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3rd international conference on machine learning, Internet of Things and Big data, ICMIB 2023

3rd International Conference on Machine Learning, Internet o...

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3rd international conference on machine learning, Internet of Things and Big data, ICMIB 2023

ISBN: (纸本)9789819939312

The proceedings contain 52 papers. The special focus in this conference is on machine learning, Internet of Things and Big data. The topics include: Smart Skin-Proto: A Mobile Skin Disorders Recognizer Model;machine learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks;real Time Air-Writing and recognition of Tamil Alphabets Using Deep learning;a Fuzzy Logic Based Trust Evaluation Model for IoT;supervised learning Approaches on the Prediction of Diabetic Disease in Healthcare;solar Powered Smart Home Automation and Smart Health Monitoring with IoT;seasonal-Wise Occupational Accident Analysis Using Deep learning Paradigms;MLFP: machine learning Approaches for Flood Prediction in Odisha State;vision-Based Cyclist Travel Lane and Helmet Detection;machine learning Algorithms Aided Disease Diagnosis and Prediction of Grape Leaf;design and Experimental Analysis of Spur Gear–A Multi-objective Approach;Chest X-Ray Image Classification for COVID-19 Detection Using Various Feature Extraction Techniques;Computer Vision and Image Segmentation: LBW Automation Technique;a Mixed Collaborative Recommender System Using Singular Value Decomposition and Item Similarity;hybrid Clustering-Based Fast Support Vector machine Model for Heart Disease Prediction;forecasting and Analysing Time Series data Using Deep learning;intelligent Blockchain: Use of Blockchain and machine learning Algorithm for Smart Contract and Smart Bidding;Weed Detection in Cotton Production Systems Using Novel YOLOv7-X Object Detector;smart Healthcare System Management Using IoT and machine learning Techniques;Automatic Code Clone Detection Technique Using SDG;Optimized Fuzzy PI Regulator for Frequency Regulation of Distributed Power System;simulated Design of an Autonomous Multi-terrain Modular Agri-bot;customer Segmentation Analysis Using Clustering Algorithms.

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Deep learning Models for Aspect-Based Sentiment Analysis Task: A Survey Paper 3rd

Deep Learning Models for Aspect-Based Sentiment Analysis Tas...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Hammi, Sarsabene Hammami, Souha Mezghani Belguith, Lamia Hadrich ANLP Research Group MIRACL Lab FSEGS University of Sfax Sfax Tunisia

ISBN: (纸本)9783031463372

Due to the significant increase in the volume of data shared on the web, Aspect-Based Sentiment Analysis (ABSA) has become essential. This task ensures a detailed sentiment analysis. It identifies firstly the aspect terms (e.g., price, food, etc.) and then classifies their sentiment polarity as positive, negative, or neutral. Many approaches have been used to treat this task including the machine learning-based approach, the rule-based approach, etc. However, with the important increase in the content of the internet, these approaches became relatively unable to analyze this volume of information, resulting in the emergence of the deep learning-based approach which is the subfield of the machine learning-based approach. Recently many researchers used the deep learning-based approach to address the ABSA. This paper provides a summary of the deep learning models that have been developed for ABSA, as well as a survey of studies that have employed these models to address different subtasks of the ABSA task. Finally, we discuss the implications of our work and potential avenues for future research. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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ML_recognition: A Robust Model for Handwritten Digit recognition Using machine learning Classifiers 3

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Ibrahim Abd Elatif Gomaa Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%. © 2023 IEEE.

关键词： Support vector machines

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Design and Implementation of an English Teaching System Based on Virtual Environment Technology 3rd

Design and Implementation of an English Teaching System Base...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Ying, Sun Yao, Jiang Qi, Hongda Gongqing College Nanchang University Jiangxi Gongqing Jiujiang China Jingdezhen Vocational University of Art Jingdezhen China Nanchang Vocational University Jiangxi Nanchang330500 China

ISBN: (纸本)9783031631290

For most non computer professionals and some computer professionals, data mining and machine learning are two advanced fields. In the author’s opinion, this may be a habitual misconception, rather than an added aloof of attributive shere. This article applies data mining technology to an English online learning platform, analyzes learners and learning content through clustering algorithms and association rules, and develops a donetics algorithm based on a content organization system. In this example, we use this algorithm to provide personalized learning content for learners, hoping that this system can be extended to online learning platforms in the future. The concept of data driven alerts (DDL) has attracted the attention of all researchers. data driven English machines are an inevitable trend to improve college students’ English learning efficiency and autonomous learning ability. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： data mining

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Chronic Kidney Disease Analysis using Various Biological Parameters under machine learning Techniques 3

Chronic Kidney Disease Analysis using Various Biological Par...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Suresh, M. Nirmala, E. Maragatharajan, M. Yadav, Manish School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Kotri Kalan India School of Computing Science engineering and Artificial Intelligence VIT Bhopal University Madhya Pradesh Kotri Kalan India

ISBN: (纸本)9798331532420

Kidney chronic disease (KCD) has become one of the most critical illnesses which should be diagnosed properly within a certain limit of time for better treatment. Nowadays different algorithms being used for treatment of patients. By using the best predictory model, doctor can easily predict the disease on time. For this verdict, Kidney Chronic Disease Prediction has been discussed in this article. In this article various datasets have been used by the different article authors to predict the disease which are pre-processed with different methods like Univariate Feature Selection (UFS) and Recursive Feature Elimination (RFE), data mining techniques, etc. which are further used to train and test various ML models get the accuracy by predictory models. The scores gained by the predictory models is then validated to check the precision of the output accuracy of by the model using methods like performance matrices, various cross validation techniques, Mathew Correlation Coefficient (MCC). This whole article is all around the analyses of different articles based on the machine learning and validation techniques with respect to the accuracy. Most of the models attained the accuracy of 99 percent and above. In this article, along with machine learning models it also discussed about two neural networks, which attained the accuracy between 93 to 98% with a good precision value. © 2024 IEEE.

