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检索条件"任意字段=2nd International Conference on Software Engineering and Machine Learning, CONF-SEML 2024"

共 232 条记录，以下是11-20 订阅

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Comparative Study of machine learning and Holt-Winters Exponential Smoothing Models for Prediction of CPI's Seasonal Data 2

Comparative Study of Machine Learning and Holt-Winters Expon...

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2nd international conference on software engineering and Information Technology, ICoSEIT 2024

作者： Afrah, Ashri Shabrina Sari, Nur Fitriyah Ayu Tunjung Utama, Shoffin Nahwa Holle, Khadijah Fahmi Hayati Lestandy, Merinda Sintiya, Endah Septa Rizdania Universitas Islam Negeri Maulana Malik Ibrahim Malang Department of Informatics Engineering Malang Indonesia Universitas Muhammadiyah Malang Department of Electrical Engineering Malang Indonesia State Polytechnic of Malang Information Technology Department Malang Indonesia University of Pgri Wiranegara Computer Science Department Pasuruan Indonesia

ISBN: (纸本)9798350317503

Inflation is one of the factors influencing price stability. Inflation affects people's purchasing power and impacts their decisions as economic actors. Consumer Price Index (CPI) is one of the factors used by economists to measure the inflation or deflation in a country. This research focuses on comparing the prediction results of the CPI for the Education Goods and Services using the Holt's Winters Exponential Smoothing and machine learning Methods, namely Long Short-Term Memory (LSTM), Extreme learning machine (ELM), Ridge, and Least Absolute Shrinkage and Selection Operator (LASSO). The data used is univariate data on the CPI for the Education Goods and Services in Malang City in 1996-2013, which was obtained from the publication of the Statistics Indonesia (BPS), entitled 'Malang City in Figures' which was published in 1997-2014. The results of this research show that the Ridge Method produces the smallest Mean Absolute Percentage Error (MAPE) value compared to other machine learning Methods and the Multiplicative Holt-Winters Exponential Smoothing Method, with MAPE= 2.10723%%. machine learning models that have been simulated have very good accuracy values, with MAPE values © 2024 IEEE.

关键词： Forecasting

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software Bug Report Detection Methods Based on machine learning Techniques 2

Software Bug Report Detection Methods Based on Machine Learn...

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2nd international conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Srinivasarao, Ulligaddala Chaganti, Akhil Mannam, Ajay Kotrikeline, Dhanush Katta, Hemanth University Department of Computer Science and Engineering Hyderabad India University Department of Computer Science and Engineering and Hyderabad Hyderabad India

ISBN: (纸本)9798350371314

software defect prediction, an integral facet of software engineering, proactively identifies and resolves potential flaws before they escalate into disruptive issues during production. Insights gleaned from dedicated research serve as solid support for development teams, pinpointing vulnerable areas within their codebase and optimizing resource allocation. This strategic approach significantly reduces overall expenditure on defect rectification. This paper conducts a comprehensive study, meticulously examining various machine learning algorithms in this domain. It intricately dissects deployment, functionality, and the comparative effectiveness of models like Logistic Regression, K-Nearest Neighbours, Naive-Bayes Classifier, Random Forest Classifier, and Recurrent Neural Networks. This thorough exploration enhances understanding of defect prediction methodologies. It provides a detailed roadmap for optimal strategies, reinforcing software quality measures and mitigating the impact of potential coding irregularities on system integrity and reliability. According to the experimental findings, RNN outperformed the baseline methods in terms of accuracy, F1 score, recall, and precision when identifying software bug reports. © 2024 IEEE.

关键词： Nearest neighbor search

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Improving Diagnostics for Brain Cancer: An Application of Deep learning 2

Improving Diagnostics for Brain Cancer: An Application of De...

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2nd international conference on Artificial Intelligence and machine learning Applications, AIMLA 2024

作者： Sharma, Neeraj Umakanth, S. Moharana, Alok Kumar Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Management School of Mangement - UG Karnataka Bangalore India ARKA JAIN University Department of Pharmacy Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

The software of deep mastering to improve diagnostics for mind cancer has the potential to master healthcare and scientific imaging. This painting provides the improvement and evaluation of a deep getting-to-know-based system for the type of brain cancer Magnetic Resonance Imaging (MRI) scans of actively treated patients. The version combines deep convolution neural networks (CNN) and switches learning for you to identify and classify brain tumours appropriately. The gadget was tested on a large dataset of 204 MRI scans and executed with a normal accuracy of 91.67%, a sensitivity of ninety 60.95%, and a specificity of 87%. The results exhibit the effectiveness of gaining knowledge for the diagnosis of most brain cancers and its potential position in improving medical outcomes. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Anxiety Assessment System 2

