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17th International Conference on Information Technology and Applications, ICITA 2023

作者： Salma, Syeda Umme Sakib, Md Sadman Yasaar, Nahiyan Alvee, Mohammed Moinul Morshed Reza, Md Tanzim Parvez, Mohammad Zavid Department of Computer Science and Engineering BRAC University Dhaka Bangladesh School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW Australia

ISBN: (纸本)9789819983230

Prostate cancer detection is an extremely challenging problem due to the socially sensitive nature of the data. As such, it negatively impacts early detection, including new procedures becoming difficult to develop owing to a lack of enough datasets. The proposed approach, which is based on Federated Learning, is a decentralized strategy that uses weight averaging and can quickly find anomalies while retaining data confidentiality. Additionally, the results of the customized basic CNN model with three Conv2D layers and two state-of-the-art models, VGG19 and Xception, were compared to those obtained in centralized and decentralized configurations. To recapitulate, this research’s findings indicate that the decentralized technique yields results that are very consistent with those yielded by state-of-the-art centralized methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medical imaging

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Enhancing Text Detection in Natural Scenes: A Hybrid Approach with MSER, Connected Components, and Norm-CLAHE 3

Enhancing Text Detection in Natural Scenes: A Hybrid Approac...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Selvam, Prabu Nevgi, Akshaj Gunasundari, C. Sowrish, V.K. Natarajan, B. Jessika, S. Sharon School of Computer Science and Engineering VIT University Chennai India School of Computer Science and Engineering VIT Chennai Vellore India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India VIT University Department of Computer Science and Engineering Vellore India School of Computer Science and Engineering VIT Vellore Vellore India

ISBN: (纸本)9798350317022

The textual information present in natural scene images is a valuable clue for many vision-based applications. Locating text areas becomes particularly challenging due to issues like varying lighting conditions, complex backgrounds, non-uniform text sizes, and more. Detecting curved text, in particular, is a demanding task in the fields of computer vision and pattern recognition. This research work proposes a robust approach for the detection of text in natural scene images with different orientations. The proposed method is divided into three key stages: (i) Pre-Processing: The original image is converted to grayscale, and this research propose a new Norm-Contrast-limited Adaptive Histogram Equalization (CLAHE) to enhance the contrast in the grayscale image by limiting noise amplification and prevents saturation in dark and bright text regions. (ii) Text Region Extraction: this research combined Otsu's thresholding, Maximally Stable Extremal Region (MSER) and Sobel edge de-tector to extract text regions and enhance edges in the image. (iii) Non-Text Region Removal: In this stage, this research combine Connected Component (CC), Morphological operations, MSER region filters, and basic text geometric properties to create a framework for removing non-text regions and drawing rectangle bounding boxes around the detected text regions. this research assessed the performance of the proposed method utilizing the ICDAR 2017 text dataset, which yielded an overall accuracy of 93.47%, demonstrating highly satisfactory results. © 2023 IEEE.

关键词： connected component edge detector image processing MSER Text detection

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Klotski v2: Improved DNN Model Orchestration Framework for Dataflow Architecture Accelerators

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1045-1058页

作者： Bai, Chen Wei, Xuechao Zhuo, Youwei Cai, Yi Zheng, Hongzhong Yu, Bei Xie, Yuan The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Alibaba DAMO Academy Computing Technology Laboratory Hangzhou311121 China Peking University School of Integrated Circuits Beijing100871 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

Dataflow architecture accelerators are a new kind of scalable DNN accelerators. For an instruction, the availability of input operands solely determines the beginning of executions. DNN model orchestration determines how to partition, schedule, and map the computation to the underlying hardware. In this article, we propose the Klotski v2 framework to solve DNN model orchestration for dataflow architecture accelerators. First, a Bayesian optimization-based entropy-directed partition algorithm is proposed to transform a DNN model into μ ops. Second, a unified formal formulation for μ ops scheduling and mapping is presented. Third, a two-stage methodology is proposed to decouple the scheduling and mapping. Fourth, a Hilbert curve-based mapping heuristic is proposed to enhance problem-solving efficiency, improving the tradeoff between solution quality and algorithm runtime. Extensive results show that Klotski v2 can achieve an average of 21.57% higher execution performance improvement than the previous methodologies. With the Hilbert curve-based mapping heuristic, we improve the algorithm efficiency by an average of 63.50% across different DNN workloads. © 1982-2012 IEEE.

