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Energy-efficient online resource provisioning for cloud-edge platforms via multi-armed bandits 34

Energy-efficient online resource provisioning for cloud-edge...

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34th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Rey-Jouanchicot, Jordan del Castillo, Juan Angel Lorenzo Zuckerman, Stephane Veronica Belmega, E. CY Cergy Paris Univ CY Tech Cergy France CY Cergy Paris Univ ETIS UMR 8051 ENSEA CNRS F-95000 Cergy France Univ Gustave Eiffel CNRS LIGM F-77454 Marne La Vallee France

ISBN: (数字)9781665451574

ISBN: (纸本)9781665451574

Edge computing is a new paradigm in which data are locally collected, aggregated and preprocessed before being sent to a Cloud platform. Edge devices, typically IoT objects, are characterized by limited computational capabilities, alongside high energy efficiency operations. In smart building or smart city applications, the overall amount of available IoT devices may account for an important computational capacity. Aggregating the idle CPU cycles of several devices would allow performing on-site parallel and distributed computing, while reusing the available resources in the network. Timely and important computational tasks such as video surveillance processing, HPC jobs, or deep neural network training, can be performed with local available resources. this includes local servers or a private, on-site cloud, as opposed to the use of public clouds, thus avoiding security issues and fostering energy efficiency. An ongoing research topic in such a distributed and volatile context is to design novel algorithms for resource provisioning that allow tasks to be distributed over a set of available IoT devices in an efficient manner. this work presents two online, adaptive and robust scheduling techniques, based on the UCB and EXP3 Multi-Armed Bandit algorithms (MABs), for resource management in Cloud-Edge computing based environments while improving performance and reducing energy consumption. the novelty of our adapted algorithms lies in the fact that we explicitly account for unavailable computing IoT devices resulting in a time-varying available set of arms at each stage. Our numerical results show the high relevance of our approach in reaching optimal provisioning policies in time-varying environments.

关键词： Cloud Edge computing resource provisioning online learning multi-armed bandits

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Enhancing Brain Tumor Identification through Convolutional Neural Networks and Transformer Models 15

Enhancing Brain Tumor Identification through Convolutional N...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Dudam, Nerajdattu Chato, Lina Motupalli, Nagamani Kashyap, Kristipati thoyajaksha Sane, Madhusree Bollina, Mounika University of South Dakota Dept. Computer Science Vermillion United States

ISBN: (纸本)9798331507695

An automatic diagnosis of brain tumors based on Deep Learning (DL) is a promising research field to enhance the diagnosis and prognosis of brain tumors. this paper presents an exploration into the use of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) for brain tumor classification, specifically identifying glioma, meningioma, pituitary tumors, and no tumor from brain Magnetic Reasons Imaging (MRI) scans. We implemented different approaches, such as 1) developing a custom CNN model from scratch, 2) leveraging pre-trained models based on Transfer Learning (TL) (including VGG16 and ResNet50), 3) developing a custom ViT model from scratch, and 4) leveraging pre-trained model in vision transformers, such as ViT B16. A publicly available labeled dataset is used to develop and test our proposed classification Deep Learning models. Our findings reveal that both custom-trained and pre-trained CNN and ViT models achieve high accuracy. the model ViT B16 based on transferred learning achieves the best accuracy of 97.6%, which is slightly better than the CNN models. To enhance the accuracy of the prediction model and reduce the misclassifications, ensemble model based on the combination of ViT B16 and CNN model;this approach increases the accuracy to 98%. © 2025 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Wavelet and Fourier Transform for Image Fusion 15

A Comparative Analysis of Wavelet and Fourier Transform for ...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kuppuraj, T. Saini, Ramesh Jayadurga, R. Aggarwal, Diksha Eti, Muktha Birare, Komal M. Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Business Administration Chennai India Quantum University Research Center Quantum University India School of Engineering and Technology Electrical and Electronics Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Information Technology Pune India

ISBN: (纸本)9798350370249

the purposes of image fusion are to acquire a comprehensive picture which can show extra unique visual facts while compared to its original person supply pix. On this regard, a comparative evaluation of Fourier remodel (feet) and Wavelet transform (WT) for photo fusion is proposed. Firstly, a wavelet-based totally fusion method is advanced using Discrete Wavelet transform (DWT) followed by singular fee decomposition (SVD). the proposed approach uses a pair of images which includes low-pass and high-skip images, with which the fusion method is completed. then again, a feet-primarily based fusion technique is advanced through making use of the 2d-ft at the pair of snap shots after which performing SVD on them. Secondly, each fusion algorithms are applied on a set of pictures which will compare the overall performance. In the end, several metrics together with PSNR (peak signal-to-Noise Ratio), SSIM (Structural Similarity Index) and EN (Entropy) are used to assess the fusion performance in terms of each objective and subjective criteria. the received effects virtually display that the wavelet-based totally fusion method is greater green for photograph fusion in comparison to that primarily based at the toes. the better overall performance is attributed to the better © 2024 IEEE.

