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7th International Conference on Trends in electronics and Informatics, ICOEI 2023

作者： Balde, Govind Hanmantrao Ala Walid, Md. Abul Srinivasan, P. Yella, Venkata Rajesh Soumya, M. Rastogi, Ravi N.T.V.S's G. T. Patil Arts Commerce and Science College Research and P.G. Department of Zoology M.S. Nandurbar425412 India Department of Computer Science and Engineering Khulna9203 Bangladesh Sona College of Technology Department of Ece Tamilnadu Salem India Koneru Lakshmaiah Education Foundation Department of Biotechnology Andhra Pradesh Vaddeswaram India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal India Nielit Electronics Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350397284

Cell density prediction can be defined as the process of predicting the number of cells in a given quantity of a culture or cell suspension. It is considered a common practice in cell biology since cell density had a significant impact on cell behavior and can be utilized for monitoring the health and growth of cell culture. Precise prediction of cell density was significant for a range of applications in cell biology., which includes bioprocessing, cell-based assays, and cell culture. Therefore, this article develops a novel Cell Density Prediction design using Optimal Deep Learning with Salp Swarm Algorithm (CDP-ODLSSA) technique. The presented CDP-ODLSSA technique predicts the cell densities accurately on the images of cell suspensions or cultures. To do so, the presented CDP-ODLSSA technique employs Long Short Term Memory-Autoencoder (LSTM-AE) model for prediction of cell densities. In addition, the hyperparameter tuning of the LSTM-AE model takes place by the use of Salp Swarm Algorithm (SSA). For experimental validation of the CDP-ODLSSA technique, a wide range of simulations was taken place. The obtained values highlighted the superiority of the CDP-ODLSSA technique compared to other approaches. © 2023 IEEE.

关键词： Long short-term memory

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Deep Distributed Learning and Spatial-Temporal Features for Facial Depression Detection 7

Deep Distributed Learning and Spatial-Temporal Features for ...

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7th International Conference on electronics, Communication and Aerospace Technology, ICECA 2023

作者： Arunachalam, Sripriya Ghate, Shubhangi N. Kopperundevi, N. Balamurugan, M.S. Kanagajothi, D. Srimathi, S. SRM Institute of Science and Technology Computer Science and Application Vadapalani Campus College of Science and Humanities India Ramrao Adik Institute of Technology Electronics and Telecommunications Engg. D. YiPatil University Maharashtra Navi-Mumbai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Panimalar Engineering College Department of ECE India Vel Tech Rangarajan Dr Shangunthala R&D Institute of Science and Technology Department of Mathematics India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350340600

One of the most frequent and debilitating mental illnesses is major depression. Video-based identification of depression using facial expressions has been proposed by a number of deep learning architectures. These structures are frequently modeled for regression with Euclidean loss to foretell the degree of depression. The ordinal correlation between depressive symptoms and exposure to facial depictions is not explored, and the models' resilience to noisy and ambiguous labeling is constrained as a result. Famous deep architectures have shown guarantee in facial depression acknowledgment, but they still lack proper discriminative power because of problems with a lack of labeled depression information for deep transfer learning, large variations in facial expression between people who have identical symptom severity, and subtle variations in facial expression between varying depression severities. This study models the challenge of facial depression identification as one of the learnings to distribute labels, and this study offers a deep combined label distribution and metric learning approach to this problem. Using distribution learning, this research study provides a deep learning architecture for predicting depressive symptoms. The proposed deep learning on spatiotemporal feature improves the process of depression prediction because the two learning modules work together to improve the feature's representation ability and discriminatory strength. The results of empirical evaluation of the proposed method on publicly available datasets prove the system efficacy. © 2023 IEEE.

