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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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13th International conference on Intelligent Human Computer Interaction, IHCI 2021

13th International Conference on Intelligent Human Computer ...

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13th International conference on Intelligent Human Computer Interaction, IHCI 2021

ISBN: (纸本)9783030984038

The proceedings contain 68 papers. The special focus in this conference is on Intelligent Human Computer Interaction. The topics include: Simulation Model of a Spare Parts Distribution Network in the Airline Industry for Reducing Delays and Improving Service Levels: A Design of Experiments Study;alzheimer’s Dementia Recognition Using Multimodal Fusion of Speech and Text Embeddings;reSenseNet: Ensemble Early Fusion Deep learning Architecture for Multimodal Sentiment Analysis;technological Evaluation of Virtual and Augmented Reality to Impart Social Skills;attitude Control for Fixed-Wing Aircraft Using Q-learning;Using Mask-RCNN to Identify Defective Parts of Fruits and Vegetables;evaluating the Efficiency of Several machine learning algorithms for Fall Detection;face and Face Mask Detection Using Convolutional Neural Network;WTM to Enhances Predictive Assessment of Systems Development Practices: A Case Study of Petroleum Drilling Project;A Built-in Concentration Level Prediction Device for Neuro Training System Based on EEG Signal;Night Vision, Day & Night Prediction with Object Recognition (NVDANOR) Model;gender Detection Using Voice Through Deep learning;facial Recognition Technologies: A Survey and Comparison of Systems and Practical Applications;AI-Based Syntactic Complexity Metrics and Sight Interpreting Performance;A Novel Metric of Continuous Situational Awareness Monitoring (CSAM) for Multi-telepresence Coordination System;methods for Determining the Optimal Sampling Step of Signals in the Process of Device and Computer Integration;algorithms and Service for Digital Processing of Two-Dimensional Geophysical Fields Using Octave Method;Performance Analysis of the IBM Cloud Quantum computing Lab Against MacBook Pro 2019;determination of Dimensions of Complex Geometric Objects with Fractal Structure;preface.

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Detection of Malicious Node in VANETs Using Digital Twin

Detection of Malicious Node in VANETs Using Digital Twin

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International conference on Big Data Intelligence and computing, DataCom 2022

作者： Arya, Varsha Gaurav, Akshat Gupta, Brij B. Hsu, Ching-Hsien Baghban, Hojjat Insights2Techinfo Jaipur India Ronin Institute MontclairNJ07043 United States International Center for AI and Cyber Security Research and Innovation and Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Lebanese American University Beirut1102 Lebanon Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan

ISBN: (纸本)9789819922321

Vehicular ad-hoc network (VANET) plays an essential role in helping the development of smart cars and intelligent transportation systems. By easing congestion and speeding up data transfer, VANET makes it possible for connected cars to function more smoothly. However, VANETs are affected by different types of cyber attacks, such as distributed denial of services (DDoS) attacks. A digital twin (DT) is a replica of a physical system that operates in tandem with the actual thing, allowing for continuous monitoring and management. The DT prepares the way for the monitoring of a physical entity on a regular basis and for its automated management. The improved efficiency in keeping tabs on the physical world is largely attributable to DT. For this reason, academics are advocating for its use in a variety of settings. In this research, we use DT to solve the problem of identifying and stopping malignant nodes on a VANET infrastructure. In this paper, we proposed a framework that uses the concepts of DT for the identification of malignant nodes. Our suggested approach employs machine learning to distinguish between regular traffic and attack traffic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicular ad hoc networks

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4th International conference on Soft computing and its Engineering Applications, icSoftComp 2022

4th International Conference on Soft Computing and its Engin...

