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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Zahid Khan Nauman Khan Anis Koubaa Adel Ben Mnaouer Wadii Boulila College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer Science and Information Technology University of Malakand Chakdara (Dir Lower) KPK Pakistan

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimal routing in Vehicular Ad-hoc networks (VANETs) presents significant challenges due to the dynamic nature of vehicular movements. Cluster-based routing schemes have emerged as promising strategies to manage the scalability and efficiency of routing in VANETs. However, they often face limitations in coverage due to their inherent reliance on distributive route discovery methods. This paper introduces the Software-Defined networking-enabled cluster-based Evolving Graph Routing Scheme (SE-CEGRS), designed to address these coverage challenges by integrating the flexibility and centralized control of Software-Defined Networking (SDN) with the established Cluster-based VANET-oriented Evolving Graph (CVoEG) framework. Unlike traditional CVoEG, which employs a distributive approach with Evolving Graph Dijkstra (EG-Dijkstra) for both intra- and inter-cluster communications, SE-CEGRS maintains this strategy for intra-cluster scenarios but shifts to a centralized model for inter-cluster communications. Our evaluation demonstrates that SE-CEGRS significantly outperforms conventional CVoEG in inter-cluster communication scenarios by offering reduced discovery times, lower communication overhead, and shorter average path lengths. The findings underscore SECEGRS’s potential to enhance the efficiency and scalability of routing in VANETs, pointing toward a new direction for future research and development in vehicular communication technologies.

关键词： Wireless communication Scalability Routing Communications technology Software defined networking Vehicle dynamics Research and development

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Recent ViT based models for Breast Cancer Histopathology Image Classification

Recent ViT based models for Breast Cancer Histopathology Ima...

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International Conference on Computing and Networking Technology (ICCNT)

作者： ArunaDevi Karuppasamy Department of Computer Science and Management Information System Oman College of Management and Technology Muscat Oman

Breast cancer is one of the most common type of cancer affecting women worldwide, and a leading cause of cancer-related deaths. Histopathology image analysis plays a vital role in breast cancer diagnosis. Early diagnosis and accurate classification of breast cancer histopathology images can help for effective treatment. In recent years, deep learning-based image classification methods have shown promising results in automated breast cancer diagnosis. Earlier, Convolutional Neural Network(CNN) had a huge attention in image classification tasks, and also obtained a good performances in histopathology image classification. Recently, Vision Transformer (ViT) based on self-attention mechanism has shown promising results in computer vision tasks, including image classification. In this paper, we review recent ViT-based model for breast cancer histopathology image classification. Furthermore, we summarize the results obtained from our experiments with both CNN and ViT-based models in breast cancer histopathology image classification.

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Digital Transformation and its Impact on Operational Efficiency and Competitive Advantage in Islamic Banks 2

Digital Transformation and its Impact on Operational Efficie...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Shehadeh, Maha Almajali, Dmaithan Abu-Alsondos, Ibrahim A. Alkhwaldi, Abeer F. Al-Gasaymeh, Anwar S. Applied Science Private University Finance and Banking Science Department Amman Jordan Applied Science Private University Management Information System Department Amman Jordan American University in the Emirates College of Computer Information Technology Dubai United Arab Emirates Mutah University Department of Management Information Systems Karak Jordan

ISBN: (纸本)9798350335644

This study aimed to identify the impact of digital transformation on Islamic banks operating in Jordan by using the descriptive analytical method. Primary data were obtained through a questionnaire and distributed to the study sample, which consisted of 68 employees of 4 Islamic banks operating in Jordan. The structural equation model was used in the process of testing the study hypotheses. We obtained a number of results, the most important of which is the presence of a statistically significant effect at the significance level (α ≤ 0.05) of digital transformation in Islamic banks operating in Jordan in relation to operational efficiency and competitive advantage. We recommend that Islamic banks need to develop a strategy for digital transformation aimed at innovation and competition, without prejudice to the controls of Islamic finance. There is a need to focus on managing the risks associated with digital transformation, as well as treating the risks associated with modern technologies, especially those that threaten Islamic banks and financial stability. © 2023 IEEE.

