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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 3288 条记录，以下是201-210 订阅

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th international conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

the COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. this research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. the system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. the methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. the results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. the efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. this innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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Another Look at Non-homogeneous Markovian software Reliability Modeling 10

Another Look at Non-homogeneous Markovian Software Reliabili...

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10th international conference on Dependable Systems and their Applications, DSA 2023

作者： Qiu, Nanxiang Li, Siqiao Dohi, Tadashi Okamura, Hiroyuki Hiroshima University Graduate School of Advanced Sciences and Engineering Higashi-Hiroshima739-8527 Japan

ISBN: (纸本)9798350304770

this paper explores yet another software reliability modeling framework based on non-homogeneous Markov processes (NHMPs). For two subclasses of NHMPs;generalized binomial processes (GBPs) and generalized Polya processes (GPPs), we formulate 22 novel NHMP-based software reliability models (SRMs) with 11 kinds of baseline intensity functions, which are different from the existing NHMP-based SRMs by Li et al. (2023). Our evaluation of NHMP-based SRMs focuses on assessing their goodness-of-fit and predictive performances using 8 sets of software fault detection time-interval data (group data). the results are compared with the infinite-failure NHPP-based SRMs. through comprehensive numerical experiments, we show that our new modeling framework could provide the strengths in the goodness-of-fit performance but have some limitations on the predictive performance in the early testing phase. © 2023 IEEE.

关键词： Markov processes

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Lightning Nowcasting Based on Depthwise Separable Residual Convolution with Attention Mechanism by Using Multisource Data 6

Lightning Nowcasting Based on Depthwise Separable Residual C...

引用

6th international conference on Electronics and Communication, Network and Computer Technology, ECNCT 2024

作者： Xie, Dan Luo, Fei School of Software Engineering Chengdu University of Information Technology Chengdu610225 China

ISBN: (纸本)9798350366143

Lightning is a common natural phenomenon with great destructive power, capable of damaging buildings, igniting fires, and causing casualties. Timely prediction of lightning occurrences and the implementation of appropriate precautions are crucial for maintaining human productivity and safety. As the traditional method of lightning prediction, the extrapolation method of thunderstorm monomer is unable to grasp the generation and disappearance of lightning accurately. Meanwhile, the lightning forecast relies on numerical simulations and parameterizations have problems in the spatiotemporal deviations. In this paper, we propose a depth separable residual convolution block(ResCDBlock) with an attention mechanism and construct a ResCD-3DUnet model for lightning short-term prediction. the utilization of multi-source data, including satellite, radar, and lightning location data with high spatiotemporal resolution, enables the model to learn the lightning characteristics more effectively. the results show that the constructed ResCD-3DUnet scores higher than other baseline models for lightning prediction. the proposed ResCDBlock and the fusing of multi-source data are demonstrated more effectively in the ablation studies. © 2024 IEEE.

关键词： Deep neural networks

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Estimating Water Quality in Telangana Using Classification Modeling: A Comparison of CatBoost and LGBM Classifiers 6

Estimating Water Quality in Telangana Using Classification M...

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

作者： Singh, Preet Ramkumar, K.R. Hasija, Taniya Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350369748

For sustainable living there is a need to plan resource assessment to use it efficiently. Water is a key resource utilized by every living being in one form or the other form such as for drinking, used by farmers for irrigation purposes, to dissolve the fertilizer sprays for the plants. In this paper, Telangana water quality is assessed by using the machine learning models CatBoosting algorithm and LGBM classifier for the classification of water quality. the model which performs better in terms of accuracy score is the CatBoosting model with an efficiency of 94% as compared to the LGBM classifier. the classification also gives the recall of the model CATBoosting as 0.89 which is higher than the LGBM classifier. To ensure the quality of drinking water this classification necessarily makes the effective decision for water treatment and to take actions for the sustainable usage of water. © 2024 IEEE.

关键词： Fertilizers

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An Enhanced Loss Function for 6G Space-Time Domain predictive Channel models 24

An Enhanced Loss Function for 6G Space-Time Domain Predictiv...

