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Multi-Path Attention Block-Loss Function for Non-Uniform Blind Image Deblurring

Multi-Path Attention Block-Loss Function for Non-Uniform Bli...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Nagendar Yamsani Pradeep Kumar S Kassem AL-Attabi Zainab Alassedi Abbas Hameed Abdul Hussein School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic university Najaf Iraq National University of Science and Technology Dhi Qar Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

Blind image deblurring is a significant and difficult task in the computer vision field. It is mainly focused on decreasing the blur caused by motion or camera shake. However, image deblurring has made great progress, but there is a need for enhancement in the visual effect and image information to enhance the overall image quality. In this research, the Multi-Path Attention Block-Loss Function (MPAB-LF) is proposed for non-uniform blind image deblurring to enhance image quality. The MPAB approach is employed to provide the foundation for blind image deblurring. To combine numerous MPABs instead of employing associated feature aggregation, the Improved One-Shot Aggregation (IOSA) is utilized. Finally, the loss functions are employed to enhance the generator and discriminator of the framework. Existing approaches like Generative Adversarial Network (GAN), MPAB, Two Convolutional Neural Networks (CNN), and Dense Dilated Block and Improved Attention Module (DDBIAM) are used to compare with the proposed MPAB-FL approach. The proposed MPAB-FL achieves a better Peak-to-Signal Noise Ratio (PSNR) of 35.65 dB and Structural Similarity Index (SSIM) of 0.9957 compared to the existing approaches like GNN, MPAB, Two-stage CNN, and DDBIAM respectively.

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Text Steganography Analysis with Embedding Techniques for Hidden Data Retrieval via Extraction Methods

Text Steganography Analysis with Embedding Techniques for Hi...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Karunakara Rai B Kassem Al-Attabi Mohammed Yousif Arabi R Archana Reddy A. H. A. Hussein Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science and Artificial Intelligence SR University Warangal Telangana India College of Pharmacy Ahl Al Bayt University Karbala Iraq

In the digital data protection, the field of text steganography through image encryption has garnered significant attention. As an essential endeavour, the development of secure and robust methods for digital image watermarking, steganography, and image concealment stands as a crucial response to the need for safeguarding sensitive information while preserving data integrity and quality. The proposed method, named LWT-DCT-SVD, presents an innovative approach by leveraging a cover image and a secret key in conjunction with embedding techniques. This process culminates in the creation of a stego image, enabling the secure concealment of text within it. To unveil the concealed information, an extraction technique is employed, making use of the stego key in combination with the embedding method. This extraction process seamlessly reveals the original secret text, guaranteeing a secure and reliable means of transferring and retrieving confidential information. To gauge its effectiveness, the proposed method is rigorously compared with existing techniques such as DGV, OMP, and FE. Numerical validation highlights its strength, with a high PSNR value of 80.7656 signifying image quality preservation and a low MSE of 0.1780, reinforcing its data security attributes.

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PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight Factor of Whale Optimization Algorithm

PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight F...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Mohammed I. Habelalmateen R Archana Reddy Chetana Reddy N. Nagarjuna Pradeep Kumar S Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor Malaysia Department of Computer Technical Engineering College of Technical Engineering the Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Telangana India Department of Electronics and Communication Oxford College of Engineering Bangalore Department of CSIT CVR College of Engineering Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In the landscape of optimization for mitigating the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, prevalent techniques like the tone reservation scheme and l-norm Cyclic Algorithm (LNCA) algorithm grapple with inherent challenges linked to computational complexity and iterative algorithm derivation. While strides have been made with models such as Twin Symbol Hybrid Optimization by the premise of Partial Transmit Sequence (TSHO-PTS) and Advanced Switching Differential Evolution (ASDE), persistent concerns related to symbol overlaps and algorithmic limitations underscore the need for further refinement in computational efficiency within OFDM-based communication systems. In response to these challenges, the proposed Nonlinear Inertia Weight Factor within the Whale Optimization Algorithm (NIWF-WOA) emerges as a solution. Comparative analysis against existing methods like LCNA, Modified Dragonfly Optimization algorithm (MDOA), TSHO-PTS, and ASDE reveals the superior performance of NIWF-WOA. Numerical validation affirms its efficacy, with a recorded in Bit Error Rate (BER) of 10 −2.5 , SNR 10 −3.4 and PAPR of . 10 −4.2 , underscoring the substantial improvement achieved by NIWF-WOA in optimizing PAPR reduction for MIMO-OFDM systems.

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Satellite Image Classification using Grey Wolf Optimizer based Stacked Bidirectional Long Short Term Memory for Agriculture Monitoring

Satellite Image Classification using Grey Wolf Optimizer bas...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Parameshachari B.D R Archana Reddy Baydaa Sh. Z. Abood Mohammed I. Habelalmateen Abbas Hameed Abdul Hussein Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq The Islamic university Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

Effective monitoring of refined crops as well as various land covers is a critical conservation and commercial concern in agricultural land organization and crop yield forecast. To solve this issue, a unique strategy for satellite image categorization in agriculture monitoring is proposed, which employs the Grey Wolf Optimizer (GWO) in conjunction with a Stacked Bidirectional Long Short Term Memory (LSTM) model. The dataset used consists of Sentinel-2 satellite surface reflectance data, specifically the Sentinel-2 Level-2A output. Digital integers are translated into reflectance values during the preprocessing stage, successfully minimizing changes in image brightness. AlexNet is used for feature extraction, and GWO is used for feature selection. The GWO-Stacked Bi-LSTM model is then used to categorize satellite images, yielding dramatically better accuracy. The analysis results suggest that the proposed Stacked Bi-LSTM model has an amazing accuracy of 99.34%, demonstrating higher performance when compared to existing approaches.

