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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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Software reliability models based on machine learning techniques: A review

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AIP Conference Proceedings 2022年第1期2463卷

作者： Bejjam Vasundhara Devi R. Kanniga Devi 1Department of Computer Science and Engineering Sreenidhi Institute of Science and Technology Hyderabad – 501301 Telangana India 2Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil-626126 Tamilnadu India

In order to create outstanding quality, reliable software, the software business has many problems. The reliability of software is important for the reliability of the system. The quality of the software is impressive. It compares with the reliability of hardware by reflecting the perfection of architecture and the reliability of hardware. This paper examines emerging literature focused on machine learning approaches of software reliability models. We can divide the reliability analysis of software into three components: modelling, measuring and improvement. Following the analysis of full applicable articles concerning defects that occur during the elimination of fault, we have proposed a method focusing on the most relevant software reliability parameters using machine learning methods.

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Convolutional neural network-based MRI brain tumor classification system

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AIP Conference Proceedings 2022年第1期2519卷

作者： M. Amanullah J. Visumathi Naresh Sammeta Maram Ashok 1Department of Information Technology Aalim Muhammed Salegh College of Engineering Chennai Tamil Nadu India 2Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India 3Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India 4Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Secunderabad Telangana India

A brain tumor, the cause of more death rate among all cancers, is diagnosed using uncontrollable cell growth and abnormal brain cell partitioning. The recent progress in Deep Learning (DL) neural network technology aids the health service department in medical imaging diagnose several death-causing diseases. The visual learning of image recognition manually may result in fault detection and can be overcome by the most prevalent task of machine learning. In our paper, the Convolutional Neural Network (CNN) model is designed with data augmentation and image processing techniques to identify the brain MRI scan images into cancerous or non-cancerous and classify various brain tumor types. The performance comparison of our proposed CNN model uses the Transfer Learning (TL) method. With a very small dataset, the experimental result shows that our model is very effective at low computational power with less complexity and achieves better accuracy compared to an existing model.

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IoT based smart intruder system for baby monitoring

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AIP Conference Proceedings 2023年第1期2690卷

作者： Santaji Krishna Shinde J. Frank Vijay S. Gomathi Meena H. Roopa S. J. Suji Prasad S. Ramesh 1Department of Computer Engineering Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering & Technology Baramati Pune Maharashtra 413133 India 2Department of Information Technology KCG College of Technology Karpakkam Chennai–600097 Tamil Nadu India 3Department of Computer Science VidhyaSagar Women’s College GST Road Vedanarayanapuram Chengalpattu Tamil Nadu 603111 India 4Department of Information Science and Engineering Bangalore Institute of Technology Bangalore Karnataka 560004 India 5Department of Electronics and Instrumentation Engineering Kongu Engineering College Thoppupalayam Perundurai Erode Tamil Nadu 638060 Tamilnadu India 6Department of Electronics and Communication Engineering St. Mother Theresa Engineering College Vagaikulam Mudivaithanenthal Post Thoothukudi Dist.–628102 Tamil Nadu India

We all know how difficult it can be for parents to properly feed their children, especially if both parents are employed. The next intolerant thing is to give someone 24 hours. As a result, must devise something special that will assist parents in providing constant care and monitoring for their children. As a result, came up with the idea of an Internet of Things (IoT)-based Smart Cradle System that will allow parents to keep tabs on their children even while they are away from home and track their every move. The cradle system is innovative, smart, and safe for taking care of a baby quickly. This method includes all of the tiniest features required for the baby’s care and safety while in the cradle. It also includes several features that will help parents keep tabs on their newborns and keep them informed about how they’re doing. An eight-day-old baby died in the Darbhanga district of Bihar’s government hospital’s neo-natal urgent care unit, allegedly from rat bites. The incident was made public when the mother went to breastfeed the baby in the morning. The incident occurred after the workers at the Darbhanga Medical College and Hospital (DMCH), located around 145 kilometres east of Patna, had gone home for the evening. The parents were natives of Madhubani’s Najra town. They claimed that the baby’s death was caused by a rat bite because blood was seeping from the boy’s toes and fingers, which had been injured and scarred. Everyone at the hospital has a distinct reason for wanting to keep such an incident from happening again. However, it was completely ignored by the parents. They stated that the child was admitted to the hospital in a severe condition and that only the therapy had been unsuccessful so far. However, the wounds discovered on the infant’s body prove that the occurrence occurred as a result of the hospital administration’s negligence. Because of the lack of infant monitoring, horrific occurrences like these occur all the time, not just in one hospi

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Design of CMOS DC-DC converter for WSN/IOT applications

