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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： M. Saravana Karthikeyan J. Relin Francis Raj R. Parvathi S. Thanga Anushya R. Santhana Krishnan K. Paul Joshua Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SaveethaInstitute of Medical and Technical Sciences Chennai India Department of Smart Computing School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology Vellore India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

U-Net-Attention-TBNet is an innovative deep-learning model developed for precisely segmenting and classifying tuberculosis (TB) lesions in chest X-ray images. This Model combines the U-Net architecture with an advanced attention mechanism, enhancing feature extraction and boosting detection accuracy. Trained on the CheXpert dataset, U-Net-Attention-TBNet demonstrates superior performance compared to existing models, including DenseNet with U-Net, ResNet with U-Net, and VGGNet with U-Net. It achieves significantly higher accuracy, precision, recall, and F1 score, showcasing its effectiveness in distinguishing between primary TB lesions, secondary TB lesions, miliary TB lesions, and calcified granulomas. The attention mechanism refines the Model's ability to focus on pertinent features, leading to improved segmentation and reduced false positives. This progress represents a significant advancement in TB diagnosis, offering enhanced reliability and efficiency in medical imaging and contributing to better management of tuberculosis.

关键词： Image segmentation Accuracy Attention mechanisms Tuberculosis Feature extraction Boosting Robustness Lesions X-ray imaging Biomedical imaging

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Analysis of Feature Parameters and Their Time-Transition for Shockable Arrhythmia and Prediction of the Effect of Electrical Defibrillation

Analysis of Feature Parameters and Their Time-Transition for...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Shunta Noguchi Yuta Yoshikawa Takayuki Okai Hidetoshi Oya Md. Masudur Rahman Minoru Yoshida Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Department of Computer Science Tokyo City University Tokyo Japan Department of Information and Communication Engineering Noakhali Science and Technology University Bangladesh Department of Applied Systems and Mathematics Kanagawa University Kanagawa Japan

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

In this paper, we propose a prediction system of the effect of electrical defibrillation. In order to develop the proposed system, we firstly analyze from pre-shock (immediately before defibrillation) ECGs (ElectroCardioGrams) by using Gabor Wavelet Transform, Poincaré plot analysis, Spectral entropy, and feature parameters are extracted by those extracted analysis results, we focus on time-transition feature parameter. Next, effective feature parameters are selected based on $\chi^{2}$ -test. Finally, “Effective” and “Ineffective” for electrical defibrillation are classified by using SVM (Support Vector Machine) with three kernels (Linear, Gaussian, and Polynomial) and regularization. In this way, we shows the effectiveness of the proposed feature extraction methods and prediction system.

关键词： Wavelet transforms Support vector machines Accuracy Arrhythmia Electrocardiography Feature extraction Wavelet analysis Entropy Defibrillation Kernel

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Automatic Concrete Surface Crack Recognition Using EfficientNetV2 Variants

Automatic Concrete Surface Crack Recognition Using Efficient...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Rama, A. Damasevicius, Robertas Arunmozhi, S. Mohammed, Mazin Abed Hussam, Ragheed Rajinikanth, V. Saveetha School of Engineering Simats Department of CSE TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas51423 Lithuania Manakula Vinayagar Institute of Technology Department of Electronics and Communication Engineering TN Puducherry605 107 India College of Computer Science and Information Technology University of Anbar Department of Artificial Intelligence Anbar31001 Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad10021 Iraq Saveetha School of Engineering Simats Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India

ISBN: (纸本)9798350315127

Building health and structural integrity are important to ensure occupant safety and long-term durability. Monitoring and assessing building condition regularly ensures that potential problems are identified early. This research aims to develop and implement a deep-learning (DL) supported tool to examine the concrete surface images collected using digital camera. The data gathering forms the first stage and the chosen DL algorithm is used for feature extraction followed by the feature selection and then fusion is done and finally a fivefold cross verification schema is applied for a binary classification. This work considered the EfficientNetV2 (ENV2) model with variants like small, medium and large for the examination. The investigation is implemented using individual and fused deep-features and the performance of the developed tool is confirmed using the achieved accuracy. The outcome of this research confirms that the proposed tool produces a betterment of >96% accuracy with individual-features and 100% accuracy with fused-features. These results confirm that the proposed scheme provides significant result during the crack detection. © 2023 IEEE.

