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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Extensive Analysis of machine learning Techniques in the Field of Heart Disease

Extensive Analysis of Machine Learning Techniques in the Fie...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Tanvi Rustagi Meenu Vijarania CSE K.R. Mangalam University Gurugram Haryana India

CAD (Coronary Artery Diseases) and CHF (Chronic Heart Failure) are two main forms of heart disease that lead to heart attacks. This disease is increasing the number of casualties worldwide. The heart is an organ that circulates filtered blood to each part of the body. The normal working of the heart is affected due to various conditions like Obesity, Hypertension, lifestyle etc. The large volume of heart diseases is avertable, but they continue to increase for the reason that protective measures are insufficient. To cure these life-threatening diseases, we need a more reliable and accurate system. machine learning procedures have been employed on numerous therapeutic datasets to industrialise the investigation of medical data. Recently, many researchers have started applying a number of machine learning techniques to assist the medical community and experts in the detection of heart-related *** technique like Support Vector machine,Artificial Neural Network and Random Forest are examples of data mining and machine learning approaches that are used to forecast heart disorders. This can help clinician to decide on the diagnosis and forecast of cardiac illness. The prime intention behind writing this research paper is to use machine learning procedures to forecast a patient's heart condition.

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Design of E-commerce Customer Churn Prediction System Based on data mining Techniques

Design of E-commerce Customer Churn Prediction System Based ...

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Social Sciences and Intelligent Management (SSIM), IEEE international conference on

作者： Jie Yang Shandong Institute of Commerce and Technology Jinan China

An e-commerce customer churn prediction system was designed based on data mining techniques. Following relevant theories, data mining, machine learning, and customer relationship management were applied for model construction. A decision tree model and the random forest model were used to build the system. The decision tree model is a decision-making model based on a tree structure, using the construction of a tree for classification or regression tasks. Random forest is an ensemble learning method that improves prediction accuracy and stability by building multiple decision trees and averaging their results. In the implementation of the system, data were preprocessed for data cleaning, handling missing values, and addressing outliers to ensure data quality and integrity. Correlation analysis and principal component analysis were conducted to identify features that significantly impacted customer churn prediction. Finally, continuous variables were determined for model construction and prediction. Using data mining techniques, an effective system was constructed. The system helps e-commerce businesses understand and manage customer relationships and enhance customer satisfaction and loyalty.

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data mining Model for Forecasting Academic Performance of Undergraduates Based on Behavioral Features and SVM in E-learning

Data Mining Model for Forecasting Academic Performance of Un...

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Electronic Communications, Internet of Things and Big data (ICEIB), international conference on

作者： Yujiao Zhang Ling Weay Ang Shaomin Shi Sellappan Palaniappan School of Science & Engineering Malaysia University of Science and Technology Petaling Jaya Malaysia School of Information Engineering Shaanxi Xueqian Normal University Xi'an China

E-learning plays an increasingly important role in modern education. Behavioral data from online education platforms make it possible to mine college students' academic performance. The existing data mining models pay little attention to the weight of features in academic performance prediction, and most of them simply treat all features equally. However, each characteristic has a different influence on the predicted outcome. Taking account of this fact, we proposed a data mining model for forecasting undergraduates' academic performance based on multi-feature and support vector machine (SVM). First, the original data was extracted from the e-learning platform and processed. Then, each feature was measured its correlationship with academic performance according to Pearson’s rule and given a weight. Finally, the data mining model was constructed based on the weighted muti-feature and SVM. Simulation of MATLAB shows that the proposed method has higher Accuracy and Recall on forecasting academic performance of undergraduates.

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The Future of DDoS Mitigation: An Exploration of machine learning Algorithms

The Future of DDoS Mitigation: An Exploration of Machine Lea...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Ravleen Kaur Chadha Gautam Sharma Pushkar Jain Apex Institute of Technology Chandigarh University Mohali Punjab

DDOS attacks are currently among of the most frequent online crimes. Its prevalence does not, however, make it any simpler to deal with. The resources of the impacted server, such as broadband and buffering size, are lowered down as a result of being unable to give resources to legitimate clients. In this research study, evaluation of many models would be carried out and the one which has the most accuracy would be determined. To identify assaults and typical situations, numerous datasets will be used to train and test the machine learning algorithms. This study will use weak data mining platforms, and the outcomes of those platforms will be compared and studied.