关键词： Comparison Fusion Greater performance Photograph Similarity

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Optimizing Road Extraction in Remote Sensing Imagery: A Lightweight Dilated Inception Deep Residual U-Net with Channel Attention (LDIDRU-CAN) Model and Pruning Strategies 15

Optimizing Road Extraction in Remote Sensing Imagery: A Ligh...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Palvi Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Dept. of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science & Mathematics Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350370249

the major challenge in Road Extraction is to extract accurate and complete road network and to get high accuracy while minimizing computational power and ensuring faster prediction. It plays important role in urban planning, disaster management, automatic vehicle navigation and GPS navigation. Several earlier studies used complex deep networks that required large amounts of computational power and large storage. In order to address issues such as high storage requirements, computational load, access to memory delays, and low hardware deployment efficiency this research work presents a novel network model called LDIDRU-CAN. the proposed model is fast, compact, and efficient and requires less storage. the objective of this work, is to compress the original model using structured pruning strategy. this strategy simplified the original model and reduced their computation cost as well as storage requirements. For the reduction of the parameters, we focused on the Row Scale Regular Pruning (RSRP) and Low Learning Rate (LLR) retraining. After applying RSRP pruning approach it reduces storage requirements, parameters, and also contributes to a more than 50% reduction in hardware resource consumption and successfully enhances the parallelism of the model. Testing and validations are performed on the Massachusetts Road Dataset. With only 45 M parameters and 180 MB model size, the suggested network achieves remarkable performance with 87.91% precision, 85.51% recall, 86.69% F1-score and 98.91% accuracy. © 2024 IEEE.

关键词： Channel Attention LLR Pruning Road Extraction RSI RSRP

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Seamless Video Quality Assessment Using Image Fusion 15

Seamless Video Quality Assessment Using Image Fusion

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Deshpande, Vivek Vijayalakshmi, V.J. Khurana, Hivam Hemalatha, S. Ahluwalia, Gunveen Traisa, Roopa Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Ug Karnataka Bangalore India

ISBN: (纸本)9798350370249

Seamless video first-class evaluation is a critical challenge in multimedia communique structures. Photograph fusion is an effective method used to evaluate the video's high quality by means of combining visible statistics from more than one asset. Image fusion can be used to hit upon variations in video frames and may be used to perform class and anomaly detection tasks. In this work, a photograph fusion method is proposed for seamless video exceptional evaluation. the proposed technique makes use of a fused photo from two videos with special resolutions as enter, which is then processed via a deep convolutional neural community for great assessment. the performance of the proposed method is evaluated on several public video satisfactory datasets. Effects display that it achieves aggressive outcomes compared to present techniques. © 2024 IEEE.

关键词： Image fusion

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Enhancing Healthcare with Early Heart Disease Prediction 15

Enhancing Healthcare with Early Heart Disease Prediction

引用

15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Bhave, Roshni Dhopte, Shilpa Raut, Gayatri Sondawale, Shweta P. Katariya, Nikita Yeshwantrao Chavan College of Engineering Nagpur India R.H.Sapat College of Engineering Management studies and Research Nashik India Sinhgad Academy of Engineering Pune India Shri Ramdeobaba College of Engineering and Management Department of Computer Science & Engineering Nagpur India

ISBN: (纸本)9798331300579

Heart disease, a widespread ailment that affects communities all over the world, is one of the most important global health concerns. the goal of this essay is to construct a machine learning model that can detect cardiac disease in its early stages because early detection is crucial for saving lives. the importance of this project rests in its potential to help the general population by enabling early intervention before heart issues worsen, potentially lowering the need for pricy medical procedures and treatments in hospitals and clinics. For a very long time, cardiac disease prediction and detection have been extremely difficult for healthcare professionals. these problems frequently result from lifestyle choices like bad eating habits, binge drinking, smoking, high blood pressure, raised cholesterol, diabetes, obesity, stress, and a sedentary lifestyle. As a result, creating a reliable prediction model has enormous potential for solving this important healthcare problem. Different supervised machine learning techniques have been used to predict cardiac disease, with an excellent accuracy rate of 94%.the performance of the proposed technique is evaluated on a real dataset known as mortality-rate-heart-patient-hospital. © Grenze Scientific Society, 2024.

