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Biometric Verification System UsingHyperparameter Tuned Deep Learning Model

computer systems science & engineering 2023年第7期46卷 321-336页

作者： Mohammad Yamin Saleh Bajaba Sarah B.Basahel E.Laxmi Lydia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India

Deep learning(DL)models have been useful in many computer vision,speech recognition,and natural language processing tasks in recent *** models seem a natural fit to handle the rising number of biometric recognition problems,from cellphone authentication to airport security *** approaches have recently been utilized to improve the efficiency of various biometric recognition *** recognition was considered the more reliable and accurate biometric detection method *** recognition has been an active research region in the last few decades due to its extensive applications,from security in airports to homeland security border *** article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition(PODTL-BIR)*** presented PODTL-BIR technique recognizes the iris for biometric *** the presented PODTL-BIR model,an initial stage of pre-processing is carried *** addition,the MobileNetv2 feature extractor is utilized to produce a collection of feature *** PODTL-BIR technique utilizes a bidirectional gated recurrent unit(BiGRU)model to recognise iris for biometric ***,the political optimizer(PO)algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique’s recognition ***-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR *** experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms.

关键词： Biometric verification iris recognition political optimizer deep learning feature extraction

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Natural Language Processing to Improve Content Retrieval Performance 3

Natural Language Processing to Improve Content Retrieval Per...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Jadhav, Varsha D. Doda, Devendra Kumar Swarup, Lakshya Kumar, Ritesh Pandian, Rajesh Rufina, Princy Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Karnataka Bangalore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India

ISBN: (纸本)9798350319125

Latest studies in herbal Language Processing (NLP) have targeted improving the accuracy of statistics retrieval through leveraging machines better to recognize the use and scope of human language. Employing NLP strategies makes it far more feasible to system massive units of files and better identify the applicable content in them. Content related to a user's query can then be taken care of and ranked based on the similarity of the language used in the file. NLP strategies permit a more accurate representation of content material and can help retrieve the maximum relevant statistics. Furthermore, NLP can be prolonged beyond traditional search engines like Google to research textual content-primarily based information, such as emails and social media posts, for areas that include sentiment analysis and customer support. This paper explores the capacity of NLP to enhance content material retrieval overall performance in more than a few situations. It additionally outlines valuable techniques for evaluating the effectiveness of such structures. Dialogue and conclusion refer to approaches by which the implementation of NLP can convey improved accuracy to content material retrieval. © 2023 IEEE.

关键词： Discussion effectiveness implementation representation similarity

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OMNI- CHANNEL RETAILING FOR ENHANCING CUSTOMER ENGAGEMENT AMIDST SUPPLY CHAIN DISRUPTION: AN EMERGING MARKET PERSPECTIVE

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2023年第1期6卷 449-473页

作者： Joshi, Sudhanshu Sharma, Manu Chatterjee, Prasanjit Operations and Supply Chain Management Research Laboratory School of Management Doon University Dehradun India School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Department of Management Studies Graphic Era Deemed to be University Dehradun India Guidhall School of Business and Law London Metropolitan University London United Kingdom MCKV Institute of Engineering West Bangal India

The research aims to explore the strength of enablers and adoption barriers in omnichannel retailing (OCR) and discuss how organizations may focus on redesigning their business models in emerging markets to manage the disruptive environment. The major enablers may enhance the omnichannel' performance to deliver a unified experience across all channels during the pandemic. The paper has used hybrid Multi-Criteria Decision-Making (MCDM) Methods. Organizations widely use these methods to explore the interrelationship among barriers and enablers affecting their performance. In the current study, 18 experts from different domains have examined and evaluated the 10 barriers and 7 enablers. The study reveals that integration, Visibility, internet accessibility, and advanced distribution centers are the primary enablers and driving the customer analytics enabler to strengthen their customer engagement and providing a unified experience to the. During the pandemic time the usage of the online channels have increased and thus retail channels may consider these enablers to enhance the unified experience level of the customers. The study also shows that inconsistency in price is the main adoption barrier followed by inconsistency in product discounts that should be minimized to engage customers effectively. The retail organizations need to understand the roadblocks in adopting OCR and should take relevant actions to minimize them. The retail organization or marketers may redesign their existing strategies based on price consistency, integration, Visibility, information systems, and coordination to develop a unified experience across channels during the pandemic situation. © 2023 by the authors.

关键词： Visibility

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A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

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computer systems science & engineering 2022年第6期41卷 1043-1056页

作者： Xiaorui Zhang Wenfang Zhang Wei Sun Xingming Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszowVoivodeship100031Poland

In a telemedicine diagnosis system,the emergence of 3D imaging enables doctors to make clearer judgments,and its accuracy also directly affects doctors’diagnosis of the *** order to ensure the safe transmission and storage of medical data,a 3D medical watermarking algorithm based on wavelet transform is proposed in this *** proposed algorithm employs the principal component analysis(PCA)transform to reduce the data dimension,which can minimize the error between the extracted components and the original data in the mean square ***,this algorithm helps to create a bacterial foraging model based on particle swarm optimization(BF-PSO),by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding,thereby achieving the optimal balance between embedding capacity and imperceptibility.A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.

