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Air Quality Assessment B ased on CNN Transformer Hybrid Architecture

International Journal of Advanced Computer science and Applications 2025年第4期16卷 273-279页

作者： Zhang, Yuchen Thinakaran, Rajermani Faculty of Data Science and Information Technology INTI International University Malaysia

Air quality assessment plays a crucial role in environmental governance and public health decision making. Traditional assessment methods have limitations in handling multi source heterogeneous data and complex nonlinear relationships. This paper proposes an air quality assessment model based on a CNN Transformer hybrid architecture, which achieves end to end prediction by integrating CNN's local feature extraction capability with Transformer's advantage in modeling global dependencies. The model employs a three layer CNN for local feature learning, combined with Transformer's multi head self attention mechanism to capture long range dependencies, and uses multilayer perceptrons for final prediction. Experiments on public datasets demonstrate that compared t o traditional machine learning methods and single deep learning models, the proposed hybrid architecture achieves a 10.2 percentage improvement in Root Mean Square Error (RMSE) and a 0.57 percentage point improvement in coefficient of determination (R2). T hrough systematic ablation experiments, we verify the necessity of each model component, particularly the importance of the CNN Transformer hybrid architecture, positional encoding mechanism, and multi layer network structure in enhancing prediction perfor mance. The research results provide an effective deep learning solution for air quality assessment. © (2025), (science and information Organization). All Rights Reserved.

关键词： Damage detection

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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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Android Application for Children to Learn Arabic Alphabets and Numbers

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第5期19卷 115-127页

作者： Al Khaldy, Mohammad Ali Shaheen, Ameen Al-Shaikh, Ala’A Alzyadat, Wael Alhroob, Aysh Department of Business Intelligence and Data Analytics University of Petra Amman Jordan Faculty of Science and Information Technology Al-Zaytoonah University Amman Jordan Faculty of Information Technology Zarqa University Zarqa Jordan

Mobile learning has become a new trend in e-learning that uses portable media such as smartphones. This offers a new space for creating kid-friendly educational game applications. Therefore, this helps parents fulfill their responsibility to teach kids. This study aims to develop and integrate an educational application that contains multimedia elements that inspire children and evaluate their trustworthiness. The waterfall model was used to develop the application of this study. The application includes four sections: numbers, alphabets, animals, and fruits. A survey was conducted and distributed among four experts and 20 children to validate our application. A selective sampling technique was used to choose these responders. Thematic analysis was used to analyze the data collected. According to the results, the application is appropriate for beginners who want to learn the Arabic alphabet and numbers can easily inspire them with the multimedia elements used and can be implemented in the classroom to enhance the effectiveness of the educational process. Future research could examine the effectiveness of the application across different learning styles or age groups. © 2025 by the authors of this article.

关键词： Contrastive Learning

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Comparative Analysis of SVM, Naïve Bayes, and Logistic Regression in Detecting IoT Botnet Attacks

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 338-343页

作者： Siswanto, Apri Aji, Luhur Bayu Efendi, Akmar Alfaruqi, Dhafin Azriansyah, M. Rafli Raihan, Yefrianda Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Faculty Data Science and Information Technology INTI International University Malaysia

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the risk of cyberattacks, particularly botnet intrusions, which pose serious security threats to IoT networks. Machine learning-based Intrusion Detection Systems (IDS) have emerged as effective solutions for detecting such attacks. This study presents a comparative analysis of three widely used machine learning classifiers—Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR)—to assess their performance in detecting IoT botnet attacks. The experiment uses the BoTNeTIoT-L01 dataset, applying preprocessing techniques such as data cleaning, normalization, and feature selection to enhance model accuracy. The models are trained and evaluated based on standard performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that SVM outperforms the other classifiers in terms of detection accuracy and robustness, particularly in detecting malware based on PE files. These findings offer valuable insights into selecting suitable machine learning models for securing IoT environments. Future work will further explore integrating advanced feature selection techniques and deep learning models to improve detection performance. © (2025), (science and information Organization). All rights reserved.

关键词： Logistic regression

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DCARES: deep convolutional neural network with neural-based optimization for image-based product recommender system

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Multimedia Tools and Applications 2025年 1-31页

作者： Hung, Bui Thanh Nhan, Nguyen Van Phuc Sy, Nguyen Tien Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh city Viet Nam

The development of recommendation systems represents a critical challenge in machine learning, particularly in today’s digital landscape. These systems play a pivotal role in delivering personalized product and service suggestions tailored to user preferences, thereby enhancing user experience and driving sales revenue. While methodologies such as collaborative filtering, content-based filtering, and hybrid recommender systems have been widely explored, creating a robust and effective recommendation system remains a complex and demanding task. This study introduces the DCARES model, a novel content-based filtering approach that leverages the similarity between product features. The DCARES model integrates a deep convolutional neural network (CNN) with machine learning techniques to form the core of the proposed recommendation system. CNNs are employed to extract rich and intricate features from product images, which are then combined with machine learning models to generate personalized recommendations based on user preferences. To optimize performance, the model is fine-tuned using various neural-based optimization algorithms, with the most effective one selected through rigorous evaluation. Experimental results demonstrate that the proposed model delivers highly accurate and effective recommendations in all metrics: Accuracy, RMSE, MAE and MAPE, showcasing its potential to outperform traditional approaches. By incorporating CNNs into recommendation systems, this study highlights a promising direction in the field of machine learning. The proposed approach not only enhances the quality and precision of recommendations but also opens new avenues for future research and applications, particularly in visually driven domains such as e-commerce and fashion. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Novel MITM attack scheme based on built-in negotiation for blockchain-based digital twins

