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28th International European conference on Parallel and Distributed computing, Euro-Par 2022

28th International European Conference on Parallel and Distr...

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28th International European conference on Parallel and Distributed computing, Euro-Par 2022

ISBN: (纸本)9783031125966

The proceedings contain 25 papers. The special focus in this conference is on Parallel and Distributed computing. The topics include: Analysing Supercomputer Nodes Behaviour with the Latent Representation of Deep learning Models;Accelerating Parallel Operation for Compacting Selected Elements on GPUs;A Methodology to Scale Containerized HPC Infrastructures in the Cloud;cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads;multi-objective Hybrid Autoscaling of Microservices in Kubernetes Clusters;two-Agent Scheduling with Resource Augmentation on Multiple machines;IP. LSH. DBSCAN : Integrated Parallel Density-Based Clustering Through Locality-Sensitive Hashing;graphGuess: Approximate Graph Processing System with Adaptive Correction;deterministic Parallel Hypergraph Partitioning;MARTINI: The Little Match and Replace Tool for Automatic Application Rewriting with Code Examples;OmpSs-2@Cluster: Distributed Memory Execution of Nested OpenMP-style Tasks;Generating Work Efficient Scan Implementations for GPUs the Functional Way;automatic Parallelization of Python Programs for Distributed Heterogeneous computing;A Hybrid Piece-Wise Slowdown Model for Concurrent Kernel Execution on GPU;accelerating Brain Simulations with the Fast Multipole Method;high-Performance Spatial Data Compression for Scientific Applications;accurate Fork-Join Profiling on the Java Virtual machine;characterization of Different User Behaviors for Demand Response in Data Centers;on-the-Fly Calculation of Model Factors for Multi-paradigm Applications;relative Performance Projection on Arm Architectures;exploring Scheduling algorithms for Parallel Task Graphs: A Modern Game Engine Case Study;Decentralized Online Scheduling of Malleable NP-hard Jobs;A Bi-Criteria FPTAS for Scheduling with Memory Constraints on Graphs with Bounded Tree-Width.

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Outbreak Prediction of COVID-19 3rd Wave in asia 4th

Outbreak Prediction of COVID-19 3rd Wave in Asia

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4th International conference on Futuristic Trends in Networks and computing Technologies, FTNCT 2021

作者： Sharma, Avinash Yadav, Dharminder Reen, L.S. Chandra, Umesh Mullana Haryana Ambala133207 India Department of Computer Science and Technology Glocal University UP Saharanpur India Seth Jai Parkash Institute of Information Technology Yamuna Nagar Haryana Radaur India Department Computer Science and Technology Banda University of Agriculture and Technology UP Banda India

ISBN: (纸本)9789811950360

Coronavirus is a pandemic for whole world and infected more than 200 countries of the world. Spreading of coronavirus started from china at the end of December and within three months, it infected whole world. Coronavirus is belonging to beta coronavirus family. Common symptoms of coronavirus are fever, dry cough, fatigue, and respiratory-related problem. This paper tries to study the infection rate of coronavirus in asian countries, rest of world countries, and overall world countries. asia is largest continent of the world which contains approximate 50 countries and highest contributes in GDP. Population of asian countries 446.27 crore, that is 60 percentage of whole world population and covers 30 percent geographical area of world. China and India are the most populated countries in asia. Total confirmed cases 2,056,051, recovered cases 502,045, and death cases 134,177 in the world. Scholar also divides the asia continent into six regions such East, South, Central, North, Southeast and Western asia for better understanding infection of coronavirus. This paper analyzes the confirmed cases, recovered cases, and death cases in asia and understands the infection pattern date wise and countries wise. machine learning algorithm is used for prediction infection in the world and also predicts future infection rate and death rate in asian countries and world. Prophet is used for future prediction of confirmed and death cases in the asian countries. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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2nd International conference on Computer Vision, High-Performance computing, Smart Devices, and Networks, CHSN 2021

2nd International Conference on Computer Vision, High-Perfor...

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2nd International conference on Computer Vision, High-Performance computing, Smart Devices, and Networks, CHSN 2021

