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A Novel WTS-EDC Network for Multi-Source ECG Signal Classification

Journal of The Institution of Engineers (India): Series B 2024年 1-10页

作者： Rana, Ankur Kumar, Vivek Gupta, Anish Department of Computer Science and Engineering Quantum University Uttarakhand Roorkee247667 India

Electrocardiography (ECG) is a commonly used diagnostic tool in the clinical setting for detecting cardiovascular diseases (CVDs). However, its manual interpretation can be time-consuming and prone to human error. With the recent advancements in deep learning techniques, there has been an increasing interest in using more robust and generalized deep learning algorithms for automated analysis and interpretation of ECG signals. This article provides a novel model, namely Wavelet Time Scattering-Ensembled Dilated Convolution Network (WTS-EDCN), for ECG signal classification. The classifier is an ensemble of dilated convolutional networks, with each network utilizing wavelet time scattering features extracted from multi-source (MS) ECG signal data. The WTS features efficiently constitute the time–frequency characterization of an ECG signal. WTS features are extracted from different ECG signals containing normal and abnormal rhythms. These features are exploited with a deep neural network (DNN) composed of an ensemble of convolutional nets (CNN) with varied dilation rates. Each varied dilation-rated CNN captures the time–frequency information at different scales, and the ensemble ensures the combined use of the information from individual CNNs. The WTS-EDCN model is experimented on MS ECG signals, which have been diagnosed as having either a normal sinus rhythm, Arrhythmia, or congestive heart failure. The model is successful in classifying the diagnosis with high accuracy. These WTS features have proved significant for multi-source ECG signal classification. The EDC classifier reflects a robust and well-generalized classifier since it constitutes an ensembled approach that is a proven strategy for creating better generalized classification models, especially with multi-source datasets. © The Institution of Engineers (India) 2024.

关键词： Electrocardiography

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Classifying the state of knowledge-based question answering: patterns, progress, and prospects

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International Journal of computers and Applications 2025年第1期47卷 93-105页

作者： Sharma, Sumit Jain, Sarika Tiwari, Mukesh Kumar lal, Sohan Department of Computer Science and Engineering Quantum University Roorkee Uttarakhand Roorkee India Department of Computer Applications National Institute of Technology Haryana Kurukshetra India

In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generation of QASs focuses on the types of inquiries that users typically enquire, the features of the data sources that are used, and the formats in which the right responses are produced. Many QASs have been developed since the 1960s when research on the subject first started. There are several cutting-edge frameworks for answering questions that can be used to categorize the QA system according to a number of criteria. The necessity for an extensive survey on QASs comes naturally in order to determine the future focus of research in this field. In this study, QASs are analyzed by the number of criteria used to categorize question-answering (QA) and provide the dimension using a variety of standards to cover all the QAS. We describe the current state of research in each QAS area and make recommendations for its future direction. Based on the dimension criteria outlined in the research methodology, research studies are chosen for additional review and comparison. Constructed some criteria to classify the QASs, we provide the classification and go over the benefits and drawbacks of each class's QAS, as well as the problems with the corresponding study. Results are detailed summarized and compared. As a result of this study, a general architecture of frameworks for QA over knowledge base (KB) is drawn in addition to the comparative analysis of systems, serving as a reference for academics working on related topics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Question answering

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Flu-Net: two-stream deep heterogeneous network to detect flu like symptoms from videos using grey wolf optimization algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7733-7745页

作者： Gupta, Himanshu Imran, Javed Sharma, Chandani Department of Computer Science and Engineering Quantum University Roorkee India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

The outbreak of COVID-19 (also known as Coronavirus) has put the entire world at risk. The disease first appears in Wuhan, China, and later spread to other countries, taking a form of a pandemic. In this paper, we try to build an artificial intelligence (AI) powered framework called Flu-Net to identify flu-like symptoms (which is also an important symptom of Covid-19) in people, and limit the spread of infection. Our approach is based on the application of human action recognition in surveillance systems, where videos captured by closed-circuit television (CCTV) cameras are processed through state-of-the-art deep learning techniques to recognize different activities like coughing, sneezing, etc. The proposed framework has three major steps. First, to suppress irrelevant background details in an input video, a frame difference operation is performed to extract foreground motion information. Second, a two-stream heterogeneous network based on 2D and 3D Convolutional Neural Networks (ConvNets) is trained using the RGB frame differences. And third, the features extracted from both the streams are combined using Grey Wolf Optimization (GWO) based feature selection technique. The experiments conducted on BII Sneeze-Cough (BIISC) video dataset show that our framework can 70% accuracy, outperforming the baseline results by more than 8%. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Egocentric Vision Action Recognition: Performance Analysis on the Coer-Egovision Dataset

Egocentric Vision Action Recognition: Performance Analysis o...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Gupta, Himanshu Kumar, Vivek Quantum University Department of Computer Science & Engineering Roorkee India