关键词： Deep learning

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Artificial Driving and Spectral Graph Theory for Automated Plant Leaf Disease Classification

Artificial Driving and Spectral Graph Theory for Automated P...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Chhavi Bajpai Manish Gaur J Roselin Suganthi Vinayagam S S. Kaliappan Manoj Senthil K Department of Computer Science & Engineering Centre for Advanced Studies Lucknow India Department of Computer Science & Engineering Institute of Engineering and Technology Lucknow India Department of ECE K. Ramakrishnan College of Engineering Trichirapalli India Department of Bioscience Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Research and Development Lovely Professional University Jalandhar India Department of Electronics and Communication Engineering Kongu Engineering College Erode India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Having a certified plant pathologist visually inspect the diseased leaves is now the gold standard for disease diagnosis in plants. This is because symptoms are present in the vast majority of plant illnesses. Because manual disease diagnosis is slow and results are highly dependent on the skill of the pathologist, this is an ideal domain for computer-aided diagnostic *** one of the suggested methods are preprocessing, step two is feature extraction, and step three is training the model. Pictures are acquired, then processed, enhanced, resized, segmented, and finally converted from RGB to HSI as part of the preprocessing phase. The topic of this study is the extraction of numerous features for the purpose of leaf disease categorization. Hexagons of colors are retrieved for classification purposes. The model was trained using Spectral Graph Theory. On average, this state-of-the-art technique achieves a higher accuracy rate of 92.48% than CNN and SVM.

关键词： Support vector machines Training Accuracy Computational modeling Crops Color Feature extraction Graph theory Medical diagnosis Diseases

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Analysis of Electroencephalographic Signals to Study the Behavior of Brain Frequencies for the Study of Academic Stress 2

Analysis of Electroencephalographic Signals to Study the Beh...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Moyeenudin, H.M. Hannah, S. Anuradha, T. Muthalagu, R. Seedha Devi, V. Jose Anand, A. Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai600117 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu 6000262 India Department of Electrical and Electronics Engineering KCG College of Technology Tamil Nadu Chennai600097 India Department of Biomedical Engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu 603308 India Department of IT Jaya Engineering College Tamil Nadu Chennai602024 India Department of ECE KCG College of Technology Tamil Nadu Chennai600 097 India

ISBN: (纸本)9798350340235

The current study perform an analysis of electroencephalographic signals to study the behavior of brain frequencies in subjects who are under academic stress generated by a cognitive task, while listening to music or being silent. Creation of a corpus of more than 10 subjects under different sound stimuli is created. Characterization of brain signals are characterized for the identification of academic stress. Protocol is designed and brain signals are collected to observe the relationship between music listening and academic stress. EEG signal classifiers are used to identify differences between different sessions. Analysis of brain frequencies are analysed obtained in the sessions for each participant. © 2023 IEEE.

关键词： Academic Stress Brain Frequencies Brainwave Patterns EEG (Electroencephalography) Stress Response

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Military Vehicle Object Detection Based on Feature Representation and Refined Localization Using Inception Recurrent Convolutional Neural Network

Military Vehicle Object Detection Based on Feature Represent...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Muntather Almusawi S G Shivaprasad Yadav Abdul Rahim Srinivas Aluvala Ramachandra A C The Islamic university Najaf Iraq Electronics and Telecommunication Engineering Ramaiah Institute of Technology Bangalore India Electronics and Communication Engineering Department Mallareddy Engineering College for Women (Autonomous) Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India

Advanced reconnaissance technologies for military vehicle components form basis for weapons and equipment reconnaissance and tracking operations in complex environments, which is crucial to allow intelligent warfare using information. Addressing limitations of conventional detection algorithms in this domain, a novel approach termed as Military Vehicle Object Detection Method based on Inception Recurrent Convolutional Neural Network (MVODM-IRCNN) and Hierarchical Feature Representation is proposed for detecting military vehicle object. first is a comprehensive data set specially designed for military vehicles. Then it is two system improvements: hierarchical feature representation and state-of-the-art localization, using Inception Recurrent Convolutional Neural Network (IRCNN), aimed at improving detection performance. Hierarchical feature representation techniques make it easier to find good feature representation levels for different object shapes, improving accuracy of these techniques boxes. combination of materials provides a significant gain in detector performance. Finally, an exploratory study conducted on own dataset shows that MVODM-IRCNN has exceptional recognition capabilities, which proves its usefulness in military vehicle recognition applications.

