android malware families based on their similarities by applying reverse engineering to extract the features and …compute fuzzy hashing of the preprocessed code. This relationship allows us to identify the families according to their features. A study was conducted using a database test of 2,288 samples from diverse ransomware families. An accuracy in classifying Android ransomware malware up to 98.46% was achieved. Show more
Keywords: Android malware analysis, android ransomware, cybersecurity, fuzzy hashing, natural language processing
DOI: 10.3233/JIFS-219367
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Arulmurugan, A. | Kaviarasan, R. | Garnepudi, Parimala | Kanchana, M. | Kothandaraman, D. | Sandeep, C.H.
Article Type: Research Article
Abstract: This research focuses on scene segmentation in remotely sensed images within the field of Remote Sensing Image Scene Understanding (RSISU). Leveraging recent advancements in Deep Learning (DL), particularly Residual Neural Networks (RESNET-50 and RESNET-101), and the research proposes a methodology involving feature fusing, extraction, and classification for categorizing remote sensing images. The approach employs a dataset from the University of California Irvine (UCI) comprising twenty-one groups of pictures. The images undergo pre-processing, feature extraction using the mentioned DL frameworks, and subsequent categorization through an ensemble classification structure combining Kernel Extreme Learning Machine (KELM) and Support Vector Machine (SVM). The paper …concludes with optimal results achieved through performance and comparison analyses. Show more
Keywords: Remote sensing, image scene classification, deep learning, feature extraction, RESNET- 101, ensemble
DOI: 10.3233/JIFS-235109
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023
Authors: Cai, Xiumei | Yang, Xi | Wu, Chengmao | Zhang, Rui
Article Type: Research Article
Abstract: Focusing on the currently available multi-view fuzzy clustering algorithms, many of which frequently lack robustness and are hence less frequently used in image segmentation. We present a multi-view fuzzy clustering image segmentation algorithm in this research, along with an autonomous view-weight learning mechanism. Firstly, to ensure that each view has the best view weight, the algorithm adds a view weight factor. Secondly, it introduces the weighted fuzzy factor and the kernel distance metric, the role of the weighted fuzzy factor is to collect the local spatial information and local grey scale information to preserve as much of the image’s detailed …information as feasible during segmentation. The role of the kernel distance metric is to lessen the influence of outliers and noisy points on image segmentation. Finally, the technique for resolving the issue of image uncertainty and fuzzy factor selection introduces the concept of interval type-2 fuzzy c-means clustering. Numerous experiments on different images demonstrate that the proposed algorithm in this paper is more robust than previous multi-view fuzzy clustering algorithms for solving noise image segmentation problems. It is also more effective at segmenting images contaminated by noise and can better retain the detailed information in the image. Show more
Keywords: Multi-view, fuzzy clustering, autonomous view-weight learning, type-2 fuzzy, image segmentation
DOI: 10.3233/JIFS-235967
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Yang, Yi | Huang, Huiling | Wu, FeiBin | Han, Jun | Ma, Mengyuan | Zhang, Yantong | Feng, Yanbing
Article Type: Research Article
Abstract: This paper introduces a novel neural network architecture and an enhanced data synthesis method that significantly boost the performance in removing complex smoke from images. The architecture features a multi-branch and multi-scale feature fusion design, which effectively integrates multiple feature streams and adaptively restores the background by identifying specific smoke characteristics within the image. A newly designed Fourier residual block is incorporated to capture frequency domain information, enabling the network to process and transform information across both spatial and frequency domains. To improve the network’s generalization ability and robustness, an in-depth analysis of the imaging process in smoky environments was …conducted, leading to an improved method for synthesizing smoke images. This methodology facilitates the creation of a more varied and realistic training dataset, substantially enhancing the neural network’s capabilities in image restoration. Experimental results show that this approach is highly effective on both synthetic and real-world smoke datasets, outperforming existing image de-smoking methods in terms of quantitative metrics and visual perception. The code for this method is available at https://github.com/Exiagit/MFSR. Show more
Keywords: Single image smoke removal, frequency domain learning, data synthesis method
DOI: 10.3233/JIFS-239146
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Nieves, Juan Carlos | Osorio, Mauricio | Rojas-Velazquez, David | Magallanes, Yazmín | Brännström, Andreas
Article Type: Research Article
Abstract: Humans have evolved to seek social connections, extending beyond interactions with living beings. The digitization of society has led to interactions with non-living entities, such as digital companions, aimed at supporting mental well-being. This literature review surveys the latest developments in digital companions for mental health, employing a hybrid search strategy that identified 67 relevant articles from 2014 to 2022. We identified that by the nature of the digital companions’ purposes, it is important to consider person profiles for: a) to generate both person-oriented and empathetic responses from these virtual companions, b) to keep track of the person’s conversations, activities, …therapy, and progress, and c) to allow portability and compatibility between digital companions. We established a taxonomy for digital companions in the scope of mental well-being. We also identified open challenges in the scope of digital companions related to ethical, technical, and socio-technical points of view. We provided documentation about what these issues mean, and discuss possible alternatives to approach them. Show more
Keywords: Conversational agents, well-being, mental health, trustworthy artificial intelligence
DOI: 10.3233/JIFS-219336
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Gomathi, S.V. | Jayalakshmi, M.
Article Type: Research Article
Abstract: This article focuses on an area of nonlinear programming problems known as linear fractional programming problems with multiple objectives. When tackling real-world linear fractional optimization problems, ambiguity and uncertainty in decision-making are inherent. This research aims to present a simple and computationally quick approach to solving multiple objective linear fractional programming problems with all decision variables and parameters described in terms of crisp. The proposed solution algorithm is based primarily on the fuzzy-based technique, and a membership function strategy. To resolve the multi-objective linear fractional programming problem, first consider the problem as a single objective function and along with the …fuzzy programming model obtain the optimal solution using LINGO software. LINGO is a software application primarily used for solving linear, nonlinear, and integer optimization problems Moreover, an e-education setup problem demonstrates the steps of the proposed method. Show more
Keywords: Linear fractional programming problem, multi-objective linear fractional programming, fuzzy mathematical programming, hyperbolic membership function
DOI: 10.3233/JIFS-234286
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Sánchez-Jiménez, Eduardo | Cuevas-Chávez, Alejandra | Hernández, Yasmín | Ortiz-Hernandez, Javier | Hernández-Aguilar, José Alberto | Martínez-Rebollar, Alicia | Estrada-Esquivel, Hugo
Article Type: Research Article
Abstract: Machine learning algorithms have been used in diverse areas among applications, including healthcare. However, to fit an effective and optimal machine learning model, the hyperparameters need to be tuned. This process is commonly referred to as Hyperparameter Optimization and comprises several approaches. We combined three Hyperparameter Optimization techniques (Bayesian Optimization, Particle Swarm Optimization, and Genetic Algorithm) with three classifiers (Random Forest, Support Vector Machine, and XGBoost) to identify the best combination of hyperparameters that maximize model performance. We use the Framingham dataset to test the proposal. For classifier performance, the Support Vector Machine obtained the best result in recall (96.40%) …and F-score (93.86%), while XGBoost obtained the best result in precision (96.30%) and specificity (96.36%). In the accuracy metric, both classifiers achieved 95%. Bayesian optimization had the best results in terms of accuracy, precision, specificity, and F-score metrics. Both Particle Swarm Optimization and Genetic Algorithm obtained the best result in the recall metric. Show more
Keywords: Bayesian optimization, framingham dataset, genetic algorithm, heart disease, hyperparameter default value, hyperparameter optimization, machine learning, particle swarm optimization, support vector machine, XGBoost
DOI: 10.3233/JIFS-219376
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Cosío-León, M.A. | Martínez-Vargas, Anabel | Rodríguez-Cortés, Gabriela
Article Type: Research Article
Abstract: It is well-known that tuning a metaheuristic is a critical task because the performance of a metaheuristic and the quality of its solutions depend on its parameter values. However, finding a good parameter setting is a time-consuming task. In this work, we apply the upper confidence bound (UCB) algorithm to automate offline tuning in a (1 + 1)-evolution strategy. Preliminary results show that our proposed approach is a less costly method.