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24th IEEE international conference on Communication Technology, ICCT 2024

作者： Zhang, Kaiyuan Huang, Chen Li, Junling Qian, Zhongyu Wang, Cheng-Xiang Purple Mountain Laboratories Pervasive Communication Research Center Nanjing211111 China Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing210096 China

ISBN: (纸本)9798350363760

Facing the sixth-generation (6G) technical vision, the machine learning (ML) based channel modeling has drawn a lot of attention. the prediction accuracy of ML-based channel modeling methods depends on the dataset size, model parameter settings, and loss functions. the design of loss function is critical to improve the accuracy of predictive channel models. this paper proposes a novel space-time-aware weighted loss function that reflects the correlation between space-time domain channel characteristics. the proposed loss function is compared to widely used loss functions, e.g., mean square error (MSE), Huber loss, and log-cosh loss. Based on the channel measurements, the performance of the proposed loss function is evaluated. According to the experiment results, the predictive channel model with the proposed loss function achieves the highest accuracy compared to the channel model with conventional loss functions. the proposed loss function can be used to design more accurate ML-based predictive channel models for 6G wireless channels. © 2024 IEEE.

关键词： Mean square error

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Neural Networks Based Behavioral Modeling of Dual-Band RF Power Amplifiers using Augmented BiLSTM Structures 6

Neural Networks Based Behavioral Modeling of Dual-Band RF Po...

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6th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Ahmed, Majid Dalbah, Ahmad Hammi, Oualid Ghannouchi, Fadhel M. College of Engineering American University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates University of Calgary Intelligent RF Radio Laboratory Department of Electrical and Software Engineering CalgaryAB Canada

ISBN: (纸本)9798350344349

this paper introduces a novel Bidirectional Long-Short-Term-Memory (BiLSTM) based Artificial Neural Network (ANN) model for the behavioral modeling of the nonlinear distortions observed in dual-band radiofrequency power amplification systems. Addressing the demand for energy-efficient and green wireless infrastructures in the era of multi-band transmissions, the proposed Augmented BiLSTM uses selected features of the input signals in each band to concurrently predict the output waveform in each band. Experimental benchmarking against the established 2D-Memory Polynomial (2D-MP) model, conducted with a ZHL-5W-2G S+ power amplifier prototype driven by concurrent dual-band 5G signals, reveals that the proposed model is able to achieve comparable performance. In addition to its accuracy, the main advantage of the proposed neural network based model lies in its ability to simultaneously model the amplifier's behavior in both bands with a single trainable structure. this contrasts with conventional analytically defined models, which require a dedicated structure per signal transmission band. © 2024 IEEE.

关键词： Power amplifiers

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6th international conference on Innovative Computing and Communication, ICICC 2023

6th International Conference on Innovative Computing and Com...

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6th international conference on Innovative Computing and Communication, ICICC 2023

ISBN: (纸本)9789819940707

the proceedings contain 128 papers. the special focus in this conference is on Innovative Computing and Communication. the topics include: Wearable Sensor Based Cloud Data Analytics Using Federated Learning Integrated with Classification by Deep Learning Technique;comparison of Decision Tree and Random Forest for Default Risk Prediction;trusted Cloud Service Framework for Cloud Computing Security;HITR-ECG: Human Identification and Classification Simulation System Using Multichannel ECG Signals: Biometric Systems Era;brain Disorder Classification Using Deep Belief Networks;Diabetic Retinopathy Detection Using Deep CNN Architecture and Medical Prescription;an Innovative software engineering Approach to Machine Learning for Increasing the Effectiveness of Health Systems;region of Interest and Feature-based Analysis to Detect Breast Cancer from a Mammogram Image;vehicular Ad Hoc Network: A Review;Securing the Networks Against DDoS Attacks Using Blockchain Technology;the Proposed Deep Learning Combined with Knowledge Graphs for Fake News Detections on Social Networks;algorithm-Driven predictive Analysis of Blue-Chip Stocks in the Murky Indian Environment;survey on Crime Analysis, Forecasting, and Prediction Using Machine Learning;soft Computing and Data Mining Techniques for Dengue Detection: A Review;Extended HD-MAABE Scheme Supporting Delegation and Revocation for Cloud-based Organizational Data Sharing;detection of Open Metal Sites in Metal–Organic Frameworks Using Machine Learning;PCA-Based Feature Selection and Hybrid Classification Model for Speech Emotion Recognition;ANFIS and Kernel Extreme Learning Machine to the Assessment and Identification of Seismic b-value as Precursor;payroll Management Using Blockchain;centric Management of Resources in Modern Distributed Computing;enhancing Security of IoT Data Transmission in Social Network Applications.

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the FormAI Dataset: Generative AI in software Security through the Lens of Formal Verification 19

The FormAI Dataset: Generative AI in Software Security throu...