ISBN: (纸本)9789811698842

The proceedings contain 54 papers. The special focus in this conference is on Computer Vision, High-Performance computing, Smart Devices, and Networks. The topics include: EEG Signals Classification for Right- and Left-Hand Movement Discrimination Using SVM and LDA Classifiers;A Comparative Study on Network Intrusion Detection System Using Deep learning algorithms and Enhancement of Deep learning Models Using Generative Adversarial Network (GAN);Indian Sign Language Detection Using YOLOv3;Feature Extraction-Based Phishing URL Detection Using machine learning Techniques;a Slant Transform and Diagonal Laplacian Based Fusion Algorithm for Visual Sensor Network Applications;tracking Industrial Assets Using Blockchain Technology;prototype for Recognition and Classification of Textile Weaves Using machine learning;Development of Deep Neural Network Algorithm for Identification of Cerebral Microstructural Changes in Brain Tumor for Post-COVID-19 Patients;a Codebook Modification Method of Vector Quantization to Enhance Compression Ratio;multi-Perspective Reasoning Using Adaptive learning;intra Change Detection in Shelf Images Using Fast Discrete Curvelet Transform and Features from Accelerated Segment Test;mucormycosis Vaccine Design using Bioinformatic Tools;blockchain Implementation in IoT Privacy and Cyber Security Feasibility Study and Analysis;Modified ResNetModel for MSI and MSS Classification of Gastrointestinal Cancer;an IoT-Enabled Healthcare System: Auto-predictive Colorectal Cancer with Colonoscopy Images Combined with the Convolutional Neural Network;unfair Review Detection on Amazon Reviews Using Sentiment Analysis;decentralized Coded Caching for the Internet of Things (IoT) Using User Cooperation;Cryptanalysis of SIMON (32/64) Cipher Using Satisfiability Modulo Theories;Personal Safety Monitoring Devices in Wake of COVID19: Application of IoT and Sensor Technology;Ink Recognition Using TDNN and Bi-LSTM;SquashCord: Video Conferencing Application Using WebRTC.

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2nd International conference on Biologically Inspired Techniques in Many Criteria Decision Making, BITMDM 2021

2nd International Conference on Biologically Inspired Techni...

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2nd International conference on Biologically Inspired Techniques in Many Criteria Decision Making, BITMDM 2021

ISBN: (纸本)9789811687389

The proceedings contain 63 papers. The special focus in this conference is on Biologically Inspired Techniques in Many Criteria Decision Making. The topics include: Detection and Classification of Encephalon Tumor Using Extreme learning machine learning Algorithm Based on Deep learning Method;real-Time Elderly Multiple Biological Parameter Safety Monitoring System Based on mCloud computing: Telemedicine Era;lexicon-Based Argument Extraction from Citizen’s Petition in Arabic Language;detection of Heart Disease Using Data Mining;i Hardly Lie: A Multistage Fake News Detection System;complexity Classification of Object-Oriented Projects Based on Class Model Information Using Quasi-Opposition Rao Algorithm-Based Neural Networks;Intelligent Transportation Systems (ITSs) in VANET and MANET;cloud-Based Smart Grids: Opportunities and Challenges;prediction of Used Car Prices Using machine learning;covid-19 and Awareness of the Society: A Collection of the Important Facts and Figures Related to the Global Pandemic;Detection of COVID-19 Cases from Chest Radiography Images;monitoring the Heart Rate—An Image Processing Approach;brain Image Classification Using Optimized Extreme Gradient Boosting Ensemble Classifier;software Design for Mobile Phone Auto-Silencer;traffic Flow Prediction: An Approach for Traffic Management;BertOdia: BERT Pre-training for Low Resource Odia Language;a machine learning Approach to Analyze Mental Health from Reddit Posts;digitalization of Education: Rural India’s Potential to Adapt to the Digital Transformation as New Normality;COVID-19 Severıty Predıctıons: An Analysis Usıng Correlatıon Measures;Image Colorization Using CNNs;smart Agriculture Framework Implemented Using the Internet of Things and Deep learning;accuracy Analysis for Predicting Heart Attacks Based on Various machine learning algorithms;preface.

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11th International Advanced computing conference, IACC 2021

11th International Advanced Computing Conference, IACC 2021

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11th International Advanced computing conference, IACC 2021

ISBN: (纸本)9783030955014

The proceedings contain 51 papers. The special focus in this conference is on Advanced computing. The topics include: An Analysis of the Psychological Implications of COVID-19 Pandemic on Undergraduate Students and Efforts on Mitigation;network-Based Identification of Module Biomarker Associated with Hepatocellular Carcinoma;identifying Hub Nodes and Sub-networks from Cattle Rumen Microbiome Multilayer Networks;machine learning-Based Psychology: A Study to Understand Cognitive Decision-Making;predicting Stock Market Prices Using Sentiment Analysis of News Articles;Sentimental Analysis on Multi-domain Sentiment Dataset Using SVM and Naive Bayes Algorithm;artificial Intelligence in Online Stores’ Processes;early Warning Indicators for Financial Crisis During Covid-19;facial Recognition Based Attendance Monitoring System;Covid Alert System: A Smart Security System to Alert Violations of Covid Protocol Using OpenCV;transfer learning Using Variational Quantum Circuit;gait learning Using Reinforcement learning;data Science in the Business Environment: Architecture, Process and Tools;a Logarithmic Distance-Based Multi-Objective Genetic Programming Approach for Classification of Imbalanced Data;multiview Classification with Missing-Views Through Adversarial Representation and Inductive Transfer learning;Deep Reinforcement learning Based Throughput Maximization Scheme for D2D Users Underlaying NOMA-Enabled Cellular Network;An Intrusion Detection System for Blackhole Attack Detection and Isolation in RPL Based IoT Using ANN;evaluating the Efficacy of Different Neural Network Deep Reinforcement algorithms in Complex Search-and-Retrieve Virtual Simulations;post-hoc Explainable Reinforcement learning Using Probabilistic Graphical Models;ghostbusters: How the Absence of Class Pairs in Multi-Class Multi-Label Datasets Impacts Classifier Accuracy;Corona Virus Detection Using EfficientNet from CT Scans;reLearner: A Reinforcement learning-Based Self Driving Car Model Using Gym Enviro

