Deep atrous context convolution generative adversarial network with corner key point extracted feature for nuts classification M. Shyamala Devi, M. Jaiganesh, S. Priya, E. Elakkiya Scientific Reports, 2026 Deep learning-based nut classification has emerged as a viable way to automate the detection and categorization of different nut varieties in the food processing and agriculture sectors. Conventional techniques for classifying nuts mostly rely on manually created characteristics like texture, color, shape, or edges. These characteristics frequently fall short of capturing the image’s complete complexity, particularly when nuts show tiny visual variances. This research proposes Deep Atrous Context Convolution Generative Adversarial Network (DAC-GAN) model that categorize the 8 classes of nuts like brazil nuts, cashew, peanut, pecan nut, pistachio, chest nut, macadamia and Walnut. This research uses Common Nut KAGGLE dataset with 4,000 nuts images of 8 nuts classes. The DAC-GAN approach overcomes the difficulties of having limited labelled data for nut classification tasks by employing DCGANs’ ability to produce high-quality, synthetic nut images to supplement the dataset. The DCGAN comprises of a discriminator and a generator block. The discriminator block develops the ability to differentiate between synthetic and real images, while the generator block generates realistic nut images from random noise. The real images along with the DCGAN generated images are processed with feature filtering methods to extract the Corner Key Points Featured (CKPF) nuts images. To further enhance the feature selection, the CKPF edges are extracted from the image that provides unique, geometrically distinctive critical corners to further process for representative learning. To proceed with the effective feature extraction and model learning, the CKPF nuts images are processed with atrous convolution that capture the intricate details by expanding the receptive field without losing resolution. The novelty of this work exists by appending the filtration and atrous convolution that acquire the spatial data features from the nut’s images at various resolutions. Atrous convolution was refined by appending the pre-context and post-context block that add the image level information to the features. The effectiveness of the DAC-GAN model was validated with the traditional augmented dataset with all existing filtering images and CNN models. Implementation outcome shows that DAC-GAN found to exhibit high accuracy of 99.83% towards the nuts type classification. The superiority of the DAC-GAN method over traditional approaches is demonstrated by extensive experiments on augmented and DCGAN generated datasets, which achieve higher classification accuracy and generalization across a variety of nut type categorization. The outcome demonstrates that the DCGAN together with atrous convolution have the potential to be an effective tool for automating nut sorting in food industry.
Compact antenna design for breast cancer diagnostics Pavithra R., Thilagavathi S., Kiruthikaa R., Rajam K., Elakkiya E. Proceedings of SPIE the International Society for Optical Engineering, 2026 A wide band, size reduced noval I shaped patch antenna fed using coplanar waveguide with wide bandwidth used for medical imaging applications. The patch is made in the shape of an I, which improves the impedance bandwidth of the antenna to 7.9 GHz at a centered frequency of 5.5 GHz while still maintaining the area to be compact at 10 mmx 10 mmx1.6 mm. The I-shaped patch, along with the ground plane, enables uniform current distribution over the radiating bandwidth, and the coupling capacitance is strong. The proposed antenna, when simulated, results in stable radiation with a peak directivity of 3.7 dBi. The wide bandwidth performance and form factor make it best suitable for microwave breast imaging applications that can penetrate the biological tissues while maintaining the spatial resolution. The antenna fabricated using Rogers material helps to achieve radiation efficiency of 85% by maintaining the proper optimization of the gap between the ground plane slot and feed line. The antenna configuration of the I shape provides improved matching of impedance at operating frequency while maintaining the directional radiation pattern.
Dynamic RBFN with vector attention-guided feature selection for spam detection in social media E Elakkiya, Sumalatha Saleti, Arunkumar Balakrishnan Complex and Intelligent Systems, 2026 Online social media platforms have emerged as primary engagement channels for internet users, leading to increased dependency on social network information. This growing reliance has attracted cybercriminals, resulting in a surge of malicious activities such as spam. Consequently, there is a pressing need for efficient spam detection mechanisms. Although several techniques have been proposed for social network spam detection, spammers continually evolve their strategies to bypass these systems. In response, researchers have focused on extracting additional features to better identify spammer patterns. However, this often introduces feature redundancy and complexity, which traditional machine learning-based feature selection methods struggle to manage in highly complex datasets. To address this, we propose a novel attention network-based feature selection method that assigns weights to features based on their importance, reducing redundancy while retaining relevant information. Additionally, an adaptive Radial Basis Function Neural Network (RBFN) is employed for spam classification, enabling dynamic weight updates to reflect evolving spam behaviors. The proposed method is evaluated against state-of-the-art feature selection, deep learning models, and existing spam detection techniques using accuracy, F-measure, and false-positive rate. Experimental results demonstrate that our approach outperforms existing methods, offering superior performance in detecting spam on social networks.
