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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

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Convolutional Neural Network and Long Short-Term Memory based Ensemble Learning for modulation classification in 5G Network 2

Convolutional Neural Network and Long Short-Term Memory base...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Devi, T. Alkhayyat, Ahmad Armoogum, Vinaye Buvaneswari, B. Manjula, B.M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Technology School of Innovative Technologies and Engineering Mauritius Panimalar Engineering College Department of Information Technology Chennai India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350372892

Modulation classification is important for its fundamental role in dynamic spectrum access which supports 5G (fifth generation) wireless communications. When the receiver receives multiple signals, multipath fading exists at the receiver side it is a big challenge to classify the modulation type. When many signals are transmitting to the receiver side, there exists a heterogeneous traffic, where there is a lot of disturbances occur. Due to these reasons, the block error rate for modulation increases simultaneously. To overcome the changing demands like multipath fading, minimize block error rate and to reduce the traffic. In this research a modulation classification algorithm is developed using the combination architectures of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based Ensemble Learning (EL). Dimensionality is reduced through principal component analysis;it also minimizes the training time. The proposed method outperformed same as the ordinary CNN in terms of classification accuracy, but it reduced the training time with the help of Principal Component Analysis (PCA). The results proved that the proposed CNN-LSTM-EL achieved the best classification accuracy of 99.5 % than all other existing methods like CNN, LSTM. © 2024 IEEE.

关键词： Principal component analysis

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The Application of Ethernet and its Network Software in A computer Control System 2

The Application of Ethernet and its Network Software in A Co...

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2nd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022

作者： Pan, Dongying Wang, Chunhua Ju, Haoxue Pareek, Piyush Kumar Shandong Institute of Commerce and Technology Jinan China Dept. of Computer Science Engineering AI-ML & IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9781665491112

This paper, with the help of engineering examples to express the application of Ethernet in computer control system, proves that Ethernet has good reliability, then studies the principle of Ethernet local area network in transmission control, and finally introduces the specific application of DECNET network software for reference. © 2022 IEEE.

关键词： Ethernet

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A Segmentation Residual Network with Adaptive Normalization for Crop Type Classification using Satellite Imagery

A Segmentation Residual Network with Adaptive Normalization ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Kankala, Kumaraswamy Meghana, A. Adnan, Myasar Mundher Sudheer Kumar, Komuravelly Mouli, Kathi Chandra Hyderabad India Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

Nowadays, crop type classification with satellite images has become an emerging study in remote sensing data. Till now, various calibrated images captured by satellite are included to monitor the food growth across the world. The analysis of remote sensing images has some difficulties in classification such as identifying patterns and crop type covers and also overfitting of crops with noisy data. To overcome these issues, the model of Segmentation Residual Network with Adaptive Normalization (SegResNet-AN) is proposed to resolve the limitations of Batch Normalization BN) and variations in spatial images. The proposed SegResNet-AN gives an accurate rate in reducing overfitting of crop classification and also handles the noisy data for the large data images while capturing. For those normalization processes, different image features are extracted from the encoding and decoding. Segmentation is done through the SegResNet which works by dividing individual images into maps of pixel regions according to each crop cover type, removes all duplicated images by data redundancy. In the results, the proposed SegResNet-AN method gives results better than the other compared existing Fusion Net model in terms of precision (0.986) recall (0.934) and f1-score (0.956) respectively. © 2024 IEEE.