关键词： Competition

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LLM-Assisted Reinforcement Learning: Leveraging Lightweight Large Language Model Capabilities for Efficient Task Scheduling in Multi-Cloud Environment

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IEEE Transactions on Consumer Electronics 2024年

作者： Tang, Xuhao Liu, Fagui Xu, Dishi Jiang, Jun Tang, Quan Wang, Bin Wu, Qingbo Philip Chen, C.L. South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Pengcheng Laboratory Department of New Networks Shenzhen518066 China National University of Defense Technology College of Computer Changsha410073 China

In the contemporary landscape of large language models (LLMs) development, it is crucial to address the challenges of deploying these models on hardware-constrained consumer electronic devices (CEDs), especially within complex dynamic task scheduling in multi-cloud environments (MCE). We propose a novel methodology leveraging a lightweight LLM to enhance task scheduling decisions in *** approach involves creating a task scheduling expert database informed by optimization objectives to fine-tune the lightweight LLM. This enables the model to generate a schedulable candidate set of tasks based on the current state of tasks and operational conditions within CEDs across MCE, optimizing scheduling decisions and enhancing overall efficiency. Simulations using both synthetic and real-world datasets demonstrate that our method outperforms three other algorithms in cost minimization, makespan reduction, and energy consumption. In summary, our methodology empowers CEDs to optimize the utilization of multi-cloud resources and harness the capabilities of lightweight LLMs to effectively minimize makespan, operational costs, and energy consumption during the task scheduling process, thereby facilitating efficient task scheduling. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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IoT-Enhanced Gesture Recognition system for Healthcare Applications

IoT-Enhanced Gesture Recognition System for Healthcare Appli...

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Telecommunications Conference (ITC-Egypt), International

作者： Krithika L. B Nallakaruppan MK Mohamed Baza Mahmoud Badr Yuvraj Singh Deora School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Deparment of Computer Science College of charlsron charlsron SC USA Department of Networks and Computer Security College of Engineering State University of New York (SUNY) Polytechnic Institute Utica NY USA

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

In response to the prevalent language barriers in medical settings, this paper proposes an innovative solution leveraging gesture recognition technology. Through the integration of deep learning models and Internet of Things (IoT) tools, the system aims to facilitate seamless communication between patients and healthcare providers. Initially, a deep learning model is developed using a personalized dataset containing various medical gestures, allowing for the recognition of nuanced patient expressions such as “I have Fever” or “Shoulder pain.” Additionally, the system incorporates IoT sensors to monitor vital signs like body temperature and pulse in real-time, ensuring comprehensive patient assessment. With the capability to continuously evolve and adapt to new gestures, this solution not only enhances accessibility but also improves the quality of healthcare delivery, particularly in underserved communities. By bridging the gap between language barriers and medical communication, this innovative approach holds promise for fostering inclusive and effective healthcare practices worldwide. In addressing the challenges posed by language barriers in medical interactions, this paper presents a comprehensive solution that combines gesture recognition technology with deep learning models and IoT devices. By capturing and interpreting patient gestures, such as indicating specific symptoms or discomfort, the system facilitates clear communication between patients and doctors. Furthermore, the integration of IoT sensors enables continuous monitoring of vital signs, allowing for timely interventions and personalized care. With its ability to adapt and expand to accommodate new gestures and medical scenarios, this solution represents a significant advancement in healthcare accessibility and quality. By leveraging cutting-edge technology, this approach has the potential to revolutionize healthcare delivery, particularly in areas with limited access to linguistic resources or medi

关键词： Temperature sensors Deep learning Temperature measurement Shoulder Medical services Gesture recognition Sensors

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TailClip: Mitigating Tail Latency in Cloud Gaming via Smart Video Frame Generation

TailClip: Mitigating Tail Latency in Cloud Gaming via Smart ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Qianyun Gong Kunheng Jiang Jingjing Wen Xinjing Yuan Jianxin Shi Lingjun Pu Institute of Systems and Networks College of Computer Science Nankai University Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University Tianjin China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Latency is one of the most significant issues in cloud gaming, among which tail latency, mainly attributed to dynamic network environments (i.e., transmission) and limited device computing capacity (e.g., decoding), has attracted increasing attention. To mitigate the tail latency, different from existing researches considering resource adaptation such as bitrate adaptation, we propose TailClip, whose novel idea is to enable video frame generation at the client if the tail latency is about to happen. TailClip consists of two innovative components: a Deep Reinforcement Learning (DRL) driven tail latency trigger that jointly decides a series of subsequent frame generation regarding multidimensional features of historical tail latency; a lightweight frame generation model derived by adaptive pruning in terms of device computing capacity at runtime. Extensive evaluations indicate the superior performance of TailClip. For example, it can remove the high-latency frames (i.e., over 100 ms) by 77% with an acceptable video quality (i.e., 0.45 dB reduction on average).

