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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 3308 条记录，以下是311-320 订阅

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predictive Modeling for Disease Progression in Chronic Conditions Using Machine Learning 6

Predictive Modeling for Disease Progression in Chronic Condi...

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

作者： Umamaheswari, T.S. Dhaygude, Amol Dattatray Dewangan, Omprakash Krishnan, T. Yerpude, Poonam Swarnkar, Suman Kumar Jayagovind Harigopal Agarwal Agarsen College Department of Computer Applications Chennai India University of Washington Department of Data Science Seattle United States Kalinga University Department of CS & IT Chhattisgarh Raipur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Indian Institute of Technology Department of Computer Science & Engineering Bhilai India Shri Shankaracharya Institute of Professional Management and Technology Department of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350304480

predictive modeling of disease progression in chronic conditions is a crucial task in healthcare, as it enables early identification and personalized intervention for patients at risk of adverse outcomes. In recent times, there has been a notable advancement in the field of machine learning techniques, which has demonstrated considerable potential in accurately forecasting the evolution of diseases. this development has proven to be highly beneficial for healthcare professionals, as it equips them with vital insights that can significantly enhance the management of patients. this research paper provides a thorough examination and comparative evaluation of different machine learning algorithms in the context of forecasting the course of diseases in chronic situations. We explore the use of diverse datasets containing longitudinal patient records, clinical variables, and disease-specific markers to train and evaluate predictive models. the study also investigates the impact of feature selection and data preprocessing techniques on the model's performance. Moreover, we assess the generalizability and robustness of the models across different chronic diseases to determine their potential for broader clinical applications. the results demonstrate that machine learning models can effectively predict disease progression, with certain algorithms outperforming others in specific scenarios. the research findings presented in this study offer significant contributions to the understanding and implementation of machine learning techniques for predicting disease development. the aforementioned realizations have the potential to be of substantial assistance to medical professionals in terms of making well-informed decisions, which will ultimately lead to better outcomes for patients. © 2023 IEEE.

关键词： Chronic conditions Clinical applications Clinical variables Data preprocessing Disease progression Feature selection Generalizability Healthcare Longitudinal patient records Machine learning Patient outcomes predictive modeling

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Forecasting Criminal Activity: An Empirical Approach for Crime Rate Prediction 6th

Forecasting Criminal Activity: An Empirical Approach for Cri...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Prabhu, B. P. Aniruddha Sharma, Tushar Taranath, N.L. Dilip, Kumar Department of Computer Science and Engineering Alliance University Bangalore India University Dehradun India

A60;crime is an act that is prohibited by law and is punishable by a fine, imprisonment, or other legal action. Every day, the news of criminal activity fills our news channels and social media platforms, portrayin... 详细信息

ISBN: (纸本)9789819728381

A crime is an act that is prohibited by law and is punishable by a fine, imprisonment, or other legal action. Every day, the news of criminal activity fills our news channels and social media platforms, portraying an image of a society in which crime is an ever-present concern. Crime rate prediction using machine learning is a critical undertaking in today’s society to handle the ever-present concern of criminal activities. this paper presents a comprehensive approach to predicting crime rates by leveraging machine learning algorithms and data analysis techniques. the proposed system utilizes historical crime data to develop predictive models that identify high-risk areas and potential future crime trends. the dataset, sourced from the National Crime Rate Bureau (NCRB), undergoes preprocessing, which includes feature engineering and data augmentation. Five distinct models—nearest neighbor, support vector machine, random forest, decision tree, and XGBoost—are assessed for their predictive performance. Among these, the XGBoost Regressor exhibits the highest accuracy in predicting crime rates for 8 distinct crime categories in 19 Indian metropolitan cities. the results indicate a promising accuracy of 93.20% using the selected model, showcasing the potential of machine learning in crime prediction. By targeting resources toward high-risk regions, law enforcement authorities may successfully suppress criminal activity and promote community safety. this study highlights the significance of predictive policing and its role in designing future crime measures. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Measuring Design Compliance using Neural Language models: An Automotive Case Study 18

Measuring Design Compliance using Neural Language Models: An...

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18th ACM international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Parthasarathy, Dhasarathy Ekelin, Cecilia Karri, Anjali Sun, Jiapeng Moraitis, Panagiotis Volvo Grp Gothenburg Sweden Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781450398602

As the modern vehicle becomes more software-defined, it is beginning to take significant effort to avoid serious regression in software design. this is because automotive software architects rely largely upon manual review of code to spot deviations from specified design principles. Such an approach is both inefficient and prone to error. In recent days, neural language models pre-trained on source code are beginning to be used for automating a variety of programming tasks. In this work, we extend the application of such a Programming Language Model (PLM) to automate the assessment of design compliance. Using a PLM, we construct a system that assesses whether a set of query programs comply with Controller-Handler, a design pattern specified to ensure hardware abstraction in automotive control software. the assessment is based upon measuring whether the geometrical arrangement of query program embeddings, extracted from the PLM, aligns with that of a set of known implementations of the pattern. the level of alignment is then transformed into an interpretable measure of compliance. Using a controlled experiment, we demonstrate that our technique determines compliance with a precision of 92%. Also, using expert review to calibrate the automated assessment, we introduce a protocol to determine the nature of the violation, helping eventual refactoring. Results from this work indicate that neural language models can provide valuable assistance to human architects in assessing and fixing violations in automotive software design.

