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2021 International Conference on Intelligent Technologies, CONIT 2021

作者： Pradhan, Devasis Sahu, Prasanna Kumar Dash, A. Tun, Hla Myo Acharya Institute of Technology Department of Electronics and Communication Engineering Dr. Sarvepalli Radha Krishnan Road Bengaluru India NIT Rourkela Department of Electrical Engineering Odisha India Department of Electronics and Telecommunication Engineering India Yangon Technological University Faculty of Electrical and Computer Engineering Department of Electronic Engineering Myanmar

ISBN: (纸本)9781728185835

As the world pushes toward the utilization of greener innovation and limits energy squander, energy productivity in the remote organization has gotten more basic than any other time in recent memory. The cutting edge network, for example, 5G, are being intended to improve energy productivity and accordingly establish a basic part of the exploration and network planning. The 5G Green era required to convey a wide scope of administrations that incorporates upgraded versatile broadband, gigantic machine-type correspondence and ultra-dependability, and low inertness. Presently various difficulties and open doors for the plan of modern client affiliation instruments. Thus, considerable examination endeavors are committed to the issues of client relationship in HetNets, massive MIMO, mmWave, and energy harvesting. The paper further discussed the new security highlights including various innovations applied to 5G Green Communication, such as heterogeneous networks, gadget to-gadget interchanges or D2D Communication, enormous different info numerous yield, programming characterized networks, and the Internet of Things(IoT). © 2021 IEEE.

关键词： Heterogeneous networks

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Investigating and Implementing the Efficiency of Image Restoration Techniques in Digital Image Processing

Investigating and Implementing the Efficiency of Image Resto...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Kalyan Acharjya S. Yuvaraj Varsha D. Jadhav Amritpal Sidhu Deepak Kumar Sinha Ojasvee Kaneria Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

This look at explores the efficiency of diverse photo recovery strategies, inclusive of noise reduction, histogram change, facet enhancement, polishing, and smoothing, in digital picture processing. An extensive evaluation of the techniques becomes conducted to assess their effectiveness in restoring degraded virtual pictures. Numerous objective metrics, together with Structural Similarity (SSIM) Index, peak sign-to-Noise Ratio (PSNR), and mean Squared blunders (MSE), have been used to quantitatively examine the first-rate of the restored photographs. Effects indicated that noise discount and area enhancement furnished the finest improvements within the snap shots. Histogram changes and sprucing also stepped forward the high-quality of the pictures, at the same time as the smoothing technique ended in a moderate lower in pleasant. This examines offers a detailed insight into the efficiency of various photo healing techniques and can serve as a reference for in addition investigations.

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Implementing Long Short-Term Memory Network for Enhancing Hyper Spectral Time Series Modeling

Implementing Long Short-Term Memory Network for Enhancing Hy...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Muktha Eti Jyoti Shekhawat Manish Nagpal Srisainath R Pranali G. Chavhan Rakesh Kumar Yadav Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai-127 Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This take a look at proposes using lengthy quick-time period reminiscence (LSTM) networks as a tool for enhancing the accuracy of Hyper Spectral Time series (HSTS) fashions. via the use of this recurrent neural community approach, the model can account for the temporal dating among the hyper spectral spectral time collection variables. additionally, with this utility, the version can research from the spectral time series over a longer time period. This study examined the efficacy of the carried out LSTM network with the aid of comparing the results to the Hyper Spectral Time collection model performance performed prior to using this method. The effects recommend that the addition of the LSTM network accelerated the accuracy of the version through about 25%. This generation has the potential to substantially beautify the accuracy of temporal records inside the discipline of faraway sensing records analysis. In conclusion, this examine indicates that LSTM networks are a promising tool for boosting the predictive energy of HSTS models. long quick-term memory Networks (LSTM) are a type of Recurrent Neural Networks (RNNs) that have been applied to many duties in synthetic Intelligence including language information, herbal language processing, and image processing. more recently, they had been used to enhance hyper spectral time series modeling. The shape of LSTM permits it cope with and keep lengthy-term sequences of statistics, making it particularly suitable for modeling hyper-spectral time series. At whenever step, an LSTM community takes as input a vector representing the hyper-spectral values, and outputs a vector which represents the intermediate representation of the time series. these intermediate representations are then used to generate the output at every next time step. LSTM networks are for his or her ability to capture lengthy-time period dependencies, and because of their structure, they are additionally nicely-appropriate to hyper-spectral time collecti