ISBN: (纸本)9798350382723

Egocentric activity recognition, also known as first-person vision, captures human actions and activities from the perspective of a wearable camera, providing a personalized and contextualized viewpoint. This unique perspective is crucial for various real-world applications, including healthcare monitoring, sports analy-sis, augmented reality, and assistive technologies. In this paper, we present a comparative analysis of various state-of-the-art algorithms for egocentric action recognition using the recently curated dataset, 'Coer-Egovision.' This dataset captures human actions and activities from the unique perspective of a wearable camera, providing a personalized and context-aware viewpoint. Such a perspective holds significant value for real-world applications, including healthcare monitoring, sports analysis, augmented reality, and assistive *** 'Coer-Egovision' dataset encompasses diverse actions, such as 'Going-Downstairs', 'Going-Upstairs', 'Texting', 'Walking' and 'Writing' allowing for a challenging benchmark to evaluate algorithms in this domain. By leveraging the wearable camera perspective, the dataset addresses the need for a more com-prehensive understanding of human interactions with the *** the evaluated algorithms, the Long-term Recurrent Convolutional Network (LRCN) emerges as the top performer, achieving an impressive accuracy of 88% in effectively modeling long-term temporal dependencies. Additionally, the Histogram of Oriented Gradients (HOG) combined with Support Vector Machine (SVM) approach demonstrates robust performance with 82% accuracy, highlighting the efficacy of feature-based methods. The study also reveals promising results for the 2-Stream Approach with 3D CNN and LSTM, achieving an accuracy of 74%. Furthermore, pretrained models like VGG16 and DenseNet201 along with LSTM exhibit competitive performances with accuracies of 80% *** results offer valuable insights into the performance of diverse algo

关键词： Support vector machines

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Unveiling the web of privacy: A comprehensive review of threats to information security in social networks 4th

Unveiling the web of privacy: A comprehensive review of thre...

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4th International Conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Singh, Shalini Baloni, Dev Sarkar, Manash Department of Computer Science and Engineering Quantum University Roorkee India Department of Computer Science and Engineering Atria Institute of Technology Banglore India

ISBN: (纸本)9781032911571

This review paper explores emerging threats to information privacy and security within the dynamic landscape of Online Social Networks (OSNs), which serve as repositories of vast amounts of user data. The rise in social media use emphasizes how crucial it is to inform users about the dangers that come with it, like cyber-attacks and privacy violations. Even though various social media platforms have been made possible by technological advancements, consumers’ ignorance of these platforms creates serious security risks. Although academics have come up with creative security solutions for social networks, there are obstacles in the way of their actual application. It is essential to continuously assess security vulnerabilities in light of technology improvements. This study examines several dangers to online social networks and suggests countermeasures based on a variety of models, frameworks, and encryption methods. Despite advancements, privacy concerns remain unaddressed, requiring parents to actively supervise their children’s online activity. © 2025 the Author(s).

关键词： Differential privacy

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Ensemble Explainable Artificial Intelligence Model for COVID-19 Detection Using Chest X-Ray Images 1

Ensemble Explainable Artificial Intelligence Model for COVID...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Pandey, Krishna Mohan Baloni, Dev Quantum University Department of Computer Science and Engineering Uttrakhand Roorkee India

ISBN: (纸本)9798350367720

After the virus was initially detected in Wuhan, China, in December 2019, it was announced in the midst of 2020 that an outbreak of COVID-19 had occurred, marking the onset of a global pandemic. Experts have put forth the hypothesis that this particular virus has the capability to infect both the upper respiratory system, including the sinuses, nose, and throat, as well as the lower respiratory system, encompassing the trachea and lungs. The most prevalent modes of transmission are through airborne droplets and direct contact with individuals carrying the COVID-19 virus. Currently, there are countless individuals across the globe who are grappling with this illness. Despite widespread preventive measures, the number of individuals affected by this disease and the death toll associated with it continue to rise at a disconcerting pace. There is a pressing need for computerAided Diagnosis (CAD) methods that can accurately identify this disease, yet many existing models are not utilized due to their lack of explainability. In this particular investigation, we present a methodology for swiftly and precisely identifying COVID-19 in Chest X-Ray (CXR) images, as this is the most commonly used and easily accessible medical diagnostic tool. To accomplish this, we employ DenseNet 121, a Deep Learning Model, on our dataset and achieve an impressive accuracy rate of 98.88%. Furthermore, we integrate Explainable Artificial Intelligence (XAI) models, specifically GRAD CAM ++ and SHAP, as an ensemble approach to enhance the interpretability of our classification model. This integration allows for a clearer visualization of the decision-making process and supports medical professionals in understanding how the algorithm arrives at its decisions. © 2024 IEEE.