关键词： neural programming language models language model evaluation software design patterns

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6th international conference on Intelligent Sustainable Systems, ICISS 2023

6th International Conference on Intelligent Sustainable Syst...

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6th international conference on Intelligent Sustainable Systems, ICISS 2023

ISBN: (纸本)9789819917259

the proceedings contain 66 papers. the special focus in this conference is on Intelligent Sustainable Systems. the topics include: Recommendation System Based on Clustering Techniques Using Collaborative Filtering Method;compartmented Proactive Secret Sharing Scheme;devOps Challenges and Practices in software engineering;the Effects of Climate Change on Crop Yield;Finetuned-VGG16 CNN Model for Tissue Classification of Colorectal Cancer;effective Heart Disease Prediction and Classification Using Intelligent System;a Machine Learning Approach for Aeroponic Lettuce Crop Growth Monitoring System;a Novel Approach for Privacy Preserving Technique in IoT Fog and Cloud Environment;IOT-Based Fertilizer Recommendation System Using a Hybrid Boosting Algorithm;development of a System for Controlling IoT Devices Using Gaze Tracking;towards Human-Like Robotic Grasping for Industrial Applications Using Computer Vision;an Extensive Study on Unattended Object Detection in Video Surveillance;Analysis of Digital Data on Social Network TikTok During COVID-19;an Hybrid Edge Algorithm for Vehicle License Plate Detection;gesture Controlled Drone Swarm System for Violence Detection Using Machine Learning for Women Safety;healthcare System Modeling and Security engineering;hierarchical Classification of Disaster News Using Local Classifier per Parent Node;Working from Home During a Pandemic: the Impact COVID-19 Had on software and Web Development;stochastic Processes with Trend Stationarity in High-Clustered Growth Networks;smart Vest for Women Undergoing Menopause;analysis of Digital Data About the Preference of Audiovisual Audiences;Cloud Infrastructure Security Using a Hybrid AES Encryption Model;development of Deep Learning Based models for Detecting the Significance of Non-Manual Parameters for Indian Sign Language Interpretation.

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Lightweight and Heterogeneous Conversion of Power Transmission Model Based on CATIA V6 6

Lightweight and Heterogeneous Conversion of Power Transmissi...

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

作者： Lin, Shan Zheng, Zhou Yang, Yan Jian, Qian Huang, Jianye Wu, Linting Fujian Power Co. Ltd. Electric Power Research Institute Fuzhou Fujian350007 China

ISBN: (纸本)9798350305623

CATIA V6 (Computer-Aided three-Dimensional Interactive Application), as an advanced computer-aided design software, can help engineers quickly realize the design and optimization of power transmission models. this article aims to conduct research on lightweight and heterogeneous conversion of power transmission models based on CATIA V6. this article constructed a control group and two experimental groups, and conducted 10 tests each. the control group used the original CATIA V6 power transmission model as a comparison group. the experimental group 1 lightweighted the model to simplify the structural elements and ensure that accuracy requirements were met. the experimental group 2 used heterogeneous transformation to replace some structural elements with an adaptation framework or chip that adapts to a specific environment. then, the article determined the input parameters and keeps them consistent to ensure the accuracy of the comparison. the results showed that experimental group 1 and experimental group 2 had positive impact on model accuracy, computational efficiency and performance evaluation compared with the control group. this shows that lightweight and heterogeneous conversion have a positive impact on the model, providing useful suggestions and guidance for improving the CATIA V6 power transmission model. © 2023 IEEE.

关键词： CATIA V6 Heterogeneous Conversion Light Weight Network Topology Optimization Transmission Model

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Stock Price Prediction using Recurrent Neural Network and LSTM 6

Stock Price Prediction using Recurrent Neural Network and LS...

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6th international conference on Computing Methodologies and Communication, ICCMC 2022

作者： Sandhya, Pasala Bandi, Raswitha Himabindu, D. Dakshayani Department of Computer Science and Engineering AP Visakhapatnam India VNR Vignana Jyothi Institute of Engineering and Technology Department of Information Technology Hyderabad India

ISBN: (纸本)9781665410281

Predicting stock prices is a tough task simulated to anticipate stock returns by machine learning. A range of approaches and techniques are used for stock market forecasting. the stock market is considered quite active and advanced. A precise estimate of future pricing could give investors an increased profit margin. According to the estimates, investors can select equities to produce a higher return. Various approaches to machine learning have been used in stock markets over the years. Still, deep learning models, which have shown superior to prior machine learning methods as far as predictive accuracy and speed are concerned, are being used with the growing data and wish for forecasts. In this research, a common deeper study model for stock market prediction, the Long-Short Memory (LSTM) recurring neural network has been utilized. In this task, Python modules are used to automatically download historical market data to forecast future stock prices by fitting an LSTM model to data. © 2022 IEEE.

