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machine learning Method for Optimal Route Calculation

Machine Learning Method for Optimal Route Calculation

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IEEE international conference on Methods and Systems of Navigation and Motion Control (MSNMC)

作者： Serhii Migel Maksym Zaliskyi Maryna Maloied Alina Osipchuk Roman Odarchenko Oleksandr Bondarev Department of Telecommunication and Radioelectronic Systems National Aviation University Kyiv Ukraine

the industrialization of society and the transition to Industry 4.0 requires the use of advanced data processing and decision-making technologies. In connection with the utilization of perfect measuring systems and monitoring of a large number of parameters, the volume of statistical data increases when it is necessary to process them in real time. On the other hand, the computing possibilities of information systems is also increasing, which contributes to expanding the opportunity of using artificial intelligence technologies based on machine learning and deep learning methods. this paper is devoted to the development and analysis of a new machine learning method for determining the optimal route of an unmanned system or a guided vehicle. the initial information for building a route is a map with the designation of permitted and prohibited zones of movement from a given point to the final destination. the proposed method is an improvement of DBSCAN nonparametric clustering technologies and Dijkstra's algorithm.

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“Ensemble Incremental Model” for Misinformation Detection in Healthcare

“Ensemble Incremental Model” for Misinformation Detection ...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Yashoda Barve Jatinderkumar R. Saini Suryadatta College of Management Information Research & Technology Pune India Symbiosis Institute of Computer Studies and Research Symbiosis International (Deemed) University Pune India

With the growth of social media usage, information sharing has become a concern due to the spread of misinformation and its strong impact on society. Researchers have developed innovative techniques to detect and classify false information online. Improving the accuracy of these models using ensemble learning methods has gained popularity recently. However, these ensemble models are based on standard machine learning and ensemble classifiers and don't consider the novel classifiers related to false information detection. In addition, the ensemble models developed so far consider only textual features, leading to over fitting the models. Further, the changing data is a concern as the models show poor performance with old data. Considering these limitations, authors have proposed the 'Ensemble Incremental Model (ElM),’ which extracts novel user and textual features, implements the ensemble method using recently developed Content Similarity Measure (CSM) algorithm and standard machine learning classifiers, and incorporates incremental learning approach. It was observed that the novel approach EIM showed the highest accuracy of 93.44%.

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Optimised Home Electricity Management using machine learning

Optimised Home Electricity Management using Machine Learning

引用

international conference on computing Methodologies and Communication (ICCMC)

作者： M. Arunkumar S. Devadharshini Dept of Information Technology IFET College of Engineering Villupuram India

the interactive home automation system that is based on the Internet of things (IoT) is a cutting-edge technical breakthrough that allows remote control and monitoring of home appliances in addition to the actual purchase of items. the Internet of Objects (IoT) aims to bring everything in the world together under a unified infrastructure, which will give us the ability to manage the things that are around us and provide us with continual information on the state of those things. the fundamental objective of the study is to provide a recommendation for an effective Internet of things implementation that may be put to use to carry out remote monitoring and control of household appliances. the user interface for this system is implemented in a manner that makes advantage of portable devices. In addition, it provides control of home appliances using mobile devices by employing Wi-Fi as the communication protocol and the Raspberry Pi Uno as the controller. the home user will be responsible for following the system as it travels over the Internet via the web-based user interface. For the purpose of anticipating people's usage of power and so enabling them to become less reliant on it, machine learning is being utilised here.

关键词： Home appliances Protocols machine learning User interfaces software Mobile handsets Internet of things

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Applications of Artificial Intelligence and machine learning in Contemporary Quality Engineering

Applications of Artificial Intelligence and Machine Learning...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Aneesh Pradeep Software Engineering New Uzbekistan University Tashkent Uzbekistan

the increasing complexity and global competition in various industries have compelled organizations to prioritize quality engineering as a critical factor for success. In recent years, Artificial Intelligence (AI) and machine learning (ML) have emerged as transformative technologies with the potential to revolutionize quality engineering practices. this paper explores the applications of artificial intelligence (AI) and machine learning (ML) in contemporary quality engineering. With the rapid growth of technology and increased demand for quality products, there is a need for efficient and effective methods for quality control. AI and ML provide innovative solutions for improving quality engineering processes' accuracy, speed, and *** discuss the role of AI and ML in data analysis and decision-making processes, enabling quality engineers to identify patterns, anomalies, and correlations within vast datasets. these techniques empower organizations to make data-driven decisions, optimize processes, and detect potential quality issues in realtime. the limitations and difficulties of these technologies are discussed, along with potential future lines of investigation. the research presented in this paper suggests that AI and ML hold great promise for revolutionizing quality engineering and raising standards across the board for goods and services.