关键词： Performance evaluation Runtime Computational modeling Tail Cloud gaming Streaming media Deep reinforcement learning

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Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 3203-3219页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related *** exponential generation of text data on the Internet must be leveraged to gain knowledgeable *** extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and *** Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the *** current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)*** presented ALTS-MLODL technique aims to summarize the text documents in the English *** accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of ***,the MLO algorithm is used for the effectual selection of the extracted *** the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter *** exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization *** validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were *** experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

关键词： Text summarization deep learning hyperparameter tuning applied linguistics multi-leader optimizer

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Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

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computers, Materials & Continua 2021年第11期69卷 2217-2230页

作者： Muzamil Ahmed Muhammad Ramzan Hikmat Ullah Khan Saqib Iqbal Muhammad Attique Khan Jung-In Choi Yunyoung Nam Seifedine Kadry Deparment of Computer Science and Information Technology The University of LahoreSargodha CampusSargodha40100Pakistan Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan School of System and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan College of Engineering Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Science HITEC University TaxilaTaxilaPakistan Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityLebanon

Violence recognition is crucial because of its applications in activities related to security and law *** semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less *** approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based *** studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other ***,the their performance must be *** study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video *** the proposed framework,the keyframe extraction technique eliminates duplicate consecutive *** keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate *** feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the *** empirical analysis,four widely used standard datasets are used with diverse *** results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.

关键词： Violence detection violence recognition deep learning convolutional neural network inception v4 keyframe extraction

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MYSTERIOUS ACTION DETECTION by SPEED and DISPLACEMENT ANALYSIS in IMAGE PROCESSING 1

MYSTERIOUS ACTION DETECTION by SPEED and DISPLACEMENT ANALYS...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Vishnupriya, S. Darwin Nesakumar, A. Mounika, S. Pavithra, K.N. R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering India R.M.K. Engineering College Department of Electronics and Communication Engineering India R.M.D.Engineering College Department of Computer Science and Business System India R.M.D.Engineering College Department of Computer Science Engineering India

ISBN: (纸本)9781665456876

There are now three moving target detection algorithms: The optical flow methodology, the background difference technique, and the inter-frame difference method are all employed. The optical flow technique has the most complex algorithm and takes the longest to process data compared to other approaches. The background subtraction(BS) approach, on the other hand, is recognised for its high sensitivity to light changes. Finally, while the frame difference approach is very straightforward and quick to use, the findings are insufficiently accurate due to backdrop brightness fluctuations causing mistakes. In this paper, the objective is to discover strange actions in image processing using speed and displacement analysis. Motion is detected using Incremental and Multi-feature Tensor Subspace Learning (IMTSL) Applied for Background Modeling and Subtraction. Following IMTSL Background Subtraction, different image analysis were performed on the resulting pictures for displacement estimate, followed by speed computation. The results demonstrate improved backdrop modelling and action prediction. © 2022 IEEE.

关键词： Optical flows

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Advancing agriculture with machine learning:a new frontier in weed management

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Frontiers of Agricultural science and Engineering 2025年第2期12卷 288-307页

作者： Mohammad MEHDIZADEH Duraid K.A.AL-TAEY Anahita OMIDI Aljanabi Hadi Yasir ABBOOD Shavan ASKAR Soxibjon TOPILDIYEV Harikumar PALLATHADKA Renas Rajab ASAAD Department of Agronomy and Plant Breeding Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili Ilam Science and Technology Park Department of Horticulture College of Agriculture University of Al-Qasim Green Department of GIS and Remote Sensing Faculty of Geography University of Tehran Medical Laboratories Techniques Department Al-Mustaqbal University College Erbil polytechnic university Technical engineering college information system engineering department Department of Fundamental Economics Tashkent State University of Economics Manipur International University Department of Computer Science Nawroz University

Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental *** have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed *** learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate *** learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application.

关键词： Weed management herbicides machine learning agricultural practices environmental impact

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