M. Saifur Rahman

Riad Ahmed Anonto, Sardar Md. Saffat Zabin, M. Saifur Rahman

Grounding vision--language models in low-resource languages remains challenging, as they often produce fluent text about the wrong objects. This stems from scarce paired data, translation pivots that break alignment, and English-centric pretraining that ignores target-language semantics. We address this with a compute-aware Bengali captioning pipeline trained on LaBSE-verified EN--BN pairs and 110k bilingual-prompted synthetic images. A frozen MaxViT yields stable visual patches, a Bengali-native mBART-50 decodes, and a lightweight bridge links the modalities. Our core novelty is a tri-loss objective: Patch-Alignment Loss (PAL) aligns real and synthetic patch descriptors using decoder cross-attention, InfoNCE enforces global real--synthetic separation, and Sinkhorn-based OT ensures balanced fine-grained patch correspondence. This PAL+InfoNCE+OT synergy improves grounding, reduces spurious matches, and drives strong gains on Flickr30k-1k (BLEU-4 12.29, METEOR 27.98, BERTScore-F1 71.20) and MSCOCO-1k (BLEU-4 12.00, METEOR 28.14, BERTScore-F1 75.40), outperforming strong CE baselines and narrowing the real--synthetic centroid gap by 41%.

Joydeb Kumar Sana, Mohammad Zoynul Abedin, M. Sohel Rahman et al.

Data transformation (DT) is a process that transfers the original data into a form which supports a particular classification algorithm and helps to analyze the data for a special purpose. To improve the prediction performance we investigated various data transform methods. This study is conducted in a customer churn prediction (CCP) context in the telecommunication industry (TCI), where customer attrition is a common phenomenon. We have proposed a novel approach of combining data transformation methods with the machine learning models for the CCP problem. We conducted our experiments on publicly available TCI datasets and assessed the performance in terms of the widely used evaluation measures (e.g. AUC, precision, recall, and F-measure). In this study, we presented comprehensive comparisons to affirm the effect of the transformation methods. The comparison results and statistical test proved that most of the proposed data transformation based optimized models improve the performance of CCP significantly. Overall, an efficient and optimized CCP model for the telecommunication industry has been presented through this manuscript.