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Design and Analysis of DC-DC Two-Phase Interleaved Buck Converter for EV Charging Applications 18

Design and Analysis of DC-DC Two-Phase Interleaved Buck Conv...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Yousuf, Muhammad Zeb, Kamran School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan

ISBN: (纸本)9798350328172

While the world is booming with Electric Vehicles (EVs), countries where bikes are a common mode of transport still require adequate charging infrastructure to cope up with the slow EV growth. Efficient power conversion is necessary for EV chargers to be feasible. This paper presents an efficient solution for the DC-DC stage of the EV chargers for two wheelers by designing a two-phase Interleaved Buck Converter (IBC). The converter has been equipped with a PI controller for corrective measures against the disturbances, reducing the steady state error and the settling time of the system. The designed PI controller eliminates the need for an additional controller for current balancing between the two branches of the IBC. With significant advantages of reduced ripple, mitigated stress on the components and efficient conversion of power, IBC looks out as a great breakthrough to shift the dynamics of two-wheeler chargers. IBC in this paper has been designed using MATLAB/Simulink and is evaluated in Proteus with the non-ideal components that can be used in the prototype along with a small-scale hardware validation of 12W prototype. © 2023 IEEE.

关键词： Controllers

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Breaking Through the Spike: Spike Window Decoding for Accelerated and Precise Automatic Speech Recognition

Breaking Through the Spike: Spike Window Decoding for Accele...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Wei Zhang, Tian-Hao Luo, Chao Zhou, Hui Yang, Chao Qian, Xinyuan Yin, Xu-Cheng *** GROUP Shanghai China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Recently, end-to-end automatic speech recognition has become the mainstream approach in both industry and academia. To optimize system performance in specific scenarios, the Weighted Finite-State Transducer (WFST) is extensively used to integrate acoustic and language models, leveraging its capacity to implicitly fuse language models within static graphs, thereby ensuring robust recognition while also facilitating rapid error correction. However, WFST necessitates a frame-by-frame search of CTC posterior probabilities through autoregression, which significantly hampers inference speed. In this work, we thoroughly investigate the spike property of CTC outputs and further propose the conjecture that adjacent frames to non-blank spikes carry semantic information beneficial to the model. Building on this, we propose the Spike Window Decoding algorithm, which greatly improves the inference speed by making the number of frames decoded in WFST linearly related to the number of spiking frames in the CTC output, while guaranteeing the recognition performance. Our method achieves SOTA recognition accuracy with significantly accelerates decoding speed, proven across both AISHELL-1 and large-scale In-House datasets, establishing a pioneering approach for integrating CTC output with WFST. © 2025 IEEE.

关键词： automatic speech recognition spike window decoding wfst

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Enhancing Intrusion detection: Leveraging Federated Learning and Hybrid Machine Learning Algorithms On ToN_IoT Dataset 20

Enhancing Intrusion detection: Leveraging Federated Learning...

引用

20th International Conference on Frontiers of Information technology, FIT 2023

作者： Naeem, Faiza Malik, Asad Waqar Abbas Khan, Safdar Jabeen, Farzana National University of Science and Technology School of Electrical Engineering and Computer Sciences Islamabad Pakistan

ISBN: (纸本)9798350395785

An efficient and productive IoT application may ease some real-time tasks however, it is at risk of cyber-attack. Intrusion Detection Systems (IDS) are of significant importance for the security measures of IoT applications. IoT/IIoT devices that deal with large data volumes are at risk of malicious attacks and as a result, anomaly-based IDS are developed. Anomaly-based intrusion detection systems perform more efficaciously than other methods. But, the question that arises is whether the performance of models meets the required standards. The research intends to improve the efficiency of IDS with a focus on the Telemetry data of IoT/IIoT sensors data from the ToN_IoT dataset. It includes data about seven IoT/IIoT devices. Federated Learning based on Deep auto-encoder is adopted to design a model with efficiently identifies attacks while solving the issue of privacy concerns. Hybrid models use Machine Learning algorithms with increased detection rates. The algorithms used for the Hybrid model are Random Forest and XGBoost. The XGBoost algorithm improves the accuracy of the Hybrid model with better predictions. The hybrid model ensures efficient pre-processing and feature selection for intrusion detection. The accuracy percentage of the Federated model is 97% on the dataset of Garage Door and 88% on the Motion Light dataset while the Hybrid model outperforms on all IoT/IIoT devices with an average accuracy score of 99.99%. © 2023 IEEE.