Stacked hybrid model for load forecasting: integrating transformers, ANN, and fuzzy logic Elakkiya E, Antony Raj S, Arunkumar Balakrishnan, Bhavyasri Sanisetty, Revanth Balaji Bandaru Scientific Reports, 2025 Modern energy management systems must include load forecasting in order for utilities to plan and optimize electricity distribution, lower operating costs, and improve grid stability. With the addition of renewable energy sources and the advancement of smart grid technology, energy systems have become increasingly complex, making accurate forecasting increasingly challenging. Conventional techniques, including regression models and ARIMA, frequently perform less well because they are unable to capture the complex multivariate relationships and temporal dependencies present in energy data. Furthermore, these techniques are prone to errors in the presence of noisy data and have scalability issues when used on big, high-dimensional datasets. This paper presents a hybrid forecasting framework that combines artificial neural networks with Time Series Transformers and Fuzzy Logic Transform in order to overcome these drawbacks. The Transformer architecture excels in capturing long-term dependencies and interdependencies between features through its self-attention mechanism. Meanwhile, FLT + ANN effectively preprocesses noisy, irregular data and models short-term nonlinear patterns. The combination of these techniques creates a robust framework capable of handling complex energy datasets while maintaining high accuracy. Extensive tests on actual energy datasets show that the suggested hybrid model outperforms both conventional and stand-alone methods. With RMSE and MAE reductions of up to 15–20%, the model outperforms baseline models such as Random Forests, Decision Trees, and Linear Regression. These findings demonstrate how the suggested paradigm has the potential to transform load forecasting and enable more intelligent, effective energy systems.
Optimizing Deep Learning for Pneumonia Diagnosis Using Chest X-Ray Data Ramdas Kapila, Alluri Sai Sunanda, Sumalatha Saleti, E Elakkiya Sensor Data Analytics for Intelligent Healthcare Delivery, 2025 Fine-tuning of deep learning models to maximize their performance and predict pneumonia using check X-ray images is the main objective of this chapter. The exploration begins with a careful examination of each model architecture’s foundations, highlighting its advantages and disadvantages in medical imaging. This chapter then delves into the complexities of training the model, stressing the need to prepare the dataset and use augmentation methods. To improve the model’s prediction abilities, this chapter also presents a methodical strategy for hyperparameter tuning, in which important parameters like batch sizes and epochs are meticulously tuned. The procedure entails a thorough analysis of how changes in these parameters affect the training dynamics and convergence of the model. Upon hyperparameter adjustment, the results show that tuned ResNet50 attains the greatest accuracy of 96.63%. The significant influence of fine-tuning on forecasting efficiency is highlighted by the performance variance between models with and without hyperparameter tweaking.
FLAGaTST: Fuzzy Logic Transformed Adversarial GAN and Time Series Transformer for Robust MPPT Under Partial Shading Conditions E. Elakkiya, S. Antony Raj, S. Priya IEEE Access, 2025 Partial shading poses a significant challenge in photovoltaic systems by creating multiple peaks in the power-voltage curve, complicating the task of accurately tracking the Maximum Power Point. Traditional maximum power point tracking methods often struggle to identify the true Global Maximum Power Point, leading to suboptimal energy harvesting. This paper proposes a novel hybrid tracking framework that integrates fuzzy logic, synthetic data generation using Generative Adversarial Networks (GANs), and time-series modeling with Transformer architectures. Fuzzy logic improves resilience to input uncertainties by translating raw data into interpretable fuzzy values. GANs augment the dataset by generating realistic synthetic samples, thereby improving generalization. The Transformer model leverages self-attention mechanisms to capture long-term temporal patterns in solar irradiance and power profiles. By combining these strengths, the proposed method delivers a robust and accurate global maximum power point tracking solution, particularly under dynamic and partially shaded environments. Experimental results demonstrate its superior performance and scalability compared to conventional maximum power point tracking approaches.
