The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation’s economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2’s pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.

Students’ emotional state and attention significantly impact how they handle stress and interact with their studies. These factors are crucial in defining their learning objectives and general personal growth, influencing their academic achievement. Because Bangladesh has distinct educational and mental health difficulties, it is important to comprehend these dynamics. Time series analysis is a useful technique for tracking lecture activities in class and how they affect student participation since it provides insightful information about behavior patterns across time. This investigation aims to address these problems by using MotionWatch 8 and a comprehensive questionnaire to analyze class lecture activities. This study employs ensemble methodologies, deep Learning algorithms, and a diverse range of machine learning models to assess and predict student behavior. A hybrid model is one of the techniques that produced the most stunning results out of all of them, proving how well it could capture complex patterns in time series data. To assess the robustness of the algorithms, the study also looked at how different datasets performed. Ultimately, the models’ interpretability was improved, and their decision-making processes were given a deeper understanding through the utilization of explainable AI methods, including SHAP, LIME, and permutation importance. The effort establishes new standards for improving student engagement and well-being through data-driven insights using sophisticated models and explainable AI.

Depression is a common mental health disorder. It significantly affects millions of people's daily lives all around the world. Despite the high incidence of depression and limited treatment options, many people are hesitant to seek professional help. Researchers are now exploring the use of wearable technology to collect personalized physiological data, which has recently been applied to provide healthcare services. Depression can be recognized with the help of machine learning using motor activity data collected by the ActiGraph wristband. Large data samples and higher effectiveness are necessary for developing a reliable and robust machine-learning model. However such quantitative and relevant clinical data are not openly available. This study used the depresjon (depression in Norwegian) dataset consisting of time-series data on the activity of only 23 depressed and 32 healthy individuals. However, a data augmentation technique was used that increased the number of samples from 693 to 5500. Among these algorithms, XGBoost obtained an impressive accuracy of 98.8% in 10-fold cross-validation, which met the requirements to accurately detect depression. In addition, this investigation highlighted different hidden patterns included in the time series of the two classes. Furthermore, this study utilized Explainable AI approaches, such as SHAP. These approaches further improve model transparency and interpretability. The explanation highlighted the significance of the statistical feature Median activity among features like Mean, Zero crossing rate, Standard deviation, etc. that significantly influence the model's decision-making process.

For many purposes, such as document digitalization and linguistic study, handwritten character recognition is essential. This experiment offers a novel method for reading handwritten characters in Bangla using the modern EfficientNetV2B2 model. The complex structure of the Bangla alphabet, with its range of writing styles and variants, poses a significant difficulty. The innovative method makes use of the modern EfficientNetV2B2 model’s efficiency and superior performance, which have been adjusted on the dataset to produce notable increases in recognition accuracy, particularly for complex character types and styles. Analyzed the model’s interpretability and provided insights into the recognition procedure. Also discussed the wider societal effects of using technology to advance literacy and safeguard cultural heritage. On the two datasets, the experiment utilized had pretty excellent accuracy. Using the first dataset, EfficientNetV2B2 model was able to reach 100% training accuracy, 99.33% validation accuracy, and 98.67% test accuracy. On the second dataset, the model got 95.65% training accuracy, 97.75% validation accuracy, and 94.95% test accuracy.