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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Salsabila, Nurhusna Siswanto, Apri Bayuaji, Luhur Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Fac. Data Science and Information Technology INTI International University Malaysia

ISBN: (纸本)9798331522667

This research introduces the design and implementation of an advanced IoT-based home security system aimed at improving both convenience and safety for homeowners. The system integrates a smart doorbell with a mobile application, enabling real-time alerts for guest arrivals. Key features include facial recognition for automated door unlocking and notification of unrecognized visitors. When an unknown face is detected, the system sends an image along with a notification to the homeowner via a Telegram bot. The architecture incorporates the ESP32-CAM microcontroller for face detection, a solenoid lock for automatic door access, and an ultrasonic sensor to detect visitor presence. Extensive testing under various environmental conditions was conducted to assess the system's performance, particularly in terms of face recognition accuracy and notification delivery. The results indicate that the system significantly enhances home security by ensuring timely notifications and automating door access for verified individuals. While the system performs reliably in well-lit environments, challenges remain in low-light conditions. Software testing confirmed the robustness of the underlying logic. This innovative IoT solution overcomes the limitations of traditional security systems, offering a more secure, user-friendly, and efficient alternative for modern home security. © 2025 IEEE.

关键词： Ultrasonic sensors

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Enhancing Multimodal Rumor Detection with Statistical Image Features and Modal Alignment via Contrastive Learning 21st

Enhancing Multimodal Rumor Detection with Statistical Image...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhou, Chenyu Li, Xiuhong Li, Zhe Chen, Fan Sheng, Jiabao Chen, Bin Wang, Haoyu School of Computer Science and Technology Xinjiang University Xinjiang China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong Department of Electrical Engineering Stanford University CA United States Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9789819601219

The swift proliferation of multimodal rumors on social media, particularly those with manipulated images and complex intermodal interactions, significantly challenges current detection methods. In response, we utilize statistical image features, including mean and variance, to capture spatial attributes effectively and improve the detection of image-tampered tweets. To tackle complex intermodal correlations, we introduce a contrastive learning approach that aligns features across modalities efficiently. Additionally, we introduce a cross-attention fusion module (CAFM) that enhances the integration of image and text modalities, thereby improving multimodal rumor detection performance. In conclusion, we propose the cross-attention fusion network (ConCAFN), leveraging contrastive learning for robust multimodal rumor detection. Extensive experiments on two real-world datasets confirm the model’s enhanced capability to detect multimodal rumors accurately, demonstrating our methods’ effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Nonvolatile Control of Optical and Electronic Responses in Two-Dimensional MoS2 via Ferroelectric ScAlN Thin Films

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ACS Applied Materials and Interfaces 2025年第18期17卷 26920-26930页

作者： Zha, Zhengxian Hou, Xinyi Wang, Hu Li, Haoran Kozadaev, Konstantin V. Li, Yongjiang Dai, Jianxun Han, Xue Liu, Kun Huang, Huolin Sun, Changsen Gao, Junfeng Tolstik, Alexei Pan, Lujun Li, Dawei School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian116024 China School of Biomedical Engineering Faculty of Medicine Dalian University of Technology Dalian116024 China Physical Optics and Applied Informatics Department Belarusian State University Minsk220030 Belarus School of Physics Dalian University of Technology Dalian116024 China Laser Physics and Spectroscopy Department Belarusian State University Minsk220030 Belarus

Wurtzite-structured ScAlN is a recently discovered ferroelectric material, and unique functionalities can emerge at the heterointerface between ScAlN and two-dimensional (2D) semiconductors, a territory that has yet to be fully explored. In this work, we report the controlled fabrication of inch-scale ScAlN thin films with high ferroelectric properties via reactive co-sputtering as well as the nonvolatile control of photoluminescence (PL) emission and electronic responses in 2D MoS2 by interfacing with ScAlN. The results show that as-grown ScAlN thin films are uniform at wafer scale (≥2 in.) and possess an ultrasmooth surface (≤1.7 Å), ultralow coercive field (≤0.04 V/nm), and out-of-plane polar axis. By interfacing monolayer MoS2 with ScAlN, we realize nonvolatile modulation of PL intensity and position in MoS2, which is attributed to ScAlN’s ferroelectric polarization-induced exciton-to-trion conversion, as evidenced by pizoresponse force microscopy and PL mapping analyses. Moreover, we fabricate high-performance ScAlN/2D MoS2 ferroelectric field-effect transistors, which exhibit a high current switching ratio (≥4.3 × 106) and an ultralow subthreshold swing (≤4.17 mV dec-1), exceeding most of the previously reported ScAlN/semiconductor devices. This study not only offers a cost-effective route to inch-scale fabrication of high-quality ScAlN films but also promotes the development of advanced optoelectronic devices based on III-nitride ferroelectric/2D heterostructures. © 2025 American Chemical Society.

