Several speaker recognition algorithms failed to get the best results because of the wildly varying datasets and feature sets for classification. Gender information helps reduce this effort since categorizing the classes based on gender may help lessen the impact of gender variability on the retrieved features. This study attempted to construct a perfect classification model for language‐independent gender identification utilizing the Common Voice dataset (Mozilla). Most previous studies are doing manual extracting characteristics and feeding them into a machine learning model for categorization. Deep neural networks (DNN) were the most effective strategy in our research. Nonetheless, the main goal was to take advantage of the wealth of information included in voice data without requiring significant manual intervention. We trained the deep learning network to choose essential information from speech spectrograms for the classification layer, performing gender detection. The pretrained ResNet 50 fine‐tuned gender data successfully achieved an accuracy of 98.57% better than the traditional ML approaches and the previous works reported with the same dataset. Furthermore, the model performs well on additional datasets, demonstrating the approach’s generalization capacity.

The 2019 coronavirus pandemic (COVID-19) struck without warning, and existing medical screening and clinical management systems were unprepared, causing a high fatality rate. Given the virus’s ongoing evolution, there is still a potential for reemergence; earlier weak preparedness will not be accepted in such a situation. Therefore, it is vital to understand and rectify past diagnostic work’s flaws. RT-PCR and antigen tests, both widely used, have experienced problems in the past. They either were too sluggish or produced an excessive number of false negatives. Another issue was a lack of test kits. As a result, chest X-ray image-based disease classification has emerged. However, managing a variety of chest X-ray pictures for COVID-19 and pneumonia patients is complicated and error-prone. As a result, the only way to improve the current diagnosis is to apply deep learning algorithms that learn from radiography pictures and anticipate COVID-19 development. We constructed our own convolutional neural network (CNN) by incorporating transfer learning from the most popular ResNet, VGG, and InceptionNet models. The endeavor necessitated the creation of a sizable dataset that accurately depicted the patient population. Before importing the model, the images were enhanced to remove artifacts caused by noise, motion, or blurring that could impair the detection of infection. Preprocessing has a substantial impact on the model’s accuracy. The results indicated that the VGG16 architecture, with a detection accuracy of 95.29%, is optimal for COVID-19 identification from X-ray images. Furthermore, most generated models outperformed current state-of-the-art research in the same field.

To enhance my research, I would like to share my knowledge of Herpes simplex virus-2 (HSV-2) and HIV/AIDS (Acquired Immunodeficiency Syndrome) through a mathematical model. The study’s objective is to develop and examine the co-disease model for the modern era using mathematical modeling. Based on their modes of transmission, HSV-2 and HIV/AIDS are currently the leading causes of death from infectious and severe chronic diseases. Depending on the severity of HIV-2’s chronic disease, the model is divided into five phases: the first two HIV-2 stages and the remaining three HIV stages. Ordinary differential equations (ODEs) are arranged differently by each individual. Investigations into the mathematical equation model have revealed the points of equilibrium between the free and endemic models. A study of the developed model was conducted using the basic reproduction numbers [R0] of HSV-2 and HIV. The results demonstrate that if R01, equilibrium endemic states are regarded as existing. Finally, MATLAB software was used to simulate the numerical equations of the model.