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Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

computers, Materials & Continua 2024年第9期80卷 4561-4576页

作者： Atta Rahman Mustafa Youldash Ghaida Alshammari Abrar Sebiany Joury Alzayat Manar Alsayed Mona Alqahtani Noor Aljishi Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision *** detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and ***,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of *** this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB *** approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.

关键词： Diabetic retinopathy transfer learning machine learning fundus images binary classification APTOS

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Compact Deep Learning Architecture for Oral Cancer Classification

Compact Deep Learning Architecture for Oral Cancer Classific...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Kalli, Sivanagireddy Kalaimani, G. Sivaprakash, S. Suvathi, P. Kavitha, G. Srinivasan, C. Department of Electronics and Communication Engineering Sridevi Women's Engineering College Telangana Hyderabad India Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India Tamil Nadu Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

A compact deep learning model is designed to classify oral cancer based on histopathological images in this work. It has the capacity to learn varying quantities of contextual information by making use of image patches. The proposed model consists of four convolution layers and four sub-sampling layers to extract the features. It uses softmax classification in the fully connected layer for the classification. Because of its straightforward architecture, it is a lightweight model that is suitable for use in settings with limited access to resources and in which it may provide superior results in terms of the amount of space and time required. The performance is evaluated using 1224 histopathological images in two distinct resolutions. The first set has 528 images (100x magnifications) and the second set has 696 images (400 x magnifications). Results prove that the proposed system provides an accuracy of ~98.5% for both sets when data augmentation is employed. © 2024 IEEE.

关键词： Lung cancer

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Mapping the Knowledge Structure and Research Evolution of Deep Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第10期51卷 1404-1412页

作者： Yuan, Ke Zhao, Chenmeng Chen, Yayun Shen, Lin Tang, Qian Jia, Chunfu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Spatial Information Processing Engineering Research Center Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China Yayun Chen is an undergraduate School of Computer and Information Engineering Henan University Kaifeng475004 China College of Geography and Environmental Science Henan University Kaifeng475004 China College of Cybersecurity Nankai University Tianjin 300350 China

Deep learning is a significant research field in today’s society, especially in the field of artificial intelligence. To gain a more comprehensive understanding of the current state of research in the field of deep learning and to identify areas of intense interest, this paper uses Citespace for research and learning, samples 1863 articles from the Web of science (WoS) core dataset, which spans the years 2017 to 2023, and adopts the methods of co-occurrence analysis, keyword burst analysis, and co-citation analysis. Information visualization is used to conduct bibliometric analysis, examining keywords, research institutions, authors, and countries. The analysis demonstrates a significant increase in the literature record from 2017 to 2022, followed by a decline from 2022 to 2023. The research on deep learning spans various fields, including computer science, engineering, and telecommunications, which are considered crucial areas of study. China, the USA, and England are leading the way in the field of deep learning research. These countries have established extensive cooperative relationships with many countries, with research institutions and authors playing a vital role in the cooperative network. The current theoretical research hotspots of deep learning mainly include "convolutional neural networks", "neural networks", "models", "classification", etc. The focus of research in deep learning has shifted from theoretical research to practical applications. It is now being applied in various areas, such as deep reinforcement learning, bearing fault, and semi-supervised learning. In the future, deep learning technology will be applied to more cutting-edge technologies. © (2024), (International Association of Engineers). All rights reserved.

关键词： Deep reinforcement learning

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Blockchain-Integrated Metaverse for Academia: A Solution for Virtual Classes, Lectures, and Project Fundings 8th

Blockchain-Integrated Metaverse for Academia: A Solution fo...

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Ramya, R.S. Ramesh, Babu D. R. Kumudavalli, M.V. Joshi, Pareekshit Venugopal, K.R. Department of Computer Science and Engineering Dayananda Sagar College of Engineering Banglore India Dayananda Sagar College of Arts science and commerce Banglore India Bangalore University Bengaluru India

ISBN: (纸本)9789819712595

The rapid advancement of technology has paved the way for innovative approaches to online education, aiming to provide a more engaging and immersive learning experience. In this paper, a college metaverse is built using *** and React Three Fiber, offering an interactive and collaborative virtual classroom environment. Students can join the virtual classroom as avatars, enabling them to actively participate in classes and interact with their peers. The synchronized whiteboard fosters real-time collaboration, allowing students to share ideas, contribute to discussions, and visualize concepts. This dynamic environment transforms online classes into interactive and immersive experiences, closely resembling traditional in-person learning. To enhance communication, we integrated Peerjs, enabling real-time audio transmission through WebRTC technology. Students can engage in live discussions, ask questions, and interact with the teacher and fellow classmates, fostering an engaging and interactive classroom atmosphere. In addition to the virtual classroom, metaverse also includes a library section where students and faculty can explore ongoing research projects undertaken by college members. Visitors can support these projects by funding them through a simple click, promoting collaboration and innovation within the college community. The seamless integration of technologies allows for a multi-user, interactive, and immersive learning environment that surpasses the limitations of traditional online classes. Overall, the paper presents an innovative solution to transform online education into a more interactive and engaging experience. It opens up new possibilities for students and faculty to connect, collaborate, and learn in a virtual environment that mirrors the dynamics of a physical classroom. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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Privacy preserving large language models: ChatGPT case study based vision and framework

