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共 1147 条记录，以下是161-170 订阅

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3rd international conference on Computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

3rd International Conference on Computer Vision, High Perfor...

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3rd international conference on Computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

ISBN: (纸本)9789819966899

The proceedings contain 43 papers. The special focus in this conference is on Computer Vision, High Performance Computing, Smart Devices, and Networks. The topics include: machine learning-Based Path Loss Estimation Model for a 2.4 GHz ZigBee Network;Comparative Analysis of CNN Models with Vision Transformer on Lung Infection Classification;classification of Alzheimer’s Disease Using Stacking-Based Ensemble and Transfer learning;heart Device for Expectation of Coronary Illness Utilizing Internet of Things;Parallel Programming in the Hybrid Model on the HPC Clusters;an Extensive Study of Frequent mining Algorithms for Colossal Patterns;a Review Paper on Progressive Approach to Reduce Context Switching in Round Robin Scheduling Algorithm;Syn Flood DDoS Attack Detection with Different Multilayer Perceptron Optimization Techniques Using Uncorrelated Feature Subsets Selected by Different Correlation Methods;Ensemble Model Detection of COVID-19 from Chest X-Ray Images;Evolutionary, Protein–Protein Interaction (PPI), and Domain–Domain Analyses in Huntington’s Disease;Transfer learning-Based Effective Facial Emotion Recognition Using Contrast Limited Adaptive Histogram Equalization (CLAHE);transformer Model for Human Activity Recognition Using IoT Wearables;design and Development of a Chatbot for Personalized learning in Higher Education;performance Evaluation of Concentric Hexagonal Array for Smart Antenna Applications;a Comprehensive Study on Bridge Detection and Extraction Techniques;vegetation Change Detection of Multispectral Satellite Images Using Remote Sensing;performance Evaluation of Neural Networks-Based Virtual machine Placement Algorithm for Server Consolidation in Cloud data Centres;low-Resource Indic Languages Translation Using Multilingual Approaches;DCC: A Cascade-Based Approach to Detect Communities in Social Networks;an Improved Way to Implement Round Robin Scheduling Algorithm.

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Diabetes Prediction based on Supervised and Unsupervised learning Techniques - A Review 3

Diabetes Prediction based on Supervised and Unsupervised Lea...

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

作者： Sivaraman, M. Sumitha, J. Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Diabetes is a disorder that develops in the human body when blood glucose or sugar levels are extremely high. machine learning (ML) is subfield of Artificial Intelligence (AI) that is built on the idea that systems and machines will evaluate and interpret data, learn from it, and make a decision with no human participation. Medical fields that operate with massive amounts of data have recognized by the of machine learning technology. Health care systems can work more effectively and gain a competitive advantage by extracting insights from this data. The general goal of this study is to discover and observe the working and performance of various machine-learning algorithms. This paper work discusses the possibilities of employing machine-learning technology in healthcare. © 2022 IEEE.

关键词： data mining

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Research on Sales Forecasting Model Based on GRU Neural Network and machine learning Model 3

Research on Sales Forecasting Model Based on GRU Neural Netw...

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2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Zhang, Chunchang Shi, Ruoxuan Harbin Institute of Technology Weihai Weihai China Lanzhou University Lanzhou China

ISBN: (纸本)9798350341546

In the age of big data, the retail industry generates a vast amount of sales data daily. Analyzing and mining these data can facilitate management decision-making and provide great benefits to enterprises. With competition intensifying in the retail industry, precise sales forecasts are essential to assist companies in effectively managing their inventory of specific commodities, developing marketing plans, and formulating timely and effective strategic policies to reduce costs and increase efficiency. In this paper, the STL method is combined with SVR and GRU to build a daily sales forecasting model for retail enterprises. The trend, seasonal, and residual sub-series decomposed from past sales are used to directly predict future sales to achieve a better forecasting effect, and are compared with some traditional models and STL-ML combined models based on the decomposition integration idea. The results show that the method used in this paper has higher forecasting accuracy, and the forecasting ideas proposed in this paper have important reference significance for the sales forecasting practice of retail enterprises. © 2023 IEEE.

关键词： GRU neural network sales forecasting STL decomposition SVR time series

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Cardio Vascular Disease Prediction and Classification Report Generation using data mining Technique 3

Cardio Vascular Disease Prediction and Classification Report...

