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International Conference on Data Science, Computation and Security, IDSCS 2023

作者： Chandravanshi, Kamlesh Soni, Gaurav Mishra, Durgesh Kumar School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India School of Computer Science and Information Technology Symbiosis University of Applied Sciences Madhya Pradesh Indore India

ISBN: (纸本)9789819709748

One of the most important aspects of any network, whether wired or wireless, is keeping the transmitter and receiver’s time in sync. In a mobile ad hoc network, each node is constantly adapting to its immediate surroundings. Without oversight or centralized communication, the data sending and receiving is executed autonomously. A novel method for ensuring consistent communication in dynamic networks using clock synchronization and multicast routing is presented in this study. Group communication is possible using the multicast routing protocol;each group consists of many nodes, each of which has its own unique clock time and a designated coordinator. According to the average time beacon of all groups in MANET, the clocks of all mobile nodes are synchronized in the proposed study. One way to boost network efficiency is using the suggested multicast Average Time Clock beacon Synchronization (Avg-SYN-CLK) protocol. Compared to Asynchronies Clock Time Performance (ASYN-CLK) and the Maximum Clock Time Synchronization (MAX-SYN-CLK) beacon broadcasting method, the suggested global clock synchronization scheme outperforms both in terms of end-to-end delay and packet loss. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Synchronization

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New Approaches to the Prognosis and Diagnosis of Breast Cancer Using Fuzzy Expert Systems

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Journal of Computer and Communications 2024年第5期12卷 151-169页

作者： Elias Ayinbila Apasiya Abdul-Mumin Salifu Peter Awon-Natemi Agbedemnab Department of Computer Science School of Computing and Information Sciences C. K. Tedam University of Technology and Applied Sciences Navrongo Ghana

Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.

关键词： EMFES Breast Cancer Type-2 Fl Soft computing Membership Functions Fuzzy Set Fuzzy Rules Risk Factors.

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FAST ORTHOGONAL MATCHING PURSUIT FOR EXACT RECOVERY AND SPARSE APPROXIMATION

arXiv

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arXiv 2024年

作者： Yu, Huiyuan He, Jia Cheng, Maggie X. Department of Applied Mathematics College of Computing Illinois Institute of Technology ChicagoIL60616 United States

Orthogonal Matching Pursuit (OMP) has been a powerful method in sparse signal recovery and approximation. However OMP suffers computational issue when the signal has large number of non-zeros. This paper advances OMP in two fronts: it offers a fast algorithm for the orthogonal projection of the input signal at each iteration, and a new selection criterion for making the greedy choice, which reduces the number of iterations it takes to recover the signal. The proposed modifications to OMP directly reduce the computational complexity. Experiment results show significant improvement over the classical OMP in computation time. The paper also provided a sufficient condition for exact recovery under the new greedy choice criterion. For general signals that may not have sparse representations, the paper provides a bound for the approximation error. The approximation error is at the same order as OMP but is obtained within fewer iterations and less time. © 2024, CC BY.

关键词： Compressed sensing

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An Exemplifying Study of the E-Learning Adoption during Covid-19 Pandemic in Oman

An Exemplifying Study of the E-Learning Adoption during Covi...

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2022 applied Informatics International Conference, AiIC 2022

作者： Al Maashri, Issaa Mohamed Hassan Ghosh, Shounak Bukar, Umar Ali University of Technology and Applied Sciences Academic Electronic Systems Center Muscat Oman School of Computing and Technology Malaysia Universiti Putra Malaysia Faculty of Computer Science and Information Technology Malaysia

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

As a consequence of the Covid-19 outbreak, students are required to stay at home to prevent the virus from spreading. This has resulted in a quick acceptance of online learning, as students and faculty cannot participate in in-class activities. This study examines the determinants affecting E-Learning uptake in Oman during the Covid-19 outbreak, using 223 student responses and structural equation modeling (SEM). Four hypotheses were supported, while two were rejected. Specifically, perceived ease of use, perceived usefulness, and institutional support affect behavioral intention to adopt E-Learning. However, infrastructure support and privacy risk, on the other hand, were not. Thus, this research serves as a springboard for higher education officials to increase the use of E-Learning and adjust to student hurdles for students to comprehend and effectively use the system. © 2022 IEEE.

关键词： E-learning

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Single-atom nanozymes for electrochemical sensing oxidative stress biomarkers

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Trends in Chemistry 2025年第6期7卷 333-344页

作者： Mani, Veerappan Tostado-Blazquez, Guillermo Ramalingam, Vinoth Salama, Khaled Nabil Thuwal23955-6900 Saudi Arabia Centre for Advanced Measurement Research & Health Translation Department of Pure and Applied Chemistry University of Strathclyde GlasgowG1 1XL United Kingdom School of Computing Engineering and Technology Robert Gordon University Garthdee Road AberdeenAB10 7GJ United Kingdom

Developing reliable and low-cost sensors for oxidative stress biomarkers has gained significant interest for understanding and managing chronic diseases. Recently, single-atom nanozymes (SANs) with atomically dispersed metal sites and unique metal–nitrogen–carbon (M–N–C) structures have emerged as promising redox enzyme mimetics, offering superior catalytic efficiency and specificity, and with exceptional sensing capabilities for oxidative stress biomarkers such as hydrogen peroxide (H2O2), nitric oxide (NO), glutathione (GSH), and uric acid (UA). This review discusses key progress, challenges, and future directions in SANs for electrochemical determination of oxidative stress biomarkers, aiming to guide future research toward practical and impactful solutions in healthcare and disease management. © 2025 Elsevier Inc.

