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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Peddarapu, Rama Krishna Anogna, Sudagoni Harshini, M.N.S. Sri Sireesha, J. Rathna, K. Guna Department of Computer Science and Engineering Faculty of Computer Sciences and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

To address the need for summarizing and extracting information efficiently, this paper highlights the growing challenge posed by the increasing number of PDF files. Reading lengthy documents is a tedious and time-consuming task. To save time and quickly comprehend the key points of a PDF, a PDF summarizer tool has been developed to tackle these issues. In today's professional environments, gathering and managing data from documents is critical. This article introduces an innovative solution called DocSum, which automates the process of summarizing extracted data. Powered by *** Core, the DocSum system allows users to upload PDF documents for processing and receive concise summaries in return. The system features a user-friendly interface that encourages interaction and engagement, utilizing AI and machine learning techniques to streamline document handling. Users can request specific summaries, enabling efficient document management workflows. By empowering users to seamlessly interact with vast amounts of information, DocSum enhances productivity and explores new ways to optimize document management. This solution is ideal for professionals seeking to stay informed and manage data effectively, while also keeping track of advancements in document handling technologies across multiple industries. It is not only helpful for professionals but also helpful for students who need quick revision or examinations, teachers to gain brief knowledge on the concepts. © 2025 The Authors. Published by Elsevier B.V.

关键词： Data handling

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Exploration and Exploitation Mechanism in Pairwise Test Case Generation: A Systematic Literature Review

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IEEE Access 2025年 13卷 82342-82377页

作者： Yahaya, Muhammad Sabo Hashim, Ahmad Sobri B. Oluwagbemiga Balogun, Abdullateef Aminu Muazu, Aminu Sabo Usman, Fatima Adamu Aliyu, Dahiru Uwaisu Muhammad, Abdullahi Universiti Teknologi PETRONAS Faculty of Science Management and Computing Department of Computing Perak32610 Malaysia Faculty of Engineering and Technology Federal Polytechnic Computer Engineering Technology Department Katsina Daura824101 Nigeria Umaru Musa Yar’adua University Faculty of Natural and Applied Sciences Computer Science Department Katsina820102 Nigeria Gombe State University Faculty of Science Computer Science Department Gombe760101 Nigeria Federal University Dutse Faculty of Computing Computer Science Department Jigawa 720223 Nigeria

Software systems are essential in modern life and require rigorous testing to ensure reliability. Pairwise testing, a combinatorial testing methodology, optimizes test case generation by minimizing redundancy while maintaining defect detection effectiveness. This review systematically examines the role of metaheuristic algorithms in pairwise test case generation, focusing on their exploration and exploitation mechanisms to address local optima challenges. Covering research from 2014 to 2024, the review evaluates hybrid and metaheuristic strategies, including Pairwise Migrating Birds Optimization-Based Strategies (PMBOS), Pairwise Gravitational Search Algorithm Strategy (PGSAS), Pairwise hybrid Artificial Bee Colony (PhABC), Genetic and Particle Swarm Optimization (GAPSO) algorithm, Hybrid Optimization Algorithm (HOA), and Parameter Free Choice Function based Hyper-Heuristic (PCFHH), among others. These methods consistently outperform traditional techniques by reducing test suite sizes and enhancing fault detection capabilities. The findings underscore the growing adoption of the one-test-at-a-time (OTAT) approach over the one-parameter-at-a-time (OPAT) approach and identify key challenges in scalability, hyperparameter tuning, and constraint handling, highlighting the need for hybrid strategies and adaptive optimization techniques. This review offers valuable insights into advancing Software testing methodologies and presents recommendations for future research to improve efficiency, scalability, and fault detection in pairwise testing. © 2013 IEEE.

关键词： Software reliability

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

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computers, Materials & Continua 2025年第2期82卷 2931-2949页

作者： Hairani Hairani Triyanna Widiyaningtyas Didik Dwi Prasetya Afrig Aminuddin Department of Electrical Engineering and Informatics Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Computer Science Universitas BumigoraMataram83127Indonesia Department of Computer Graphic and Multimedia Faculty of ComputingCollege of Computing and Applied SciencesUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia

An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t

关键词： SMOTE modification Clustering-SMOTE manhattan distance

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Knowledge augmented significant language model-based chatbot for explainable diabetes mellitus prediction 19

Knowledge augmented significant language model-based chatbot...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Elfayoumi, Mazen AbouElazm, Mohamed Mohamed, Omar Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user’s understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT-3.5, and Gemma2. This approach augments the chatbot’s responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures. © 2025 IEEE.