关键词： Crack detection

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Reputation systems for Supply Chains: The Challenge of Achieving Privacy Preservation

arXiv

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arXiv 2023年

作者： Bader, Lennart Pennekamp, Jan Thevaraj, Emildeon Spiß, Maria Kanhere, Salil S. Wehrle, Klaus Cyber Analysis & Defense Fraunhofer FKIE Germany Communication and Distributed Systems RWTH Aachen University Germany Institute for Industrial Management RWTH Aachen University Germany School of Computer Science and Eng. University of New South Wales Australia

Consumers frequently interact with reputation systems to rate products, services, and deliveries. While past research extensively studied different conceptual approaches to realize such systems securely and privacy-preservingly, these concepts are not yet in use in business-to-business environments. In this paper, (1) we thus outline which specific challenges privacy-cautious stakeholders in volatile supply chain networks introduce, (2) give an overview of the diverse landscape of privacy-preserving reputation systems and their properties, and (3) based on well-established concepts from supply chain information systems and cryptography, we further propose an initial concept that accounts for the aforementioned challenges by utilizing fully homomorphic encryption. For future work, we identify the need of evaluating whether novel systems address the supply chain-specific privacy and confidentiality needs. Copyright © 2023, The Authors. All rights reserved.

关键词： Supply chains

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Fuzzy Querying in the Cloud-based Environment for Data Stream-driven Predictive Maintenance in AGV-enabled Smart Factories

Fuzzy Querying in the Cloud-based Environment for Data Strea...

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IEEE International Conference on Big Data

作者： Bożena Małysiak-Mrozek Dominik Romanów Piotr Grzesik Paweł Benecki Alexandre Niyomugaba Theodore Habimana Daniel Kostrzewa Krzysztof Tokarz Che-Lun Hung Dariusz Mrozek Department of Distributed Systems and Informatics Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science INES Ruhengeri - Institute of Applied Sciences Rwanda Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models.

关键词： Remotely guided vehicles Program processors Filtering Pipelines Predictive models Logic gates Data processing Monitoring Predictive maintenance Smart manufacturing

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Compressing Time Series Towards Lightweight Integrity Commitments

Compressing Time Series Towards Lightweight Integrity Commit...

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South Eastern European Design Automation, computer Engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Angeliki Katsika Konstantinos Papageorgiou Alexandros Fakis Athanasios Kakarountas Fotis Andritsopoulos Vassilis Plagianakos Georgios Spathoulas Department of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece Department of Information and Communication Systems Engineering University of the Aegean Samos Greece iTrack Athens Greece Department of Information Security and Communication Technology Norwegian University of Science and Technology (NTNU) Gjovik Norway

In the current era of big data, the generation and preservation of time series data has seen widespread adoption in various sectors such as trade, healthcare, and industry. At the same time, data have become a prized asset, as we are witnessing an unprecedented explosion in the volume of data generated and collected. This includes data from various sources, such as sensors, social media, and transactions, offering a competitive edge to organizations that harness the power of data, leading them to informed decisions and rapid responses to changing conditions. However, it is essential to responsibly manage data, addressing privacy and security concerns to ensure their integrity and availability, while efficiently managing the cost of storage and operations. To address these challenges, there is a compelling de-mand for methods that compress these time series while ensuring that the compressed versions faithfully represent the original data and remain secure against tampering or unauthorized alterations. This paper introduces a novel approach to condensing time-series data into lightweight integrity commitments, leveraging polynomial vector commitments as a key component. These integrity commitments are integrated with blockchain technology, serving as a cornerstone in guaranteeing data integrity and authenticity, particularly in supply chain scenarios where trust and transparency are of the utmost importance.

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MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-Visual Class Incremental Tasks 24

MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yue, Xianghu Zhang, Xueyi Chen, Yiming Zhang, Chengwei Lao, Mingrui Zhuang, Huiping Qian, Xinyuan Li, Haizhou Department of Electrical and Computer and Engineering National University of Singapore Singapore Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China School of Electronic Electrical and Communication Engineering University of the Chinese Academy of Sciences Beijing China National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangdong Guangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shenzhen China

ISBN: (纸本)9798400706868

Class-incremental learning poses a significant challenge under an exemplar-free constraint, leading to catastrophic forgetting and sub-par incremental accuracy. Previous attempts have focused primarily on single-modality tasks, such as image classification or audio event classification. However, in the context of Audio-Visual Class-Incremental Learning (AVCIL), the effective integration and utilization of heterogeneous modalities, with their complementary and enhancing characteristics, remains largely unexplored. To bridge this gap, we propose the Multi-Modal Analytic Learning (MMAL) framework, an exemplar-free solution for AVCIL that employs a closed-form, linear approach. To be specific, MMAL introduces a modality fusion module that re-formulates the AVCIL problem through a Recursive Least-Square (RLS) perspective. Complementing this, a Modality-Specific Knowledge Compensation (MSKC) module is designed to further alleviate the under-fitting limitation intrinsic to analytic learning by harnessing individual knowledge from audio and visual modality in tandem. Comprehensive experimental comparisons with existing methods show that our proposed MMAL demonstrates superior performance with the accuracy of 76.71%, 78.98%, and 76.19% on AVE, Kinetics-Sounds, and VGGSounds100 datasets, respectively, setting new state-of-the-art AVCIL performance. Notably, compared to those memory-based methods, our MMAL, being an exemplar-free approach, provides good data privacy and can better leverage multi-modal information for improved incremental accuracy. © 2024 ACM.

