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Fingerprint Matching for Noisy and Distorted Patterns using a Siamese Network with ResNet50 and Multihead Attention

IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Lehana, Parveen Kumar Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii AlZubi, Ahmad Ali University of Jammu DSP Lab Department of Electronics India University of Jammu Department of Computer Science and IT India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Manipal University Jaipur Jaipur India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa Poland National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Faculty of Informatics and Software Engineering Kyiv Ukraine King Saud University Community College Department of Computer Science Riyadh Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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Progressive Semantic-Visual Alignment and Refinement for Vision-Language Tracking

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IEEE Transactions on Circuits and systems for Video Technology 2025年第5期35卷 4271-4286页

作者： Liang, Yanjie Wu, Qiangqiang Cheng, Lin Xia, Changqun Li, Jia Peng Cheng Laboratory Shenzhen518000 China City University of Hong Kong Department of Computer Science Hong Kong Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China

In recent years, vision-language tracking has drawn emerging attention in the tracking field. The critical challenge for the task is to fuse semantic representations of language information and visual representations of vision information. For this purpose, several vision-language tracking methods perform early or late fusion to fuse visual and semantic features. However, these methods cannot take full advantage of the transformer architecture to excavate useful cross-modal context at various levels. To this end, we propose a new progressive joint vision-language transformer (PJVLT) to progressively align and refine visual embedding with semantic embedding for vision-language tracking. Specifically, to align visual signals with semantic signals, we propose to insert a semantic-aware instance encoder layer (SAIEL) into each intermediate layer of transformer encoder to perform progressive alignment of visual and semantic features. Furthermore, to highlight the multi-modal feature channels and patches corresponding to target objects, we propose a unified channel communication patch interaction layer (CCPIL), which is plugged into each intermediate layer of transformer encoder to progressively activate target-aware channels and patches of aligned multi-modal features for fine-grained tracking. In general, by progressively aligning and refining visual features with semantic features in the transformer encoder, our PJVLT can adaptively excavate well-aligned vision-language context at coarse-to-fine levels, therefore highlighting target objects at various levels for more discriminative tracking. Experiments on several tracking datasets show that the proposed PJVLT can achieve favorable performance in comparison with both conventional trackers and other vision-language trackers. © 1991-2012 IEEE.

关键词： Semantics

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Quantum-Secure Hybrid Communication for Aviation Infrastructures

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2283-2294页

作者： Dowling, Benjamin Wimalasiri, Bhagya King's College London Department of Informatics LondonWC2R 2LS United Kingdom University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom

The rapid digitization of aviation communication and its dependent critical operations demand secure protocols that address domain-specific security requirements within the unique functional constraints of the aviation industry. These secure protocols must provide sufficient security against current and possible future attackers, given the inherent nature of the aviation community, that is highly complex and averse to frequent upgrades as well as its high safety and cost considerations. In this work we propose a pair of quantum-secure hybrid key exchange protocols (PQAG-KEM and PQAG-SIG) to secure communication between aircrafts in-flight and ground stations. PQAG-KEM leverages post-quantum and classical Key Encapsulation Mechanisms (KEMs) to ensure the hybrid security of the protocol against classical as well as future quantum adversaries. PQAG-SIG, alternatively, uses quantum-safe digital signatures to achieve authentication security. We provide an implementation of both PQAG-KEM and PQAG-SIG, and compare favourably with current state-of-the-art secure avionic protocols. Finally, we provide a formal analysis of our new PQAG protocols in a strong hybrid key exchange framework. © 2004-2012 IEEE.

关键词： Quantum cryptography

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IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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The Intersection of Genetics and Neuroimaging: A Systematic Review of Imaging Genetics in Neurological Disease for Personalized Treatment

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Journal of Molecular Neuroscience 2025年第2期75卷 1-26页

作者： Mashhour, Mahinaz A. Youssef, Ibrahim Wahed, Manal Abdel Mabrouk, Mai S. Biomedical Engineering Department Misr University for Science and Technology Giza Egypt Systems and Biomedical Engineering Department Cairo University Giza Egypt Center for Informatics Science (CIS) School of Information Technology and Computer Science Nile University Giza Egypt