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4th International conference on Soft computing and its Engineering Applications, icSoftComp 2022

ISBN: (纸本)9783031276088

The proceedings contain 36 papers. The special focus in this conference is on Soft computing and its Engineering Applications. The topics include: Explainable AI for Predictive Analytics on Employee Attrition;graph Convolutional Neural Networks for Nuclei Segmentation from Histopathology Images;Performance Analysis of Cache Memory in CPU;extraction of Single Chemical Structure from Handwritten Complex Chemical Structure with Junction Point Based Segmentation;automatic Mapping of Deciduous and Evergreen Forest by Using machine learning and Satellite Imagery;RIN: Towards a Semantic Rigorous Interpretable Artificial Immune System for Intrusion Detection;SVRCI: An Approach for Semantically Driven Video Recommendation Incorporating Collective Intelligence;deep learning Based Model for Fundus Retinal Image Classification;one True Pairing: Evaluating Effective Language Pairings for Fake News Detection Employing Zero-Shot Cross-Lingual Transfer;reliable Network-Packet Binary Classification;semKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization;extraction and Analysis of Speech Emotion Features Using Hybrid Punjabi Audio Dataset;human Activity Recognition in Videos Using Deep learning;Brain Tumor Classification Using VGG-16 and MobileNetV2 Deep learning Techniques on Magnetic Resonance Images (MRI);five-Year Life Expectancy Prediction of Prostate Cancer Patients Using machine learning algorithms;an Ensemble MultiLabel Classifier for Intra-Cranial Haemorrhage Detection from Large, Heterogeneous and Imbalanced Database;A Method for Workflow Segmentation and Action Prediction from Video Data - AR Content;convolutional Neural Network Approach for Iris Segmentation;reinforcement learning algorithms for Effective Resource Management in Cloud computing;FedCLUS: Federated Clustering from Distributed Homogeneous Data;corpus Building for Hate Speech Detection of Gujarati Language;a Software

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machine learning Combined with Thresholding - A Blended Approach to Potholes Detection

Machine Learning Combined with Thresholding - A Blended Appr...

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Advancements in Smart, Secure and Intelligent computing (ASSIC), International conference on

作者： Noor Jehan Ashaari Muhamad Muhammad Ehsan Rana Vazeerudeen Abdul Hameed Hrudaya Kumar Tripathy School of Computing Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

Potholes are a regular occurrence that can cause discomfort and harm everyday road users. In recent times many studies have been done on automated pothole detection as there is a need to assess the road condition in a more affordable and timely manner. This research aims to explore the different motion-based approaches used in pothole detection. Motion sensors such as accelerometers and gyroscopes are commonly utilised to acquire movement information, and these data can be used not only to detect the presence of potholes but also have been used to classify general road conditions. It has been found that the approaches can be divided into two categories: threshold-based and machine learning. For both approaches, statistical features are extracted from the motion data and used in determining the threshold values or as inputs to train the classifier models. Further opportunities for improvement in data labelling and the need to classify pothole severity levels using a standard metric are also discussed in the paper.

关键词： Vibrations Roads Support vector machine classification machine learning Transforms Feature extraction Labeling

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A Review of machine learning Applications for Li-Ion Battery State Estimation in Electric Vehicles

A Review of Machine Learning Applications for Li-Ion Battery...

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IEEE Innovative Smart Grid Technologies - asia (ISGT asia)

作者： Wesley Qi Tong Poh Yan Xu Robert Thiam Poh Tan School of Electrical & Electronic Engineering Nanyang Technological University Singapore Department of Automotive System Engineering Infineon Technologies AP Pte Ltd Singapore

ISBN: (纸本)9798350399677

The advent of machine learning (ML) has led to an exponential rise in the exploration of new methods to estimate the states of lithium-ion batteries (LIBs) in electric vehicle (EV) applications. Data-driven methods involving ML are increasingly engaged to estimate the state-of-charge (SOC) and state-of-health (SOH) due to greater availability of battery datasets in the public domain; alongside improvements in computing system efficiency. At present, the battery management system (BMS) of EVs tend to face challenges in attaining highly accurate state estimation results using traditional methods, since the LIBs possess strong time-varying and non-linear traits, whilst remaining susceptible to the influence of external factors. Therefore, this paper provides a comprehensive review of the existing ML methods in SOC and SOH estimation of EV application-based LIBs. The insights gained from this review will contribute towards the future development of advanced LIB state estimation algorithms.

