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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 811 条记录，以下是561-570 订阅

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Investigation of Renewable Energy Integration Challenges and Condition Monitoring Using Optimized Tree in three Phase Grid System

Investigation of Renewable Energy Integration Challenges and...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Naila Shah Ahteshamul Haque Mohammad Amir Anil Kumar Dept. of Electrical Engineering Advanced Power Electronics Research Lab Jamia Millia Islamia India Ministry of New & Renewable Energy (MNRE) Government of India

the stochastic nature of distributed renewable energy resources (DREs) poses many operational and technical challenges in integrating renewable energy resources. Voltage instability is one of the challenges due to integration of renewable energy resources in which the supply is less than the demand of energy at the distribution side. the major challenge associated with integration of renewable energy resources is the islanding scenario which needs to be addressed. In this paper, an efficient and accurate machine-learning algorithm is developed for the islanding detection and classification process. For this purpose, simulation and compilation perform on a 12kW 3Ø grid-connected photovoltaic (PV) model in Typhoon Hardware-in-Loop (HIL) simulator in which data is extracted for low voltage ride through (LVRT) grid fault. Furthermore, this data is trained with the proposed machine-learning algorithm for the islanding classification mechanism. the proposed Optimizable Decision Tree (O-DT) technique has a training accuracy of 99.7% and can efficiently predict islanding classification with a speed of about 140000bs/sec.

关键词： Training Renewable energy sources Tropical cyclones machine learning Voltage Data models Real-time systems

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Network Intrusion Detection using Deep Reinforcement learning

Network Intrusion Detection using Deep Reinforcement Learnin...

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international conference on computing Methodologies and Communication (ICCMC)

作者： V. Sujatha Kodimala Lakshmi Prasanna Kakarla Niharika Vanukuri Charishma Kamma Bhavya Sai Computer Science and Engineering Vignan's Nirula Institute of Technology and Sciences for Women Guntur India

the number of internet-connected systems has increased by a huge amount in recent years. these systems are highly vulnerable and increasingly at risk from cyber- attacks. these cyber-attacks need more advanced and intelligent cyber defense systems that include agents that can learn to make judgements on their own without consulting human specialists. Deep learning-based reinforcement learning (DRL) is very good at handling complicated, dynamic, and especially high-dimensional cyber protection problems. this article provides a modern network intrusion detection technology, which employs a deep feed-forward neural network method and reinforcement learning, which is based on Q- learning. In order to detect various sorts of intrusions in the network using an automated trial- and-error method and continually improve its detection skills, the Deep Q-learning (DQL) model is proposed. the accuracy of the model proposed is 91.4%, while the accuracy of other self-taught learning models is 88.4% and it is a similar case for recall rate and precision as well which are 90.2% and 92.8%. Our experimental findings further demonstrate that our suggested DQL beats other comparable machine learning methods and is very good at identifying various intrusion classifications.

关键词： Training Deep learning Q-learning machine learning algorithms Computational modeling Network intrusion detection Predictive models

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Automatic Attendance System based on FaceRecognition using machine learning

Automatic Attendance System based on FaceRecognition using M...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Sk. Sharmila G. Kavya Nagasai M. Sowmya A.Sai Prasanna S. Navya Sri N. Meghana Department of Information Technology Vignan'sNirula Institute of Technology and Science for Women PedaPalakaluru Guntur Andhra Pradesh

Advancements have been made in the field of face recognition technology. Controlling aperson's attendance in real time via facial recognition technology. Face recognition is the process of recognizing a person by their facial characteristics. Various computer vision algorithms, including those used for face detection, expression recognition, and video surveillance, can make use of a person's unique facial features. A face detection and recognition- based attendance monitoring system might very well rapidly and accurately locate and identify people in photographs or video footage. In addition to being laborious to maintain, the time-honored practice of physically ticking off attendees is inefficient. this research work presents the working of a cascade classifier built with machine learning to improve the face detection results. this has been done by comparing the face images in the current image to a database of previously trained faces. the acquired image contributions are searchedfor a previously registered face, and once found, the person's attendance is recorded automatically.

关键词： Portable computers Webcams Face recognition machine learning Video surveillance software Real-time systems

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machine learning Methodologies in Social Platform: An Analytical Survey 7th

Machine Learning Methodologies in Social Platform: An Analyt...

