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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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A Comprehensive Assessment on Predictive Analytics using data mining Techniques 3

A Comprehensive Assessment on Predictive Analytics using Dat...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Vaidehi, B. Arunesh, K. Sri S.Ramasamy Naidu Memorial College Department of Computer Science Sattur India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Educational data mining is a fast emerging multidisciplinary field that reveals unexpected patterns in educational organizations. Considering the issues and demanding situations faced through instructional establishments in studying people overall performance and enhancing their instructional management, the diverse automated studying strategies have been examined. In this work, more Research contribution were reviewed and evaluated in revealing alternative ways for evaluating students' educational attainment to improve quality and support students in attaining remarkable achievements in their academics. The most typical applications of educational data mining encompass: enhancing the study process, intensifying course completion, assisting students in curriculum selection, student profiling, classifying factors that predispose to dropout, student targeting, curriculum planning, predicting student performance, and as a support for decision-making at the time of student enrolment. Predictive analytics is a machine learning and data mining method that is commonly utilized in educational data mining, to foresee expected future trends, events, and behaviors based on prior data. As a result of data mining algorithms, academic establishments will be able to make people academic outcomes accessible in aims to enhance the quality of learning they deliver. © 2022 IEEE.

关键词： data mining

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Enhancing the Long-Term Sustainability of Healthcare data Optimization Through Deep Federated learning 3

Enhancing the Long-Term Sustainability of Healthcare Data Op...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kumar, Balraj Evin, Munni Byeon, Haewon Sivaprakash, M. Sheeba, P. Maria Sunil, J. School of Computer Application Lovely Professional University Punjab Phagwara India School of Engineering and Computing American International University Kuwait College of Ai Convergence Inje University Department of AI-Big Data Gimhae50834 Korea Republic of Stella Mary's College of Engineering Department of Mechanical Engineering Tamil Nadu 629202 India St. Mother Theresa Engineering College Department of Electronics and Communication Engineering Thoothukudi India Annai Vailankanni College of Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798350361186

The medical industry regards the prevention and detection of skin diseases as a top priority. The number of people suffering from skin diseases is increasing faster than ever before due to increased levels of pollution and poor diet. Although it's not the major priority, having bad skin makes us less certain. Regularly inspecting your skin is an important step in identifying any degenerative or early changes in your skin that could lead to skin disease. machine learning algorithms can be utilized to create robust models that can categorize a wide range of skin conditions. First, the skin and non-skin must be distinguished in order to identify skin conditions. In order to determine the unambiguous a group of skin conditions A selection of five distinct machine learning algorithms has been employed to compile a dataset of skin infections. These algorithms include Kernel Self- pattern, Random Forest, Naive Bayes, Logistic Regression, and CNN, among others. A comparable investigation based on training accuracy and skepticism matrix criterion has been carried out and illustrated with graphs. It has been determined that, of all the applicants, CNN offers the highest level of training accuracy for the appropriate assumption of skin disorders. © 2024 IEEE.

关键词： Deep learning

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A Sample-Quality-Based Imitation Approach for Offline Reinforcement learning 3rd

A Sample-Quality-Based Imitation Approach for Offline Reinfo...

引用

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

作者： Li, You Liu, Juntao Zeng, Cheng Tang, Hainan Gong, Hui Wuhan Digital Engineering Institute Wuhan430205 China Hubei University Wuhan430062 China

ISBN: (纸本)9789819624676

The objective of offline reinforcement learning is to derive an optimal policy using a static dataset. Due to the estimation errors of out-of-distribution (OOD) action value, most offline reinforcement learning algorithms constrain the policy within the actions in the dataset. TD3 + BC is one of these methods to alleviate OOD problem. In TD3 + BC, the agent employs behavior cloning to align its policy closely with that found in the dataset. Although TD3 + BC performances well in some environments, it is too conservative. TD3 + BC simply imitates the existing policy in the dataset. It disregards the quality of the state-action pairs within the dataset. This leads to a failure to learn good policies. Therefore, in this paper, a sample quality based imitation approach for offline reinforcement learning (SQIRL) is proposed. In the proposed method, the quality of each training sample in the dataset is evaluated, and then the high-quality samples are imitated. Therefore, the proposed method not only alleviates the OOD problem as original TD3 + BC, but also learns a better policy. This algorithm is compared with the existing methods, such as imitation learning methods, policy regularization methods, uncertainty penalty methods, and policy constraints methods. It is found that this algorithm outperforms the comparison methods in the MuJoCo environment. It is important to highlight that this approach is especially effective in scenarios where the overall data quality is low. For example, the average normalized rewards of the proposed algorithm are 200%, 181% higher than those of TD3 + BC in the Random dataset of halfcheetah and hopper environment. Meanwhile, in the Full-Replay dataset of halfcheetah, hopper and walker2d environments, compared with the TD3 + BC, the average normalized rewards obtained by the proposed method increase by 81.4%,157%, and 21.8%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Research on short term stock selection strategy based on machine learning 3

Research on short term stock selection strategy based on mac...

