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dual-discriminator conditional generative adversarial network-based LDPC-Coded OFDM Free-Space OCS for Improving BER Analysis

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IETE JOURNAL OF RESEARCH 2025年第3期71卷 1016-1024页

作者： Malini, A. Hema Selvi, M. Saveetha Engn Coll Dept ECE Chennai India

This paper proposes a dual-discriminator conditional generative adversarial network-based LDPC-Coded OFDM Free-Space OCS for Improving BER Analysis (LDPC-OFDM-FSO-DDcGAN). The proposed LDPC-OFDM-FSO-DDcGAN method containsthe transmitter model, the channel model and the receiver model. The binary data are given to the encoder of LDPC in the transmitter model and the input data encode previously controlling the situation using QAM. To decrease the effects of fading caused by turbulence, the channel model intends a probability density function with FSO-DDcGAN. The maximal ratio combing (MRC), equal gain combining (EGC) and selection combining (SC) can be employed by the combiner to integrate the signal once it has been received from the FSO channel. The combiner is used to improve one signal by combining the several signals received. The bit of data is transformed towards a sequential stream after combiner. The proposed Probability Density Function (PDF) offers high SNR gain, BER reduction and power efficiency when combined with DDcGAN. To achieve large coding gain, the standard coding methods of LDPC are used through only a code percentage as realistic to imitation data. The proposed LDPC-OFDM-FSO-DDcGAN technique is executed in MATLAB. The LDPC-OFDM-FSO-DDcGAN method provides higher power efficiency and lower bit error rate analysed with the existing models.

关键词： Bit error rate dual-discriminator conditional generative adversarial network

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Cluster head based secure routing using optimized dual-discriminator conditional generative adversarial network in wireless ad-hoc networks

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PEER-TO-PEER networkING AND APPLICATIONS 2023年第6期16卷 2747-2760页

作者： Prabha, R. Senthil, G. A. Suganthi, SU. Sri Sai Ram Inst Technol Dept Elect & Commun Engn Chennai Tamil Nadu India Agni Coll Technol Dept Informat Technol Chennai Tamil Nadu India Sri Sai Ram Inst Technol Dept Artificial Intelligence & Data Sci Chennai Tamil Nadu India

Designing a routing protocol that can against the attacks of malicious nodes is very essential because open wireless channels make a wireless ad hoc network (WAN) affected by different security attacks. To overcome this problem, dual-discriminator conditional generative adversarial network Optimized with Namib beetle optimization algorithm is proposed in this manuscript for cluster head based secure routing in Wireless Ad-hoc networks (CH-SR-DDCGAN-NBOA-WAN). Here, the proposed CH-SR-DDCGAN-NBOA-WAN method consists of two phases: (i) to find the optimal CHs (ii) to find optimal trust path. At first, dual-discriminator conditional generative adversarial network is utilized for selecting Cluster Head (CH) depending on the multi-objective fitness function. The multi-objective fitness function considers the factors, namely set of nodes, determining short security paths connecting sources and destinations, and links schedule with the routes that fulfills the request of all nodes to decrease the energy consumption of whole network, and also increase the defense against spiteful nodes. Therefore, in this work, proposed Namib beetle optimization algorithm (NBOA) is utilized for optimizing the dual-discriminator conditional generative adversarial network, which selects the CH accurately. After CH selection, Nomadic People Optimization Algorithm (NPOA) is used to identify the trust path from that are confirmed by using trust parameters, such as integrity, consistency, forwarding rate, availability factor. The proposed CH-SR-DDCGAN-NBOA-WAN approach is activated in network stimulator NS-2 and its performance is examined under performance metrics, such as drop, normalized network energy, network lifetime, delay, throughput, energy consumption, Packet Delivery Ratio. The proposed CH-SR-DDCGAN-NBOA-WAN approach attains 23.31%, 24.5%, and 30.01% lower packet drop, 12.45%, 17.34% and 24.67% higher network life time and 11.45%, 17.34% and 29.56% lower average delay time compared wit

关键词： Clustering Energy efficacy and trust management dual-discriminator conditional generative adversarial network Namib beetle optimization algorithm Nomadic people optimization algorithm

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Brain tumor classification for MRI images using dual-discriminator conditional generative adversarial network

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ELECTROMAGNETIC BIOLOGY AND MEDICINE 2024年第1-2期43卷 81-94页

作者： Selvi, T. Kalai Begum, A. Sumaiya Poonkuzhali, P. Aarthi, R. Easwari Engn Coll Dept Artificial Intelligence & Data Sci Chennai Tamil Nadu India RMD Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India

This research focuses on improving the detection and classification of brain tumors using a method called Brain Tumor Classification using dual-discriminator conditional generative adversarial network (DDCGAN) for MRI images. The proposed system is implemented in the MATLAB programming language. In this study, images of the brain are taken from a dataset and processed to remove noise and enhance image quality. The brain pictures are taken from Brats MRI image dataset. The images are preprocessed using Structural interval gradient filtering to remove noises and improve the quality of the image. The preprocessing outcomes are given to feature extraction. The features are extracted by Empirical wavelet transform (EWT) and the extracted features are given to the dual-discriminator conditional generative adversarial network (DDCGAN) for recognizing the brain tumor, which classifies the brain images into glioma, meningioma, pituitary gland, and normal. Then, the weight parameter of DDCGAN is optimized by utilizing Border Collie Optimization (BCO), which is a met a heuristic approach to handle the real world optimization issues. It maximizes the detection accurateness and reduced computational time. Implemented in MATLAB, the experimental results demonstrate that the proposed system achieves a high sensitivity of 99.58%. The BCO-DDCGAN-MRI-BTC method outperforms existing techniques in terms of precision and sensitivity when compared to methods like Kernel Basis SVM (KSVM-HHO-BTC), Joint Training of Two-Channel Deep Neural network (JT-TCDNN-BTC), and YOLOv2 including Convolutional Neural network (YOLOv2-CNN-BTC). The research findings indicate that the proposed method enhances the accuracy of brain tumor classification while reducing computational time and errors. This research focuses on improving the detection and classification of brain tumors using a method called Brain Tumor Classification using dual-discriminator conditional generative adversarial network (DDCGAN) for

关键词： Border collie optimization dual-discriminator conditional generative adversarial network empirical wavelet transform glioma

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