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6th International conference on Big Data Cloud and Internet of Things, BDIoT 2022

6th International Conference on Big Data Cloud and Internet ...

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6th International conference on Big Data Cloud and Internet of Things, BDIoT 2022

ISBN: (纸本)9783031283864

The proceedings contain 49 papers. The special focus in this conference is on Big Data Cloud and Internet of Things. The topics include: Knowledge Embeddings for Explainable Recommendation;Design of Blended learning Course Based on SPOC for Primary School Teachers: Case of Soft Skills;the Impact of the Online Flipped Classroom on the learning Outcomes and Motivation of Nursing Students in Computer Science;digitizing Writing: An Empirical Exploratory Study Towards Students’ Writing Skill Fluency Improvement in ‘Padletosphere’;AraBERT with GANs for High Performance Fine-Grained Dialect Classification;genetic-Novelty Oversampling Technique for Imbalanced Data;neural Network for Arabic Text Diacritization on a New Dataset;processing Time Performance Analysis of Scheduling algorithms for Virtual machines Placement in Cloud computing Environment;Business - IT Alignment in Cloud Environment Comparative Study;machine learning-Based Intrusion Detection System: Review and Taxonomy;reduce Cheating in e-Exams Using machine learning: State of the Art;comparative Study of Bayesian Optimization Process for the Best machine learning Hyperparameters;The Effect of Normalization and Batch Normalization Layers in CNNs Models: Application to Plant Disease Classifications;the Role of Ground Truth Annotation in Semantic Image Segmentation Performance for Autonomous Driving;fine-Tuning Pre-trained Vision Transformer Model for Anomaly Detection in Video Sequences;An Efficient Real-Time Moroccan Automatic License Plate Recognition System Based on the YOLO Object Detector;evaluation of Selected machine learning Models and Features for Electrical Consumption Prediction in Educational Institutions;Credit Card Fraud Detection Using SVM, Decision Tree and Random Forest Supervised machine learning algorithms;SNDAE: Self-Normalizing Deep AutoEncoder for Recommendation;Intelligent Evaluation System Using NLP;retrospective Study and Evaluation of School Failure (Junior High School) in Scientific Subj

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International conference on Advances in Data Science and computing Technologies, ASDC 2022

International Conference on Advances in Data Science and Com...

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International conference on Advances in Data Science and computing Technologies, ASDC 2022

ISBN: (纸本)9789819936557

The proceedings contain 77 papers. The special focus in this conference is on International conference on Advances in Data Science and computing Technologies. The topics include: Effect of Dropout on Convolutional Neural Network for Hyperspectral Image Classification;state Derivative Optimal Control Law for Submersible Autonomous Robotic Vehicle in Steering Plane;ASR for Indian Regional Languages Using Fine-Tuned Wav2Vec2 Model;Intelligent Online Voting System for Twenty-First Century and Smart Cities 5.0: An Empirical Approach through Blockchain with ML Techniques;an Empirical Study on Credit Risk Assessment Using Ensemble Classifiers;audio Based Text Summarization Using Natural Language Processing;blockchain-Enabled Security in Vehicular Ad Hoc Network;stock Selection Using Ontological Financial Analysis;Prediction of Future Career Course of Students Through RF Algorithm;components of Information Diffusion and Its Models in Online Social Networks;a Comparative Study;development of a Residual Neural Network Architecture for the Detection of Diabetic Retinopathy in Retinal Fundus Images;prediction Markets Using machine learning;evaluation of Optimum Batting Order Based on Partnership Analysis Using Ant Colony Optimization Technique;identification of Rock Images in Mining Industry: An Application of Deep learning Technique;evaluation of Teachers’ Performance Using Fuzzy Inferences System;a Detailed Analysis on Intrusion Detection Systems, Datasets, and Challenges;object Detection Under Low-Lighting Conditions Using Deep learning Architectures: A Comparative Study;terahertz Image Processing: A Boon to the Imaging Technology;developing a Talent Identification Model for Predicting Player Position in Football Using machine learning algorithms;Survey of Various machine learning Techniques for Analyzing IoMT-Based Remote Patient Monitoring System;PSO-Based Traffic Signals in a Real-World City.

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Classification of Tumor Metastasis Data by Using Quantum kernel-based algorithms 22

Classification of Tumor Metastasis Data by Using Quantum ker...