关键词： COVID-19

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Beyond quantum Shannon decomposition: Circuit construction for n-qubit gates based on block-ZXZ decomposition

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Physical Review Applied 2024年第3期22卷 034019页

作者： Anna M. Krol Zaid Al-Ars Quantum & Computer Engineering Department

This paper proposes an optimized quantum block-ZXZ decomposition method that results in more optimal quantum circuits than the quantum Shannon decomposition, which was presented in 2005 by M. Möttönen, and J. J. Vartiainen [in Trends in quantum computing research, edited by S. Shannon (Nova Science Publishers, 2006) Chap. 7, p. 149, arXiv:quant-ph/0504100]. The decomposition is applied recursively to generic quantum gates, and can take advantage of existing and future small-circuit optimizations. Because our method uses only single-qubit gates and uniformly controlled rotation-Z gates, it can easily be adapted to use other types of multi-qubit gates. With the proposed decomposition, a general three-qubit gate can be decomposed using 19 cnot gates (rather than 20). For general n-qubit gates, the proposed decomposition generates circuits that have 22484n−322n+53 cnot gates, which is less than the best-known exact decomposition algorithm by (4n−2−1)/3 cnot gates.

关键词： Optimization problems quantum algorithms & computation quantum circuits quantum gates

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Detection of Brain Tumor Using U-Net Model Approach

Detection of Brain Tumor Using U-Net Model Approach

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Patra, Arunima Paul, Krishnangshu Paul, Prithwineel Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science and Engineering University of Engineering and Management Kolkata India Department of Computer Science and Engineering Centre of Excellence for Quantum Computing Institute of Engineering & Management University of Engineering and Management Kolkata India

ISBN: (纸本)9789819613472

This study introduces an innovative deep learning methodology leveraging the U-Net framework for medical image segmentation and lesion detection in brain tumors. U-net architecture contains encoder and decoder blocks which in turn are capable of capturing and propagation of information of the features. In this paper, for brain tumor segmentation, a U-Net Convolutional Neural Network (CNN) is employed, utilizing multimodal magnetic resonance imaging (MRI) data and skip connections for precise tumor localization and segmentation. Through comprehensive experiments, we show that our method outperforms traditional techniques and current CNN models for medical image segmentation and lesion detection in brain tumors. Moreover, the proposed method can perform the task with higher accuracy. This tailored U-Net architecture demonstrates the potential for advancing medical image analysis, enabling precise segmentation and lesion detection, ultimately leading to improved patient care and treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Genetic Algorithm Based Deterministic Workflow Scheduling with Load Balancing 2

Genetic Algorithm Based Deterministic Workflow Scheduling wi...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Mittal, Punit Kumar, Satender Sharma, Swati Department of Computer Science and Engineering Quantum University Roorkee India Department of Information Technology Meerut Institute of Engineering and Technology Meerut India

ISBN: (纸本)9798331530389

A crucial problem in cloud computing is load balancing, which makes it challenging to guarantee that services operate as intended in accordance with quality of service (QoS), performance reviews, and service contracts that cloud service providers (CSPs) demand of businesses. The main goal is to distribute workloads among computer resources in order to improve performance. Load balancing is categorized as an "NP-hard" problem due to the ample solution space and the time needed to identify the optimal solution. While only a few approaches may provide an exact answer in polynomial time, prior research shows that metaheuristic-based tactics can find correct solutions in a fair amount of time. We use a framework that integrates Neural Network Task Classification (N2TC) and Genetic Algorithm Task Assignment (GATA) for load balancing. By taking load balancing and task completion time into account, this approach seeks to maximize load distribution and reduce process execution time. Extensive simulations of application operations were carried out to evaluate the effectiveness of the suggested load-balancing strategy. Results have shown that the proposed framework outperforms tournament selection genetic algorithm (TSGA), Heterogeneous Earliest Finish Time (HEFT), and Performance Effective Task Scheduling (PETS) heuristics in terms of load balancing up to 4.52% and makespan by 6.47%. © 2025 IEEE.

关键词： Genetic algorithms

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Thermoelastic damping in MEMS gyroscopes at high frequencies

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Microsystems & Nanoengineering 2023年第1期9卷 137-148页

作者： Daniel Schiwietz Eva M.Weig Peter Degenfeld-Schonburg Robert Bosch GmbH Corporate Research71272RenningenGermany Department of Electrical&Computer Engineering Technical University of Munich85748GarchingGermany Munich Center for Quantum Science and Technology(MCQST) 80799MunichGermany TUM Center for Quantum Engineering(ZQE) 85748GarchingGermany

Microelectromechanical systems(MEMS)gyroscopes are widely used,e.g.,in modern automotive and consumer applications,and require signal stability and accuracy in rather harsh environmental *** many use cases,device reliability must be guaranteed under large external loads at high *** sensitivity of the sensor to such external loads depends strongly on the damping,or rather quality factor,of the high-frequency mechanical modes of the *** this paper,we investigate the influence of thermoelastic damping on several high-frequency modes by comparing finite element simulations with measurements of the quality factor in an application-relevant temperature *** measure the quality factors over different temperatures in vacuum,to extract the relevant thermoelastic material parameters of the polycrystalline MEMS *** simulation results show a good agreement with the measured quantities,therefore proving the applicability of our method for predictive purposes in the MEMS design ***,we are able to uniquely identify the thermoelastic effects and show their significance for the damping of the high-frequency modes of an industrial MEMS *** approach is generic and therefore easily applicable to any mechanical structure with many possible applications in nano-and micromechanical systems.

关键词： uniquely generic quantities

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