Keywords: Upper confidence bound algorithm, meta-optimizer, bandit problems, reinforcement learning
DOI: 10.3233/JIFS-219362
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Akhmetova, Dilyara | Akhmetov, Iskander | Pak, Alexander | Gelbukh, Alexander
Article Type: Research Article
Abstract: The paper focuses on the importance of coherence and preserving the breadth of content in summaries generated by the extractive text summarization method. The study utilized the dataset containing 16,772 pairs of extractive and corresponding abstractive summaries of scientific papers specifically tailored to increase text coherence. We smoothed the extractive summaries with a Large Language Model (LLM) fine-tuning approach and evaluated our results by applying the coefficient of variation approach. The statistical significance of the results was assessed using the Kolmogorov-Smirnov test and Z-test. We observed an increase in coherence in the predicted texts, highlighting the effectiveness of our proposed …methods. Show more
Keywords: Coherence, cohesion, extractive summary, abstractive summary, GPT2, summarization, seq2seq, random forest
DOI: 10.3233/JIFS-219353
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ibarra Carrillo, Mario Alfredo | Montiel Pérez, Jesús Yaljá | Molina Lozano, Herón
Article Type: Research Article
Abstract: Today, it is the amount of data that defines the existence of mankind. Scientists respond to the large amount of required calculations by developing hardware in several directions. One of them is to increase the number of arithmetic elements. Another direction is to create new architectures that represent new algorithms for processing numerical data. We have chosen the second direction by developing a new systolic core architecture, which implies an improvement in efficiency, i.e. performing the same task with the same number of arithmetic elements but reducing the latency. Measurements are made in terms of computational capacity and the number …of arithmetic elements involved in the operations. The results of the tests are compared with data from a number of selected articles. Today, we have achieved 3.2GFlops with only two modules. In the future, we plan to integrate up to four of our cores in a system with its own memory and management processor and at a higher operating frequency. Show more
Keywords: Systolic array, systolic tensor core, accelerated matrix multiplication, accelerated convolution
DOI: 10.3233/JIFS-219361
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: El Alaoui Elfels, Mohamed | Douiri, Moulay Rachid | Raoufi, Mustapha
Article Type: Research Article
Abstract: The generation of power in Photovoltaic systems is reduced when they operate far from their maximum power point. For optimal operation, it is essential to continuously track the maximum power point of the PV solar array. However, identifying the maximum power point is a challenge due to the nonlinear relationship of electrical characteristics of PV panels with external factors. To address this issue, we present a novel design approach for a self-organizing, self-tuning fuzzy logic controller, applied to the problem of maximum power point tracking in photovoltaic systems. We outline the basic structure of the fuzzy logic controller and address …the design problems typically associated with conventional trial-and-error schemes. We also discuss the suitability of the genetic algorithm optimization technique for determining and optimizing the fuzzy logic controller design. In our proposed approach, we translate the normalization factors, membership function parameters, and controller policy into bit-strings, which are then processed by the genetic algorithm to find a near-optimal solution. To achieve high dynamic performance, we choose a particular objective function as a performance index. We compare our approach with two variants of the maximum power point algorithm, one based on genetic algorithms and the other based on fuzzy logic, as well as with the methods described in references [34 ] and [35 ], in order to evaluate its effectiveness. Show more
Keywords: Fuzzy logic controller, genetic algorithm optimisation, optimal power, photovoltaic system
DOI: 10.3233/JIFS-231710
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Tang, Ao | Wang, Xiaofeng | Peng, Qingyuan | Wang, Junxia | Yang, Yi | He, Fei | Hua, Yingying
Article Type: Research Article
Abstract: A CNF formula with each clause of length k and each variable occurring 4s times, where positive occurrences are 3s and negative occurrences are s , is a regular (3s + s , k )-CNF formula (F 3s +s ,k formula). The random regular exact (3s + s , k )-SAT problem is whether there exists a set of Boolean variable assignments such that exactly one literal is true for each clause in the F 3s +s ,k formula. By introducing a random instance generation model, the satisfiability phase transition of the solution is analyzed by …using the first moment method, the second moment method, and the small subgraph conditioning method, which gives the phase transition point s* of the random regular exact (3s + s , k )-SAT problem for k ≥3. When s < s* , F 3s +s ,k formula is satisfiable with high probability; when s > s* , F 3s +s ,k formula is unsatisfiable with high probability. Finally, through the experimental verification, the results show that the theoretical proofs are consistent with the experimental results. Show more
Keywords: Random regular exact (3s + s, k)-SAT problem, first moment method, second moment method, small subgraph conditioning method, phase transition
DOI: 10.3233/JIFS-238254
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Kolesnikova, Olga | Yigezu, Mesay Gemeda | Gelbukh, Alexander | Abitte, Selam | Sidorov, Grigori
Article Type: Research Article
Abstract: Twitter has experienced a tremendous surge in popularity over recent years, establishing itself as a prominent social media platform with a large user base. However, with this increased usage, there has been a concerning rise in the number of individuals resorting to derogatory language and expressing their opinions in a demeaning manner toward others. This surge in hate speech has drawn significant attention to the field of sentiment analysis, which aims to develop algorithms capable of detecting and analyzing emotions expressed in social networks using intuitive approaches. This paper focuses on addressing the complex task of detecting hate speech and …aggressive behavior while performing target classification. We explored various deep-learning approaches, including LSTM, BiLSTM, CNN, and GRU. Each offers unique capabilities for capturing different aspects of the input data. We proposed an ensemble approach that combines the top three performing models. This ensemble approach benefits from the diverse strengths of each individual model showing F1 score of 0.85 for English-HS, 0.94 for English-TR, 0.92 for English-AB, 0.84 for Spanish-HS, 0.86 for Spanish-TR, 0.97 for Spanish-AB, 0.74 for multilingual-HS, 0.94 for multilingual-TR, and 0.88 for multilingual-AB. Show more
Keywords: Hate speech, aggressive behavior, target classification, ensemble learning, deep learning, target classification
DOI: 10.3233/JIFS-219350
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Valencia-Valencia, Alex I. | Gomez-Adorno, Helena | Stephens Rhodes, Christopher | Bel-Enguix, Gemma | Trueba, Ojeda | Fuentes Pineda, Gibran