关键词： Heart

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YOLOv8-AF: A Novel Customized YOLOv8-Accurate Fast Deep Learning Model for Enhancing Border Security in Various Global Regions 15

YOLOv8-AF: A Novel Customized YOLOv8-Accurate Fast Deep Lear...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Muduli, Debendra Toppo, Aman Ujwal Singh, Vikrant Singh, Mahendra Tiwari, Divy Prakash C. V. Raman Global University Dept. of Computer Science Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

the proliferation of ground-pit tunnels along international borders poses significant security threats, including illegal immigration, smuggling, human trafficking, and potential terrorist activities. To address these challenges, this paper presents an innovative surveillance system that integrates drones equipped with high-resolution cameras and a deep learning-based object detection model, specifically the YOLOv8-AF (You Only Look Once v8-Accurate Fast) variant. the proposed model has been designed to autonomously identify ground pits from aerial imagery, providing real-time alerts to security authorities. the YOLOv8-AF model, trained on a publicly available comprehensive Ground Pit Image Dataset (GPID), demonstrates superior performance with over 95% accuracy and a mean average precision (mAP) of 0.895, outperforming other models like Faster-RCNN, Fast-RCNN, and SSD. the proposed model ability to process 116.28 frames per second makes it highly efficient for real-time applications, offering a cost-effective and robust solution for enhancing border security in various global regions. © 2024 IEEE.

关键词： Fast-RCNN GPID mAP ML YOLOv8-AF

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PPQSort: Pattern Parallel Quicksort 15th

PPQSort: Pattern Parallel Quicksort

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Hévr, Gabriel Šimeček, Ivan Department of Computer Systems Faculty of Information Technology Czech Technical University in Prague Thákurova 9 Prague16000 Czech Republic

ISBN: (纸本)9783031856969

this paper presents PPQSort (Pattern Parallel Quicksort), a new parallel quicksort algorithm that provides high performance and ease of use. PPQSort uses C++ threads for parallelization, achieving efficient sorting without external libraries and allowing seamless integration across different computing environments. this paper describes novel quicksort optimizations, including branchless partitioning and their efficient parallel implementation. PPQSort is compared with existing parallel quicksort algorithms on different machines and with different input data. Experimental evaluation results demonstrate that PPQSort is fast and robust, consistently outperforming the fastest available parallel quicksort implementations for almost all inputs. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： C++ (programming language)

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Fusion of Extractive and Abstractive Text Summarization Techniques for Legal documents - An Experimental Evaluation 15

Fusion of Extractive and Abstractive Text Summarization Tech...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Avaniya, A. Sathya, S. Siva Sophie, S. Lourdumarie Department of Computer Science Pondicherry University Pondicherry India

ISBN: (纸本)9798331300579

Summarizing legal case documents is a very daunting task as law practitioners have to traverse via a hundreds of case reports before determining the relevant case which may aid as a good resource material in an ongoing case. Numerous summarization algorithms have been proposed over time, including generic text summarization and a few specifically for summarizing legal documents. this paper proposes different hybrid text summarization techniques by the fusion of extractive and abstractive text summarizer for legal domain. the fusion techniques are aimed at generating high-quality abstractive summaries that balance original content preservation with enhanced readability and coherence. the extractive methods namely LSTM-based summarization, CaseSummarizer, and Maximum Marginal Relevance (MMR), are explored in conjunction with Legal Pegasus, a state-of-the-art abstractive summarization model tailored for legal texts. Evaluation using ROUGE metrics demonstrates the effectiveness of these hybrid approaches in generating coherent and contextually relevant summaries within the legal domain. the CaseSummarizer and Legal Pegasus combination emerges as the most promising, achieving the highest F-score across all evaluated metrics, highlighting its potential for improving legal text comprehension and summarization. the proposed approach aims to elevate performance and deliver summaries that are not only contextually relevant but also more comprehensive. © Grenze Scientific Society, 2024.

关键词： Natural language processing systems

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Forehand to Deep Learning: A CNN-LSTM Framework for Serve Shot Analytics in Table Tennis 15

Forehand to Deep Learning: A CNN-LSTM Framework for Serve Sh...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Vikrant Mehta, Shiva Graphic Era Hill University Computer Science and Engineering Dehradun248002 India Graphic Era Deemed to Be University Dehradun248002 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

It introduces a novel method for detecting and recognizing the table tennis serve-shot motions based on the advanced convolutional and Long Short-Term Memory models. the model is designed to classify serve shots into five categories: side-spin, backspin, shots with no spin, slicing, and illegal service. It consists of CNNs for precision image classification and the LSTM network for considering the time movement of people through a sequence of shots. Experimental data proved the viability of a model, so the overall accuracy was achieved at 90.39%. the accuracy metrics for the varied classes can stretch from 84.6% to 94.22%. Another key indicator is the high recall rates for Serve shots. this table has an astonishing rate of 97.03% for class 3, demonstrating how the model can effectively recognize serve shots. the high F1 scores illustrate the balance between the Precision and the Recall, the values of which exceed 87% for all instances. the model's performance is guaranteed as performance evaluations are based mainly on macro, weighted, and micro averages. We usually have 90-95 percent accuracy, recall, and F1-Score scores. the experiment proves that the classifier finds the answer to the issue, which is indeed a significant achievement considering the job's toughness and numerous data. the CNN-LSTM model's fantastic accuracy and dependability are a reference tool for coaches and athletes they can rely upon. While some argue that it can help trainers and give a macro view into the course, others cannot stop them for fear that this will create doubts about the authenticity of the competition. © 2024 IEEE.

关键词： Tennis

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