关键词： Ferroelectric thin films

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity 5

A Review on Leveraging Language Agnostic Automated Text Data...

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

作者： Wijesundera, Sarith Peiris, Suresh Michael University of Westminster Department of Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages. © 2025 IEEE.

关键词： Transfer learning

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Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising

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IEEE Access 2025年 13卷 71880-71907页

作者： El-Nabi, Samy Abd Ibrahim, Ahmed F. El-Rabaie, El-Sayed M. Hassan, Osama F. Soliman, Naglaa F. Ramadan, Khalil F. El-Shafai, Walid Faculty of Computer Science and Engineering Department of Artificial Intelligence Engineering South Sinai 46511 Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Suez Canal University Faculty of Computers and Informatics Information Systems Department Ismailia41522 Egypt Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Computer Science Department Riyadh11586 Saudi Arabia

With the rapid development of intelligent transportation systems and growing emphasis on driver safety, real-time detection of driver drowsiness has become a critical area of research. This study presents a robust and scalable driver drowsiness detection framework that integrates a Swin Transformer-based deep learning model with a diffusion model for image denoising. While conventional convolutional neural networks (CNNs) are effective in standard vision tasks, they often suffer performance degradation in real-world driving scenarios due to noise, poor lighting, motion blur, and adversarial attacks. To address these challenges, the proposed model focuses on eye-state detection, specifically, prolonged eye closure, as a primary indicator of driver disengagement and fatigue. Our system introduces a novel preprocessing stage using a denoising diffusion model built on a U-Net encoder-decoder architecture, effectively mitigating the impact of Gaussian noise and adversarial perturbations. Additionally, we incorporate adversarial training with Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks, demonstrating significant improvements in classification accuracy and resilience. Evaluations are conducted on two benchmark datasets, Eye-Blink and Closed Eyes in the Wild (CEW), under both clean and noisy conditions. Comparative experiments show that the proposed system outperforms several state-of-the-art models, including ViT, ResNet50V2, InceptionV3, MobileNet, DenseNet169, and VGG19, in terms of accuracy (up to 99.82%), PSNR (up to 41.61 dB), and SSIM (up to 0.984), while maintaining competitive inference times suitable for practical deployment. Moreover, a detailed sensitivity analysis of data augmentation strategies reveals that techniques such as rotation and horizontal flip substantially enhance the model’s generalization across variable visual inputs. The system also demonstrates improved robustness under real-world black-box scenarios and adver

关键词： Sensitivity analysis

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HPPH: Computer Vision-Based Service for High-Performance Pavement Health Recognition

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IEEE Internet of Things Journal 2025年第11期12卷 15987-15996页

作者： Tsung, Chen-Kun Kristiani, Endah Chiu, Chen-Kang Liu, Jung-Chun Yang, Chao-Tung National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung City411030 Taiwan Tunghai University Department of Computer Science Taichung City407224 Taiwan Krida Wacana Christian University Department of Informatics Jakarta11470 Indonesia Tunghai University Research Center of Smart Sustainable Circular Economy Taichung City407224 Taiwan

Identifying pavement damage is a crucial component in road maintenance and infrastructure management. Prompt detection and corrective action of pavement defects can prevent severe deterioration, maintain safety, and prolong the useful life of road infrastructure. Computer vision is widely applied to vehicle applications, such as driver assistance and self-driving, and that is a good platform to equip the pavement damage recognition service. Therefore, this work applies computer vision to develop the pavement damage detection service named high-performance pavement health recognition (HPPH) to detect common pavement damages, including longitudinal cracks, transverse cracks, alligator cracks, and potholes. The proposed HPPH can be deployed on a vehicle for regular road detection. To fit the requirements of vehicle applications, HPPH considers state-of-the-art techniques to optimize the performance of the recognition service on vehicles, e.g., DeepStream is applied to increase the inference performance. At the same time, YOLOv8s (You Only Look Once, YOLO) provides real-time inference. The experimental results reveal that HPPH provides an accuracy of 90.3% and an average precision 0.5 of 74.3%. Moreover, the optimized HPPH provides three times better than the pure YOLOv8s in terms of the inference speed. In summary, the proposed HPPH provides high recognition accuracy with high efficiency and is feasible to be applied to vehicle applications to realize distributed road maintenance. © 2025 IEEE.

关键词： Deep learning

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A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

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Multimedia Tools and Applications 2025年第8期84卷 5349-5375页

作者： Shafique, Arslan Mehmood, Abid Alawida, Moatsum Elhadef, Mourad Rehman, Mujeeb Ur School of Biomedical Engineering University of Glasgow Glasgow United Kingdom Department of Computer Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Cyber Technology Institute School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications. © The Author(s) 2024.

