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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Selvaraj, Srividhya
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Computer Science & IT Electrical & Electronics Engineering

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Optimizing feature extraction for tampering image detection using deep learning approaches Muniappan, Ramaraj; Sabareeswaran, Dhendapani; Jothish, Chembath; Raja, Joe Arun; Selvaraj, Srividhya; Nainan, Thangarasu; Ilango, Bhaarathi; Sumbramanian, Dhinakaran
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1853-1864

Abstract

Tamper image detection approach using deep learning involves, creating a model that can accurately identify and localize instances of image tampering, by employing advanced feature extraction methods, object detection algorithms, and optimization techniques that could be manipulated on need basis. Enhance the integrity of visual content by automating the detection of unauthorized alterations, to ensure the reliability of digital images across various applications and domains. The problem addressing the optimization feature extraction techniques involves the detection of subtle manipulations, handling diverse tampering techniques, and achieving robust performance across different types of images and scenarios. The proliferation of sophisticated image editing tools makes it challenging to detect tampered regions within images, necessitating proposed techniques for automated tamper image detection. The research work will focus on four different feature extraction algorithms such as non-negative factorization (NNF), singular value decomposition (SVD), explicit semantic analysis (ESA), principal component analysis (PCA), which are outsourced. Detecting tampered images through deep learning necessitates the meaningful selection and adjustment of several parameters to enhance the model's effectiveness. Integrating the feature extraction algorithm with the suggested methods effectively identifies critical features within the dataset, thereby improving the detection capabilities and achieving higher accuracy.
Optimization techniques applied on image segmentation process by prediction of data using data mining techniques Muniappan, Ramaraj; Selvaraj, Srividhya; Vanathi Gurusamy, Rani; Thiyagarajan, Velumani; Sabareeswaran, Dhendapani; Prasanth, David; Krithika, Varadharaj; Ilango, Bhaarathi; Subramanian, Dhinakaran
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2161-2171

Abstract

The research work presents an enhanced method that combines rule-based color image segmentation with fuzzy density-based spatial clustering of applications with noise (FDBSCAN). This technique enhances super-pixel robustness and improves overall image quality, offering a more effective solution for image segmentation. The study is specifically applied to the challenging and novel task of predicting the age of tigers from camera trap images, a critical issue in the emerging field of wildlife research. The task is fraught with challenges, particularly due to variations in image scale and thickness. Proposed methods demonstrate that significant improvements over existing techniques through the broader set of parameters of min and max to achieve superior segmentation results. The proposed approach optimizes segmentation by integrating fuzzy clustering with rule-based techniques, leading to improved accuracy and efficiency in processing color images. This innovation could greatly benefit further research and applications in real-world scenarios. Additionally, the scale and thickness variations of the present barracuda panorama knowledge base offer many advantages over other enhancement strategies that have been proposed for the use of these techniques. The experiments show that the proposed algorithm can utilize a wider range of parameters to achieve better segmentation results.
An optimized deep learning framework based on LEE for real time student performance prediction in educational data Muniappan, Ramaraj; Devi Devarajan, Sowmya; Subbarayalu Ramamurthy, Lavanya; Balakumar, Ayshwarya; Gunaseelan, Prathap; Palanisamy, Shyamala; Selvaraj, Srividhya; Sabareeswaran, Dhendapani; Bhaarathi, Ilango
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9773

Abstract

Predicting student performance in real-time remains a critical challenge in educational data mining (EDM), especially with large, noisy, and high-dimensional datasets. This study proposes an advanced deep learning framework that integrates learning entropy estimation (LEE) with models such as support vector machines (SVM), you only look once (YOLO), recurrent convolutional neural networks (RCNN), and artificial neural networks (ANN) to enhance feature selection and classification accuracy. The framework follows a systematic pipeline involving data preprocessing, LEE-based feature extraction, and model training on a real-time academic dataset comprising student demographics, attendance, and performance metrics. Among the proposed models, the LEE-based YOLO (LBYOLO) achieved the highest testing accuracy of 93% and the fastest execution time of 1.84 seconds, while the LEE-based ANN (LBANN) demonstrated consistent performance across precision, recall, and F1-score. The results confirm the superiority of deep learning methods over traditional machine learning techniques for educational prediction tasks. This approach enables early detection of at-risk students and supports timely, data-driven educational interventions. Future work will focus on adaptive learning systems and multi-platform student behavior analysis to support personalized education strategies.
Co-Authors Balakumar, Ayshwarya Bhaarathi, Ilango Devi Devarajan, Sowmya Gunaseelan, Prathap Ilango, Bhaarathi Jothish, Chembath Krithika, Varadharaj Muniappan, Ramaraj Nainan, Thangarasu Palanisamy, Shyamala Prasanth, David Raja, Joe Arun Sabareeswaran, Dhendapani Subbarayalu Ramamurthy, Lavanya Subramanian, Dhinakaran Sumbramanian, Dhinakaran Thiyagarajan, Velumani Vanathi Gurusamy, Rani