关键词： Financial markets

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Construction Equipment Performance with Cloud Data Analysis for predictive Maintenance 6

Construction Equipment Performance with Cloud Data Analysis ...

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6th IEEE international conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Seeni, Senthil Kumar Maheswari, P. Konidhala, Jothibabu Suguna, M. Raja Ishwarya, M.V. Srinivasan, S. Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chennai India University of Technology and Applied Sciences- Al Musannah Department of Electronics and Communication Engineering South Al Batina Musanna Oman Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381689

the research uses Internet of things technology for predictive maintenance in the construction sector. the system addresses incorporating Internet of things sensors into construction machinery to enable real-time monitoring of operating parameters. the study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. the abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. the study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. this validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry. © 2024 IEEE.

关键词： predictive maintenance

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Comparative Analysis of Machine Learning Algorithms for Parkinson's Disease Prediction 6

Comparative Analysis of Machine Learning Algorithms for Park...

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6th international conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Yadav, Dhruv Jain, Ishaan Delhi Technological University Software Engineering Delhi India

ISBN: (纸本)9781665410359

Parkinson's Disease, a brain disorder which has symptoms like stiffness in the body, shaking, difficulty in walking, maintaining balance and body coordination. Parkinson's Disease usually begins gradually and worsens over time. Its effects on an individual can be controlled if there is a possibility to detect it in its initial stages. through various tests, it has been found out that in the most initial stage it affects the speech of an individual. In this work six Machine Learning models - K Nearest Neighbors, Gaussian Naïve Bayes, Logistic Regression, Support Vector Machine, Random Forest and Decision Tree have been compared based on six evaluation metrics - Recall, Accuracy, Precision, F1-Score, False Positive Rate and Area under ROC Curve. © 2022 IEEE.

关键词： Decision trees

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Opposition based multi-objective binary horse herd optimization algorithm for customer churn prediction in Telecommunication 6

Opposition based multi-objective binary horse herd optimizat...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Sharma, Ashish Mishra, Nitin Natani, Mihir Kshetija, Kalash Pareek, Kinshuk Department of Electronics & Communication Engineering Jaipur Engineering College and Research Centre Rajasthan Jaipur302017 India

ISBN: (纸本)9798350369748

In the fast-paced world of telecommunications, keeping customers satisfied and retaining their loyalty is crucial. Predicting when a customer might leave, known as" churn prediction," is vital for telecom companies. they analyze customer behavior, usage patterns, and other indicators to identify potential churners. Special models are used to classify customers into groups, distinguishing between those likely to leave and those likely to stay. An important aspect of this process is selecting the most relevant features for analysis. Metaheuristic algorithms, like the Horse Herd Optimization Algorithm (HOA), can assist in this task. this article introduces a new method for churn prediction using HOA. Initially, the continuous HOA was adapted into a different algorithm. then, an" opposite" aspect was incorporated to enhance the inputs, resulting in a more sophisticated framework. this method effectively predicts when customers might churn. Research findings demonstrate that this new algorithm outperforms similar methods in predicting customer churn while requiring less time for calculations. © 2024 IEEE.

关键词： Sales

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Accurate Prediction of Leaf Disease in Pepper Bell using Deep Learning Algorithm 6

Accurate Prediction of Leaf Disease in Pepper Bell using Dee...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Renuka, N. Kannan, N. Jayavarshini, A. Raj, A. Aakash Priyadharshini, M. KSR College of Engineering Tamil Nadu India KSR Institute for Engineering and Technology Tamil Nadu India

ISBN: (纸本)9798350369748

Aim: To develop and validate a deep learning-based model for the accurate prediction of leaf diseases in pepper bell plants. Materials and Methods: the system collects diverse images of healthy and diseased bell pepper plants for training. Group 1 focuses on precision agriculture, utilizing SVM for plant disease detection due toits proficiency in handling high-dimensional data. Group 2 emphasizes the significance of dataset selection in RandomForest experiments for plant disease detection. Group 3 introduces a proposed system integrating CNN for disease detection in bell pepper, tomato, and potato plants using ResNet and MobileNet. Result: In the specific context of the CNN utilized in this study, an impressive accuracy of 99.74% and a low loss of 0.0064 highlight its effectiveness in accurately discerning between healthy and diseased bell pepper plants. Conclusion: In Deep learning models, CNNs and vision-based approaches, show promise in accurately predicting bell pepper leaf diseases. Timely detection of such diseases in agricultural crops is crucial for ensuring food security and sustainable agriculture. © 2024 IEEE.

关键词： Pepper

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