关键词： Cryptography

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Precision Multiband Terahertz Metamaterial Biosensor With Targeted Spectral Selectivity for Early Detection of MCF-7 Breast Cancer Cells

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IEEE Sensors Journal 2025年第9期25卷 14970-14987页

作者： Hamza, Musa N. Alibakhshikenari, Mohammad Virdee, Bal S. Lavadiya, Sunil Ud Din, Iftikhar Cavalcante De Souza Sanches, Bruno Koziel, Slawomir Iffat Naqvi, Syeda Panda, Abinash Farmani, Ali Rashid, Nasr Kouhalvandi, Lida Shariatifar, Milad University of Raparin College of Science Department of Physics Sulaymaniyah46012 Iraq University of Rome Tor Vergata Department of Electronics Engineering Rome00133 Italy London Metropolitan University Department of Center for Communications Technology LondonN7 8DB United Kingdom Marwadi University Department of Information and Communication Technology Gujarat Rajkot360003 India University of Engineering and Technology Department of Telecommunication Engineering Mardan23200 Pakistan Escola Politécnica da Universidade de São Paulo Department of Electronic Systems Engineering São Paulo05508 Brazil Reykjavik University Engineering Optimization and Modeling Center Reykjavik102 Iceland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland University of Engineering and Technology Department of Telecommunication Engineering Taxila34109 Pakistan CMR Institute of Technology Department of Electronics and Communication Engineering Bengaluru560037 India Lorestan University Department of Electronics engineering Khorramabad68151-44316 Iran Jouf University College of Engineering Department of Electrical Engineering Sakaka72388 Saudi Arabia Al-Azhar University Faculty of Engineering Department of Electrical Engineering Cairo11884 Egypt Dogus University Department of Electrical and Electronics Engineering Istanbul34775 Turkey

Cancer remains one of the leading causes of mortality worldwide, with breast cancer (BC) being a particularly prevalent form. Projections estimate nearly 20 million new cases globally over the next two decades. Early detection is critical for effective treatment;however, conventional diagnostic techniques often lack the necessary sensitivity and specificity, with some methods being invasive and labor-intensive. Recent advancements in microwave imaging (MWI) have shown significant potential as efficient, noninvasive tools for monitoring various cancer types. MWI operating in the terahertz (THz) range has emerged as a promising approach for biosensing, offering the precision needed to differentiate between healthy and cancerous tissues by analyzing small-scale biological features. Among the methods for BC detection, the identification and analysis of MCF-7 BC cells are particularly significant. THz waves interact uniquely with the intrinsic properties of MCF-7 cells, making THz-based biosensors ideal candidates for diagnostic tools. However, many existing sensors are limited in key performance areas, including operating bandwidth and absorption efficiency. This study introduces a novel multiband metamaterial (MTM)-based biosensor specifically designed for the detection of MCF-7 BC cells. The sensor features a compact geometry composed of multiple resonators made from 200-nm-thick aluminum (Al) layers on a 50-$\mu $ m-thick polyethylene terephthalate (PET) substrate. With dimensions of only $198\times 198μ $ m2, the proposed device is exceptionally compact. It operates in the 0.5-1.6-THz frequency range and achieves near-perfect absorption rates (>99%) across multiple bandwidths. These results are achieved through precise tuning of the sensor's geometry and architectural optimization, significantly enhancing its sensitivity for cancer detection. Comprehensive validation of the sensor is performed using full-wave electromagnetic analysis, which includes evaluating elect

关键词： Terahertz waves

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A dynamic simulation model to improve the livability of transportation systems

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Public Transport 2025年第1期17卷 177-220页

作者： Suryani, Erma Hendrawan, Rully Agus Adipraja, Philip Faster Eka Widodo, Basuki Chou, Shuo-Yan Az-Zahra, Alifia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Informatics Engineering Institut Teknologi Dan Bisnis Asia Malang Surabaya65113 Indonesia Department of Mathematics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department of Civil Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

Transportation users often face shortcomings, such as unreliable service causing uncertainty in transit, low quality of service, and loss of time due to road and highway congestion. This study aims to improve the livability of the transportation system by considering several factors. System dynamics is used as a model development method to analyze the relationship between complex and nonlinear variables affecting the livability of the transportation system. The system structure of a valid model can be modified by adding multiple feedback loops, new parameters, and changing the feedback loop structure to see how it affects other variables. The scenario model is built from a valid model by changing the model structure to improve the livability of the transportation system through improvements in the perception, safety, security, and health level of public transport, land use accessibility, and mitigation of air pollution. These strategies are expected to increase the livability of the transportation system by 71%, driven by the improvement in perception, safety, security level, and health level of public transport, land use accessibility, and decreased air pollution. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Land use

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