Hybrid Models for Ehanced Intrusion Detection on NSL KDD and KDD CUP 99 Data Set Elakkiya E, Bhavana Chukka, Krishna Sai Teja Kadiyam, Poojitha Pulagam, S Antony Raj 2025 4th Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 5 0 Otcon 2025, 2025 Intrusion detection is essential for safeguarding computer networks against malicious activities. This work integrates three advanced approaches to achieve robust intrusion detection, leveraging two distinct datasets. Firstly, a Graph Neural Network (GNN) and Tabular Transformer model utilize the KDD Cup 99 dataset to classify network intrusions, achieving best-in-class accuracy by effectively modeling complex relationships within the data. Secondly, a Generative Adversarial Network (GAN)-augmented Multilayer Perceptron (MLP) employs the NSL-KDD dataset to enhance data diversity, generating realistic synthetic samples that improve classification performance. Lastly, a hybrid framework combining Variational Autoencoders (VAEs) and GANs, also leveraging the NSL-KDD dataset, addresses class imbalance and data synthesis challenges, producing high-quality synthetic data while retaining essential features. Each approach achieves its best accuracy on its respective dataset, demonstrating significant advancements in intrusion detection accuracy, reducing false alarm rates, and ensuring computational efficiency.
Artificial intelligence based on multi objective algorithm for effective load forecasting S. Antony Raj, S. V. D. Anil Kumar, E. Elakkiya, Girija Kumari Palamarthi, Sumitra Pelepu, Shaik Bashida Integrated Technologies in Electrical Electronics and Biotechnology Engineering, 2025 In recent years, researchers have directed more attention towards accurately predicting and maintaining stable loads, recgnizing their profound impact on the economy and the crucial need for effective power system management. However, the majority of past studies have focused solely on either decreasing forecast errors or improving stability, with few delving into both simultaneously. Developing a forecasting model that addresses both objectives concurrently presents a formidable task, primarily due to the intricate nature of load behavior patterns. Hence, in order to concurrently accomplish both objectives, we propose and implement an Artificial intelligence based multi objective algorithm (AIMOA). The suggested model demonstrates superior performance compared to baseline models across different real-world electricity datasets, with results indicating strong performance of our proposed approach.
Optimized CNN-Transformer Hybrid Model for Enhanced Brain Tumor Detection in Medical Imaging Deepak Das Tatwa, Elakkiya E, S. Antonyraj, Anurag Nayak, Losta Roy Bikram Sah, et al. 2025 4th Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 5 0 Otcon 2025, 2025 Detecting brain tumors manually from MRI scans is challenging, time-consuming, and often inaccurate due to similarities in tissue and tumor appearance. This highlights the need for an efficient automatic tumor detection system. We propose a deep learning-based model for brain tumor detection from 2D MRI scans. The model utilizes convolutional neural networks with transformer blocks to enhance spatial and contextual feature recognition. Trained on diverse tumor images, Compared to traditional methods like SVM, our approach showed superior performance. Implemented using TensorFlow and Keras, this method supports accurate and rapid tumor detection for clinical applications. In our study, the CNN model achieved an accuracy of 99.46%, surpassing the current state-of-the-art results. This CNN-based approach can assist doctors in accurately detecting brain tumors in MRI images, potentially significantly speeding up the treatment process.
