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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1857 条记录，以下是221-230 订阅

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application of Natural Language Processing (NLP) in machine learning 3

Application of Natural Language Processing (NLP) in Machine ...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Shetty, Pallavi Udhayakumar, R. Patil, Akanksha Manwal, Manika Vadar, Parashuram Shankar Pavitha, N. Department of Mca India VelTech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Chennai India Computer Engineering PHCET Rasayani Mumbai University Maharashtra Rasayani India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India Vishwakarma University Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350330724

In recent years, research and development in the domains of artificial intelligence and its related sub - fields, such machine learning, deep learning, and natural language processing, have grown at an unheard-of rate. The availability of powerful operating systems at more affordable prices and the variety of applications available in these research domains are the new sources of enthusiasm. The capability of computer systems to explain human languages is known as natural language processing, and that is a challenging undertaking where various learning techniques have performed a crucial part in correct analysis. The NLP sector is flourishing, and one current debate assessment of professionals estimated that just by 2050, all smart computers will be capable of executing any intellectual work which an individual could accomplish. NLP and statistics-based 'machine learning' (ML) have demonstrated accomplishment in challenging learning challenges. Automation, self-management technologies, speech synthesis, and deep learning (DL) advancements have empowered machines to achieve heretofore unthinkable feats. Secondary method of data analysis has been done for this study to collect relevant and statistical information related to research topic. In this article, mathematical expressions and detailed analysis has been conducted. © 2023 IEEE.

关键词： Convolution

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A CNN Deep learning Technique for Botnet Attack Detection for IoT application 3

A CNN Deep Learning Technique for Botnet Attack Detection fo...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Batham, Yashi Kumar Tiwari, Rakesh Technocrats Institute of Technology and Science Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350322842

The Internet of Things (IoT) is a significant technology that enables us to develop many helpful online applications. The Internet of Things allows remote operating items using preexisting network infrastructure. Services that are based on information technology, computers, and other technologies are included in the cyber world. The cyber-world sees many protocols and technological advancements that lead to improvements. When it comes to cyber-based services, security is an extremely significant issue. Classification methods based on machine learning (ML) and deep learning (DL) can accurately forecast the most effective model for a dataset. A cyber assault on a wide scale carried out by devices infected with malware and managed remotely is known as a botnet attack. Under the context of IoT applications, an effective method for detecting botnet attacks is presented in this research. © 2023 IEEE.

关键词： Internet of things

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Data-driven multi period operation optimization of active distribution networks 3

Data-driven multi period operation optimization of active di...

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2023 IEEE 3rd international conference on Data Science and computer application, ICDSCA 2023

作者： Xiaoning, Tong Yueqiang, Wang Zhangquan, Qiu Yang, Huang State Grid Shanghai Electric Power Company Changxing Power Supply Company Shanghai201913 China

ISBN: (纸本)9798350341546

Traditional physical models are difficult to model and analyze the uncertainty factors of the power grid, and have weak adaptability to changes in ADN structure and new elements. This paper proposes a data-driven active distribution network operation optimization strategy, which uses data-driven replacement process simulation to directly obtain control strategies from real-time environmental operation information. The implicit internal logic between the optimal operation of the operation scenario and the state of the control equipment can be explored through extreme learning machine, so the mapping relationship between the operation control strategy and distribution network operation scenario can be established. The data-driven reactive power optimization method proposed in this article does not rely on actual system power flow calculations, achieving optimized scheduling of active distribution networks under data-driven conditions, and providing a foundation for control strategies of active distribution networks. © 2023 IEEE.

关键词： Active Distribution Network Data driven Extreme learning machine Operational optimization

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3rd international conference on Optimization, learning Algorithms and applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on Optimization, learning Algorithms and applications, OL2A 2023