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7th international conference on soft computing and Signal Processing, ICSCSP 2024

作者： Pandey, Priyam Paul, Anuruddha Pradhan, Roshni Pattnaik, Aradhana Kar, Rwik School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India

ISBN: (纸本)9789819799251

Social media platforms have become a breeding ground for various criminal activities, leading to the need for a comprehensive survey to understand their nature and detection methods. the vast amount of data available on these platforms requires advanced analytical techniques to derive meaningful insights. In this paper, we present valuable insights from multiple studies, addressing crimes, event detection, and machine learning applications on social media. We propose methodologies for crime detection, event detection, and the utilization of machine learning algorithms, emphasizing the significance of interchangeability and efficiency in this context. Our findings suggest that advanced analytical techniques can help improve crime detection and event extraction, and machine learning algorithms can be effectively utilized to achieve this goal. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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Intelligent Virtual Laboratory Development and Implementation using the RASA Framework

Intelligent Virtual Laboratory Development and Implementatio...

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international conference on computing Methodologies and Communication (ICCMC)

作者： R. Josphineleela S. Kaliappan Natrayan L Upendra Mohan Bhatt Department of Computer Science Engineering Panimalar Engineering College poonamallee India Department of Mechanical Engineering Velammal Institute of Technology Chennai India Department of Mechanical Engineering Saveetha School of Engineering SIMATS India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

Virtual environments for learning with intelligent support are in high demand right now. this is due to the fact that smart e-learning environments must be built internally in order to increase students' hands-on abilities. through inquiry and demonstration in a smart e-lab environment, these talents are linked to several disciplines of science and engineering. Students and instructors may make the transition from working in virtual environments to doing experiments using actual materials. Lab assistants often spend the bulk of their time making the same mistakes. As a result, this research looked at the technology, proposed approach, and implementation of an intelligent virtual C programming laboratory. this ensures that virtual assistants employ chatbots to fix student faults. the environment is based on his NLP (natural language processing) and machine learning models, with web application development using his MERN stack and virtual assistant production using the RASA, Artificial Intelligence (AI) framework.

关键词： Training Electronic learning Virtual assistants Memory management Virtual environments Production machine learning

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A Deep learning Approach for Recognizing Malayalam Sign Language Gestures

A Deep Learning Approach for Recognizing Malayalam Sign Lang...

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international conference on ent computing and Control Systems (ICICCS)

作者： Kevin Issac Esther Daniel Bright Gee Varghese Dept. of Computer Science & Engineering Karunya Institute of Technology and Sciences Coimbatore India Dept. of Computer Science Maharishi International University Fairfield USA

Communication involves the sharing or exchanging of information, ideas, and emotions, which requires a common language between individuals. For those with speech impairments, sign language serves as a means of communication. the lack of understanding of sign language can create difficulties in communication between individuals with speech impairments and those without, posing a significant challenge. One solution to this challenge is to create a machine learning-based model that recognizes various sign language motions and converts them into a shared language. this model can be trained to benefit those seeking to interact and communicate with individuals with speech impairments. Although Sign Language Recognition systems exist, they are limited in the number of languages that can be recognized. this article introduces a real-time Sign Language Recognition system that employs transfer learning with TensorFlow and utilizes a webcam for the purpose of detecting and interpreting sign language gestures specific to the Malayalam language.

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Efficient Time Series Photovoltaic Production Forecasting using FLAML with Traditional machine learning Algorithms

Efficient Time Series Photovoltaic Production Forecasting us...

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international conference on ent computing and Control Systems (ICICCS)

作者： Dora Praveen Kumar Navjot Kaur Shyam Sundar Kushana Computer Science and Engineering Lovely Professional University Punjab India Aerospace Engineering Lovely Professional University Punjab India

this study explores the use of automatic photovoltaic production forecasting. the library is applied with five traditional machine learning algorithms, including Xgb_Limitdepth, Random-Forest, Extra-Tree, XGBoost, and lightGBM. the dataset used in this study is obtained from a photovoltaic power plant located in a sunny region in Europe, Greece, and it includes hourly power production data for a period of four years. the results show that FLAML can automatically optimize hyperparameters and select the best algorithm for the given data, achieving accurate forecasts for photovoltaic power production. the experiment shows that the combination of FLAML with traditional machine learning algorithms performs better than using each algorithm individually. In addition, the study compares the performance of the different algorithms and demonstrates that XGBoost and LGBM outperform the other algorithms. Overall, this study demonstrates the usefulness of FLAML in time series forecasting and its potential to optimize the forecasting process by automating the selection of the best algorithm and hyperparameters. this study’s empirical evidence can be useful for researchers and practitioners in the field of renewable energy, particularly in photovoltaic power production, and similar can be used for wind power production and tidal power production.