关键词： 3-D medical watermarking robust watermarking PCA BF-PSO

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Edge-Cloud Computing systems for Unmanned Aerial Vehicles Capable of Optimal Work Offloading with Delay 2

Edge-Cloud Computing Systems for Unmanned Aerial Vehicles Ca...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Narayana Tinnaluri, V.S. Vyankatesh Ghamande, Manasi Singh, Shashank Kuchipudi, Ramu Dattatraya Bhosale, Minakshi Dharani, R. Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vijayawada India Vishwakarma Institute of Information Technology Engineering and Applied Sciences Pune411037 India Integral University Lucknow Department of Computer Science and Engineering Uttar Pradesh India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Faculty of Mca Yashoda Technical Campus Satara Maharashtra Wadhe India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur India

ISBN: (纸本)9798350346640

Mobile terminals' limited processing power and memory make it challenging to meet the needs of increasingly complex applications like autonomous vehicles and augmented reality. That's why there's been a rise in the demand for edge cloud computing power from endpoint gadgets. Due to its adaptability and proximity to the user, an unmanned aerial vehicle (UAV) may be used to support mobile edge computing (MEC) via job offloading, potentially relieving strain on edge clouds. Due to limitations in processing power and battery life, Unmanned Aerial Vehicles (UAV) devices face a huge issue with the rise of Applications and services for mobile devices that can't afford to be delayed and that need a lot of processing power. Task offloading is one way that mobile cloud computing may help you get around these restrictions. The biggest difficulties with this paradigm, however, are the significant latency and security concerns. The edge-cloud computing model was then proposed and has since seen widespread adoption as a means of dealing with these complications. However, the present task offloading models allow UAVs to perform their heavy jobs at the linked edge server, leading to unnecessary loads owing to the huge amount of UAVs and, in turn, increasing the latency. For this reason, this study has presented a delay-optimal task offloading strategy for multi-tier edge-cloud computing with many users. In this research, a system model has been proposed to assist an intelligent and perceptive agent in determining the best computational offloading strategy, minimizing both the time it takes to complete a job and the amount of power it needs to do so. The efficacy of an agent was shown, and simulation results revealed that its adoption may greatly cut delay and energy usage. © 2023 IEEE.

关键词： Mobile edge computing

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Bottom-Up Cracks Detection in Road Pavement in Artificial Neural Network 1

Bottom-Up Cracks Detection in Road Pavement in Artificial Ne...

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1st International Conference on Software, systems and information technology, SSITCON 2024

作者： Sunil Kumar, V. Alzubaidi, Laith H. Ramesh, G.P. Sreenath, M.V. Gayathri, T. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India The Islamic University of Babylon Department of Computers Techniques Babylon Iraq Department of Electronics and Communication Engineering Chennai India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bangalore India Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350352931

In recent years, road crack detection is a significant task for detecting surface defects, road safety and road maintenance. But there is lack of approach that able to detect the concealed cracks especially bottom-up cracks, and this is challenging due to poor continuity of cracks, various crack's widths, and low contrast among cracks and the surrounding pavement. The Artificial Neural Network (ANN) algorithm is proposed for detecting and classifying the structural health status of bottom-up crack in road pavements, which allows detecting surface defects and failures. The approach aims at collection of these signatures by utilizing acoustic-sensors, located at roadside, and also classification of SHS in pavements over the ANN approaches. The various Machine Learning (ML) classifiers such as Multilayer Perceptron (MLP), 2-Dimensional Convolutional Neural Network (2D-CNN), Support Vector Classifier (SVC) and Random Forest Classifier (RFC) are utilized as well as compared for the analysis of crack in road pavement. The experimental results illustrate the probability of associating with great accuracy 92.7 %of MLP, 96.7 %of 2D-CNN, 92.5 %of RFC and 9 9. 3 %of SVC respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Deep Learning Algorithms for Image Recognition in Medical Imaging 2

Comparative Analysis of Deep Learning Algorithms for Image R...

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Ashokkumar, S. Kumar, S. Magesh Sugumaran, D. Rajaraman, R. Saranya, N. Naga Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics Saveetha Engineering College Tamil Nadu Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Tamil Nadu Chennai India

ISBN: (纸本)9798350383867

This research paper compares deep learning models through the imaging tasks of medical imaging. Modern-age architectures such as CNNs i.e., AlexNet, VGG, Resnet, Densensnet, and U-Net are the foundations of state of the art medical image analysis. They are applied to various tasks like detection of cancer, organ segmentation, and classification of diseases among others. Reports indicate that there are specific networks of neurons which perform optimization for specified applications in this sense of which the ResNet architecture is better than others in tumor detection and U-Net is better than SegNet in organ segmentation tasks. Leveraging the transfer learning techniques trained on the ImageNet, our approach to disease classification succeeded;in addition, we utilized ensemble methods that combined the predictions from multiple models, which were pretty effective. This research explores the choice and fine tuning deep learning strategies for medical image analysis and deals with a huge push to diversify imaging techniques diagnostics and treatment planning. ©2024 IEEE.

