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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining, MLDM 2003"

共 1147 条记录，以下是171-180 订阅

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Exploring the Potential Benefits of machine learning for Optimizing Real-Time data Analysis 3

Exploring the Potential Benefits of Machine Learning for Opt...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Ravindra Sakhare, Nitin N. Thangarasu, N. Senthilkumar, K.K. Wawage, Pawan S. Rengarajan, A. Vivekananda Global University Department of Civil Engineering Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This studies paper examines the capability blessings of implementing device getting to know algorithms to optimize real-time information evaluation. The subjects mentioned consist of techniques for predicting traits in actual-time information, the ability of gadget studying algorithms to discover anomalies and outliers in datasets, and how using records mining and herbal language processing (NLP) can improve the accuracy of real-time facts insights. It also evaluates the demanding situations that can arise while applying machine gaining knowledge of algorithms to actual-time statistics, along with scalability problems and overfitting of records. In end, the authors argue that system studying has notable potential to enhance the accuracy and timing of actual-time facts analysis, and that they provide steerage on choosing and enforcing appropriate gadget getting to know algorithms. © 2023 IEEE.

关键词： machine learning

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A Survey on Wireless Network Traffic Analysis using machine learning Algorithms 3

A Survey on Wireless Network Traffic Analysis using Machine ...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Praveen Kumar, M. Ashwitha Noble, P. Esha Malavika, V.S. Geethanjali, G. Farheen, A.S. Sri Eshwar College of Engineering Department of CSE Coimbatore India

ISBN: (纸本)9798350343632

Analyzing and forecasting network traffic for different applications has gained popularity in recent years. Several tests have been carried out to find issues with the computer network apps that are in use today. Anticipating and evaluating network traffic can assist in implementing preemptive steps to guarantee safe, dependable, and excellent network connection. Numerous methods, including data mining approaches and neural network-based techniques, have been suggested and assessed for the purpose of predicting patterns of network traffic. For this goal, other linear and nonlinear models have also been developed. The main objectives of combining different network analysis and prediction techniques are efficiency and optimization. The goal of this survey study is to present a thorough review of the variety of methods used in traffic prediction and network analysis. The paper delves into the distinctive features and findings of previous research efforts and brings to light the patterns that have emerged from these studies. Furthermore, it provides an overview of the various areas in which network traffic analysis and prediction have shown their effectiveness. © 2023 IEEE.

关键词： data mining Techniques dataset NTA Preprocessing

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ML-Based AI Conceptual Framework for Assessing SMEs Digitalization Through the Lens of Sentiment Analysis 3

ML-Based AI Conceptual Framework for Assessing SMEs Digitali...

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3rd international conference on Digitalization and Management Innovation, DMI 2024

作者： Pelova, Boryana Hristova, Gloria Faculty of Economics and Business Administration Sofia University "St. Kliment Ohridski" Bulgaria

ISBN: (纸本)9781643685823

Tools from the machine learning and data mining domain become even more popular in the fields of economics, entrepreneurship, and policy-making. At the same time, the research on small and medium-sized enterprises (SMEs) is getting amplifying importance for governments and policy-makers especially when it comes to support of SME’s digitalization. A good understanding of the current level of digitalization of SMEs by industries is a prerequisite for design and integration of effective national policies. The goal of this paper is to design the architecture of an ML-based AI conceptual framework for assessing SMEs digitalization. We do this from the perspective of customers assuming that their preferences are absorbed in the publicly available (online) data that they generate in social media and community forums. This approach forms a significant contribution of this paper. Furthermore, we define an algorithm for data preparation, and we develop an algorithm based on sentiment analysis, which generates a set of industry-specific digitalization indices, which is another important contribution of this paper. © 2025 The Authors.

关键词： Adversarial machine learning

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data mining and Visualization to Understand Employee Attrition and Work Performance 3

Data Mining and Visualization to Understand Employee Attriti...