ISBN: (纸本)9783031530357

The proceedings contain 26 papers. The special focus in this conference is on Optimization, learning Algorithms and applications. The topics include: Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models;Using LiDAR Data as Image for AI to Recognize Objects in the Mobile Robot Operational Environment;an Evaluation of Image Preprocessing in Skin Lesions Detection;an Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills;vehicle Industry Big Data Analysis Using Clustering Approaches;enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data;An Efficient GPU Parallelization of the Jaya Optimization Algorithm and Its application for Solving Large Systems of Nonlinear Equations;Multi-objective Optimal Sizing of an AC/DC Grid Connected Microgrid System;sub-system Integration and Health Dashboard for Autonomous Mobile Robots;movement Pattern Recognition in Boxing Using Raw Inertial Measurements;optimization Models for Hydrokinetic Energy Generated Downstream of Hydropower Plants;deep Conditional Measure Quantization;fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods;Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation;deep learning and machine learning Techniques Applied to Speaker Identification on Small Datasets;performance of Heuristics for Classifying Leftovers from Cutting Stock Problem;Deep learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach;identification of Late Blight in Potato Leaves Using Image Processing and machine learning;adaptive Convolutional Neural Network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario;Impact of EMG Signal Filters on machine learning Model Training: A Comparison with Clustering on Raw Signal;assessing the 3D Position of a Car with a Single 2D Camera Usin

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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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machine learning for Privacy Preservation with Minimal Information Leakage 3

Machine Learning for Privacy Preservation with Minimal Infor...

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

作者： Verma, Mr. Vikas Kaur, Simerjeet Vivekananda Global University Department of Mechanical Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

A wide range of application sectors are progressively using machine learning (ML). A successful machine learning (ML) model often requires a lot of training data and powerful processing resources, especially the newly developing deep neural network model. Due to the possibility of information leakage and the changing legislative landscapes that progressively limit Privacy-sensitive data access and use, the need for large amounts of available data, raises significant privacy concerns. Additionally, adversarial techniques. Therefore, carefully thought-out privacy-preserving machine learning (PPML) solutions are essential and are generating more and more research attention from academia and business. More and more PPML initiatives are being put forth through the incorporation of privacy-preserving methods into machine learning algorithms. As existing PPML techniques intersect with ML, systems, security, and privacy, among others, there is an important requirement to understand current research, associated barriers, and roadmaps for future research. This study offers a PGU model to help evaluate various PPML solutions by intricately breaking down each solution's privacy-preserving functions. It also evaluates and summarizes existing privacy-preserving methodologies in a methodical manner. Phase, Guarantee, and Technical Utility make up the PGU model's trinity. We also address the special features and difficulties of PPML and suggest future study avenues that will be beneficial to a wide variety of research communities in the fields of machine learning, security systems, distributed systems and privacy system. © 2023 IEEE.

关键词： Deep neural networks

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application of computer-based support vector machine technology in power customer credit rating 3

Application of computer-based support vector machine technol...

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3rd international conference on computer Science and Management Technology, ICCSMT 2022

作者： Xiao, Lan Wu, Yan Fu, Xiao Zhang, Ziwen Xiao, Yuhao State Grid Sichuan Information & Telecommunication Company Chengdu610000 China

ISBN: (纸本)9798350320404

With the rapid development of the times, the field of computer technology is becoming wider and wider. In view of the research status of customer risk management in electric power enterprises, this paper establishes a customer risk assessment model based on computer support vector machine, and does some research work on the analysis and prediction of power customer credit. The experiment shows that the statistical learning method based on computer support vector machine can evaluate the credit of electric power customers, help electric power enterprises to select high-quality customers, and provide decision-making basis for electric power enterprises to actively prevent and avoid marketing risks. © 2022 IEEE.

关键词： Support vector machines

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3rd international conference on Optimization, learning Algorithms and applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on Optimization, learning Algorithms and applications, OL2A 2023

ISBN: (纸本)9783031530241

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions 3

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yashas, D. Kashyap, M. Shivani Charan, P.R. Hemanth Kumar, G.P. Abhishek, B.K. Deekshith, C. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Dept of Information Science and Engineering Bengaluru India Bnm Institute of Technology Dept of Artificial Intelligence and Machine Learning Bengaluru India Siddaganga Institute of Technology Dept of Artificial Intelligence and Data Science Tumkur India Vidyavardhaka College of Engineering Dept of Computer Science and Engineering Mysore India

ISBN: (纸本)9798331515911

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field. © 2025 IEEE.

关键词： Convolutional neural networks

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Advancing Phishing Detection with Random Forest: A machine learning Approach for Enhanced Cybersecurity 3

Advancing Phishing Detection with Random Forest: A Machine L...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chandra, Sucheta Das, Priyanka Mandal, Ankita Chakroborty, Sriparno Chowdhury, Ishika Ghosh, Kathamrita University of Engineering & Management Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798331523893

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection. © 2025 IEEE.

关键词： Random forests

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