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Materials Today: Proceedings 2022年 62卷 4844-4852页

作者： Sriraman, Harini Ravikumar, Aswathy Pattabiraman, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

As transistor technology scales beyond 32 nm, the reliability of the processors and servers reduces at an exponential rate. In recent times, the qualification of reliability of servers has reduced from 5 years to 3 years. With this current trend, the lifetime of servers can be expected to shrink further. In a cloud data center, with hundreds and hundreds of servers, the ill effect of shorter lifetime reliability will be magnified. Reliability of servers influences the performance, cost and power efficiency of a cloud data center. In this paper, a cost-effective technique to continuously monitor the reliability of servers and scheduling tasks based on these reliability values is proposed. The proposed technique monitors the hardware parameters for the current executing workload to calculate the reliability metrics. The reliability metric used in ReAD is MTTF (Mean Time to Fail) and this is calculated for individual servers. The load balancer reads the MTTF of individual servers and performs the load balance, considering reliability as one of the parameters. The proposed technique is non-invasive in nature and does not require the server to go into a special test mode. This saves the testing time. The experimental analysis shows on an average 25% improvement in Server Usage Metric with the minimum improvement around 5% and the maximum is around 50%. © 2022

关键词： Reliability

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Deep Learning-Based Epileptic Seizure Classification using Empirical Mode Decomposition Algorithm 9

Deep Learning-Based Epileptic Seizure Classification using E...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Durga, R. Prabha, Jeya A. Maansi, J. Fathimal, Sameera M. Niranjan, Vijay Kirubha, Angeline S.P. Departmentof Biomedical Engineering SRM Institute of Science and Technology Tamil Nadu Chengalpattu Kattankulathur India

ISBN: (纸本)9798350383201

Uncontrollable, spontaneous seizures are a hallmark of the neurological condition epilepsy. Epilepsy diagnosis and monitoring frequently include electroencephalogram (EEG) data. It takes time and the skill of a qualified medical professional to interpret EEG data. In order to enhance the diagnosis and treatment of epilepsy, automated approaches for identifying epileptic episodes are required. The empirical mode decomposition (EMD) technique is used in this research to present a deep learning-based strategy for detecting epileptic seizures. The intrinsic mode functions (IMFs) that record various frequency components of the signal are used by the EMD algorithm to decompose EEG signals. We use a Deep Neural Network (DNN) to ascertain whether the input signal has seizure activity or not. The proposed technique improves existing methods, according to our experimental findings, which demonstrate high accuracy. © 2023 IEEE.

关键词： Empirical mode decomposition

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Optimal social welfare:A many-to-many data transaction mechanism based on double auctions

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Digital Communications and Networks 2023年第5期9卷 1230-1241页

作者： Jingyuan Duan Ling Tian Jianqiao Mao Jiaxin Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Shenzhen Institute of Information Technology China Department of Electronic and Electrical Engineering University College LondonGower StreetLondonUnited Kingdom Amazon(China)Investment Ltd China

With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and *** can achieve maximal data value and prepare data for smart city *** to data's unique characteristics,such as dispersion,heterogeneity and distributed storage,an unbiased platform is necessary for the data trading market with rational trading ***,there are multiple buyers and sellers in a practical data trading market,and this makes it challenging to maximize social *** solve these problems,this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism(SOMTM),which integrates three entities,a trading process and an algorithm named Many-to-Many Trading Algorithm(MMTA).Based on the market scale,market dominated-side and market fixed-side,simulations verify the convergency,economic properties and efficiency of SOMTM.

关键词： Data trading Double auction Social welfare Game theory Smart city

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Performance Improvement through Novel Adaptive Node and Container Aware Scheduler with Resource Availability Control in Hadoop YARN

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computer Systems science & engineering 2023年第12期47卷 3083-3108页

作者： J.S.Manjaly T.Subbulakshmi School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

The default scheduler of Apache Hadoop demonstrates operational inefficiencies when connecting external sources and processing transformation *** paper has proposed a novel scheduler for enhancement of the performance of the Hadoop Yet Another Resource Negotiator(YARN)scheduler,called the Adaptive Node and Container Aware Scheduler(ANACRAC),that aligns cluster resources to the demands of the applications in the real *** approach performs to leverage the user-provided configurations as a unique design to apportion nodes,or containers within the nodes,to application ***,it provides the flexibility to the applications for selecting and choosing which node’s resources they want to manage and adds limits to prevent threshold breaches by adding additional jobs as *** or container awareness can be utilized individually or in combination to increase *** top of this,the resource availability within the node and containers can also be *** paper also focuses on the elasticity of the containers and self-adaptiveness depending on the job *** results proved that 15%–20%performance improvement was achieved compared with the node and container awareness feature of the *** has been validated that this ANACRAC scheduler demonstrates a 70%–90%performance improvement compared with the default Fair *** results also demonstrated the success of the enhancement and a performance improvement in the range of 60%to 200%when applications were connected with external interfaces and high workloads.