Imaging genetics is one of the important keys to precision medicine that leads to personalized treatment based on a patient’s genetics, phenotype, or psychosocial characteristics. It deepens the understanding of the mechanisms through which genetic variations contribute to neurological and psychiatric disorders. This systematic review overviews the methods and applications of imaging genetics in the context of neurological diseases, mentioning its potential role in personalized medicine. Following PRISMA guidelines, this review systematically analyzes 28 studies integrating genetic and neuroimaging data to explore disease mechanisms and their implications for precision medicine. Selected research included multiple neurological disorders, including frontotemporal dementia, Alzheimer’s disease, bipolar disorder, schizophrenia, Parkinson’s disease, and others. Voxel-based morphometry was the most common imaging technique, while frequently examined genetic variants included APOE, C9orf72, MAPT, GRN, COMT, and BDNF. Associations between these variants and regional gray matter loss (e.g., frontal, temporal, or subcortical regions) suggest that genetic risk factors play a key role in disease pathophysiology. Integrating genetic and neuroimaging analyses enhances our understanding of disease mechanisms and supports advancements in precision medicine. © The Author(s) 2025.

关键词： Imaging genetics Neurological diseases Precision medicine Radiogenomics Voxel-based morphometry

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Health security disparities in the Eastern Mediterranean Region: A comparative analysis using an integrated MCDM and clustering approach

Journal of Biosafety and Biosecurity

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Journal of Biosafety and Biosecurity 2025年第1期7卷 38-51页

作者： Nasser, Adel A. Alghawli, Abed Saif Ahmed Saleh, S. Elsayed, Amani A.K. Department of Information Systems and Computer Science Sa'adah University Sa'adah Yemen Department of Artificial Intelligence Modern Specialized University Sana'a Yemen Department of Computer Science College of Sciences and Humanities Prince Sattam Bin Abdulaziz University Building No: 16 A 3 Al-Kharj 16700 Saudi Arabia Department of Mathematics Hodeidah University Hodeidah Yemen Department of Computer Science Cihan University-Erbil Iraq Department of Computer Science College of Sciences and Humanities Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Objectives: This study aimed to analyze and compare health security performance (HSP) between Gulf Cooperation Council and non-Gulf Cooperation Council countries within the Eastern Mediterranean Region (EMR) for 2019 and 2021, recognizing the critical role of health security in managing global health threats. Methods: The study utilized data from the Global Health Security Index (GHSI) for 2019 and 2021. Key health security priorities were identified using the entropy objective weighting method. The VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method was employed to rank countries based on overall performance. K-means clustering was applied to group countries with similar health security profiles. Pearson's and Spearman's rank correlation coefficients were used to assess relationships between independent HSP indicators and overall performance scores. Results: A significant shift in health security priorities within the EMR was observed between 2019 and 2021, with prevention gaining prominence. Gulf Cooperation Council countries emphasized detection and reporting, while non-Gulf Cooperation Council countries prioritized health systems and prevention. Gulf Cooperation Council countries, particularly Qatar and Saudi Arabia, consistently demonstrated strong HSP. Conversely, non-Gulf Cooperation Council countries facing conflict and instability, such as Yemen, Somalia, and Syria, exhibited weaker performance. Health system capacity, prevention, detection and reporting, and risk environment showed robust correlations with overall HSP. Conclusion: This study underscores the necessity for tailored, adaptive policies to address HSP disparities across regions, highlighting investment in prevention, detection, and reporting. It stresses international collaboration, improved policy implementation, and ongoing research to enhance global health security systems across diverse contexts. © 2025 KeAi Communications Co., Ltd

关键词： Eastern Mediterranean Region Entropy weighting method Global health security Gulf Cooperation Council K-means clustering VIKOR method

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Customized IoT based Smart Irrigation for Terrace Plants 6

Customized IoT based Smart Irrigation for Terrace Plants

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

作者： Priya, S. Padma Pavalarajan, S. Abarna, P.S. Manjula devi, C. Abinaya, B. Sukaina, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India Department of Information Technology Velammal college of Engineering and Technology Madurai India

ISBN: (纸本)9798331522667

An Internet of Things based smart irrigation system for terrace plants is a cutting-edge solution designed to automate and optimize the watering process, ensuring efficient use of water and promoting healthier plant growthSmart irrigation system may vary from field with crops of same variety to terrace plantation with plants of different varieties. As the water requirement may vary based on plant type, environmental condition, growth stage, it is intelligent to provide varying water supply. This Smart irrigation system employs soil moisture to monitor the water content of the terrace garden in real time. Some variety of plants may require minimum quantity of water. The sensors continuously collect data and send it to a central IoT platform or a cloud-based server, where intelligent algorithms analyse the data and determine the precise amount of water needed by the plants. Based on this analysis, the system automatically activates the irrigation mechanism whether it's a drip system, sprinklers, or any other appropriate watering method only when required, thus preventing water wastage and avoiding overwatering or underwatering. © 2025 IEEE.

