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All Journal Emerging Science Journal
Joseph Mani
Department of Mathematics and Computer Science, Modern College of Business and Science, Bowshar, Muscat
Environmental Science

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Leveraging Hybrid Deep Q-Learning for Early Identification of At-Risk Students P. Vijaya; Joseph Mani; Basant Kumar; Hothefa Shaker; Rajeev Rajendran
Emerging Science Journal Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-SIED1-06

Abstract

Student performance prediction is employed to predict the learning performance to identify at-risk students. However, prediction models should also consider external factors along with learning activities, such as course duration. The student’s performance gets affected, which leads to a high decreasing rate and meets the risk of failing to complete the course on time. To overcome these challenges, this work proposed a Sea Lion Search Optimization (SLnSO) based on the Deep Q network (DQN) for predicting at-risk students. Here, the input data is taken from the dataset and forwarded to the data transformation phase, which is performed by Yeo-Johnson (YJ) transformation. Then, in the feature selection stage, the most relevant features are selected using the Damerau-Levenshtein technique. Then, Data Augmentation (DA) is performed to increase the dimension of the features, which is followed by the Deep Q Network (DQN) that is utilized for predicting the students at risk. Finally, by implementing the proposed SLnSO, the predicted results will be executed by DQN. The SLnSO-DQN is the combination of both Sea Lion Optimization (SLnO) and Squirrel Search Algorithm (SSA). The outcomes of the proposed model SLnSO-DQN attain significant performance that is based on various parameters, such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root MSE (RMSE), and also obtained better values of 0.327, 0.265, and 0.514, respectively.
Fractional White Smell Agent Optimization for CNN-Based Transfer Learning in Melanoma Classification Vijaya P; Basant Kumar; Joseph Mani; Satish Chander; Roshan Fernandes; Mohamed Sirajudeen Yoosuf
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-02

Abstract

Melanoma is the deadliest form of skin cancer, and early diagnosis and treatment can significantly reduce mortality rates. However, existing strategies for classifying melanoma from dermoscopic skin images still face significant challenges. Therefore, this study aims to develop an accurate method for melanoma classification using dermoscopic skin images. A novel melanoma classification framework, termed Fractional White Smell Agent Optimization-enabled Convolutional Neural Network-based Transfer Learning (FWSAO_CNN-based TL), is proposed. First, the input skin image is preprocessed using an Adaptive Kalman Filter. Subsequently, skin lesion segmentation is performed using LinkNet, where the network is trained using White Smell Agent Optimization (WSAO). Following segmentation, image augmentation is applied, and feature extraction is conducted. Finally, melanoma classification is performed using a CNN-based transfer learning model trained with the proposed Fractional White Smell Agent Optimization (FWSAO), which integrates the Fractional concept, Smell Agent Optimization (SAO), and White Shark Optimizer (WSO). The CNN utilizes hyperparameters derived from a pretrained GoogLeNet model. The performance of the proposed FWSAO_CNN-based TL framework was evaluated using accuracy, True Positive Rate (TPR), and True Negative Rate (TNR). The proposed method achieved values of 91.565%, 90.090%, and 91.269%, respectively. Furthermore, the proposed model demonstrated performance improvements of 18.4%, 8.1%, 17.5%, 12.55%, 8.2%, and 6.23% compared with conventional approaches.
Co-Authors Basant Kumar Hothefa Shaker Mohamed Sirajudeen Yoosuf P. Vijaya Rajeev Rajendran Roshan Fernandes Satish Chander Vijaya P