• Assistant Professor in Dept. of Computer Applications, Dr. B. C. Roy Academy of Professional Courses (Formerly Dr. B. C. Roy Engineering College)
Today the importance of going green have been realized both in terms of environmental issues and cost minimization by implementing different strategies and policies by the ICT industry. Going green suggests the environmentally responsible practice of computers and related resources of ICT. For a sustainable environment, today Green Computing is an emerging topic, because of efficient power usage, minimal or no emission of carbon footprint, also proper disposal of electronic waste (e-waste), and many more, thus to take less participation in the global warming phenomenon. In this study, emphasis has given on diminishing the energy and carbon footprint of computer and its related resources like- monitors, printers using green computing.<br>
Abstract—Selection of the proper higher educational courses is absolutely necessary for the prospective students. Selecting appropriate courses are really cumbersome job for the students who are having less information about present trend of education relating to get placements or jobs and for better development in the future. In this paper, trend analysis and forecasting has proposed to predict the prospects of the selected higher educational courses in the field of computer science/technology. An online survey has done to get the dataset for analysis and there were altogether 151 data selected for the study. A Feed-Forward Artificial Neural Network model has proposed and the best network architecture has been selected among the top five NN considering the parameters like fitness value, AIC (Akaike’s Information Criterion) value, training, validation, test error values. The best network architecture is further analyzed using Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) algorithms for finding the accuracy of the trend. The study focuses on important input parameters during training of network architecture. Correct Classification Rate (CCR) for training and validation has been prepared to find the best network after a number of iterations. A comparative study<br> between the LM and CGD algorithm has primed with a focus on confusion matrix. This study recommends and predicts the future trends of the selected higher educational computer science/technology courses by using ANN.<br>
Depression among undergraduates is a growing global concern, with prevalence rates rising from 3.82% to 4.36% globally and 3.76% to 4.51% in India (2017 -2021) for the 15-24 age group, exacerbated by the COVID-19 pandemic. Early detection is crucial, but self-reported questionnaires often lack objectivity. This study explores the efficacy of ensemble machine learning models for predicting early depression among undergraduates, leveraging a dataset of 12,617 Indian students compiled from a Kaggle dataset and a 2025 survey, augmented to balance depressed (44%) and not depressed (56%) classes. Three heterogeneous ensemble models—Model Averaged Neural Network (MANN), LogitBoost, and Weighted Subspace Random Forest (WSRF)—were applied to a feature set, after assessing multicollinearity using Tolerance and Variance Inflation Factor, and addressing feature redundancy with Pearson Correlation Coefficient (PCC). AUC-ROC, Accuracy, Precision, Recall, F1 Score, and Cohen's Kappa were employed to evaluate the model's performance, with 10-fold cross-validation repeated thrice for robustness. WSRF excelled, achieving 0.9829 accuracy, 0.9910 precision, 0.9782 recall, and AUC values of 99.90% (training), 92.10% (testing), and 91.50% (validation), compared to MANN (AUC 93.20% testing) and LogitBoost (AUC 87.40% validation). WSRF’s superior performance supports its use in identifying at-risk students, enabling timely mental health interventions. Future research should focus on cross-demographic generalizability, advanced methods like deep learning, and real-time monitoring to enhance global mental health strategies.
Climate change is exacerbating the risks of floods a more significant threat to global stability and ecosystems, particularly in vulnerable regions like the Teesta River Basin in India with an estimated flood risk rate of 85%. The study generates comprehensive flood susceptibility maps by combining other important geospatial elements with digital elevation model (DEM) derived from Cartosat-1 satellite data. This study employed a novel approach implementing strategic data augmentation and Boruta analysis to optimize the selection of flood-conditioning factors, with advanced machine learning techniques- K-nearest neighbors (KNN), Naïve Bayes (NB), and decision trees (DT), ensuring the robustness of the predictive models for the study area. To determine flood-prone area prediction, these models were assessed using accuracy, precision, recall, F1-score, and receiver operating characteristics area under the curve (ROC-AUC). The findings show that the KNN model outperforms the NB and DT models in terms of accuracy of 91.62% and demonstrating robustness with ROC-AUC of 97.80%, 94.20%, 95.00% in training, testing and validation respectively, effectively delineating areas with varying levels of flood risk. This research provides flood risk management insights and a methodological framework for flood preparedness and resilience in flood-prone regions.
