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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: 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: Ali, Zeeshan | Yin, Shi | Yang, Miin-Shen
Article Type: Research Article
Abstract: In the context of fuzzy relations, symmetry refers to a property where the relationship between two elements remains the same regardless of the order in which they are considered. Natural language processing (NLP) in engineering documentation discusses the application of computational methods or techniques to robotically investigate, analyze, and produce natural language information for manufacturing contents. The NLP plays an essential role in dealing with large amounts of textual data normally recovered in engineering documents. In this paper, we expose the idea of a bipolar complex hesitant fuzzy (BCHF) set by combining the bipolar fuzzy set (BFS) and the complex …hesitant fuzzy set (CHFS). Further, we evaluate some algebraic and Schweizer-Sklar operational laws under the presence of BCHF numbers (BCHFNs). Additionally, using the above information as well as the idea of prioritized (PR) operators, we derive the idea of BCHF Schweizer-Sklar PR weighted averaging (BCHFSSPRWA) operator, BCHF Schweizer-Sklar PR ordered weighted averaging (BCHFSSPROWA) operator, BCHF Schweizer-Sklar PR weighted geometric (BCHFSSPRWG) operator, and BCHF Schweizer-Sklar PR ordered weighted geometric (BCHFSSPROWG) operator. Basic properties for the above operators are also discussed in detail, such as idempotency, monotonicity, and boundedness. Moreover, we evaluate the best way in which NLP can be applied to engineering documentations with the help of the proposed operators. Therefore, we illustrate the major technique of multi-attribute decision-making (MADM) problems based on these derived operators. Finally, we use some existing operators and try to compare their ranking results with our proposed ranking results to show the supremacy and validity of the investigated theory. Show more
Keywords: Fuzzy set (FS), hesitant FS, bipolar complex hesitant FS, Schweizer-Sklar prioritized aggregation operators, natural language processing, multi-attribute decision-making
DOI: 10.3233/JIFS-240116
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2024
Authors: Shi, Jing | Zhang, Xiao-Lin | Wang, Yong-Ping | Gu, Rui-Chun | Xu, En-Hui
Article Type: Research Article
Abstract: Deep neural networks (DNNs) are susceptible to adversarial attacks, and one important factor is that adversarial samples are transferable, i.e., adversarial samples generated by a particular network may deceive other black-box models. However, existing transferable adversarial attacks tend to modify the input features of images directly without selection to reduce the prediction accuracy in the alternative model, which would enable the adversarial samples to fall into the model’s local optimum. Alternative models differ significantly from the victim model in most cases, and while simultaneously attacking multiple models may improve transferability, gathering numerous different models is more challenging and expensive. We …simulate various models using frequency domain transformation to close the gap between the source and victim models and improve transferability. At the same time, we destroy important intermediate layer features that influence the decision of the model in the feature space. Additionally, smoothing loss is introduced to remove high-frequency perturbations. Extensive experiments demonstrate that our FM-FSTA attack generates more well-hidden and transferable adversarial samples, and achieves a high deception rate even when attacking adversarially trained models. Compared to other methods, our FM-FSTA improved attack success rate under different defense mechanisms, which reveals the potential threats of current robust models. Show more
Keywords: Deep neural networks, adversarial samples, transferable attacks
DOI: 10.3233/JIFS-234156
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Zhao, Xianhao | Wang, Mingyang | Xin, Chaoqun | Wang, Xianjie
Article Type: Research Article
Abstract: In the field of autonomous driving, driving systems need to understand and quickly respond to changes in road scenes, which makes it equally important to enhance the accuracy and real-time performance of semantic segmentation tasks in road scenes. This article proposes a lightweight road scene semantic segmentation model LR3S that integrates global contextual information based on the DeepLabV3+ framework. LR3S utilizes a lightweight GhostNetV2 network as the backbone to capture rich semantic information in images, and uses ASPP_eSE module to enhance the capture of multi-scale and detail level semantic information. In addition, a lightweight CARAFE upsampling operator is utilized to …upsample feature maps, taking advantage of CARAFE’s large receptive field and low computational cost to prevent the loss of fine-grained features and ensure the integrity of semantic information. Experimental results demonstrate that LR3S achieves an MIoU of 74.47% on the Cityscapes dataset and obtains an MIoU of 76.01% on the PASCAL VOC 2012 dataset. Compared to baseline semantic segmentation models, LR3S significantly reduces the parameter amount while maintaining segmentation accuracy, achieving a good balance between model accuracy and real-time performance. Show more