关键词： Performance evaluation Recurrent neural networks Shape Time series analysis Predictive models Vectors Robustness

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Deep Robust Reversible Watermarking

arXiv

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arXiv 2025年

作者： Chen, Jiale Wang, Wei Shi, Chongyang Dong, Li Li, Yuanman Hu, Xiping School of Computer Science Beijing Institute of Technology Beijing100081 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China

Robust Reversible Watermarking (RRW) enables perfect recovery of cover images and watermarks in lossless channels while ensuring robust watermark extraction under lossy channels. However, existing RRW methods, mostly non-deep learning-based, suffer from complex designs, high computational costs, and poor robustness limiting their practical applications. To address these issues, this paper proposes Deep Robust Reversible Watermarking (DRRW), a deep learning-based RRW scheme. DRRW introduces an Integer Invertible Watermark Network (iIWN) to achieve an invertible mapping between integer data distributions, fundamentally addressing the limitations of conventional RRW approaches. Unlike traditional RRW methods requiring task-specific designs for different distortions, DRRW adopts an encoder-noise layer-decoder framework, enabling adaptive robustness against various distortions through end-to-end training. During inference, the cover image and watermark are mapped into an overflowed stego image and latent variables. Arithmetic coding efficiently compresses these into a compact bitstream, which is embedded via reversible data hiding to ensure lossless recovery of both the image and watermark. To reduce pixel overflow, we introduce an overflow penalty loss, significantly shortening the auxiliary bitstream while improving both robustness and stego image quality. Additionally, we propose an adaptive weight adjustment strategy that eliminates the need to manually preset the watermark loss weight, ensuring improved training stability and performance. Experiments on multiple datasets demonstrate that DRRW achieves notable performance advantages. Compared to state-of-the-art RRW methods, DRRW improves robustness and reduces embedding, extraction, and recovery complexities by 55.14×, 5.95×, and 3.57×, respectively. The auxiliary bitstream is shortened by 43.86×, and reversible embedding succeeds on 16,762 images in the PASCAL VOC 2012 dataset, marking a significant step toward pra

关键词： Image enhancement

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Reliable Free-Space Optical Communication System Performance for Matching Multi-level Customer Needs: Using Hybrid Modulation System with Deep Reinforcement Learning

Reliable Free-Space Optical Communication System Performance...

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IEEE Symposium on computers and Communications (ISCC)

作者： Alaa A. Algamal Ahmed H. Abd El-Malek Hossam M. H. Shalaby School of Electronics Communications & Computer Engineering Egypt-Japan University of Science & Technology Alexandria Egypt Department of Electrical Engineering Faculty of Engineering Alexandria University Alexandria Egypt

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

In the recent decades, free-space optical (FSO) communication technology has gained significant importance owing to its promising unique features: high user capacity, license-free spectrum, ease and quick deploy-ability. However, the performance of FSO communication systems depends on the uncontrollable terrestrial atmospheric effects. The second main challenge is that the FSO system performance degrades as a line-of-Sight (LoS) technology due to the misalignment transmitter and receiver. The third challenge is the consideration of time value of money, which is central to most engineering economic analyses in employing communication systems. The opportunity cost of making one choice over another must also be considered. This paper presents a proposed FSO system design model for mitigating the three performance challenges we mentioned. The results show an enhancement by 83.34% in system efficiency in case of moderate scintillation (using gamma-gamma model). This proposed hybrid model is proved to be applicable to any atmospheric channel conditions.

关键词： Multiplexing Free-space optical communication Communication systems Atmospheric modeling System performance Computational modeling Reinforcement learning

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A DWT-Based Model with CNN-based Decision Fusion Strategy for Medical Image Encryption Scheme for Healthcare Network

A DWT-Based Model with CNN-based Decision Fusion Strategy fo...