Article Type: Research Article
Abstract: Social media platforms, such as Twitter (now X), are a major source of communication. Identifying communicative intentions is useful, as it encapsulates the latent motivations that drive text creation. This intention is also helpful in understanding the message, context, and audience. This study proposes a method for detecting communicative intentions in tweets using Jakobson’s language functions. We constructed a meticulously annotated dataset, drawing from the extensive RepLab2013 corpus. Our dataset underwent rigorous scrutiny by linguistic annotators who analyzed over 12,000 tweets individually. These experts identified the dominant language function within each tweet by employing diverse strategies to ensure precise labeling …quality. The outcome demonstrated a noteworthy Kappa agreement score of 0.6, reflecting a strong inter-annotator reliability. Subsequently, these functions were mapped to the corresponding intention categories. We employed logistic regression and support vector machines (SVM) algorithms to classify intention in tweets and explored various pre-processing techniques, incorporating n-grams and bag-of-words representations. Furthermore, we expanded our research using pre-trained large language models, incorporating the latest state-of-the-art techniques in natural language processing. Show more
Keywords: Intention, communicative intention, tweets, language functions, intention identification, n-grams, logistic regression, SVM, deep learning
DOI: 10.3233/JIFS-219357
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Rasham, Tahair | Kutbi, Marwan Amin | Hussain, Aftab | Chandok, Sumit
Article Type: Research Article
Abstract: The objective of this research is to propose some new fixed point theorems for fuzzy-dominated operators that satisfy a nonlinear contraction on a closed ball in a complete b -multiplicative metric space. Our strategy involves the use of a combination of two distinct kinds of mappings: one belongs to a weaker class of strictly increasing mappings, and the other is a class of dominated mappings. In order to demonstrate the validity of our new findings, we provide instances that are both illustrative and substantial. Finally, in order to illustrate the novelty of our findings, we provide applications that allow us …to derive the common solution to integral and fractional differential equations. Our findings have a significant impact on the interpretation of a large number of previously published studies, both present and historical. Show more
Keywords: Fixed point, b-multiplicative metric space, generalized nonlinear contraction, fuzzy dominated operators, graph contraction, ordered fuzzy mappings, integral equation, fractional differential equation
DOI: 10.3233/JIFS-238250
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhang, Xin-jie | Li, Jun-qing | Liu, Xiao-feng | Tian, Jie | Duan, Pei-yong | Tan, Yan-yan
Article Type: Research Article
Abstract: Enterprises have increasingly focused on integrated production and transportation problems, recognizing their potential to enhance cohesion across different decision-making levels. The whale optimization algorithm, with its advantages such as minimal parameter control, has garnered attention. In this study, a hybrid whale optimization algorithm (HWOA) is designed to settle the distributed no-wait flow-shop scheduling problem with batch delivery (DNWFSP-BD). Two objectives are considered concurrently, namely, the minimization of the makespan and total energy consumption. In the proposed algorithm, four vectors are proposed to represent a solution, encompassing job scheduling, factory assignment, batch delivery and speed levels. Subsequently, to generate high-quality candidate …solutions, a heuristic leveraging the Largest Processing Time (LPT) rule and the NEH heuristic is introduced. Moreover, a novel path-relinking strategy is proposed for a more meticulous search of the optimal solution neighborhood. Furthermore, an insert-reversed block operator and variable neighborhood descent (VND) are introduced to prevent candidate solutions from converging to local optima. Finally, through comprehensive comparisons with efficient algorithms, the superior performance of the HWOA algorithm in solving the DNWFSP-BD is conclusively demonstrated. Show more
Keywords: Distributed no-wait flow shop, batch delivery, hybrid whale optimization algorithm, path-relinking
DOI: 10.3233/JIFS-238627
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Fan, Zhou | Yanjun, Shen | Zebin, Wu
Article Type: Research Article
Abstract: In this article, a non-fragile adaptive fuzzy observer is proposed for nonlinear systems with uncertain external disturbance and measurement noise. Firstly, the nonlinear system is augmented by an output filtered transformation. The output with measurement disturbance is put into the state equation of the augment system. Then, we introduce fuzzy logic system (FLS) to approximate the measurement disturbance, and construct an augmented non-fragile adaptive fuzzy observer for the augment system. A Lyapunov function is constructed to reveal that the characteristic of estimation errors is uniformly ultimately boundedness (UUB). Finally, two experimental simulations are offered to confirm the validity of the …proposed design method. Show more
Keywords: Non-fragile, high-gain observer, adaptive observer, fuzzy logic system
DOI: 10.3233/JIFS-237271
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Rajesh Kannan, A. | Thirupathi, G. | Murali Krishnan, S.
Article Type: Research Article
Abstract: Consider the graph G , with the injection Ω from node set to the first p + q natural numbers. Let us assume that the ceiling function of the classical average of the node labels of the end nodes of each link is the induced link assignment Ω * . If the union of range of Ω of node set and the range of Ω * of link set is all the first p + q natural numbers, then Ω is called a classical mean labeling. A super classical mean graph is a graph …with super classical mean labeling. In this research effort, we attempted to address the super classical meanness of graphs generated by paths and those formed by the union of two graphs. Show more
Keywords: Labeling, super classical mean labeling, super classical mean graph
DOI: 10.3233/JIFS-232328
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Ihtisham, Shumaila | Mustafa, Ghulam | Qureshi, Muhammad Nauman | Manzoor, Sadaf | Alamgir, | Khan, Adnan
Article Type: Research Article
Abstract: This study explores the distribution of order statistics of the Alpha Power Pareto (APP) distribution. Alpha Power Pareto is a more flexible distribution proposed by adding an extra parameter in the well-known Pareto distribution. This paper focuses on the derivation of single and product moment of the APP order statistics. Additionally, a recurrence link for single moments of order statistics is established. Moreover, analytical formulas of Rényi and q-entropy for APP order statistics are obtained.
Keywords: Order statistics, q-entropy, rényi entropy, recurrence relation, single and product moments
DOI: 10.3233/JIFS-231873
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Shafi, Smd | Sathiya Kumar, C.
