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

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Transfer Learning-Based Recommendation System 6

Transfer Learning-Based Recommendation System

引用

6th IEEE international conference on Advances in Science and Technology, ICAST 2023

作者： Pate, Aaditee Francis, Bryan Sathe, Vardaan Kalantri, Rakhi Rajguru, Shagufta Fr. C. Rodrigues Institute of Technology Computer Engineering Department Maharashtra Navi Mumbai400703 India

ISBN: (纸本)9798350359817

E-commerce refers to online business operations carried out electronically. It also includes the buying and selling of intangible items like services and digital goods. Essentially, it pertains to business internet sales. the development of this industry is unquestionable given that over 4.9 billion people now have access to the internet, and it is actively altering the operating business models across all sectors. the recent COVID-19 pandemic has acted as a catalyst in this and created the perception that online transactions and shopping can be secure, producing results in product satisfaction comparable to those of offline shopping. E-commerce is the way of the future and the present for businesses to serve their customers, gather feedback and reviews, and make it simple for customers to buy without having to physically go anywhere. An e-commerce platform cannot be built without user-friendly software programs that facilitate exchanges between customers and businesses. We aim to deliver a software for building an ecommerce website that provides services such as: Product search, reverse image search, product recommendation, sales analytics, and a chatbot for the user experience. © 2023 IEEE.

关键词： Sales

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An Artificial Neural Network Model based on Binary Particle Swarm Optimization for enhancing the efficiency of software Defect Prediction 23

An Artificial Neural Network Model based on Binary Particle ...

引用

6th international conference on software engineering and Information Management, ICSIM 2023

作者： Malhotra, Ruchika Chawla, Sonali Sharma, Anjali Department of Software Engineering Delhi Technological University Bawana Road Delhi110042 India Department of Planning Monitoring and Evaluation CSIR-National Physical Laboratory Delhi110012 India

ISBN: (纸本)9781450398237

With the rise in the growth of the software industry, it is essential to identify software defects in earlier stages to save costs and improve the efficiency of the software development lifecycle process. We have devised a hybrid software defect prediction (SDP) model that integrates Binary Particle Swarm Optimization (Binary PSO), Synthetic Minority Oversampling Technique (SMOTE), and Artificial Neural Network (ANN). BPSO is applied as a wrapper feature selection process utilizing AUC as a fitness function, SMOTE handles the dataset imbalance, and ANN is used as a classification algorithm for predicting software defects. We analyze the proposed BPSO-SMOTE-ANN model's predictive capability using the AUC and G-mean performance metrics. the proposed hybrid model is found helpful in predicting software defects. the statistical results suggest the enhanced performance of the proposed hybrid model concerning AUC and G-mean values. Also, the hybrid model was found to be competitive with other machine learning(ML) algorithms in determining software defects. © 2023 ACM.

关键词： Neural networks

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Research of Integration and Simulation Technology for Multi-source Heterogeneous models of Trains 6th

Research of Integration and Simulation Technology for Multi-...

引用

6th international conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Lu, Yiming Zhao, Jiaying Liu, Siyu Wang, Haifang Li, Kuanxin CRRC Tangshan Co. Ltd. Tangshan064000 China

ISBN: (纸本)9789819993185

this article introduces the research scheme of multi-source heterogeneous model integration and simulation technology, which integrates and interacts model data composed of multiple software and hardware sources (multi-source) and multiple data types (electrical, control, mechanical, and other heterogeneous data). the application of the combined simulation test of electrical, control and mechanical data of the door system of a Multiple unit is introduced. the integration of multi-source heterogeneous models and the opening of the simulation Technology roadmap can verify the integrity and correctness of the system design by means of digital simulation verification, so as to eliminate potential design errors to the greatest extent, quickly locate faults, improve quality and efficiency, and shorten the actual vehicle commissioning cycle. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Integration

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Comparison of Regression Techniques and Integration models Using Machine Learning 6

Comparison of Regression Techniques and Integration Models U...

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

作者： Kumar, Ashwani Devi, Kavita Katoch, Munish Sharma, Anup Angra, Prikshat Kumar Bose, Siddhartha Farhan, Mohd. Singh, Pritpal Mittal School of Business Lovely Professional University Punjab Phagwara India School of Computer Science Engineering Lovely Professional University Punjab Phagwara India School of Computer Applications Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350304480

Regression problems help extract the law from massive amounts of data to predict. Hence, regression algorithms have been a hot topic in machine learning research in recent years. In actual problems related to regression have complex internal and external factors, and different machine learning algorithms affect scalability and predictive performance. this research uses BP neural network, extreme learning machine, and support vector machine to examine the applicability of machine learning methods in regression problems. Comparing the single model and integrated model regression results of each machine learning algorithm shows its pros and cons. Finally, simulated experiments using four data sets show how well each machine learning method predicts regression. Research on machine learning algorithms and integration models appears to be viable and logical. © 2023 IEEE.