A fully decentralized federated adversarial vision transformer with blockchain and secure aggregation for visual-based intrusion and malware forensics MM Belal, S Saleti, E E International Journal of Data Science and Analytics 22 (1), 31 , 2026 2026
Deep atrous context convolution generative adversarial network with corner key point extracted feature for nuts classification MS Devi, M Jaiganesh, S Priya, E Elakkiya Scientific Reports , 2026 2026
Dynamic RBFN with vector attention-guided feature selection for spam detection in social media E Elakkiya, S Saleti, A Balakrishnan Complex & Intelligent Systems 12 (1), 34 , 2026 2026 Citations: 1
Optimizing Deep Learning for Pneumonia Diagnosis Using Chest X-Ray Data R Kapila, AS Sunanda, S Saleti, E Elakkiya Sensor Data Analytics for Intelligent Healthcare Delivery, 176-193 , 2025 2025
FLAGaTST: Fuzzy Logic Transformed Adversarial GAN and Time Series Transformer for Robust MPPT Under Partial Shading Conditions E Elakkiya, A Raj, S Priya IEEE Access , 2025 2025
Stacked hybrid model for load forecasting: integrating transformers, ANN, and fuzzy logic Elakkiya E, Antony Raj S, Arunkumar Balakrishnan, Bhavyasri Sanisetty ... Scientific Reports 15 (1), 19688 , 2025 2025 Citations: 13
Optimized CNN-Transformer Hybrid Model for Enhanced Brain Tumor Detection in Medical Imaging DD Tatwa, E Elakkiya, S Antonyraj, A Nayak, LRB Sah, SK Sah 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing … , 2025 2025
Hybrid Models for Ehanced Intrusion Detection on NSL KDD and KDD CUP 99 Data Set E Elakkiya, B Chukka, KST Kadiyam, P Pulagam, SA Raj 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing … , 2025 2025
Artificial intelligence based on multi objective algorithm for effective load forecasting SA Raj, SVDA Kumar, E Elakkiya, GK Palamarthi, S Pelepu, S Bashida Integrated Technologies in Electrical, Electronics and Biotechnology … , 2025 2025
EEG Signal Processing for Action Recognition Using Machine Learning Paradigms SP Abirami, M Chandar, G Karthikeyan, E Elakkiya 2024 OITS International Conference on Information Technology (OCIT), 246-252 , 2024 2024
DDOS Attack Detection using DeepDDOS: A Hybrid Approach using CNN GRU and MLP model E Elakkiya, KA Chiratanagandla, J Jethy, NK Shah, VK Singh 2024 5th IEEE Global Conference for Advancement in Technology (GCAT), 1-5 , 2024 2024 Citations: 1
RBFN-Augmented DDoS Detection with CNN-GRU Fusion E Elakkiya, RB Bista, C Shah, A Rajput, AK Gupta, R Chaudhary 2024 15th International Conference on Computing Communication and Networking … , 2024 2024 Citations: 4
Deep learning approach for disaster tweet classification E Elakkiya, RB Bista, C Shah 2024 15th International Conference on Computing Communication and Networking … , 2024 2024 Citations: 1
CGFSSO: the co-operative guidance factor based salp swarm optimization algorithm for MPPT under partial shading conditions in photovoltaic systems SA Raj, E Elakkiya, S Rajmohan, GG Samuel International Journal of Information Technology, 1-16 , 2024 2024 Citations: 5
Multiple Granularity Context Representation based Deep Learning Model for Disaster Tweet Identification E Elakkiya, S Rajmohan, MKS Prasad 2024 5th International Conference on Innovative Trends in Information … , 2024 2024
MS3A: Wrapper-Based Feature Selection with Multi-swarm Salp Search Optimization R Shathanaa, SR Sreeja, E Elakkiya Advances in Data-driven Computing and Intelligent Systems: Selected Papers … , 2023 2023
Multi-cohort whale optimization with search space tightening for engineering optimization problems S Rajmohan, E Elakkiya, SR Sreeja Neural Computing and Applications 35 (12), 8967-8986 , 2023 2023 Citations: 17
Stratified hyperparameters optimization of feed-forward neural network for social network spam detection (SON2S) E. Elakkiya, S. Selvakumar E Elakkiya, S Selvakumar Soft Computing 26 (21), 11915-11934 , 2022 2022 Citations: 9
TextSpamDetector: textual content based deep learning framework for social spam detection using conjoint attention mechanism E Elakkiya, S Selvakumar, R Leela Velusamy Journal of Ambient Intelligence and Humanized Computing 12 (10), 9287-9302 , 2021 2021 Citations: 35
CIFAS: Community Inspired Firefly Algorithm with fuzzy cross-entropy for feature selection in Twitter Spam detection E Elakkiya, S Selvakumar, RL Velusamy 2020 11th International Conference on Computing, Communication and … , 2020 2020 Citations: 12
