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DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficient Deep Learning Clusters in the Cloud

DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficien...

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IEEE/ACM International symposium on Cluster computing and the Grid (CCGRID)

作者： Yoochan Kim Kihyun Kim Yonghyeon Cho Jinwoo Kim Awais Khan Ki-Dong Kang Baik-Song An Myung-Hoon Cha Hong-Yeon Kim Youngjae Kim Dept. of Computer Science and Engineering Sogang University Seoul Republic of Korea LG Electronics Oak Ridge National Laboratory TN USA ETRI Daejeon Republic of Korea

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Distributed Deep Learning (DDL), as a paradigm, dictates the use of GPU-based clusters as the optimal infrastructure for training large-scale Deep Neural Networks (DNNs). However, the high cost of such resources makes them inaccessible to many users. Public cloud services, particularly Spot Virtual Machines (VMs), offer a cost-effective alternative, but their unpredictable availability poses a significant challenge to the crucial checkpointing process in DDL. To address this, we introduce DeepVM, a novel solution that recommends cost-effective cluster configurations by intelligently balancing the use of Spot and On-Demand VMs. DeepVM leverages a four-stage process that analyzes instance performance using the FLOPP (FLoating-point Operations Per Price) metric, performs architecture-level analysis with linear programming, and identifies the optimal configuration for the user-specific needs. Extensive simulations and real-world deployments in the AWS environment demonstrate that DeepVM consistently outperforms other policies, reducing training costs and overall makespan. By enabling cost-effective checkpointing with Spot VMs, DeepVM opens up DDL to a wider range of users and facilitates a more efficient training of complex DNNs.

关键词： Training Deep learning Checkpointing Measurement Cloud computing Costs Artificial neural networks Linear programming

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A Detailed Analysis of Machine Learning Models to Predict Water Potability

A Detailed Analysis of Machine Learning Models to Predict Wa...

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International Conference on computing and Networking Technology (ICCNT)

作者： Vishnu Sreekumar Faraz Ihsan Surya Reghuram S Sarath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Water is one of the most indispensable things in our life. However, getting clean drinking water has become more difficult for a lot of people lately. this research paper uses machine learning techniques to tackle the crucial problem of predicting the potability of water. Reliability in water potability assessment is critical, as concerns about water quality and its effects on public health are becoming more widespread worldwide. We present a novel predictive model to assess the potability of water sources by utilizing an extensive dataset that includes various water quality parameters. In order to examine intricate relationships within the dataset, the study uses a cutting-edge machine learning framework that integrates algorithms like Random Forest, KNN, SVM, Decision tree, and Gradient boosting. To extract pertinent information, feature engineering is used, and model performance is rigorously tuned using hyper parameters. To determine the best method for predicting water potability, a comparative analysis of several models is carried out. the proposed model is validated on different datasets, which guarantees its robustness and generalizability in a range of environmental conditions. the outcomes show that the predictions of water potability have a high degree of sensitivity, specificity, and accuracy.

关键词： Support vector machines Analytical models Accuracy Water quality Predictive models Nearest neighbor methods Boosting Robustness Public healthcare Random forests

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Utilizing many-core accelerators for halo and center finding within a cosmology simulation

Utilizing many-core accelerators for halo and center finding...

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IEEE symposium on Large Data Analysis and Visualization (LDAV)

作者： Christopher Sewell Li-ta Lo Katrin Heitmann Salman Habib James Ahrens Los Alamos National Laboratory Argonne National Laboratory

