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

共 1140 条记录，以下是51-60 订阅

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Efficient Temporal data mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation Systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM machine learning

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ECG data mining Approach for Detection of Arrhythmia Using machine learning

ECG Data Mining Approach for Detection of Arrhythmia Using M...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Rahema Naaz Shafeeq Ahmad Department of Computer Science & Engineering Azad Institute of Engineering and Technology Lucknow India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

The Electrocardiogram (ECG) is very important signal generated from human body that provides information for assessment of variation in cardiac activities. These ECG data are generally distorted due to high power line or magnetic field associated noise that results as a problem in error during diagnosis of heart related disorders on analysis through biomedical analysis software. This work contributes for very significant stage that is introduced to reduce the anomaly in the ECG signals to perform accurate automated diagnosis. This work follows a methodology of an automated machine learning algorithm is developed for accurate estimation of ECG data segment cycle using clustering approach integrated with Discrete Wavelet Transform (DWT). The key findings of this works cover the development and analysis of an approach to overcome the errors in detection of T-wave using Implantable Cardioverter Defibrillator (ICD). The work reports the solution of issues using fuzzy C-means clustering behind prediction of fatal Ventricular Arrhythmias (VA) for performing standard classifications.

关键词： machine learning algorithms Accuracy databases Arrhythmia Clustering algorithms Transforms Electrocardiography Discrete wavelet transforms Classification algorithms Standards

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2025 international conference on Automation and Computation, AUTOCOM 2025

2025 International Conference on Automation and Computation,...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

ISBN: (纸本)9798331542375

The proceedings contain 296 papers. The topics discussed include: integrating automation and sustainability: Arduino-based solar panel cleaning system for renewable energy optimization;utilizing transforming portfolio management through automation using advanced deep reinforcement learning algorithms for optimized investment strategies;leveraging quantum computing for multi-criteria decision analysis in strategic business planning;secure multi-party computation for privacy preservation in collaborative networks;integration of continuous improvement approach in sustainable e-government services towards citizen satisfaction in health sector development of conceptual model;unlocking scholarly insights using deep learning-based evaluation method for research impact assessment in academic institutions;balancing technology with pedagogy in smart classrooms for English language;empirical study to select a place to stay in NCR area using data mining and machine learning;and the impact of ultra-wideband sensors in industrial automation and robotics applications.

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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)9783031317224

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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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

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Predictive Models for Career Guidance, Based on Academic and Behavioral data with data mining and machine learning Technique

Predictive Models for Career Guidance, Based on Academic and...

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Disruptive Technologies (ICDT), international conference on

作者： Brijesh Kumar Verma Nidhi Srivastava Ajay Kumar Bharti Amity Institute of Information Technology (AIIT) Amity University Lucknow U.P INDIA Ambalika Institute of Management and Technology (AIMT) Lucknow U.P INDIA

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

A person's future is greatly influenced by career counseling, especially for students making decisions about their education and careers. This project focuses on using data mining and machine learning approaches to leverage behavioral and academic data to create prediction models for career advising. To produce individualized career suggestions, the suggested system combines data gathering, preprocessing, feature engineering, and predictive analytics. Academic performance indicators (e.g., grades, test scores), behavioral characteristics (e.g., learning styles, personality tests), and demographic information are important sources of data. While clustering techniques like K-Means make it easier to group students with similar profiles, machine learning algorithms like Random Forest, Support Vector machines, and Neural Networks are used for classification. Furthermore, hybrid models that combine clustering and classification improve the accuracy and customization of the system. Metrics including precision, recall, F1 score, and clustering validation indices are used to evaluate the system, guaranteeing solid and trustworthy results. By assigning students to appropriate career groupings and recommending certain positions, the prediction algorithms offer useful insights. This study fills in the gaps in conventional career counseling approaches and opens the door for scalable, effective, and customized career advice systems by showing how cutting-edge data-driven approaches can enable students to make well-informed career decisions.

关键词： Support vector machines Employee welfare Accuracy Engineering profession Stability criteria Neural networks Predictive models data models data mining Random forests

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The personalized university English learning system in computer-driven research 24

The personalized university English learning system in compu...

