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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

3rd International Conference on Machine Intelligence and Sma...

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

ISBN: (纸本)9783031691140

The proceedings contain 59 papers. The special focus in this conference is on machine Intelligence and Smart Systems. The topics include: Chronic Kidney Disease Prediction and Interpretation Using Explainable AI;systematic Review and Analysis of Artificial Intelligence-Based Breast Cancer Classification and Detection;war of Tweets: Sentiment Analysis on Ukraine Russia Conflict;Implementing HRRN for Evaluating Cloud Performance Using Reinforcement learning;using machine learning for Prediction of Obstructions for Indoor Location Systems;privacy Threats and Protection in Artificial Intelligence and machine learning;Combining Linguistic Information with BERT for Span Based End-to-End Aspect Based Sentiment Analysis;a Dimensionality Reduction Model: A Retrospective Approach on Dementia Triggering Parameters and Feature Ranking;effective Identification of Lung Diseases Using Few-Shot learning;comparative Study on Classification Based-data mining Techniques in Early Diabetes Prediction;optimize machine learning Model for Sentiment Analysis of Online Education During Covid-19 Pandemic;review on the Challenges and Future Directions of Deep learning-Based Techniques for Advance Prediction of Cardiac Attack;different Techniques for Detecting Plant Leaf Disease Using machine learning;proposed Framework of Extensive Humanoid Design Cycle and Recent Developments in Bipedal Walk;natural Language Processing for Waste Management Using Public Opinions in Smart Cities;prediction of Diabetes During Pregnancy Through Fog Environment;empirical Wavelet Transform Grounded Poignant Ground Target recognition and Classification by Seismic Signal Processing;A Powered-Up Classification of Disabling Distributed Network Cloud-Based Attacks Using MLPNN-BP and MLPNN-LM;stroke Prediction Framework Based on Missing Value Information and Outlier Detection by Using machine learning Techniques in E-Healthcare;magnetic Resonance Imaging Digitization for Brain Abnormality recognition.

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A Survey on Various Representation learning of Hypergraph for Unsupervised Feature Selection 1

引用

3rd international conference on data, Engineering, and Applications, IDEA 2021

作者： Singh, Rana Pratap Ojha, Divyank Jadon, Kuldeep Singh Department of Computer Science and Engineering ITM Gwalior India

ISBN: (数字)9789811946875

ISBN: (纸本)9789811946868

Since multimedia technology is increasingly being developed, vast quantities of unlabeled data must be processed with a high dimension. Unsupervised feature selection considered to minimize the dimensionality of many machine learning and data mining activities is generally accepted as an important and challenging preliminary step. Feature selection is a big problem in machine learning and in recent years has been very important. Representation learning has become a field in itself in the community of machine learning, and both academia and industry are followed and feeding a substantial string of empirical successes by an exponential growth in research activity in the field of representation learning. Latent representation learning may help with multiple tasks in machine learning and data mining and also recently has become ever more drawn to network data particularly. A vertex set and hyperedge set are constructed of Hypergraph. In a hypergraph, each hyperedge can connect several vertices and easily extend the hyperedge, which contributes to a flexible hypergraph edge degree. In hypergraph learning, hypergraph construction is the first step for data modeling. In this survey paper, we have described various technologies like data mining, machine learning, and their types and also we deeply described feature selection methods and their types;classification methods explained the various learning methods such as hypergraph learning and Latent representation learning. Various optimization techniques are also discussed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Performance of machine learning Methods Using Tweets 1

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3rd international conference on Mathematics and its Applications in Science and Engineering, ICMASE 2022

作者： Tuğ, İlkay Kan-Kilinç, Betül Department of Statistics Faculty of Science Eskisehir Technical University Eskisehir26470 Turkey

ISBN: (数字)9783031217005

ISBN: (纸本)9783031216992

Public opinions shared in common platforms like Twitter, Facebook, Instagram, etc. act as the sources of information for experts. Transportation and analysis of such data is very important and difficult due to data regulations and its structure. The pre-processing approaches and word-based dictionaries are used to understand the unprocessed data and make possible the opinions/tweets to be analyzed. machine learning algorithms learn from past experience and use a variety of statistical, probabilistic and optimization algorithms to detect useful patterns from unstructured data sets. Our study aims to compare the performance of classification algorithms to predict individuals with COVID-19(+ ) or COVID-19(−) using the emotions among the tweets by text mining procedures. Logistic Regression (LR), Support Vector machine (SVM), Naive Bayes (NB), Decision Trees (DT), Random Forest (RF), Artificial Neural Networks (ANN), Gradient Boost (GBM) and XGradient algorithms were used to extract the accuracy of model performance of each model for the detection and identification of the disease related to the COVID-19 virus, which has been on the agenda recently. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Architectural Decorative Image pattern recognition in the Context of Iconography for Efficient Information mining