关键词： Scheduling algorithms

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EduFrenzy: A One-Stop Learning Platform 12

EduFrenzy: A One-Stop Learning Platform

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Sharma, Mehul Sivakumar, Shruti Singh, Aishwary Nair, Prashant R Amrita Vishwa Vidyapeetham Amrita School of Engineering Coimbatore Department of Electrical and Electronics Engineering India Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Department of Artificial Intelligence India Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science & Engineering India

ISBN: (纸本)9798350340204

EduFrenzy is an innovative multilingual online learning platform designed to bridge the educational gap for underserved communities. By offering localized and culturally relevant content, multilingual support, and comprehensive community engagement tools, EduFrenzy addresses the unique educational challenges faced by these communities. The platform features curated courses across various domains, financial support integration, and offline access to educational materials. Leveraging advanced technological frameworks and user-centered design principles, EduFrenzy provides equitable access to quality education, empowering individuals to enhance their knowledge and skills regardless of their geographical or economic constraints. This paper outlines the development process, technological framework, and implementation strategy of EduFrenzy, highlighting its potential to transform educational experiences and foster social and economic development in underserved communities. © 2024 IEEE.

关键词： Federated learning

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PESTA: An Elastic Motion Capture Data Retrieval Method

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Journal of computer science & Technology 2023年第4期38卷 867-884页

作者：蒋子飞李伟黄艳尹义龙郭宗杰彭京亮 School of Software Shandong UniversityJinan 250101China Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles 90089U.S.A Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan 250022China School of Information Science and Engineering University of JinanJinan 250022China

Prevalent use of motion capture(MoCap)produces large volumes of data and MoCap data retrieval becomes crucial for efficient data *** clips may not be neatly segmented and labeled,increasing the difficulty of *** order to effectively retrieve such data,we propose an elastic content-based retrieval scheme via unsupervised posture encoding and strided temporal alignment(PESTA)in this *** retrieves similarities at the sub-sequence level,achieves robustness against singular frames and enables control of tradeoff between precision and *** firstly learns a dictionary of encoded postures utilizing unsupervised adversarial autoencoder techniques and,based on which,compactly symbolizes any MoCap ***,it conducts strided temporal alignment to align a query sequence to repository sequences to retrieve the best-matching sub-sequences from the ***,it extends to find matches for multiple sub-queries in a long query at sharply promoted efficiency and minutely sacrificed *** performance of the proposed scheme is well demonstrated by experiments on two public MoCap datasets and one MoCap dataset captured by ourselves.

关键词： motion capture(MoCap) content-based retrieval adversarial autoencoder temporal alignment

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Machine Learning Applications for Online Partial Discharge Detection, Classification, and Localization in Power Transformers: A Review 4

Machine Learning Applications for Online Partial Discharge D...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Tag, Ameera Refaat, Shady S. Kameli, Sayed Mohammad Saleh, Mohammad Alshaikh Texas A&M University at Qatar Electrical and Computer Engineering Department Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Texas A&M University Department of Electrical and Computer Engineering College Station United States

ISBN: (纸本)9798350306262

The power transformer is a crucial asset and a fundamental component of the power grid. Assets undergo aging due to the stresses present in insulation materials. Partial discharges (PDs) are the most common fault source in power transformers and an excellent indicator of aging. The detection, classification, and localization of PD activities in power transformers are persisting challenges, while techniques utilizing machine learning (ML) are widely sought to deal with those challenges. Existing ML techniques show promising results with an elevated level of accuracy and precision. However, there is a lack of conventional ML-based real-time monitoring capability. Therefore, this paper presents a comprehensive review of the application of ML techniques for online PD activity detection, classification, and localization in power transformers, focusing on supervised, unsupervised, semi-supervised, and reinforcement learning techniques. In addition, this paper explores the challenges, future trends, perspectives, and outlook of machine learning for online transformer fault analysis. © 2024 IEEE.