关键词： Lithium-ion batteries Training Support vector machines Maximum likelihood estimation asia machine learning Electric vehicles

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A Comprehensive Review on Epilepsy Diagnosis and Prognosis using machine learning and Deep learning Approaches

A Comprehensive Review on Epilepsy Diagnosis and Prognosis u...

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IEEE International WIE conference on Electrical and Computer Engineering (WIECON-ECE)

作者： Oishika Khair Esha Nazmul Haque Foysal Ahmed Nasima Begum Department of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh

Epilepsy is an illness that affects the nervous system and may lead to abnormal behavior, sensations, and even loss of consciousness in certain people. When someone is having a seizure, abnormal patterns of brain activity might be seen. About 50 million individuals all over the globe suffer from epilepsy 1 . It is essential to begin treatment as soon as possible after an epileptic seizure, and it is also essential to diagnose or anticipate an approaching seizure as early as possible. This will allow for a reduction in the intensity and frequency of any potential bad effects. The rapidly decreasing cost of noninvasive EEG collecting equipment as well as the rapid development of machine learning algorithms have fueled passionate debate in this field of study. This paper investigates the methods that have been applied to the CHB-MIT scalp EEG database of epileptic signals, as well as a number of machine learning and deep learning algorithms that have been developed over the last several years and are based on a variety of characteristics and classifiers. The results of this study will help to choose the best machine learning or deep learning models for predicting epilepsy.

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1st International conference on Emerging Technology Trends in Internet of Things and computing, TIOTC 2021

1st International Conference on Emerging Technology Trends i...

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1st International conference on Emerging Technology Trends in Internet of Things and computing, TIOTC 2021

ISBN: (纸本)9783030972547

The proceedings contain 26 papers. The special focus in this conference is on Emerging Technology Trends in Internet of Things and computing. The topics include: A Lightweight Algorithm to Protect the Web of Things in IOT;Proof of Good Service Based on DAGs-To-Blockchain for IoT Applications;approaches for Forgery Detection of Documents in Digital Forensics: A Review;novel Approximation Booths Multipliers for Error Recovery of Data-Driven Using machine learning;A Spectrum Sensing Profile Based SDR for Cognitive Radio System: An Experimental Work;named Data Networking Mobility: A Survey;Integration Femtocells Based on Hybrid Beamforming with Existing LTE Macro-cell for Improving Throughput Towards 5G Networks;cardiac Arrhythmia Diagnosis via Multichannel Independent Component Analysis: An Approach Towards a Better Health Care System;an Intelligent Flood Alert System Using LoRa on Basis of Internet of Things;investigation on the Impact of Video Games on People Who Use New Technology;An Improved Video Coding Model for Future IMT Requirements;eye Gaze Based Model for Anxiety Detection of Engineering Students;Automated Brain Tumor Segmentation and Classification Through MRI Images;the Efficiency of Classification Techniques in Predicting Anemia Among Children: A Comparative Study;brain Tumours Classification Using Support Vector machines Based on Feature Selection by Binary Cat Swarm Optimization;a New Secret Sharing Scheme Based on Hermite Interpolation and Folded Magic Cube Rules;training Adaptive Neuro Fuzzy Inference System Using Genetic algorithms for Predicting Labor Productivity;Applying an Efficient AI Approach for the Prediction of Bearing Capacity of Shallow Foundations;preface.

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1st International conference of IoT and its Applications, ICIA2020

1st International Conference of IoT and its Applications, IC...