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AIP Conference Proceedings 2022年第1期2463卷

作者： Kumudham R. Sathish Kumar P. Rajendran V. V. Devi Gopi Krushna R. A. Sam Chelladurai S. 1Department of Electronics & Communication Engineering School of Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India 2Department of Computer Applications Gurunanak College Chennai India

This paper proposed DC-DC converter with DT-CMOS Switch. The proposed circuit is designed for evaluating and compared with CMOS switch by both circuit simulation and device simulation. This DT-CMOS SWITCH reduces the output ripple and the conduction loss through a low on resistance. Therefore, proposed converter has excellent performance efficiency than converter with conventional CMOS switch. Simulations of eight phases phase locked loop circuit in 0.13 m standard CMOS technology reveal that the proposed charge pump circuit has given an input supply voltage of 1 V that has the output voltage of 6.7 V, which reaches the efficiency is 75%. Supplying a 5-mA load current maintains a power supply as well as 50 percent efficiency and a voltage output of above 5 volts. The proposed charge pump circuit employs body biasing and charge transfer switches to drastically reduce leakage while also increasing voltage. The result of simulation reveals that the proposed charge pump circuits perform better in terms of voltage pumping gain and power efficiency.

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Development of a sit-to-stand assistance chair for elderly people

Development of a sit-to-stand assistance chair for elderly p...

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作者： Aharari, Ari Yang, Won-Seok Department of Computer and Information Sciences SOJO University 4-22-1 Ikeda Nishi-Ku Kumamoto860-0082 Japan College of Engineering and Design Shibaura Institute of Technology 3-9-14 Shibaura Minato-Ku Tokyo108-8548 Japan

According to the survey on the actual situation of elderly persons at home or nursing home care, the first item after concerning about disease under treatment is 'weak legs and difficulties to stand from the chair'. Muscle strength further decreases with aging and make feeling burden when standing from chair. Also, people who are suffering from secondary symptoms such as bedsores and keep sitting in a chair for a long time are on the rise. The most burdensome for elderly persons when trying to stand up from the chair is to bear the weight themselves. In this paper, we introduce 'Rakutateru' which is especially designed to support elderly persons to easily stand up from the chair and keep people to more active and independent. We also evaluate the validity of an assist unit which is contained inside the lower part of the Rakutateru surface. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Assist chair Elderly support chair Lifting unit

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RETRACTED ARTICLE: An evaluation of AI-based college music teaching using AHP and MOORA

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Soft Computing 2023年第2期28卷 689-689页

作者： Han, Xiao Chen, Fuyang Ullah, Ijaz Faisal, Mohammad College of Automation Engineering Control Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing China University of Rennes 1 Rennes France Department of Computer Science and Information Technology University of Malakand Chakdara Pakistan

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An Efficient Credit Card Fraud Detection System using Deep-learning based Approaches 23

An Efficient Credit Card Fraud Detection System using Deep-l...

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23rd IEEE International Multi-Topic Conference, INMIC 2020

作者： Ali, Ishtiaq Aurangzeb, Khursheed Awais, Muhammad Ul Hussen Khan, Raja Jalees Aslam, Sheraz Comsats University Islamabad Computer Science Department Islamabad44000 Pakistan College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Computing and Communications Lancaster University Bailrigg LancasterLA1 4YW United Kingdom Cyprus University of Technology Department of Electrical Engineering Cyprus

ISBN: (纸本)9781728198934

During the past few years, use of e-commerce has been grown to a large scale. Due to which, the use of credit card has also been increased. Many people now use credit cards for online shopping, e-billing, and other online payments. This frequent use of credit cards is pushing the organizations and banks to implement credit card fraud detection systems to distinguish between illicit and legitimate transactions. These systems have been trained in pre-existed datasets and then applied to the new transactions. Many techniques are used to detect fraudulent transactions, such as Genetic Algorithm (GA), Support Vector Machine (SVM), and Artificial Immune System (AIS). In most of the techniques, the classification results are biased towards the majority class due to this biasness False Positive Rate (FPR) and False Negative Rate (FNR) are maximized. To overcome this problem, we have implemented three techniques, i.e., Naive Bayes (NB), Generative Adversarial Networks (GAN), and Neural Networks (NN). The final results are then compared in terms of accuracy, precision, recall, and f-measure. Our main objectives are to minimize the FPR and FNR, which ultimately improves the identification of fraudulent and legitimate transactions. The results show that NN outperforms NB and GAN in term of accuracy, precision, and f-measure. © 2020 IEEE.

关键词： Generative adversarial networks

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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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