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Near-field Localization with Dynamic Metasurface Antennas 48

Near-field Localization with Dynamic Metasurface Antennas

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Qianyu Guerra, Anna Guidi, Francesco Shlezinger, Nir Zhang, Haiyang Dardari, Davide Wang, Baoyun Eldar, Yonina C. Nanjing University of Posts and Telecommunications School of Communication and Information Engineering Nanjing China National Research Council of Italy Institute of Electronics Computer and Telecommunication Engineering Bologna Italy Ben-Gurion University School of ECE Beer-Sheva Israel University of Bologna Electronic and Information Engineering Guglielmo Marconi-DEI-CNIT Department of Electrical Cesena Italy Weizmann Institute of Science Faculty of Math and CS Rehovot Israel

ISBN: (纸本)9781728163277

Sixth generation (6G) cellular communications are expected to support enhanced wireless localization capabilities. The widespread deployment of large arrays and high-frequency bandwidths give rise to new considerations for localization applications. Emerging antenna architectures, such as dynamic metasurface antennas (DMAs), are expected to be frequently utilized thanks to the achievable high angular resolution and low hardware complexity. Further, wireless localization is likely to take place in the radiating near-field (Fresnel) region, which provides new degrees of freedom, because of the adoption of arrays with large apertures. While current studies mostly focus on the use of costly fully-digital antenna arrays, in this paper we investigate how DMAs can be applied for near-field localization of a single user. We use a direct positioning estimation method based on curvature-of-Arrival of the impinging wavefront to obtain the user location, and characterize the effects of DMA tuning on the estimation accuracy. Next, we propose an algorithm for configuring the DMA to optimize near-field localization, by first tuning the adjustable DMA coefficients to minimize the estimation error using postulated knowledge of the actual user position. Finally, we propose a sub-optimal iterative algorithm that does not rely on such knowledge. Simulation results show that the DMA-based near-field localization accuracy could approach that of fully-digital arrays at lower cost. © 2023 IEEE.

关键词： Iterative methods

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Comparative Analyses of Multi-Wavelength Converters Based on Electro Absorption Modulators at Various Data Rates

Comparative Analyses of Multi-Wavelength Converters Based on...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Taranjeet Kaur Jatinder Kaur Mohit Jambu Aditi Saxena Sanjeev Kumar Shah Antima Saxena Department of ECE Chandigarh Engineering College Jhanjeri Mohali India EEE Department Gulzar Group of Institutes Khanna Punjab India Department of ECE GLA University Mathura India Electronics & Communication Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of Computer Science & Engineering IES College of Technology Bhopal

The performance analysis of an electro absorption modulator based multi-wavelength converter that uses a radio frequency generator has been investigated at various input powers (ranging from -3 dBm to 10 dBm) and at various data rates (5 Gbps, 10 Gbps, and 20 Gbps). This was done in order to eliminate the need for an additional pump signal by utilising the RZ (Return to Zero) modulation format. This was done in order to make the solution a more cost-effective one. In this article, a comprehensive investigation of the trade-off between diverse power and varied data rate has been carried out. The input power and data rate are the two most essential operational parameters for wavelength conversion. Because of this, the effect of input power and data rate on the performance of wavelength conversion is investigated in great depth by monitoring the timing diagrams of the signal that has been wavelength converted.

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Security Metrics and their Measurements for Wireless Communications 11

Security Metrics and their Measurements for Wireless Communi...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Renuka, K. Singh, Rajesh Geetha, B.T. Gangodkar, Durgaprasad Veeramalai, G. Prasad, Ch. Raghava Rathinam College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India Uttaranchal Institute of Technology Uttaranchal University India Saveetha School of Engineering Simats Department of ECE Tamil Nadu India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Department of Mathematics Thalavapalayam Tamil Nadu Karur639113 India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781665487320

Safety indicators are becoming more significant in the conceptualization, creation, actualization, and management of wirelessly systems and communication systems. Health measurement studies sometimes have a specific scope or objective, notwithstanding their significance. Security procedures provide both and quantitative information about the degree of security of a network system or system. Able to tighten are often used to determine a system's overall security posture and reach security objectives. There isn't a systematic taxonomy of security protocols based on distant or remote contact, despite the fact that several security vetting procedures have been put in place. The goal of our research is to better analyse the risk metrics already in use. We suggest a reasonable arrangement of traditional security indicators based on mobile transmission to debate this. For risk and risk concept, policy choices, and practical practise, cybersecurity and durability are becoming more and more crucial. In addition to estimates, a defined and widely accepted metrics approach is essential to provide the best protection and adaptability of platforms. Given the above, this proposed research paper categorizes and presents the most commonly used security metrics for wireless communication systems. To begin with, we catalogue security measurements into network-based and host-based measurements. Host-based measurements are classified as non-probability in regardless of that internet measures are categorised as direction in the increase to semi, advancement is probable. Another method to assess the security of distant networks and frameworks is provided by our creative categorization of security metrics. © 2022 IEEE.

