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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 3308 条记录，以下是761-770 订阅

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Finite-Control-Set Model predictive Control with Neutral Point Potential Balance for Single-phase three-level Voltage Source Inverter 6

Finite-Control-Set Model Predictive Control with Neutral Poi...

引用

6th IEEE international conference on predictive Control of Electrical Drives and Power Electronics (PRECEDE)

作者： Chen, Shengwei Yang, Ya Wang, Kaixin Yang, Yong Fan, Mingdi Xie, Menxi Chen, Rong Gang, Fang Zeng, Weibo Soochow Univ Sch Rail Transportat Suzhou Peoples R China Jiangsu Goodwe Power Supply Technol Co Ltd Suzhou Peoples R China

ISBN: (纸本)9781665425575

A finite-control-set model predictive control (FCS-MPC) with neutral point potential balance for single-phase three-level voltage source inverter(3L-VSI) is proposed in this paper. Firstly, predictive models are rebuilt according to T-type 3L-VSI. For the sake of the solution of voltage potential imbalance of neutral point potential, the voltage difference is assigned a certain weight to the cost function to realize enhancement of wave quality of output voltage. Simulated results of the proposed FCS-MPC are accomplished, which is used to verify the effectiveness of this method.

关键词： three-level finite-control-set model predictive control imbalance of neutral point potential

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Minimizing User Connectivity Costs and Latency Between Controllers and Switch-Controllers for software Defined Networking 19th

Minimizing User Connectivity Costs and Latency Between Cont...

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Mobile Web and Intelligent Information Systems - 19th international conference, MobiWIS 2023, proceedings

作者： Viveros, Andres Adasme, Pablo Firoozabadi, Ali Dehghan Department of Electrical Engineering Universidad de Santiago de Chile Avenida Víctor Jara No 3519 Santiago9170124 Chile Department of Electricity Universidad Tecnológica Metropolitana Av. Jose Pedro Alessandri 1242 Santiago7800002 Chile

ISBN: (纸本)9783031397639

In this paper, we consider the problem of assigning users to switches and controllers at minimum connectivity costs. We simultaneously minimize the existing latency between controllers and switch controllers for software-defined networks (SDN). In particular, we propose two mixed-integer quadratic programming formulations and their standard linearization counterparts. All the proposed models are solved with the CPLEX solver. More precisely, the quadratic models are solved with the branch and cut (B &C) algorithm of the solver. Whilst the linear ones are solved with both, the B &C and with the automatic Bender’s decomposition algorithmic option of the CPLEX solver. To the best of our knowledge, this work constitutes a first attempt to propose mathematical formulations while including user connectivity to a backbone software-defined network. Notice that we consider which is the best strategy for connecting users to the backbone, either connecting them to switches or to the controller nodes of the network. Our preliminary numerical results indicate that for most of the benchmark instances considered the second strategy which connects users to switches has a better performance in terms of objective function values. On the other hand, we observe that the linear models offer better performance in terms of CPU times to get the optimal or best objective values. Finally, we see that the linear models can be solved faster with the branch and cut algorithm than using Bender’s decomposition approach. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Controllers

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A CNN-Transformer Knowledge Tracking Model Combining with Forgotten Factors

A CNN-Transformer Knowledge Tracking Model Combining with Fo...

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Computer Science and Technologies in Education (CSTE), international conference on

作者： Xiaosheng Huang Yin Wu Yixin Luo School of Software East China Jiaotong University NanChang China

ISBN: (数字)9798350351804

ISBN: (纸本)9798350351811

Knowledge tracking plays a crucial role in intelligent education, contributing significantly to promoting educational equity and enhancing educational quality. To address the limitations of the Transformer-based knowledge tracking model in terms of forgetting characteristics and local modeling, this paper proposes a knowledge tracking approach that integrates convolutional neural network (CNN) and Transformer, drawing on the Ebbinghaus forgetting curve theory and memory trace decay theory. this paper incorporates three forgetting factors into the Transformer model: the time interval of learning, the number of times a learner revisits the same knowledge points, and the learner's mastery of knowledge points to address its deficiencies in forgetting characteristics. Simultaneously, one-dimensional convolution is used to model the learner's historical interaction sequence to address the relative shortcomings of the Transformer model in local modeling. Finally, the outputs of the global modeling Transformer layer and the local modeling convolution layer are concatenated to obtain the learner's knowledge state level, thus enabling predictions of the learner's future performance. Experiments on four main datasets show that compared with BKT, DKT, DKVMN, SAKT and SAINT, the proposed model has achieved a significant improvement in AUC performance and running time.