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

作者： Ren, Yanzheng Beijing Inst Technol Sch Mechatron Beijing Peoples R China

ISBN: (纸本)9781665417907

Based on theoretical analysis, this paper constructs a short-term stock selection strategy based on machine learning. The sample set is constructed based on the closing price trend of individual stocks in the last 20 trading days, and the machine learning algorithms GBDT and GBRank are used for training, and machine learning is used to automatically perform pattern recognition on the processing capabilities of high-dimensional nonlinear data. When forecasting, the former will rank stocks with higher rising probability in the next 3 trading days, and the latter will rank stocks with greater gains in the nest 3 trading days. The stock selection strategy swaps positions every 3 trading days, and each time the top 10 stocks given by the equal-weight buying algorithm are used to construct an investment portfolio. The experimental results show that the short-term quantitative stock selection strategy based on the GBDT algorithm can outperform the market combination, namely the Shanghai and Shenzhen 300 Index, and has certain practicability.

关键词： machine learning pattern recognition stock selection strategy algorithm

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Fault Diagnosis in Micro Grid Using Wavelet Transform in Combination with machine learning 3

Fault Diagnosis in Micro Grid Using Wavelet Transform in Com...

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3rd Odisha international conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2024

作者： Saiprakash, Chidurala Kumar Joga, S Ramana Sowmya, Bera Krishna, Teki Vamsee Narayana, V. Lakshmi Manoj, Vasupalli Sr University Department of Electrical and Electronics Engineering Warangal India Dadi Institute of Engineering and Technology Eee Department Anakapalle India Department of EEE Tadepalligudem India Dadi Institute of Engineering and Technology Bs&h Department Anakapalle India Gmrit Eee Department Andhra Pradesh Rajam India

ISBN: (纸本)9798350354379

Microgrids, which are defined by their decentralized methods of generating and distributing electricity, have a vital role in improving the resilience and sustainability of energy systems. Ensuring the stability and efficiency of micro grids relies heavily on the identification of faults. Conventional fault detection techniques often lack the necessary sophistication to effectively manage the intricacy and variety of contemporary micro grid systems. This paper focuses on the issue of fault identification in micro grids. It presents a new method that combines wavelet transform and data mining approaches to tackle this difficulty. The goal is to provide a reliable approach that can accurately detect and categorize defects, resulting in reduced downtime and enhanced system performance. The wavelet transform allows for the analysis of signals by breaking them down into several frequency components, thereby offering a comprehensive depiction of the dynamics of a system. The suggested technique provides various benefits over existing fault detection methods, which often depend on unsophisticated threshold-based approaches. As a consequence, there is an enhancement in precision and dependability, which allows for prompt detection and resolution of errors, while reducing interruptions in the provision of energy. Moreover, the use of sophisticated data analytics improves the capability to distinguish between typical operational circumstances and distinct fault situations, hence minimizing inaccurate alerts and enhancing the efficiency of the system. The effectiveness of the suggested technique in improving the accuracy of fault detection and assuring the dependable functioning of micro grids is confirmed via simulation results. © 2024 IEEE.

关键词： Artificial intelligence

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2022 3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

2022 3rd International Conference on Computer Vision, Image ...

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3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

ISBN: (纸本)9781665459112

The proceedings contain 253 papers. The topics discussed include: testing the feasibility of quantizing the progress of stroke patients’ rehabilitation with a computer vision method;recognition of surface defects of aluminum profiles based on convolutional neural network;a novel generative adversarial network simulating the complementary structure of DNA genetic information;an improved lightweight method of convolutional neural network for face recognition;a calculation method for line-of-sight stable attitude of networked optical transceiver based on depth feedforward neural network;a grapevine virus disease detection method based on convolution neural network;a predict method of measuring equipment operation performance based on improved local weighted partial least squares;research on multi-scale network computer modeling based on mobile vendor management;and identification method of dress pattern drawing based on machine vision algorithm.