Article Type: Research Article
Abstract: Identifying diseases using chest X-rays is challenging because more medical professionals are needed. A chest X-ray contains many features, making it difficult to pinpoint the factors causing a disease. Moreover, healthy individuals are more common than those with illnesses, and various diseases occur at different rates. To diagnose the disease accurately using X-ray images, extracting significant features and addressing unbalanced data is essential. To resolve these challenges, a proposed ensemble self-attention-based deep neural network aims to tackle the problem of unbalanced information distribution by creating a new goal factor. Additionally, the InceptionV3 architecture is trained to identify significant features. The …proposed objective function is a performance metric that adjusts the ratio of positive to negative instances, and the suggested loss function can dynamically mitigate the impact of many negative observations by reducing each cross-entropy term by a variable amount. Tests have shown that ensemble self-attention performs well on the ChestXray14 dataset, especially regarding the dimension around the recipient’s characteristics curves. Show more
Keywords: Deep neural networks, cross-weighted entropy loss, data with discrepancies, feature extraction, X-ray
DOI: 10.3233/JIFS-236444
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Venkatesan, S. | Kempanna, M. | Arogia Victor Paul, M. | Bhuvanesh, A.
Article Type: Research Article
Abstract: At present, Non-Orthogonal Multiple Access (NOMA) has become the most efficient technique to solve Data Rate (DR) requirements in Visible Light Communication (VLC) systems. However, present NOMA systems show high interference and increase the Peak-to-Average Power Ratio (PAPR), especially in wider applications. To overcome this issue, several techniques have been undertaken in the past and proven to better communication performance. However, the existing studies fail to provide a better Quality of Services (QoS) for the recent multi-carrier Optical Communication System (OCS). Hence, this study put forth a novel Generalized Frequency Division Multiplexing (GFDM) scheme to minimize the PAPR in an …indoor-based NOMA-VLC system. To enhance the performance of the GFDM system, a novel Offset-based Quadrature Amplitude Modulation (OQAM) technique is introduced that enhances the signal quality and prevents the Co-Channel Interference (CCI) problems effectively. Moreover, the proposed study introduces a novel Quantum-enabled Rabbit Optimization (QRO) technique for solving Resource Allocation (RA) problems in the NOMA-VLC system. The proposed method is processed via the MATLAB platform and various performance measures like Sum Rate (SR), Signal-to-Interference Noise Ratio (SINR), and Symbol Error Rate (SER) are analyzed and distinguished with various existing studies. In the simulation scenario, the proposed method achieves the SR of 178Mbps, SINR of 16 dB, and SER of compared to conventional techniques. Show more
Keywords: Indoor optical communication, non-orthogonal multiple access, light fidelity, generalized frequency division multiplexing, resource allocation, quantum rabbit optimization, offset quadrature modulation
DOI: 10.3233/JIFS-237800
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Yang, Wenyang | Li, Mengdi
Article Type: Research Article
Abstract: The development of computer vision and artificial intelligence provides technical support for objective evaluation of classroom teaching, and promotes the implementation of personalized teaching by teachers. In traditional classroom teaching, due to limitations, teachers are unable to timely understand and evaluate the effectiveness of classroom teaching through students’ classroom behavior, making it difficult to meet students’ personalized learning needs. Using artificial intelligence, big data and other digital technologies to analyze student classroom learning behavior is helpful to understand and evaluate students’ learning situation, thus improving the quality of classroom teaching. By using the method of literature analysis, the paper sorts …out relevant domestic and foreign literature in the past five years, and systematically analyzes the methods of student classroom behavior recognition supported by deep learning. Firstly, the concepts and processes of student classroom behavior recognition are introduced and analyzed. Secondly, it elaborates on the representation methods of features, including image features, bone features, and multimodal fusion. Finally, the development trend of student classroom behavior recognition methods and the problems that need to be further solved are summarized and analyzed, which provides reference for future research on student classroom behavior recognition. Show more
Keywords: Behavior recognition, object detection, skeleton pose, deep learning
DOI: 10.3233/JIFS-238228
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Geetha, R. | Priya, E. | Sivakumar, Kavitha
Article Type: Research Article
Abstract: Purpose: Automated diagnosis of acute cerebral ischemic stroke lesions (ACISL) is an evolving science. Early detection and exact delineation of ACISL automatically from diffusion-weighted magnetic resonance (DWMR) images are crucial for initiating prompt treatment. Thus, this work aims to determine the appropriate slice out of 60 pieces using multi-fractal analysis (MFA) and to segment the lesions in DWMR images using a hybrid optimization method. Features extracted from the segmented images were clinically correlated with the modified Rankin Scale (mRS). Methods: Thirty-one real-time stroke patients’ images were collected from Rajiv Gandhi Government General Hospital, Chennai, India. Multiple …MRI slices were taken from each patient and filtered using an anisotropic diffusion filter (ADF). These filtered images were skull-stripped automatically by the maximum entropy thresholding technique incorporating mathematical morphological operations (MEM). The multi-fractal analysis (MFA) identifies the prominent slice with the significant infarct lesion. An isodata algorithm that integrated differential evolution with the particle swarm optimization method based on Kapur’s (IDPK) and Otsu’s (IDPO) approaches was attempted to segment the ACISL. Finally, the geometric and moment features extracted from the segmented lesions categorized the stroke severity and were correlated with the mRS. Results: The findings of the experimental work confirm that the suggested IDPK approach achieved usual normalized values for image similarity indices such as Sokal-Michener Coefficient (98.51%), Roger-Tanimoto Coefficient (90.16%), Sokel-Sneath-2 (91.04%), and Sorenson Index (90.04%) are superior to IDPO. Statistical significance proved that the segmented lesions’ area (r = 0.820, p < 0.0001) and perimeter (r = 0.928, p < 0.0001) were strongly correlated with the mild and moderate criteria of mRS. Conclusion: The proposed work effectively detected ischemic stroke lesions and their severity within the studied image groups. It could be a promising and potential tool to aid radiologists in validating their diagnosis. Show more
Keywords: Ischemic stroke lesion, magnetic resonance imaging, multi-fractal analysis, isodata algorithm, differential evolution with particle swarm optimization, modified Rankin Scale
DOI: 10.3233/JIFS-233883
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Luo, Long
Article Type: Research Article
Abstract: This paper proposes a lightweight human action recognition algorithm for pedestrian behavior recognition. First, the skeleton feature information is input into the HRNet network model. In order to selectively enhance more details containing the target features and suppress irrelevant or weak features, an external attention mechanism is added to the HRN child model. Secondly, in order to extract the temporal characteristics of the target feature vector and ensure the continuity of actions in human behavior recognition, a dual-stream network based on HRNet and Long Short-Term Memory (LSTM) is constructed; finally, due to the huge model, it cannot be well transplanted …to embedded. Therefore, this paper uses depthwise separable convolution to lightweight the network model. The experimental results show that in terms of human behavior recognition, the method in this paper has better recognition accuracy than Two-stream, Multi-streamCNN, Cov3DJ, ConvNets, JTM, ASM-3, RF+SW, hd-CNN and TPSMMs. Show more
Keywords: External attention mechanism, lightweight, the network model, depthwise separable convolution, dual-stream network introduction
DOI: 10.3233/JIFS-239704
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Lavanya, J. | Kavi Priya, S.
