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Comparative Analysis of Machine Learning Techniques for Arabic Text Classification Using the SANAD dataset 11

Comparative Analysis of Machine Learning Techniques for Arab...

引用

11th International Conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Attar, Hani Sharrab, Yousef Smadi, Saja Jbaily, Safa Zarqa University Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Isra University Department of Data Science and Artificial Intelligence Amman Jordan Yarmouk University Dept. Computer Science Irbid Jordan

ISBN: (纸本)9798331509293

Applying Machine Learning (ML) models for Arabic text classification is a growing research field that has recently raised a large number of research. Generally, classifying text involves selecting a category based on its content from a list of pre-established categories. data classification is among the most popular Natural Language Processing (NLP) activities. Moreover, automatic article classifications, such as the Single-label Arabic News Articles dataset (SANAD), are desperately needed to improve the retrieval of the requested data, which this paper investigates. The ML leverages large datasets to improve efficiency;indeed, by locating focal spots within the text, the learning mechanisms facilitate attention tools better suited for handling lengthy texts. The proposed work employs a dataset of 7,000 articles of separately classified Arab news items and then breaks them into seven categories of 1,000 news stories each: politics, education, sports, religion, culture, finance, and medicine. The investigated SANAD consists of three primary datasets from three news portals: Akhbarana, AlArabiva, and AlCtrl. Finally, to obtain trustable information, this study implements a variety of Conventional ML techniques, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression model to classify the investigated dataset. Our results show that the suggested solution outperformed the analytical search in the Arabic language in terms of accuracy, whereas the Logistic Regression model shows the best results among the seven utilized publicly available dataset classifications. © 2025 IEEE.

关键词： Logistic regression

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Beyond the Blink: IoT's Next-Gen Drowsiness Detection System for Drivers 3

Beyond the Blink: IoT's Next-Gen Drowsiness Detection System...

引用

3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Routray, Sidheswar Patel, Nachiket Patel, Dhruv Srivastava, Jyoti Dash, Rudra Narayan Pandit Deendayal Energy University Dept. of Computer Science & Engg. Gandhinagar India Indrashil University Dept. of Computer Science & Engg. Mehsana India Ganpat University Dept. of Computer Engineering Mehsana India School of Electrical Engineering KIIT University Bhubaneswar India

ISBN: (纸本)9798331533205

Drowsiness during driving includes high risks for the driver, the co-passengers, and the other people on the road. Advance Driver Assistance Systems (ADAS) have been proposed to reduce these risks. This work introduces a new Internet of Things (IoT) seizure detection system to improve driver safety using Raspberry Pi, which is equipped with various sensors and utilizes machine learning for real-time monitoring and analysis. Using sensor fusion technology, including monitoring yawning and eye-closing patterns, the system aims to detect early signs of driver sleepiness. Being a low-cost, versatile, and tiny device, it becomes an ideal core processor for integration into an in-car environment. Additionally, the simplicity enables seamless communication with various sensors and facilitates effective data collection. Extensive tests and simulations were conducted to evaluate the effectiveness and reliability of the proposed system for real-world driving situations. The result outcomes prove the system's accuracy and efficiency in detecting early signs of driver fatigue to enhance driver safety. The integration of Raspberry Pi and sensor fusion technology makes it economical as well as scalable approaches supporting multiple platforms. © 2025 IEEE.

关键词： Advanced driver assistance systems

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Zero-Trust Architecture for Smart City Healthcare Systems 2

Zero-Trust Architecture for Smart City Healthcare Systems

引用

2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Alsofyani, Shahd Ishaque, Mohammad Nofal, Mostafa Jedah International College Dept. of Computer Science and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

This paper introduces a tailored Zero-Trust Architecture (ZTA) for healthcare systems in smart cities, addressing the security challenges posed by interconnected infrastructures. The model enforces continuous access validation through micro-segmentation, identity and access management (IAM) with multi-factor authentication (MFA), and device trust verification, significantly reducing vulnerabilities. AI-powered real-time monitoring and anomaly detection enhance the system's ability to identify and mitigate advanced persistent threats (APTs) and insider risks. Secure communication protocols, end-to-end encryption, and audit logging ensure data protection, regulatory compliance, and forensic capabilities. The proposed ZTA provides a scalable, future-proof solution that strengthens patient data security and safeguards healthcare infrastructure against evolving cyber threats in smart cities. © 2025 IEEE.

关键词： Patient treatment

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LSTM-Based Classification of Psychiatric Disorders Using Ultra-Short Raw ECG Signals

LSTM-Based Classification of Psychiatric Disorders Using Ult...