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Digit recognition by the Implementation of Supervised learning Using a Convolutional Neural Network

Digit Recognition by the Implementation of Supervised Learni...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Mrinal Paliwal Sunil Kumar Chawla Punit Soni Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The recognition of handwritten numbers is a significant challenge in the field of computer vision, as it finds applications in various domains including banking, medical diagnostics, and handwriting recognition. In recent years, there has been a notable enhancement in our technological capabilities due to the progress made in internet accessibility and technology. Deep learning algorithms have emerged as valuable computational tools for addressing several challenges, including the task of digit recognition. The utilization of convolutional neural networks in the task of digit recognition is driven by their ability to acquire hierarchical representations from picture data. This scholarly article provides a comprehensive examination of the application of Convolutional Neural Networks (CNNs) in the field of machine learning for the purpose of digit recognition. This research examines the architectural components of Convolutional Neural Networks (CNNs), the training procedures employed, and the various methodologies employed for the purpose of digit recognition. The paper also provides an overview of the different datasets used for training and testing CNNs for digit recognition and a discussion of the results obtained from various studies.

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Improving Human Activity recognition Through Multisensor data Fusion Techniques in Wireless Sensor Networks

Improving Human Activity Recognition Through Multisensor Dat...

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Computing and machine Intelligence (ICMI), international conference on

作者： Rasha S. Gargees Department of Computer Science Central Michigan University Michigan USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Wireless Sensor Networks (WSNs) have revolutionized data collection, especially in Human Activity recognition (HAR). Multisensor datasets are crucial for a comprehensive understanding of human behavior, enabling more advanced classification techniques. This study explores the essential role of machine learning in categorizing activities, especially given the abundance of available multi-sensor data from WSN. The research utilizes information fusion as a pivotal mechanism to boost the accuracy of activity classifications. Employing Support Vector machine (SVM) and Decision Tree (DT) algorithms, the project utilizes advanced data fusion techniques, specifically Kalman Filter (KF) and Covariance Intersection (CI), to optimize information extraction from the provided data. The study encompasses six experiments, including applying SVM and DT on raw data, SVM and DT on data fused by CI, and SVM and DT on data fused by KF. The results of these experiments reveal a significant improvement in the accuracy of SVM and DT classification when incorporating CI and KF. This emphasizes the effectiveness of information fusion techniques in refining the outcomes of human activity recognition systems, showcasing their vital role in enhancing the reliability and precision of activity classifications. This research not only contributes to the field of HAR but also establishes a foundation for further advancements in real-world applications where precise activity classification holds utmost importance.

关键词： Support vector machines Wireless communication Wireless sensor networks Accuracy machine learning algorithms Refining data integration

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Application of slack variable-optimized SVM on piano teaching reform

Application of slack variable-optimized SVM on piano teachin...

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2021 3rd international conference on Applied machine learning and data Science, ICAMLDS 2021

作者： Li, Tianjiao Shangdong Management University Shandong Province Jinan City China

The data mining task of the classification algorithm is mainly to classify the data and classify them into each known category. As a classification algorithm, SVM has many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition. Based on the advantages of the SVM algorithm, this paper optimizes the slack variables, establishes a teaching quality evaluation model, and takes 500 subjects of the questionnaire survey as data samples by comparing with a variety of classification methods to evluate, and the results show that the accuracy of the SVM model is 92.70%, which proves the effectiveness of the research model. © Published under licence by IOP Publishing Ltd.

关键词： pattern recognition

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Long-Term Traffic Characterization in a Large-Scale Cellular Network Based on Limited Time Series 3

Long-Term Traffic Characterization in a Large-Scale Cellular...