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3rd international conference on Smart data Intelligence, ICSMDI 2023

作者： Jebaseeli, T Jemima Navin Kumar, M. Subagar, Angeleen Santhosh, A. Karunya Institute of Technology and Sciences Department of Cse Coimbatore641114 India

ISBN: (纸本)9781665464871

Cardiovascular disease is one of the primary reasons for death in the world today. It has evolved into one of the most challenging illnesses to identify. By a recent WHO research, heart disorders are on the rise. As a result, 17.9 million people die each year. As the population increased, this became increasingly difficult to diagnose and initiate treatment during the initial stages. When it comes to forecasting coronary heart disease, medical analysis of data encounters a huge challenge. Electronic health record systems are currently used to handle the data of patients in hospitals. The huge amount of information created by the medical industry is being misused. A new approach is required to reduce costs and accurately predict heart disease. Hospitals can use appropriate decision support systems to reduce the cost of clinical tests. Several types of research offer barely a glimpse of optimism for employing machine learning approaches for predicting cardiac disease. The proposed study suggests a unique strategy for finding key characteristics via a machine learning approach throughout this work, which would also improve the precision of cardiovascular risk diagnosis. Diverse characteristic correlations and classification algorithms are used to establish the statistical model. Using the Improved random forest with Hyper - parameters tweaking in the classification algorithm for cardiovascular disease, a better reliability with an acceptable accuracy of 94.5% has been obtained. This approach may be valuable to healthcare professionals in their treatment as a decision assistance system. © 2023 IEEE.

关键词： Classification (of information)

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Predicting Laptop Prices in the Tunisian Market Using data mining and machine learning Methods 3

Predicting Laptop Prices in the Tunisian Market Using Data M...

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3rd IEEE international conference on Signal, Control and Communication, SCC 2023

作者： Balti, Ala Yassin, Mannai Lakhoua, Mohamed Najeh Sayadi, Mounir University of Tunis National High School of Engineering of Tunis Ensit Research Laboratory LR13ES03 Sime Tunisia University of Carthage National Engineering School of Carthage ENICarthage Tunisia University of Carthage National Engineering School of Carthage ENICarthage Research Laboratory Smart Electricity & Ict SEICT Tunisia

ISBN: (纸本)9798350326390

The laptop industry has undergone swift expansion and technological enhancements, resulting in a diverse array of laptop models and capabilities to cater to a broad spectrum of consumer demands. However, for businesses, the task of identifying the most suitable laptop that aligns with specific requirements can be quite formidable. To tackle this challenge, this study introduces a solution centered around predicting laptop prices through the application of supervised machine learning, with a particular focus on utilizing the Gradient Boost method. The system attains an impressive predictive accuracy of 94% when compared against actual market prices. Moreover, this investigation delves into the connections between laptop prices and various attributes including model, RAM, storage types (HDD/SSD), GPU, and CPU. By making use of this system and comprehending these correlations, enterprises can streamline the laptop selection process. This research significantly contributes to the domain of laptop price prognostication, providing enterprises with the tools to make well-informed decisions and optimize their laptop acquisition strategies. © 2023 IEEE.

关键词： Forecasting

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data mining for news content: a case of Cantonese opera news topic modelling analysis 3

Data mining for news content: a case of Cantonese opera news...

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3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Wang, Bifeng Xu, Xiaotong Chen, Haocih Xie, Xinyi Chen, Jiawei Liu, Qian Fu, Yong School of Education GuangZhou Academy of Fine Arts Guangdong Guangzhou510006 China School of Journalism and Communication Jinan University Guangdong Guangzhou510632 China School of Literature Zhejiang University Zhejiang Hangzhou310058 China College of Liberal Arts Jinan University Guangdong Guangzhou510632 China

ISBN: (纸本)9781510661301

Topic modelling approach is widely used for text data mining in NLP(Natural Language Processing). Text mining has been used for analysis of ICH (intangible cultural heritage), where Cantonese opera is a representative ICH of Lingnan culture. This study retrieved news content on Cantonese Opera and used machine learning analysis (LDA topic modelling) method to find out the distribution of the topics. Four main themes are concluded: the development, cooperation, and inheritance of Cantonese opera(taken up to 45.1% in all data);The traditional form(23.5%);Innovative forms(18.3%);Education and cultural inheritance of Cantonese opera(13.2%). This research further explored how to better promote Cantonese opera by analysing the topics as well as the data, and suggested that emphasis should be placed on the innovation of traditional elements in Cantonese opera, keeping them close to life, and education. © 2023 SPIE.

关键词： machine learning

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Exploring machine learning Applications for Additive Manufacturing 3rd

Exploring Machine Learning Applications for Additive Manufac...

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

作者： Chouhan, Kshitij Gupta, Sumit Chaudhary, Vijay Gupta, Pallav Kumar, Sundeep Department of Mechanical Engineering ASET Amity University Uttar Pradesh Noida201313 India Management Studies Engineering College Ajmer Rajasthan305025 India

ISBN: (纸本)9789819918935

The use of data and algorithms to open the ways that human learn, which gradually increase the accuracy, is all considered under a branch of computer science and machine learning known as machine learning (ML). This is a rapidly growing field of data science majorly includes the use of trained algorithms and statistical methods to make classifications, which uncovering basic insights of data mining projects. AM is a relatively new technology that has the potential to drastically alter the future of digital manufacturing. In recent years, machine learning (ML) has attracted a lot of attention in AM because of its amazing performance in data tasks including classification, regression, and clustering. One of the primary justifications is this very fact. The complexity of production systems leads to a variety of problems with regard to design principles, standardization, and quality control. There is a growing need, however, for more sophisticated and superior products. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D printing