关键词： (ML) Machine learning BP neural network Integrated model Multiple Regression Vector machine

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Ground Reaction Forces and Moments in Stroke Survivors: Experimental versus AnyBody Model Predictions 46

Ground Reaction Forces and Moments in Stroke Survivors: Expe...

引用

46th Annual international conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Hulleck, Abdul Aziz Vaqar Abdullah, Muhammed Hamed, Farah Katmah, Rateb Khalaf, Kinda El Rich, Marwan Khalifa University Department of Mechanical Engineering Abu Dhabi United Arab Emirates Khalifa University Department of Biomedical Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350371499

Multibody dynamics models and simulations offer an efficient alternative to traditional methods, such as force plates, pressure sensing mats, and instrumented treadmills, for computing ground reaction forces (GRF) and moments (GRM), valuable in the quantification of the gait of neurological patients. Accurate determination of GRF and GRM magnitudes, with a specific focus on the disruptive shear components affecting gait, is essential for post-stroke gait assessment and rehabilitation. this study explored the predictive capability of musculoskeletal models equipped with foot contact elements, by comparing them with experimental data from a published dataset. the results yielded peak normalized Root Mean Square Errors (n-RMSE) of 0.51±0.31% and 0.46±0.28% for mediolateral shear components, 0.4±0.13% and 0.35±0.16% for anteroposterior shear components, and 0.34±0.16% and 0.32±0.12% for compressive components of GRF for the right and left foot respectively. For GRM, nRMSE peaks in the sagittal plane were 4.72±3.55%, followed by 2.51±2% in the frontal plane, and 2±1.44% in the transverse plane for the right foot. On the left side, nRMSE peaks were 3.73±3.12% in sagittal plane, 2.75±2.7% in frontal plane, and 2.65±2% in the transverse plane. this study underscores the potential of musculoskeletal modeling and simulation software, such as AnyBody, as a time and cost-effective alternative for evaluating the biomechanics of stroke survivors. © 2024 IEEE.

关键词： Musculoskeletal system

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An Intelligent Platform for Cricket Technique and Performance Improvement 6

An Intelligent Platform for Cricket Technique and Performanc...

引用

6th international conference on Advancements in Computing, ICAC 2024

作者： Madushan, Anjana Fernando, Priyantha Mahendra, Hesara Kottage, Nowen Kasthuriarachchi, Sanvitha Rajapaksha, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

In cricket training, preventing injuries and developing skills are of utmost importance, especially concerning the school cricketers. this research presents a web application, 'CricBoost' which would serve to address all these vital issues through a multifaceted strategy. this study offers batsman pose accuracy assessment and correction using MediaPipe and Random Forest algorithm, Analysis of bowling and grip using MediaPipe, VGG16 and Gradient Boost models for classification and Shap for providing suggestions on the optimal methods, Suggestion of Shot selection for Batsman, where YOLOv8 and OpenCV are applied to ball pitch analysis and the player is given suggestions about the right shot to take, and cricket drill recommendation system focused on targeted skill improvement of players. CricBoost will deliver a unique combination that will increase players' technical skills, strategic intelligence, and performance while simultaneously focusing on injury prevention by teaching correct techniques. © 2024 IEEE.

关键词： Cricket (Sports)

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Power Flow Calculation of Vehicle Network Coupling in Traction Power Supply System 6th

Power Flow Calculation of Vehicle Network Coupling in Tracti...

引用

6th international conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Li, Cong Bao Liu, Li Peng Liu, Yang Tian, Xing Jun Song, Wei Cao, Shuai Shuohuang Railway Development Co. LTD National Energy Group Hebei Cangzhou China School of Electrical and Electronic Engineering Shijiazhuang Tiedao University Hebei Shijiazhuang China

ISBN: (纸本)9789819993185

this paper presents an analysis of the fully parallel AT traction network using a six conductor transmission line. An equivalent model is derived and simulation models for traction substations, section posts, AT posts, traction networks, and electric locomotives are comprehensively built using RT-plus/Simulink to enhance the practicality of the simulation. Based on these models, a simulation software for electrified railway traction power supply system is developed using vehicle network coupling power flow calculation. the results of the example calculation demonstrate that this method effectively considers the interaction between the train and the traction power supply network, leading to a more accurate reflection of power flow distribution in the traction power supply system. So, the proposed method has significant application value in electrified railway engineering. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Electric power distribution

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Deep Learning-Based Stock Price Prediction: A Comprehensive Approach Using Bi-LSTM 4

Deep Learning-Based Stock Price Prediction: A Comprehensive ...

