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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251-260 订阅

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Performance simulation experiment analysis of extended range electric vehicles 4

Performance simulation experiment analysis of extended range...

引用

4th international conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2024

作者： Liu, Chunhui School of Mechanical Engineering Shandong Huayu Institute of Technology Shandong Dezhou China

ISBN: (数字)9781510679887

ISBN: (纸本)9781510679870

Based on the performance requirements of the entire vehicle, the parameters of the vehicle's components were determined through power matching and component selection. then select the car simulation system software and establish mathematical models corresponding to each component. Finally, establish a complete computer simulation model of various components of the car. After software simulation, optimize the performance of the car based on the simulation results. In the development process of extended range electric vehicles, performance simulation and optimization of the entire vehicle system is a very important step. © 2024 SPIE.

关键词： Vehicle performance

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Cardiovascular predictive Analysis with Machine Learning Fusion 6th

Cardiovascular Predictive Analysis with Machine Learning Fus...

引用

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

作者： Ahire, Aaditya Mehta, Dimple Amith Shekhar, C. Dharrao, Deepak Bongale, Anupkumar M. Maharashtra Pune412115 India Department of Computer Science and Engineering B N M Institute of Technology Bengaluru India Maharashtra Pune412115 India

ISBN: (纸本)9789819734412

Predicting heart strokes is a critical task in the field of healthcare, and it is further complicated by the multifaceted nature of cardiovascular health. In recent years, machine learning algorithms have emerged as valuable tools for forecasting heart stroke occurrences. Among these methods, ensemble models have shown significant promise by amalgamating the predictions of multiple base models. To overcome the existing limitations and enhance the accuracy of heart stroke prediction, the research introduces a novel ensemble model, combining stacked long short-term memory (LSTM) and XGBoost, which leverages the strengths of both these algorithms. the proposed model is compared with alternative methods, including individual stacked LSTM and XGBoost models, to assess its effectiveness in predicting heart strokes. the ensemble model is trained and evaluated using extensive historical health data. Evaluation metrics such as accuracy, precision, F1 score, and recall are employed to assess the performance of the forecasting model. the results of this study reveal that the ensemble stacked LSTM-XGBoost model outperforms individual stacked LSTM and XGBoost models, demonstrating superior predictive accuracy in capturing heart stroke occurrences. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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Hybrid Convolutional Neural Networks for Enhanced Detection of Mango Leaf Diseases 6

Hybrid Convolutional Neural Networks for Enhanced Detection ...

引用

6th international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Porna, Shweta Bhattacharjee Kabir, Md. Firoz Rana, Md. Imran Chowdhury Sajol, Md. Saiful Islam Roy, Tonmoy Khan, Mohammad Aman Ullah Adnan, Md. Al Bhavani, Girigula Durga Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Master's in Information Technology University of the Cumberlands United States Bachelor of Business Administration International American university Los Angeles CA United States Louisiana State University Department of Computer Science and Engineering LA United States Data Analytics & Information Systems Utah State University Logan United States Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka1212 Bangladesh Noakhali Science And Technology University Bsc in software engineering Bangladesh Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9798331505790

the classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. this paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. these included 4,000 training samples and 3,524 testing samples. the images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. the suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model's performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of 100 % on the test set. these findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture. © 2024 IEEE.

关键词： Fruits

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A Basic Iterative Non-Sign Average Gradient Adversarial Attack on Brain Tumor Image 6

A Basic Iterative Non-Sign Average Gradient Adversarial Atta...