Article Type: Research Article
Abstract: The paper addresses the optimization challenges in cloud resource task execution within the container paradigm, introducing the Multi-Objective Comprehensive Container Scheduling and Resource Allocation (MOCCSRA) scheme. It aims to enhance cost-effectiveness and efficiency by utilizing the Tuna Swarm Optimization (TSO) technique to optimize task planning and resource allocation. This novel approach considers various objectives for task scheduling optimization, including energy efficiency, compliance with service level agreements (SLAs), and quality of service (QoS) metrics like CPU utilization, memory usage, data transmission time, container-VM correlation, and container grouping. Resource allocation decisions are guided by the VM cost and task completion period factors. …MOCCSRA distinguishes itself by tackling the multi-objective optimization challenge for task scheduling and resource allocation, producing non-dominated Pareto-optimal solutions. It effectively identifies optimal tasks and matches them with the most suitable VMs for deploying containers, thereby streamlining the overall task execution process. Through comprehensive simulations, the results demonstrate MOCCSRA’s superiority over traditional container scheduling methods, showcasing reductions in resource imbalance and notable enhancements in response times. This research introduces an innovative and practical solution that notably advances the optimization field for cloud-based container systems, meeting the increasing demand for efficient resource utilization and enhanced performance in cloud computing environments. Show more
Keywords: Cloud container, task scheduling, resource allocation, DSTS, multi-objective optimization, tuna swarm optimizer, pareto optimality
DOI: 10.3233/JIFS-234262
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Su, Jiafu | Xu, Baojian | Liu, Hongyu | Chen, Yijun | Zhang, Xiaoli
Article Type: Research Article
Abstract: As an emerging concept in knowledge management (KM), green knowledge management plays a crucial role in the sustainable development of enterprises. A reasonable assessment of an enterprise’s green knowledge management capabilities can help the company effectively manage the embedded green knowledge within its operational processes, thereby achieving self-reinforcement of competitive advantages for the enterprise. Therefore, this paper proposes a multi-criteria classification method based on interval-valued intuitionistic fuzzy entropy weight method-TOPSIS-Sort-B (EWM-TOPSIS-Sort-B) to assess the green knowledge management capabilities of enterprises. In this method, expert assessments are expressed using interval-valued intuitionistic fuzzy sets. A new entropy weight method is introduced into …TOPSIS-Sort-B to determine the weights of various evaluation indicators, and TOPSIS-Sort-B is employed to classify and rate each evaluation scheme. It is worth noting that this paper has improved the TOPSIS-Sort-B method by not converting interval-valued intuitionistic fuzzy sets into precise values throughout the entire evaluation process, thus avoiding information loss. Finally, we applied a case of knowledge management capability assessment to validate the proposed method, and conducted sensitivity analysis and comparative analysis on this approach. The analysis results indicate that variations in the parameter ϑ of the interval-valued intuitionistic fuzzy aggregation operator lead to changes in criterion weights and the comprehensive evaluation matrix, resulting in unordered changes in the final classification results. Due to the absence of transformation of interval values in this study, compared to the four classification methods of TOPSISort-L, the classification results are more detailed, and the evaluation levels are more pronounced. Show more
Keywords: Interval-valued intuitionistic fuzzy set, TOPSIS-Sort-B, entropy weight method, green knowledge management capability
DOI: 10.3233/JIFS-239001
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Xiao, Le | Chen, Xiaolin | Shan, Xin
Article Type: Research Article
Abstract: News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using Large Language Model(LLM) with powerful natural language understanding and generative capabilities. We also designed News Summary Generator (NSG), …which aims to select and evolve the event pattern population and generate news summaries, so that using LLM extracts structured event patterns from events contained in news paragraphs, evolves the event pattern population using a genetic algorithm, and selects the most adaptive event patterns to input into LLM in order to generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability. Show more
Keywords: News summary generation, large language model, genetic algorithm, evolution
DOI: 10.3233/JIFS-237685
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zheng, Quanchang
Article Type: Research Article
Abstract: We investigate the semi-online problem of MapReduce scheduling on two parallel machines. We aim to minimize the makespan. Jobs are released over-list, and each job includes a map task and a reduce task. The job’s map task can be preemptive and scheduled simultaneously onto different machines, however, the reduce task is non-preemptive. The job’s reduce task needs to wait for its map task to complete before starting. We consider the following two versions: Firstly, we know the processing time of the largest reduce task beforehand, and then design a 4/3-competitive optimal semi-online algorithm. Secondly, we know in advance the value …of the reduce task with the largest processing time and the the total sum of the processing times. Then we present a 4/3-competitive semi-online algorithm. We conclude that the algorithm is the best possible when the largest reduce task meets certain conditions. Show more
Keywords: MapReduce system, semi-online, scheduling, competitive ratio, makespan
DOI: 10.3233/JIFS-239276
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Cui, Jinrong | Sun, Haosen | Kuang, Ciwei | Xu, Yong
Article Type: Research Article
Abstract: Effective fire detection can identify the source of the fire faster, and reduce the risk of loss of life and property. Existing methods still fail to efficiently improve models’ multi-scale feature learning capabilities, which are significant to the detection of fire targets of various sizes. Besides, these methods often overlook the accumulation of interference information in the network. Therefore, this paper presents an efficient fire detection network with boosted multi-scale feature learning and interference immunity capabilities (MFII-FD). Specifically, a novel EPC-CSP module is designed to enhance backbone’s multi-scale feature learning capability with low computational consumption. Beyond that, a pre-fusion module …is leveraged to avoid the accumulation of interference information. Further, we also construct a new fire dataset to make the trained model adaptive to more fire situations. Experimental results demonstrate that, our method obtains a better detection accuracy than all comparative models while achieving a high detection speed for video in fire detection task. Show more
Keywords: Object detection, fire detection, efficient, multi-scale feature learning, interference immunity
DOI: 10.3233/JIFS-238164
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Lu, Mingzhen
Article Type: Research Article