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FairdR: Ensuring Fairness in Mixed data of Fairly and Unfairly Treated Instances 1

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3rd CAAI international conference on Artificial Intelligence (CICAI)

作者： Liu, Yuxuan Kuang, Kun Zhang, Fengda Wu, Fei Zhejiang Univ Hangzhou Peoples R China

ISBN: (数字)9789819991198

ISBN: (纸本)9789819991181;9789819991198

Fairness has emerged as a crucial topic in data mining and machine learning applications, driven by ethical and legal considerations. It is important to recognize that not all samples are treated unfairly, resulting in data heterogeneity in fairmachine learning. Existing fair models primarily focus on achieving fairness across all heterogeneous data, yet they often fall short in ensuring fairness within specific subgroups, such as fairly treated and unfairly treated data. This paper presents a novel problem of training a fair model on heterogeneous data, aiming to achieve fairness for both types of data, with a particular emphasis on the unfairly treated subset. To address this challenge, an effective approach is to recover the distribution of both fairly and unfairly treated data. In this study, we adopt the Structural Causal Model (SCM) to model the heterogeneous data as a mixture of causal structures. Leveraging the perspective of SCM, we propose a framework called FairdR, which utilizes the Hirschfeld-Gebelein-R ' enyi (HGR) correlation to accurately recover the distribution of both fairly and unfairly treated data. FairdR can serve as a pre-processing method for other fair machine learning models, providing protection for the unfairly treated members. Through empirical evaluation on synthetic and real-world datasets, we demonstrate that the presence of heterogeneous data can introduce unfairness in previous algorithms. However, FairdR successfully recovers the distribution of fairly and unfairly treated data, thus improving the fairness of downstream algorithms when dealing with heterogeneous data.

关键词： Fairness Causality Heterogeneity

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Privacy-Preserving data mining Methods for Sensitive Information 3

Privacy-Preserving Data Mining Methods for Sensitive Informa...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Sukhvinder Jasim, L.H. Dari Dhote, Vijay Suseela, D. Venkatasubramanian, R. Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

This study explores innovative Privacy-Preserving data mining (PPDM) methods for handling sensitive information, addressing the critical balance between data utility and privacy. We identify prevalent issues arising from traditional data mining techniques and evaluate advanced PPDM approaches like differential privacy, homomorphic encryption, and federated learning using real-world datasets. Through a comprehensive analysis, our results indicate that hybrid models, which combine multiple PPDM techniques, are particularly effective in enhancing privacy without compromising data utility. The findings underscore the need for adaptable and robust PPDM strategies in the era of Big data, contributing valuable insights to both the academic and practical domains. Our research paves the path for future studies and developments in creating a harmonious ecosystem where data mining efficiency and privacy coexist, promoting an insightful yet private data-centric world. © 2023 IEEE.

关键词： Analytically Valuable Patterns data Privacy data Repositories Homomorphic Encryption and Utility Privacy-Preserving data mining Sensitive Information

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Predicting Coronary Heart Disease Through machine learning Algorithms 3rd

Predicting Coronary Heart Disease Through Machine Learning A...

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3rd international conference on Innovations in Computing Research, ICR 2024

作者： Mariettou, Savina Koutsojannis, Constantinos Triantafillou, Vassilios Electrical and Computer Engineering Department University of Peloponnese Patras Greece Health Physics and Computational Intelligence Lab Physiotherapy Department School of Health Rehabilitation Sciences University of Patras Patras Greece Network Technologies and Digital Transformation Lab Electrical and Computer Engineering Department University of Peloponnese Patras Greece

ISBN: (纸本)9783031655210

machine learning has gained popularity in medical fields due to the increasing availability of health data and the improvement of machine learning algorithms. It can be used to create predictive models that diagnose diseases, predict disease progression, tailor treatment to individual patient needs, and improve the functioning of medical systems. The right use of data can have a positive impact on improving the quality of patient care, reducing healthcare costs, and creating tailored and effective medical approaches. The healthcare sector benefits greatly from the accurate interpretation of medical data as it contributes to the early prediction of diseases in patients. Early detection of a disease can help control the symptoms and provide the correct treatment. In our work, we analyzed actual measurements from the Framingham Heart Study and we created a medical database with 78001 records. Our ultimate goal is to develop an expert Artificial Intelligence system and an Artificial Neural Network that can predict the development of coronary heart disease by employing intelligent knowledge-mining algorithms. We have created two intelligent systems that predict the progression of coronary heart disease using machine learning algorithms such as Random Forest, Decision Trees and Neural Networks. In our experimental analysis, the Decision Tree and Neural Network achieved an accuracy of 90.08% and 84.56% respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neural networks

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