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IEEE 22nd International conference on Bioinformatics and Bioengineering (BIBE)

作者： Li, Tai-Yue Mekala, Venugopala Reddy Ng, Ka-Lok Su, Cheng-Fang Natl Synchrotron Radiat Res Ctr Hsinchu Taiwan Asia Univ Dept Bioinformat & Med Engn Taichung Taiwan China Med Univ Dept Med Res Taichung Taiwan Natl Yang Ming & Chiao Tung Univ Dept Appl Math Hsinchu Taiwan

ISBN: (数字)9781665484879

ISBN: (纸本)9781665484879

Tumor metastasis is a dynamic process, and its fatality rate is relatively high. Different genes are regulated during the metastasis process. Next-generation sequencing technology is a new approach that enables rapid and highthroughput whole-transcriptome measurements. Support vector algorithms make use ofkernel functions to classify data effectively. A few studies suggest that quantum support vector machine algorithms can perform well in classification problems. If biomarkers can be identified to predict tumor metastasis accurately, it will be an important step toward precision medicine. In this study, we use both the SVM and QSVM classifiers with the addition of a certain number of features, we can achieve very good distinctions between patients with or without metastasis. This is a positive result for precision medicine studies. Also, we evaluate the performance of quantum and classical algorithms in classifying tumor metastasis data. Our preliminary study indicates that the classical kernel-based classifier performs better than the quantum version.

关键词： tunwr metastasis biomarkers preciSion medicine quantum kernel-based classifier quantum machine learning

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International conference on machine learning and Big Data Analytics, ICMLBDA 2022

International Conference on Machine Learning and Big Data An...

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International conference on machine learning and Big Data Analytics, ICMLBDA 2022

ISBN: (纸本)9783031151743

The proceedings contain 44 papers. The special focus in this conference is on machine learning and Big Data Analytics. The topics include: Bilingual Documents Text Lines Extraction Using Conditional GANs;Performance Comparison of YOLO Variants for Object Detection in Drone-Based Imagery;a Microservice Architecture with Load Balancing Mechanism in Cloud Environment;an IoT Application for Detection and Monitoring of Manhole;resource Allocation in 5G and Beyond Edge-Slice Networking Using Deep Reinforcement learning;preface;a Comprehensive Analysis on Mobile Edge computing: Joint Offloading and Resource Allocation Perspective;the Important Influencing Factors in machine Translation;damaged Units Return Investigation in Printer-Producing Industry Utilizing Big Data;colorization of Grayscale Images: An Overview;evolutionary Approaches Toward Traditional to Deep learning-Based Chatbot;analysis of machine learning algorithms for Detection of Cyberbullying on Social Networks;sentiment Analysis of Political Tweets for Israel Using machine learning;a Novel Approach for Real-Time Vehicle Re-identification Using Content-Based Image Retrieval with Relevance Feedback;Extractive and Abstractive Text Summarization Model Fine-Tuned Based on BERTSUM and Bio-BERT on COVID-19 Open Research Articles;RevCode for NLP in Indian Languages;Application for Mood Detection of Students Using TensorFlow and Electron JS;finding Significant Project Issues with machine learning;Using CNN Technique and Webcam to Identify Face Mask Violation;a Review on Internet of Things-Based Cloud Architecture and Its Application;prediction of Maneuvering Status for Aerial Vehicles Using Supervised learning Methods;HRescue: A Modern ML Approach for Employee Attrition Prediction;using machine learning to Detect Botnets in Network Traffic.

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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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2022 IEEE International Women in Engineering (WIE) conference on Electrical and Computer Engineering, WIECON-ECE 2022

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

ISBN: (纸本)9798350311563

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. © 2022 IEEE.

关键词： Electroencephalography

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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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2022 International conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2022

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

ISBN: (纸本)9781665461092

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. © 2022 IEEE.

关键词： machine learning

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Sonic Waves Travel-time Prediction: When machine learning Meets Geophysics

Sonic Waves Travel-time Prediction: When Machine Learning Me...

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International conference on Green Energy, computing and Sustainable Technology (GECOST)

作者： Wong, W. K. Nuwara, Yohanes Juwono, Filbert H. Motalebi, Foad Curtin Univ Malaysia Dept Elect & Comp Eng Miri Malaysia Asia Pulp & Paper Sinarmas Corp Informat Technol Div Jakarta Indonesia Univ Southampton Malaysia Comp Sci Program Johor Baharu Malaysia

ISBN: (纸本)9781665486637

Sonic wave travel-time prediction is an important task in oil and gas exploration as it provides important information on the content and lithography of the rocks. Travel-time data, however, are not always accessible due to practical considerations. Currently, machine learning methods have been used to infer these values. In this paper, we look at the application of machine learning in predicting sonic wave travel-time, specifically in terms of challenges, benchmarks, and datasets. In addition, we present some preliminary results of sonic wave travel-time prediction using existing machine learning regression methods, namely curve fitting artificial neural network and multiple linear regression. Finally, this paper is aimed to act as a "bridge" between machine learning practitioners and domain-specific oil and gas engineers.