Efficiently finding and computing statistics about “halos” (regions of high density) are essential analysis steps for N-body cosmology simulations. However, in state-of-the-art simulation codes, these analysis operators do not currently take advantage of the shared-memory data-parallelism available on multi-core and many-core architectures. the Hybrid / Hardware Accelerated Cosmology Code (HACC) is designed as an MPI+X code, but the analysis operators are parallelized only among MPI ranks, because of the difficulty in porting different X implementations (e.g., OpenMP, CUDA) across all architectures on which it is run. In this paper, we present portable data-parallel algorithms for several variations of halo finding and halo center finding algorithms. these are implemented with the PISTON component of the VTK-m framework, which uses Nvidia's thrust library to construct data-parallel algorithms that allow a single implementation to be compiled to multiple backends to target a variety of multi-core and many-core architectures. Finally, we compare the performance of our halo and center finding algorithms against the original HACC implementations on the Moonlight, Stampede, and Titan supercomputers. the portability of thrust allowed the same code to run efficiently on each of these architectures. On Titan, the performance improvements using our code have enabled halo analysis to be performed on a very large data set (81923 particles across 16,384 nodes of Titan) for which analysis using only the existing CPU algorithms was not feasible.

关键词： Algorithm design and analysis computer architecture Computational modeling Graphics processing units Analytical models Acceleration Pistons

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A Data-Driven Approach to Heart Disease Prognosis using XG Boost Algorithm

A Data-Driven Approach to Heart Disease Prognosis using XG B...

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International Conference on computing and Networking Technology (ICCNT)

作者： K Kavitha K Gopalakrishnan S Kavitha K Sobiya M Suveetha R Sridhar CSE Kongu engineering college perundurai India IT Velalar College of engineering and Technology perundurai India CSE Nandha engineering college perundurai India Department of computer science and engineering Kongu engineering college perundurai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Heart disease remains a critical public health challenge, responsible for a high mortality rate and significant socio-economic burdens, including substantial healthcare costs and productivity losses. this study explores the application of advanced computational techniques, specifically deep learning and machine learning models, to enhance the prediction accuracy of heart disease. the focus is on the XGBoost algorithm, renowned for its superior performance in classification tasks. By leveraging a comprehensive dataset comprising diverse patient information, this research aims to improve the precision and efficacy of early heart disease detection. We rigorously evaluate XGBoost’s predictive capabilities against various other machine learning and deep learning models to identify the most effective approach for early diagnosis. the findings of this study have the potential to contribute significantly to the development of reliable predictive tools for heart disease, ultimately aiding in better clinical decision-making and patient outcomes.

关键词： Heart Deep learning Machine learning algorithms Computational modeling Predictive models Prediction algorithms Risk management Reliability Socioeconomics Diseases

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Exploring Intelligent UAV Route Planning in Autonomous Telecommunication Systems

Exploring Intelligent UAV Route Planning in Autonomous Telec...

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International Conference on computing and Networking Technology (ICCNT)

作者： Rakhi Chakraborty Deepak Kumar K Preetham Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India Department of Mathematics Vivekananda Global University Jaipur India Department of Management School of Management - UG JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this paper gives a singular approach to permitwise unmanned aerial car (UAV) direction planning in self-sufficient telecommunication systems. the proposed technique utilizes the aggregate of heuristic algorithms, artificial intelligence, and dynamic programming strategies for unmanned airborne vehicles to determine the most useful route to supply the services correctly. the proposed technique is evaluated on simulated 3-D surroundings. It has proven its effectiveness in offering high-acting direction-making plans with quick flight routes and offers excellent real-time performance. the heuristics and synthetic intelligence strategies used in this method can efficiently examine the environment for higher route planning and allow the UAVs to evolve to extraordinary terrains, climates, and boundaries, which can affect the conversation shipping. the principle application of this technique is in offering reliable verbal exchange offerings via UAVs in antagonistic and indoor environments. thru the proposed approach, this paper immediately explores the green routing solutions for practical deployment eventualities.

关键词： Oral communication Autonomous aerial vehicles Routing Real-time systems Planning Telecommunications Maintenance Indoor environment Optimization Meteorology

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Improved Squeeze and Excitation-Based YOLOv8-SE for Object Detection

Improved Squeeze and Excitation-Based YOLOv8-SE for Object D...