关键词： Internet of things

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Federated Learning-Based Network Intrusion Detection Using Deep Auto Encoder 6

Federated Learning-Based Network Intrusion Detection Using D...

引用

6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Jeyavim Sherin, R.C. Parkavi, K. Vit University School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350394702

The progress of information technology is directly related to different elements of daily life, providing people with services that make living more comfortable. As the network infrastructure grows to support new services, it necessarily produces various weak points that cybercriminals might exploit. Building a reliable network intrusion detection system with deep learning methodologies demands a substantial dataset. In the past, the conventional approach involved collecting data for centralized learning, which was then transmitted to a central server for model training. Nevertheless, this approach raises valid concerns regarding the potential exposure of personal information contained within raw data, which could lead to legal repercussions for data providers. Consequently, this study presents a Modified FedAvg algorithm to enhance the existing FedAvg algorithm for network intrusion detection models. This modified approach maximizes the potential of federated learning, enhancing data privacy and minimizing the transmission of model weights. Employing a Deep Auto Encoder within a federated setting, we evaluated our methodology using the CICIDDoS 2019 dataset, attaining a notable accuracy rate of 98.82% in effectively identifying intrusions. © 2023 IEEE.

关键词： Network security

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Dependency Tree Distance for Aspect Level Sentiment Analysis 4

Dependency Tree Distance for Aspect Level Sentiment Analysis

引用

4th International Conference on computer engineering and Application, ICCEA 2023

作者： Zhao, Yifan Zhang, Xinping Wang, Junhua School of Computer Science and Engineering Changchun University of Technology Jilin Province Changchun China

ISBN: (纸本)9798350347548

When dealing with complex statements containing multiple aspects in the existing model, most of them ignore the dependencies between context and aspect words, this makes it difficult for the model to learn local information about aspects. To solve this problem, an aspect-level emotion analysis model based on dependency tree distance is proposed. It can calculate the dependency tree distance between aspect words and other context words according to their position in the syntactic dependency tree structure, then the sentence is divided into local information based on dependency tree distance. In the design of the model structure, the fusion learning method is used. The multi-head attention mechanism is used to fuse the local information, global information and aspect information, and the hidden state containing rich sentence expression is obtained as the classification basis. To verify the effectiveness of the model, experiments are carried out on three benchmark datasets, including Restaurant and Laptop datasets of SemEval 2014, and Twitter datasets. In addition, a comparative experiment is designed for the two processing methods and the multi-group dependency tree distance threshold, and the processing method and the optimal threshold suitable for different datasets are selected. © 2023 IEEE.

关键词： Sentiment analysis

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Safely Knowledge Transfer from Source Models via an Iterative Pruning Based Learning Approach

Safely Knowledge Transfer from Source Models via an Iterativ...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Xiaoyu Sean Zhang, Jianan Yao, Siya Huang, Bo School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210000 China College of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China Nanjing University of Science and Technology Computer Science and Engineering Nanjing210000 China

ISBN: (纸本)9781665410205

Transfer learning has become a key technique in deep learning, widely adopted in the industry and academia for developing customized models, especially for specific and downstream tasks solving. Despite the prevalence of transfer learning, the target model can easily inherit defects from the source model during the learning process, such as vulnerability to backdoor attacks and adversarial attacks. Thus, this work proposes a novel approach, iterative pruning learning approach (IPLA), that reduces the inheritance of potential defects during the transfer learning process. In order to reduce the vulnerability to attacks and improve the robustness of target model, IPLA evaluates the importance of weights from the source model and retains ones that are critical to the target task, then prunes the redundant weights through an iterative pruning process. Experiments are performed on 4 datasets over 2 backbone source models. Results demonstrate the satisfactory performance of our proposed method. © 2024 IEEE.