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Digital Communications and Networks 2025年第1期11卷 256-267页

作者： Xin Liu Rui Zhou Shohei Shimizu Rui Chong Qingguo Zhou Xiaokang Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China Faculty of Data Science Shiga UniversityHikone 522-8522Japan Beijing Zhongxin Xingkong Network Technology Co Beijing 100005China

As smart contracts,represented by Solidity,become deeply integrated into the manufacturing industry,blockchain-based Digital Twins(DT)has gained momentum in recent *** of the blockchain infrastructures in widespread use today are based on the Proof-of-Work(PoW)mechanism,and the process of creating blocks is known as“mining”.Mining becomes increasingly difficult as the blockchain grows in size and the number of on-chain business systems *** lower the threshold of participation in the mining process,“mining pools”have been *** can cooperate and share the mining rewards according to the hashrate they contributed to the *** is the most widely used communication protocol between miners and mining *** security is essential for the *** this paper,we propose two novel Man-In-The-Middle(MITM)attack schemes against Stratum,which allow attackers to steal miners'hashrate to any mining pool using hijacked TCP *** with existing attacks,our work is more secretive,more suitable for the real-world environment,and more *** Proof-of-Concept(PoC)shows that our schemes work perfectly on most mining softwares and ***,we present a lightweight AI-driven approach based on protocol-level feature analysis to detect Stratum MITM for blockchain-based *** detection model consists of three layers:feature extraction layer,vectorization layer,and detection *** prove that our detection approach can effectively detect Stratum MITM traffic with 98%*** work alerts the communities and provides possible mitigation against these more hidden and profitable attack schemes.

关键词： Digital twins MITM Blockchain Stratum Anomaly detection

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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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Feature-Based Machine Unlearning for Vertical Federated Learning in IoT Networks

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5031-5044页

作者： Pan, Zijie Ying, Zuobin Wang, Yajie Zhang, Chuan Zhang, Weiting Zhou, Wanlei Zhu, Liehuang City University of Macau Faculty of Data Science China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

In the era of the Internet of Things (IoT), managing the deluge of data generated by distributed devices presents unique challenges, particularly concerning privacy and the efficient use of computational resources. Vertical Federated Learning (VFL) offers a promising avenue for collaborative machine learning without centralizing data, thereby addressing privacy concerns inherent in traditional approaches. However, as data privacy laws and personal data deletion requests become more prevalent, the necessity for effective machine unlearning strategies within VFL frameworks grows increasingly important. To this end, this paper introduces a novel approach to feature-based machine unlearning tailored specifically for VFL systems in IoT networks. Our methodology enables the selective removal of data influence from trained models without the need for full retraining, thus preserving model utility while ensuring compliance with privacy requirements. By integrating a combination of feature relevance measuring techniques and efficient communication protocols, our solution minimizes the data footprint on network nodes, reduces bandwidth consumption, and maintains the integrity and performance of the learning models. To the best of our knowledge, our proposed framework represents the first practical approach to enable machine unlearning within vertical federated learning environments. We demonstrate the effectiveness of our approach through rigorous evaluation using several IoT datasets, highlighting significant improvements in unlearning efficiency and model robustness compared to existing techniques. Our work not only furthers the development of sustainable and compliant machine learning models in IoT but also sets a foundational framework for future research in secure and efficient data management within federated environments. © 2002-2012 IEEE.

关键词： Federated learning

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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

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Research in Astronomy and Astrophysics 2025年第3期25卷 236-248页

作者： Zhen-Hong Shang Long Chen Zhen-Ping Qiang Yi Bi Run-Xin Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Observatories Chinese Academy of Sciences

Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.

关键词： methods: data analysis Sun: fundamental parameters techniques: image processing

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Heart Disease Prediction Model Using Feature Selection and Ensemble Deep Learning with Optimized Weight

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Computer Modeling in Engineering & sciences 2025年第4期143卷 875-909页

作者： Iman S.Al-Mahdi Saad M.Darwish Magda M.Madbouly Department of Information Technology Institute ofGraduate Studies and ResearchAlexandriaUniversityAlexandria21526Egypt Faculty of Computers and Data Science Alexandria UniversityAlexandria5432042Egypt

Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare *** problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical *** challenges often lead to inefficient and less *** predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational *** work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel *** features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction *** hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the *** enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional *** proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over ***,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medi

关键词： Heart disease prediction feature selection ensemble deep learning optimization genetic algorithm(GA) ensemble deep learning tunicate swarm algorithm(TSA) feature selection

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