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Fraud Detection in Online Payments using machine learning Techniques

Fraud Detection in Online Payments using Machine Learning Te...

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international conference on ent computing and Control Systems (ICICCS)

作者： U. Siddaiah P. Anjaneyulu Y. Haritha M Ramesh Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

People rely on online transactions for nearly everything in today’s environment. Online transactions offer several benefits, such as ease of use, viability, speedier payments, etc., but they also have some drawbacks, such as fraud, phishing, data loss, etc. As online transactions grow, there is a continuing risk of frauds and deceptive transactions that could violate a person’s privacy. In order to prevent high risk transactions, numerous commercial banks and insurance firms invested millions of rupees in the development of transaction detection systems. this research study has introduced a feature-engineered machine learning-based model for detecting transaction fraud. By processing as much data as it can, the algorithm can gain experience, strengthen its stability, and increase its performance. the effort to detect online fraud transactions can use these algorithms. In this, a dataset of specific online transactions is obtained. then, with the aid of machine learning algorithms, unusual or distinctive data patterns that will be helpful in identifying any transactions that are fraudulent are discovered. the XGBoost algorithm is a cluster of decision trees, which will be utilized in order to achieve the best outcomes. this algorithm has recently taken control of the ML industry. Comparing this approach to other ML algorithms reveals that it is faster and more accurate.

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software Effort Estimation using ANN (Back Propagation)

Software Effort Estimation using ANN (Back Propagation)

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international conference on computing Methodologies and Communication (ICCMC)

作者： Ravi Kumar B N Yeresime Suresh Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India Dept. of Computer Science and Engineering Ballari Institute of Technology and Management Ballari India

Project managers often use Effort Estimating strategies to manage the human resources of current or upcoming software projects. Prior to project implementation, cost, time, and personnel estimation are basically necessary. For every project of software, getting an accuracy in Effort Estimation has always been difficult. In case of this study, estimating the labor involved in developing software determined using a Back Propagation Model. this model's goal is to investigate the capabilities and potential uses of utilizing Artificial Neural Networks (ANN) as a tool for forecasting the effort required for software development. In order to estimate the software work, we are attempting to implement a machine learning technique in this research. Out of all machine learning methods, we are applying an algorithm based on Artificial Neural Networks that is Back propagation. the Desharnais dataset, a well-known publicly available dataset for estimation of software effort, is used to test the approach. the performance and accuracy of the tested model have been evaluated using three metrics: MMRE, MRE, and Pred (0.25). In the sections below that follows, I explain the algorithm and its results.

关键词： Backpropagation Computational modeling software algorithms Estimation Artificial neural networks machine learning Predictive models

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Exploring Aromatase Inhibitors to Breast Cancer with machine learning Approach Based Quantitative Structure-Activity Relationships Model Technique

Exploring Aromatase Inhibitors to Breast Cancer with Machine...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Sahebrao B. Pawar N.K. Deshmukh School of Computational Sciences Swami Ramanand Teerth Marathwada University Nanded India

the conversion of androgen depends in large part on the overexpression of aromatase in many females. Many therapeutic and pharmaceutical drugs, as well as inhibitors, have been developed, but some of them either don't have the strength to control diseases caused by the aromatase enzyme or they encourage unwelcome mammary epithelial cells inside the breast. the prospect and potential for creating medicines with unique pharmacological and therapeutic capabilities using the Multi-machine learning Classifier Based-Quantitative Structure-Activity Relationship (MMLCB-QSAR) process model were looked into. In order to forecast the chemical classes with the highest potential for Aromatase, the model uses chemical descriptors and supervised machine learning to inhibit utilizing real multidimensional Aromatase inhibitors that were collected from a curated database. the multi-dimensional dataset is mainly explored and visualized using the t-SNE algorithm. Despite having excellent predictive abilities, the Bootstrap aggregation or Bagging Classifier (RandomForest Classifier) algorithm excels. More specifically, regularization and hyperparameter tuning are responsible for outstanding model results in statistics and more accurate prediction using 10-fold cross-validation. Model parameters like the Matthews correlation coefficient score (0.9397), the accuracy Score (0.973%), and the log loss probability (0.1208).

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