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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Gururamasenthilvel, P. Duraimutharasan, N. Esther T, Hannah Rose Periyasamy, P.N. Rajesh Kumar, K. Saveetha School of Engineering Department of Computer Science and Engineering Chennai602 105 India Amet University Department of Information Technology Tamil Nadu Kanathur603112 India Velammal Engineering College Department of Artificial Intelligence and Data Science India Hindusthan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore641032 India Chendu College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurantakam603311 India

ISBN: (纸本)9798350315905

Human capital is crucial to the success of businesses in the new knowledge economy. There is a compelling financial justification for developing a system to assess and enhance employee performance, making performance measurement at the level of the individual worker a must. With data Mining, HR professionals may find new and relevant information (HRM). The success of an employee is not dependent on a single trait. Humans vary greatly in their innate talents and the traits that define their personalities. Hence, data from many sectors must be collected for performance monitoring. The purpose of this article is to present an overall grade of an employee's performance taking into account a number of criteria. We begin by contrasting many popular Machine Learning algorithmsagainst one another. We then recommend an ensemble method that draws from the aforementioned methods. The empirical findings show that the suggested model outperforms the state-of-the-art models in terms of precision, recall, F1-score, and accuracy, as compared to the more conventional models. © 2023 IEEE.

关键词： data mining

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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Emerging Trends in Information Technology and Engineering (ic-ETITE), Conference on

作者： Elumalaivasan Poongavanam Prathik. A Munirathinam. T Kayalvizhi. V Rajesh. R Mahavishnu. C Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Sona College of Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Shreenivasa Engineering College Dharmapuri India

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases.

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Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification

arXiv

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arXiv 2025年

作者： Gao, Yibo Zhou, Hangqi Gao, Zheyao Wang, Bomin Gao, Shangqi Wang, Sihan Zhuang, Xiahai School of Data Science Fudan University China Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Dept. of Oncology University of Cambridge United Kingdom

The pursuit of decision safety in clinical applications highlights the potential of concept-based methods in medical imaging. While these models offer active interpretability, they often suffer from concept leakages, where unintended information within soft concept representations undermines both interpretability and generalizability. Moreover, most concept-based models focus solely on local explanations (instance-level), neglecting the global decision logic (dataset-level). To address these limitations, we propose Concept Rule Learner (CRL), a novel framework to learn Boolean logical rules from binarized visual concepts. CRL employs logical layers to capture concept correlations and extract clinically meaningful rules, thereby providing both local and global interpretability. Experiments on two medical image classification tasks show that CRL achieves competitive performance with existing methods while significantly improving generalizability to out-of-distribution data. The code of our work is available at https://***/obiyoag/crl. © 2025, CC BY.

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Exploring Artificial Neural Network Models for c-VEP Decoding in a Brain-Artificial Intelligence Interface

Exploring Artificial Neural Network Models for c-VEP Decodin...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhengqing Miao Anja Meunier Michal Robert Žák Moritz Grosse-Wentrup Research Group Neuroinformatics Faculty of Computer Science University of Vienna Vienna Austria Department of Psychology Justus Liebig University Giessen Giessen Germany Doctoral School Computer Science Faculty of Computer Science University of Vienna Vienna Austria Vienna Cognitive Science Hub University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The Conversational Brain-Artificial Intelligence Interface (BAI) is a novel brain-computer interface (BCI) that uses artificial intelligence (AI) to help individuals with severe language impairments communicate. It translates users’ broad intentions into coherent, context-specific responses through an advanced AI conversational agent. A critical aspect of intention translation in BAI is the decoding of code-modulated visual evoked potentials (c-VEP) signals. This study evaluates five different artificial neural network (ANN) architectures for decoding c-VEP-based EEG signals in the BAI system, highlighting the efficacy of lightweight, shallow ANN models and pre-training strategies using data from other participants to enhance classification performance. These results provide valuable insights for the application of ANN models in decoding c-VEP-based EEG signals and may benefit other c-VEP-based BCI systems.