关键词： Knowledge engineering Convolution Education Psychology predictive models Transformers Market research

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Trajectory Prediction for V2X Collision Warning Using Pruned Transformer Model

Trajectory Prediction for V2X Collision Warning Using Pruned...

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), international conference on

作者： Jinquan Tan Haoran Li Qiang Zhang China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

this study explores the use of a pruned Transformer model for vehicle trajectory prediction in V2X collision warning systems, focusing on balancing accuracy and computational efficiency. By employing weight pruning, the model significantly reduces parameters and inference time while maintaining competitive performance with traditional LSTM models. Real-world experiments across diverse traffic scenarios demonstrate the pruned Transformer’s strengths in trajectory prediction error and false warning rates. However, increased missed warnings in dynamic scenarios and longer prediction times highlight areas for further optimization. these findings establish the pruned Transformer as a promising candidate for real-time V2X applications under resource constraints.

关键词： Adaptation models Computational modeling predictive models Transformers Real-time systems Robustness Trajectory Vehicle dynamics Vehicle-to-everything Long short term memory

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Evaluating the Efficacy of Feature Selection Methods in Cardiovascular Disease Prediction with Machine Learning

Evaluating the Efficacy of Feature Selection Methods in Card...

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international conference on Electrical Information and Communication Technology (EICT)

作者： Rezwanul Haque S. M. Mahedy Hasan Azmain Yakin Srizon Anwar Hossain Efat Md. Farukuzzaman Faruk Mahjabin Oishe Nahrin Jannat Mostarina Mitu Department of Computer Science and Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

Cardiovascular disease (CVD) remains a primary contributor to global mortality, necessitating refined predictive models. Machine learning (ML) has emerged as a pivotal tool in augmenting clinical decisions, particularly in CVD prediction. Nonetheless, the performance of ML classifiers is substantially influenced by the judicious selection of relevant features. this manuscript presents a focused comparative analysis of feature selection methodologies in CVD prediction through ML classifiers. Utilizing a clinical dataset, the study assesses the efficacy of various classifiers based on distinct feature subsets. Empirical results indicate that the Random Forest classifier, with a mere five selected features, attains a remarkable 99.70% accuracy, thus qualifying as an optimal choice for clinical settings. the research underscores the necessity of scrupulously evaluating diverse combinations of feature selection and learning algorithms to derive the most effective model. Collectively, this work provides pivotal insights into optimal feature selection for precise CVD prediction, thereby contributing to the development of more efficient predictive strategies.

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Multiclass Objects Localization Using Deep Learning Technique in Autonomous Vehicle 6

Multiclass Objects Localization Using Deep Learning Techniqu...

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6th IEEE international conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2022

作者： Juyal, Amit Sharma, Sachin Matta, Priya Graphic Era Deemed to Be University Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665456982

In a conventional automobile, the vehicle is controlled by a human brain. the eyes, which gather visual data from surrounding traffic, are the most important human sense. this visual input is then sent to the brain through the eyes. the eyes then communicate this visual information to the brain, which is in charge of operating the car by applying brakes, accelerating, or turning left or right. Autonomous vehicles or self-driving cars, on the other hand, rely on sensors, LiDAR, RADAR, and cameras to gather traffic data. this data from multiple sensors and cameras must be processed for the car to travel smoothly and securely. Intelligent software that can simulate a human brain, classify, and localize numerous sorts of traffic objects in real- time is required for autonomous vehicles. the image is the major input of an autonomous vehicle, and the program must detect the object from the input image. Object localization, or predicting the true position of a recognized object in an image, is a critical problem. the software can only predict the next step or decide what distance to keep between the car and other traffic objects, as well as whether to brake and accelerate, after exact object localization. We are proposing an object localization method using YOLOv5 as an object detector and LiDAR to calculate the distance. We created a custom dataset of 6000 images of five different classes and achieved 89.2% mAP value and at a.95 IoU threshold, our model achieved 52.1 mAP. We have also compared several conceivable computer vision challenges and real-time deep learning models in object localization in this work. © 2022 IEEE.

关键词： Autonomous vehicles

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Enhancing Source Code Vulnerability Detection Using Flattened Code Graph Structures

Enhancing Source Code Vulnerability Detection Using Flattene...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

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

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

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.

