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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3232 条记录，以下是481-490 订阅

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4th international Informatics and software engineering conference - Symposium Program, IISEC 2023

4th International Informatics and Software Engineering Confe...

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4th international Informatics and software engineering conference, IISEC 2023

ISBN: (纸本)9798350318036

The proceedings contain 57 papers. The topics discussed include: application of ChatGPT in the tourism domain: potential structures and challenges;oblique logistic function for the rank-frequency distribution of letters;innovation in corporate cyber security;a digital twin framework for edge server placement in mobile edge computing;mobile app for kidney patients to provide a comprehensive solution to manage disease;development of a genetic algorithm for vehicle routing problem in military logistics distribution;analyzing customer churn: a comparative study of machine learning models on Pay-TV subscribers in turkey;a study on slice-aware industrial edge applications for next-generation private networks;and a comparison of optimization methods for path finding problem.

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Exploring machine learning in IoT Smart Home Automation 8

Exploring Machine Learning in IoT Smart Home Automation

引用

8th IEEE international conference on software engineering and Computer Systems, ICSECS 2023

作者： Waseem, Quadri Wan Din, Wan Isni Sofiah Bin Ab Rahman, Azamuddin Nisar, Kashif Faculty of Computing Pekan Malaysia AnalytiCray Kuala Lumpur55100 Malaysia Swinburne University of Technology Sydney Professional Computing Data Science ParramattaNSW2150 Australia

ISBN: (纸本)9798350310931

The Internet of Things (IoT) has evolved in these years. Various types of organizations, industries, research domains and almost all types of intelligent future applications are utilizing the advantages of IoT. These applications include smart homes, smart cities, smart infrastructure smart communities, smart healthcare, smart agriculture and many more. 'Smart Homes' has emerged as one the latest Internet of Things (IoT) applications known to automate household equipment's using remote or automated functioning from remote locations to improve the quality of life for its inhabitants. For a smart home system to function effectively, the machine learning (ML) implementation must go beyond basic remote control and simple automation. To fully realize its potential and provide homeowners with tremendous and unexpected benefits, more research and development in the fields of machine intelligence and smart home automation are required. In this research work, we aim to traverse ML in IoT smart home automation by classifying the home automation applications. We propose a taxonomy of machine learning (ML) for smart homes based on its application. This research also includes related surveys and literature reviews along with open challenges and issues as well as future directions in detail. © 2023 IEEE.

关键词： Automation

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Improving machine learning Algorithm’s Accuracy for Detecting Code Smell Using Data Balancing and Parameter Optimization 5th

Improving Machine Learning Algorithm’s Accuracy for Detecti...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Nandini, Abhishilpa Singh, Randeep Rathee, Amit Department of Computer Science and Engineering IEC University Solan India Department of Computer Science GC Barota Haryana Sonipat India

ISBN: (纸本)9789819773701

Detecting code smells is vital for improving software quality, improving its maintainability, and reducing software failures and faults. Code smell detection using machine learning-based methods has become more prevalent in recent years, as they easily overcome the limitations of heuristic-based methods that are based mostly on rules that must be manually created. Data imbalance and parameter optimization are found to be directly affecting performance of underlying machine learning models. However, to the best of our knowledge about literature of the field, little experimental evaluation is carried out in the direction of impact of data balancing and parameter tuning on machine learning models. Therefore, this paper considers it important to propose a new data balancing and parameter optimization technique. Further, the proposed approach is experimentally validated by considering standard code smell datasets. The empirical evaluation considers four code smells taken from two categories (two at method level and rest of the two are considered at class level) and studied four machine learning models, viz., Decision Tree, Random Forest, kNN, and SVM. The obtained experimental results justify the suitability of proposed data balancing and parameter optimization techniques in improving performance of machine learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Crime-Intent Sentiment Detection on Twitter Data Using machine learning 8

Crime-Intent Sentiment Detection on Twitter Data Using Machi...