关键词： Convolutional neural networks

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Optimized BIGRU-Attention Network for OSA and Cardiovascular Disease Prediction with Risk Factor Classification 4

Optimized BIGRU-Attention Network for OSA and Cardiovascular...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Yadav, Sameer Joseph, J. Sharmila Nagar, Mayank Dakshinamurthi, Vidyabharathi Usha, M. Dubey, Vedant Gyan Ganga Institute of Technology & Sciences Dept of Cse Jabalpur India Vit Bhopal University School of Computing Science and Engineering India Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Sona College of Technology Dept. of Cse TN Salem India Measi Institute of Information Technology India Ggits MP India

ISBN: (纸本)9798331518981

Cardiac illness is a rapidly rising worldwide killer. The development of an automated prediction framework for OSA-related cardiovascular problems is tough given that to the extensive information needed to effectively forecast heart illness. Several algorithms have been developed for use in IoT-based medical oversight systems, which utilize sensor data to identify ailments *** deploy new learning models to improve prediction results. The existing methods lack the ability to forecast accurately, making them unsuitable for successful diagnosis. This study proposes BGEAN, a revolutionary framework that combines deep learning with computing devices to identify cardiac issues accurately. Data are pre-processed utilizing Studentized Residual Methods to address missing values and outliers. Improved Gated Recurrent Neural Networks help classify pre-processed data as normal or abnormal. Thorough testing and comparison to top deep learning techniques are conducted. Performance indicators, such as accuracy (99.99%), recall (99.35%), specificity (90.12%), precision (99.5), F1-score (99.99%), and Model Building Time (MBT) (0.212sec), are computed. The findings show that the technology that was suggested surpassed current algorithms in accuracy, error, and reaction time. With its many user situations, BGEAN provides the most diverse environment for forecasting cardiac diseases. © 2024 IEEE.

关键词： Recurrent neural networks

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Realization of Machine Learning Algorithms for Diagnosing Winding Faults in Induction Motors

Realization of Machine Learning Algorithms for Diagnosing Wi...

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International Conference on Power Electronic Drives

作者： W. Abitha Memala Mercy Paul Selvan S. Mohan Ram T. V. Brindavan R. Raja Singh Dept. of Electrical and Electronics Engg. Sathyabama Institute of Science and Technology Chennai India Dept. of Computer Science and Engg. Sathyabama Institute of Science and Technology Chennai India School of Electrical Engineering Vellore Institute of Technology Vellore India Automotive Research Centre Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

The reliability and efficiency of squirrel cage induction motors (SCIMs) are most important in industrial applications, where failures can lead to significant downtime and economic losses. Among the common faults, stator winding faults are particularly detrimental, leading to degraded motor performance and potentially catastrophic failures if undetected at the incipient stage. Various approaches aiding machine learning algorithms are adapted for diagnosing machine faults. This paper realizes different machine learning algorithms to diagnose the winding faults of SCIM. A comprehensive dataset of motor current signatures is trained and validated with several machine-learning models using Python in the Anaconda platform. A detailed comparative analysis of the model's performance is conducted based on metrics such as accuracy, precision, recall, F1 score, and computational efficiency. This research highlights the potential of integrating machine learning techniques into SCIM monitoring systems, offering a proactive approach to maintenance and reducing unscheduled downtimes. Future work will explore the deployment of these models in real-time monitoring systems and the expansion of the dataset to include various motor types and fault conditions.

关键词： Fault diagnosis Adaptation models Machine learning algorithms Induction motors Computational modeling Biological system modeling Windings Stator windings Machine learning Real-time systems

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Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

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computers, Materials & Continua 2022年第5期71卷 3035-3050页

作者： Xiaorui Zhang Xun Sun Xingming Sun Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

The leakage of medical audio data in telemedicine seriously violates the privacy of *** order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio *** scheme decomposes the medical audio into two independent embedding domains,embeds the robust watermark and the reversible watermark into the two domains *** order to ensure the audio quality,the Hurst exponent is used to find a suitable position for watermark *** to the independence of the two embedding domains,the embedding of the second-stage reversible watermark will not affect the first-stage watermark,so the robustness of the first-stage watermark can be well *** the second stage,the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible *** experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db,additive white Gaussian noise(AWGN)of 20 db,low-pass filtering,resampling,re-quantization and other attacks,and has good imperceptibility.

关键词： Telemedicine privacy protection audio watermarking robust reversible watermarking two-stage embedding

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