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Smart Technologies, Communication and Robotics (STCR)

作者： G. Ramkumar Osama Jamal Jamil Abed Al Jawad J. Lefty Joyson P. Nancy Navdeep Singh Rowsonara Begum Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Faculty of Economic and Administrative Sciences BIT Zarqa University Zarqa Jordan College of Business Administration University of Business and Technology Jeddah Saudi Arabia Department of Computer Science and Engineering J.P. College of Engineering Tenkasi School of Electronics and Electrical Engineering Lovely Professional University India Department of Computer Science & Engineering (Data Science) MLR Institute of Technology Dundigal Hyderabad Telengana

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

Telemedicine and digital health are expanding rapidly, increasing the demand for secure and precise transmission of medical images. This study introduces an innovative hybrid model that combines DWT, CNN, and encryption techniques for safe image processing. The Proposed DWR-CNN model secures image embedding, hybrid encryption, and deep learning classification techniques. Medical images are concealed within a reference image using methods such as SVD to maintain their confidentiality while remaining unaltered. The image is subsequently transformed using DWT to enhance its resilience against attacks. A blend of AES and ECC protects the image transfer. VGG16 and YOLOv8 are to extract features and identify errors. The DWT-CNN model encourages precision and robust safety in evaluations using medical imaging datasets.

关键词： Deep learning Accuracy Telemedicine Watermarking Transforms Elliptic curve cryptography Feature extraction Discrete wavelet transforms Encryption Biomedical imaging

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A Reliable Method to Estimate the Bispectral Index Value Using a Single Frontal EEG Channel for Intra and Inter Subject Variability

A Reliable Method to Estimate the Bispectral Index Value Usi...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Shahbakhti, Mohammad Beiramvand, Matin Krycinska, Roza Nasiri, Erfan Chen, Wei Sole-Casals, Jordi Wierzchon, Michal Broniec-Wojcik, Anna Augustyniak, Piotr Marozas, Vaidotas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Tampere University Faculty of Information Technology and Communication Tempere Finland Jagiellonian University Medical College Doctoral School in Medical and Health Sciences Krakow Poland Jagiellonian University Collegium Medicum Department of Interdisciplinary Intensive Care Krakow Poland Allameh Tabataba'i University Faculty of Statistics Mathematics and Computer Tehran Iran Fudan University Center for Intelligent Medical Electronics School of Information Science and Technology Shanghai China University of Vic-Central University of Catalonia Data and Signal Processing Research Group Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Agh University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland

ISBN: (纸本)9781665493840

Objective: Monitoring the depth of anesthesia (DoA) plays an important role for administering the drug injection during a surgery, i.e., preventing undesired awareness and inordinate anesthetic depth. Although the bispectral index (BIS) monitor is the golden standard system for the DoA monitoring, it is still not affordable for the developing countries. Alternatively, a low-cost electroencephalogram (EEG) headband can be used. The objective of this paper is to present a new algorithm for estimating the BIS values using a single frontal EEG channel. Method: In the first step, the EEG signal is filtered for the elimination of artifacts and is split into its sub-bands. In the second step, several linear and nonlinear features are extracted from each sub-band and fed to a random forest regression model in order to estimate the BIS. The performance of the proposed algorithm is assessed using EEG data recorded from twenty-four subjects during the general anesthesia and is validated in terms of correlation coefficient (CC) and absolute error (AE) between the reference and estimated BIS values. Results: The proposed algorithm achieved the mean CC of 0.83 and AE of 6.5 for intra subject variability and mean CC of 0.87 and AE of 5.5 for inter subject variability. Significance: Given the similar results for both intra and inter subject variability, the proposed algorithm has the potential to be used in the real-world scenario. © 2023 IEEE.

关键词： Electroencephalography

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

SSRN

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Algarni, Abeer D. Soliman, Naglaa F. El-Samie, Fathi E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