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8th international conference on Automation, Control and Robotics engineering, CACRE 2023

作者： Bokolo, Biodoumoye George Ogegbene-Ise, Ebikela Chen, Lei Liu, Qingzhong Sam Houston State University Dept. of Computer Science HuntsvilleTX United States Kennesaw State University Dept. of Comp. and Software Eng. KennesawGA United States Georgia Southern University Dept. of Info. Tech. Statesboro Georgia

ISBN: (纸本)9798350302776

This research examines sentiment analysis in the context of crime intent using machine learning algorithms. A comparison is made between a crime intent dataset generated from a Twitter developer account and Kaggle's sentiment140 dataset for Twitter sentiment analysis. The algorithms employed include Support Vector machine (SVM), Naïve Bayes, and Long Short-Term Memory (LSTM). The findings indicate that LSTM outperforms the other algorithms, achieving high accuracy (97%) and precision (99%) in detecting crime tweets. Thus, it is concluded that the crime tweets were accurately identified. © 2023 IEEE.

关键词： Support vector machines

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Diabetic Foot Ulcer Identification using machine learning 2

Diabetic Foot Ulcer Identification using Machine Learning

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2nd international conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Gayatri, Gottumukkala Varun, Aitha Sai Sail, Manne Jaswanth Chowdari, Ch. Pratyusha Jamal, K. Griet Electronics and Communication Engineering Telangana Hyderabad India

ISBN: (纸本)9781665491990

Diabetic Foot Ulcer (DFU) is the main complexity of Diabetes, and might lead to cutoff of foot. One of the advancements of DFU treatment depends on the alertness of doctors and patients. This treatment approach has drawbacks such as the high cost of the diagnosis as well as the length of treatment. The analyzers had proposed many methods based on the technology which depends on both hardware as well as software (That includes sensors, portable devices, and deep learning algorithms). To have an efficient technique for diabetic foot ulcer detection, there is a need to enrich the process by deep learning and machine learning models to get high performance in the result. This study has used an image dataset of around 1500 images. The model is trained with advanced convolutional neural networks (CNN) of 11 layers with utmost 70 epochs with a best possible accuracy. The comparative analysis is done and result can be easily accessible by using a web application. © 2023 IEEE.

关键词： learning algorithms

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Suicidal Ratio Prediction Among the Continent of World: A machine learning Approach

Suicidal Ratio Prediction Among the Continent of World: A Ma...

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2023 international conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023

作者： Biplob, Khalid Been Badruzzaman Bijoy, Md. Hasan Imam Bitto, Abu Kowshir Das, Aka Chowdhury, Amit Hossain, Sayed Md. Minhaz Daffodil International University Department of Software Engineering Dhaka Bangladesh Daffodil International University Department of Cse Dhaka Bangladesh Premier University Department of Cse Chattogram Bangladesh

ISBN: (纸本)9781665456272

Suicide is a global health issue with significant negative effects. Individuals at risk of suicide often avoid seeking help due to stigma or fear of forced treatment, and those with mental illnesses, who make up the majority of suicide victims, may not be aware of their condition or risk. Detecting those at risk of suicide is a challenge for healthcare providers. However, advances in artificial intelligence (AI) may lead to the development of new suicide prediction technologies. This study used machine learning to predict suicide rates across different continents using six common classification algorithms: Stochastic Gradient Descent Classifier (SGDC), Random Forest Classifier (RFC), Gaussian Naive Bayes Classifier (GNBC), K-Neighbors Classifier (KNNC), Logistic Regression Classifier (LRC), and Linear Support Vector Classifier (LSVC). The KNNC algorithm had the highest training accuracy at 100%, and a 97% test accuracy. The RFC algorithm achieved the highest test accuracy at 99%, with a corresponding training accuracy of 99%. © 2023 IEEE.

关键词： Stochastic systems

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Estimation of the optimal record interval of the technological parameters 5

Estimation of the optimal record interval of the technologic...

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5th international Youth conference on Radio Electronics, Electrical and Power engineering, REEPE 2023

作者： Sultanov, Mahsud M. Boldyrev, Ilya A. Evseev, Kirill V. Khlyustov, Nikita S. Volzhsky Branch of National Research University MPEI Volzhsky Russia National Research University MPEI Moscow Russia

ISBN: (纸本)9798350399523

In this paper an algorithm for assessing the optimal recording interval technological parameters is considered. The purpose of the work is to determine an approach for finding the optimal period for recording the values of the parameters during their archiving in the hardware-software complex to reduce the amount of data transmitted through the information and measurement channels, and disk space required for their storage. As a result of the work, a technique has been developed for signal restoration by filling in empty values between adjacent known points on the basis of machine learning models, the proposed approach has been validated on the thermal power plant control system technological parameters. The results of the estimation are presented. © 2023 IEEE.