MOST CITED SCHOLAR PUBLICATIONS
Citation semantic based approaches to identify article quality S Sendhilkumar, E Elakkiya, GS Mahalakshmi Proceedings of international conference ICCSEA, 411-420 , 2013 2013 Citations: 44
TextSpamDetector: textual content based deep learning framework for social spam detection using conjoint attention mechanism E Elakkiya, S Selvakumar, R Leela Velusamy Journal of Ambient Intelligence and Humanized Computing 12 (10), 9287-9302 , 2021 2021 Citations: 35
GAMEFEST: Genetic Algorithmic Multi Evaluation measure based FEature Selection Technique for social network spam detection E Elakkiya, S Selvakumar Multimedia tools and applications 79 (11), 7193-7225 , 2020 2020 Citations: 21
Multi-cohort whale optimization with search space tightening for engineering optimization problems S Rajmohan, E Elakkiya, SR Sreeja Neural Computing and Applications 35 (12), 8967-8986 , 2023 2023 Citations: 17
Stacked hybrid model for load forecasting: integrating transformers, ANN, and fuzzy logic Elakkiya E, Antony Raj S, Arunkumar Balakrishnan, Bhavyasri Sanisetty ... Scientific Reports 15 (1), 19688 , 2025 2025 Citations: 13
CIFAS: Community Inspired Firefly Algorithm with fuzzy cross-entropy for feature selection in Twitter Spam detection E Elakkiya, S Selvakumar, RL Velusamy 2020 11th International Conference on Computing, Communication and … , 2020 2020 Citations: 12
Stratified hyperparameters optimization of feed-forward neural network for social network spam detection (SON2S) E. Elakkiya, S. Selvakumar E Elakkiya, S Selvakumar Soft Computing 26 (21), 11915-11934 , 2022 2022 Citations: 9
Modified Sequential Pattern Mining Using Direct Bit Position Method” K Subramanian, E Elakkiya International Journal of Science and Research (IJSR) 2319, 7064 , 2016 2016 Citations: 6
CGFSSO: the co-operative guidance factor based salp swarm optimization algorithm for MPPT under partial shading conditions in photovoltaic systems SA Raj, E Elakkiya, S Rajmohan, GG Samuel International Journal of Information Technology, 1-16 , 2024 2024 Citations: 5
RBFN-Augmented DDoS Detection with CNN-GRU Fusion E Elakkiya, RB Bista, C Shah, A Rajput, AK Gupta, R Chaudhary 2024 15th International Conference on Computing Communication and Networking … , 2024 2024 Citations: 4
Dynamic RBFN with vector attention-guided feature selection for spam detection in social media E Elakkiya, S Saleti, A Balakrishnan Complex & Intelligent Systems 12 (1), 34 , 2026 2026 Citations: 1
DDOS Attack Detection using DeepDDOS: A Hybrid Approach using CNN GRU and MLP model E Elakkiya, KA Chiratanagandla, J Jethy, NK Shah, VK Singh 2024 5th IEEE Global Conference for Advancement in Technology (GCAT), 1-5 , 2024 2024 Citations: 1
Deep learning approach for disaster tweet classification E Elakkiya, RB Bista, C Shah 2024 15th International Conference on Computing Communication and Networking … , 2024 2024 Citations: 1
Initial Weights Optimization Using Enhanced Step Size Firefly Algorithm for Feed Forward Neural Network Applied to Spam Detection E Elakkiya, S Selvakumar TENCON 2019-2019 IEEE Region 10 Conference (TENCON), 942-946 , 2019 2019 Citations: 1
A fully decentralized federated adversarial vision transformer with blockchain and secure aggregation for visual-based intrusion and malware forensics MM Belal, S Saleti, E E International Journal of Data Science and Analytics 22 (1), 31 , 2026 2026
Deep atrous context convolution generative adversarial network with corner key point extracted feature for nuts classification MS Devi, M Jaiganesh, S Priya, E Elakkiya Scientific Reports , 2026 2026
Optimizing Deep Learning for Pneumonia Diagnosis Using Chest X-Ray Data R Kapila, AS Sunanda, S Saleti, E Elakkiya Sensor Data Analytics for Intelligent Healthcare Delivery, 176-193 , 2025 2025
FLAGaTST: Fuzzy Logic Transformed Adversarial GAN and Time Series Transformer for Robust MPPT Under Partial Shading Conditions E Elakkiya, A Raj, S Priya IEEE Access , 2025 2025
Optimized CNN-Transformer Hybrid Model for Enhanced Brain Tumor Detection in Medical Imaging DD Tatwa, E Elakkiya, S Antonyraj, A Nayak, LRB Sah, SK Sah 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing … , 2025 2025
Hybrid Models for Ehanced Intrusion Detection on NSL KDD and KDD CUP 99 Data Set E Elakkiya, B Chukka, KST Kadiyam, P Pulagam, SA Raj 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing … , 2025 2025
