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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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Structure learning of Bayesian networks using sparrow optimization algorithm

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

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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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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps:An Ultra-Wide Range Dynamics for Image Encryption

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computers, Materials & Continua 2025年第5期83卷 2161-2188页

作者： De Rosal Ignatius Moses Setiadi T.Sutojo Supriadi Rustad Muhamad Akrom Sudipta Kr Ghosal Minh T.Nguyen Arnold Adimabua Ojugo Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceDian Nuswantoro UniversitySemarang50131Indonesia Department of Cyber Forensics and Information Security Behala Government Polytechnic756Upendra Nath Banerjee RoadParnasreeBehalaKolkata700060India Thai Nguyen University of Technology Thai Nguyen UniversityThai Nguyen240000Vietnam Department of Computer Science Federal University of Petroleum ResourcesEffurun330102Nigeria

Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption *** paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter *** proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance *** numerical experiments validate the effectiveness of *** proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in *** bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher *** system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force ***,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution *** approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption *** method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor *** robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential ***,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s *** results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.

关键词： Single qubit quantumchaotic quantumchaotic map quantumimage encryption quantumlogistic map quantum sine map

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Awake at the Wheel: Enhancing Automotive Safety Through EEG-Based Fatigue Detection 27th

Awake at the Wheel: Enhancing Automotive Safety Through EEG-...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Siddhad, Gourav Dey, Sayantan Roy, Partha Pratim Iwamura, Masakazu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Osaka Sakai599-8531 Japan

ISBN: (纸本)9783031781940

Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employing advanced pattern recognition technologies, including machine learning and deep neural networks, EEG signals are meticulously analyzed to discern patterns indicative of fatigue. This methodology combines feature extraction with a classification framework to improve the accuracy of fatigue detection. The proposed NLMDA-Net reached an impressive accuracy of 83.71% in detecting fatigue from EEG signals by incorporating two novel attention modules designed specifically for EEG signals, the channel and depth attention modules. NLMDA-Net effectively integrate features from multiple dimensions, resulting in improved classification performance. This success stems from integrating temporal convolutions and attention mechanisms, which effectively interpret EEG data. Designed to capture both temporal and spatial characteristics of EEG signals, deep learning classifiers have proven superior to traditional methods. The results of this study reveal a substantial enhancement in detection rates over existing models, highlighting the efficacy of the proposed approach for practical applications. The implications of this research are profound, extending beyond academic realms to inform the development of more sophisticated driver assistance systems. Incorporating this fatigue detection algorithm into these systems could significantly reduce fatigue-related incidents on the road, thus fostering safer driving conditions. This paper provides an exhaustive analysis of the dataset, methods employed, results obtained, and the potential real-world applications of the findings, aiming to contribute significantly to advancements in automotive safety. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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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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Holistic Digital Doctor for X-Ray Reading Using VR-Enhanced 3D Touch Bone Model User Interface

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IEEE Access 2025年 13卷 72790-72811页

作者： Bibi, Asma Majeed, Muhammad Faran Frnda, Jaroslav Abbasi, Almas Bures, Vladimir Anwar, Muhammad Shahid Gamzatdin, Bekbaev Kohsar University Murree Department of Computer Science Punjab Murree47150 Pakistan International Islamic University Islamabad Department of Computer Science Islamabad44000 Pakistan University of Žilina Faculty of Operation and Economics of Transport and Communications Department of Quantitative Methods and Economic Informatics Zilina01026 Slovakia University of Hradec Králové Faculty of Informatics and Management Hradec Kralove 500 03 Czech Republic Gachon University Department of AI and Software Seongnam-si13120 Korea Republic of Tashkent State University of Economics Department of Finance and Digital Economics Tashkent100066 Uzbekistan

