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

共 3244 条记录，以下是221-230 订阅

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

3rd International Conference on Mechanical and Energy Techno...

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

ISBN: (纸本)9789819749461

The proceedings contain 50 papers. The special focus in this conference is on Mechanical and Energy Technologies. The topics include: A Numerical Investigation on Estimation and Validation of Power Law Constants for TiB2 Reinforced AA2024 Composite Material;Power Flow Control of the Grid-Integrated DG System Using Hybrid Aquila Optimizer‑Tangent Search Algorithm;Design and Analysis of Manual Pallet Stacker by Using FEM Method;Design and Development of an IoT Counter Using ESP8266 for 6 Axis MIG Welding Robot;In-Depth Examination of the Mechanical Properties of AA6061/MoS2/SiC Hybrid Composites;optimization of Microchannel Heat Sink Shapes to Enhancing Electronic Cooling Efficiency;empowering Next-Generation Energy Infrastructure Through IoT for Climate Resilience;safeguarding Cryptocurrency Transactions: Leveraging Blockchain and machine learning for Enhanced Financial Security;utilizing machine learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media Platforms;improving Image Clarity with Artificial Intelligence-Powered Super-Resolution Methods;revolutionising Tumour Diagnosis: How Clinical Application of Artificial Intelligence and machine learning Enhances Accuracy and Efficiency;harnessing Medical databases and data mining in the Big data Era: Advancements and Applications in Healthcare;machine learning-Powered Design and Implementation for Classification of Missing data in IoT Applications;ioT-Based Security Detection for Cloud Web Applications: Leveraging Internet of Things Approaches;Exploring Ethical Considerations: Privacy and Accountability in Conversational Agents like ChatGPT;navigating Cross-Lingual Natural Language Processing: Challenges, Strategies, and Applications;fostering Understanding: Bridging the Gap Between Black-Box Models and Human Interpretability with Explainable Artificial Intelligence;semantic Analysis and machine learning Techniques for Enhancing Content-Based Image Retrieval.

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Enhancing Cloud Security with machine learning: Tackling data Breaches and Insider Threats 3

Enhancing Cloud Security with Machine Learning: Tackling Dat...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Nair, Sreejith Sreekandan Lakshmikanthan, Govindarajan Parthasarathy, J. Dinesh, P.S. Shanmugakani, Kavitha Jegajothi, B. Anna University Chennai India Jayalakshmi Institute of Technology Thoppur Dharmapuri India Sathyamangalam India Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College avadi India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331509675

Cloud computing remains susceptible to internal threats and data compromises, notwithstanding its foundational role in contemporary business operations within this age of automation. To mitigate this concern, a machine learning framework is suggested for the preemption of prospective threats. Most, breaches of organizational internal systems are concerns about user non-compliant behaviors aimed at messing up activity logs, which can be combatted using supervised models and change detection. There seems to be a class of threats that go undetected that the authors would refer to as behaviour patterns that do not follow standard operating behaviour for business. An interface of RF and LSTMs would be able to establish common patterns and knit rule-based threats to create business insights and acceptable threats. User and System exhaustion of RF was used to minimise covert system identifiers of users to examine access pattern data streams, which were unlimited in dimensional centers. Evaluation of detection of anomalous content, such as R- scoring, classification of log events as generic clustering, etc. It could prevail on the challenges of log records analysis and the checkpoints of Structural Semantic Communication principles. It can free up a range of security checkpoints by glaring at user access limits and ensuring multi-channel compliance. This approach would significantly strengthen data security on the Cloud by targeting high-risk actions across oriented sections of breaching data storage centers. © 2025 IEEE.

关键词： Cloud security

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Financial Forecasting Through Hybrid Algorithms of machine learning & Deep learning 3

Financial Forecasting Through Hybrid Algorithms of Machine L...

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

作者： Raman, Ramakrishnan Kafila Gehlot, Anita Shah, Heli Sanjay, C.P. Ponnusamy, R. Institute of Business Management Maharashtra Pune India SR University School of Business Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India Parul University Mechatronics & Robotics PIT India Sri Balaji University Department of IT& Business Analytics SBUP Pune India Chennai Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350399264

This article serves as a primer where integration of different techniques related to machine learning in the realm of economic prediction, planning, and analysis (FP&A). With its ability to extract insights quickly and automatically from massive datasets, machine learning looks to be a natural fit for supporting FP&*** this article, we highlight the caveats of utilizing classic machine learning approaches for scheduling and data allocation, since most of these methods are designed for forecasting (prediction) (causal inference). Newly designed double machine cognitive approach may answer causal concerns of interest, although naïve use of machine learning often fails in this context. In this paper, we survey the existing literature on the use of machine learning to financial planning and analysis, and we utilize a case study from the field of simulation research to demonstrate the utility of this technique for both decision making and planning. We also look at how better predicting and planning may be achieved with more data sets. © 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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2023 3rd international conference on Computing and Information Technology, ICCIT 2023

2023 3rd International Conference on Computing and Informati...