关键词： Sprinkler systems (irrigation)

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Sentinel Shield DNS Filtering Reinvented with Threat Intelligence and AI/ML

Sentinel Shield DNS Filtering Reinvented with Threat Intelli...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Vignesh, S. Praveen Kumar, G. Santhosan, R. Sivachandran, S. K.S. Rangasamy College of Technology Department of Computer Science and Business Systems Tiruchengode Tamil Nadu Namakkal India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Traditional solutions of firewalls usually fail to keep pace with the emerging threats. For this purpose, it offers a new solution that integrates ML with a firewall system to dynamically identify malicious domain requests, such as a dataset consisting of 50 features and 100,000 records, which used authentic logs enriched with Open Source Intelligence carefully analyzed. Supervised algorithms were used, which resulted in very high accuracy levels of 90% to 98%, with very fast classification times ranging from 0.01 to 2.38 seconds. This research not only provides a valuable public dataset but also a methodology that can be exploited by researchers. Practically, the study provides the foundation for an in-band firewall that can proactively identify malicious domains in real time, thus enhancing organizational cybersecurity postures. Further optimization of the performance of the firewall can be made using ensemble learning techniques such as Random Forests and Gradient Boosting. Both these ensemble techniques use several models to improve predictive accuracy for complicated datasets and add robustness in general. Additionally, online learning algorithms should also be used. These are techniques used when new data are being presented one by one so that the model learns and adjusts from time to time, responding dynamically to evolving threats. Further performance improvements can be achieved by fine-tuning the hyperparameters of the chosen algorithms and experimenting with feature engineering. Updating the models with new data and monitoring their performance regularly is essential to keep them effective against changing threats. This paper opens up the transformative potential of ML-enhanced firewalls in modern cybersecurity frameworks and sets the stage for future developments in this critical domain. © 2025 IEEE.

关键词： Cybersecurity

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Hybridization of deep learning models with crested porcupine optimizer algorithm-based cybersecurity detection on industrial IoT for smart city environments

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Alexandria Engineering Journal 2025年 127卷 239-250页

作者： Alzakari, Sarah A. Aljebreen, Mohammed Asiri, Mashael M. MANSOURI, Wahida Alahmari, Sultan Alqahtani, Mohammed Sorour, Shaymaa Bedewi, Wafi Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Community College King Saud University P.O. Box 28095 Riyadh11437 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Information Technology Faculty of Sciences and arts Turaif Northern Border University Arar91431 Saudi Arabia Cybersecurity Institute Riyadh Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Management Information Systems School of Business King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Smart cities have attracted extensive coverage from multidisciplinary studies, and many artificial intelligence (AI) solutions have been designed. Conversely, cybersecurity has constantly been a crucial issue and is becoming gradually dangerous in smart cities. The attack defence models are inappropriate for perceiving multistep assaults as the recognition rules are restricted, and efficacy is partial due to many false security alarms. Therefore, an innovative solution is immediately required to progress cybersecurity defence ability. Machine learning (ML) methods are commonly employed to recognize numerous attacks because they could help network administrators grab analogous initials to avert intrusion. This study presents Cybersecurity using a Crested Porcupine Optimizer Algorithm with Hybrid Deep Learning Models (CCPOA-HDLM). The foremost intention of this study is to improve cybersecurity detection and classification in smart city environments. To accomplish that, the CCPOA-HDLM method comprises distinct processes such as min-max normalization, improved Salp swarm algorithm (ISSA)-based feature selection, Multi-Channel Convolutional Neural Network - Recurrent Neural Network (MCNN-RNN)-based cybersecurity detection, and crested porcupine optimizer (CPO)-based parameter selection process. Primarily, data normalization utilizing min-max normalization is implemented. Next, the CCPOA-HDLM method utilizes ISSA based feature selection method to select optimum features. The CCPOA-HDLM technique employs a hybrid of the MCNN-RNN model for the cybersecurity detection and classification process. Moreover, the hyperparameter range of the hybrid of DL techniques occurs using the CPO technique. The experimental validation of the CCPOA-HDLM approach is performed on the UNSW-NB15 dataset, and the outcome portrayed a superior accuracy value of 99.04 % over other recent approaches under various measures. © 2025 The Authors

关键词： Convolutional neural networks

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Optimizing Secure Multi-User ISAC systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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