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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71-80 订阅

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An Empirical Understanding of Code Clone Detection by ChatGPT 6

An Empirical Understanding of Code Clone Detection by ChatGP...

引用

6th international conference on Data Science and Information Technology, DSIT 2023

作者： Wang, PeiJie Zhu, Lu Wang, Qianlu Jaiteh, Ousainou Guo, Chenkai Nankai University College of Computer Science Tianjin China Nankai University College of Cyber Science Tianjin China University of Gambia School of Information Communication and Technology Gambia

ISBN: (纸本)9798350304442

As one of the most popular NLP models recently, ChatGPT has achieved remarkable applications in various NLP tasks. Code clone detection serving as a typical prediction task of software engineering has been studied for years. However, there is a lack of systematic evaluation for the ChatGPT in code clone detection. To fill in this gap, we construct a specific dataset covers multiple types of code data and conduct the first empirical study in the clone detection task for ChatGPT on both source code and binary code. Our study found that ChatGPT can successfully detect the code clones and accurately explain the code semantics for most simple cases. However, in complex binary code scenarios, ChatGPT gets limited performance. Our work shows that ChatGPT has difficulty in identifying the semantics of long assembly code. the results and findings of our research support developers to better apply the big intelligent models to the prediction tasks of software engineering field. © 2023 IEEE.

关键词： software engineering

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Enhancing Deep Learning Performance through Parallel Processing: A Comprehensive Research Study 6

Enhancing Deep Learning Performance Through Parallel Process...

引用

6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： thakur, Rudraditya Goel, Naman Bhavesh Vijaykumar, Vaidehi Vellore Institute of Technology Chennai School of Computer Science Engineering Chennai India

ISBN: (纸本)9798350394702

the computational demands of training complex deep learning models have hindered the widespread adoption of this transformative technology. Parallel processing techniques have emerged as a promising approach to address this challenge by distributing the computational workload across multiple processors. this research delves into the multifaceted dimensions of enhancing deep learning performance through parallel processing. It explores the theoretical underpinnings, parallelization strategies, hardware and software infrastructures, and application-specific impact of parallel processing in deep learning. © 2023 IEEE.

关键词： Application programs

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Enhancing Demand Response Forecasting through Random Forest Regression: A Case Study Analysis 6

Enhancing Demand Response Forecasting Through Random Forest ...

引用

6th international conference on Energy, Power and Environment, ICEPE 2024

作者： Chatuanramtharnghaka, Benjamin Deb, Subhasish Singh, Ksh. Robert Mizoram University Electrical Engineering Department Aizawl India

ISBN: (纸本)9798350379297

Demand response (DR) plays a crucial role in optimizing energy consumption and ensuring grid reliability. this study investigates the efficacy of Random Forest Regression (RFR) as a predictive tool for DR forecasting, focusing on its performance within the context of a specific dataset. Utilizing data on residential energy consumption patterns, we conducted a comparative analysis of RFR against alternative regression algorithms. the results reveal promising outcomes, suggesting that RFR outperforms other models in terms of predictive accuracy and generalizability, particularly within our dataset. Furthermore, the study explores parameter optimization strategies and assesses the impact of varying 'n-estimators' on model performance. the findings underscore the potential of RFR in forecasting load dynamics, thereby facilitating informed decision-making in DR program planning and implementation. this research contributes valuable insights into enhancing DR forecasting methodologies, paving the way for more effective and sustainable energy management practices. © 2024 IEEE.

关键词： Demand response

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Automated AI Tool for Log File Analysis 6

Automated AI Tool for Log File Analysis

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Lohar, Prerna Baraskar, Trupti Dept.of Computer Engineering & Technology Dr. Vishwanath Karad MIT World Peace University Pune India

ISBN: (纸本)9798331522667

Log file analysis has a critical role in monitoring and maintaining software systems, yet the manual inspection of logs becomes increasingly impractical with the growing volume of data. this survey paper explores recent advancements in automated log file analysis, with a particular focus on the integration of AI techniques, which includes ML models and NLP. the study identifies key challenges in traditional methods, such as the need for human interpretation, difficulty in detecting new errors, and issues in backtracking within continuous data streams. Moreover, we examine state-of-the-art AI approaches, like LLaMA 2, to streamline log analysis by automating error detection, summarization, and anomaly identification. Research deficiencies are identified, notably the necessity for advanced methodologies to manage variety of log formats, automated model optimization, and ongoing learning processes. this comprehensive review endeavors to provide a thorough examination of the current landscape, encompassing perspectives on potential outcomes and prospective trajectories in the domain of AI-enhanced log file analysis. In addition to providing insights into prospective solutions and future directions in AI-driven log file analysis, this study attempts to give an in-depth analysis of the current situation. © 2025 IEEE.