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Facial Emotional Recognition Using Faster Regional Convolutional Neural Network with VGG16 Feature Extraction Model

Facial Emotional Recognition Using Faster Regional Convoluti...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Nagendar Yamsani Muhammed Basim Jabar Myasar Mundher Adnan A. H. A. Hussein Subhra Chakraborty School of Computer Science and Artificial Intelligence SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq The Islamic university Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Facial Emotion Recognition (FER) poses a significant challenge when training deep learning (DL) models like Artificial Neural Network (ANN) and machine learning (ML). The primary issue in FER is the detection of the face, the creation of bounding boxes, and the classification of emotional expressions. This paper proposes the use of Faster Regional Convolutional Neural Network (RCNN) for facial recognition and employs a median filter to filter the image dataset. The model leverages the Facial Emotional Recognition (FER 2013) dataset for training and predicting accurate classifications. The Median Filter is used to eliminate noise in the image, such as Salt and Pepper noise, and data augmentation is utilized to generate synthetic data for addressing minor imbalances in classes. Subsequently, the VGG 16 architecture is implemented for precise feature extraction to reduce complexity in the classification process. This paper, achieved accuracy, precision, recall, specificity, and f1-score values of approximately 78.22%, 75.40%, 80.20%, 85.90%, and 71.40%, respectively, when compared to other classification methods like Convolutional Neural Network, ResNet 50, Divide-And-Conquer-Based learning, and Resnet 18.

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Optimizing Resource Allocation in Mobile Edge Computing for IIoT through Reinforcement Learning and Multiobjective Particle Swarm Optimization

Optimizing Resource Allocation in Mobile Edge Computing for ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sudhakar AVV Mohan M K. Maharajan Mohammed I. Habelalmateen N. Naga Saranya Department of Electrical and Electronics Engineering SR University Telangana India Department of ISE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Bangalore Krishnankoil India Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor Malaysia Department of Computer Technical Engineering College of Technical Engineering the Islamic University Najaf Iraq Department of Computer Applications Saveetha College of Liberal Arts and Science SIMATS Chennai India

Mobile networks are flexible enough to support a range of resource allocation models and service-based choices in computing domains, which affects both virtual reality and the Industrial Internet of Things (IIOT). Virtual resource management is made easier by the Mobile Edge Computing (MEC) paradigm, which also makes edge connectivity between data terminals and execution in the core network under heavy load possible. Meeting customer requirements efficiently is achieved through thoughtful planning, aided by cognitive agents. User data, incorporating behavioral patterns, is amalgamated to cater to IIOT service types. Neural caching for memory during task execution is made easier by the use of swarm intelligence and reinforcement learning techniques. Prediction strategies optimize business operations and caching to reduce execution delays. Predictive models evaluate performance by taking into account variables like nearby user equipment and mobile edge computing resources. This method's efficacy is demonstrated by a cognitive agent model that manages resource distribution and creates networks of communication to improve service quality. For accurate resource distribution among end users, reinforcement learning techniques—more especially, Multi-Objective Particle Swarm Optimization (MOPSO) algorithms—are used. This includes building cost mapping tables and optimizing allocation in MEC. The proposed method outperforms existing algorithms like Task-Offloading and Resource Allocation Strategy and achieves a better throughput value of 785.32.

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Optimization of Renewable Energy Using Hybrid Multi-Objective Slime Mould and Simulated Annealing Algorithms

Optimization of Renewable Energy Using Hybrid Multi-Objectiv...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： V. Malathy Jagadevi N Kalshetty Kassem Al-Attabi M.R. Kamesh Abhijeet Das Department of ECE SR University Warangal Telangana India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Mechanical Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Civil Engineering C.V. Raman Global University (CGU) Bhubaneswar Odisha India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

During the past few years, increase in population resulted in increase in energy demand all over the world. The increased energy demand resulting depletion of fossil fuels which are now being replaced with Renewable Energy (RE) systems. However, operating on different energy sources for cost management and energy demand are difficult to handle due to its uncertainties like varying weather, load demand. In order to address these issues, a Hybrid RE System (HRES) is proposed in this study with a Hybrid Multi-Objective Slime Mould Algorithm and Simulated Annealing (HMOSMASA) method. The Simulated Annealing (SA) with advanced local search ability improves the effectiveness of Multi-Objective Slime Mould (MOSMA) exhibiting decentralized behavior and finds optimal solutions efficiently. Primarily, the search space is explored by MOSMA then solutions are exploited for local search by SA to obtain best optimal solution. The HMOSMASA ensures optimal sizing of HRES, load demand effectively and which is trained on historically collected data. The HMOSMA method is evaluated by Annual System Cost, Net Present Cost, Levelized Cost of Energy that achieved optimized results of 6374${\$}$, 2109732${\$}$, 0.231${\$}$/kWh respectively. The HMOSMASA demonstrated best results compared to Particle Swarm Optimization (PSO) and Hybrid Grey Wolf Optimization (GWO).