关键词： Partial discharges

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Multimodal Emotion Recognition Using computer Vision: A Comprehensive Approach 15

Multimodal Emotion Recognition Using Computer Vision: A Comp...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohan, G. Bharathi Sarrvesh, N. Hrishikeasan, N.P. Boopalamani, J. Doss, Srinath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho university Botswana

ISBN: (纸本)9798350370249

Real-time understanding and response to human emotions have become critical in today’s connected world of prevalent human-computer interaction. This study presents a novel approach to real-time emotion detection using a camera-based system that analyzes both voice and facial expressions. Our method advances the state-of-the-art by integrating deep learning techniques with parallel models for audio and visual inputs through a weighted fusion approach, a strategy not commonly employed in existing systems. Utilizing datasets such as CK+48 and the Ryerson Audio-Visual Database of Emotional Speech and Song (Ravdess), our system achieves superior performance metrics, including an accuracy of up to 92.92% and an F1 score of 92.94%. Encapsulated within a user-friendly microservice with an intuitive online interface, our solution enables seamless real-time interaction using only a webcam and microphone. Ongoing efforts focus on refining the system’s ability to recognize diverse voices and expressions, aiming to create technology that empathizes with human emotions and enhances interpersonal interactions. ©2024 IEEE.

关键词： Emotion Recognition

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Cryptocurrency Price Prediction Using Deep Learning 9th

Cryptocurrency Price Prediction Using Deep Learning

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Tharun, S.V. Saranya, G. Tamilvizhi, T. Surendran, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SaveethaInstitute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9783031440830

As technology advances daily, are advancing our lives into the digital sphere. The introduction of these cryptocurrencies aims to prevent the financial crisis. Due to its decentralized nature, high level of security, and restrictions on the number of coins that may be created, cryptocurrencies have attracted investors. Predicting the future price includes several limits and determinants because it involves capital. It varies according to market share. Using block chain technology and encryption, the transactions are encrypted from the beginning to end. Predicting prices to encourage consumers to invest during a specific period and earn a profit. They include a variety of elements, such as market analysis, sentiment analysis on Twitter, trading volume, and open and closing prices. Typical models that can be used to forecast bitcoin prices include regression techniques, neural networks, and support vector machines. Predictions of cryptocurrency prices based on their closing prices give investors additional insight into whether to wait until the closing period if prices are low for the entire day or to invest the following day. Using deep learning and bidirectional Long Short-Term Memory (LSTM) suggested this model to anticipate the price of digital currencies including Bitcoin, Litecoin, Ethereum, and Cardano. In this model, predictions are made using historical price statistics, and the graph is created by evaluating several performance indicators. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Automated object recognition in high-resolution optical remote sensing imagery

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National science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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Fault Diagnosis of Tenessee Eastman Process with Detection Quality Using IMVOA with Hybrid DL Technique in IIOT

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SN computer science 2023年第5期4卷 1-10页

作者： Anitha, Cuddapah Rajesh Kumar, T. Balamanigandan, R. Mahaveerakannan, R. Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai 602105 India

This work presents a deep learning (DL) based approach to defect identification in continuous systems. The Tennessee Eastman Procedure was chosen as a use case for this study because it involves a dispersed network of sensors throughout a manufacturing facility. To improve detection quality for fault diagnosis, a hybrid DL approach is presented to choose the most representative sensors. Using an enhanced version of the Multi-Verse Optimization technique, the learning velocity of a Recurrent Neural Network is maximised (IMVOA). An exhaustive design space of solutions varying in sensing and detected quality has been made available by the suggested technique. It is an alternative to the standard Industry 4.0 setup, in which a plethora of decentralised sensor networks report their findings to a single cloud repository. In contrast, the suggested method takes a decentralised approach, which means that processing may occur closer to the sensors that create the data, or even at the advantage of the Internet of Things. Bandwidth, privacy, and latency issues, which often plague centralised methods, are circumvented here. Experimental results demonstrate that the suggested technique delivers fault finding solutions for the Tennessee Eastman Procedure at state-of-the-art detection quality levels. Solution times are 35 times faster than the application with the best detection quality, while feature counts are reduced by an average of 1.99 times. It is important to note that the framework’s scalability creates a design space from which the best possible implementation may be selected based on the requirements of the application. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Fault diagnosis Improved multi-verse optimization algorithm Recurrent Neural Network Tennessee Eastman process

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