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6th International Conference on computing and Informatics, ICCI 2024

作者： Elshami, Nada E. Salah, Ahmad Mohsen, Heba Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computers and Informatics Zagazig University Zagazig Egypt

ISBN: (纸本)9798350373875

Human Pose Estimation is a computer vision technique utilized in various fields such as healthcare, security, and sports to detect the pose of single or multi-person utilizing various machine learning and deep learning methodologies. These methodologies can be applied in a variety of ways, including top-down and bottom-up approaches. Furthermore, this technique can be used with images or videos, as well as in 2D or 3D domains. This paper delves into recent advancements in deep learning methods used for estimating the pose, covering commonly employed datasets, evaluation metrics, and the challenges encountered in this field. It aims to assist new researchers in getting familiar with this technique based on work published between 2019 and 2023, with a spotlight on 2D and omitting 3D. © 2024 IEEE.

关键词： Deep learning

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Spider Inspired Hexapod Mobile Robot With Ultrasonic SLAM Exploration In Hazards Environment

Spider Inspired Hexapod Mobile Robot With Ultrasonic SLAM Ex...

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Advances in technology and computing (ICATC), International Conference on

作者： Thisal Induwara Thisara Pathirana Thushani Mallikarathne Department of Applied Computing Faculty of Computing and Technology University of Kelaniya Kelaniya Sri Lanka Department of Mechanical and Manufacturing Faculty of Technology Wayamba University of Sri Lanka

ISBN: (数字)9798331518219

ISBN: (纸本)9798331518226

This research starts with issues of human operations in hazardous environments and the need for robots capable of handling such issues, then introduces the main concept a six legged robot, inspired by the biological structure and movement of spiders, designed and fabricated using 3d printing technology. This research is focused on the development and evaluation of a bio inspired hexapod mobile robot for mapping and exploration in hazardous environments. It incorporates slam technology, with a view to creating an accurate map in complex and hazardous terrains using the hector slam mapping tool. The results obtained show that the walking speed of the robot on flat surfaces is 4.8 cm/s and on uneven terrains, it walks at a speed of 4.2 cm/s with mapping accuracy of about 83%. Additionally, the robot's stability was confirmed through torque tests, showing that the motors could handle the required load, maintaining balance even under additional weight. This study concludes that the spider inspired hexapod robot is a viable solution for autonomous exploration in hazardous environments, offering significant potential for applications in search and rescue operations, disaster recovery, and inspection of dangerous sites. The study concludes that the spider inspired robot is a promising solution for navigating and mapping hazardous environments, with potential applications in disaster management and exploration.

关键词： Legged locomotion Simultaneous localization and mapping Accuracy Torque Navigation Inspection Three-dimensional printing Motors Hazards Acoustics

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Optimized Vaccine Selection Using Machine Learning and Genetic Algorithms: A Study on Side Effects of COVID-19 Vaccines 12th

Optimized Vaccine Selection Using Machine Learning and Genet...

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12th International Conference on Soft computing for Problem Solving, SocProS 2023

作者： Soni, Vishal Joshi, Shubham Deep, Kusum Pant, Millie Department of Mechatronics Indian Institute of Information Technology Bihar Bhagalpur813210 India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India Department of Mathematics Indian Institute of Technology Uttarakhand Roorkee247667 India Mehta Family School for Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India

ISBN: (纸本)9789819732913

The COVID-19 pandemic has necessitated large-scale vaccination campaigns to control the virus's spread. Given the scale of these efforts, it's crucial to scrutinize potential adverse effects, especially in vulnerable population groups. This study combines machine learning and Genetic Algorithms (GA) to analyze the patterns of patient recovery post-vaccination. We use a range of classifiers, including the Gradient Boosting Decision Tree (GBDT), to identify key features and symptom categories that influence patient recovery. We also use Principal Component Analysis (PCA) to reduce dimensionality and uncover patterns within high-dimensional data. The data used in this study have taken from the Vaccine Adverse Event Reporting System (VAERS). With the help of GA, we aim to guide optimal vaccine selection for susceptible demographics, minimizing the risk and severity of side effects. Our research paves the way for more personalized vaccination strategies, promoting healthier outcomes and reducing risks associated with mass vaccination. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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The Smart Assistive technology in Education for Students with Disabilities in Oman

The Smart Assistive Technology in Education for Students wit...

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computing and Data Analytics (ICCDA), International Conference on

作者： Junath Naseer Ahamed Laryam Nasser Albalushi Hind Maktoom Almamari Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The research in Oman assessed smart assistive technology's impact on students with disabilities, focusing on academic improvement and user satisfaction. Using a mixedmethods approach, it found significant gains in student engagement and academic outcomes. Recommendations include expanding assistive tools and training educators. Limitations were a small sample size and uneven tech access. The study concluded that such technology greatly enhances inclusive education in Oman.

关键词： Training Data analysis Focusing Assistive technologies

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A Study on the Importance of Cybersecurity and its Significance in Omani Society

A Study on the Importance of Cybersecurity and its Significa...

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computing and Data Analytics (ICCDA), International Conference on

作者： Junath Naseer Ahamed Aisha Abdullah Mohammed Hamda Khamis Al Ghaithi Rawya Khamis Al Salty Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This study investigates the critical role of cybersecurity in Omani society, analyzing its significance in safeguarding digital assets and maintaining societal well-being. By examining the current cybersecurity landscape in Oman, including challenges and vulnerabilities, the study highlights the pressing need for robust cybersecurity measures. Additionally, it explores the implications of cyber threats on various sectors, such as government, finance, healthcare, and education, underscoring the far-reaching consequences of inadequate cybersecurity practices. Furthermore, the study assesses existing cybersecurity initiatives and policies in Oman and identifies areas for improvement to enhance overall cyber resilience. Through empirical research and data analysis, this study contributes valuable insights into the importance of cybersecurity in protecting national interests, promoting economic growth, and preserving social stability in Oman.