关键词： Training Visualization Translation Artificial neural networks Brain modeling data models Electroencephalography Decoding Calibration Artificial intelligence

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CAT: A Collaborative Annotation Tool for Chinese Genealogy Textual Documents 26

CAT: A Collaborative Annotation Tool for Chinese Genealogy T...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Jiang, Huan Wang, Zihao Li, Rongrong Peng, Yuwei Peng, Zhiyong Xu, Bin School of Computer Science Wuhan University Wuhan430072 China School of History Wuhan University Wuhan430072 China The Big Data Institute Wuhan University Wuhan430072 China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan430072 China

ISBN: (纸本)9798350331684

The annotation for Chinese genealogy textual documents is helpful for constructing genealogy knowledge graph, training effective machine learning models for knowledge extraction, etc. However, this kind of documents is difficult to annotate. The primary reason is that the texts are written in both classical and vernacular Chinese. These texts also contain numerous ancient characters and are usually without punctuation. Understanding genealogy texts requires sufficient expertise. When multiple users labeling the same text, conflicts may occur. Existing annotation tools are inappropriate for this work. In this paper, we propose a novel interactive labeling tool, which provides text segmenting, entity and relationship tagging etc. With the annotated information, it is convenient to construct knowledge graph from textual documents, which can be used to analyze Chinese genealogy texts. Furthermore, we introduce a weak supervised mechanism with Hidden Markov Model for collaborative annotating with crowdsourcing. The practice shows that our approach is effective for collaborative annotation. It also facilitates the construction of knowledge graph and obtains more high-quality data sets. At present, this annotation tool has been applied into service. © 2023 IEEE.

关键词： Hidden Markov models

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SWIPTNet: a Unified Deep Learning Framework for SWIPT Based on GNN and Transfer Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Han, Hong Lu, Yang Song, Zihan Zhang, Ruichen Chen, Wei Ai, Bo Niyato, Dusit Kim, Dong In Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

This paper investigates the deep learning based approaches for simultaneous wireless information and power transfer (SWIPT). The quality-of-service (QoS) constrained sumrate maximization problems are, respectively, formulated for power-splitting (PS) receivers and time-switching (TS) receivers and solved by a unified graph neural network (GNN) based model termed SWIPT net (SWIPTNet). To improve the performance of SWIPTNet, we first propose a single-type output method to reduce the learning complexity and facilitate the satisfaction of QoS constraints, and then, utilize the Laplace transform to enhance input features with the structural information. Besides, we adopt the multi-head attention and layer connection to enhance feature extracting. Furthermore, we present the implementation of transfer learning to the SWIPTNet between PS and TS receivers. Ablation studies show the effectiveness of key components in the SWIPTNet. Numerical results also demonstrate the capability of SWIPTNet in achieving nearoptimal performance with millisecond-level inference speed which is much faster than the traditional optimization algorithms. We also show the effectiveness of transfer learning via fast convergence and expressive capability improvement. © 2002-2012 IEEE.

关键词： Laplace transforms

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Server Cloud Based Security Surveillance Systems: a Design science Research

Server Cloud Based Security Surveillance Systems: a Design S...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Alexander Nurenie Arief Ramadhan Edi Abdurachman Agung Trisetyarso Muhammad Zarlis Computer Science Department BINUS Graduate Program – Computer Science Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia

In this research, the author addresses the prevalent issues faced by users of cloud services, especially those using Peer-to-Peer (P2P) technology, such as connection losses, security concerns, and poor video quality. The study does not delve into the complex problems at the data center level but instead proposes a novel Design science Research approach. This new method, tested with five clients in varied locations, utilizes port forwarding to establish a direct server-client connection, bypassing traditional P2P frameworks. The results show that this approach significantly improves quality over the P2P system. Feedback from clients before and after the experiment indicates a positive response to this new method. This research offers insights into overcoming P2P-related issues in cloud services and contributes to the development of cloud-based server science, presenting a cost-effective, independent alternative to data center-dependent P2P services, with plans for ongoing research in this area.

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Identification of Cardiovascular Disease using ECG Images based on Deep Learning Procedure 2

Identification of Cardiovascular Disease using ECG Images ba...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Esther T, Hannah Rose Periyasamy, P.N. Rajesh Kumar, K. Gururamasenthilvel, P. Duraimutharasan, N. Velammal Engineering College Department of Artificial Intelligence and Data Science India Hindusthan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore641032 India Chendu College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurantakam603311 India Saveetha School of Engineering Department of Computer Science and Engineering Chennai602 105 India Amet University Department of Information Technology Tamil Nadu Kanathur603112 India

