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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

共 3307 条记录，以下是371-380 订阅

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predictive Modeling of Students' Stress Levels Using Machine Learning Algorithm 4

Predictive Modeling of Students' Stress Levels Using Machine...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Ali, Hassan Amin, M. Hamza Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9798350353266

the increasing prevalence of stress among university students has raised concern about its impact on academic performance and overall well-being. this conference paper explores the applications of machine learning algorithms to predict and analyze the student's stress level. A comprehensive dataset encompassing various stress-related factors, including academic workload, social interactions, and lifestyle, was collected from Kaggle which consists of 1100 instances and 19 attributes. Our methodology places a premium on human connection, integration of qualitative data to refine the accuracy of stress predictions. By seamlessly merging the capability of machine learning with an empathetic understanding of the human experience, our approach strives to pave the way for a more holistic and personalized educational ecosystem. the insights derived from this study aspire to guide educators, administrators, and policy makers in crafting nurturing environments that empower students to excel both academically and emotionally. Embarking on a journey to unravel the myriad factors influencing students' stress, we consider academic pressures, social dynamics, and personal experiences. Harnessing machine learning algorithms such as decision trees, neural networks, we navigate expansive student populations. Acknowledging the nuanced nature of stress, our study integrates a compassionate approach with cutting-edge technology. the study employs state-of-the-art machine learning techniques using various classification algorithms, to build predictive models for assessing stress levels out of which random forest gives 97.45% accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of a predictive IoT-Based System for Real-Time Air Quality Monitoring and Route Optimization 6

Development of a Predictive IoT-Based System for Real-Time A...

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6th international conference on Advancements in Computing, ICAC 2024

作者： Inthikhaff, M.I.M. Rimas, M.J.M. Kumari, J.M.D. Pasan, M.G.R. Kahandawaarachchi, K.A.D.C.P. Wijesiri, M.P.M. Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517878

Sri Lanka faces significant air pollution challenges, primarily from vehicular emissions, posing serious public health risks and impacting the quality of life. To address this, we developed a real-time air quality monitoring system that visualizes pollution data through an interactive heatmap. Our system integrates gas sensors that measure methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter (PM.), connected to an ESP32 board and Wi-Fi module, and strategically deployed around smart buildings. the collected data is transmitted to a central hub using IoT technology and visualized on a mobile app developed with Flutter, featuring a color-coded scheme to represent pollution levels. this allows users to easily identify high-risk areas and select cleaner routes for travel. In testing, we focused on predicting PM. concentrations in Colombo using machine learning models, including Random Forest, XGBoost, and Prophet. Random Forest excelled in PM. prediction, while Prophet was highly effective for CO, demonstrating machine learning's potential in air quality forecasting. the system also includes a MIMI Air device that collects, processes, and transmits air quality data every 30 seconds, alerting users in real-time when pollutants exceed WHO thresholds, providing a cost-effective, reliable solution for public health. © 2024 IEEE.

关键词： Decision trees

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A Novel Survey on Hand Sign Recognition 6

A Novel Survey on Hand Sign Recognition

引用

6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumavat, Kavita S. Jain, Kajal S. Kazi, Aman J. Vishwakarma University Department of Computer Engineering Pune India

ISBN: (纸本)9798350304480

Motion recognition is an essential research part of computer vision with many applications including human-computer interaction and signal interpretation. In this article, we propose a convolutional neural network (CNN) model for motion recognition using live cameras. We gathered information about hand-drawn illustrations of the English alphabet (A-Z) before removing the background and converting them to black-and-white images. Previous images were used to train the CNN model and 95% accuracy was achieved on the test data. We implemented a model in Python and combined it with a user-friendly software interface that provides a real-time prediction of hand movements using a camera system. Our system has the potential to be an essential tool in many industries and businesses, including managing electronic devices and helping people with communication disabilities. the proposed models and methods provide robust and accurate solutions for real-time operational information and can be used in a variety of situations. © 2023 IEEE.

关键词： Computer Gestures Image Neural-Networks Preprocessing

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Artificial Intelligence and Industry 4.0 Trends: A Comprehensive Review and Case Study Analysis in the Moroccan Context 6

Artificial Intelligence and Industry 4.0 Trends: A Comprehen...