Keywords: Semantic segmentation, road scenes, attention mechanism, GhostNetV2, CARAFE
DOI: 10.3233/JIFS-239692
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Haennah, J.H. Jensha | Christopher, C. Seldev | King, G.R. Gnana
Article Type: Research Article
Abstract: Accurate SARS-CoV-2 screening is made possible by automated Computer-Aided Diagnosis (CAD) which reduces the stress on healthcare systems. Since Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is highly contagious, the transition chain can be broken through an early diagnosis by clinical knowledge and Artificial Intelligence (AI). Manual findings are time and labor-intensive. Even if Reverse Transcription-Polymerase Chain Reaction (RT-PCR) delivers quick findings, Chest X-ray (CXR) imaging is still a more trustworthy tool for disease classification and assessment. Several studies have been conducted using Deep Learning (DL) algorithms for COVID-19 detection. One of the biggest challenges in modernizing healthcare is extracting …useful data from high-dimensional, heterogeneous, and complex biological data. Intending to introduce an automated COVID-19 diagnosis model, this paper develops a proficient optimization model that enhances the classification performance with better accuracy. The input images are initially pre-processed with an image filtering approach for noise removal and data augmentation to extend the dataset. Secondly, the images are segmented via U-Net and are given to classification using the Fused U-Net Convolutional Neural Network (FUCNN) model. Here, the performance of U-Net is enhanced through the modified Moth Flame Optimization (MFO) algorithm named Chaotic System-based MFO (CSMFO) by optimizing the weights of U-Net. The significance of the implemented model is confirmed over a comparative evaluation with the state-of-the-art models. Specifically, the proposed CSMFO-FUCNN attained 98.45% of accuracy, 98.63% of sensitivity, 98.98% of specificity, and 98.98% of precision. Show more
Keywords: COVID-19 classification, deep Learning, U-Net, Convolutional Neural Network (CNN), Moth Flame Optimization (MFO)
DOI: 10.3233/JIFS-230523
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Liu, Zhaohui | Wang, Xiao
Article Type: Research Article
Abstract: Pedestrians have random distribution and dynamic characteristics. Aiming to this problem, this paper proposes a pedestrian object detection method based on improved YOLOv5 in urban road scenes. Firstly, the last C3 module was replaced in the Backbone with the SE attention mechanism to enhance the network’s extraction of pedestrian object features and improve the detection accuracy of small-scale pedestrians. Secondly, the EIOU loss function was introduced to optimize the object detection performance of the detection network. To validate the effectiveness of the algorithm, experiments were conducted on a dataset composed of filtered Caltech pedestrian detection data and images taken by …ourselves. The experiments showed that the improved algorithm has P -value, R -value, and mAP of 98.4%, 95.5%, and 98%, respectively. Compared to the YOLOv5 model, it has increased P -value by 1.4%, R -value by 2.7%, and mAP by 1.3%. The improved algorithm also boosts the detection speed. The detection speed is 0.8 ms faster than the YOLOv5 model. It is also faster than other mainstream algorithms including Faster R-CNN and SSD. The improved algorithm enhances the effectiveness of pedestrian detection significantly and has important application value. Show more
Keywords: Road traffic safety, YOLOv5, pedestrian object detection
DOI: 10.3233/JIFS-240537
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Zhan, Huawei | Han, Chengju | Li, Junjie | Wei, Gaoyong
Article Type: Research Article
Abstract: Aiming at the problems of slow speed and low accuracy of traditional neural network systems for real-time gesture recognition in complex backgrounds., this paper proposes DMS-yolov8-a gesture recognition method to improve yolov8. This algorithm replaces the Bottleneck convolution module in the backbone network of yolov8 with variable row convolution DCNV2, and increases the feature convolution range without increasing the computation amount through a more flexible feeling field. in addition, the self-developed MPCA attention module is added after the feature output layer of the backbone layer, which improves the problem of recognizing the accuracy of difference gestures in complex backgrounds by …effectively combining the feature information of the contextual framework, taking into account the multi-scale problem of the gestures in the image, this paper introduces the SPPFCSPS module, which realizes multi-feature fusion and improves real-time accuracy of detection. Finally, the model proposed in this paper is compared with other models, and the proposed DMS-yolov8 model achieves good results on both publicly available datasets and homemade datasets, with the average accuracy up to 97.4% and the average mAP value up to 96.3%, The improvements proposed in this paper are effectively validated. Show more
Keywords: Gesture recognition, yolov8, DCNV2, MPCA, feature fusion
DOI: 10.3233/JIFS-238629
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Meenakshi, A. | Bramila, M.