Architectural Decorative Image Pattern Recognition in the Co...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Yu Qin College of Art and Design Guangzhou Panyu Polytechnic Guangzhou City China

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

This study addresses the crucial task of architectural decorative image pattern recognition in the context of iconography, with an emphasis on efficient information mining. The proposed research work presents a novel algorithm that combines edge detection, ResNet50, and the triplet attention mechanism for image style analysis. The proposed methodology is organized into comprehensive sections, including a literature review that explores various studies in image pattern recognition. The proposed algorithm focuses on overcoming the challenges faced by existing deep learning methods in capturing aesthetic style features. It introduces a multifaceted self-supervised task and incorporates edge detection using the Canny method, followed by the application of ResNet50 with a triplet attention mechanism. The paper then focuses on the optimization of the network structure for the targeted task of architectural decorative image recognition, introducing a feature embedding encoder (FEE) to effectively handle multi-level structures. In the experimental phase, the proposed model is tested against traditional CNN and FCM models, demonstrating superior performance with recognition accuracy consistently ranging from 97% to 99%. The comparative analysis highlights the effectiveness of the proposed algorithm in achieving high accuracy, positioning it as a promising solution for image recognition tasks.

关键词： Deep learning Surveys Visualization Sentiment analysis Accuracy Attention mechanisms Image edge detection

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pattern mining of Smart Home User Behavior in The Context of The Internet of Things: based on Sensor Networks 3

Pattern Mining of Smart Home User Behavior in The Context of...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Nie, Bailing School of Network and Communication Nanjing Vocational College of Information Technology Jiangsu Nanjing210023 China

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

This research study is focused on analyzing the sensor networks based behavior recognition and incident response system of smart home users. The study has mainly considered the existing research results to build a basic incident response framework. The system structure is divided into three levels: situational awareness layer, situational management layer, and situational application layer. At the same time, a complete and practical information model is defined. Having a complete and used information model is the basis for the system to correctly perceive the state of the environment and understand user behavior. In this article, the user's behavior patterns and the meaning of events are defined, and a mechanism for mapping and transforming the original data to the information model is designed. © 2022 IEEE.

关键词： Metadata

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3rd international conference on Soft Computing and its Engineering Applications, icSoftComp 2021

3rd International Conference on Soft Computing and its Engin...

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3rd international conference on Soft Computing and its Engineering Applications, icSoftComp 2021

ISBN: (纸本)9783031057663

The proceedings contain 33 papers. The special focus in this conference is on Soft Computing and its Engineering Applications. The topics include: Sentiment Analysis of Twitter data Using machine learning Approaches;mining Spatio-Temporal Sequential patterns Using MapReduce Approach;a Split-Then-Join Lightweight Hybrid Majority Vote Classifier;identification of Barriers in Adoption of IoT: Commercial Complexes in India;a Dynamically Adapting Framework for Stock Price Prediction;evaluating Binary Classifiers with Word Embedding Techniques for Public Grievances;database Concentration Method for Efficient Image Retrieval Using Clustering and Image Tag Comparison;microstructure Image Classification of Metals Using Texture Features and machine learning;segregation of Areca Nuts Using Three Band Photometry and Deep Neural Network;Early Diagnosis of Alzheimer’s Disease from MRI Images Using Scattering Wavelet Transforms (SWT);constraint Pushing Multi-threshold Framework for High Utility Time Interval Sequential pattern mining;KTSVidRec: A Knowledge-Based Topic Centric Semantically Compliant Approach for Video Recommendation on the Web;intelligent Facial Expression Evaluation to Assess Mental Health Through Deep learning;intelligent Mobility: A Proposal for Modeling Traffic Lights Using Fuzzy Logic and IoT for Smart Cities;crop Disease Prediction Using Multiple Linear Regression Modelling;radial Basis Function Network Based Intelligent Scheme for Software Quality Prediction;generating the Base Map of Regions Using an Efficient Object Segmentation Technique in Satellite Images;KCEPS: Knowledge Centric Entity Population Scheme for Research Document Recommendation;weighted Hybrid Recommendation System Using Singular Value Decomposition and Cosine Similarity;CT/MRI 3D Fusion for Cerebral System Analysis;text Analysis and Classification for Preprocessing Phase of Automatic Text Summarization Systems;arabic Cyberbullying Detection from Imbalanced dataset Using machine learning;in

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Deep Transfer learning and Intelligent Item Packing in Retail Management 3rd

Deep Transfer Learning and Intelligent Item Packing in Retai...