引用

6th international conference on software engineering and Computer Science, CSECS 2023

作者： Wei, Jiaxuan Tang, Jiawei Liu, Shifa Chen, Weilong School of Information Science & Engineering Lanzhou University Lanzhou China

ISBN: (纸本)9798350306378

As one of the major diseases that pose a threat to human health, brain tumors' diagnosis plays a vital role in the intervention and rehabilitation process. Classification models based on deep neural networks (DNNs) have been proven effective in aiding doctors to diagnose brain tumors through MRI images. However, the injection of tiny perturbations to the images can have a significant impact on the diagnosis, indicating the vulnerability of DNNs. therefore, research on adversarial attack methods are highly significant in the medical diagnosis field. In this paper, we combined the non-sign gradient attack method and average gradient to propose a non-sign average gradient attack method(ABINM). By performing attack experiments on brain tumor MRI images under three different classification models, it was verified that the ABINM method can achieve higher rate of attack success and the generated adversarial sample shows stronger transferability on white-box and black-box testing compared to the baseline gradient-based adversarial attack methods. © 2023 IEEE.

关键词： Deep neural networks

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Adapting Pedagogical Frameworks to Online Learning: Simulating the Tutor Role with LLMs and the QUILT Model 6

Adapting Pedagogical Frameworks to Online Learning: Simulati...

引用

6th international conference on Advancements in Computing, ICAC 2024

作者： Madhubhashana, Sandaru Ranhinda, Vibhashana Jayakody, Dineth Shavinda, Lakmuthu Nawarathne, Malithi Haddela, Prasanna Sri Lanka Institute of Information Technology Dept. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

this paper presents SoloScholar, a novel asynchronous online learning platform designed to deliver personalized education to large undergraduate student groups by integrating established pedagogical frameworks with Large Language models (LLMs). Leveraging Bloom's Revised Taxonomy, learners are categorized into beginner, intermediate, and advanced levels to personalize the educational content. the platform employs the Questioning and Understanding to Improve Learning and thinking (QUILT) model to structure tutorial sessions and simulate the teaching role of a tutor at a Higher Education Institute (HEI). To generate contextually relevant and cognitively appropriate questions, carefully crafted prompts are fed into LLMs, synthesizing information from reliable knowledge sources. the efficacy of SoloScholar was evaluated through a dual assessment approach: a technical evaluation using the Retrieval Augmented Generation Assessment (RAGAS) framework and a human evaluation involving academic experts. the technical evaluation demonstrated high scores in answer relevancy and context utilization across all learning levels. Similarly, quantitative responses from academic experts indicated strong satisfaction with the quality and appropriateness of the generated content. the findings underscore the potential of integrating LLMs with pedagogical models to enhance personalization in online education. SoloScholar effectively addresses challenges associated with large-group teaching by providing adaptive learning experiences that cater to diverse cognitive needs. © 2024 IEEE.

关键词： Contrastive Learning

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Integrated Load Modeling of Induction Motors Considering Frequency Characteristics 6

Integrated Load Modeling of Induction Motors Considering Fre...

引用

6th international conference on Information Technologies and Electrical engineering, ICITEE 2023

作者： Yang, Jinshi Hai, Shifang Yu, Pengpeng Bai, Zuqiang Xiao, Fan High-tech Institute Fan Gong-ting South Street on the 12th QingZhou ShanDong China

ISBN: (纸本)9798400708299

In response to the current problem of limited frequency characteristic analysis in load modeling, this article establishes three models: static load model, dynamic load model and comprehensive load model considering distribution network branches. Analyzes the mechanism of the three models, uses MATLAB and PSD-BPA software to establish electromagnetic transient and electromechanical transient simulation models of power system load respectively, and simulates and analyzes the influence of internal parameters, dynamic load ratio and equivalent parameters of the distribution network on the load of the dynamic load model. the accuracy of the theoretical derivation is verified by using the simulation data to calculate the frequency factor under different models and comparing the simulation results with the theoretical derivation. © 2023 ACM.

关键词： Induction motors

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Application to Enhance the Programming Knowledge of Children 6

Application to Enhance the Programming Knowledge of Children

引用

6th international conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

this proposal outlines the development of a comprehensive educational platform aimed at bridging the Information Technology knowledge gap among Tamil-speaking students in Sri Lanka. the platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. the application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. this model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. this component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical Information Technology skills. the second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. the platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. the platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances Information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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Quick Transient Stability Evaluation Method for Wind-thermal-bundled AC/DC Transmission System 6

Quick Transient Stability Evaluation Method for Wind-thermal...