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19th international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Tihanyi, Norbert Bisztray, Tamas Jain, Ridhi Ferrag, Mohamed Amine Cordeiro, Lucas C. Mavroeidis, Vasileios Technol Innovat Inst Abu Dhabi U Arab Emirates Univ Oslo Oslo Norway Univ Manchester Manchester Lancs England

ISBN: (纸本)9798400703751

this paper presents the FormAI dataset, a large collection of 112, 000 AI-generated compilable and independent C programs with vulnerability classification. We introduce a dynamic zero-shot prompting technique constructed to spawn diverse programs utilizing Large Language models (LLMs). the dataset is generated by GPT-3.5-turbo and comprises programs with varying levels of complexity. Some programs handle complicated tasks like network management, table games, or encryption, while others deal with simpler tasks like string manipulation. Every program is labeled with the vulnerabilities found within the source code, indicating the type, line number, and vulnerable function name. this is accomplished by employing a formal verification method using the Efficient SMT-based Bounded Model Checker (ESBMC), which uses model checking, abstract interpretation, constraint programming, and satisfiability modulo theories to reason over safety/security properties in programs. this approach definitively detects vulnerabilities and offers a formal model known as a counterexample, thus eliminating the possibility of generating false positive reports. We have associated the identified vulnerabilities with Common Weakness Enumeration (CWE) numbers. We make the source code available for the 112, 000 programs, accompanied by a separate file containing the vulnerabilities detected in each program, making the dataset ideal for training LLMs and machine learning algorithms. Our study unveiled that according to ESBMC, 51.24% of the programs generated by GPT-3.5 contained vulnerabilities, thereby presenting considerable risks to software safety and security.

关键词： Dataset Vulnerability Classification Large Language models Formal Verification software Security Artificial Intelligence

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Enhancing Solar Energy Production Forecasting with Ensemble-based Learning Techniques 6

Enhancing Solar Energy Production Forecasting with Ensemble-...

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6th international conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Abdelsalam, Hany Souri, Alireza İnanç, Nihat Department of Software Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey

ISBN: (纸本)9798331539603

the global trend in renewable energy solutions has emphasized the urgent need for accurate forecasting of solar energy production. this study examines the potential of ensemble-based learning techniques in predicting solar energy production by leveraging historical data collected from 11 solar photovoltaic installations in Calgary, Canada, spanning from September 2015 to March 2023. the dataset was carefully preprocessed to handle missing values, duplicates, and to extract meaningful features that align with the nature of time series data. A range of ML models were implemented and assessed by several forecasting evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2). the experimental results demonstrate the superior performance of ensemble methods, particularly XGBoost and Light GBM, in solar energy forecasting. XGBoost achieved the best performance, with an MAE of 25.36, an MSE of 3599.66, and R2 of 0.957, outperforming models such as Random Forest and Neural Networks. the practical implications of these findings suggest that accurate solar energy forecasting can significantly enhance operational planning and optimize energy management in solar power plants, contributing to more reliable and efficient renewable energy systems. © 2024 IEEE.

关键词： ensemble methods machine learning renewable energy solar energy forecasting time series analysis XGBoost

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MediMind: A Comprehensive Health Prediction and Record-Keeping Platform 6th

MediMind: A Comprehensive Health Prediction and Record-Keep...

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6th international conference on Information Systems and Management Science, ISMS 2023

作者： Naik, Darshana A. Sharma, Aditya Mahajan, Danish Khandelwal, Dhruv Gajya, Arpit Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

the "MediMind" project signifies a revolutionary fusion60;of predictive analytics, centered on diabetes and heart disease60;risk assessment, coupled with state-of-the-art skin disease prediction and ro... 详细信息

ISBN: (纸本)9783031707889

the "MediMind" project signifies a revolutionary fusion of predictive analytics, centered on diabetes and heart disease risk assessment, coupled with state-of-the-art skin disease prediction and robust Electronic Health Record (EHR) management. By employing advanced machine learning models like Logistic Regression and Support Vector Machines (SVM) for diabetes and heart disease predictions, alongside the cutting-edge Convolutional Neural Network (ResNet-50) for precise skin disease classification, "MediMind" achieves unparalleled accuracy and applicability. the predictive models exhibit exceptional performance metrics, boasting high accuracy, precision, recall, and F1-score. Seamless integration into Electronic Health Records (EHR) ensures immediate implementation in clinical decision-making, providing healthcare professionals with precise insights into diabetes and heart disease risks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electronic health record

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