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Proceedings of the 2024 3rd international conference on Artificial Intelligence and Education

作者： Jing Ming School of Public Fundamental Courses Wuhan Institute of Design and Sciences Wuhan Hubei China

ISBN: (纸本)9798400712692

The aim of this study is to develop a computer-assisted personalised university English learning system to improve learning efficiency and effectiveness. machine learning algorithms were used to analyse student data and build a personalised learning model. Experimental results show that the system significantly outperforms traditional methods in vocabulary acquisition (22% improvement), listening comprehension (18% improvement) and oral fluency (15% improvement). The conclusion suggests that a personalised learning system based on artificial intelligence and big data can effectively improve university English teaching, and provide a new direction for the future development of educational technology. During the Eastern Han period, the production of everyday pottery, pottery sculptures, architectural bricks and tiles, a variety of ceramic sculptures, living statues, horses, acrobatics and other funerary objects, as well as new products such as large lofts, chariots and horses, became the main features of the industry.

关键词： Artificial Intelligence Adaptive learning data mining

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SRv6-INT Enabled Network Monitoring and Measurement: Toward High-Yield Network Observability for Digital Twin 3rd

SRv6-INT Enabled Network Monitoring and Measurement: Toward...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Zhang, Xu Feng, Chuan Han, Pengchao Liu, Wei Gong, Xiaoxue Zhang, Qiupei Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9789819616978

Network monitoring and measurement is an important part of realizing the network digital twin. However, it introduces the problem of high cost when obtaining the status data of physical networks. Therefore, to efficiently achieve network observability, we first introduce potential techniques and network scenarios for implementing network measurements. Then, a High-Yield Segment Routing over IPv6 based In-band Network Telemetry (HY-SRv6-INT) system is proposed in this paper. In particular, we design the HY-SRv6-INT system architecture. It consists of IPv6 switches implemented by the P4 language and a Software-Defined Networking (SDN) controller containing the HY-SRv6-INT application. Next, we experimentally demonstrate a packet-level path-associated network measurement process. In addition, the performance of the HY-SRv6-INT system is also quantitatively evaluated to verify its advantages. Finally, we discuss some research challenges and open issues for future network telemetry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet protocols

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A No-Code Approach to Classifying Neurological Disorders Using Orange data mining

A No-Code Approach to Classifying Neurological Disorders Usi...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Yabez Davidraj P Elavarasan S Vivek T Ramesh Munirathinam Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Neurodegenerative diseases are progressive disorders causing neuron damage, leading to cognitive and motor impairments. This study presents a machine learning approach for classifying multiple neurological disorders using MRI data with the Orange data analytics software. A total of 165 MRI images representing conditions such as Alzheimer’s disease, Huntington’s disease, motor neuron disease, and Pick’s disease were sourced from the AANLIB database. Image features were extracted using the VGG-16 deep learning model, and classification was conducted via Support Vector machine (SVM) with polynomial, linear, radial basis function (RBF), and sigmoid kernels. The polynomial and sigmoid kernels achieved the highest classification accuracy of 93.3%, outperforming the linear and RBF kernels. These findings highlight that MRI-based classification using deep learning features and SVM in Orange can effectively differentiate neurological disorders, showing promise for early and accurate diagnosis.

关键词： Support vector machines Neurological diseases Accuracy Magnetic resonance imaging Neurons Motors Polynomials Software Kernel Alzheimer's disease

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Performance Enhancement of DBSCAN Density-Based Clustering Algorithm in data mining

Performance Enhancement of DBSCAN Density-Based Clustering A...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Neha Tyagi Rashmi Mishra Sitanshu Kumar Patel Somraj Karki Department of Computer Science and Engineering Amity University Noida India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In order to facilitate better data analysis, clustering is a prominent data mining approach which separates data into separate categories based on their similarities and distinctions. It’s a method of unsupervised learning which may generate a model with a training dataset. Throughout this study, the Euclidian distance and EPS (Epsilon) value has been determined using the DBSCAN technique based on the similarities as well as disparity for the input data. Moreover, Euclidian distance is dynamically determined using the back propagation method; a simulation study is carried out that demonstrates both speed and precision improvements. DBSCAN, or spatial clustering based on density of application with noise, is a common clustering technique for geographic datasets that can locate clusters of any shape and automatically detect noise points. To deal with the problem, an improved DBSCAN technique based on the selection of features (FS-DBSCAN) has been suggested. However, the algorithm proved unstable while handling huge dimensional data. Many simulation via real-world datasets testify the algorithm’s efficiency. Comparison of the proposed algorithm to the DBSCAN algorithm demonstrates its benefits. Structures within the collections may be identified by dividing of collections of products into classes and clusters, with a focus on increasing the resemblances of objects within each cluster and lowering the similarities of things belonging to different clusters.

关键词： Backpropagation Training machine learning algorithms Shape Noise Clustering algorithms Spatial databases data mining Object recognition Unsupervised learning

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