关键词： Privacy Data analysis Pressing Passwords Stability analysis Loss measurement Computer security Computer crime Protection Resilience

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TNEST: Training Sparse Neural Network for FPGA Based Edge Application 2nd

TNEST: Training Sparse Neural Network for FPGA Based Edge A...

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2nd International Conference on Advances in computing Research, ACR 2024

作者： Das, Rammi Karn, Rupesh Raj Heikkonen, Jukka Kanth, Rajeev Nepal College of Information Technology Pokhara University Kathmandu Nepal Center for Cyber Security New York University Abu Dhabi United Arab Emirates Department of Computing University of Turku Turku Finland Department of Information Technology Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9783031569494

Machine learning (ML) hardware inference has developed ultra-low-power edge devices that accelerate inferential applications performance. An FPGA (Field Programmable Gate Array) is a popular option for such systems. The FPGA has power budget, memory, compute resource, area, etc. constraints but possesses several key advantages, including bandwidth saving, speed, real-time inference, offline activity, etc. Neural networks have been extensively used in Edge systems due to their significant popularity in AI. Requirements of complex neural networks have only been recently realized for edge applications, so the research community has yet to develop a standard model for such applications. The sparse neural architecture reduces the active neurons and connections, making the entire system computationally more efficient and utilizing less memory. Such essential facts are more and more evident in edge-based IoT applications. In this work, we have customized neural network training algorithms to fit precisely for the Edge systems. Rather than a traditional top-down approach where MLs are fully trained and then pruned to fit within edge device resources, we adopted a generative approach where MLs are prepared with the least number of parameters, and further components are added as the need arises to improve inference accuracy. Our generative model shows significant savings in FPGA resource consumption compared to the top-down approach for the same precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： High level synthesis

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InputJump: Augmented reality-facilitated cross-device input fusion based on spatial and semantic information

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虚拟现实与智能硬件（中英文） 2024年第6期6卷 502-526页

作者： Xin ZENG Xiaoyu WANG Tengxiang ZHANG Yukang YAN Yiqiang CHEN Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101408China Goertek Inc. Bejing 261031China The Hong Kong University of Science and Technology Hong Kong 999077China Department of Computer Science Hajim School of Engineering and Applied SciencesUniversity of RochesterRochesterNY 14627US

The proliferation of computing devices requires seamless cross-device *** reality(AR)headsets can facilitate interactions with existing computers owing to their user-centered views and natural *** this study,we propose InputJump,a user-centered cross-device input fusion method that maps multi-modal cross-device inputs to interactive elements on graphical *** input jump calculates the spatial coordinates of the input target positions and the interactive elements within the coordinate system of the AR *** also extracts semantic descriptions of inputs and elements using large language models(LLMs).Two types of information from different inputs(e.g.,gaze,gesture,mouse,and keyboard)were fused to map onto an interactive *** proposed method is explained in detail and implemented on both an AR headset and a desktop *** then conducted a user study and extensive simulations to validate our proposed *** results showed that InputJump can accurately associate a fused input with the target interactive element,enabling a more natural and flexible interaction experience.

关键词： Mixed reality Spatial Semantic Large language models

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Resource Allocation in Cloud computing Using Quasi-Opposition Learning Based Aquila Optimizer

Resource Allocation in Cloud Computing Using Quasi-Oppositio...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Kumari, K. Aruna Muvva, Vijaya Bhaskar Reddy Kumar, K. Sudheer Rai, B. Karunakara Shakir, Aboothar Mahmood Department of CSE SRKR Engineering College Bhimavaram Chinaamiram India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences -Shinas Shinas Oman School of Computer Science and Artificial Intelligence SR University Warangal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9789819778751

The resource allocation is focus on enhancing the service provider proceedings and obtaining customer satisfaction by Service Level Agreements (SLA). Cloud technology has significantly transformed the organizations to manage and allocate their resources. The cloud users are choosing sources of application distribution exclusive of cooperating Quality of Service (QoS) necessities. To overcome this issue, this paper proposed a Quasi-Opposition Learning based Aquila Optimizer (QOL-AO) for resource allocation in cloud. The virtualization supports to implement task using resource availability and reduces the response time. The QOL-AO employs the hunting approach for obtain the effective and efficient resource allocation in cloud. The Performance of QOL-AO is estimate through execution time, response time and energy consumption for various number of Virtual Machines (VMs). The QOL-AO attained less execution time of 18.568 ms and response time of 0.095 s than existing algorithms like Emperor Penguin Optimization (EPO) and Particle Swarm Optimization (PSO). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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A Study for Smart Technologies for Autism Spectrum Disorders Children in Oman to Facilitate Interactive Communication

A Study for Smart Technologies for Autism Spectrum Disorders...

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computing and Data Analytics (ICCDA), International Conference on

作者： Junath Naseer Ahamed Manal AlGhafri Shima Al-Zeid Misoon Al Mamari Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The study investigates the use of smart technologies in Oman to improve communication skills and social interactions among children with autism spectrum disorders (ASD). The Sultanate of Oman is focusing on fostering an inclusive environment through technological innovation and inclusive education. The study concludes that smart technology interventions can significantly improve communication abilities and social interactions, promoting a more inclusive society for ASD children.

关键词： Autism Technological innovation Data analysis Education Focusing

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