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2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Tsakmaki, Paraskevi V. Tasoulis, Sotiris Georgakopoulos, Spiros V. Plagianakos, Vassilis P. Dept. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece Dept. of Mathematics University of Thessaly Lamia Greece

ISBN: (纸本)9798331508333

Psychiatric disorders, including bipolar disorder, depression, and schizophrenia, pose substantial public health challenges due to the intricate nature of their diagnosis and management. Electrocardiogram (ECG) signals offer a noninvasive method to assess autonomic nervous system function, which is frequently dysregulated in these conditions. However, conventional ECG analysis techniques often require prolonged recordings and extensive preprocessing, limiting their applicability in real-time or large-scale settings. This study presents an approach leveraging Long Short-Term Memory (LSTM) networks for medical data classification, specifically for the identification of psychiatric disorders using ultra-short, raw ECG signals. Our methodology efficiently captures the temporal dynamics of ECG signals within brief windows, significantly reducing the need for extensive preprocessing and manual feature extraction. The proposed LSTM-based classifier achieved an impressive mean accuracy of 97.12% ± 0.006, underscoring its precision and efficacy in distinguishing between different psychiatric conditions. Our findings indicate that LSTM networks, with their capacity to effectively model complex temporal patterns in short ECG recordings, represent a promising advancement in the diagnostic landscape of psychiatry. © 2025 IEEE.

关键词： Electrocardiography

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Forensic Insights into SenseCAP: A Comprehensive Examination of Technical and LoRa Connectivity Dimensions

Forensic Insights into SenseCAP: A Comprehensive Examination...

引用

20th International Conference on Cyber Warfare and Security, ICCWS 2025

作者： Senol, Nurettin Selcuk Rasheed, Amar Dept. of Computer Science Sam Houston State University HuntsvilleTX United States

ISBN: (纸本)9781917204316

The rapid expansion of Internet of Things (IoT) devices has reshaped various sectors by improving connectivity, efficiency, and convenience. Central to this transformation are LoRa (Long Range) and LoRaWAN (Long Range Wide Area Network) technologies, which provide reliable, low-power, long-range communication critical for IoT applications. As these systems evolve, examining security vulnerabilities and forensic challenges becomes increasingly essential. This paper explores digital forensics within IoT environments, focusing on the methodologies and tools required to secure and maintain the integrity of IoT deployments. By analyzing artifacts and log data from SenseCAP devices, the study offers insights into device operations and user interactions. Additionally, frequency analysis conducted via Software-Defined Radio (SDR) confirmed LoRa communication within expected frequency bands. The findings highlight the importance of robust forensic investigations to protect IoT ecosystems from cyber threats. Through an extensive literature review and empirical analysis, this paper contributes to advancing IoT device forensics, proposing strategies to address emerging challenges and enhance the resilience of IoT infrastructures in an increasingly interconnected world. © 2025 Proceedings of the International Conference on Information Warfare and Security (ICCWS). All rights reserved.

关键词： computer forensics

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Quantum Cryptography and Post-Quantum Security: Safeguarding Cryptographic Protocols Against Quantum Threats 2

Quantum Cryptography and Post-Quantum Security: Safeguarding...

引用

2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Ishaq, Muhammad Al-Anesi, Bushra Jeddah International College Computer Science & Information Technology Dept. Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

As quantum computing progresses towards practical realization, the security of existing cryptographic protocols, such as RSA and Elliptic Curve Cryptography (ECC), faces unprecedented risks. Quantum algorithms like Shor's Algorithm threaten to undermine the security of these widely used encryption schemes, necessitating the development of quantum-resistant alternatives. This paper explores the vulnerabilities of current cryptographic protocols in the quantum era and investigates quantum-safe cryptographic techniques such as lattice-based, hash-based, and code-based cryptography. Additionally, we delve into Quantum Key Distribution (QKD), a method that leverages the principles of quantum mechanics to securely distribute cryptographic keys, ensuring secure communication even in the presence of quantum adversaries. The integration of QKD with post-quantum cryptographic algorithms is proposed as a robust approach to future-proofing cybersecurity. The paper concludes with a discussion of practical challenges in implementing quantum-safe systems and highlights future research directions to strengthen cryptographic resilience against quantum threats. © 2025 IEEE.

关键词： Qubits

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Hybrid Fake News Detection Model: Bagging and Logistic Regression Approach 14

Hybrid Fake News Detection Model: Bagging and Logistic Regre...

引用

14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Shivam Tiwari, Naveen Kumar Bajpai, Abhishek Yadav, Shashank Dept. of Computer Science and Engineering Rajkiya Engineering College Kannauj UP Kannauj India

ISBN: (纸本)9798331531935

In Today’s world or from Past Scenario’s Fake News has a Huge Impact on Everyone’s Lives including Politics, Sports, Education, External Foreign Affairs, Defense, etc. This project aims to develop a model for identifying and detecting fake news. In this model, we implement an ensemble method that leverages the TF IDF vectorization technique to extract meaningful textual features and employ a Bagging Classifier with Logistic Regression as the base estimator to enhance classification accuracy. To build this classifier, we utilized the scikit learn library. Our work was implemented using Jupyter Notebook, an online platform. For feature extraction, we employed a well-known vectorizer called Term Frequency Inverse Document Frequency (TF-IDF). We split our data into training sets (75%) and test sets (25%). The proposed model achieves an accuracy of 99.15% on the test dataset, with a precision of 99.45%, a recall of 99%, and an F1-score of 99.17%. It outperforms standalone Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting classifiers. © 2025 IEEE.

