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Stylometry-driven framework for Urdu intrinsic plagiarism detection: a comprehensive analysis using machine learning, deep learning, and large language models

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Neural computing and Applications 2025年第9期37卷 6479-6513页

作者： Manzoor, Muhammad Faraz Farooq, Muhammad Shoaib Abid, Adnan Department of Computer Science University of Management and Technology Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the passages have been written by a single author;(2) extrinsic: where a suspicious document is compared with a given set of source documents to figure out sentences or phrases which appear in both documents. In the pursuit of advancing intrinsic plagiarism detection, this study addresses the critical challenge of intrinsic plagiarism detection in Urdu texts, a language with limited resources for comprehensive language models. Acknowledging the absence of sophisticated large language models (LLMs) tailored for Urdu language, this study explores the application of various machine learning, deep learning, and language models in a novel framework. A set of 43 stylometry features at six granularity levels was meticulously curated, capturing linguistic patterns indicative of plagiarism. The selected models include traditional machine learning approaches such as logistic regression, decision trees, SVM, KNN, Naive Bayes, gradient boosting and voting classifier, deep learning approaches: GRU, BiLSTM, CNN, LSTM, MLP, and large language models: BERT and GPT-2. This research systematically categorizes these features and evaluates their effectiveness, addressing the inherent challenges posed by the limited availability of Urdu-specific language models. Two distinct experiments were conducted to evaluate the impact of the proposed features on classification accuracy. In experiment one, the entire dataset was utilized for classification into intrinsic plagiarized and non-plagiarized documents. Experiment two categorized the dataset into three types based on topics: moral lessons, national celebrities, and national events. Both experiments are thoroughly evaluated through, a fivefold cross-validation analysis. The results show that the random forest classifier achieved an ex

关键词： Deep learning

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Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

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Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

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IEEE Internet of Things Magazine 2025年第3期8卷 72-80页

作者： Xie, Wenwen Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Dobre, Octavia A. Jilin University China The College of Computing and Data Science Nanyang Technological University Singapore The University of Hong Kong China Memorial University The Faculty of Engineering and Applied Science Canada

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAl) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAl in improving energy harvesting IoT networks in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting IoT networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting IoT networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data col-lection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some import-ant open directions. © 2018 IEEE.

关键词： Costs Generative AI Reviews Energy exchange data collection Autonomous aerial vehicles Batteries Energy harvesting Internet of Things Battery management systems Energy storage

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The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation 29

The Evolution of Zero Trust Architecture (ZTA) from Concept ...

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29th International Conference on Information Technology, IT 2025

作者： Nasiruzzaman, Md Ali, Maaruf Salam, Iftekhar Miraz, Mahdi H. Xiamen University Malaysia School of Computing and Data Science Malaysia Universiteti Metropolitan Tirana Faculty of Computer Science and IT Albania The Doctoral College United Kingdom Xiamen University Malaysia Mahdi H. Miraz Is with the School of Computing and Data Science Malaysia Wrexham University School of Computing Faculty of Arts Science and Technology United Kingdom University of South Wales Faculty of Computing Engineering and Science United Kingdom

ISBN: (纸本)9798331517649

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA's benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats. © 2025 IEEE.

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

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Job Generator for Evaluating and Comparing Scheduling Algorithms for Modern GPU Clouds 15

Job Generator for Evaluating and Comparing Scheduling Algori...

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15th International Conference on Cloud computing and Services science, CLOSER 2025

作者： Konopa, Michal Fesl, Jan Beránek, Ladislav Department of Data Science and Computing Systems Faculty of Agriculture and Technology University of South Bohemia České Budějovice Czech Republic

ISBN: (纸本)9789897587474

The steep technological and performance advances in GPU cards have led to their increasing use in data centers in the recent years, especially in machine learning jobs. However, high hardware performance alone does not guarantee (sub) optimal utilization of computing resources, especially when the cost associated with power consumption also needs to be increasingly considered. As consequence of these realities, various job scheduling algorithms have been and are being developed to optimize the power consumption in data centers with respect to defined constraints. Unfortunately, there is still no known, widely used, parametrizable dataset that serves as a de facto standard for simulating scheduling algorithms and the resulting ability to compare their performance against each other. The goal of this paper is to describe a simple job set generator designed to run on modern GPU architectures and to introduce the newly created data set suitable for evaluation the scheduling algorithms. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Scheduling algorithms

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ERBM: A Machine Learning-Driven Rule-Based Model for Intrusion Detection in IoT Environments