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

ISBN: (纸本)9798350321487

The proceedings contain 114 papers. The topics discussed include: multi-attribute featured layout generation for graphic design using capsule networks;combining CNNs and Bi-LSTMs for enhanced network intrusion detection: a deep learning approach;IoT-based monitoring of refrigerated vaccine storage: a literature review and a proposed solution;review the recent IoT systems for healthcare applications;combining data mining with rigorous whole-genome phylogenetics enables detailed comparative genomics from over 2.3 million genomes across Coronaviridae;fake news detection using cellular automata based deep learning;a new approach to sentiment analysis on twitter data with LSTM;action localization and recognition through unsupervised I3D and TSN;and improving prospective healthcare outcomes by leveraging open data and explainable AI.

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Post-COVID Cardiovascular Risk Assessment Using an Efficient machine learning Model 3

Post-COVID Cardiovascular Risk Assessment Using an Efficient...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Srinuvasa Rao, D. Reddy, Desidi Narsimha Ramisetti, Lova Naga Babu Mani, M S Radha Manga Pathipati, Harikrishna Pathuri, Siva Kumar Mendu, Mohana Prasad GMR Institute of Technology Dept of CSE AP Rajam India Soniks Consulting LLC 101 E park blvd PlanoTX United States Information Technology 10333 Harwin Dr. #375e HoustonTX77036 United States SRKR Engineering College Department of Information Technology AP Bhimavaram India Information Technology ITG Technologies 10998 S Wilcrest Dr HoustonTX77099 United States Koneru Lakshmaih Education Foundation Dept of CSE AP Vaddeswaram India Malineni Suseelamma Womens's Engineering College Dept of CSE PrakasamDst AP Singarayakonda India

ISBN: (纸本)9798350354171

Individuals who were exposed to COVID-19 in different parts of their bodies, including heart attacks, faced major consequences. The frequency of cardiovascular diseases is increasing in the healthcare industry. This illness most commonly affects people aged 20 to 48. In supervised learning, data mining mainly depends on categorization algorithms. Using various combinations of calculations and algorithms, the information obtained from hospital data analysis is used for early-stage prediction of post-Covid cardiovascular disease. machine learning is one of the strange inventions that have been widely used in a variety of fields, including the application of medical services to disease prediction. We examined the accuracy of machine learning algorithms that can be used in this study to predict heart disease analyzes and predict overall risk. One of the most current data mining methods is classification. The classification method's primary goal is to keep the data in the correct class. In this work, classification methods such as Logistic Regression Classifier, Naive-Bayes, and Ensemble Cardiovascular Prediction Classification Algorithm were used (ECVDPCA) and the dataset is taken from Kaggle repository. The goal of this study is to find the most exact classification approach and to improve accuracy in predicting Post-Covid Cardio-Vascular illness. Python was used to get the ideal solution in this case to predict the accuracy of the classifier in which the proposed classifier gave the best accuracy when compared with the other two classifiers i.e., 95. © 2024 IEEE.

关键词： COVID-19

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Innovations in Media C: Federated learning and BiLSTM Integration for Image and Video Analysis 3

Innovations in Media C: Federated Learning and BiLSTM Integr...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Kumar, A Suresh Balavivekanandhan, A. Al Ansari, Mohammed Saleh Syamala Rao, M.P.N.V. Peera, D Gouse Muthuperumal, S. Rathinam Technical Campus Department of Cse Coimbatore India Prof. Dhanapalan College of Science and Management Department of Computer Science Chennai India University of Bahrain Department of Chemical Engineering Bahrain Srm University Department of Cse Andhra Pradesh India Annamacharya University Department of Civil Engineering India K.Ramakrishnan College of Engineering Department of Mathematics Trichy India

ISBN: (纸本)9798350369083

In the ever-evolving landscape of media, the demand for efficient and robust analysis of images and videos has intensified. Traditional methods often struggle to keep pace with the scale and complexity of media data. In response, this study introduces a novel approach that integrates Federated learning (FL) and Bidirectional Long Short-Term Memory (BiLSTM) networks to enhance the analysis of images and videos in media applications. Federated learning, a decentralized machine learning technique, enables collaborative model training across multiple edge devices without centralized data aggregation, thus addressing privacy concerns and data silo issues inherent in traditional approaches. By leveraging FL, The proposed framework facilitates the aggregation of insights from diverse sources while preserving data privacy. Furthermore, the integration of BiLSTM networks offers enhanced temporal modeling capabilities, allowing for the extraction of contextual information from sequential data such as video *** experimentation on diverse media datasets, including images and videos, demonstrate the effectiveness of approach in tasks such as object recognition, scene understanding, and action recognition. The results showcase significant improvements in accuracy and efficiency compared to baseline methods, highlighting the potential of Federated learning and BiLSTM integration for advancing image and video analysis in media ***, This study contributes to the ongoing efforts to innovate media analysis techniques by harnessing the power of decentralized learning and advanced sequential modeling, paving the way for more intelligent and privacy-preserving media analysis systems. This method achieves an accuracy of 97.5% and has been implemented in Python. © 2024 IEEE.