Facial expression recognition plays a pivotal role in comprehending emotions and social interactions, particularly in individuals grappling with autism spectrum disorder (ASD), who often encounter challenges in non-verbal communication. For some with ASD, even subtle shifts in their facial expressions can prove elusive, rendering it arduous to categorize their emotional states accurately. Consequently, the analysis and processing of facial expression data hold the potential to facilitate early autism diagnosis, thereby enabling timely therapeutic interventions and alleviating the economic and psychological burdens experienced by patients. In this study, we introduce an enhanced approach that leverages a fuzzy convolutional neural network (CNN) and an Improved Binary Genetic Algorithm (IBGA) for autism diagnosis based on facial expression data. Our model confronts the intricate task of recognizing facial images by selecting salient features employing the IBGA method. Moreover, it employs fuzzy weights to ascertain the probability of similarity within the classifier layer. Our proposed model has yielded impressive outcomes, boasting an average accuracy of 0.9405 for ASD detection and 0.9365 for the recognition of six distinct facial expressions. These results hold profound implications for the enhancement of emotion recognition systems and the refinement of interventions aimed at bolstering social interactions for individuals grappling with ASD. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Graph Neural Networks for Resource Allocation Optimization in Healthcare Industry

Graph Neural Networks for Resource Allocation Optimization i...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. K. Manivannan Venkatesh Kavididevi D. Muthukumaran R Nandhini V. Vakula C. Srinivasan Faculty of Management SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Information Technology Vardhaman College of Engineering Hyderabad Telangana India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Optimizing the allocation of healthcare resources has never been easier than with the help of Graph Neural Networks (GNNs). In healthcare systems, where complications abound, GNNs may help with the allocation of resources in a way that improves both patient outcomes and operational efficiency. The goal is to use GNNs’ graph-modelling capabilities to describe healthcare networks, with hospitals, doctors, and other medical resources acting as nodes and potential transfers of patients or shared resources as edges. Using both past and present data, the goal is to optimize resource allocation by forecasting the most effective distribution patterns. The decision-making process is improved by using important methods including attention mechanisms, message-passing algorithms, and node embeddings. The accuracy and scalability of healthcare resource management are both enhanced by GNNs, which capture spatial and temporal dynamics. The general efficacy of healthcare delivery systems is improved by this method, which permits more precise forecasts and efficient use of resources. The results highlight how GNNs can change healthcare resource allocation models. From GitHub repository data the short interfering RNAs (siRNAs) and microRNAs (miRNAs) data with efficacy is calculated. Out of the 8 data collected as samples for various siRNAs the efficacy minimum value is 0.07 and the maximum value is 0.946 and in other data samples collected it is ${0. 3 8 6}$ and 0.777. For miRNAs it is 0.934 and 0.394 and in other data sample collected it is 0.738 and 0.432.

关键词： Data privacy Hospitals Scalability RNA Medical services Graph neural networks Real-time systems Resource management Streams Software development management

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Optimized Spherical Network for Clustering based Fault-Tolerant and Reliable Routing in IoT-Enabled WSNs

Optimized Spherical Network for Clustering based Fault-Toler...

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

作者： Md Asif M. Prabha P. Santhosh Srinivasan S. Prabagaran Department of Electronics and Computer Engineering JB Institute of Engineering and Tecnology Hyderabad Telangana India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics Panimalar Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Faculty of Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research presents an innovative approach to optimize the performance and longevity of Internet of Things (IoT)-assisted Wireless Sensor Networks (WSNs). The proposed Osprey-Based Bowerbird Spherical Convolutional Network (OBSCN-GIA) framework addresses the critical challenges of energy consumption and fault tolerance. The OBSCN-GIA technique utilizes the Osprey Optimization Algorithm (OOA) for cluster head selection and cluster construction, ensuring efficient network organization. The Bowerbird Optimization Algorithm (BOA) is integrated to enhance fault tolerance and network survivability. A Spherical Convolutional Network (SCN)-based routing technique is employed to optimize WSN route selection, while the Geyser-Inspired Algorithm (GIA) fine-tunes SCN settings for optimal data transfer. The proposed approach was evaluated against existing models, demonstrating superior performance in terms of survivability, packet delivery ratio, network longevity, energy consumption, delay, and throughput. The OBSCN-GIA achieved a remarkable throughput of 99.94% and an average energy consumption rate of 0.02J for a network of 100 nodes. This research contributes to the development of more efficient and sustainable WSNs, paving the way for the widespread adoption of IoT technologies in various applications.

关键词： Wireless sensor networks Fault tolerance Energy consumption Fault tolerant systems Clustering algorithms Routing Throughput Data transfer Convolutional neural networks Optimization

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