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3rd international conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819708918

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM Communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite Image Classification;primitive Roots and Euler Totient Function-Based Progressive Visual Cryptography;approach for Fire Detection Using Image Processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine learning Model for Traffic Prediction and pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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Harnessing Keypoints with Deep Metric learning for Lane Line Instance Segmentation 3

Harnessing Keypoints with Deep Metric Learning for Lane Line...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Danso, Juliana Mantebea Fengli, Zhang Gyamfi, Enoch Opanin Botchey, Francis Effirim Adu-Gyamfi, Daniel Kwabena, Sarpong Affum, Eric Chengdu610054 China Upper East Region Navrongo Ghana Uestc Sise Chengdu610054 China Koforidua Technical University Department of Computer Science Eastern Region Koforidua Ghana Upper East Region Navrongo Ghana Chengdu611731 China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

The paper mainly studies methods on lane line detection in autonomous driving applications. The specific aim of paper is to experiment on the instance-based segmentation technique for a lane line detection in images. We incorporate the process of selecting portions or patches of lane line pixels as keypoints. We then improve the proposed method with deep metric learning techniques, more specifically, with an associative embedding having an angular loss function, to ensure that, keypoints with similar features belongs to the same instance of lane line. Next, the instance detection of the lane line is completed by cluster fitting using Kmeans. We also experiment our modified lane line detection model on TuSimple and CULane datasets. To confirm the important applications of our proposed methods, we evaluated it and achieved some competitive accuracies. The experimental results show the feasibility of the proposed method © 2022 IEEE.

关键词： Embeddings

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

3rd International Conference on Mobile Radio Communications ...

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

ISBN: (纸本)9789811979811

The proceedings contain 56 papers. The special focus in this conference is on Mobile Radio Communications and 5G Networks. The topics include: Predictive Analysis of Air Pollutants Using machine learning;machine learning Techniques Applied of Land Use—Land Cover (LULC) Image Classification: Research Avenues Challenges with Issues;crime Analysis Using Computer Vision Approach with machine learning;natural Language Processing Implementation for Sentiment Analysis on Tweets;VLSI Implementation of BCH Encoder with Triple DES Encryption for Baseband Transceiver;design and Implementation of Image De-hazing Using Histogram Equalization;Improved Hybrid Unified Power Flow Controller Using Fractional Order PID Controlled Systems;Optimized Activation Function-Based SAR Ship Detection;framework for Implementation of Smart Driver Assistance System Using Augmented Reality;Power-Efficient Hardware Design of ECC Algorithm on High Performance FPGA;A Creative Domain of Blockchain Application: NFTs;sign Language recognition Using machine learning;an IoT-Based Health Monitoring System for Stress Detection in Human Beings;ioT-Based Driving pattern Analysis and Engine Sensor Damage Prediction Using Onboard Diagnostics;Solving the Element Detecting Problem in Graphs via Quantum Walk Search Algorithm (QWSA);critical Analysis of Secure Strategies Against Threats on Cloud Platform;pareto Optimal Solution for Fully Fuzzy Bi-criteria Multi-index Bulk Transportation Problem;automatic Candidature Selection by Artificial Natural Language Processing;elimination and Restoring Deduplicated Storage for Multilevel Integrated Approach with Cost Estimation;vulnerability Assessment of Cryptocurrency Wallet and Exchange Websites;greedy Theory Using Improved Performance Prim’s Algorithm, Big Bang Speedup of the Bellman–Ford Algorithm;performance Analysis of High-Speed Optical Communication Systems Under the Impact of Four Wave Mixing.

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Proceedings of 3rd international conference on Intelligent Engineering and Management, ICIEM 2022

Proceedings of 3rd International Conference on Intelligent E...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

ISBN: (纸本)9781665467568

The proceedings contain 168 papers. The topics discussed include: design for ergonomics: application of single- passenger electric car via design of experiments;detection of breast cancer using machine learning and deep learning methods;automobile insurance fraud detection: an overview;a survey on detection of lung cancer using different image processing techniques;deep learning methods for automatic number plate recognition system: a review;skin cancer detection using machine learning: a survey;transforming education system through artificial intelligence and machine learning;model for retailers with price dependent demand rates;neuromarketing: an emerging domain in the formal education system;design of dual slot microstrip antenna for wireless application;analysis of ancillary services provided by distributed energy resources in smart distribution grid;and grey scale image skeletonization of vein pattern using Hausdroff image binarization technique in biometric security system.

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