关键词： Satellite imagery

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An Effective Hybrid Image Watermarking Encrypted and Decrypted Technique for Copyright Protection 2

An Effective Hybrid Image Watermarking Encrypted and Decrypt...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Al-Jawahry, Hassan M. Kumar, Bura Vijay Chandra, N Subhash Patil, Rajeshwari Unnisa, Raheem The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Cvr College of Engineering Telangana India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India Cmr Technical Campus Hyderabad India

ISBN: (纸本)9798350317558

Image watermarking serves as a visual deterrent, signaling potential infringers that the image is protected by copyright. While various watermarking methods have been developed in the past, existing models encounter limitations such as a lack of suitability for diverse data types and ineffective data generalization. Additionally, the absence of standardization in watermarking techniques poses a significant challenge. In this proposed approach, images are input into an improved discrete Fourier transform (IDFT) to convert signals from time domain. The Arnold Transform is initially applied to convert images into a signal format. Subsequently, Walsh-Hadamard Transform (WHT) is employed for non-sinusoidal, orthogonal signal transformation. Finally, Singular Value Decomposition (SVD) is implemented to decrease number of dimensions in high-dimensional data, facilitating easier visualization and analysis. The average Peak Signal-to-Noise Ratio (PSNR), Normalized Cross-Correlation (NCC), and Bit Error Rate (BER) of the hybrid IDFT-WHT-SVD watermarking scheme are reported as 56.58, 0.2651, and 0.9902, respectively. This proposed model underwent comparison with other existing models, including CNN-based robust watermarking, Hyper-Chaotically Encrypted, and Cryptographic Technique. © 2024 IEEE.

关键词： Decrypting Embedding Improved Descried Fourier Transform Singular Value Decomposition Walsh-Hadamard Transform Watermarkingg

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Monarch Butterfly Optimization based Multi-Convolution Neural Network for Lung Cancer Classification 2

Monarch Butterfly Optimization based Multi-Convolution Neura...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Uma Maheswari, S. Adnan, Myasar Mundher Lathakumari, K.R. Pradeep, V. Praveena, Hirald Dwaraka Karunya Institute of Technology and Sciences Data Science and Cyber Security Coimbatore India The Islamic University Department of Computer Science Najaf Iraq Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India Alva's Institute of Engineering and Technology Department of Information Science and Engineering Mangalore India School of Engineering Mohan Babu University Erstwhile Sree Vidya Nikethan Engineering College Department of Ece Andhra Pradesh India

ISBN: (纸本)9798350382952

The lung cancer generally presented a pulmonary nodule on images of diagnostic and correct estimation in malignant pulmonary nodules is difficult for diagnosis and protecting of lung cancer. However, the existing methods has less classification accuracy because of irrelevant features on classification of lung cancer. In this research, proposed a Monarch Butterfly Optimization based Multi-Convolution Neural Network (MBO based M-CNN) method for lung cancer classification. Dataset used for lung cancer classification is LIDC-IDRI dataset and it is pre-processed by Histogram Equalization (HE). Then, the features are extracted by Gray Level Co-occurrence Matrix (GLCM) and extracted features are selected by Monarch Butterfly Optimization (MBO) algorithm. Then, the lung cancer classification is performed by Multi-Convolution Neural Network (M-CNN). The performance of proposed algorithm is tested in terms of accuracy, precision, sensitivity and specificity. Developed technique obtained accuracy 99.71%, precision 95.39%, sensitivity 99.46%, f1-score 97.45% that is better than previous method like Adaptive Boosting Self-Normalized Multiview Convolution Neural Network (AdaBoost-SNMV-CNN). © 2024 IEEE.

关键词： Adaptive boosting

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Contrast Limited Adaptive Histogram Equalization based Multi-Objective Improved Cat Swarm Optimization for Image Contrast Enhancement

Contrast Limited Adaptive Histogram Equalization based Multi...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Borra, Subba Reddy Tejaswini, N.P. Malathy, V. Magesh Kumar, B. Habelalmateen, Mohammed I. Malla Reddy Engineering College for Women Hyderabad India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India SR University Department of Ece School of Engineering Warangal India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India The Islamic University Najaf Iraq