关键词： Contrastive Learning

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Development of Efficient Wireless Sensor Network for IoT Applications 3

Development of Efficient Wireless Sensor Network for IoT App...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Shankar, S. Deepika, G. Devi, G. Ramesh, S. Srivastava, Suraj Kumar, S Sandeep Sri Krishna Arts and Science College Department of Electronics and Communication Systems Tamil Nadu Coimbatore641008 India St. Peter's Engineering College Department of Electronics and Communication Engineering Telangana Hyderabad500100 India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Dindigul624622 India Institute of Artificial Intelligence and Machine Learning Saveetha University Depattment of Computational Intelligence Tamil Nadu Chennai602105 India I.K. Gujral Punjab Technical University Department of Computer Science and Engineering Mohali Campus-1 Punjab Sahibzada Ajit Singh Nagar160055 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350322842

This research study provides an overview of the effort that went into developing reliable 'Wireless sensor networks' for IoT applications, including both theoretical study and practical testing. The report also includes the findings of an analysis of variance and regression analysis, which pinpoint the significance of such aspects as network architecture, traffic load, and ambient conditions on the performance of Wireless Sensor Network (WSN). Finally, study provides a discussion on the future research directions, as well as the design and implementation of a Wireless Sensor Network (WSN) for Internet of Things (IoT) applications. © 2023 IEEE.

关键词： Electric power utilization

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Bayesian Optimization in Wasserstein Spaces 16th

Bayesian Optimization in Wasserstein Spaces

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16th International Conference on Learning and Intelligent Optimization, LION 16 2022

作者： Candelieri, Antonio Ponti, Andrea Archetti, Francesco Department of Economics Management and Statistics University of Milano-Bicocca Milan Italy Department of Computer Science Systems and Communication University of Milano-Bicocca Milan Italy Oaks s.r. l. Milan Italy Consorzio Milano Ricerche Milan Italy

ISBN: (纸本)9783031248658

Bayesian Optimization (BO) is a sample efficient approach for approximating the global optimum of black-box and computationally expensive optimization problems which has proved its effectiveness in a wide range of engineering and machine learning problems. A limiting factor in its applications is the difficulty of scaling over 15–20 dimensions. It has been remarked that global optimization problems often have a lower intrinsic dimensionality which can be exploited to construct a feature mapping the original problem into low dimension manifold. In this paper we take a novel approach mapping the original problem into a space of discrete probability distributions endowed with a Wasserstein metric. In this new approach both the Gaussian process model and the acquisition function work in a 1-dimensional Wasserstein (WST) space. The results in the WST space are then mapped back to the original space using a neural network. Computational results show that, at least for high dimension additive test functions, the exploration in the Wasserstein space is significantly more effective.

关键词： Gaussian distribution

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A Proposal of Code Typing Problem for Basic Java Programming Learning

A Proposal of Code Typing Problem for Basic Java Programming...

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International Electronics Symposium (IES)

作者： Mustika Mentari Nobuo Funabiki Huiyu Qi Yan Watequlis Syaifudin Imam Fahrur Rozi Komang Candra Brata Department of Information Technology State Polytechnic of Malang Malang Indonesia Department of Information and Communication Systems Okayama University Okayama Japan Department of Computer Science Brawijaya University Malang Indonesia

Java is an object-oriented programming language that helps programmers to run programs easily. A web-based Java Programming Learning Assistant system (JPLAS) has been developed to help improve skills through self-learning. Learning Java programming for beginners requires quick practice routines in code typing problems, but JPLAS didn’t have that problem type yet. In this paper, we propose Code Typing Problems (CTP) as a new type in JPLAS. CTP is a new instance that displays code references that must be rewritten that cannot be copied and pasted. CTP evaluation was given to 21 second-semester students taking a Java programming course. The problem-solving did on 8 Java programming topics, where each case consisted of at least two Java files with 624 lines of program code. The results show that the highest average level of truth is 99.1%, and the trend of the ratio of the average number of submissions to the number of question lines is decreasing. A decreasing ratio indicates an increase in students’ understanding while using CTP.

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