ISBN: (纸本)9798350315905

When it comes to finding out what's wrong with your heart, cardiac imaging is crucial (CVD). Its previous purpose was restricted to qualitative and quantitative evaluations of the heart. The advent of big data and machine learning, however, has opened up new possibilities for developing AI tools that can aid the physician in the diagnosis of CVDs. Our study offers a unique approach to ECG illness prediction by combining cardiology, signal processing techniques, and a deep learning model. Then, using wavelet transformation and cardiology, we convert the ECG data into a time-frequency representation. Next, a deep convolutional network is used to categories the results of the time-frequency analysis. We find that by combining the benefits of signal processing with those of deep learning, we can significantly enhance the precision of categorization using our approach. The suggested methodology has the potential to enhance performance and reduces staffing costs for major general hospitals, all while lowering the rates at which primary care facilities make diagnostic errors. © 2023 IEEE.

关键词： Electrocardiograms

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SOCA-DOM:A Mobile System-on-Chip Array System for Analyzing Big data on the Move

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Journal of computer science & Technology 2022年第6期37卷 1271-1289页

作者： Le-Le Li Jiang-Yi Liu Jian-Ping Fan Xue-Hai Qian Kai Hwang Yeh-Ching Chung Zhi-Bin Yu Center for Heterogeneous and Intelligent Computing Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Science and Engineering The Chinese University of Hong KongShenzhenShenzhen 518172China Center for High Performance Computing Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Shenzhen 518055China Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos Angeles CA 90089-0001U.S.A. Department of Computer Science University of Southern CaliforniaLos AngelesCA 90089-0001U.S.A. School of Data and Science The Chinese University of Hong KongShenzhenShenzhen 518172China

Recently,analyzing big data on the move is *** requires that the hardware resource should be low volume,low power,light in weight,high-performance,and highly scalable whereas the management software should be flexible and consume little hardware *** meet these requirements,we present a system named SOCA-DOM that encompasses a mobile system-on-chip array architecture and a two-tier“software-defined”resource manager named ***,we design an Ethernet communication board to support an array of mobile ***,we propose a two-tier software architecture for Chameleon to make it ***,we devise data,configuration,and control planes for Chameleon to make it“software-defined”and in turn consume hardware resources on ***,we design an accurate synthetic metric that represents the computational power of a computing *** employ 12 Apache Spark benchmarks to evaluate ***,SOCA-DOM consumes up to 9.4x less CPU resources and 13.5x less memory than Mesos which is an existing resource *** addition,we show that a 16-node SOCA-DOM consumes up to 4x less energy than two standard Xeon *** on the results,we conclude that an array architecture with fine-grained hardware resources and a software-defined resource manager works well for analyzing big data on the move.

关键词： edge computing mobile architecture resource management big data analytics software-defined systems

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A Precoding Perturbation Method in Geometric Optimization: Exploring Manifold Structure for Privacy and Efficiency

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IEEE Transactions on Information Forensics and Security 2025年 20卷 6220-6235页

作者： Azadeh Pourkabirian Wei Ni Xiaolin Zhou Kai Li Mohammad Hossein Anisi Real-Time and Embedded Computing Systems Research Centre (CISTER) Porto Portugal Data61 CSIRO Sydney NSW Australia School of Information Science and Technology Fudan University Shanghai China School of Computer Science and Electronic Engineering University of Essex Colchester U.K.

Inherent broadcast characteristics can raise privacy risks of wireless networks. The specifics of antenna ports, antenna types, orientation, and beamforming configurations of a transmitter can be susceptible to manipulation by any device within range when the signal is transmitted wirelessly. Personal and location information of users connected to the transmitter can be intercepted and exploited by malicious actors to track user movements and profile behaviors or launch targeted attacks, thus compromising user privacy and security. In this paper, we propose a novel precoding perturbation approach for privacy preservation in wireless communications. Our approach perturbs the precoding matrix of the transmitter using a Riemannian manifold (RM) structure that adaptively adjusts the magnitude and direction of perturbation based on the geometric properties of the manifold. The approach ensures robust privacy protection while minimizing the distortion of the transmitted signals, thus balancing privacy preservation and data utility. Privacy can be preserved without relying on additional cryptographic mechanisms, resulting in the computational and communication overhead reduction. Our approach operates directly on the transmission of signals, making them inherently secure against eavesdropping and interception. Simulation results underscore the superiority of the approach, showing a 17.21% improvement in privacy preservation while effectively maintaining data utility.

关键词： Precoding Privacy Perturbation methods data privacy Manifolds Vectors Streams Symmetric matrices Radio transmitters Interference

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