IET Blockchain

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IET Blockchain 2024年第S1期4卷 706-724页

作者： Ullah, Imdad Hassan, Najm Gill, Sukhpal Singh Suleiman, Basem Ahanger, Tariq Ahamed Shah, Zawar Qadir, Junaid Kanhere, Salil S. School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW Australia Higher Colleges of Technology Abu Dhabi United Arab Emirates School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Management Information Systems Department College of Business Administration Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology Sydney International School of Technology and Commerce SydneyNSW Australia Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Sydney Australia

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre-processing and preserving private context and the private training process for large-scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy;specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and

关键词： Blockchain

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Offload Decision Making for Web Applications

Offload Decision Making for Web Applications

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Sudha Sadasivam, G. Barath Kumar, G. Tarun Visva, R. Hrithik, B. Aravind, M. Bhooshaan, A. PSG College of Technology Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335835

Mobile devices play a significant role, in our day-to-day lives, enabling us to work with computationally intensive applications. Implementation of these applications requires a rich set of resources such as high processing power, large memory, and an efficient power supply. However, a huge range of mobile devices fails to meet these requirements. Offloading involves the delegation of the compute units in web applications from low-end local devices to high-end edge devices, to improve performance. In this paper, we propose various machine learning-based prediction models, which take into account parameters such as execution time and transfer time to predict whether a compute unit can be executed locally or must be Offloaded to a remote device. The strategy proposed involves a machine learning algorithm, that is trained by examining the time of execution of a sample application in multiple devices with different computational capabilities. Once the execution time of the local device is predicted using a suitable regressor, the proposed model can predict the time of execution of each edge device and correspondingly Offload the compute units by comparing this execution time and the aforementioned factors. Thus, the computationally intensive applications can be selectively offloaded to the remote edge servers, to improve productivity. © 2023 IEEE.

关键词： Clustering algorithms

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Exploiting Human Pose and Scene Information for Interaction Detection

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computers, Materials & Continua 2023年第3期74卷 5853-5870页

作者： Manahil Waheed Samia Allaoua Chelloug Mohammad Shorfuzzaman Abdulmajeed Alsufyani Ahmad Jalal Khaled Alnowaiser Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has attractedmany researchers to this field. Inspired by the existing recognition systems,this paper proposes a new and efficient human-object interaction recognition(HOIR) model which is based on modeling human pose and scene featureinformation. There are different aspects involved in an interaction, includingthe humans, the objects, the various body parts of the human, and the backgroundscene. Themain objectives of this research include critically examiningthe importance of all these elements in determining the interaction, estimatinghuman pose through image foresting transform (IFT), and detecting the performedinteractions based on an optimizedmulti-feature vector. The proposedmethodology has six main phases. The first phase involves preprocessing theimages. During preprocessing stages, the videos are converted into imageframes. Then their contrast is adjusted, and noise is removed. In the secondphase, the human-object pair is detected and extracted from each image *** third phase involves the identification of key body parts of the detectedhumans using IFT. The fourth phase relates to three different kinds of featureextraction techniques. Then these features are combined and optimized duringthe fifth phase. The optimized vector is used to classify the interactions in thelast phase. TheMSRDaily Activity 3D dataset has been used to test this modeland to prove its efficiency. The proposed system obtains an average accuracyof 91.7% on this dataset.

关键词： Artificial intelligence daily activities human interactions human pose information image foresting transform scene feature information

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Control Structures and Algorithms for Force Feedback Bilateral Teleoperation Systems: A Comprehensive Review

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computer Modeling in engineering & sciences 2025年第2期142卷 973-1019页

作者： Jiawei Tian Yu Zhou Lirong Yin Salman A.AlQahtani Minyi Tang Siyu Lu Ruiyang Wang Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Force feedback bilateral teleoperation represents a pivotal advancement in control technology,finding widespread application in hazardous material transportation,perilous environments,space and deep-sea exploration,and healthcare *** paper traces the evolutionary trajectory of force feedback bilateral teleoperation from its conceptual inception to its current *** elucidates the fundamental principles underpinning interaction forces and tactile exchanges,with a specific emphasis on the crucial role of tactile *** this review,a quantitative analysis of force feedback bilateral teleoperation development trends from 2011 to 2024 has been conducted,utilizing published journal article data as the primary source of *** review accentuates classical control frameworks and algorithms,while also delving into existing research advancements and prospec-tive breakthrough ***,it explores specific practical scenarios ranging from intricate surgeries to hazardous environment exploration,underscoring the technology’s potential to revolutionize industries by augmenting human manipulation of remote *** underscores the pivotal role of force feedback bilateral teleoperation as a transformative human-machine interface,capable of shaping flexible control strategies and addressing technological *** research endeavors in force feedback bilateral teleoperation are expected to prioritize the creation of more immersive experiences,overcoming technical hurdles,fortifying human-machine collaboration,and broadening application domains,particularly within the realms of medical intervention and hazardous *** the continuous progression of technology,the integration of human intelligence and robotic capabilities is expected to produce more innovations and breakthroughs in the field of automatic control.

关键词： Force feedback bilateral teleoperation robot development history application prospect

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