The Digital Medical Doctor (DD) platform takes a unique approach by allowing X-ray interpretation through touch devices, eliminating the requirement for medical visits simply to read X-rays. In the context of joint analysis, our suggested joint track methodology departs from the traditional keyboard and mouse matching technique, proposing an image-matching approach employing a joystick instead. However, we innovate this using a touch interface, which is more accurate and effective than joystick-based X-ray picture matching. Given the growing incidence of knee problems among women, a dataset concentrating only on knee examinations is chosen. Our study employs a hybrid image-matching technique, incorporating the Touch Bone Model (TBM) designed on a 3D modeler for knee matching on touch screens. Utilizing the Joint Track dataset, the results of DD are verified by the doctor. The results reveal whether the report is positive or negative. We saw increased efficiency and lower resource usage by incorporating TBM into the mobile version of DD, resulting in time and cost savings. This novel method is in line with the expanding environment of healthcare technology, intending to improve diagnostic processes while encouraging ease and resource efficiency. © 2013 IEEE.

关键词： Diagnosis

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Detect and Conquer: Template-Based Analysis of Processes Using Complex Event Processing

Detect and Conquer: Template-Based Analysis of Processes Us...

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International Workshops which were held in conjunction with the 6th International Conference on Process Mining, ICPM 2024

作者： Imenkamp, Christian Akili, Samira Weidlich, Matthias Koschmider, Agnes Business Informatics and Process Analytics University of Bayreuth Bayreuth Germany Department of Computer Science Humboldt-Universität zu Berlin Berlin Germany

ISBN: (纸本)9783031822247

Online process analysis aims at identifying behavioral regularities or abnormalities in processes in near-real-time from continuous event streams. Yet, its realization is challenging, due to the requirements in terms of scalability and accuracy imposed by processes in Internet-of-Things environments. Against this background, this paper presents an approach for online process analysis that is based on standard models and systems for complex event processing (CEP). We present the "Detect and Conquer" approach that includes generic process templates to accurately capture behavioral regularities or deviations, which are then mapped to CEP queries to achieve their efficient evaluation. We evaluated our approach against synthetic and real-world datasets. The results demonstrate the feasibility and efficiency of our approach. © The Author(s) 2025.

关键词： Structured Query Language

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A Multi-Modal Assessment Framework for Comparison of Specialized Deep Learning and General-Purpose Large Language Models

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IEEE Transactions on Big Data 2025年第3期11卷 1001-1012页

作者： Nadeem, Mohammad Sohail, Shahab Saquib Madsen, Dag Oivind Alzahrani, Ahmed Abrahim Ser, Javier Del Muhammad, Khan Aligarh Muslim University Department of Computer Science Aligarh India VIT Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India University of South-Eastern Norway Norway King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia TECNALIA Derio48160 Spain Department of Communications Engineering Bilbao48013 Spain Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Recent years have witnessed tremendous advancements in Al tools (e.g., ChatGPT, GPT 4, and Bard), driven by the growing power, reasoning, and efficiency of Large Language Models (LLMs). LLMs have been shown to excel in tasks ranging from poem writing and coding to essay generation and puzzle solving. Despite their proficiency in general queries, specialized tasks such as metaphor understanding and fake news detection often require finely tuned models, posing a comparison challenge with specialized Deep Learning (DL). We propose an assessment framework to compare task-specific intelligence with general-purpose LLMs on suicide and depression tendency identification. For this purpose, we trained two DL models on a suicide and depression detection dataset, followed by testing their performance on a test set. Afterward, the same test dataset is used to evaluate the performance of four LLMs (GPT 3.5, GPT 4, Google Bard, and MS Bing) using four classification metrics. The BERT-based DL model performed the best among all, with a testing accuracy of 94.61%, while GPT 4 was the runner-up with accuracy 92.5%. Results demonstrate that LLMs do not outperform the specialized DL models but are able to achieve comparable performance, making them a decent option for downstream tasks without specialized training. However, LLMs outperformed specialized models on the reduced dataset. © 2015 IEEE.

关键词： Contrastive Learning

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