关键词： Adversarial machine learning

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on machine Vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI Images Using Deep learning;software Maintenance Prediction Using Stack Ensemble Deep learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar Signal Processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare data;multimodal Face recognition System Using Hybrid Deep learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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2023 3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

2023 3rd International Conference on Advances in Electrical,...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

ISBN: (纸本)9781665494007

The proceedings contain 204 papers. The topics discussed include: a Bengali word identification and verification using machine learning approach;a framework for big data adoption and sustainable institutional performance;conceptual design of an automated irrigation and fixation system for long-term crop care of cherry tomatoes for applications in microgravity machines;comparative study of deep learning models for COVID 19 diagnosis;improved double-level blending model (IDLBM): customer churn estimating in insurance industry;a comparative survey of maize leaf diseases using pre-trained convolutional neural networks;chromatic partitioning of a network and an optimum scheduling problem;comparison of performance of machine learning algorithms for diabetes detection;fine - grained sentiment analysis on online reviews;an approach to predict and classify retinopathy stages in diabetic patients using deep learning;intelligent recognition of ancient Brahmi characters using transfer learning;and comparative study and performance analysis of deep neural networks for sign language recognition using transfer learning.

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Clothing material classification-based deep learning 3

Clothing material classification-based deep learning

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3rd international conference on Electronic Information Engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Fan, Feiqiang Zhang, Diming Jiangsu University of Science and Technology Jiangsu Zhenjiang212100 China

ISBN: (纸本)9781510680449

Aiming at the problems of poor classification and recognition of traditional clothing images and low recognition accuracy, this paper proposes a convolutional neural network clothing material classification and recognition method based on the improved deep residual network (ResNet). By introducing an attention mechanism module in each residual block, a clothing material image classification and recognition model based on improved ResNet is constructed. In order to better carry out the related research, a self-constructed dataset specifically used for clothing material classification and recognition. In order to verify the superiority of the improved network in clothing material recognition, the traditional machine learning methods and the classical neural networks in deep learning are explored respectively, and the parameters of the network are adjusted to make it more suitable for clothing material recognition. The improved model is compared with other models. The experimental results show that the performance of the clothing material recognition network based on the improved ResNet network is superior (99.2% recognition accuracy, 98.46%precision, 98.83%recall, and 0.9864 F1_Score), and it can satisfy the needs of commercial networks for recognizing clothing materials © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image classification

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machine learning and data mining 1

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3rd international conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2023

作者： Kurasov, Dmitry A. Kutuzov, Anton S. Zvonarev, Dmitry S. Devyatkov, Anton P. University of Tyumen 6 Street Volodarskogo Tyumen625003 Russia Chelyabinsk State University 129 Street Kashirin Brothers Chelyabinsk454001 Russia

ISBN: (数字)9783031603181

ISBN: (纸本)9783031603174

The article discusses the main tasks of machine learning. The functional structure of a computer algorithm for solving machine learning problems and a data mining model are considered. The solution of the simplest machine learning problem using the classification method of linear regression is proposed. The analysis of existing learning algorithms based on a decision tree is carried out. Based on the analysis performed, a decision tree was selected for implementation using the so-called C4.5 algorithm. The article builds a decision tree using specific training data. The application of a simple and understandable algorithm for building trees is to create all possible trees, calculate the number of erroneously classified data for each of them and select a tree with a minimum number of errors. As a result, an optimal learning algorithm is formed for the decision tree in terms of data errors in the learning process. The top-down algorithm implemented in the article for building a decision tree selects the attribute with the largest increase in information at each step. Entropy is used as a metric of the amount of information in the training data set D. In the process of implementation, this algorithm is analyzed to identify opportunities for its application to a more complex task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Entropy

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Research on expression recognition based on deep learning in classroom learning situation analysis 3

Research on expression recognition based on deep learning in...

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3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Lu, Na Luo, Yanling Wuhan Technology And Business University Hubei Wuhan430065 China

ISBN: (纸本)9781510661363

With the rapid development of artificial intelligence technology, the automatic recognition of students' learning state and emotion by target detection and expression recognition technology has attracted more and more attention. In order to solve the problem that the detection accuracy and speed can not be considered in the application, an intelligent lightweight classroom learning situation analysis solution is proposed. Firstly, face is recognized by face detection. By designing a small convolution kernel for continuous convolution and extracting the key feature points of the face in parallel, five kinds of learning expression intensity are output. Finally, the learning emotion score of the weighted sum of the students' overall head up rate obtained by face detection and the expression intensity obtained by expression recognition is used as the evaluation result of learning emotion analysis. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Face recognition

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