引用

6th IEEE international Electrical and Energy conference, CIEEC 2023

作者： Li, Boyang Chao, Pupu Cheng, Xueting Zheng, Huiping Li, Weixing Dalian University of Technology Department of Electrical Engineering Dalian China State Grid Shanxi Electric Power Research Institute Taiyuan China

ISBN: (纸本)9798350346671

For wind-thermal-bundled power transmission through AC/DC hybrid system, when the proportion of power transmitted by the AC line is high, the transient instability of voltage and power angle is prone to arise after faults, threatening the system's safe operation. In this paper, an evaluation method is proposed to obtain the transient stability of the system quickly. First, the equivalent models of the synchronous generator, the wind turbine, and the HVDC transmission line are established, and the equivalent circuit model of the transmission system is further obtained. Next, the calculation procedure of the quick evaluation method for transient voltage and power angle is elaborated according to the equivalent circuit model of the system. Finally, the effectiveness and accuracy of the proposed evaluation method are verified by comparing it with the result obtained by simulation software. © 2023 IEEE.

关键词： Computer software

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Enhanced Image Classification through Customized Convolutional Spiking Neural Network 6

Enhanced Image Classification through Customized Convolution...

引用

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

作者： Saini, Ashok Kumar Kumar, Rajesh Gehlot, Naveen Verma, Seema Department of Electrical Engineering Malaviya National Institute of Technology Jaipur India Department of Electrical and Electronics Engineering Education NITTTR Bhopal India

ISBN: (纸本)9798350369748

Spiking Neural Networks (SNNs) are deemed to provide biological realism. Also, it has more computational power than Artificial Neural Networks (ANNs) due to its utilization of spikes for information transmission and encoding. However, their shallow structures impose structural limitations, restricting the feature extraction capabilities of conventional SNNs. this study aims to improve the feature extraction capability of SNNs by leveraging the proficient feature extraction skills of Convolutional Neural Networks (CNNs). Our proposed model, Customized Convolutional Spiking Neural Network (CCSNN), combines CNN for feature learning with SNNs for cognitive skills. On the Digit-MNIST, Fashion-MNIST, and Letter-MNIST datasets, CCSNN surpasses previous models using fewer neurons and less training data, enhancing the biological realism of image classification models. In this study, CCSNN achieved impressive results on the Digit-MNIST, Fashion-MNIST, and Letter-MNIST datasets, with accuracies of 99.10%, 91.80%, and 99.30%, respectively, compared to conventional SNN. © 2024 IEEE.

关键词： Convolutional neural networks

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Distributed Core Network Traffic Prediction Architecture Based on Vertical Federated Learning 13th

Distributed Core Network Traffic Prediction Architecture Bas...

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13th international conference on Computer engineering and Networks (CENet)

作者： Li, Pengyu Guo, Chengwei Xing, Yanxia Shi, Yingji Feng, Lei Zhou, Fanqin China Telecom Res Inst 6G Res Ctr Beijing 102209 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9789819992492;9789819992478;9789819992461

Network traffic prediction has always been an important research topic, frequently employed in intelligent network operations for load awareness, resource management, and predictive control. Most existing methods adopt a centralized training and deployment approach, neglecting the involvement of multiple parties in the prediction process and the potential for training prediction models using distributed methods. this study introduces a novel wireless traffic prediction framework based on split learning, addressing the limitations of existing centralized methods. the proposed framework enables multiple edge clients to collaboratively train high-quality prediction models without transmitting large amounts of data, thus mitigating latency and privacy concerns. Each participant trains a dimension-specific prediction model using its local data, which are then aggregated through a collaborative interaction process. A partially global model is trained and shared among clients to tackle statistical heterogeneity challenges. Experimental results on real-world wireless traffic datasets demonstrate that our approach outperforms state-of-the-art methods, showing its potential and accuracy in Internet traffic prediction.

关键词： Traffic prediction Core network Federated learning Split learning

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