Abstract: The idea of sustainable development has become more important in resolving environmental issues and fostering a healthy coexistence of human endeavors with the natural world. Internet of Things (IoT) technology is expanding across many industries, and it is also advancing in agriculture and the agricultural environment. The planning and design for intelligent gardens using a unique Sunflower Optimized-Enhanced Support Vector Machine (SFO-ESVM) is thoroughly analyzed and researched in this study. The development and plan of intelligent gardens are investigated using agricultural IoT technologies and agricultural landscapes. First, we used the SFO method to select the best garden plan inspired by …the mathematical patterns observed in sunflower seed groupings. Next, we use an ESVM model to assess how well each plant species fits into the planned garden. The SFO-ESVM considers several variables, such as soil qualities, climatic information, plant traits, and ecological requirements, to choose the best plants. Additionally, we create an intelligent control system that combines sensors, actuators, and IoT technologies to track and regulate the environmental parameters of the garden. The SFO-ESVM-based conceptual planning and design framework for smart gardens is proposed and systematically extended to give scientific direction for the agricultural IoT of smart gardens. The proposed method was then tested in a real-world garden environment. The outcomes show that the SFO-ESVM framework-based intelligent design and execution of the sustainable development-oriented garden combines ecological principles with innovative optimization methods. Show more
Keywords: Intelligent design and realization, garden, internet of things (IoT), sustainable development, sunflower optimized-enhanced support vector machine (SFO-ESVM)
DOI: 10.3233/JIFS-234540
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: He, Shun | Li, Chaorong | Wang, Xingjie | Zeng, Anping
Article Type: Research Article
Abstract: This paper proposes a watermarking method that can be used for the copyright protection of DNN models, utilizing learnable block-wise image transformation techniques and a secret key to embed a watermark. A black-box watermarking approach is used, which does not require a specific predefined training or trigger set, allowing for the remote verification of model ownership. As a result, this method can achieve copyright protection using authentication methods for DNN models. Results of experiments on established datasets [1, 2 ] indicate that the original watermark is not easily overwritten by pirated watermarks. Moreover, its performance in pruning attack experiments is …similar to that observed in the studies cited above. However, our approach demonstrates stronger robustness against fine-tuning attacks, while also achieving higher image classification accuracy. Show more
Keywords: DNN watermark, block-wise image transformation, black-box watermark, robustness
DOI: 10.3233/JIFS-240274
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Han, Xinyue | Yao, Wei
Article Type: Research Article
Abstract: The aim of this paper is to present basic concepts of lattice-valued fuzzy mathematical morphology. We use a complete residuated lattice as the codomain of fuzzy sets, a pair of fuzzy powerset operators, called the fuzzy erosion operator and the fuzzy dilation operator, is defined and their properties and relationships are studied. The pair of two operators forms a Galois adjunction and so that the induced fuzzy opening operator and fuzzy closing are an interior operator and a closure operator respectively. It is shown that the dilation stable sets and the erosion stable sets are equivalent, which form a fuzzy …Alexandrov topology. Show more
Keywords: Fuzzy mathematical morphology, complete residuated lattice, fuzzy dilation, fuzzy erosion, dilation stable set, erosion stable set, fuzzy Alexandrov topology
DOI: 10.3233/JIFS-238540
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Long, Huimin | Zheng, Hang | Chen, Ming | Liu, Chengjian
Article Type: Research Article
Abstract: The detection of communication signals in heterogeneous electromagnetic environments currently relies primarily on a one-dimensional statistical feature threshold method. However, this approach is highly sensitive to dynamic changes in the environment, fluctuations in signal-to-noise ratios, and complex noise. To address these limitations, this paper proposes a novel time-frequency diagram based on high-order accumulation for signal detection. Traditional time-frequency diagrams suffer from poor noise suppression ability and unclear features. However, higher-order cumulants can effectively overcome these shortcomings. Currently, methods based on higher-order cumulants are typically limited to one-dimensional signals. Yet, two-dimensional time-frequency signal diagrams can represent a broader array of features. …This paper employs higher-order accumulation to extract time-frequency features from the received signal, thereby transforming the conventional radio detection problem into an image recognition challenge. By merging the advantages of higher-order accumulations and time-frequency diagrams, we propose the use of higher-order accumulation time-frequency diagrams for signal detection. Extensive experimental simulations demonstrate that the proposed time-frequency diagram exhibits strong anti-noise performance and effectively suppresses frequency bias from multiple perspectives. The performance of the Higher-Order Cumulant-Time Frequency (HOC-TF) indicated lower Root Mean Square Error (RMSE) compared with the Short-Time Fourier Transform-Time Frequency (STFT-TF) and Wavelet Transform-Time Frequency (WT-TF). Additionally, compared to the STFT-TF and WT-TF methodologies, the novel time-frequency diagram introduced demonstrates superior stability using the Singular Value Decomposition (SVD) method. Moreover, by combining the new time-frequency diagram with the deep learning YOLOV5 network, signal detection and modulation identification of communication signals can be achieved. Show more
Keywords: Signal detection, higher-order cumulant, novel time-frequency diagram
DOI: 10.3233/JIFS-237988
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Ruth Isabels, K. | Arul Freeda Vinodhini, G. | Anandan, Viswanathan
Article Type: Research Article
Abstract: This work tackles the problem of maximizing machining parameters to improve the strength and resilience of 17-4 precipitation hardening (17-4 PH SS) stainless steel, which is renowned for its strong ductility but challenging machinability. We investigate different turning input parameter combinations and machining environments (dry, oil, ionic liquid), focusing on cutting temperature and flank wear as critical parameters. We analyze eighteen experimental outcomes using a VIKOR multi-criteria decision-making (MCDM) technique using CRITIC and intuitionistic fuzzy VIKOR. Expert analyses emphasize how important the machining environment is, especially when using ionic liquids (IL). Expert preferences are taken into consideration as the hybrid …CRITIC intuitionistic fuzzy R-VIKOR technique balances flank wear and cutting temperature. Criteria similarity is evaluated by the Jaccard distance coefficient, but opponent’s subjective regret and group utility are given priority in the R-VIKOR method. Compromise values are determined by an enhanced normalization technique, and parameter analysis shows that the approach is more accurate and effective than previous ones. The machining parameters for (17-4 PH SS) are being optimized by this research, which is important for businesses that need high-performance materials with intricate machining requirements. Show more
Keywords: Cutting temperature, flank wear, CRITIC, IF R-VIKOR MCDM, Jaccard coefficient
DOI: 10.3233/JIFS-241509
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: Tino Merlin, R. | Ravi, R.