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3rd international conference on Computing and Information Technology, ICCIT 2023

作者： Gollapalli, Mohammed Rahman, Ata-Ur Osama, Abdurrahman Alfaify, Ahmad Yassin, Muhammad Alabdullah, Ahmad Imam Abdulrahman Bin Faisal University College of Computer Science & Information Technology Department of Computer Information Systems Dammam31441 Saudi Arabia Imam Abdulrahman Bin Faisal University College of Computer Science & Information Technology Department of Computer Science Dammam31441 Saudi Arabia

ISBN: (纸本)9798350321487

Throughout the course of careers, a large number of employees encounter a variety of normal life events that can have an impact on their performance and job environment satisfaction. Under these circumstances, some employees are compelled to leave their positions, not because of issues with their work environment, but due to personal circumstances. This is what we refer to as Employee Attrition, a pervasive phenomenon affecting numerous businesses and work environments in the present day. Employee attrition could occur in a variety of ways and be determined by several entities. Therefore, we look into various conditions, causes, indicators, and entities in order to study the attrition problem and determine its answers and enhancements. This study investigated the attrition of employees based on IBM data repository, together with the machine learning based experimental analysis and knowledge discoveries, to identify the most effective approaches to the attrition problem. The experimental data contained 1,471 records with 35 distinct characteristics. We formally employed Nave Bayes Classifier in this study using WEKA datamining software, and adequate knowledge has been extracted to better comprehend employee turnover and their work performance facets. © 2023 IEEE.

关键词： data mining

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Research on the Application of Artificial Intelligence in the Library Sector 3

Research on the Application of Artificial Intelligence in th...

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3rd international conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Xu, Zihan College of Information and Intelligence Hunan Agricultural University Changsha410128 China

ISBN: (纸本)9781510663473

This study examines the literature on AI and libraries, examines the significant roles AI has played recently in industries related to libraries, and briefly describes relevant technical functions and their application characteristics in the library field. It begins with six key technologies: OCR, data mining, natural language processing, face recognition, knowledge mapping, and machine learning, and then makes a thorough analysis of each. Detailed analysis and summary of the results achieved in the practical application of AI, an analytical overview of the business functions related to AI in the library field on the development and reform of libraries and the current application status of various technologies, and the problems that libraries may encounter in the practical implementation of AI-related technologies are pointed out. © 2023 SPIE.

关键词： data mining

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3rd international conference on Communication, Networks and Computing, CNC 2022

3rd International Conference on Communication, Networks and ...

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3rd international conference on Communication, Networks and Computing, CNC 2022

ISBN: (纸本)9783031431449

The proceedings contain 53 papers. The special focus in this conference is on international conference on Communication, Networks and Computing. The topics include: Prevention and Detection of Poisoning Attacks in Medical-Based machine learning Web Applications;machine learning-Based Solutions for Securing IoT Systems Against Multilayer Attacks;securing Internet of Things Using machine learning Techniques: A Systematic Review;predicting Students’ Performance Employing Educational data mining Techniques, machine learning, and learning Analytics;COVID-19 Detection Using State-of-the-Art Deep learning Models on X-Ray and CT Images;Edge Discerning Using Improved PSO and Canny Algorithm;sniffit: A Packet Sniffing Tool Using Wireshark;design of a Dielectric Resonator Antenna with Two Drilled Hollow Cylinders for Wide Bandwidth;an Analytic Assessment of Medical Applications of the Internet of Things (IoT) During Covid-19 and Possible Future Pandemics;Triple-Wideband Antenna for RF Energy Harvesting;dual-Band Antenna and Low Pass Filter Design for Wireless Energy Harvesting;performance Analysis of Various Digital Adders;Implementation of Low-Power Full Adder Using GNRFET Technology;Design of Dual-Polarized Square Microstrip Patch Antenna in KU Band for Weather Applications;Tumor Detection Using DWT and SVM in MRI Image;TiGen – Title Generator Based on Deep NLP Transformer Model for Scholarly Literature;Patient Monitoring System for COVID Care Using Biosensor Application;enhance the Tourism of Unpopular Places by Fuzzy Distance Method;analysis and Comparison of Posture Detection Using Deep learning Algorithm;Design and Analysis of 16 Channel WDM-OFC System with Minimum BER for 5G Applications;classification Techniques in Remote Sensing: A Review.

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3rd international conference on Communication, Networks and Computing, CNC 2022

3rd International Conference on Communication, Networks and ...

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3rd international conference on Communication, Networks and Computing, CNC 2022

ISBN: (纸本)9783031431395

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Smart Use of machine learning in Heart Disease Identification 3

Smart Use of Machine Learning in Heart Disease Identificatio...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raman, Ramakrishnan Shintre, Vaijanath Joshi, Kapil Prasanthi, G.S.L.B.V. Kaleem, Afshan Rajput, Kapil Symbiosis Institute of Business Management Maharashtra Pune India Sanjay Ghodawat University Department of Electronics Engg Maharashtra Kolhapur India Uttaranchal University Uttaranchal Institute of Technology India Muffakham Jah College of Engineering and Technology India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