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Computers, Materials and Continua 2025年第3期83卷 5155-5179页

作者： Mehmmod, Arshad Batool, Komal Sajid, Ahthsham Alam, Muhammad Mansoor Su'ud, Mazliham MohD Khan, Inam Ullah Department of Information Security and Data Science Riphah Institute of Systems Engineering Riphah International University Islamabad46000 Pakistan Faculty of Computing Riphah International University Islamabad46000 Pakistan Faculty of Computing and Informatics Multimedia University Cyberjaya63100 Malaysia

Traditional rule-based Intrusion Detection Systems (IDS) are commonly employed owing to their simple design and ability to detect known threats. Nevertheless, as dynamic network traffic and a new degree of threats exist in IoT environments, these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy. In this study, we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model (ERBM) to classify the packets better and identify intrusions. The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic, where three membership functions are created to classify all the network traffic features as low, medium, or high to remain situationally aware of the environment. Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty. Also, for further classification, machine learning classifiers such as Decision Tree (DT), Random Forest (RF), and Neural Networks (NN) learn complex ways of attacks and make the detection process more precise. A thorough performance evaluation using different metrics, including accuracy, precision, recall, F1 Score, detection rate, and false-positive rate, verifies the supremacy of ERBM over classical IDS. Under extensive experiments, the ERBM enables a remarkable detection rate of 99% with considerably fewer false positives than the conventional models. Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions, the ERBM system provides a unique, scalable, data-driven approach to IoT intrusion detection. This research presents a major enhancement initiative in the context of rule-based IDS, introducing improvements in accuracy to evolving IoT threats. Copyright © 2025 The Authors.

关键词： Membership functions

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Latency- and Privacy-Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 365-377页

作者： Hu, Yuhao Xu, Xiaolong Qi, Lianyong Zhou, Xiaokang Xia, Xiaoyu Nanjing University of Information Science and Technology School of Software Nanjing210044 China College of computer science and technology Qingdao266850 China Shiga University Faculty of Data Science Hikone522-8522 Japan RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Reliable artificial intelligence (AI) systems not only propose a challenge on providing intelligent services with high quality for customers but also require customers' privacy to be protected as much as possible during process of the services. Given the ultrahigh computing load brought by deep-learning-based intelligent services to edge devices and the ultralong distance between edge and cloud, low-latency requirement of intelligent services is hard to meet in single edge computing or cloud computing. Edge-cloud collaborative inference of deep neural networks (DNNs) is considered a feasible solution to the problem. However, former work has not reduced the inference latency to the greatest extent and has not considered privacy protection in distributed systems. To solve the problem, we first establish a novel queue mechanism. Then, the convolution layer split decisions are made based on deep reinforcement learning (DRL) to realize the parallel inference of convolutional neural networks (CNNs) for inference latency reduction. Next, for each CNN, the partition decision is made based on brute force algorithm to further reduce inference latency and protect customers' privacy. Finally, simulation results show that our method performs better than the existing other methods. © 2020 IEEE.

关键词： Cloud computing

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Enhancing Match Detection Process Using Chi-Square Equation for Improving Type-3 and Type-4 Clones in Java Applications

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 777-784页

作者： Abdullah, Noormaizzattul Akmaliza Mubarak-Ali, Al-Fahim Hamza, Mohd Azwan Mohamad Yaacob, Siti Salwani Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan Malaysia Centre For Artificial Intelligence & Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob Gambang Pahang Kuantan26300 Malaysia

Generic Code Clone Detection (GCCD) is a code clone detection model that use distance measure equation, enabling detection of all types of code clones, naming clone Type-1, Type-2, Type-3 and Type-4 in Java programming language applications. However, the detection process of GCCD did not focus on detecting clones of Type-3 and Type-4. Hence, this paper suggested two experiments to incorporate enhancements to the GCCD in order to improve the detection rate of clone Type-3 and clone Type-4. The implementation of Chi-square distance in the match detection process produced a significant result increase in the experiment specifically on clones Type-3 and Type-4, in comparison with the Euclidean distance in GCCD, which allows the increase of detection rate due to the dissimilarity of the distance measures. Based on the results, the suggested enhancement using Chi-square distance on match detection process outperforms GCCD in terms of improving code clone detection results based on clone Type-3 and Type-4, as the objectives for each experiment are carried, contributes to the research on improving the code clone detection result. © (2025), (science and Information Organization). All rights reserved.

关键词： Java programming language

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