关键词： Big data Hadoop YARN hadoop distributed file system(HDFS) MapReduce scheduling fair scheduler

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Multi-stage deep investigation pipeline on detecting malign network traffic

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Materials Today: Proceedings 2022年 62卷 4726-4731页

作者： Nasreen Fathima, A.H. Syed Ibrahim, S.P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

To detect intrusions effectively, we need vulnerable information. To collect the network traffic transactions and assess the anomaly which includes essential details related to network traffic data, we explore the datasets that have intrusion related network traffic. Based on this assessment, to predict attacks in the network transaction efficiently, we use the most upgraded benchmark datasets NSL-KDD and CIC-IDS-2017. In this work, a novel multistage deep learning model is developed to evaluate the optimal features for training. Our proposed model identifies weird patterns of network traffic with probabilistic unsupervised learning technique variational autoencoder. By pre-training the model in the first and second stage to classify the attack with fusion of random forest at the final stage, we improve the outcome of a very biased class dataset. Experimental results unveil the improvisation of the overall computational and time complexity. The proposed model accuracy measures as 99% with improved precision for CIC-IDS-2017 dataset. Class imbalance handling techniques have to be combined as future work in order to reduce the high false positive rates and accordingly increase the low false negative rates for the minority classes. © 2022

关键词： Decision trees

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CapsENet: Deep Learning based Acute Lymphoblastic Leukemia Detection Approach 8

CapsENet: Deep Learning based Acute Lymphoblastic Leukemia D...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lalithkumar, K. Naveen Raj, B. Priyanga, M.A. Sandhya, S. Karthiga, M. Bamnari Amman Institute of Technology Department of Information Technology Tamilnadu Sathyamangalam India Jct College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Bamnari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

The abnormal formation of lymphocytes in the bone marrow causes one of the dangerous forms of blood cancer, which is called Acute LymphoblasticLeukaemia (ALL). While it is most commonly diagnosed in children, when it is often curable, it can also occur in adults;and poor outcomes are typically due to a late stage at diagnosis. This work proposes a high-level screening method that incorporates an intelligent deep learning (DL) system to enhance the first-stage detection of this possibly fatal disease. The described method is based on the integration of CapsuleNetworks (CapsNet) with EfficientNet (ENet) for the classification of leukemic and healthy blood cells by analyzing the images of small blood smears. Based on the microscopic image databases obtained from the public domain, the designed CapsENet model was thoroughly tested and trained. Training was conducted on the GoogleColaboratory platform at optimal performance with the Nvidia Tesla P-100 GPU. The outcomes achieved by the model are as follows;96. 95% accuracy, 96. Specificity is 88%, sensitivity - 96%, Cohen's kappa coefficient - 96. 97% F1-score, and 97. 96% precision. These results proof the high efficiency of the classifier in detecting leukemic cells and its applicability for the peripherical blood test and CBC preliminary screening. This could lead on to earlier interventions in patient management and improved patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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UNet3+: Modified InceptionV3 for Multiclass Brain Tumor Classification Using MRI Images

UNet3+: Modified InceptionV3 for Multiclass Brain Tumor Cla...

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International Conference on Computing and Machine Learning, CML 2024

作者： Saha, Himanshu Prasad Sinha, Ankita Computer Science and Engineering Siliguri Institute of Technology Sukna Salbari WestBengal Siliguri734009 India

ISBN: (纸本)9789819778386

Brain tumors pose significant challenges in the field of medical image processing. Tumor that shows no symptoms at all in its early stages is referred to as asymptomatic tumor. Therefore, the implementation of an automated method to detect brain tumor in MRI at an early stage will aid radiologists in their detection process. Deep learning models are gaining a lot of attention due to their quick advancement in MRI imaging, especially in recognizing the presence of tumor. However, if the MRI scans are complex, the accuracy of disease prediction is significantly decreased. Existing approaches, on the other hand, have limits in terms of scalability and performance, which highlights the need to conduct additional research. To overcome this problem we have proposed a comprehensive UNet3+ model that performs the detection, segmentation, and classification of tumor in a single step. The performance of the proposed model is assessed by evaluating its ability to accurately segment and classify tumor using publicly available datasets BRATs. The proposed model achieves an accuracy of 99.37%, and a F1-score of 99.64%. Based on the findings, the suggested model demonstrates superior performance compared to the existing techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Study on sonic-Assisted processing method for micro pattern formation on Cu film 1

Study on sonic-Assisted processing method for micro pattern ...

