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Optimizing Monkeypox Lesions Detection with a Lightweight Hybrid Model

IEEE Access 2025年

作者： Al-Gaashani, Mehdhar S. A. M. Alkanhel, Reem Hassan, Dina S. M. Ba Mahel, Abduljabbar S. Aziz, Ahmed Khayyat, Mashael M. Muthanna, Ammar University of Electronic Science and Technology of China School of Resources and Environment 1st Ring Rd East 2 Section Sichuan Chengdu610056 China Princess Nourah bint Abdulrahman University Department of Information Technology College of Computer and Information Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia University of Electronic Science and Technology of China School of Life Science and Technology Chengdu610054 China Benha university Department of computer science Faculty of computer and Artificial intelligence Egypt Central Asian University Engineering school Tashkent Uzbekistan University of Jeddah Department of Information Systems and Technology Collage of Computer Science and Engineering Jeddah Saudi Arabia Peoples'Friendship University of Russia RUDN University Department of Applied Probability and Informatics 6 Miklukho-Maklaya St Moscow117198 Russia

The increasing global prevalence of monkeypox (mpox) has necessitated the development of accurate, efficient, and interpretable diagnostic models for timely disease identification. Although deep learning has advanced medical image analysis, diagnosing diseases with complex lesions, such as monkeypox, remains challenging in resource-limited settings. Existing methods often rely on large architectures that demand high computational resources and lack interpretability. To address these issues, we propose a lightweight, interpretable architecture that truncates MobileNetV2 for efficient feature extraction, incorporates Ghost Modules to reduce redundancy, and integrates a SA-MobileViT block to capture both local and global dependencies in lesion images. Grad-CAM visualizations enhance interpretability, and Optuna is used for hyperparameter optimization. Our model achieved an accuracy of 98.72%, precision of 98.76%, recall of 98.72%, and F1-score of 98.72%, outperforming existing approaches, especially in handling irregular lesion patterns. The model's design is ideal for deployment in resource-constrained settings, offering a robust solution for real-world healthcare applications. Grad-CAM, saliency maps, and other visualization techniques help improve clinical trust by highlighting relevant regions in input images, emphasizing key features of the lesions. The proposed model is lightweight with only 630,204 parameters, making it 92.3× smaller than ResNet152V2 (58,152,004 parameters) and 28.7× smaller than DenseNet201 (18,100,612 parameters). It requires 321.61M FLOPs, 35.8× fewer than ResNet152V2 (11.51G FLOPs) and 13.4× fewer than DenseNet201 (4.29G FLOPs). With a size of just 72.92 MB, it is 8.14× smaller than ResNet152V2 (594.08 MB) and 4.57× smaller than DenseNet201 (333.47 MB), making it an efficient solution for mobile and edge deployment in resource-constrained environments. © 2013 IEEE.

关键词： Deep learning

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A virtual reality research of Gait analysis in the medicine fields

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AIP Conference Proceedings 2022年第1期2426卷

作者： A. Sampath Dakshina Murthy B. Omkar Lakshmi Jagan K. Raghava Rao P. Satyanarayana Murty 1Department of Electronics and Communication Engineering Vignan’s Institute of Information Technology Duvvada Visakhapatnam India 2Department of Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram India

Therapeutic effects for rehabilitation of the older adult are provided by virtual realities therapy, but the body’s balance remains unclear. The objective was to characterize the effect of senior physiotherapy therapy treatments with VRT on their balance-rehabilitation. Virtual Reality is a technology often used in many fields of cinema, sport, entertainment and simulation. Machine imaging and materials seek in the present circumstances to simulate a real presence. Their sensory abilities in medicine are of practical value. His significance in the field of healthcare should thus be explored. Older individuals are most frequently affected by injury and injury-related deaths. This study evaluated the effect on the balance and the gait capabilities of falling elderly individuals. Ten articles met the inclusion criteria, which demonstrated variety in treatment types (70%) and evaluated outcomes (60 percent). The article focuses on a review of literature in which we comprehend how this technology solves a medical issue to save the lives and vital applications of a patient.