关键词： machine learning

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Research on Fault Monitoring Method of Information System Based on machine learning 6

Research on Fault Monitoring Method of Information System Ba...

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6th IEEE international conference on Automation, Electronics and Electrical engineering, AUTEEE 2023

作者： Guan, Jianchao Lu, Jun Wang, Wei Information and Telecommunication Branch State Grid Anhui Electric Power Co. Ltd. Hefei China

ISBN: (纸本)9798350305623

A monitoring system based on machine learning has been established to achieve real-time early warning and positioning. By using MSP430F149 single chip microcomputer and FPGA, we form a communication cable fault detection system with high speed and variable collection rate. The combination of DTMF FM single-chip microcomputer MT8888 and MSM7512 single-chip microcomputer realizes remote alarm and data transmission. The process of program management and serial communication in Windows system is analyzed. The real-time communication corresponding to the serial port is realized by using low-level software design methods such as process injection and API hooking based on the serial port. Based on the network operation mode under the SNMP mode, the polling and reporting information under the network operation mode is analyzed. An alarm probability model based on SNMP mode is proposed. The actual test proves that this method is practicable, and provides a practicable method for the analysis of communication protocol mechanism and communication fault analysis. © 2023 IEEE.

关键词： communication information failure fault monitoring machine learning algorithm network monitoring

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5th international conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

5th International Conference on Entrepreneurship, Innovation...

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5th international conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

ISBN: (纸本)9789819716814

The proceedings contain 41 papers. The special focus in this conference is on Entrepreneurship, Innovation, and Leadership. The topics include: A Study on Indian Digital Banking Online Customer Experience;a Comparative Analysis and Statistical Inference of Diabetes Cases;impact of Gamification on Student learning: An Empirical Evidence;Measuring Impact of Generative AI in software Development and Innovation;Sustainability in the Digital Era: Case Study of a Security Equipment Manufacturing SME;Using Monte Carlo Methods for Retirement Simulations of the 401K and IRA;a Novel Classification Scheme for Credit Card Fraud Detection Using Data Mining;Analyzing the Impact of UPI on Supply Chain Performance: A Natural Language Processing Approach with Generative Pre-trained Transformers;mental Health: Prediction and Analysis of Anxiety, Depression, Stress and Happiness Using machine learning;detecting Diabetes Retinopathy Through machine learning;extracting Relevant Features for software Transplantation;robust Method for Accessing IoT Devices and Blockchain for Secure Data Management;comparative Analysis of Financial Datasets Predictive Modeling Using Feature Extraction Methods;Cancer Detection Techniques: An Overview of Traditional and AI-Based Methods and Their Comparative Analysis;The Role of Generative Artificial Intelligence (GAI) in Education: A Detailed Review for Enhanced learning Experiences;employing machine learning Models in Prediction of Harmful Gases from Agri-Waste;emerging Trends and Perspectives on Challenges and Opportunities in Cloud Computing: A Systematic Literature Review;tagTrackr: A Smart Asset Tracking Solution;Assessing the Effectiveness of the ELSA App for English Language learning of Indian Native Speakers;safeMeds: Electronic Health Records Using Blockchain;depression Detection Using Linear Regression Model.

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Risks’ Attribute Values Evaluation in software engineering by Monte Carlo Simulation 13

Risks’ Attribute Values Evaluation in Software Engineering ...

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13th IEEE international conference on Electronics and Information Technologies, ELIT 2023

作者： Lyashkevych, Maria Lyashkevych, Vasyl Shuvar, Roman System Design Department Ivan Franko Lviv National University Lviv Ukraine

ISBN: (纸本)9798350383096

It is a good practice in the industry to carry out a risk assessment even before the stages of software production because we can prevent the costs of both human and material resources. In the era of the success of artificial intelligence with machine learning, it is very important to have accumulated expert knowledge on the basis of which we fuel datasets. Usually, it is very difficult to obtain numerical values of risk attributes, and especially to determine their importance among others. The values of risk attributes can be balanced in various ways, one of which is Monte Carlo simulation, which is presented in the publication. © 2023 IEEE.

关键词： Risk assessment

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