Bangla handwritten character recognition is a popular research topic as its difficulty is higher than the recognition of other languages because of multiple formats of compound characters. State of the art Convolutional neural network (CNN) architectures are very much useful in computer vision applications. Some works have been carried out in Bangla handwritten character recognition but most of them either not very efficient or they can not classify a lot of characters. In this work, state of art pre-trained CNN architectures is used to classify 231 different Bangla handwritten characters using CMATERdb dataset. The images were first converted to B&W form with white as the foreground color. The size of the images is reduced to 28 × 28 form. These images are used as input to the CNN architectures. The weights of the state-of-the-art CNN models are kept as it was. The training learning rate was set to 0.001 and categorical cross-entropy as the error function. After 50 epochs, InceptionResNetV2 achieved the best accuracy (96.99%). DenseNet121 and InceptionNetV3 also provided remarkable recognition accuracy (96.55 and 96.20%, respectively). We also considered combination of trained InceptionResNetV2, InceptionNetV3 and DenseNet121 architectures which provided better recognition accuracy (97.69%) than other single CNN architectures but it is not feasible for using as it requires a lot of computation power and memory. The models were tested in the cases where characters look confusing to humans, but all the architectures showed equal capability in recognizing these images. Considering computational complexity, memory and capability of recognizing confused characters, InceptionResNetV2 can be said as the best performing model.

Autism Spectrum Disorder (ASD) is a chronic condition characterised by impairments in social interaction and communication. Early detection of ASD is desired, and there exists a demand for the development of diagnostic aids to facilitate this. A lightweight Involutional Neural Network (INN) architecture has been developed to diagnose ASD. The model follows a simpler architectural design and has less number of parameters than the state‐of‐the‐art (SOTA) image classification models, requiring lower computational resources. The proposed model is trained to detect ASD from eye‐tracking scanpath (SP), heatmap (HM), and fixation map (FM) images. Monte Carlo Dropout has been applied to the model to perform an uncertainty analysis and ensure the effectiveness of the output provided by the proposed INN model. The model has been trained and evaluated using two publicly accessible datasets. From the experiment, it is seen that the model has achieved 98.12% accuracy, 96.83% accuracy, and 97.61% accuracy on SP, FM, and HM, respectively, which outperforms the current SOTA image classification models and other existing works conducted on this topic.

The escalating incidence of depression has brought attention to the increasing concern regarding the mental well-being of university students in the current academic environment. Given the increasing mental health challenges faced by students, there is a critical need for efficient, scalable, and accurate screening methods. This study aims to address the issue by using the General Health Questionnaire-12 (GHQ-12), a well recognized tool for evaluating psychological discomfort, in combination with machine learning (ML) techniques. Firstly, for effective screening of depression, a comprehensive questionnaire has been created with the help of an expert psychiatrist. The questionnaire includes the GHQ-12, socio-demographic, and job and career-related inquiries. A total of 804 responses has been collected from various public and private universities across Bangladesh. The data has been then analyzed and preprocessed. It has been found that around 60% of the study population are suffering from depression. Lastly, 16 different ML models, including both traditional algorithms and ensemble methods has been applied to examine the data to identify trends and predictors of depression in this demographic. The models' performance has been rigorously evaluated in order to ascertain their effectiveness in precisely identifying individuals who are at risk. Among the ML models, Extremely Randomized Tree (ET) has achieved the highest accuracy of 90.26%, showcasing its classification effectiveness. A thorough investigation of the performance of the models compared, therefore clarifying their possible relevance in the early detection of depression among university students, has been presented in this paper. The findings shed light on the complex interplay among socio-demographic variables, stressors associated with one's profession, and mental well-being, which offer an original viewpoint on utilizing ML in psychological research.

In recent years, the detection of depression among university students has become an increasingly critical issue. This paper presents a depression detection network (DDNet), a novel approach utilizing a three-stage stacked ensemble model to address this challenge. The proposed model incorporates two different Multilayer Perceptron (MLP) and a Stochastic Gradient Descent (SGD) as the 1st stage base classifier, an MLP, and CatBoost (CB) as 2nd stage base classifiers, with a Lasso Regressor (LASSO) serving as the meta-classifier. The hyperparameter of the used models has been optimized using random search. The optimal configuration has been determined through extensive experimentation with various machine learning (ML) models and settings to ensure high performance. Two different datasets (Dataset-1, Dataset-2) of depression detection among university students have been used to evaluate the model. The model has achieved 98.98% and 99.16% accuracy in Dataset-1 and Dataset-2, respectively. Paired t-test has been performed to ensure the statistical significance of the proposed model. To guarantee the model’s transparency, SHapley Additive exPlanations (SHAP) were employed, providing interpretability of the predictive factors. Additionally, a variantion of Monte Carlo Dropout (MCD) has been used to assess the uncertainty of the model’s predictions, ensuring reliability. The results indicate that the proposed model is a promising tool for mental health professionals seeking effective, interpretable, and reliable solutions for early depression detection in educational settings.

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition that challenges early diagnosis due to its diverse manifestations across different developmental stages. Timely and accurate detection is essential to enable interventions that significantly enhance developmental outcomes. This study introduces a robust and interpretable machine learning framework to diagnose ASD using questionnaire data. The proposed framework leverages a stacked ensemble model, combining Random Forest (RF), Extra Tree (ET), and CatBoost (CB) as base classifiers, with an Artificial Neural Network (ANN) serving as the meta-classifier. The methodology addresses class imbalance using Safe-Level SMOTE, dimensionality reduction via Principal Component Analysis (PCA), and feature selection using Mutual Information and Pearson correlation. Evaluation on publicly available datasets representing toddlers, children, adolescents, adults, and a merged dataset (Combining children, adolescents, and adults dataset) demonstrates high diagnostic accuracy, achieving 99.86%, 99.68%, 98.17%, 99.89%, and 96.96%, respectively. Comparative analysis with standard machine learning models underscores the superior performance of the proposed framework. SHapley Additive exPlanations (SHAP) were used to interpret feature importance, while Monte Carlo Dropout (MCD) quantified uncertainty in predictions. This framework provides a scalable, interpretable, and reliable solution for ASD screening across diverse populations and developmental stages.