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An efficient conjunctive keyword searchable encryption for cloud-based IoT systems

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Digital Communications and Networks 2025年

作者： Tianqi Peng Bei Gong Chong Guo Akhtar Badshah Muhammad Waqas Hisham Alasmary Sheng Chen Faculty of Information Technology Beijing University of Technology Beijing 100124 China Department of Software Engineering University of Malakand Dir Lower 18800 Pakistan School of Computing and Mathematical Sciences Faculty of Engineering and Science University of Greenwich UK School of Engineering Edith Cowan University Perth 6027 WA Australia Department of Computer Science College of Computer Science King Khalid University 61421 Abha Saudi Arabia School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK Faculty of Information Science and Engineering Ocean University of China Qingdao 266100 China

Data privacy leakage has always been a critical concern in cloud-based Internet of Things (IoT) systems. Dynamic Symmetric Searchable Encryption (DSSE) with forward and backward privacy aims to address this issue by enabling updates and retrievals of ciphertext on untrusted cloud server while ensuring data privacy. However, previous research on DSSE mostly focused on single keyword search, which limits its practical application in cloud-based IoT systems. Recently, Patranabis (NDSS 2021) [1] proposed a groundbreaking DSSE scheme for conjunctive keyword search. However, this scheme fails to effectively handle deletion operations in certain circumstances, resulting in inaccurate query results. Additionally, the scheme introduces unnecessary search overhead. To overcome these problems, we present CKSE, an efficient conjunctive keyword DSSE scheme. Our scheme improves the oblivious shared computation protocol used in the scheme of Patranabis, thus enabling a more comprehensive deletion functionality. Furthermore, we introduce a state chain structure to reduce the search overhead. Through security analysis and experimental evaluation, we demonstrate that our CKSE achieves more comprehensive deletion functionality while maintaining comparable search performance and security, compared to the oblivious dynamic cross-tags protocol of Patranabis. The combination of comprehensive functionality, high efficiency, and security makes our CKSE an ideal choice for deployment in cloud-based IoT systems.

关键词： Symmetric searchable encryption Conjunctive keyword search Forward and backward privacy Cloud server

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Berry Curvature and Bulk-Boundary Correspondence from Transport Measurement for Photonic Chern Bands

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Physical Review Letters 2023年第13期131卷 133601-133601页

作者： Chao Chen Run-Ze Liu Jizhou Wu Zu-En Su Xing Ding Jian Qin Lin Wang Wei-Wei Zhang Yu He Xi-Lin Wang Chao-Yang Lu Li Li Barry C. Sanders Xiong-Jun Liu Jian-Wei Pan Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China National Laboratory of Solid State Microstructures School of Physics Nanjing University Nanjing 210093 China Department of Physics Southern University of Science and Technology Shenzhen 518055 China The Physics Department and the Solid State Institute Technion–Israel Institute of Technology Haifa 3200003 Israel Department of Physics University of Konstanz D-78457 Konstanz Germany School of Computer Science Northwestern Polytechnical University Xi’an 710129 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Institute for Quantum Science and Technology University of Calgary Alberta T2N 1N4 Canada International Center for Quantum Materials School of Physics Peking University Beijing 100871 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing 100190 China International Quantum Academy Shenzhen 518048 China

Berry curvature is a fundamental element to characterize topological quantum physics, while a full measurement of Berry curvature in momentum space was not reported for topological states. Here we achieve two-dimensional Berry curvature reconstruction in a photonic quantum anomalous Hall system via Hall transport measurement of a momentum-resolved wave packet. Integrating measured Berry curvature over the two-dimensional Brillouin zone, we obtain Chern numbers corresponding to −1 and 0. Further, we identify bulk-boundary correspondence by measuring topology-linked chiral edge states at the boundary. The full topological characterization of photonic Chern bands from Berry curvature, Chern number, and edge transport measurements enables our photonic system to serve as a versatile platform for further in-depth study of novel topological physics.

