Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of the brain anatomy and functions. The mechanism of image processing has a widespread usage in the area of medical science for improving the early detection and treatment phases. Deep Neural Network (DNN), till date have demonstrated a wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper a technique for image compression using a Deep Wavelet Autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers like Autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods.

Machine learning reveals order in crumpled sheets using simulated flat-folding patterns as data surrogate in a data-limited regime.

Gene Regulatory Network (GRN) has always gained considerable attention from bioinformaticians and system biologists in understanding the biological process. But the foremost difficulty relics to appropriately select a stuff for its expression. An elementary requirement stage in the framework is mining relevant and informative genes to achieve distinguishable biological facts. In an endeavor to discover these genes in several datasets, we have suggested a strategic gene selection algorithm called Support Vector Machine Bayesian T-Test Recursive Feature Elimination algorithm (SVM-BT-RFE), which is an extended variation of support vector machine recursive feature elimination (SVM-RFE) algorithm and support vector machine t-test recursive feature elimination (SVM-T-RFE). Our algorithm accomplishes the goal of attaining maximum classification accuracy with smaller subsets of gene sets of high dimensional data. Each dataset is said to contain approximately 5000–40,000 genes out of which a subset of genes can be selected that delivers the highest level of classification accuracy. The proposed SVM-BT-RFE algorithm was also compared to the existing SVM-T-RFE and SVM-RFE where it was found that the proposed algorithm outshined than the latter. The proposed SVM-BT-RFE technique have provided an improvement of approximately 25% as compared to the existing SVM-T-RFE and more than 40% of improvement as compared to the existing SVM-RFE. The comparison was performed with regard to the classification accuracy based on the number of genes selected and classification error rate of 5 runs of the algorithm

This paper presents an analysis and subsequent discussion of the self focusing of a dark hollow Gaussian electromagnetic beam (HGB) in a magnetoplasma, considering ponderomotive and collisional nonlinearities. A paraxial-like approach, in which the relevant parameters are expanded in terms of radial distance from the maximum of the irradiance rather than that from the axis, has been adopted to analyze the propagation of the HGB. The nature of self focusing is highlighted through the critical curves as a plot of dimensionless radius versus power of the beam. The effect of the magnetic field and the nature of the nonlinearity on self focusing of various order HGBs has also been explored.

Poverty is a glaring issue in the twenty-first century, even after concerted efforts of organizations to eliminate the same. Predicting poverty using machine learning can offer practical models for facilitating the process of elimination of poverty. This paper uses Multidimensional Poverty Index Data from the Oxford Poverty and Human Development Initiative across the years 2019 and 2021 to make predictions of multidimensional poverty before and during the pandemic. Several poverty indicators under health, education and living standards are taken into consideration. The work implements several data analysis techniques like feature correlation and selection, and graphical visualizations to answer research questions about poverty. Various machine learning, such as Multiple Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost, AdaBoost, Gradient Boosting, Linear Support Vector Regressor (SVR), Ridge Regression, Lasso Regression, ElasticNet Regression, and K-Nearest Neighbor Regression algorithm, have been implemented to predict poverty across four datasets on a national and a subnational level. Regularization is used to increase the performance of the models, and cross-validation is used for estimation. Through a rigorous analysis and comparison of different models, this work identifies important poverty determinants and concludes that overall, Ridge Regression model performs the best with the highest R² score.

With the increasing reliance on technology in traffic management systems, ensuring road safety and protecting the integrity of these systems against cyber threats have become critical concerns. This research paper investigates the potential of reinforcement learning techniques in enhancing both road safety and cyber security of traffic management systems. The paper explores the theoretical foundations of reinforcement learning, discusses its applications in traffic management, and presents case studies and empirical evidence demonstrating its effectiveness in improving road safety and mitigating cyber security risks. The findings indicate that reinforcement learning can contribute to the development of intelligent and secure traffic management systems, thus minimizing accidents and protecting against cyber-attacks.