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International Conference on computing and Networking Technology (ICCNT)

作者： Riya Chauhan Maroti Deshmukh Akshay Singh Dept. of Computer Science and Engineering NIT Uttarakhand Srinagar Garhwal India Managing Director Evigway Technologies

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the rapid advancement of deep learning has made major advancements surveillance applications, yet detecting targets remains challenging due to size variations and environmental factors. In this paper, we integrated the Squeeze-and-Excitation (SE) module into various blocks of YOLOv8 to enhance feature discrimination and detection performance. the SE module, added to the backbone, neck, and head of YOLOv8, adaptively recalibrates channel-wise feature responses. this SE-enhanced YOLOv8 (YOLOv8-SE) model improves object detection on the challenging MS-COCO dataset, crucial for real-time surveillance across diverse day-night scenarios. Experimental results show that YOLOv8-SE attains high precision and recall, with a Mean Average Precision at 50 Intersection over Union (mAP50) of 0.5199 and mAP50-95 of 0.3647. Despite the increased number of parameters, the model maintains real-time inference speeds with reduced PyTorch inference time (in ms) and unchanged GFLOPs, establishing it as a reliable solution for real-time object detection tasks.

关键词： Deep learning Adaptation models Surveillance Computational modeling Object detection Feature extraction Real-time systems Environmental factors Neck Reliability

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SMART: Serverless Module Analysis and Recognition Technique for Managed Applications

SMART: Serverless Module Analysis and Recognition Technique ...

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IEEE/ACM International symposium on Cluster computing and the Grid (CCGRID)

作者： Adi Ashkenazi Edita Grolman Aviad Elyashar Dudu Mimran Oleg Brodt Yuval Elovici Asaf Shabtai Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Cyber Labs@BGU Ben-Gurion University of the Negev Israel Department of Computer Science Shamoon College of Engineering Beer-Sheva Israel

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Serverless Function-as-a-Service (FaaS) environments enable developers to build and run cloud applications without the need to manage the underlying servers and computing infrastructure, allowing them to focus on implementing the application logic. Such environments contain numerous functions and dynamic resources, e.g., APIs and databases, making it challenging to gain insight and context of internal events i.e., recognize modules. Module in a serverless application is a set of functions and resources, that represents a functional unit that shares logical context. this paper presents SMART, a method for automatic analysis and recognition of modules for managed serverless applications. the proposed method creates an event-based graph by analyzing the standard serverless logs that document events involving the application’s functions and resources and utilizes well-known community detection algorithms (such as Louvain), with graph centrality metrics (such as degree centrality) to recognize the modules. SMART enables high-level visibility of the application’s structure and logical context which can facilitate security analysis and contribute to improved decision-making of incident response handlers, who typically do not have direct access to the application’s design and code, which can lead to challenges in fully understanding the system’s intricacies. We focused on the popular Amazon Web Services (AWS) Lambda serverless computing platform and evaluated the proposed method on three different demo applications (Airline Booking, VOD, and E-commerce). We compared SMART’s performance to four overlapping community detection algorithms and showed that it outperformed them in the task of module recognition, with a maximum improvement of 61% on the omega index metric compared to the Speaker-Listener Label Propagation algorithm. In addition, we demonstrate that the use of large language models (LLMs) with the knowledge gained by SMART can enrich security analysis insights.

关键词： Measurement Web services Serverless computing computer architecture Security Indexes Electronic commerce Detection algorithms Standards Airline industry

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Enhanced Sports performance Analysis: CNNLSTM Based Detection of Tennis Serve Variations

Enhanced Sports Performance Analysis: CNNLSTM Based Detectio...

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International Conference on computing and Networking Technology (ICCNT)

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

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this study looks at the applicability of a special network structure referred to as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) in identifying minor changes in motion in the handling of the tennis racket during serves. the desired performance in rare movements, such as the incorporation of spatial feature extraction capabilities of CNNs and temporal sequence learning of LSTMs, attains serve. this model was trained and tested using a video-based dataset containing videos of professional/semi-professional and amateur tennis players. Next, a considerable number of small motion changes were extracted and labelled for many samples in the dataset. the observation made from the current research analysis shows that the CNN-LSTM model achieves an accuracy of 94%.5% on the detection of the disease with a precision of 92.3%, a recall of 93 - it is noticed that the above result is that which consolidates the fundamental constancy between the recall and the result it portrays. 8%, precision 93%, and F1-score 93%. But even in this regard, it stands at 0%, which is even higher than many state-of-the-art models. the analysis of the performance at each epoch shows gradual improvements; consequently, the training and validation sets attain high accuracy after 50 epochs. the optimization of the form of the batch size implies that an ideal batch size has been estimated as 32, with a corresponding classification accuracy estimate of 94 with a standard error of 0. Per cent: 5% and the decrease on 0. 25. the heatmap of the confusion matrix shows high classification rates for several of the motion variation classes with very little confusion, and the ROC curve analysis yields an average AUC of 0.93 for all classes.