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Chy, Md. Kalim Amzad Masum, Abdul Kadar Muhammad Alam, Mohammed Shamsul Azad, Md. Abul Kalam Hassan, Md. Maruf Bhuiyan, Touhid International Islamic University Dept. of Computer Science and Engineering Chittagong Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Islamic University of Technology Dept. of Business and Technology Management Gazipur Bangladesh School of It Washington University of Science and Technology VA United States

ISBN: (纸本)9798331529635

this study delves into the prediction of stock prices in the Bangladesh stock market using advanced deep learning frameworks, with a particular focus on the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Traditional statistical models often struggle to effectively capture the complex, non-linear characteristics of financial data, leading to inconsistent results. the Bi-LSTM model, however, addresses this challenge by processing data in both forward and backward sequences, making it particularly suitable for analyzing the intricate dynamics of the Bangladesh stock market. A rigorous feature engineering process is undertaken, incorporating 64 distinct features that include market trends, stock-specific indicators, technical and fundamental variables, as well as crucial macroeconomic factors like gold prices and foreign exchange reserves. the dataset spans historical stock market data from 2012 to 2022, with comprehensive preprocessing steps taken to ensure data quality and consistency. Hyperparameter tuning is executed through a grid search to optimize model performance. the evaluation of the Bi-LSTM model is carried out using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and the R2 score, which demonstrate its enhanced predictive power compared to traditional models. this research contributes to the growing field of deep learning in financial forecasting, offering new perspectives for stock price prediction in emerging markets, while also addressing ethical issues to minimize risks like market manipulation. © 2024 IEEE.

关键词： Mean square error

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User Centric Evaluation of Code Generation Tools 6

User Centric Evaluation of Code Generation Tools

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6th IEEE international conference on Artificial Intelligence Testing (AITest) as a part of the IEEE international Congress on Intelligence and Service-Oriented Systems engineering (CISOSE)

作者： Miah, Tanha Zhu, Hong Oxford Brookes Univ Sch Engn Comp & Math Oxford OX33 1HX England

ISBN: (纸本)9798350365054

With the rapid advance of machine learning (ML) technology, large language models (LLMs) are increasingly explored as an intelligent tool to generate program code from natural language specifications. However, existing evaluations of LLMs have focused on their capabilities in comparison with humans. It is desirable to evaluate their usability when deciding on whether to use a LLM in software production. this paper proposes a user centric method for this purpose. It includes metadata in the test cases of a benchmark to describe their usages, conducts testing in a multi-attempt process that mimics the uses of LLMs, measures LLM generated solutions on a set of quality attributes that reflect usability, and evaluates the performance based on user experiences in the uses of LLMs as a tool. the paper also reports a case study with the method in the evaluation of ChatGPT's usability as a code generation tool for the R programming language. Our experiments demonstrated that ChatGPT is highly useful for generating R program code although it may fail on hard programming tasks. the user experiences are good with overall average number of attempts being 1.61 and the average time of completion being 47.02 seconds. Our experiments also found that the weakest aspect of usability is conciseness, which has a score of 3.80 out of 5.

关键词： Machine learning Large language models ChatGPT Code generation Performance evaluation Usability R programming language

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Vehicle Axle Acceleration Prediction: An Interpolation Approach

Vehicle Axle Acceleration Prediction: An Interpolation Appro...

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18th IEEE international conference on Advanced Motion Control, AMC 2024

作者： Aboutorabi, Ahmad Brockmann, Matthias Münster University of Applied Sciences Department of Mechanical Engineering Steinfurt Germany

ISBN: (纸本)9798350342819

this article explores the development of vehicle axle acceleration prediction models utilizing interpolation techniques for 4-poster device testing in the automotive industry. the main objective is to estimate acceleration data for a specific speed based on data collected at other speeds, employing different interpolation techniques. To enhance the precision of predicted data, a refinement model is introduced, allowing for the fine-tuning of predicted acceleration magnitudes. the research methodology involves data collection through simulation in MSC ADAMS software, and data processing, algorithm development, and result comparisons using MATLAB software. the accuracy of these models is assessed through statistical evaluations, incorporating metrics like Root Mean Square (RMS) and Absolute Mean for insights into predictive performance. the research emphasizes the quest for precision in predicting acceleration data, and the outcomes demonstrate substantial improvements in accuracy. © 2024 IEEE.

关键词： Forecasting

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