Anxiety Assessment System

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2nd international conference on Artificial Intelligence and machine learning Applications, AIMLA 2024

作者： Prabakar, S. Akshaya, S.N. Janani, S. Pavithra, S. Sona College of Technology Dept. Biomedical Engineering Salem India Sona College of Technology Dept. Biomedical Engineering Salem India

ISBN: (纸本)9798350349221

In this project, an innovative anxiety assessment system created especially for children with multiple disorders is presented. With the use of intuitive software and sensors that monitor vital signs like temperature, respiration rate, heart rate, and galvanic skin response. The system seeks to accurately analyze the mental and behavioral health of these children with a few simple questions that are asked by the mobile application to caregivers;each answer adds to the caregiver's final score. An Arduino processes the information that has been collected, which includes the score and physiological markers, and classifies the child's anxiety level into a suitable category. This methodology provides an efficient tool for timely identification and tailored intervention, enhancing the well-being of multiple disordered children. © 2024 IEEE.

关键词： Electrophysiology

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AI based Smart Boot Space Management System 2

AI based Smart Boot Space Management System

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2nd international conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Radhika, V Jaganathan, V Kanish, V Surya, S. P. Subash, R. Sri Ramakrishna Engn Coll Elect & Instrumentat Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

The boot space is often an overlooked part of a vehicle, representing a missed opportunity for efficiency and convenience. Boot space of a car, remains underutilized due to the lack of automation and intelligent planning. Firstly, the inability to fulfil the optimal utilization of available space leads to inefficient packing, often resulting in wasted room and compromised comfort during travel. Secondly, the absence of a smart planner of luggage causes the problem, especially when coordinating luggage from multiple passengers or pick-up points. This lack of coordination not only impedes efficient space allocation but also prolongs the loading process, causing delays and inconvenience. The technical implementation of the project involves hardware, software, and machine learning principles. Hardware components such as raspberry pi, force sensors, and camera module will form the backbone of the system. On the software side, a cloud-based Android Auto application will facilitate communication between the vehicle and a centralized server, where the machine learning model resides. This model will continuously analyze incoming data, learning from past experiences to generate optimized packing plans. Finally, user interfaces on the vehicle's infotainment system through the Android auto application provides smart access to the system, allowing passengers to input their luggage details and receive real-time packing recommendations.

关键词： Boot space Raspberry pi Force sensor Car Camera module machine learning Android auto

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A Federated learning Algorithm Based on Combination of Prototype and Comparative learning 2

A Federated Learning Algorithm Based on Combination of Proto...

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2nd IEEE international conference on Sensors, Electronics and Computer engineering, ICSECE 2024

作者： Chen, Yanwei Huang, Wenbo Software Institute Nanjing University Nanjing210008 China School of Communication Engineering Jilin University Changchun130015 China

ISBN: (纸本)9798350373646

Federated learning is a modern distributed machine learning paradigm that has garnered a lot of interest because of its benefits for collaborative model training and data privacy. However, federated learning is confronted with many challenges, including client data distribution heterogeneity and model drift. These factors place greater demands on model optimization and generalization capabilities. Traditional federated learning methods typically rely on gradient exchange and aggregation of global model parameters. This paper puts forward a privatized federated learning algorithm that combines prototype federated learning and model comparison federated learning, aiming to use prototype exchange and comparison learning techniques to increase the federated learning models' training effectiveness and classification accuracy. This algorithm significantly reduces the communication overhead by exchanging abstract class prototypes between the client and the server. Additionally, it uses comparison learning to enhance the loss algorithm, which improves the model's capacity for generalization. This, in turn, reduces the representation drift between the local and global models, thus improving the learning effectiveness of the model. © 2024 IEEE.

关键词： Contrastive learning

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Prediction of software Defects for Quality Assurance and Improvement Based on machine learning Methods 2

Prediction of Software Defects for Quality Assurance and Imp...

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2nd international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Patel, Kishan Gupta, Sandeep SATI M.P. Vidisha India

ISBN: (纸本)9798331541217

software defect prediction (SDP) is increasingly vital in today's software development landscape, where delivering high-quality products on time is essential. Defects in software can lead to significant financial losses and damage to reputations;hence, identifying error-prone modules early in the development lifecycle is crucial. This research aims to leverage machine learning (ML) models to enhance software quality and minimize testing costs. This methodology begins with data collection from the CM1 dataset in the PROMISE repository, followed by comprehensive preprocessing steps, including addressing missing values, removing duplicate entries, and detecting outliers. Feature selection and Z-score normalization further refine the dataset. Then, implement a variety of ML algorithms-Extreme Gradient Boosting (XGBoost), Naïve Bayes (NB), RF, and ANN-to evaluate their efficacy in SDP. Performance metrics such as precision, recall, accuracy, and F1-score are employed for assessment. These findings indicate that XGBoost achieves the highest accuracy of 98%, outperforming other models. This study makes a substantial contribution to software engineering by showing how ML approaches may improve software quality assurance procedures and by giving practitioners practical insights to build defect prediction systems. © 2024 IEEE.

