The proliferation of digital communication and social media platforms has led to an upsurge in spam messages across various languages, including Bangla. These intrusive messages not only cause user annoyance but also elevate the risk of important messages going unnoticed. To address this concern, we harnessed the power of natural language processing (NLP) techniques in conjunction with machine-learning models. This study delves into the efficacy of diverse machine-learning and deep-learning models in detecting spam messages in the Bangla language. Particularly, deep-learning methodologies such as ELMo and BERT have exhibited promising results in spam detection across other languages. However, the realm of Bangla spam detection remains underexplored, with a dearth of deep-learning approaches. To bridge this gap, we constructed a unique Bangla SMS dataset, given the scarcity of available Bangla Spam SMS datasets, and proceeded to evaluate the performance of ELMo, BERT, and conventional machine learning models. The findings from our experiments underscore the superior performance of deep learning-based models over traditional methods, with Elmo surpassing BERT in Bangla spam detection, achieving an impressive accuracy of approximately 94%. This endeavor bears the potential to not only enhance SMS utilization efficiency but also bolster cybersecurity measures against phishing attacks.

In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posing prediction leaves a lot of challenges to face. This paper proposed to use different data mining techniques and classification algorithm like KNN, decision tree, support vector machine, naïve bayes classifier, random forest classifier, multilayer perceptron and deep neural network to predict a job post if it is real or fraudulent. We have experimented on Employment Scam Aegean Dataset (EMSCAD) containing 18000 samples. Deep neural network as a classifier, performs great for this classification task. We have used three dense layers for this deep neural network classifier. The trained classifier shows approximately 98% classification accuracy (DNN) to predict a fraudulent job post.

Background: Thalassemia has been recognized as a critical public health issue in Bangladesh, especially among pregnant women, due to its hereditary nature and the lack of early screening infrastructure. Early identification of at-risk individuals is essential to prevent the transmission of this genetic disorder to future generations and to reduce the burden on an already strained healthcare system. Methods: In this study, an innovative framework for thalassemia risk assessment has been developed by integrating Multi-Criteria Decision-Making (MCDM) methods—specifically AHP-TOPSIS—with machine learning algorithms including Random Forest, XGBoost, and CatBoost. Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME have also been incorporated to improve model transparency and trustworthiness. Real-world clinical and demographic data, consisting of 16 features and 1200 samples, have been collected through a structured survey and processed using rigorous feature selection and ranking methods. Risk stratification has been performed to classify patients into high, medium, and low categories, enabling targeted intervention. Results: Among all models, the XGBoost classifier trained on AHP–TOPSIS–prioritized features achieved a consistent accuracy of 99.28% under stratified 20-fold cross-validation, demonstrating robust diagnostic classification performance. The model predominantly captures hematologic patterns characteristic of thalassemia manifestations, functioning as an assistive diagnostic framework rather than a causal risk predictor. The explainability of predictions, ensured through comprehensive visual and statistical analyses, further enhances the model’s clinical transparency and reliability. Conclusions: The proposed MCDM–machine learning framework demonstrates strong potential for improving thalassemia risk assessment, enabling early detection and informed decision-making in maternal healthcare. The proposed framework should be regarded as a preliminary proof-of-concept system that demonstrates the feasibility of integrating Multi-Criteria Decision-Making (AHP–TOPSIS) with advanced machine learning and explainable-AI techniques for thalassemia assessment. Although the model achieved strong diagnostic performance under nested cross-validation, additional external validation and inclusion of causal predictors are required before clinical deployment.