关键词： Adversarial machine learning

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Ventricular Vision: AI-Driven Heart Disease Detection with CNN-SVM-Based Classification

Ventricular Vision: AI-Driven Heart Disease Detection with C...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Gupta, Richa Garg, Navin Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

This study builds upon the CNN-SVM model as a sound diagnostic method for identifying five distinct heart diseases. We utilize the effectiveness of the deep learning procedures of various CNNs and the ability of the SVMs, which, in the end, might be customized and optimized to solve particular cardiac diseases. Two classifications examined are atrioventricular block, arrhythmia, bradycardia, tachycardia, second-degree heart block, and annexation. The appraisal of CNN-SVM model performance accomplished through various parameters was detailed. The precision of the correct classes arrayed from 94.89% to 97.00%, the recall ranged between 93.70% and 98.91%, and the F1-scores were also high, extending from 94.87% to 97.25%. A significant enhancement in the precision and accuracy of the model was registered, which demonstrated statistics close to 0.99 for Class 1, 0.98 for Classes 2 and 3, and 0.99 for Classes 4 and 5, highlighting its high performance. Various types of averages were used to evaluate the model's effectiveness. The superiority of the macro average in accuracy, recall, and F1-score was demonstrated. It is calculated for all classes equally. The results were 96.14%, 96.20%, and 96.16%. We applied the weighted average (taking into account class imbalance) and got 96.15% for the straight as well as for the recall and the F1-score. The micro average, which selects the best from among all classes with identical scores, is kept at 96.15% for all tips, showing a well-suited classifier. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Consumer Behavior in E-Commerce with AI Personalization and Market Trends Analysis Models 3

Exploring Consumer Behavior in E-Commerce with AI Personaliz...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kumar, Gaurav Devayani, G. Saraswathi, C. Melba Kani, R. Dongre, Sandeep Mallick, M. S. Mohamed School of Computer Application Lovely Professional University Phagwara India M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Mar Ephraem college of Engineering and Technology Kanyakumari India Nagpur India Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331508456

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTM Based on key criteria, the AI model beat LSTM and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences. © 2025 IEEE.

关键词： Marketplaces

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Graph Based Frequent Item Set Generation in Association Rule Mining

Graph Based Frequent Item Set Generation in Association Rule...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Amalanathan, Geetha Mary School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350319088

This research proposes a new technique that uses a reconstruction strategy for categorical datasets to preserve the privacy of the association rules. In order to identify all frequent item association sets and determine sets comprising frequent items based on the association sets, the suggested technique takes advantage of the two-item permutation set to create an association part. The enhanced method scans the database only once, addressing a flaw in the conventional approach that scans the database twice and so increasing scanning costs. The database and association sets can be mined using the two-item permutation set, which has the advantages of operating more quickly, requiring less memory, and being simple to maintain. © 2023 IEEE.

关键词： Association rules

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Research on Network Security Game Based on System Dynamics 24

Research on Network Security Game Based on System Dynamics

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2nd International Conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Zeng, Qiufen Pei, Tingrui School of Mechanical Engineering and Mechanics Xiangtan University Hunan Xiangtan China School of Information Science and Engineering Hunan Institute of Engineering Hunan Xiangtan China College of Information Science and Technology Jinan University Guangdong Zhuhai China School of Computer Science School of Cyberspace Science Xiangtan University Hunan Xiangtan China

ISBN: (纸本)9798400711237

Network security game was usually considered as attack-defense confrontation;however, we need to think out of the box because any network entity may get a chance to launch an attack. The problem of network security which was described as the game between regulatory authority and network entity needs to be analyzed from the perspective of macro management. The model of system dynamic which was combined with traditional game theory was established. Several strategies which were most commonly used were verified and evaluated through simulation model. In order to weaken the instability of network security game, selective punishment strategy was proposed and the simulation result showed that not only in astringency but also in network security maintenance the proposed strategy can achieve good results. © 2024 Copyright held by the owner/author(s).

关键词： System theory

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