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Research Landscapes, Hotspots and Emerging Frontiers of Combinatorial Optimization algorithms on Graphs: A Scientometric Analysis (1987 - 2022)

Research Landscapes, Hotspots and Emerging Frontiers of Comb...

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International conference on Big Data and Information Analytics (BigDIA)

作者： Boliang Sun Hai Chen Ziqi You Laboratory for Big Data and Decision National University of Defense Technology ChangSha People’s Republic of China College of System Engineering National University of Defense Technology ChangSha People’s Republic of China

This paper presents a scientometric analysis of the research landscapes, hotspots, and emerging frontiers of combinatorial optimization algorithms on graphs from 1987 to 2022. The study investigates technical maturity, country/region collaboration, institutional cooperation, journal co-citation, dual graph overlay, and hotspot frontier analysis based on co-citation and keyword co-occurrence using tools like CiteSpace, Bibliometrics R, and VOSviewer. The analysis reveals that research in this field has entered a mature stage, and countries such as the United States, China, and Japan have produced rich output in this field. The University of Tokyo, MIT, the Chinese Academy of Sciences, Shanghai Jiao Tong University, Stanford University, and the University of California have formed 10 distinct collaborative clusters. The findings also suggest that research in the field of combination optimization mainly comes from two clusters, namely, computer science and mathematics, and that cross-disciplinary knowledge is also involved. In the field of graph-based combination optimization, deep learning, machine learning, graph neural networks, and reinforcement learning are now areas of intense investigation. The frontier direction of research in this field is represented by quantum computing technologies such as coherent ising machines, hybrid quantum-classical algorithms, and quantum algorithms. The research problems in this field are categorized into three main types from a keyword perspective: graph theory problems, classical optimization problems, and open problems. The corresponding methods for solving these problems are also categorized into combinatorial optimization techniques, stochastic optimization methods, and machine learning-based optimization methods. The study highlights the need for interdisciplinary collaboration, more research on quantum computing technologies, advanced optimization techniques, and the exploration of emerging research frontiers in combination o

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Performance Evaluation of Different Word Embedding Techniques Across machine learning and Deep learning Models

Performance Evaluation of Different Word Embedding Technique...

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International conference on Computer and Information Technology (ICCIT)

作者： Tanmoy Mazumder Shawan Das Md. Hasibur Rahman Tanjina Helaly Tanmoy Sarkar Pias Department of CSE University of Asia Pacific Dhaka Bangladesh Department of Computer Science Virginia Tech USA Blacksburg USA

Sentiment analysis is one of the core fields of Natural Language Processing(NLP). Numerous machine learning and deep learning algorithms have been developed to achieve this task. Generally, deep learning models perform better in this task as they are trained on massive amounts of data. This, however, also poses a disadvantage as collecting sufficient amounts of data is a challenge and training with this data requires devices with high computational power. Word embedding is a vital step in applying machine learning models for NLP tasks. Different word embedding techniques affect the performance of machine learning algorithms. This paper evaluates GloVe, CountVectorizer, and TF-IDF embedding techniques with multiple machine learning models and proves that the right combination of embedding technique and machine learning model(TF-IDF+Logistic Regression: 87.75% accuracy) can achieve nearly the same performance or more as deep learning models (LSTM: 87.89%).

关键词： Deep learning Performance evaluation Training Sentiment analysis machine learning algorithms Computational modeling Natural languages

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Prediction of Compressive Strength of Green Concrete by Artificial Neural Network 1st

Prediction of Compressive Strength of Green Concrete by Arti...