关键词： Quantum simulation Quantum walks Topological effects in photonic systems Topological phase transition Topological phases of matter

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A survey on various image analysis techniques

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AIP Conference Proceedings 2022年第1期2555卷

作者： Naresh Kumar Pegada Vetrithangam Azra Fathima Arunadevi 1Department of Computer Science & Engineering KG Reddy College of Engineering and Technology Hyderabad Telangana India 2Department of Computer Science & Engineering Chandigarh University Punjab India 3Department of Electronics and Communication Engineering Dr.N.G.P Institute of Technology Coimbatore Tamilnadu India

The image processing area has become increasingly important in real-time applications in the current world. Such image processing techniques assist in carrying out processes on digitized images in order to deliver superior results. There are a variety of algorithms for data classification, some of which are rule-based and others which are learning-based. Various image analysis techniques and related challenges in the medical field are investigated in this research. This paper presents effective strategies for overcoming the limitations of image analysis approaches, as well as a brief discussion of image pre-processing before focusing on Image Classification and Segmentation. Our research can help readers learn more about many aspects of medical image analysis. This paper will review the most relevant studies on this topic to date and will describe existing image analysis methods for Data Preprocessing, Image Classification, and Image Segmentation. Image analysis will help readers understand how to increase the performance of their models and expand constrained datasets to take use of the possibilities of other data sets.

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A secure mist-fog-assisted cooperative offloading framework for sustainable smart city development

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Digital Communications and Networks 2024年

作者： Subhranshu Sekhar Tripathy Sujit Bebortta Mazin Abed Mohammed Muhammet Deveci Haydar Abdulameer Marhoon Radek Martinek School of Computer Engineering KIIT Deemed to be University Bhubaneswar 751024 India Department of Computer Science Ravenshaw University Cuttack 753003 India Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava 70800 Czech Republic Department of Telecommunications VSB-Technical University of Ostrava Ostrava 70800 Czech Republic Department of Industrial Engineering Turkish Naval Academy National Defence University 34942 Tuzla Istanbul Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon The Bartlett School of Sustainable Construction University College London 1-19 Torrington Place London WC1E 7HB UK Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Iraq College of Computer Sciences and Information Technology University of Kerbala Karbala Iraq

Practical applications of smart cities and the Internet of Things (IoT) have multiplied, posing many difficulties in network performance, dependability, and security. Concerns of accessibility, reliability, sustainability, and security too have arisen correspondingly because of the decentralized character of the smart city and IoT systems. Fog computing offers a foundation for various applications, including cognitive support, health and social services, intelligent transportation systems, and pervasive computing and communications. Fog computing can help enhance these apps' productivity and lower the end-to-end delay experienced by such time-sensitive applications. In this research, we propose a reliable and secure service delivery strategy at the network edge for smart cities. To improve the availability and dependability, along with the security of smart city applications, the approach employs a combined method uniting distributed fog servers in addition to mist servers with the help of an intrusion detection system. Simulation findings suggest a reduction of 40.3% in the delay incurred by each service request for highly dense areas and 60.6% for moderately dense environments. Furthermore, the system has low false-negative rates and high detection and accuracy rates, decreasing service requests 2%.

关键词： Security Reliability Trustworthy computing Resource provisioning Threat detection Fog computing Internet of things

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Synergy of edge intelligence and blockchain: a comprehensive survey

TechRxiv

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TechRxiv 2021年

作者： Wang, Xiaofei Ren, Xiaoxu Qiu, Chao Xiong, Zehui Yao, Haipeng Leung, Victor C.M. College of Intelligence and Computing Tianjin University Tianjin300350 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100088 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Edge intelligence (EI), as an emerging technology, has been attracting significant attention. It pushes the frontier of artificial intelligence (AI) from the cloud to the network edge, aiming to embrace and support the next-generation communications while unleashing AI services. However, it faces challenges in its decentralized management and security, which limit its capabilities to support services with numerous requirements. On the other side, blockchain (BC), as a promising decentralized technology, is beneficial to tackle the above issues. However, there exist some technical challenges for BC, such as transaction capacity, scalability, and fault tolerance. Motivated by significant current interest around EI and BC, this survey examines whether the synergy of EI and BC can make a powerful network with combined functionalities of both cutting-edge technologies. Accordingly, we develop EI-chain and Chain-intelligence to realize reliable computing-power management, data administration, and model optimization at the edges, while improving the functions of BC by leveraging complementary characteristics of EI and BC, further making up for their own limitations. In this survey, a wide spectrum of literature is carefully reviewed to enable the EI-chain and Chain-intelligence. Moreover, we cover the technological aspects of EI-chain and Chain-intelligence: overview, motivations, and frameworks. Finally, some challenges and future directions are explored. We believe this survey will provide developers and researchers a comprehensive view on the synergy of EI and BC, while accelerating the design of a powerful integrated network. © 2021, CC BY-NC-SA.