Article Type: Research Article
Abstract: This study introduces a tailored data acquisition and communication framework for IoT smart applications, focusing on enhancing efficiency and system performance. The proposed Quality-Driven IoT Routing (EQR-SC) for smart cities utilizes IoT-enabled wireless sensor networks. Additionally, a noteworthy contribution is the introduction of the Chaotic Firefly Optimization (CFOA) algorithm for IoT sensor cluster formation, potentially optimizing the organization and efficiency of IoT sensor networks in smart cities. Trust-based cluster Head Selection is enhanced by employing the Weighted Clustering Algorithm (WCA), which assigns weights to nodes based on trustworthiness and relevant metrics to select reliable cluster heads. The proposal of a …lightweight data encryption technique enhances data security among IoT sensors, ensuring the privacy and integrity of transmitted information. To optimize pathfinding within the IoT platform, the research employs the Bellman-Ford algorithm, ensuring efficient data routing while accommodating negative edge weights when necessary. Finally, a thorough performance analysis, conducted through network simulation (NS2), provides insights into the effectiveness of the proposed OQR-SC technique, allowing for valuable comparisons with existing state-of-the-art methods. Show more
Keywords: QoS, IoT smart applications, wireless sensor networks
DOI: 10.3233/JIFS-240308
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Deng, Lulu | Zhang, Changlun | He, Qiang | Wang, Hengyou | Huo, Lianzhi | Mu, Haibing
Article Type: Research Article
Abstract: The semantic segmentation of high-resolution remote sensing images has broad application prospects in land cover classification, road extraction, urban planning and other fields. To alleviate the influence of the large data volume and complex background of high-resolution remote sensing images, the usual approach is to downsample them or cut them into small pieces for separate processing. Even if combining the two methods can improve the segmentation efficiency, it ignores the differences between the middle and the edge regions. Therefore, we consider the characteristics of large and irregular region in high-resolution remote sensing images, and then propose an irregular adaptive refinement …network to locate the irregular edge region, which will be refined adaptively. Specifically, on the basis of effectively preserving the global and local information, the prediction confidence is calculated to locate pixel points that are poorly segmented, so as to form irregular regions requiring further refinement, avoiding to ‘over-refine’ intermediate region with good segmentation. At the same time, considering the difference in the refinement degree of different pixels, we propose to adaptively integrate the local segmentation results to refine the coarse segmentation results. In addition, in order to bridge the gap between the two extreme ends of the scale space, we introduce a multi-scale framework. Finally, we conducted experiments on the Deepglobe dataset showing that the proposed method performed 0.37% to 0.87% better than the previous state-of-the-art methods in terms of mean Intersection over Union (mIoU). Show more
Keywords: High spatial resolution, remote sensing image, semantic segmentation, adaptive
DOI: 10.3233/JIFS-232958
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Du, Xueke | Li, Wenli | Wei, Xiaowen
Article Type: Research Article
Abstract: The fees of different certification services are charged in different ways: For example, T-mall.com (one of the leading e-commerce platforms in China) uses a total certification service , where each type of seller participating in the platform must purchase certification services; Pinduoduo.com (another Chinese e-commerce platform) uses an alternative certification service , where after paying a transaction fee, each seller participating in the platform can choose whether to purchase certification services. This paper studies how the choice of certification services affects the participation decisions of both sellers and buyers, as well as the revenue and quality level (the proportion of …high-quality sellers of all participating sellers) of a platform. According to previous research, network externalities also affect sellers’ and buyers’ participation strategies. Studies on the effectiveness of different certification services for e-commerce platforms have rarely considered both positive and negative network externalities. The results of constructed game-theoretic models show that both the certification capability and the certification cost play critical roles in determining which certification services can generate more revenue. If a platform provides certification services, the total certification service always generates a higher quality level than the alternative certification service. Furthermore, the applicable scope of certification services (defined as the certification strategy space), can be broadened by increasing both the profit ratio (the ratio between the profit of H-type sellers and L-type sellers) and the value ratio (the ratio between the value of H-type sellers and L-type sellers). Counterintuitively, a higher certification capability does not always yield a higher certification fee. Show more
Keywords: Certification services, E-commerce platforms, information asymmetries, network externalities, certification capability
DOI: 10.3233/JIFS-234621
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Wang, Hanpeng | Xiong, Hengen
Article Type: Research Article
Abstract: An improved genetic algorithm is proposed for the Job Shop Scheduling Problem with Minimum Total Weight Tardiness (JSSP/TWT). In the proposed improved genetic algorithm, a decoding method based on the Minimum Local Tardiness (MLT) rule of the job is proposed by using the commonly used chromosome coding method of job numbering, and a chromosome recombination operator based on the decoding of the MLT rule is added to the basic genetic algorithm flow. As a way to enhance the quality of the initialized population, a non-delay scheduling combined with heuristic rules for population initialization. and a PiMX (Precedence in Machine crossover) …crossover operator based on the priority of processing on the machine is designed. Comparison experiments of simulation scheduling under different algorithm configurations are conducted for randomly generated larger scale JSSP/TWT. Statistical analysis of the experimental evidence indicates that the genetic algorithm based on the above three improvements exhibits significantly superior performance for JSSP/TWT solving: faster convergence and better scheduling solutions can be obtained. Show more
Keywords: Improved genetic algorithm, total weight tardiness, minimum local tardiness, PiMX
DOI: 10.3233/JIFS-236712
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Vaikunta Pai, T. | Singh, Manmohan | Shaik, Nazeer | Ashokkumar, C. | Anuradha, D. | Gangopadhyay, Amit | Rao, Goda Srinivasa | Reddy, T.Sunilkumar | Nagaraju, D.
Article Type: Research Article
Abstract: As the demand for energy in India continues to surge, accurate forecasting becomes paramount for efficient resource allocation and sustainable development. This study proposes an innovative approach to forecasting Indian primary energy demand by integrating Artificial Intelligence (AI) techniques with Fuzzy Auto-regressive Distributed Lag (FADL) models. FADL models, incorporating fuzzy logic, allow for a nuanced representation of uncertainties and complexities within the energy demand dynamics. In this research, historical energy consumption data is analysed using FADL models with both symmetric and non-symmetric triangular coefficients, enhancing the model’s adaptability to the inherent uncertainties associated with energy forecasting. This study addresses the …urgent need for enhanced energy planning models in the context of sustainable development. Our research aims to provide a comprehensive framework for predicting future Total Final Consumption (TFC) in alignment with the Indian National Energy Plan’s net-zero emissions target by 2035. Recognizing the limitations of current models, our research introduces a novel approach that integrates advanced algorithms and methodologies, offering a more flexible and realistic assessment of TFC trends. The primary objective of this study is to develop an improved energy planning model that surpasses existing projections by incorporating sophisticated algorithms. We aim to refine Show more
Keywords: Auto-regressive, distributed lag, energy consumption, forecast, triangular coefficient
DOI: 10.3233/JIFS-240729
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Ma, Chengfei | Yang, Xiaolei | Lu, Heng | He, Siyuan | Liu, Yongshan
Article Type: Research Article
Abstract: When calculating participants’ contribution to federated learning, addressing issues such as the inability to collect complete test data and the impact of malicious and dishonest participants on the global model is necessary. This article proposes a federated aggregation method based on cosine similarity approximation Shapley value method contribution degree. Firstly, a participant contribution calculation model combining cosine similarity and the approximate Shapley value method was designed to obtain the contribution values of the participants. Then, based on the calculation model of participant contribution, a federated aggregation algorithm is proposed, and the aggregation weights of each participant in the federated aggregation …process are calculated by their contribution values. Finally, the gradient parameters of the global model were determined and propagated to all participants to update the local model. Experiments were conducted under different privacy protection parameters, data noise parameters, and the proportion of malicious participants. The results showed that the accuracy of the algorithm model can be maintained at 90% and 65% on the MNIST and CIFAR-10 datasets, respectively. This method can reasonably and accurately calculate the contribution of participants without a complete test dataset, reducing computational costs to a certain extent and can resist the influence of the aforementioned participants. Show more
Keywords: Federated aggregation algorithm, contribution assessment, cosine similarity, Shapley value, equitable distribution
DOI: 10.3233/JIFS-236977
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Pandey, Sakshi Dev | Ranadive, A.S. | Samanta, Sovan | Dubey, Vivek Kumar
Article Type: Research Article
Abstract: Several methodologies have been proposed in the literature of graph theory for depicting collaboration among entities. However, in these studies, the measure of collaboration is taken based on the crisp graphical properties and discusses only its positive effects. In this manuscript, we discuss the simultaneous collaboration and competition that are observed among individuals, organizations, countries, communities and many others. The notion of bipolar fuzzy bunch graph (BFBG) is introduced in this study to effectively capture the positive and negative effects of both the terms collaboration and competition, which is jointly called coopetition. The goal of this paper is to introduce …an improved representation and analytical measure for coopetition. To further enrich the literature on competition graphs, the notion of survival and winning competition among species has been introduced and also provides its bipolar fuzzy competition degrees. We also introduce two types of coopetition measures to understand the ranking structure of entities (i.e. which node batter collaborates and competes with other nodes) in the network: a) bipolar fuzzy coopetition degree and b) bipolar fuzzy coopatition index. In the form of a bipolar fuzzy coopetition graph, we find evidence to validate our framework and computations. We gathered research articles on COVID-19 and their citations over a specific time period from a specific journal. To demonstrate our approach, we displayed bipolar fuzzy collaboration and competition of various countries on COVID-19 and classified their rankings based on their positive and negative coopetition indices. Show more
Keywords: Bipolar fuzzy bunch degree, communication potential effect (CPE), bipolar fuzzy mixed graph, winning and survival competition, coopetition degree, coopetition index
DOI: 10.3233/JIFS-234061
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Rachamadugu, Sandeep Kumar | Pushphavathi, T.P.