关键词： Biomarkers

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Evaluation of Spatiotemporal Fetal Cardiac Imaging Using Deep Learning Techniques

Evaluation of Spatiotemporal Fetal Cardiac Imaging Using Dee...

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International Health Informatics Conference, IHIC 2022

作者： Nidhi, Dipak Kumar Srivastav, Khushboo Heikkonen, Jukka Kanth, Rajeev Tribhuvan University IOE Pulchowk Campus Kathmandu Nepal Kathmandu Nepal Department of Computing University of Turku Turku Finland School of Information Technology Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9789811990892

Fetal echocardiography is a standard diagnostic tool used to evaluate and monitor fetuses with a defective heart associated with a number of fetal conditions. Deep learning is a machine learning technology which can perform specific tasks with specific goals. Deep learning techniques are used to evaluate fetal cardiac ultrasound cine loops and improve the evaluation of fetal abnormalities. Convolutional neural network along with recurrent neural network was applied in this study as CNN + LSTM, CNN + GRU, and 3D CNN for the processing and classification of ultrasonographic cine loops into various classes. The models were able to sort the fetal cardiac cine loops into five standard views with 92.63%, 94.99%, and 82.69% accuracy, respectively. Furthermore, the models were able to diagnose Tricuspid atresia (TA) and Hypoplastic left heart syndrome (HLHS) with an accuracy of 94.61%, 91.99%, and 86.54%, respectively. These deep learning-based algorithms were found to be effective tools for evaluating and monitoring normal and abnormal fetal heart cine loops. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heart

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Artificial Intelligence and Machine Learning for Accessibility: Smart Reader as an Assistive Tool for the Visually Impaired

Artificial Intelligence and Machine Learning for Accessibili...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Suresh Palarimath Venkateswaran Radhakrishnan Devarajan Veerasamy Prajoona Valsalan Vimbi Viswan Muni Balaji Thumu College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Electrical and Computer Engineering Department College of Engineering Dhofar Univesity Salalah Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Sohar Sultanate of Oman

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The amalgamation of artificial intelligence (AI) and machine learning (ML) has greatly improved accessibility for visually impaired individuals, fostering increased autonomy through pioneering assistive technology. This study introduces the Smart Reader, a device that integrates optical character recognition (OCR) and text-to-speech (TTS) technologies to enhance accessibility to printed documents. The Smart Reader utilizes a camera to take text images, translates them into digital representation through OCR, and converts the text into audible speech via TTS. The system has an intuitive user interface, an automatic page-turning mechanism, and a dictionary lookup function, guaranteeing a seamless reading experience. The findings indicate that the Smart Reader significantly improves access to written information, hence enhancing the quality of life for visually impaired individuals, especially in educational environments such as schools and libraries. The device is engineered for user-friendliness, enhancing accessibility and autonomy. The Smart Reader utilizes AI and ML, marking a significant progression in assistive technology, promoting an inclusive society where visually impaired individuals can engage with printed material with assurance. In addition to functionality, it seeks to empower people by connecting capability with opportunity, guaranteeing equitable access to information for everyone.

关键词： Visualization Translation Optical character recognition Visual impairment Machine learning Assistive technologies User interfaces Turning Libraries Text to speech

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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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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wasim Haidar SK Wilfred Blessing N. R Sudhakar Kumar Chaubey Zahid Mehmood Kavitha S. J Sutherlin Subitha G College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission.

关键词： Wireless sensor networks Data analysis Smart cities Decision making Throughput Routing protocols Real-time systems Energy efficiency Delays Predictive maintenance

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Sign the Voice: An Embedded Software for Voice to Sign Language Translation

Sign the Voice: An Embedded Software for Voice to Sign Langu...

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computing and Data Analytics (ICCDA), International Conference on

作者： Donna Lyn C. Peleo Myra M. Patalay Jesfer M. Dela Cruz Emmanuel A. Ramirez College of Computing and Information Technologies National University Manila Philippines College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Sultanate of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This qualitative research study entitled Sign the Voice: An Embedded Software for Voice to Sign Language Translation aimed to bridge the communication barriers between hearing teachers and auditory-challenged students. This explored the current technologies used in the development of systems for Persons with Disabilities particularly the Deaf Community. This study aimed to develop an embedded software to translate in real-time a human's voice to sign language, making communication accessible between them. The researchers conducted interviews, focus groups, observation, and case studies. This study investigated the design process and usability of embedded software. Software testing and thematic analysis will then be conducted to test its effectiveness. The researchers considered the diverse needs of auditory-challenged students, real-time processing, and the accuracy of the translation to make sure the communication process is effective between hearing teachers and auditory-challenged students. Preliminary findings suggested promising results in terms of user satisfaction, highlighting the potential of the proposed embedded software to provide accessible communication between the hearing teachers and auditory-challenged students. The implications of this study will provide more IT opportunities for these auditory-challenged students, give diversity in the IT industry, and could lead to more opportunities for applied research by developing improved technological solutions. The cultural, societal, and ethical impacts of this proposed system were of high consideration.

关键词： Software testing Sign language Technological innovation Translation Auditory system Assistive technologies Real-time systems Cultural differences Usability Embedded software

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