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Attack Detection on Internet of things Devices using machine learning Techniques

Attack Detection on Internet of Things Devices using Machine...

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international conference on ent computing and Control Systems (ICICCS)

作者： Yatharth Sharma Vansh Kumar Himanshi Chaudhary Department of Computer Science and Engineering KIET Group of Institutions Ghaziabad India

Today, the exponential growth of technology and the boom in connection demand has resulted in significant growth of Internet of things (IoT) devices. In light of the enormous increase in smart devices, secure communication between them has become a major challenge due to an increase in cyberattacks. Adding hardware-based security is challenging due to the complex architecture of IoT devices and limited computation ability. Since IoT devices produce enormous amount of data, machine learning (ML) can be utilized to detect attacks on IoT devices. this study proposes some machine learning models to detect and prevent Botnet IoT attacks. the Network-based Detection of Bait-and-Switch IoT Attacks (N-BaIoT) dataset is used to train the models. the performance of models is analyzed using precision, recall and F1-score by carrying out multiclass classification. Experimental result shows that some of the proposed models are efficient in detecting botnet attacks with 99% accuracy. the model can also extend to use complete dataset and new ML techniques.

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Prognosis of Stroke using machine learning Algorithms

Prognosis of Stroke using Machine Learning Algorithms

引用

international conference on computing Methodologies and Communication (ICCMC)

作者： Kanaga Suba Raja. S B. Chandra K. Kausalya Ciddarth RM Gokul Ranjith V Easwari Engineering College Chennai Tamilnadu India Department of Information Technology Easwari Engineering College Chennai India

Stroke is an major causes of death and widest disability worldwide. It is one of great importance to predict the risk of having stroke for better prevention and early treatment. this brief report presents my attempt to develop a machine learning (ML) model to accurately and quickly predict whether or not a person suffered stroke based on the Random Forest dataset, K-Nearest Neighbor (KNN) Algorithm, Logistic *** the following process of classification and regression are proposed in the data moreover training of predefined hospital dataset are been performed. Exploratory Data Analysis (EDA) process has been implemented to investigating or discover the dataset outliers to achieve better classification. the main objective of this paper is to develop an effective stroke detection health care service to predict stroke in better performance. In addition to providing performance benchmark across various ML models, the report also examines which features are useful for stroke prediction.

关键词： Training machine learning algorithms Hospitals Predictive models Stroke (medical condition) Prediction algorithms Classification algorithms

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machine learning Based CH Selection for Energy Efficient Routing in WSN

Machine Learning Based CH Selection for Energy Efficient Rou...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Shankar Madkar Sanjay Pardeshi M.S. Kumbhar Shivaji University Kolhapur Maharashtra India Government Women's Polytechnic Tasgaon Maharashtra India Dept. of ETC Rajarambapu Institute of Technology Rajaramnagar Sangli Maharashtra India

In wireless sensor networks (WSNs), energy efficiency is a crucial consideration due to the limited battery power of sensor nodes. this paper proposes a machine learning-based energy-efficient routing protocol (ML-EERP) that takes into account traffic awareness. the protocol incorporates initial clustering using the K-medoid algorithm and utilizes machine learning techniques for cluster head selection based on factors such as distance, traffic, and energy levels. the protocol's experimental analysis focuses on evaluating the effectiveness of selecting the most suitable candidate nodes as cluster heads and assessing their impact on energy efficiency. Comparative analysis is performed by comparing the results of the proposed machine learning-based protocol with the traditional LEACH protocol. the performance evaluation of the proposed protocol includes metrics such as network lifetime, throughput, packet delivery ratio, and energy consumption analysis. the experimental results demonstrate significant improvements in energy efficiency compared to the LEACH protocol. this highlights the effectiveness of using machine learning techniques in the selection of cluster heads, leading to enhanced energy efficiency and improved network performance. By employing a machine learning-based approach, the proposed protocol offers a promising solution for achieving energy efficiency in WSNs. It leverages the power of machine learning algorithms to adaptively select cluster heads based on various parameters, resulting in improved energy utilization and extended network lifespan.