Article Type: Research Article
Abstract: Molecular structures are characterised by the Hosoya polynomial and Wiener index, ideas from mathematical chemistry and graph theory. The graph representation of a chemical compound that has atoms as vertices and chemical bonds as edges is called a molecular graph, and the Hosoya polynomial is a polynomial related to this graph. As a graph attribute that remains unchanged under graph isomorphism, the Hosoya polynomial is known as a graph invariant. It offers details regarding the quantity of distinct non-empty subgraphs within a specified graph. A topological metric called the Wiener index is employed to measure the branching complexity and size …of a molecular graph. For every pair of vertices in a molecular network, the Wiener index is the total of those distances. In this paper, discussed the Hosoya polynomial, Wiener index and Hyper-Wiener index of the Abid-Waheed graphs (AW)a 8 and (AW)a 10 . This graph is similar to Jahangir’s graph. Further, we have extended the research work on the applications of the described graphs. Show more
Keywords: Wiener index, Abid-Waheed, Hosoya polynomial, diameter, distance, connected graph
DOI: 10.3233/JIFS-236051
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Lin, Jiayi
Article Type: Research Article
Abstract: At this stage, network communication technology is increasingly mature, and intelligent wearable products are also widely used in human daily life. Wearable products are popular with users because of their numerous types, complete functions and convenient services. Wearable products integrate interaction technology, and users can interact with products. However, how to improve the user’s interaction experience and reduce the user’s cognitive burden on the interaction interface is an urgent problem in the current product interaction design. Therefore, based on the analysis of the types and related technologies of wearable products, this paper made a specific analysis of the interaction design …of wearable products, and established an interaction design model. At the same time, the wearable fall detection system was also tested by machine learning algorithm. The experimental results showed that the average test result of the algorithm in this paper was 87.39%, while the average test result of the traditional algorithm was 83.79%. In terms of the missed alarm rate of fall detection, the average test result of this algorithm was 6.4%, while the average test result of the traditional algorithm was 12.33%. In terms of fall detection sensitivity, the average test result of this algorithm was 92.50%, while the average test result of the traditional algorithm was 88.24%. Compared with traditional algorithms, this method performs better, with lower missed detection rate and higher sensitivity. Innovative combination of machine learning algorithm, through three-dimensional coordinate system, differentiation and vector sum formula, improves the accuracy and reliability of fall detection. In conclusion, the algorithm in this paper can effectively optimize the relevant performance of the system, thus improving the accuracy of the system’s fall detection. Show more
Keywords: 5 G network communication technology, wearable products, interaction design, wearable fall detection system
DOI: 10.3233/JIFS-237837
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Li, Hongjun | Zhang, Jinlong
Article Type: Research Article
Abstract: This paper presents a sophisticated four-stage optimization and intelligent control algorithm tailored for two-way electric vehicle charging (EVC) stations integrated with advanced photovoltaic systems and fixed battery energy storage in commercial buildings. The primary objective is to minimize operating costs while prioritizing customer satisfaction within a dynamic and uncertain energy landscape. Our algorithm optimizes the scheduled charging and discharging of electric vehicles (EVs), local battery storage (BS) units, grid power supply, and deferred loads to balance instantaneous supply and demand. The first stage focuses on developing optimal energy management plans for the day ahead, considering factors such as projected energy …production, anticipated EVC demand, and building energy consumption patterns. Building on this foundation, the second stage introduces multilayer EV charging price structures and optimizes participation rewards for discharging, dynamically addressing EV charging patterns and price sensitivities. Approaching the commissioning timeline, the third stage refines energy management plans for the upcoming hours using real-time data and forecasts, adapting to evolving conditions for optimal resource allocation. The final stage involves real-time control and the implementation of optimized programs, dynamically adjusting charge/discharge processes, grid interactions, and load deferral to maintain supply-demand balance and minimize operating costs. Our algorithm enhances system resilience in unpredictable conditions, providing compelling incentives for active EV user participation. Coordinating the integrated system efficiently, including the commercial building’s energy load, ensures reliable service to customers while reducing costs. Extensive case studies and a comparative analysis validate the algorithm’s efficiency in significantly reducing operating costs and enhancing resilience to uncertainty. The paper concludes by highlighting the algorithm’s pioneering role in intelligent EV charging station (CHS) management, offering a cost-effective, customer-oriented, and dynamic energy control strategy for advancing global energy practices. Show more