关键词： Sonic wave lithography ANN MLR

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2022 2nd International conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

2022 2nd International Conference on Advance Computing and I...

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2nd International conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

ISBN: (纸本)9781665437899

The proceedings contain 516 papers. The topics discussed include: data augmentation based on 3d model data for machine learning;transaction item embedding by maximizing a joint probability;book genre classification based on titles with comparative machine learning algorithms;sentiment analysis using machine learning classifiers: evaluation of performance;performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach;study on android hybrid malware detection based on machine learning;joint cognition of both human and machine for predicting criminal punishment in judicial system;carbon nanotube coordinate prediction with deep learning;a face detection framework based on deep cascaded full convolutional neural networks;a joint deep neural network model for pain recognition from face;optimal input selection for recurrent neural network in predictive maintenance;and automated fingerlings counting using convolutional neural network.

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1st EAI International conference on Intelligent Systems and machine learning, ICISML 2022

1st EAI International Conference on Intelligent Systems and ...

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1st EAI International conference on Intelligent Systems and machine learning, ICISML 2022

ISBN: (纸本)9783031350771

The proceedings contain 75 papers. The special focus in this conference is on Intelligent Systems and machine learning. The topics include: Mining Ancient Medicine Texts Towards an Ontology of Remedies – A Semi-automatic Approach;a Novel Oversampling Technique for Imbalanced Credit Scoring Datasets;a Blockchain Enabled Medical Tourism Ecosystem;Measuring the Impact of Oil Revenues on Government Debt in Selected Countries by Using ARDL Model;diagnosis of Plant Diseases by Image Processing Model for Sustainable Solutions;face Mask Detection: An Application of Artificial Intelligence;a Critical Review of Faults in Cloud computing: Types, Detection, and Mitigation Schemes;video Content Analysis Using Deep learning Methods;prediction of Cochlear Disorders Using Face Tilt Estimation and Audiology Data;F2PMSMD: Design of a Fusion Model to Identify Fake Profiles from Multimodal Social Media Datasets;quantum Data Management and Quantum machine learning for Data Management: State-of-the-Art and Open Challenges;multivariate Analysis and Comparison of machine learning algorithms: A Case Study of Cereals of America;competitive Programming Vestige Using machine learning;machine learning Techniques for Aspect Analysis of Employee Attrition;AI-Enabled Automation Solution for Utilization Management in Healthcare Insurance;Real-Time Identification of Medical Equipment Using Deep CNN and Computer Vision;design of a Intelligent Crutch Tool for Elders;an Approach to New Technical Solutions in Resource Allocation Based on Artificial Intelligence;gesture Controlled Power Window Using Deep learning;novel Deep learning Techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition;a Novel Model to Predict the Whack of Pandemics on the International Rankings of Academia.

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OSINT and AI: A Powerful Combination for Company Vulnerability Detection 21

OSINT and AI: A Powerful Combination for Company Vulnerabili...

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21st IEEE Student conference on Research and Development, SCOReD 2023

作者： Bizouarn, Kenan Malween Abdulnabi, Mohamed Tan, Joco Asia Pacific University of Technology and Innovation School of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350318821

Cybersecurity caused major concerns for organizations that protect critical data from cyber-attacks. Vulnerability scanning is a critical step as part of the proactive approach, as it allows the identification of potential vulnerabilities in the infrastructure before malicious attackers exploit them. This paper outlines the development of a tool for open-source intelligence (OSINT) designed to collect information from openly accessible sources efficiently. The collected information will then be formatted and sorted with clustering algorithms. machine learning will be employed to detect potential vulnerabilities in the collected data. The vulnerability results can be presented in a report to the organizations to ease the concern. The aim is to increase awareness of how the OSINT could detect vulnerabilities and improve cybersecurity postures. This study aims to contribute to the existing knowledge base on vulnerability detection, OSINT applications in IT security, and combining new IT security detection solutions like machine learning. © 2023 IEEE.

关键词： machine learning

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