关键词： Prediction models

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Non-Destructive Detection of Fillet Fish Quality Using MQ135 Gas Sensor and Neutrosophic Logic-Enhanced System

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Neutrosophic Sets and Systems 2025年 80卷 540-563页

作者： Shams, M.Y. Darwesh, M.R. Bhatnagar, Roheet Al-Sattary, N.S.A. Salama, A.A. Ghoname, M.S. Deptartment of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheihk33516 Egypt Department of Agriculture Engineering Faculty of Agriculture Tanta University Tanta31527 Egypt Department of Food Processing Faculty of Industry and Energy Samannoud Technological University Egypt Department of Computer Science and Engineering Manipal University Rajasthan Jaipur India Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt

This paper demonstrates the feasibility of using an electronic nose to assess fish quality by analyzing air quality and examining volatile organic compounds (VOCs) alongside physical variables, with pH, protein content, and VOCs serving as chemical reference points. Artificial intelligence algorithms were employed to predict quality and calculate regression coefficients. Using the reference neural network algorithm based on chemical and physical compounds, regression coefficients (R values) achieved were 0.99, 0.98, and 0.97, respectively. Additionally, ANFIS (Adaptive Neuro-Fuzzy Inference System) produced R values of 0.99, 0.85, and 0.98. Both fuzzy logic and ANFIS proved effective for predicting fish quality. Image processing techniques, including histogram analysis, color mapping, and edge detection, were also applied to assess fish quality. To enhance the inference process, Neutrosophic Logic-Enhanced Fuzzy Logic Systems were utilized, addressing uncertainty and imprecision in fish quality assessment. Neutrosophic logic combines fuzzy logic's partial truth with indeterminacy, represented by three membership functions: truth, indeterminacy, and falsity. Neutrosophic fuzzy inference integrates steps like neutrosophication, rule evaluation, aggregation, and defuzzification, ensuring improved expressiveness and fidelity. For instance, neutrosophic fuzzy rules evaluated fish freshness and appearance to determine quality ratings such as poor, good, or excellent. This integration enhances decision-making by accurately modeling complex real-world uncertainties. These methods, combining electronic nose technology, artificial intelligence, and neutrosophic inference, provide a robust, non-destructive, and cost-effective approach to detecting spoilage in fillet fish. © 2025, University of New Mexico. All rights reserved.

关键词： Volatile organic compounds

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SELL:A Method for Low-Light Image Enhancement by Predicting Semantic Priors

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IEEE Signal Processing Letters 2025年 32卷 1785-1789页

作者： Xiao, Quanquan Jin, Haiyan Su, Haonan Yan, Ruixia Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an710000 China Guizhou Minzu University Guiyang550000 China

In recent years, low-light image enhancement techniques have made significant progress in generating reasonable visual details. However, current methods have not yet fully utilized the full semantic prior of visual elements in low-light environments. Therefore, images generated by these low-light image enhancement methods often suffer from degraded visual quality and may even be distorted. To address this problem, we propose a method to guide low-light image enhancement by predicting semantic priors. Specifically, we train a semantic prior predictor under standard lighting conditions, which is made to learn and predict semantic prior features for low-light images by knowledge distillation on high-quality standard images. Subsequently, we utilize a semantic-aware module that enables the model to adaptively integrate these learned semantic priors, thus ensuring semantic consistency of the enhanced images. Experiments show that the method outperforms several current state-of-the-art methods in terms of visual performance on the LOL-v2 and SICE benchmark datasets. © 1994-2012 IEEE.