关键词： Economic and social effects

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Mining Haulage Road Maintenance Prediction Dashboard Utilising Machine Learning Techniques 6

Mining Haulage Road Maintenance Prediction Dashboard Utilisi...

引用

6th international conference on Advancements in Computing, ICAC 2024

作者： Purdon, Brad Piyathilaka, Lasitha Sul, Jung-Hoon Preethichandra, Daluwathu Central Queensland University School of Engineering and Technology Rockhampton Australia

ISBN: (纸本)9798331517878

this paper details the development of a predictive maintenance dashboard designed for haulage roads in surface mining operations using machine learning algorithms. We assessed various machine learning models, including Logistic Regression, Random Forest, and K-Nearest Neighbors (KNN), to determine their effectiveness in predicting the timing and location of necessary road maintenance. the research identifies the Logistic Regression model as the most effective, which was trained on data including vibration measurements, equipment counts, and speed metrics to evaluate road surface conditions. these inputs are processed through a logistic function to estimate the probability of required repairs. the predictions generated by the model are stored in an SQL database and visualized through Power BI, which offers a user-friendly interface. this interface enables operators, including those with limited experience, to interpret the data effectively and make informed maintenance decisions, thus improving operational efficiency and safety. © 2024 IEEE.

关键词： predictive maintenance

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CropCare: Advanced Crop Management System with Intelligent Advisory and Machine Learning Techniques 6

CropCare: Advanced Crop Management System with Intelligent A...

引用

6th international conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Rahman, Md. Jobayer Ahmed, Md. Shakil Biswas, Swapnil Orchi, Anika Tabassum Rahman, Raiyan Islam, A.K.M. Muzahidul United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350385779

In our modern society, agriculture plays a vital role in meeting our food needs while minimizing environmental risks and crop issues at every stage possible through sustainable farming practices adopted by farmers worldwide today. However, traditional methods cannot meet these criteria due to weather changes, soil characteristics impacting crop growth, diseases causing crop loss, or reduced productivity hurting farmers' income. To address these issues, we introduce an innovative Integrated Agriculture System that utilizes real-time weather data and IoT sensors to optimize crop management, going beyond traditional methods. the system incorporates machine learning models trained on environmental and soil parameters to predict crop type and fertilizer needs, enabling informed decisions for sustainable farming. Additionally, the system features a plant disease detection tool that captures crop photos and identifies whether a crop is infected or not;based on that, the system provides recommendations. Our study demonstrates exceptional predictive capabilities, with Naive Bayes and Random Forest achieving accuracy scores of 0.98 % and 0.99 %, and Logistic Regression and Decision Tree performing well with accuracies of 0.95% and 0.94%. these results highlight the system's effectiveness in enhancing productivity while minimizing environmental risks, making it an indispensable tool for modern and sustainable agriculture. © 2024 IEEE.

关键词： Crops

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Enhancing Source Code Vulnerability Detection Using Flattened Code Graph Structures 6

Enhancing Source Code Vulnerability Detection Using Flattene...

引用

6th international conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Sun, Zhequ Liu, Ke Yang, Yu School of Computer Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9798331541750

Source code vulnerability detection is essential for software security, but identifying subtle vulnerabilities in code is challenging. While Transformer-based pre-trained models have achieved success in this area, they typically treat code as a sequence of text, neglecting its structural characteristics. To address this, we propose a novel approach that uses a flattened code graph structure, which effectively captures both semantic and syntactic relationships in the code. By transforming the code into a structured sequence, this method allows the model to explicitly learn from both semantic and structural information. We combine the flattened code graph structure tokens with pre-trained models and fine-tune them to enhance performance in vulnerability detection. Experimental results demonstrate significant improvements in recall, particularly in detecting vulnerabilities in complex code. this approach provides an efficient and robust solution for source code vulnerability detection, advancing the field of software security. © 2024 IEEE.