关键词： Logistic regression

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Protecting Privacy and Securing Smart Home Network based on Zero Trust Network Approach 2

Protecting Privacy and Securing Smart Home Network based on ...

引用

2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Alghamdi, Maryam A. Budiarto, Rahmat College of Computing and Information Albaha University Dept. of Computer Science Alaqiq Saudi Arabia

ISBN: (纸本)9798331506995

The main goal of utilizing IoT technology in smart homes is to enhance convenience and flexibility. However, despite the numerous benefits, there are certain vulnerabilities and flaws that can jeopardize security and privacy. This research explores how Zero Trust Network (ZTN) technology can be implemented in smart homes to address crucial objectives such as privacy and security. The study focuses on developing and continuously verifying a ZTN-based system. A key challenge addressed in the research is optimizing security by categorizing home resources and devices based on their importance. To improve the efficiency of verification, authorization, and authentication processes, the research proposes algorithms that utilize Attribute-based Access Control (ABAC) and Elliptic Curve Cryptography (ECC). The simulation results demonstrate that in a Device-to- Device scenario, the attack detection success rates using ABAC and ECC are 87.28% and 94.30%, respectively. These findings suggest that implementing a Zero Trust Network in smart homes can significantly enhance security and privacy for homeowners, protecting them from potential attacks. © 2025 IEEE.

关键词： Authentication

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Enhanced Approaches to Kingfisher Species Identification Using Machine Learning and Deep Learning 5

Enhanced Approaches to Kingfisher Species Identification Usi...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Vidanagamachchi, C.S. Chathuranga, W.G.D. Chathuranga, L.L.G. Jayathilake, K.T.A.S. Yeshani, R.B. Vidanagamachchi, S.M. University of Ruhuna Dept. of Computer Science Matara Sri Lanka University of Ruhuna Dept. of Zoology Matara Sri Lanka

ISBN: (纸本)9798331530983

This study addresses the critical challenge of bird species recognition within the domains of deep learning, emphasizing its significant ecological and conservation implications. Focusing on Sri Lanka's avian biodiversity, particularly the Kingfisher family, this research systematically analyzes bird identification methodologies. The methodology encompasses data collection, pre-processing, feature extraction, classification, and analysis of results. The study employs various deep learning models, including VGG16, VGG19, SVM, a custom CNN, and a hybrid CNN-SVM model. The custom CNN model, specifically optimized for kingfisher species identification, achieved a testing accuracy of 81.21%, outperforming traditional models. However, the hybrid CNN-SVM model demonstrated slightly lower testing accuracy at 79.35%. These findings highlight the effectiveness of the proposed deep learning models in identifying Kingfisher species, particularly when dealing with complex variations in plumage and environmental factors. The results indicate that the custom CNN model is highly reliable for ornithological applications, especially for nonspecialists. The research highlights the complexity of Kingfisher species identification and underscores the critical role of advanced machine learning techniques in addressing these challenges. The findings have significant implications for wildlife conservation, enabling enhanced monitoring and preservation of bird populations. Additionally, the results reveal the potential for developing a mobile application for bird species recognition, particularly for Kingfishers, addressing the lack of existing computer-based identification solutions. © 2025 IEEE.

关键词： Feature extraction

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Enhancing Monocular dept. Estimation with Multi-Source Auxiliary Tasks

Enhancing Monocular Depth Estimation with Multi-Source Auxil...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Quercia, Alessio Yildiz, Erenus Cao, Zhuo Krajsek, Kai Morrison, Abigail Assent, Ira Scharr, Hanno Forschungszentrum Juelich IAS-8 Juelich Germany Rwth Aachen University Dept. of Computer Science Aachen Germany Forschungszentrum Juelich Jsc Juelich Germany Forschungszentrum Juelich IAS-6 Juelich Germany Aarhus University Dept. of Computer Science Aarhus Denmark

ISBN: (纸本)9798331510831

Monocular dept. estimation (MDE) is a challenging task in computer vision, often hindered by the cost and scarcity of high-quality labeled datasets. We tackle this challenge using auxiliary datasets from related vision tasks for an alternating training scheme with a shared decoder built on top of a pre-trained vision foundation model, while giving a higher weight to MDE. Through extensive experiments we demonstrate the benefits of incorporating various in-domain auxiliary datasets and tasks to improve MDE quality on average by 11 %. Our experimental analysis shows that auxiliary tasks have different impacts, confirming the importance of task selection, highlighting that quality gains are not achieved by merely adding data. Remarkably, our study reveals that using semantic segmentation datasets as Multi-Label Dense Classification (MLDC) often results in additional quality gains. Lastly, our method significantly improves the data efficiency for the considered MDE datasets, enhancing their quality while reducing their size by at least 80%. This paves the way for using auxiliary data from related tasks to improve MDE quality despite limited availability of high-quality labeled data. Code is available at https://***/ias-8/mdeaux. © 2025 IEEE.

关键词： Semantic Segmentation

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