关键词： Blockchain

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Prediction of influenza a virus infections in humans using an artificial neural network learning approach

arXiv

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arXiv 2021年

作者： Chrysostomou, Charalambos Partaourides, Harris Seker, Huseyin Computation-based Science and Technology Research Center The Cyprus Institute 20 Konstantinou Kavafi Street Aglantzia Nicosia2121 Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 30 Archbishop Kyprianou Str. Limassol3036 Cyprus Department of Computer and Information Sciences Faculty of Engineering and Environment The University of Northumbria NE1 8ST Newcastle-upon-Tyne United Kingdom

The Influenza type A virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Haemagglutinin (HA) gene of the virus has the potential to be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. In this paper, to accurately predict if an Influenza type A virus has the capability to infect human hosts, by using only the HA gene, is therefore developed and tested. The predictive model follows three main steps;(i) decoding the protein sequences into numerical signals using EIIP amino acid scale, (ii) analysing these sequences by using Discrete Fourier Transform (DFT) and extracting DFTbased features, (iii) using a predictive model, based on Artificial Neural Networks and using the features generated by DFT. In this analysis, from the Influenza Research Database, 30724, 18236 and 8157 HA protein sequences were collected for Human, Avian and Swine respectively. Given this set of the proteins, the proposed method yielded 97.36% (± 0.04%), 97.26% (± 0.26%), 0.978 (± 0.004), 0.963 (± 0.005) and 0.945 (± 0.005) for the training accuracy validation accuracy, precision, recall and Mathews Correlation Coefficient (MCC) respectively, based on a 10-fold cross-validation. The classification model generated by using one of the largest dataset, if not the largest, yields promising results that could lead to early detection of such species and help develop precautionary measurements for possible human infections. © 2021, CC BY.

关键词： Proteins

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Hybrid Reinforcement Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing

arXiv

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arXiv 2025年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe Kim, Dong In Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Exploiting quantum computing at the mobile edge holds immense potential for facilitating large-scale network design, processing multimodal data, optimizing resource management, and enhancing network security. In this paper, we propose a pioneering paradigm of mobile edge quantum computing (MEQC) that integrates quantum computing capabilities into classical edge computing servers that are proximate to mobile devices. To conceptualize the MEQC, we first design an MEQC system, where mobile devices can offload classical and quantum computation tasks to edge servers equipped with classical and quantum computers. We then formulate the hybrid classical-quantum computation offloading problem whose goal is to minimize system cost in terms of latency and energy consumption. To solve the offloading problem efficiently, we propose a hybrid discrete-continuous multi-agent reinforcement learning algorithm to learn long-term sustainable offloading and partitioning strategies. Finally, numerical results demonstrate that the proposed algorithm can reduce the MEQC system cost by up to 30% compared to existing baselines. © 2025, CC BY.

关键词： Qubits

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Data mining technology information integration for maximum entropy classification models

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AIP Conference Proceedings 2022年第1期2519卷

作者： R. Meenakshi K. Meenakshi M. D. Ashfaqul Hasan S. Prabagar 1Department of Computer Science Engineering Chennai Institute of Technology Chennai Tamil Nadu India 2Department of Computer Science Engineering SRM Institute of Science and Technology Vadapalani Campus Chennai Tamil Nadu India 3Department of Computer Science Engineering Mallareddy Institute of Engineering and Technology Hyderabad Telangana India 4Department of Computer Science Engineering Nalla Malla Reddy Engineering College Hyderabad Telangana India

If sample data disperses information like classification rules, these regulations can need to be combined and fused. This is usually done by combining either the classification outputs, like the classification ensemble, or by incorporating the collection of classification laws by independently weighing them. In this paper, we introduce a new way of assembling classifiers with parameters of classifiers. The technique is based on the use for regression analysis distributions in normative input dimensions of generative probabilistic graders and on nonparametric wave function for continuous. We also spread over classification parameters such as Dirichlet or regular Wishart. These populations are classified as populations of high or second order. We prove that two or more classificatory can be used by multiplying the hyper distributions of variables and drawing simple formulas for the assignment. Any research reveals how this modern approach is described. The primary advantage of this fusion approach is the conservation of hyper distribution in the fusion phase. For starters, the fused components can be used in the organized training steps.

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