Keywords: Electric vehicle charging, photovoltaic integration, battery energy storage, energy management optimization, commercial building integration
DOI: 10.3233/JIFS-241032
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Valadez-Godínez, Sergio | Sossa, Humberto | Santiago-Montero, Raúl
Article Type: Research Article
Abstract: The Associative Pattern Classifier (APC) was designed as an associative memory, focusing particularly on pattern classification. This implies that the training memory is constructed in a single operation and pattern classification also occurs in a single process. It is important to note that the APC translates the input patterns through a translation vector, which represents the average of all input patterns. Until now, there is no theoretical framework to explain the inner workings of the APC. Its relevance is inferred from the fact that several studies have been conducted using it as a foundation. This paper seeks to provide a …theoretical comprehension of the APC’s operation to facilitate future enhancements. We found the APC creates a system in static equilibrium through concurrent vectors at the origin (translation vector), resulting in a balanced separation of patterns. However, the APC cannot achieve complete pattern separation because of the presence of a neutral region. The neutral region is defined by all the points that define the separation hyperplanes. The points over the hyperplanes cannot be classified by the APC. Additionally, we discovered that the APC is unable to accurately classify the translation vector, which could be included as part of the input patterns. Our previous research showed that the APC is unsuccessful in achieving the linear separation of the AND function. In this research, we also broaden the examination of the AND function to illustrate that achieving linear separation is not feasible because the separation line represents a neutral region. The APC demonstrated exceptional performance when tested with artificial datasets where patterns were distributed over balanced regions, thus operating as an efficient multiclass and non-linear classifier. Nevertheless, the performance of the APC is lower when tested with real-world databases, making the APC inaccurate due to its restricted inner workings. Show more
Keywords: Classifier, pattern, associative memory, class, classification
DOI: 10.3233/JIFS-219347
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Zhang, Wei | Zheng, Hongxuan | Zhang, Runyu
Article Type: Research Article
Abstract: In this paper, a self-organizing RBF (SORBF) neural network with an adaptive threshold is proposed based on improved particle swarm optimization (IPSO) and neural strength (NS). The parameters and structure of SORBF can be optimized simultaneously and dynamically. Moreover, the tiresome problem of threshold setting is solved. Firstly, the network size and parameters of SORBF are mapped into the particle information of PSO. Secondly, an IPSO algorithm, based on diversity inertia weight and elite knowledge guiding, is proposed to reduce the probability of the population falling into the local optimum. Then, IPSO is used for optimizing the parameters of SORBF. …Based on neuron growth intensity and competition intensity, SORBF can realize the hidden neuron addition and deletion adaptively. Moreover, the thresholds during the structure adjustment can be provided adaptively based on the network scale and neuron strength, which avoids the subjectivity setting and can improve the adaptive ability. Finally, the convergence analysis of IPSO is provided to ensure the performance of SORBF. Experiment results show that the proposed SORBF has good self-organizing ability and compact network structure compared with other methods. Show more
Keywords: RBF neural network, PSO, self-organization, neural strength, adaptive threshold
DOI: 10.3233/JIFS-239569
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wei, Guangcun | Fu, Jihua | Pan, Zhifei | Fang, Qingge | Zhang, Zhi
Article Type: Research Article
Abstract: The text in natural scenes is often smaller compared to artificially designed text. Due to the small proportion of pixels, low resolution, less semantic information, and susceptibility to complex scenes, tiny text detection often results in many missed detections. To address this issue, this paper draws inspiration from small object detection methods and proposes TiTDet, a detection algorithm more suitable for tiny text. Due to the small proportion of pixels, low resolution, less semantic information, and susceptibility to complex scenes, tiny text detection often results in many missed detections. To address this issue, this paper draws inspiration from small object …detection methods and proposes TiTDet, a detection algorithm more suitable for tiny text. Firstly, this paper incorporates a context extraction module and an attention-guided module. These modules guide contextual information learning through a self attention mechanism, while eliminating the possible negative impact caused by redundant information. Regarding multi-scale feature fusion, this paper proposes a fine-grained effective fusion factor, making the fusion process emphasize small object learning more and highlight the feature expression of tiny texts. In terms of post-processing, this paper proposes a differentiable binarization module, incorporating the binarization process into model training. Leveraging the implicit information in the data to drive model improvement can enhance the post-processing effect. Lastly, this paper proposes a scale-sensitive loss, which can handle tiny texts more fairly, fully considering the positional relationship between the predicted and real regions, and better guiding the model training. This paper proves that TiTDet exhibits high sensitivity and accuracy in detecting tiny texts, achieving an 86.0% F1-score on ICDAR2015. The paper also compares the superiority of the method on CTW1500 and Total-Text. Show more