关键词： Economic and social effects

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Enhancing Privacy in IoT Networks: A Comparative Analysis of Classification and Defense Methods

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IEEE Access 2025年 13卷 71611-71646页

作者： Emre Ergun, Ahmet Can, Ozgu Kantarcioglu, Murat Ege University Graduate School of Natural and Applied Sciences Bornova35100 Turkey Izmir Katip Celebi University Faculty of Engineering and Architecture Department of Computer Engineering Cigli35620 Turkey Ege University Department of Computer Engineering Bornova35100 Turkey Virginia Tech Department of Computer Science BlacksburgVA24061 United States

The rapid proliferation of Internet of Things (IoT) devices has led to a substantial increase in network packet traffic, raising significant privacy concerns. Although traffic encryption is employed to protect the privacy of IoT devices, attackers can still leverage Machine Learning (ML) and Deep Learning (DL) techniques to classify device types by analyzing packet characteristics, such as size and timing. The main challenges in the state of the art are the lack of effective methods for exposing privacy violations in encrypted IoT traffic, and the absence of robust defense mechanisms to mitigate privacy breaches caused by network traffic analysis. Considering these challenges, this study presents two key contributions: (i) a novel vector-based classification method that enhances device-type identification from encrypted IoT traffic using advanced ML and DL techniques, and (ii) a robust defense mechanism based on Differential Privacy (DP) and advanced padding techniques against traffic analysis attacks. Therefore, the study examines privacy risks associated with sequential IoT device data and evaluates the effectiveness of ML algorithms using two datasets. The results demonstrate that the proposed vector-based classification method significantly improves the attacker’s classification accuracy, even when privacy-preserving techniques, such as padding, are used to obscure device-type classification. For this purpose, the study evaluates eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for IoT traffic classification, achieving an accuracy rate of 99.61% with XGBoost, 96.74% with LSTM, and 96.94% with GRU. Additionally, the Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (kNN), and GRU classification algorithms are also evaluated and compared with the XGBoost and LSTM classifiers for the proposed attack model. As a defense mechanism, DP is applied using the Fourier Perturbation Algorithm (FPA) to optimize padd

关键词： Decision trees

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Zero-dynamics attack detection based on data association in feedback pathway

Cognitive Robotics

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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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Privacy-Aware Secure Data Auditing for Cloud-based Intelligence of Things Environment

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IEEE Internet of Things Journal 2025年第11期12卷 15288-15303页

作者： Ullah, Fasee Pun, Chi-Man Mohmand, Muhammad Ismail Mahendran, Rakesh Kumar Khan, Arfat Ahmad Alhammad, Sarah M. Rodrigues, Joel J. P. C. Farouk, Ahmed Universiti Teknologi PETRONAS Computer and Information Sciences department Perak Darul Ridzuan Seri Iskandar32610 Malaysia Avenida da Universidade University of Macau China Sarhad University of Science and IT Department of Computer Science and IT Peshawar Pakistan Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Khon Kaen University College of Computing Department of Computer Science Khon Kaen40002 Thailand Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Riyadh11671 Saudi Arabia Amazonas State University Manaus Brazil Hurghada University Faculty of Computers and Artificial Intelligence Department of Computer Science Hurghada Egypt

Cloud-based Intelligence of Things is significant for Augmented Enterprise Management Systems. Data integrity auditing is challenging in the intelligence of things environment, mainly when the newer versions in the public cloud environment update existing encrypted data. The related literature on cloud-based intelligence relies on encrypted data uploading or locally handling encryption and decryption using user keys. Considering the security risk, storage constraints at the edge, and realtime environment, both approaches have limited applicability in the intelligence of things environment. This paper presents the Privacy-Aware Secure Data Auditing (PASDA) framework at the cluster head for online data integrity verification. Specifically, the users hide data files by the blinding process with a generation of their corresponding signatures, which achieves data auditing by utilizing homomorphic techniques. A novel automated self-triggering/ Self-auditing-based data integrity auditing system is proposed, which detects the changes made in the cloud-stored data and sends alert messages to the trusted primary cloud server and users. A data dynamics method is developed containing a timestamp with a pointer to store multiple versions of the same file without signatures re-generation for the whole same file. The user is revoked due to prolonged absence or detection of the missed behaviour with system or service expiry. With these data dynamics, the proposed PASDA framework allows CH to regenerate signatures of the revoked user using its membership key for cloud-based stored data access and data integrity auditing. In-depth security analysis and extensive simulations based on comparative performance evaluation attest to the benefits of the proposed PA. © 2014 IEEE.

关键词： Data integrity

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