关键词： Renewable energy sources Costs Uncertainty Sociology Simulated annealing Minimization Space exploration

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Transmission Line Fault Detection and Classification Using Feature Extraction Based Self-Attention Convolutional Neural Network with Time Series Image

Transmission Line Fault Detection and Classification Using F...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Muntather Almusawi D. Raja Babu Debarshi Mazumder Bala Dhandayuthapani V. N. Naga Saranya Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas campus Oman Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The transmission lines continuously experience the number of shunt faults and its effective in practical system increases the instability, load damages and line restoration cost. This work implements an advanced self-attention convolutional neural network (SAT-CNN) method for fault detection and classification (FDC) of high voltage transmission lines experiencing a constant number of shunt faults. Faults cause load damage in real-time applications, increase instability, and increase the cost of line restoration. Therefore, a precise model is needs to identify and categorize the flaws to quickly restore he problematic phases. In this research, implemented SAT-CNN feature extraction model with imaging-based on time series that can accurately detect and classify faults. By employing a number of input signals, including, current, voltage, and combined current and voltage signals, at different sampling frequencies, the efficacy of implemented SAT-CNN model is evaluated. Implemented SAT-CNN method obtains high performance when compared to existing methods including weight-sharing network (WSCN), Truncated singular value decomposition and Human urbanization algorithm based Recurrent Perceptron Neural Network (TSVD-HUARPNN), and Graph convolutional neural network (GCN), and result achieved 99.90% accuracy value.

关键词： Power transmission lines Costs Fault detection Time series analysis Urban areas Feature extraction Transmission line measurements

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Enhanced Reversible Image Watermarking using Improved Moth Flame Optimization Algorithm

Enhanced Reversible Image Watermarking using Improved Moth F...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： S Pradeep Kumar Zainab Abed Almoussawi G Sunil J Ananda Babu Muntather Almusawi Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India College of Islamic Science Ahl Al Bayt University Karbala Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of ISE Malnad College of Engineering Hassan India The Islamic university Najaf Iraq

With enhancing connectivity, undefended digital content is susceptible for attacks and hence watermarking is significant to protect copyright. In this research, an efficient image reversible watermarking method is proposed. The proposed method uses selection of optimum position for embedding in accordance with entropy and relevance threshold for selection of entropy is done through Particle Swarm Optimization (PSO) algorithm. A method in Integer Wavelet Transform (IWT) is implemented, next Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) is integrated to the process of embedding in whole selected blocks and Fractal encrypted watermark are combined to image coefficients utilizing mean proximity coefficient. The process of embedding is done through optimum scale that is produced through Improved Moth-Flame Optimization (IMFO) algorithm in accordance with fitness function. The proposed method attained superior image quality and high embedding capacity. The performance of proposed method id evaluated with performance measures of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Normalized Correlation (NC). The proposed method attained high PSNR of 60.32 dB, SSIM of 0.9948 and NC of 0.9872 in Lena image which is superior than other existing algorithms.

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Novel Ultrasonic Image Reconstruction and Deep Learning Model-Based Categorization for Accurate Liver Disease Diagnosis

Novel Ultrasonic Image Reconstruction and Deep Learning Mode...

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Data science and Network Security (ICDSNS), International Conference on

作者： Nijaguna G S Sneha Sureddy BinduMadhavi Dayanidhi Mohapatra Parameshachari B D Department of Information Science and Engineering S.E.A. College of Engineering and Technology Bangalore India Dept. of CSE GST GITAM University Bengaluru Campus Bengaluru India Department of ECE Hyderabad Institute of Technology and Management Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The diagnosis of liver illnesses is critical for efficient treatment and management of the condition. By combining deep learning model-based classification with ultrasonic image reconstruction, the developed method aims to surpass standard deep learning techniques. This method's nine primary processes include pre-processing approaches, image reconstruction, liver area segmentation, feature extraction, deep learning model training, testing, and comparing model performance. The suggested approach was tested on patients with fatty liver disease by processing huge sets of ultrasound pictures and RF data frames. The InceptionResNetV2 CNN was used to detect fatty liver disease in these persons. The proposed approach has a sensitivity of 95.2 percent, an accuracy of 92.8 percent, and a specificity of 94.1 percent. By combining deep learning model-based classification with ultrasonic image reconstruction, the technology overcomes some of the limitations of classic deep learning methods. Medical ultrasound imaging is commonly used because it provides for the non-invasive, risk-free identification of liver disorders. Ultrasonic imaging is a sort of medical image diagnostic that assists doctors in diagnosing and treating a variety of ailments. This suggests that the suggested technique might considerably improve the precision and consistency with which liver disease is detected, resulting in better patient outcomes.

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