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machine learning-based Traffic Classification and Channel Allocation in IoT: A Survey

Machine Learning-based Traffic Classification and Channel Al...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Santosh H Lavate P. K. Srivastava Research Scholar Department of Electronics and Telecommunication Engineering AISSMS Institute of Information Technology Pune Maharashtra India ISBM College of Engineering Pune Maharashtra India

the increasing number of Internet of things devices has resulted in the development of dynamic and heterogeneous traffic patterns. Efficient communication is required in order to maintain the smooth functioning of these networks. Traditional methods of channel allocation and traffic classification are not able to handle the complexity of these traffic patterns. machine learning techniques are being widely used to address the various challenges related to the development and maintenance of IoT networks. they can help improve the efficiency of the networks by identifying and prioritizing the traffic patterns. this paper reviews the current state of the art in machine learning-based channel allocation and traffic classification techniques. the goal of this paper is to provide a comprehensive analysis of the current state-of-art techniques in the field of machine learning-based traffic classification and channel allocation. It also aims to highlight the potential future directions of this technology. the paper reviews the current state of the art in machine learning-based channel allocation and traffic classification techniques for the Internet of things. the complexity of the traffic patterns encountered by the Internet of things network is a major challenge that can be solved through the use of machine learning techniques. this paper presents an overview of the various techniques that are being used to improve the efficiency of IoT networks. the paper also reviews the various open research questions and the potential directions in the domain of machine learning in the context of IoT channel allocation and traffic classification. the objective of this survey paper is to provide practitioners and researchers with a convenient reference.

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machine learning and Deep learning Techniques on Accurate Risk Prediction of Coronary Heart Disease

Machine Learning and Deep Learning Techniques on Accurate Ri...

引用

international conference on computing Methodologies and Communication (ICCMC)

作者： Mandadi Sai Gangadhar Kalyanam Venkata Sree Sai Salem Hruthik Sai Kumar Kanaparti Anil Kumar M. Kavitha S.S. Aravinth Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

Coronary artery disorder is the heart disease that is prevalent across the globe today. It's a serious issue for the humans as it requires proper diagnosis. Coronary artery disease develops over time as a result of plaque build- up in coronary arteries results in partial blockage of blood flow as the, which is primarily composed of cholesterol, calcium, and fibrin. Coronary arteries help human heart to pump oxygen-rich blood throughout the body by supplying it. However, appropriate diagnosis and early prediction will lessen the likelihood of developing it. this study explores the possibility of foreseeing cardiac illness at an early stage using deep learning algorithms. this study's primary goal is to properly determine whether a person has heart problems or not. by using deep learning techniques, Deep learning and various machine learning algorithms can both be used to implement early-stage prediction. Data mining, Decision trees, Naive Bayes, Artificial Neural Networks (ANN) are some examples through which it can be implemented. the research is going to be done using ANN Model.

关键词： Heart Deep learning machine learning algorithms Computational modeling Transfer learning Artificial neural networks Predictive models

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A Survey: machine learning and Deep learning in Wheat Disease Detection and Classification

A Survey: Machine Learning and Deep Learning in Wheat Diseas...

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international conference on ent computing and Control Systems (ICICCS)

作者： Nitin Sahu Aruna Bhat Department of Computer Science & Engineering Delhi Technological University Delhi India

Cereals are a significant and vital source of food for humans. To feed the world’s expanding population, farmers must produce more crops. Plant diseases, however, have an impact on crop production and food quality. Wheat is among the most important crops consumed worldwide. Since Wheat Disease causes production loss, early disease detection and classification are absolutely essential. this research study has conducted a literature survey on studies published from the year 2017 to 2022 and summarized the three main types of Wheat Disease (fungal, bacterial, and insects), Wheat Disease datasets, and the current state of the art from the last six years of research. this research study has examined 24 studies on disease identification and classification by using various machine learning (ML) and Deep learning (DL) algorithms. the proposed research analysis shows that the majority of the literature on Wheat Disease focused on fungal disease, and the majority of the datasets used were self-acquired. Many State-of-the-art models have already produced excellent results, and many more need to be developed.

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