关键词： Federated learning

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Terrain Classification through Satellite Images using Deep learning 3

Terrain Classification through Satellite Images using Deep L...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Khuman, Virendrasinh Arora, Shubh Siddhant Gupta, Rajeev Kumar Patel, Samir Pandit Deendayal Energy University Raisan Computer Science & Engineering Gujarat Gandhinagar382007 India

ISBN: (纸本)9798350354171

With the exponential growth of remote sensing data, particularly multispectral imagery, the integration of deep learning techniques such as Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) has become relevant for efficient classification tasks. Satellite imagery is procured in the manner of multispectral imagery, which is a collection of a few image layers enlist of the same scene, each of them acquired at a particular wavelength band. Challenges in multispectral images arise owing to the large volume of image data and the data variety due to high dimensionality and number of channels. Land Cover and Terrain classification is the process of identifying, mapping and delineating the surface of the earth into different units. It remains vital for its applications in a variety of fields. It enables environmental monitoring allowing for adaptation to ecosystem variations. Fields like Urban Planning, Disaster Management and Infrastructure development find essential advantages in their planning phases owing to such classification. Deep CNNs, or Convolutional Neural Networks, are advanced machine learning models renowned for their ability to extract intricate patterns from visual data, crucial in tasks like image recognition and classification. The reason behind the application of deep CNNs arises from their unparalleled computing efficiency. Through our work we try to explore the utilization of above mentioned deep learning and machine learning techniques in the classification of land cover, focusing on both multispectral imagery and provide an overview of the existing literature, examining the methodologies employed and the datasets utilized and the processing and handling of these large datasets. Additionally, we introduce various data sources and datasets commonly utilized in these studies, shedding light on the advancements, challenges, and future directions in the field of remote sensing-based land use and land cover classification. © 2024 IE

关键词： Convolutional neural networks

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

2023 IEEE 3rd International Conference on Data Science and C...

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

ISBN: (纸本)9798350341546

The proceedings contain 306 papers. The topics discussed include: CloneBAS: a code clone detection method based on abstract syntax tree and Simhash;research on the a-star algorithm based on path finding;study on vibration isolation characteristics of UHVDC converter valve saturable reactor;a market transaction model based on blockchain technology for multimicrogrid networks;optimization research on radar signal processing technology based on MTI-MTD;stock prediction using evolutionary attention-based LSTM;the reasons and improvement measures for the long time of replacing cross arm with 10 kV live;research on accurate three-dimensional modeling technology and intelligent mining in coal mines;evaluation on power equipment operation status monitoring system based on data mining;exploring deep learning-based visual localization techniques;and intelligent detection and recognition of pulley paying-off defects based on improved YOLOv5.

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Research on prediction model of mixed solution concentration based on neural network 24

Research on prediction model of mixed solution concentration...

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3rd international conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Xue, Bokai He, Yunkai Chen, Junhui Wang, Chaoyue Li, Zhende Qi, Jingnan Zhou, Xiaoyu Lanzhou Univ Lanzhou 730000 Peoples R China Nanchang Hangkong Univ Nanchang 330063 Jiangxi Peoples R China Jiangsu Univ Technol Changzhou 213001 Jiangsu Peoples R China Univ Elect Sci & Technol China Glasgow Coll Chengdu 611731 Peoples R China Guilin Univ Technol Nanning 532100 Peoples R China Harbin Inst Technol Weihai Weihai 264209 Peoples R China Nanjing Univ Informat Sci & Technol Nanjing 210044 Peoples R China

ISBN: (纸本)9798400716959

This study is based on a series of infrared spectroscopy data of mixed solutions, which recorded the spectral fluctuations by changing the infrared wavelength under known experimental conditions, including different concentrations of solutionU(IV), U(VI), and nitric acid. The solution of each concentration was performed in triplicate during the experiment, for a total of 256 experiments to obtain a more comprehensive and reliable data *** techniques based on neural networks are explored after obtaining datasets. We construct an efficient and accurate model of mixed solution concentration detection by fully exploiting the advantages of neural networks in automatic feature extraction and pattern learning. Studies have further explored how to achieve more comprehensive detection effects by mixing multiple models, including neural networks and other state-of-the-art prediction models. By tuning the model hyperparameters, performance analysis, and fine adjustment of weight allocation, we expect to build a hybrid model with good generalization performance and robustness. This study provides an advanced and feasible solution for the field of mixed solution concentration detection, with a wide range of research and application values. Finally, the research results of this paper aim to provide strong support for accurately predicting the concentration of various solutions in mixed solutions. By combining spectroscopy and machine learning methods, we seek to achieve higher prediction accuracy in practical applications, providing new analysis.

关键词： SG smoothing machine learning one-dimensional CNN hybrid model

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