Heart patients are increasing in number on a daily basis, and each year in every region of the globe, a significant number of people lose their lives unexpectedly as a result of sudden heart *** a result of this, it is essential to diagnose cardiovascular illness at an early stage in order to avert the death of It is necessary for the area of medicine to make use of some kind of technology-based application in order to identify cardiac patients with greater precision and in much shorter time. It is possible to effectively identify heart illness by making use of data mining methods, and there are a considerable number of people suffering from heart disease who are now being treated in hospitals all over the globe. data mining is the practice of sifting through enormous amounts of raw data in search of knowledge or information that can be put to good use. During the prediction analysis, many machine learning algorithms are put to use in order to locate useful patterns and make accurate projections on forthcoming events or trends. Through the use of a reworked machine learning algorithm, this research study will attempt to forecast the risk that individuals would suffer from coronary heart disease. Before being classified, the incoming data go through a number of processes, including preprocessing, grouping, and the selection of useful features. Four different algorithms, including random forest (RF), K Nearest Neighbor (KNN), Particle swarm optimization (PSO) and logistic regression are combined in order to find out what kind of cardiac disease the patient *** enhance performance and shorten the amount of time needed for training, this method removes features from the cardiac dataset that are not relevant to the problem at hand. The random forest approach is the one that finishes off this procedure. In order to group all of the data points that are considered to be outliers, KNN are optimized using PSO algorithm. In the end, a method known as logistic regres

关键词： Forecasting

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Advancing Medical Predictive Models with Integrated Approaches 3rd

Advancing Medical Predictive Models with Integrated Approach...

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3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Kumar, Aman Singh, Randeep IEC Univ Dept Comp Sci & Technol Solan Himachal Prades India

ISBN: (纸本)9783031640667;9783031640674

This study proposes a thorough methodology for improving the precision and dependability of liver disease prediction utilising cutting-edge methods. The study uses demographic information, blood test results, and machine learning to distinguish between healthy blood donors and people who have liver disorders including cirrhosis, hepatitis C, and fibrosis. To create a reliable prediction model, the study integrates PCA, PSO, & the XGBoost algorithm. Age, sex, and test results of patients are among the information gathered from the University of California Irvine machine learning Repository. Categorical variables are numerically encoded while missing values are substituted using column means to maintain data integrity. By using PCA to keep considerable data variance in a lower-dimensional space, dimensionality reduction is achieved. The suggested feature selection strategy combines PSO and Random Forest, with the latter being used to optimise the feature subset that has the greatest impact on prediction accuracy. The population size, iterations, cognitive and social components (C1 and C2), as well as inertia weight (w), are simulation parameters used by the PSO method. The database is categorized into training & testing sets in way to assess the performance of the design. With feature-selected data, the XGBoost algorithm known for its powerful prediction abilities-is trained. Grid search is used to fine-tune hyperparameters such as maximum depth, learning rate, and number of estimators. The conductance of the design is analyzed by a variety of assessment criteria, like accuracy, precision, sensitivity, specificity, F1 Score. The design's predictions are further illustrated by a confusion matrix and ROC curve, & the AUC measures the model's discriminating capability. In order to help doctors, diagnose liver illness early and administer efficient therapy, this research attempts to generate precise and trustworthy forecasts for the condition. The combination of PCA, PSO,

关键词： Liver Disease Prediction data mining machine learning Textual data PCA PSO Random Forest XGBoost

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Predictive Analysis of Road Accidents Using data mining and machine learning 3rd

Predictive Analysis of Road Accidents Using Data Mining and ...

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3rd international conference on Information Technology, InCITe 2023

作者： Kumar, Abhinav Arora, Harshit Jain, Prasuk Department of Information Technology ASET Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819959938

Today, one of the top concerns for governments is road safety. Given the significance of the subject, reducing the harm caused by traffic accidents has shifted its focus to finding the root causes of incidents on the road. On the other hand, it is impractical to analyze the components in this regard due to the exponential growth of data volume. Despite everything that has been done so far to increase road safety in India, many areas continue to be vulnerable to these flaws. Due to the diversity of these vulnerability-causing factors, a thorough analysis is required in order to significantly reduce the worrying numbers. The aim of this paper is to use data mining and machine learning approaches to help develop a model using LR and RF that not only smooths out the heterogeneity of the data by grouping like objects together to identify the regions of the nation that are most likely to experience accidents in relation to various accident-factors, but also assists in establishing the relationship between these parameters, factors, and casualties. The outcomes of data mining will assist businesses like transportation in exploring the police information system’s accident data, finding trends, forecasting future behavior, and making wise judgments to reduce accidents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： data mining

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