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6th international conference on Intelligent Systems and Computer Vision, ISCV 2024

作者： Benabdelouahab, Anass Chaibi, Adham Haman, Samia Bentassil, Zineb Elouadi, Abdelmajid Moumen, Aniss Ibn Tofail University Engineering Science Laboratory National School of Applied Sciences Kenitra Morocco Ibn Tofail University Advanced Systems Engineering Laboratory National School of Applied Sciences Kenitra Morocco

ISBN: (纸本)9798350350180

this paper presents a comprehensive study on the relationship between Artificial Intelligence (AI) and Industry 4.0, using a systematic literature review (SLR) approach complemented by a case study focused on Morocco. the Fourth Industrial Revolution has transformed production processes and automation globally, and our research aims to provide a thorough analysis of its implications within the Moroccan industrial landscape. We conducted an SLR across three prominent databases: Scopus, ScienceDirect, and Springer. Our aim was to use the queries 'Artificial Intelligence' and 'Industry 4.0' to identify highly cited articles published between 2014 and 2023. Subsequently, we guided a case study with interviews and employed VOS Viewer analysis to delve into the specific context of Morocco. the SLR yielded 114 relevant articles, which were analyzed using NVivo software, revealing a predominant focus on leveraging intelligent technologies, particularly deep learning & machine learning algorithms, to enhance industrial performance. the case study provided deeper insights into the Moroccan perspective, highlighting key areas of application such as Maintenance, Quality, and Supply Chain management. Our findings contribute to a better understanding of the intersection between AI and I4.0 in Morocco and offer valuable insights for researchers, policymakers, and industry stakeholders. © 2024 IEEE.

关键词： predictive maintenance

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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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the Balancing Act of Policies in Developing Machine Learning Explanations

The Balancing Act of Policies in Developing Machine Learning...

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international conference on software engineering - Companion, ICSE, Companion

作者： Jacob Tjaden Colby College Waterville Maine U.S.A.

ISBN: (数字)9798331536831

ISBN: (纸本)9798331536848

Due to the nature of opaque machine learning (ML) models, software engineers and data scientists struggle to understand how ML models make decisions [1]. Explainability research aims to provide transparency for these models [2] through two types of explanations. Global explanations describe how a model works generally and provide insight into its accuracy, biases, and fairness. Local explanations describe individual predictions made by the model in specific use cases.

关键词： Accuracy Machine learning predictive models Data models software software engineering

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End-user evaluation of an interface for clinical decision support using predictive algorithms 44

End-user evaluation of an interface for clinical decision su...

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44th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Kehoe, Iain E. Pepino, Jeremy A. Lee, Jarone Hahn, Jin-Oh Reisner, Andrew T. Massachusetts Gen Hosp Boston MA 02114 USA Univ Maryland Dept Mech Engn College Pk MD 20742 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

there have been decades of interest in advanced computational algorithms with potential for clinical decision support systems (CDSS), yet these have not been widely implemented in clinical practice. One major barrier to dissemination may be a user-friendly interface that integrates into clinical workflows. Complicated or non-intuitive displays may confuse users and may even increase patient management errors. We recently developed a graphical user interface (GUI) intended to integrate a predictive hemodynamic model into the workflow of nurses caring for patients on vasopressors in the intensive care unit (ICU). Here, we evaluated user perceptions of the usability of this system. the software was installed in the room of an ICU patient, running for at least 4 hours with the display hidden. Afterward, we showed nurses a video recording of the session and surveyed their perceptions about the software's potential safety and usefulness. We collected data for nine patients. Overall, nurses expressed reasonable enthusiasm that the software would be useful and without serious safety concerns. However, there was a wide diversity of opinions about what specific aspects of the software would be useful and what aspects were confusing. In several instances, the same elements of the GUI were cited as most useful by some nurses and most confusing by others. Our findings validate that it is possible to develop GUIs for CDSS that are perceived as potentially useful and without substantial risk but also reinforce the diversity of user perceptions about novel CDSS technology.