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3rd international conference on Electronics and Communication;Network and Computer Technology (ECNCT)

作者： Li, Sisi Chen, Yishuai Li, Naipeng Su, Jian Guo, Yuchun Zhao, Yongxiang Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

Understanding long-term (e.g., one year) traffic characteristics of cellular base stations (BSs) is of great value to network operators. However, there are rarely modeling results about them. In this paper, we characterize the long-term (i.e., one year) traffic patterns of thousands of BSs in a large-scale cellular network of China. We first find that the traffic distribution among BSs is highly skewed and BSs' traffic varies dramatically in a year. In order to cluster meaningful BS traffic patterns, we use a new clustering method, in which a BS's monthly traffic is represented by its rank in the BS's traffic time series. In this method, we find that the thousands of BSs have six typical traffic patterns, and the patterns are interpretable: they are clearly related to two important events in China: 1) Spring Festival when a lot of people return hometown to reunion with family, 2) Double 11 Shopping Festival when a lot of people shop online. They are also related to the BSs' geographic location and address information. Our measurement and analysis results provide useful information for cellular network providers to understand and plan their networks, and our clustering method can be applied in similar traffic pattern mining problems.

关键词： Cellular network Cellular traffic pattern recognition data mining Measurement analysis

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AI-Based Automated Subtitle Generation System for Multilingual Video Transcription and Embedding

AI-Based Automated Subtitle Generation System for Multilingu...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： K. Penyameen G.M Siva Suriya Rajan A. Arshath Ahamed S. Yugesh Ram J John Shiny A Periya Nayaki Department of Information Technology Velammal College of Engineering and Technology Madurai Tamilnadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The subtitle generator system proposed in this project is an intelligent tool towards automation of subtitle production for video content., using the most advanced speech recognition and transcription and video processing technologies. U sing integration with machine learning models such as Whisper by OpenAI with multimedia tools including FFmpeg and MoviePy., it manages the production of subtitles that track up and are perfectly synchronized with spoken content within video files. It gives standard SRT file generation with direct embedding abilities into videos., hence reducing the total manual effort and time taken in the production of subtitles. Support to transcribe into multiple languages is also given with an easy Graphical User Interface GUI that provides ease of interaction with the application. This solution to video accessibility., particularly for the purpose of using more than one language., is scalable and automated to provide the best possible approach in media industry subtitle generation.

关键词： Accuracy Production Speech recognition machine learning Streaming media Media Generators data models Multilingual Graphical user interfaces

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Optimized Depthwise Separable CNN-SVM Approach for Enhanced Human Activity recognition

Optimized Depthwise Separable CNN-SVM Approach for Enhanced ...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Saini, Rupesh Jangra, Manisha Boora, Ritu Dalal, Priyanka Guru Jambheshwar University of Science and Technology Hisar125001 India

Human Activity recognition (HAR) is paramount in several modern-day applications. However, the accuracy in recognizing the activity remains a challenge due to several factors such as natural variations in human movement, similar posture in various activities, physically challenged individuals, incomplete sensor data due to obstructions, unwanted signals affecting sensor readings, and more. This study introduces a hybrid model that combines the efficacy of Support Vector machine in high dimensional space and potential of Convolutional Neural Network to capture the temporal and spatial pattern in data. It allows the model to handle complex data and generalize effectively thereby reducing the chances of overfitting. The two models are integrated through a combined decision function using a weighted average ensemble technique. The performance of the proposed model is evaluated on the UCI HAR dataset and compared with state-of-the-art models like SVM, Convnet, LSTM-CNN, CNN, CNN-BiLSTM, etc. In comparison to models like LSTM-CNN (95.80%) and CNN-BiLSTM (96.37%), the proposed model showed superlative performance with overall classification accuracy of 97.18% and average class accuracy of 99.05%. Moreover, it also exhibited higher precision, recall and F1-Score thus, giving a more reliable solution for advanced HAR tasks. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Semi-supervised learning

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