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1st International Conference on Robotics, engineering, science, and technology, RESTCON 2024

作者： Yamamoto, Soma Fukushige, Yuho Nishizawa, Hideaki Khajornrungruang, Panart Suzuki, Keisuke Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350348842

In semiconductor processing to form surface shapes, photolithography and dry etching are used. In this case, the vacuum process requires improvements cost and productivity. We propose a sonic-Assisted processing method using the resonance phenomenon of stainless used steel plate diaphragms. This method is characterized by vibrating the diaphragm with sound pressure to vibrate the particles and microfabricate on Cu thin film [1][2]. To evaluate the sonic-Assisted processing method, we attempted to process Cu wafers by transmitting vibrations perpendicular to the wafer to a SUS plate diaphragm as a micro-shape transfer process using a micro-pattern pad. As a result, 100 μm length and 20 μm width grooves were formed on the Cu wafer surface. © 2024 IEEE.

关键词： Copper

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Efficacy of image based deep learning CNN models for bolt loosening detection in steel structures

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Asian Journal of Civil engineering 2025年第6期26卷 2409-2417页

作者： Alapati, Mallika Ramesh Chandra, G. Abboju, Shivani Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

Bolted connections are integral components in steel structures and often are vulnerable to loosening due to cyclic loading and fatigue. Detecting bolt loosening in early stages is critical to prevent sudden catastrophic failures. Recent advancements in image capturing and processing, combined with machine learning techniques have significantly improved the accuracy of detecting bolt loosening. Deep learning techniques, in specific those based on image analysis, have proven to be effective tool for damage detection in structural members. In this study, initially captured dataset of 120 images from a laboratory model is augmented to 576 images using the Roboflow platform. Investigations in this work are tied to one image data set and the metrics are analysed for how well a model performs. Usually, it depends on each use case as to which model would be best suited. In the present work, dataset featured two states of bolt loosening: tight and loose. Convolution Neural Network architectures AlexNet and ResNet are applied individually to the augmented dataset to predict the bolt loosening states as binary classification problem. The results demonstrated an accuracy of 87.931%, indicating that the AlexNet model effectively distinguished between loose and tight conditions. Whereas the ResNet algorithm exhibited relatively better accuracy of 90.8%. Further, an integrated hybrid model (CNN model with LSTM) is also applied on the same image data to further enhance the efficiency of the deep learning frame work. The performance of the trained CNN algorithms was further validated with image responses from various real time scenarios of various lighting conditions and found to be fairly good. This study highlights the potential of using deep learning techniques, specifically AlexNet, ResNet and CNN with LSTM for accurately detecting bolt loosening, thereby contributing to the safety and reliability of steel structures. © The Author(s), under exclusive licence to Springer Nature S

关键词： AlexNet Bolt loosening Bolted connections Convolution neural network architectures Image based deep learning ResNet architecture

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Dynamic Charging on the Go: Optimizing Mobile Charging Stations for Electric Vehicle Infrastructure 11

Dynamic Charging on the Go: Optimizing Mobile Charging Stati...

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11th International Conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2025

作者： Jain, Suhas Gupta, Arobinda Dept. of Computer Science & Engineering Indian Institute of Technology WB Kharagpur721302 India

ISBN: (纸本)9789897587450

With the growing adoption of electric vehicles (EVs) as an eco-friendly and sustainable means of transportation, availability of adequate EV charging infrastructure has become very important. While fixed charging stations are the primary means for public charging, they can be effectively augmented by mobile charging stations. In this paper, we address the problem of planning and operating a mobile charging station fleet by an operator. We propose an algorithm for the operator to use for route planning of the MCSs and for scheduling charging requests of EVs to them to try to maximize the number of charging requests served. Detailed simulation results are presented in different realistic scenarios to show that the proposed algorithm works well. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Electric vehicles

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