Keywords: Tiny text detection, context extraction module, attention-guided module, effective fusion factor, scale-sensitive loss
DOI: 10.3233/JIFS-236317
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Pandiyarajan, Abinaya | Jagatheesaperumal, Senthil Kumar | Thayanithi, Manonmani
Article Type: Research Article
Abstract: This study explores how Electronic Health Records (EHR) might be transformed in the context of the rapid improvements in cloud computing and IoT technology. But worries about sensitive data security and access management when it moves to large cloud provider networks surface. Even if they are secure, traditional encryption techniques sometimes lack the granularity needed for effective data protection. We suggest the Secure Access Policy – Ciphertext Policy – Attribute-based Encryption (SAPCP-ABE) algorithm as a solution to this problem. This method ensures that only authorized users may access the necessary data while facilitating fine-grained encrypted data exchange. The three main …phases of SAPCP-ABE are retrieval and decoding, where the system verifies users’ access restrictions, secure outsourcing that prioritizes critical attributes, and an authenticity phase for early authentication. Performance tests show that SAPCP-ABE is a better scheme than earlier ones, with faster encryption and decryption speeds of 5 and 5.1 seconds for 512-bit keys, respectively. Security studies, numerical comparisons, and implementation outcomes demonstrate our suggested approach’s efficacy, efficiency, and scalability. Show more
Keywords: Attribute-based encryption, electronic health record, access policy, cloud providers, cloud computing
DOI: 10.3233/JIFS-240341
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Huang, Ying | Li, Lang | Li, Di | Li, Yongchao
Article Type: Research Article
Abstract: AND-Rotation-XOR (AND-RX) ciphers are known for its unique round function and excellent implementation performance. As a result, AND-RX ciphers are well suited for protecting sensitive information on resource-constrained devices. AND-RX ciphers need to be passed by rigorous cryptanalysis methods before practice. Integral cryptanalysis is one of the important cryptanalysis methods. MILP-based automated model is constructed to solve the integral cryptanalysis of AND-RX ciphers. The automated model usually consumes a long time when the block length and the number of round function components are large. In this paper, we design a neural distinguisher named IABC model for fast and efficient integral …cryptanalysis. The IABC model learns to distinguish between ciphertext multisets to construct an integral distinguisher for AND-RX cipher, which ciphertext multisets from plaintext or random plaintexts. The IABC model is used for SIMON, SIMECK and SAND ciphers, which validates the neural distinguisher for AND-RX ciphers. The experimental results show that the IABC model is capable of expanding the number of rounds of integral distinguishers for AND-RX ciphers with certain accuracy. Therefore, IABC model can be effectively used for integral cryptanalysis of AND-RX ciphers. In addition, we discover that a larger number of active bits in the plaintext multiset results in a more accurate IABC model. Show more
Keywords: AND-RX cipher, integral cryptanalysis, division property, neural distinguisher
DOI: 10.3233/JIFS-238122
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ranjith, K. | Karthikeyan, K.
Article Type: Research Article
Abstract: The flow-shop scheduling problem (FSSP) has received a considerable amount of attention due to its wide-ranging applications. However, the omission of uncertainty significantly diminishes the practicality of scheduling results, underscoring its the necessity to address uncertainty in the flow shop problem. In this paper, a fuzzy two-machine flow-shop problem is considered and an effective algorithm with a fuzzy ranking method is proposed to minimize the total waiting time. The processing times are represented using trapezoidal membership functions. Furthermore, a two-stage flow shop scheduling problem is used in the proposed algorithm and various categories of fuzzy mean techniques. The experimental results …and statistical comparisons demonstrate that the proposed algorithm exhibits significant advantages in effectively solving the FFSSP (Fuzzy Flow-Shop Scheduling Problem). Show more
Keywords: Two-stage flow shop, trapezoidal fuzzy number, mean ranking techniques, waiting time
DOI: 10.3233/JIFS-235526
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
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: 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
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