关键词： Cybersecurity

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Advancing Cloud Security Frameworks Implementing Distributed Ledger Technology for Robust Data Protection and Decentralized Security Management in Cloud Computing Environments

Advancing Cloud Security Frameworks Implementing Distributed...

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International Conference on Advances in Information Technology (ICAIT)

作者： P Rajasekar K. Kalaiselvi Raju Shanmugam S. Tamilselvan A. Pasumpon Pandian Department of ECE Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India Unitedworld School of Computational Intelligence Karnavati University Gujarat Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamilnadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Cloud computing caused the paradigm shift in business performance, from scalability to flexibility as well as affordability in the management of computing resources. Nevertheless, ensuring security of the data remains a key issue in the cloud due to the centralized interactions between service provider and a single vulnerability in the network can put rest of the data at risk. Consequently, the application of DLT, in addition to other solutions such as DLT, demonstrates a big step towards the improvement of cloud security frameworks. This article focuses on the implementation of DLT for data protection applications that are based on a solid dataset and decentralized security procedures in cloud environments. DLT utilization, like blockchain, provides an organization with integrity data, transparency, and fitness to counter cyber threats. Architecture stacks up sponsors such as cloud providers, users, and applications and DLT is responsible for the security of the transaction and the security policy enforcement. Customers use those services via desktop based application with transparent security policy arisen from DLT. Moreover, DLT also does identity management in the way that is decentralized, data sharing in the manner that it is secure, and the compliance to regulatory frameworks, which in addition reduces the trust and privacy risks in the cloud ecosystems. The proposed architecture is genuinely a big jump toward protecting cloud security from centralized control interruptions and thus, enhancing resilience and stability in cloud computing.

关键词： Distributed ledger Cloud computing security Security management Scalability Data protection Organizations computer architecture Security Tuning Resilience

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Distributed feedback laser grating order effects on optical systems with NRZ-RZ line coding scheme performance signature

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Journal of Optical Communications 2024年第0期45卷 s1725-s1738页

作者： Bhoompally, Suneela Reddy, Mittamidi Madhusudhan Kak Anwer, Twana Mohammed Ahammad, Shaik Hasane Hossain, Md. Amzad Ferdous, A. H. M. Iftekharul Zaki Rashed, Ahmed Nabih Ezzat, Ahmed Department of ECE Malla Reddy Engineering College Telagana Secunderabad500043 India Department of Civil Engineering Institute of Aeronautical Engineering Hyderabad Dundigal500043 India Department of Physics College of Education Salahaddin University-Erbil Kurdistan Region Erbil44002 Iraq Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Faculty of Electrical Engineering and Information Technology Institute of Theoretical Electrical Engineering Ruhr University Bochum Bochum44801 Germany Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh Department of Electrical and Electronic Engineering Pabna University of Science and Technology Pabna6600 Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of VLSI Microelectronics Institute of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai602105 India

This work demonstrated the distributed feedback laser grating order effects on optical system in the presence of NRZ-RZ line coding schemes. Max lighted signal form power variations are studied with spectral base wavelength band variations through optical fiber based on first various distributed based band feedback laser (DFB) grating order. The total lighted form base power band through optical fiber based on first different DFB laser grating order is estimated. The max electrical base signal band power form variations with spectral base frequency form band variations through photo-detector based on various DFB laser grating order is demonstrated. Total electrical based power band form through photo-detector based on various DFB laser grating order is also clarified. Max base band power form band amplitude with time through photo-detector based on different DFB laser grating order is clarified. Max Q base band form is studied numerically with DFB laser grating order variations. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Distributed feedback lasers

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