关键词： software quality

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Stunting Classification Analysis for Toddlers in Bojongsoang: A Data-Driven Approach 2

Stunting Classification Analysis for Toddlers in Bojongsoang...

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2nd international conference on software engineering and Information Technology, ICoSEIT 2024

作者： Daffa, Muhammad Ghiyaats Gunawan, Putu Harry Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350317503

Stunting is one of the health problem priorities for children in Indonesia. Prevention of stunting in toddlers is needed to avoid the long-term effects for both the toddlers and the public. Stunting prevention can be done by monitoring the growth of toddlers. Therefore, a system that can predict stunting conditions in toddlers is needed. machine learning offers many methods that can be used to build a system to predict stunting conditions in toddlers. This research analyzes some machine learning models that are potentially suitable to predict stunting classes, which are K-Nearest Neighbor (KNN), Random Forest (RF), and Ensemble learning called Boosted KNN (BK). The dataset has an imbalance issue in this research, with the stunting data at only 1% of the total dataset. Therefore, oversampling of the dataset is done by generating a random dataset based on the distribution of the data that are classified as the minority class. The results of elaborating on this oversampling are shown to be satisfying. Applying imbalanced data gives an average of 98% accuracy for all methods used;however, the F-1 score macro average is shown not optimal for each of the methods, with 51.95% for KNN, 52.45% for RF, and 53.55% for BK. After the data is balanced by oversampling, the F-1 score macro average for all methods substantially increases. The new results were 93.55% for KNN, 97.70% for RF, and 98.00% for BK, underscoring the critical role of addressing data imbalance in improving predictive accuracy. © 2024 IEEE.

关键词： machine learning

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Research on the Application of Binary Classification Dataset Based on Integrated learning Model 2

Research on the Application of Binary Classification Dataset...

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2nd IEEE international conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Huang, Jiaqi Zheng, Chongyang Feng, Guohuan Artificial Intelligence School Wuchang University of Technology Wuhan China Software College Northeastern University Shenyang China School of Engineering and Computer Science Oakland University Auburn Hills United States

ISBN: (纸本)9798350360240

The binary classification problem is a fundamental and core problem type in machine learning, and many machine learning algorithms, such as logistic regression and tree models, are widely used to solve binary classification problems. Based on the Kaggle open-source binary classification dataset, a prediction model for binary classification data was established using five different machine learning algorithms, namely KNN, decision tree, gradient boosting tree, random forest, and extreme random decision tree integration. The parameters of the models were determined through relevant theoretical analysis cross-validation methods, and the test accuracy of the five integrated learning models were compared, and the experimental results showed that the test accuracy of the gradient boosting tree and random forest models reached 93.23%, which indicated that these two models had the best prediction performance in solving the binary classification problem. In addition, these two models also have high stability. Therefore, this study provides strong support to the field of machine learning and demonstrates the potential of gradient boosted trees and random forests in solving real-world problems. © 2024 IEEE.

关键词： Adversarial machine learning

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A Method for Automatic Code Error Detection and Repair Based on Deep learning 2

A Method for Automatic Code Error Detection and Repair Based...

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2nd IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Li, Shenzhu Jinan Engineering Polytechnic School of Liberal Education Jinan China

ISBN: (纸本)9798350317558

Deep learning (DL) frameworks possess robust feature extraction and pattern recognition abilities, enabling them to autonomously grasp and pinpoint error and vulnerability patterns within codebases. This article leverages a Deep Belief Network (DBN) framework to train and garner insights from extensive open-source code repositories, aiming to unearth and comprehend error and vulnerability patterns inherent in the code. Subsequently, these patterns are employed to scrutinize fresh codebases, pinpoint errors and vulnerabilities, and facilitate their rectification. In a comparative analysis with the Support Vector machine (SVM) algorithm, our approach demonstrates a notable reduction in Mean Absolute Error (MAE) by 31.66% and an elevation in recall by 17.69% in the realm of software vulnerability detection. This underscores the superior accuracy and reliability of our method in detecting software vulnerabilities. The DBN excels in the automated detection and remediation of code errors and vulnerabilities, owing to its proficiency in extracting pertinent features from code and pinpointing errors and vulnerabilities with greater precision. © 2024 IEEE.

关键词： auto repair deep learning software code vulnerability detection

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