ISBN: (纸本)9798350315455

The contrast enhancement technique is utilized to enhance the image quality which produce good quality images for various image processing tasks. The Histogram Equalization (HE) is one of the widely utilized technique for enhance the contrast and estimates the intensity frequency in an image. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is one of the local HE technique. Therefore, the CLAHE can handle the issues of classical HE algorithm. The CLAHE utilizes two various parameters such as number of tiles and clip limit, moreover the effectiveness depends on these two parameters. In this paper, Multi-Objective Improved Cat Swarm Optimization (MOICSO) is proposed to determine the optimum parameters for CLAHE to improve the image contrast. The first fitness function is utilized for estimating entropy and second is utilized for calculating Fast Noise Variance Estimation (FNVE) which produce local preservations and avoid noise amplification in an output image. The parameters like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-noise Ratio (PSNR) and Computing Time (CT) are utilized to evaluate the proposed methodology. The obtained result shows that the MOICSO achieves less MAE of 6.28 which is which is better than other existing methods. © 2023 IEEE.

关键词： Cat Swarm Optimization Contrast Limited Adaptive Histogram Equalization Entropy Fast Noise Variance Estimation Fitness Function

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Cluster Head Selection for Single and Multiple data Sinks in Heterogeneous WSN using Wild Horse Optimization 2

Cluster Head Selection for Single and Multiple data Sinks in...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Habelalmateen, Mohammed I. Kumar, Gotte Ranjith Nayana, B.P. Venkatramulu, S. Saranya, N. Naga Universiti Tun Hussein Onn Malaysia Electrical and Electronic Engineering Johor Malaysia College of Technical Engineering the Islamic University Department of Computer Technical Engineering Najaf Iraq SR University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India Kakatiya Institute of Technology and Science CSE Dept Telangana Warangal India Saveetha College of Liberal Arts and Science SIMATS Department of Computer Applications Chennai India

ISBN: (纸本)9798350317558

Enhancing a network lifetime by balancing energy as well as minimizing delay is a significant aim of Wireless Sensor Network (WSN). Besides, number of clustering approaches are developed to this process through selecting the optimum Cluster Heads (CHs) in heterogeneous WSN, but it has a significant challenge. In this research, the Wild Horse Optimization (WHO)-based Optimized Clustering approach for single and multiple data sinks is proposed for optimizing CH selection through combining the parameters of sink distance, residual energy in its formulated fitness function. The proposed method achieves better results and it is evaluated by various performance metrices like residual energy, network lifetime, throughput, alive nodes, FND as well as LND and it achieves the values of about each as 61.37, 98.36, 7.8, 96.39, 6129 and 8347 respectively when compared to the existing methods such as Energy-Efficient CH selection utilizing Improved version of Grey Wolf Optimization-based (EECHIGWO) and Enhanced Pelican Optimization Algorithm for the CH Selection (EPOA-CHS). © 2024 IEEE.

关键词： Cluster Head selection Multiple data sinks Network lifetime Wild Horse Optimization and Wireless Sensor Network

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APOA based Multi-scale Parallel Convolution Blocks with Hybrid Deep Learning for Gastric Cancer Prediction from Endoscopic Images 1

APOA based Multi-scale Parallel Convolution Blocks with Hybr...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Padthe, Adithya Mathapati, Mahantesh Priyanka, M.S. Nandihal, Priya University of the Cumberlands Department of Information Technology United States Dept.of Cse India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350316469

In terms of mortality rates, gastric cancer is second only to lung cancer. Manual gastric slice pathology examination is labor-intensive and prone to observer bias. Endoscopy of the upper digestive tract is commonly used for the screening of gastric cancer. An object identification model, a kind of deep learning, was presented as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. However, difficulties were encountered while attempting to reduce the sum of false positives in the identified findings. Tumour segmentation from the preprocessed pictures was carried out in this study, which is often more challenging and crucial. The research suggests a productive approach that makes use of multi-scale parallel convolution blocks (MPCs). Multi-scale parallel convolutions (MPCs) use filters of variable sizes to extract characteristics that are relevant across a range of tumour sizes. To further aid feature extraction with fewer parameters, residual connections and residual blocks can be used. In addition, the suggested study uses an Artificial plant optimisation algorithm (APOA) to fine-tune the segmentation model's parameters without resorting to post-processing approaches. Finally, gastric cancer detection from endoscopic pictures is accomplished using a hybrid classification strategy that incorporates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Experiments employing 1208 photos from healthy people and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross-validation approach. These findings show promise for the suggested method's application in automated early stomach cancer diagnosis using endoscopic images. © 2023 IEEE.