引用

3rd international conference on machine learning, Image Processing, Network Security and data Sciences, MIND 2021

作者： Alodat, Mohammad Sur University College Sur440 411 Oman

ISBN: (纸本)9789811958670

The main aim of this research is to facilitate recognition of plant items products packing service and overcome the challenge of classifying human eye without relying on human memory, and therefore, to facilitate the management of the retail market and improve the shopping experience of con- sumers in the Sultanate of Oman. Deep transfer learning (DTL) was used from pre-trained network on ImageNet of convolutional neural networks (CNN) architecture through train and improve and using fine-tuning to be able to detect plant items products before they are packaged. The results of this study revealed that the best performance was chosen among five pre-trained networks, and it was MobileNetV2 model, which showed accuracy with (99%). Our model, it is pre-trained MobileNetV2 models and fine-tuned matched with the actual product, where all volunteer shoppers and their number six were able to rate except shopper #six (94%). Traditional packing vending system with the actual product was able to rate (72%). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Research on Sports Competition Result Prediction System Based on Computer Big data machine learning Model

Research on Sports Competition Result Prediction System Base...

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Electrical Engineering, Big data and Algorithms (EEBDA), IEEE international conference on

作者： Xiaolong Lu Ming Li Chao Ye Yunnan Vocational College of Sports Kunming Yunnan China Yunnan Youth Sports Training Center Kunming Yunnan China

Using big data theory to analyze athletes' performance data, this paper finds out the changing characteristics of athletes' performance. Using the principle of big data, we find the performance characteristics of athletes in the training process. The particle swarm optimization (PSO) method is used to predict the network parameters of support vector machine (SVM) online. The validity of this method is verified by testing on college students. This method can track athletes' performance characteristics more accurately, so as to build an excellent sports performance prediction model. The model can detect future trends from athletes' performance data in data mining and machine learning. This kind of system has a wide application prospect, and can greatly improve the efficiency and quality. The method combines machine learning theory with support vector machine network, which can effectively improve the prediction accuracy. This method has a very broad application prospect.

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Research on Application of Tobacco Company Supply Chain Management Based on data mining Technology 3

Research on Application of Tobacco Company Supply Chain Mana...

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Yan, Dazhu Kunming Tobacco Co Xishan Branch Kunming 650000 Yunnan Peoples R China

ISBN: (纸本)9781665417907

As the market economy becomes more diversified, competition among enterprises is increasing. As a technological product in the information age, data mining technology is applied to enterprise supply chain management to help improve management efficiency and enhance the core competitiveness of enterprises. In this regard, this article takes a tobacco company as an example, briefly explains the basic principles of data mining technology in the application of supply chain management, outlines the supply chain management process based on data mining technology, analyzes and explores the selection of partners based on fuzzy data mining, and aims to give Enterprise supply chain management better apply data mining technology to provide some useful references.

关键词： data mining Technology Tobacco Company Supply Chain Management

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3rd international Informatics and Software Engineering conference, IISEC 2022

3rd International Informatics and Software Engineering Confe...

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3rd international Informatics and Software Engineering conference, IISEC 2022

ISBN: (纸本)9781665459952

The proceedings contain 80 papers. The topics discussed include: a secure and intelligent smart home controlling system;mobile platform for livestock monitoring and inspection;feature extraction and dimensionality reduction of cancer data using folded LDA;intrusion detection in computer networks using optimized machine learning algorithms;declarations of software engineering project managers managing remotely: provisions for hybrid working;vision transformers-based hand gesture classification;real-time automated license plate recognition and tracking of runaway vehicles;proposing an augmented reality-based system for fashion and beauty makeover;an augmented reality-based system for fashion and beauty makeover;and time series modeling and forecasting of expected monthly rainfall in some regions of northern Nigeria amid security challenges.

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