关键词： Decision support systems predictive models Prediction algorithms software Safety Hemodynamics Cultural differences

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LightGBM-based line loss prediction model for distribution networks 6

LightGBM-based line loss prediction model for distribution n...

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6th international conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2023

作者： Wu, Xiaogang Li, Zuxin Zhou, Xiaoqing Ji, Qingfeng Mao, Shuangshuang Ju, Xiaoming Lishui Power Supply Company of State Grid Zhejiang Electric Power Co. Ltd Lishui China Zhenghao Group Technology Network Branch Lishui China School of Software Engineering East China Normal University Shanghai China Zhejiang Jierui Electric Power Technology Co. LTD Ningbo China

ISBN: (纸本)9781510668249

Accurate calculation of line loss in distribution networks can better guide the power system on how to optimize the power grid and how to carry out technical loss reduction work. In response to the problems of many electrical parameters, complicated steps and low accuracy of results required for the calculation of theoretical line loss values in traditional distribution networks, this paper proposes a distribution network line loss prediction method based on the light gradient boosting machine (LightGBM) model algorithm. the method uses machine learning models to model key electrical parameters and line loss values to automatically calculate the grid line loss. To address the difficulty of tuning the LightGBM model, we use a Bayesian optimization algorithm to adjust the model parameters. In this paper, grid data from the Kaggle data platform is used for analysis and validation, and the experimental results show that the model proposed in this paper has higher prediction accuracy than the traditional BP model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Machine learning

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Enhancing Cardiac Health: Machine Learning in Coronary Artery Disease Prediction 6th

Enhancing Cardiac Health: Machine Learning in Coronary Arter...

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6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Vijayaraj, Anu Ragavi Pasupathi, Subbulakshmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819780891

the alarming surge in cardiovascular diseases, with a particular focus on Coronary Artery Disease (CAD), is causing premature fatalities. this escalation is exacerbating the inefficiency of the diagnostic process, burdened by both time constraints and the strain on human resources, as more patients endure extended wait times for diagnosis. Addressing this pressing issue calls for the fusion of analytical decision-making with digital patient data, aiming to develop a robust and efficient predictive model for early-stage cardiac illness. Machine Learning (ML) emerges as a promising tool to bolster CAD detection and prognosis within the healthcare sector. this chapter introduces an ML-based approach for CAD prediction, leveraging the Z-Alizadeh Sani dataset sourced from the UCI repository. the classification task is tackled using three distinct ML models—Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). the findings underscore the superior performance of ANN, achieving an impressive 89.01 percent accuracy in CAD diagnosis. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cardiology

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Optimization of Methods for Querying Formal Ontologies in Natural Language Using a Neural Network 16

Optimization of Methods for Querying Formal Ontologies in Na...

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16th international conference on Knowledge engineering and Ontology Development, KEOD 2024 as part of 16th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2024

作者： Ondo, Anicet Lepetit Capus, Laurence Bousso, Mamadou Dep. of Computer and Software Engineering Laval University QuebecQCG1V 0A6 Canada Dep. Computer Science Iba Der Thiam University Thies967 Senegal

ISBN: (纸本)9789897587160

A well-designed ontology must be capable of addressing all the needs it is intended to satisfy. this complex task involves gathering all the potential questions from future users that the ontology should answer in order to respond precisely to these requests. However, variations in the questions asked by users for the same need complicate the interrogation process. Consequently, the use of a question-answering system seems to be a more efficient option for translating user queries into the formal SPARQL language. Current methods face significant challenges, including their reliance on predefined patterns, the quality of models and training data, ontology structure, resource complexity for approaches integrating various techniques, and their sensitivity to linguistic variations for the same user need. To overcome these limitations, we propose an optimal classification approach to classify user queries into corresponding SPARQL query classes. this method uses a neural network based on Transformer encoder-decoder architectures, improving both the understanding and generation of SPARQL queries while better adapting to variations in user queries. We have developed a dataset on estate liquidation and Python programming, built from raw data collected from specialist forums and websites. Two transformer models, GPT-2 and T5, were evaluated, with the basic T5 model obtaining a satisfactory score of 97.22%. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Structured Query Language

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