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Prediction of software bugs from process logs, temporal access logs, behavior analysis, etc. requires estimation of a wide variety of high-density feature sets. Extracted feature sets must be able to classify these logs into different bug categories with high accuracy, and low complexity. To perform these tasks, a wide variety of Machine Learning Models (MLMs) are proposed by researchers, and each of them varies in terms of their performancelevel nuances, functional advantages, contextual limitations, and application-specific future scopes. Upon analyzing these characteristics, it was observed that existing models are highly context-specific, and cannot be applied to multidomain bug analysis datasets. Moreover, existing models do not incorporate a dynamic feature selection method, which limits their accuracy performance under multiple bug classification applications. To overcome these issues, this paper proposes design of a novel Ensemble deep learning Classifier with Feature Selection for highefficiency Multidomain Bug Prediction under different use cases. The proposed model improves bug representation performance by combining multiple feature extraction methods, including GWO-based novel feature selection techniques. The ensemble classification model, which combines Deep Random Forest, k Nearest Neighbor, Logistic Regression, Multilayer Perceptron, Support Vector Machine, and 1D Convolutional Neural Network classifiers, achieves higher accuracy, precision, recall, and low delay compared to existing models. The model also shows faster classification speeds than existing models and can be deployed for various real-time applications. This accuracy performance was compared with various state-of-the-art models, and it was observed that the proposed model showcases 4.5% higher accuracy, 3.2% better precision, 3.9% higher recall, and 5.5% faster classification performance, which was possible due to integration of intelligent feature selection process with high efficiency classification models under multidomain scenarios. [ABSTRACT FROM AUTHOR]

Accurate analysis of a person’s mood can assist medical psychiatry personnel to assist and improve personal well-being. In order to determine a person’s mood a wide variety of inputs can be used, these inputs include but are not limited to, the person’s social media patterns, facial patterns, ecommerce buying patterns, etc. Analysis of these patterns individually can assist in development of a moderate accuracy psycho-analysis system. Thus, to improve the accuracy and efficiency of mood analysis, this text proposes a deep learning model based on transfer learning, that combines training information from social media activity, and visual actions in order to analyze the person’s state-of-mind. The proposed model is compared with individual algorithms that detect person’s mood, and an accuracy improvement of 12% was achieved. The model is also compared with similar hybrid algorithms that combine multiple psychiatric ques, and it is observed that the proposed model is 8% more effective than these systems due to incorporation of cross validation and transfer learning. [ABSTRACT FROM AUTHOR]