关键词： Training Heating systems Analytical models Accuracy Sports equipment Feature extraction Convolutional neural networks Standards Sports Videos

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Analyzing the Effect of Spatial Correlation on performance of Multi-User Multiple Access Techniques

Analyzing the Effect of Spatial Correlation on Performance o...

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International Conference on computing and Networking Technology (ICCNT)

作者： I K Kantharaj Ankita Agarwal Bhawna Saraswat Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Sanskrit University Mathura Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Analyzing the impact of spatial correlation on the overall performance of multi-consumer more than one access strategy has long been a topic of hobby inside the communications field. Spatial correlation, that is, the quantity to which the sign outcomes in an area converge to one another, is an aspect that impacts the overall performance of conversation techniques within that identical place. the performance of those multi-person couples to get the right of entry to techniques alternates considerably depending on the extent of spatial correlation. For instance, spatial correlation can decrease the lateral right of entry to interference and consequently increase the system’s ability when compared to a non-correlated kind of device. On the other hand, high spatial correlation can result in low rejection of more than one access interference, railway rejection, and other severe overall performance constraints. Research approximately the effect of spatial correlation on multi-person a couple of get entry to techniques returned as a long way as 1983, in which diverse simulation analysis has been conducted. these simulations, in most cases, brought approximately the benefits and disadvantages of four essential multiple access strategies together with FDMA, TDMA, CDMA, and Daveigh these simulations have proven brilliant results, with the capability of a machine utilizing finite length are AO codes the usage of FDMA. A positive variety of spatial correlation claimed to be two times that of a conventional code-division a couple of get right of entry to machine. Extra specifically.

关键词： performance evaluation Time division multiple access Correlation Frequency division multiaccess Noise Insurance Interference Oral communication Distortion Antenna arrays

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Optimizing Shielding Techniques for high-Frequency EMC Applications in Aerospace Systems

Optimizing Shielding Techniques for High-Frequency EMC Appli...

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International Conference on computing and Networking Technology (ICCNT)

作者： Parikshit N. Mahalle S. Hemelatha Mukul Mishra G Kalanandhini Bhavuk Samrat T Ramachandran Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Mechanical Engineering JAIN(Deemed to be University) School of Engineering and Technology Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the defense of high-frequency electromagnetic compatibility (EMC) for aerospace systems is important to ensure the functionality and reliability of aerospace cars. Moreover, the performance of the system has to follow each plane’s requirements and electromagnetic regulations, such as Federal Aviation Administration (FAA) regulations, to hold protection and trustworthiness. While protecting excessive-frequency EMC application is only sometimes taken into consideration, the electrical gadgets, wiring, and numerous components may be electrically laid low with electrical fields, resulting in malfunction of the machine, mistakes in calculations, and faulty indicators. this paper evaluates the optimization of protective techniques for high-frequency EMC packages in aerospace systems through the usage of a mixture of simulation and size strategies. the objectives of the look are to expand an efficient shielding structure and optimize the design to reduce the reflection of electromagnetic wave radiation in the system. A two-layered shielding method with an internal shield of aluminum foil is used to reduce the reflection of electromagnetic wave radiation. Furthermore, the field studies and the simulations executed permit the optimization of the measurements to be greater than it should be predicted. the obtained results imply that the optimized defensive approach makes a good sized enhancement in the defensive overall performance of the device, thereby disposing of errors due to outside EMC resources.

关键词： performance evaluation Wiring Electromagnetic scattering FAA Electromagnetic compatibility Regulation Reflection Protection Optimization Aerospace engineering

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