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1st International conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Verma, Manvendra Upreti, Kamal Dadhich, Priyanka Ghosh, Soumi Khatri, Vishal Singh, Prashant Department of Civil Engineering GLA University Uttar Pradesh Mathura India Department of Computer Science and Engineering Akhilesh Das Gupta Institute of Technology and Management Delhi India Department of Big Data Analytics Asia Pacific Institute of Management Delhi India Department of Information Technology Maharaja Agrasen Institute of Technology Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India Department of Computer Science and Engineering Sunder Deep Engineering College Ghaziabad India

ISBN: (纸本)9783031250873

The concrete business has just come out with a brand-new product called geopolymer concrete. Because of its resilience in harsh environments and its high strength, it has the potential to revolutionise the whole construction industry. It is an excellent substitute for the traditional kind of concrete. It outperforms traditional concrete in terms of sustainability, ecology, durability, and economic viability. In this day and age, the use of machine learning strategies is not just the future of the research and development industry, but of all industries. These methods provide predictions about the outcomes by analysing the facts from the past. The process of determining the outcomes or worth of anything in the construction sector is very challenging, time consuming, and arduous. Without having to conduct destructive experiments or take samples, the strength of a mix design may now be more accurately predicted thanks to these procedures. By using deep learning and the random forest method, the purpose of this research is to predict the compressive strength of flyash-based geopolymer concrete. This will be accomplished by comparing the two algorithms with varying degrees of error and coefficient correlation. It has been shown via the use of simulated data that the random forest algorithm is the method that is most suited for making predictions about compressive strength. After the developing a model, the various errors were found for accuracy. The mean absolute error, root mean square error, relative absolute error, and root relative squared error are 3.46%, 5.94%, 64.32%, and 82.97%, respectively for the deep learning predicted compressive strength. The errors provide the proof of model accuracy to predict the compressive strength on the basis of ingredients proportions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Compressive strength

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An Investigation of the Bibliometric Landscape of the Many Facets of machine learning From the Years 2000 to 2022

An Investigation of the Bibliometric Landscape of the Many F...

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Computational Modelling, Simulation and Optimization (ICCMSO), International conference on

作者： Neeru Sharma Raman Chadha UIE Chandigarh University Punjab

machine learning as an emerging and applicable learning plays a vital role in the world of Computer Science. machinelearning has attracted much attention in the last decade. With the development of machine learning, huge researchers and publications opted for this as the path. This paper is about the bibliometric analysis that is deployed from the year 2000 to 2022. The main three fundamental factors that guide the progress of machine learning are Data, computing, and algorithmic advances. Mainly there are four different types of machine learning which accomplish Supervised ML, Unsupervised ML, Semi-Supervised ML, and Reinforcement machine learning which is used in different areas and applications. This paper is mainly focused on what are the major areas where machine learning has used and what are the minimal area where there is still a requirement of using machine learning is to be used.

关键词： Computer science machine learning algorithms Tree graphs Computational modeling Bibliometrics Closed box machine learning

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2nd International conference on Innovation in Electrical Power Engineering, Communication, and computing Technology, IEPCCT 2021

2nd International Conference on Innovation in Electrical Pow...

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2nd International conference on Innovation in Electrical Power Engineering, Communication, and computing Technology, IEPCCT 2021

ISBN: (纸本)9789811670756

The proceedings contain 47 papers. The special focus in this conference is on Innovation in Electrical Power Engineering, Communication, and computing Technology. The topics include: Stability Improvement of Fractional-Order Filters Using Gain Bandwidth and Step Response Approach;tidal Energy Connected with Grid for Coastal Area;design of Switched Capacitor-Based Charge Amplifier in 180 nm Technology;optimal Power Trading for a Micro-grid with Demand Response in Competitive Electric Market;A Comparative Study of Deep learning algorithms for Identification of COVID-19 Disease Using Chest X-Ray Images;electrical and Dielectric Characteristics of Ethylene Vinyl Acetate/Aluminum Nitride Polymer Composite for Usage in Flexible Electronics;advanced Adaptive Filter-Based Control Strategy for Active Switch Inverter Operation;Bypass Diodes Configurations for Mismatch Losses Mitigation in Solar PV Modules;sentiment Analysis of Bengali Facebook Data Using Classical and Deep learning Approaches;Design and Analysis of RF Optimization in 2G GSM and 4G LTE Network;analysis and Recognition of Emotions from Voice Samples Using Ant Colony Optimization Algorithm;Moth Flame Optimized Automatic Generation Control with PIDF Controller for the Integration of Plug-In Electrical Vehicles with Interconnected Power System;Cost and Feasibility Analysis for Designing a PV–Wind Hybrid Renewable Energy System (A Case Study for Campus-3, KIIT University, Bhubaneswar);Design of Optimal Multi-band PSS for Variable Solar-Penetrated Power System;TLBO Designed 2-DOFPIDF Controller for LFC of Multi-area Multi-source Power System;Automatic Generation Control of an Interconnected Power System Using 2-degree of Freedom Fractional Order PID Controller;Performance Assessment of Three-Phase Standalone PV System with machine learning-Based Controller;development of Conceptual Framework for Reinforcement learning Based Optimal Control.

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