Brain MR images are the most suitable method for detecting chronic nerve diseases such as brain tumors, strokes, dementia, and multiple sclerosis. They are also used as the most sensitive method in evaluating diseases of the pituitary gland, brain vessels, eye, and inner ear organs. Many medical image analysis methods based on deep learning techniques have been proposed for health monitoring and diagnosis from brain MRI images. CNNs (Convolutional Neural Networks) are a sub-branch of deep learning and are often used to analyze visual information. Common uses include image and video recognition, suggestive systems, image classification, medical image analysis, and natural language processing. In this study, a new modular deep learning model was created to retain the existing advantages of known transfer learning methods (DenseNet, VGG16, and basic CNN architectures) in the classification process of MR images and eliminate their disadvantages. Open-source brain tumor images taken from the Kaggle database were used. For the training of the model, two types of splitting were utilized. First, 80% of the MRI image dataset was used in the training phase and 20% in the testing phase. Secondly, 10-fold cross-validation was used. When the proposed deep learning model and other known transfer learning methods were tested on the same MRI dataset, an improvement in classification performance was obtained, but an increase in processing time was observed.

Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores.

The integration of generative artificial intelligence (AI) has the potential to revolutionize cross-cultural knowledge exchange in higher education. By leveraging its ability to create culturally sensitive and contextually relevant learning materials, generative AI can enhance personalized education for students from diverse backgrounds. Overcoming language barriers through real-time translation, this technology promotes inclusive collaboration and co-creation of knowledge. However, ethical considerations such as cultural authenticity, bias mitigation, and data privacy must be carefully navigated to ensure equitable and respectful cross-cultural interactions. In essence, generative AI offers a transformative avenue for fostering a culturally enriched learning environment in higher education, necessitating a balanced approach that maximizes benefits while upholding ethical principles.

Mission-critical IT systems are utilizing closed source software (CSS) mainly due to reasons related to “quality assurance” and “continued support” despite much better benefits of using Open Source Software (OSS). OSS permits users to access source code for assessment, amendment, and redistribution, which offers low dependency on a vendor without license or maintenance cost. This paper investigates and analyzes OSS adoption factors for “critical IT infrastructure” by conducting a comprehensive review of the relevant literature. Furthermore, this paper proposes a framework that can help the critical IT industry to have increased confidence in OSS. The proposed framework utilizes the organizing logic of the Technology, Organization, and Environment (TOE) framework, recommends factors that were recognized by critically scrutinizing the studies found in the available literature. To validate the framework, a questionnaire-based survey was conducted targeting IT experts in critical sectors. The data integrity of survey results was analyzed using Cronbach's alpha. Framework factors were validated statistically using one sample T-test. The outcome indicated that the factors were statistically significant as the p-value was less than 0.05 for all the factors.

Pandemic showed that everything goes toward an online system; everyone wants comfort and to save time. so, the core idea behind this research is to deliver services (packages) through Unmanned Aerial vehicles (UAVs) for people's comfort or to save their time and lives. In emergencies and road accidents, ambulances may not be able to reach victims because of traffic jams and other factors. Drone delivery can be lifesaving in these situations. In addition, these UAVs can assist people in Coronavirus Disease (COVID-19) with social distancing so that they need no human interaction to receive assistance. A drone could accelerate the delivery time, improve accuracy, reduce human costs, and save the lives of people. The Medi fly Drone (delivery services through UAV) consists of hardware and software. An android application was developed to take orders from users, which uses Quick Response code (QR-Code) for payment. The server is configured to communicate with the android application and UAV, which makes delivery efficient and secure. On the hardware side, a quadcopter had been developed using Pixhawk flight controller with onboard Arduino Mega 2560 microcontroller along with multiple Ultrasonic sensors for the UAV to avoid crashing into obstacles. The drone was designed to take all instructions from Ground Control Station (GCS); the communication between the quadcopter and the GCS is established through MAVLink protocol (Communication Protocol for drones)