关键词： Deep learning

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Edge-Centric IoT Health Monitoring: Optimizing Real-Time Responsiveness, Data Privacy, and Energy Efficiency 6

Edge-Centric IoT Health Monitoring: Optimizing Real-Time Res...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Swathi, B. Veerabomma, Supraja Archana, M. Bhadru, Dharavath Somu, Naga Lakshmi Bhavsingh, M. Dept of CSE G Pulla Reddy Engineering College A.P. Kurnool India Department of ECE G Pullaiah College of Engineering and Technology A.P. Kurnool India Department of Computer Science and Engineering CVR College of Engineering Vasthunagar Hyderabad India CSE Department NarSimha Reddy Engineering college Telangana Maisammaguda India Shri Jagdishprasad Jhabarmal Tibrewala University Rajasthan Jhunjhunu India Department of CSE Ashoka Women’s Engineering College Andhra Pradesh Kurnool India

ISBN: (纸本)9798331522667

the innovation of IoT and edge computing has significantly advanced real-time health monitoring systems by providing timely data processing, improved responsiveness, and enhanced data privacy. this study introduces the Edge-Centric IoT Health Monitoring System (EC-HMS), which overcomes limitations observed in cloud-based and hybrid models, including high latency, data privacy vulnerabilities, and excessive energy consumption. EC-HMS leverages localized edge processing to reduce latency, safeguard sensitive data, and optimize energy efficiency, thereby enabling real-time health interventions. the proposed architecture integrates data acquisition, filtering, and anomaly detection at the edge layer, facilitating immediate decision-making and secure data transmission. Experimental evaluations highlight that EC-HMS achieves a 50 ms average latency, consumes 40% less energy compared to cloud-based systems, and ensures robust privacy through localized data handling. this research contributes to the advancement of scalable, responsive, and secure healthcare monitoring frameworks suitable for remote and resource-constrained environments. Future enhancements aim to incorporate predictive analytics and synchronization mechanisms to improve performance and adaptability further. © 2025 IEEE.

关键词： Electronic health record

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Hybrid offshore wind turbines: Control-oriented modeling approaches 6th

Hybrid offshore wind turbines: Control-oriented modeling app...

引用

6th international conference on Renewable Energies Offshore, RENEW 2024

作者： Ahmad, I. M’zoughi, F. Aboutalebi, P. Garrido, I. Garrido, A.J. Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Engineering School of Bilbao—EIB/BIE Faculty of Engineering of Bilbao—EIB/BIE University of the Basque Country—UPV/EHU Bilbao Spain

ISBN: (纸本)9781032905570

In the pursuit of sustainable electricity generation from offshore wind and wave energy, the combination of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has emerged as a promising solution to enhance the working lifespan of this kind of systems. this study introduces a control-focused modeling approach, using empirical data collected from the widely accepted FAST environment, to implement advanced control laws for these integrated platforms. Leveraging Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN), the research yields predictive models that effectively capture the platform pitch dynamics so as to optimize control strategies. By integrating advanced machine learning techniques, these approaches address the challenges of modeling floating offshore structures in dynamic environments. In this sense, the neuro-estimators developed in this study closely match the original plant results, offering researchers a straightforward way to implement complex control schemes that could hardly be implemented over the original non-linear system. © 2025 the Author(s).

关键词： Offshore wind turbines

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CSOFS: Feature Selection Using Cuckoo Search Optimization Algorithm for software Fault Detection 4

CSOFS: Feature Selection Using Cuckoo Search Optimization Al...

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4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Rath, Pradeep Kumar Mahato, Susmita Bhowmik, Rwiddhi Gourisaria, Mahendra Kumar Das, Himansu Nist Institute of Science & Technology School of Computer Science & Engineering Berhampur India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798350349856

the method of identifying and predicting software flaws or problems in software systems involves using historical data. software fault prediction is a crucial topic of research in the field of software engineering because it enables developers to find probable flaws early on, which can save money and enhance the quality of the programme produced. the study's objective is to determine how well the Cuckoo Search Optimization Algorithm can extract the features that are most important for correctly predicting software flaws. the prediction models were tested and trained using a dataset of software metrics, and the dataset was subjected to a variety of feature selection algorithms. We have performed an analysis by comparing the efficiency of various optimization algorithms namely evolutionary based Genetic Algorithm Feature Selection (GAFS), Differential Evolution Feature Selection (DEFS), and swarm based algorithms like Ant Colony Optimization Feature Selection (ACOFS) and Particle Swarm Optimization Feature Selection (PSOFS) with respect to Cuckoo Search Optimization Algorithm (CSOFS). the results from the output tables for various metrics prove that feature selection using CSO can significantly increase the accuracy of the predictive model, as well as reduce the complexity of the models. this study's findings have crucial implications for developing a software failure prediction models that is more accurate and efficient. © 2024 IEEE.

关键词： Feature Selection

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