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1st International conference of IoT and its Applications, ICIA2020

ISBN: (纸本)9789811676369

The proceedings contain 41 papers. The special focus in this conference is on IoT and its Applications. The topics include: Industrial IoT: Development of Smart Cooler for Solder Paste Storage and Management;applications and Challenges in Internet of Vehicles: A Survey;effective View of Swimming Pool Using Autodesk 3ds Max: 3D Modelling and Rendering;secure Outsourcing of Image Editing Based on Homomorphic Encryption;survey on Botnet Detection Techniques;security of Cloud computing Using Quantum Zero-Knowledge Proof System;intrusion Detection System Performance Comparison Using Dimensionality Reduction Techniques;A Compact ZOR Antenna with Defective Ground for Wireless Data Transmission and Short-Range Radar Applications;performance Analysis of Consensus-Based Time Synchronization algorithms for Wireless Sensor Network;analysis of Energy Harvesting Techniques for Wireless Sensor Networks Deployment Scenarios;a General Data Retrieval Technique in Remote Healthcare Application;Malaria Detection Using VGG19 and Deep Convolutional Neural Network;brain Tumor Detection: A Review of Early Stage Tumor Detection Techniques;supervised machine learning Approaches for Attack Detection in the IoT Network;Feature Extraction and Classification of ECG Signals Through Dimension Reduction;regular Self-Health Monitoring and Medicine Reminder System with Emergency Alert Messaging Using IoT;a Comparative Analysis of Network Intrusion Detection System for IoT Using machine learning;Dissected Scene Character Recognition Using HOG Descriptors;students Performance Prediction Using Educational Data Mining;design of Intelligent Transportation System for Smart City;container-Based Lab-as-a-Service Application;an IoT-Based Smart Garbage Segregation System Using Deep learning;blockchain-Based Access Control Model for IoT Applications;implementation of IoT-Based Smart Healthcare Monitoring System;preface.

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4th International conference on Networking, Intelligent Systems and Security, NISS 2021

4th International Conference on Networking, Intelligent Syst...

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4th International conference on Networking, Intelligent Systems and Security, NISS 2021

ISBN: (纸本)9789811636363

The proceedings contain 62 papers. The special focus in this conference is on Networking, Intelligent Systems and Security. The topics include: Channel Estimation in Massive MIMO Systems for Spatially Correlated Channels with Pilot Contamination;On Channel Estimation of Uplink TDD Massive MIMO Systems Through Different Pilot Structures;NarrowBand-IoT and eMTC Towards Massive MTC: Performance Evaluation and Comparison for 5G mMTC;integrating Business Intelligence with Cloud computing: State of the Art and Fundamental Concepts;distributed Architecture for Interoperable Signaling Interlocking;A New Design of an Ant Colony Optimization (ACO) Algorithm for Optimization of Ad Hoc Network;real-Time Distributed Pipeline Architecture for Pedestrians’ Trajectories;Reconfiguration of the Radial Distribution for Multiple DGs by Using an Improved PSO;on the Performance of 5G Narrow-Band Internet of Things for Industrial Applications;The Evolution of the Traffic Congestion Prediction and AI Application;a Novel Design of Frequency Reconfigurable Antenna for 5G Mobile Phones;a Real-Time Smart Agent for Network Traffic Profiling and Intrusion Detection Based on Combined machine learning algorithms;privacy Threat Modeling in Personalized Search Systems;enhanced Intrusion Detection System Based on AutoEncoder Network and Support Vector machine;comparative Study of Keccak and Blake2 Hash Functions;cryptography Over the Twisted Hessian Curve Ha,d3;Method for Designing Countermeasures for Crypto-Ransomware Based on the NIST CSF;comparative Study Between Network Layer Attacks in Mobile Ad Hoc Networks;security of Deep learning Models in 5G Networks: Proposition of Security Assessment Process;effects of Jamming Attack on the Internet of Things;image-Based Malware Classification Using Multi-layer Perceptron.

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