关键词： Artificial plant optimization algorithm Convolutional Neural Network Endoscopic images Gastric cancer Multi-scale parallel convolution blocks Recurrent Neural Network

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Investigation on Digital Forensic Using Graph Based Neural Network with Blockchain technology 2

Investigation on Digital Forensic Using Graph Based Neural N...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Bhujang, Raghavi K. Pareek, Piyush Kumar Manoj, H.M. Deepika, K.S. Information Technology University of the Cumberlands United States Ms Ramaiah Institute of Management Department of Entrepreneurship Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Computer Science and Engineering Bms Institute of Technology and Management Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Digital forensics has evolved to focus on leveraging social media evidence, offering substantial support for investigating various crimes. However, scrutinizing social media data for evidence presents significant challenges for authorities. This article proposes employing blockchain infrastructure and natural language processing (NLP) techniques as foundational elements in digital forensic investigations. Specifically, it advocates for a Graph Convolutional Neural Network (GCNN) based approach to multi-class classification. NLP is integral to various stages of this process, including data collection, feature selection, classifier assessment, and vectorization. Moreover, to enhance data security against hackers and network attacks, a blockchain-based system is utilized. Real-world dataset experimentation demonstrates the efficacy of the proposed system. The findings reveal significant improvements in GCNN classification performance when incorporating topology knowledge. Furthermore, optimizing the model necessitates considering various input factors, such as document length, training data representation, and network architecture. This structured approach to digital forensic investigation holds promise for bolstering law enforcement efforts. © 2024 IEEE.

关键词： Graph neural networks

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An Effective Dynamic Cross Propagation Clustering in Wireless Sensor Network using Improved RFCM 2

An Effective Dynamic Cross Propagation Clustering in Wireles...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Al-Farouni, Mohammed Sharmila, D. Kumar, Komuravelly Sudheer Harikantha, Sangeetha Suresh Shanker, G Vinesh The Islamic University Najaf Iraq KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India SR University School of Computer Science & Artificial Intelligence Telangana India Nitte Meenakshi Institute of Technology Department of AI & DS Bengaluru India Cmr Technical Campus Department of CSE Hyderabad India

ISBN: (纸本)9798350317558

Wireless sensor networks (WSNs) often operate in environments that are dynamic and subject to unpredictable changes. Dynamic cross-propagation clustering makes sure that systems continue to be reliable and effective even in the face of changing conditions by allowing clusters to adapt to changes in the network. It is difficult to create a dynamic cluster and choose a routing strategy because of the high energy consumption and network complexity. An Improved Relational Fuzzy C-Means (IRFCM) based dynamic routing selection and clustering method is suggested for the WSN in this study. The effective routing process in a WSN is the goal of suggested IRFCM technique. The primary goals of suggested IRFCM are to locate the cluster head in dynamic cross-propagation system and to minimize energy usage and routing in wireless sensor networks. The recommended approach performs remarkably well, with an energy consumption rate of 10.30, a network time of 5900, and a First Node Death (FND) of 1700. These results show that the proposed approach is effective in improving Dynamic clustering, energy efficiency, network time, and FND. It also performs better with a node count of 300 compared to state of the art techniques, such as CSC-TC and MCR-UWSN. © 2024 IEEE.

关键词： Cluster head Dynamic clustering Energy model Improved Relational Fuzzy C-Means Clustering Network model

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