关键词： Deep learning Codes Source coding Semantics Syntactics Feature extraction Transformers software Robustness software reliability

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XGBoost Regression Algorithms for Efficient Predictions on Inventory Sales and Management

XGBoost Regression Algorithms for Efficient Predictions on I...

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Electronics and Electrical engineering Technology (EEET), international conference on

作者： Doreen Ying Ying Sim Zhenyang Wei School of Computer Science Faculty of Science and Engineering University of Nottingham (Malaysia campus) Selangor Malaysia College of Engineering Computing and Cybernetic Australian National University Canberra Australia

ISBN: (数字)9798350395594

ISBN: (纸本)9798350395600

A combined implementation of machine learning(ML) based prediction models on the stock market using mainly Extreme Gradient Boosting (XGBoost 1 ) regression algorithms has been researched upon. there have been existing implementations of the ML-based models in the prediction of other business models. the ML-based model called XGBoost 1 regression algorithm is experimented as the preferred model to predict the sale amount in diverse business areas with significant efforts to testify the performance in prediction accuracy of the results. After applying the XGBoost regression algorithm to the assigned datasets on the past sales data of market for predicting the future inventory stock sales, the result has been verified as highly accurate in the test data output in the test data environment. Evaluation on the coefficient of determination (R square) shows that majority of the test results is satisfactory, which more than half of the predicted results having extremely low error rate. the deviation rate of the majority of the remaining results is also within the acceptable range. Only a few portions have a significantly large unexplainable difference which is within our expectations. 1 Extreme Gradient Boost (XGBoost) is a strong classifier, i.e. an ensemble technique in Machine Learning classification

关键词： Training Machine learning algorithms Accuracy software algorithms predictive models Prediction algorithms software

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Traffic Prediction Based Energy Efficient Transfer for Point-to-Point Wireless Communications

Traffic Prediction Based Energy Efficient Transfer for Point...

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international conference on Communications, Information System and Computer engineering (CISCE)

作者： Lin Wang Yao Hu College of Electronic & Information Engineering Southwest University Chongqing P.R. China College of Electronic & Information Engineering Chongqing Technology and Business Institute Chongqing P.R. China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

this paper investigates the performance of Kalman filtering based energy-efficient transfer for nonlinear stochastic discrete-time systems and proposes a low complex traffic load prediction algorithm. Based on the historical values of traffic load, the transmitter estimates the traffic load of the current time slot. And with the predicted traffic load, the transmission power in the incoming time slot is adjusted by the transmitter adaptively. Simulations are conducted to validate the theoretical analysis results, the proposed traffic load prediction algorithm can improve the energy efficiency of the wireless networks. the prediction accuracy is also validated with the real data sets of a local base station.

关键词： Transmitters Wireless networks Heuristic algorithms Computational modeling Telecommunication traffic predictive models Prediction algorithms

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A Machine Learning-Based Approach for Sentiment Analysis of Movie Reviews on a Bangladeshi OTT Platform 6th

A Machine Learning-Based Approach for Sentiment Analysis of ...

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6th international conference on Intelligent Computing and Optimization, ICO 2023

作者： Jahan, Hasnur Arman, Md. Shohel Hasan, Afia Bristy, Sabikun Nahar DIU Data Science Lab Department of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031501500

Sentiment analysis is the examination of feelings and viewpoints in any kind of literature. Opinion mining is another phrase for sentiment analysis. the data’s sentiment analysis is quite helpful, to convey the collective, group, or individual viewpoint. this method is employed to ascertain a person’s attitude about a specific source. Huge amounts of data are present on social media and other online platforms in the form of tweets, blogs, status, postings, etc. the movie reviews were examined in this research using a variety of methods. On demand of movies on OTT platform several Facebook reviewer pages has been created in Bangladesh. For this work, almost 1000 Bangla reviews were gathered, containing some English word from Facebook. Customer tones were assessed in movie reviews. We use Unigram, Bigram, and Trigram features with a variety of models, including Decision Tree, Random Forest, Multinomial Naive Bayes, K-Neighbors, and Linear Support Vector Machine in n-grams. Random Forest is the most accurate, with 92.35 percent and 90.03 percent accuracy in Unigram and Trigram, respectively. the most accurate model in Bigram is Decision Tree, which has an accuracy of 89.50 percent. this proposed system will help to analysis reviews and give feedback about a movie. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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