Article Type: Research Article
Abstract: This paper introduces an innovative approach, the LS-SLM (Local Search with Smart Local Moving) technique, for enhancing the efficiency of article recommendation systems based on community detection and topic modeling. The methodology undergoes rigorous evaluation using a comprehensive dataset extracted from the “dblp. v12.json” citation network. Experimental results presented herein provide a clear depiction of the superior performance of the LS-SLM technique when compared to established algorithms, namely the Louvain Algorithm (LA), Stochastic Block Model (SBM), Fast Greedy Algorithm (FGA), and Smart Local Moving (SLM). The evaluation metrics include accuracy, precision, specificity, recall, F-Score, modularity, Normalized Mutual Information (NMI), betweenness …centrality (BTC), and community detection time. Notably, the LS-SLM technique outperforms existing solutions across all metrics. For instance, the proposed methodology achieves an accuracy of 96.32%, surpassing LA by 16% and demonstrating a 10.6% improvement over SBM. Precision, a critical measure of relevance, stands at 96.32%, showcasing a significant advancement over GCR-GAN (61.7%) and CR-HBNE (45.9%). Additionally, sensitivity analysis reveals that the LS-SLM technique achieves the highest sensitivity value of 96.5487%, outperforming LA by 14.2%. The LS-SLM also demonstrates superior specificity and recall, with values of 96.5478% and 96.5487%, respectively. The modularity performance is exceptional, with LS-SLM obtaining 95.6119%, significantly outpacing SLM, FGA, SBM, and LA. Furthermore, the LS-SLM technique excels in community detection time, completing the process in 38,652 ms, showcasing efficiency gains over existing techniques. The BTC analysis indicates that LS-SLM achieves a value of 94.6650%, demonstrating its proficiency in controlling information flow within the network. Show more
Keywords: Recommender Systems (RS), BagofWords (BoW), Pearson Correlation Co-efficient based Latent Dirichlet Allocation (PCC-LDA), Linear Scaling based Smart Local Moving (LS-SLM), Time Frequency and Inverse Document Frequency (TF-IDF), Community detection
DOI: 10.3233/JIFS-233851
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Lalitha, V. | Latha, B.
Article Type: Research Article
Abstract: The most valuable information of Hyperspectral Image (HSI) should be processed properly. Using dimensionality reduction techniques in two distinct approaches, we created a structure for HSI to collect physiological and diagnostic information. The tissue Oxygen Saturation Level (StO2 ) was extracted using the HSI approach as a physiological characteristic for stress detection. Our research findings suggest that this unique characteristic may not be affected by humidity or temperature in the environment. Comparing the standard StO2 reference and pressure concentrations, the social stress assessments showed a substantial variance and considerable practical differentiation. The proposed system has already been evaluated on …tumor images from rats with head and neck cancers using a spectrum from 450 to 900 nm wavelength. The Fourier transformation was developed to improve precision, and normalize the brightness and mean spectrum components. The analysis of results showed that in a difficult situation where awareness could be inexpensive due to feature possibilities for rapid classification tasks and significant in measuring the structure of HSI analysis for cancer detection throughout the surgical resection of wildlife. Our proposed model improves performance measures such as reliability at 89.62% and accuracy at 95.26% when compared with existing systems. Show more
Keywords: Hyperspectral Image, dimensionality reduction, stress tests, cancer detection, fourier coefficients
DOI: 10.3233/JIFS-236935
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Fan, Jianping | Chai, Mingxuan | Wu, Meiqin
Article Type: Research Article
Abstract: In this manuscript, we construct a Multi-Criteria Decision-Making (MCDM) model to study the new energy vehicle (NEV) battery supplier selection problem. Firstly, we select criteria to build an evaluation index system. Secondly, SAWARA and MEREC methods are used to calculate subjective and objective weights in the ranking process, respectively, and PTIHFS (Probabilistic Triangular Intuitionistic Hesitant Fuzzy Set) is employed to describe the decision maker’s accurate preferences in performing the calculation of subjective weights. Then, the game theory is used to find the satisfactory weights. We use TFNs to describe the original information in the MARCOS method to obtain the optimal …alternative. Finally, a correlation calculation using Spearman coefficients is carried out to compare with existing methods and prove the model’s validity. Show more
Keywords: PTIHFS, SWARA, MEREC, MARCOS, game theory
DOI: 10.3233/JIFS-231975
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Devi, Salam Jayachitra | Doley, Juwar | Gupta, Vivek Kumar
Article Type: Research Article
Abstract: Object detection has made significant strides in recent years, but it remains a challenging task to accurately and quickly identify and detect objects. While humans can easily recognize objects in images or videos regardless of their appearance, computers face difficulties in this task. Object detection plays a crucial role in computer vision and finds applications in various domains such as healthcare, security, agriculture, home automation and more. To address the challenges of object detection, several techniques have been developed including RCNN, Faster RCNN, YOLO and Single Shot Detector (SSD). In this paper, we propose a modified YOLOv5s architecture that aims …to improve detection performance. Our modified architecture incorporates the C3Ghost module along with the SPP and SPPF modules in the YOLOv5s backbone network. We also utilize the Adam and Stochastic Gradient Descent (SGD) optimizers. The paper also provides an overview of three major versions of the YOLO object detection model: YOLOv3, YOLOv4 and YOLOv5. We discussed their respective performance analyses. For our evaluation, we collected a database of pig images from the ICAR-National Research Centre on Pig farm. We assessed the performance using four metrics such as Precision (P), Recall (R), F1-score and mAP @ 0.50. The computational results demonstrate that our method YOLOv5s architecture achieves a 0.0414 higher mAP while utilizing less memory space compared to the original YOLOv5s architecture. This research contributes to the advancement of object detection techniques and showcases the potential of our modified YOLOv5s architecture for improved performance in real world applications. Show more
Keywords: Object detection, YOLO, convolutional neural networks, pig, and computer vision
DOI: 10.3233/JIFS-231032
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
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