Automatically generating image captions poses one of the most challenging applications within artificial intelligence due to its integration of computer vision and natural language processing algorithms. This task becomes notably more formidable when dealing with a language as intricate as Bengali and the overall scarcity of Bengali-captioned image databases. In this investigation, a meticulously human-annotated dataset of Bengali captions has been curated specifically for the encompassing collection of pictures. Simultaneously, an innovative end-to-end architecture has been introduced to craft pertinent image descriptions in the Bengali language, leveraging an attention-driven decoder. Initially, the amalgamation of images’ spatial and temporal attributes is facilitated by Gated Recurrent Units, constituting the input features. These features are subsequently fed into the attention layer alongside embedded caption features. The attention mechanism scrutinizes the interrelation between visual and linguistic representations, encompassing both categories of representations. Later, a comprehensive recursive unit comprising two layers employs the amalgamated attention traits to construct coherent sentences. Utilizing our furnished dataset, this model undergoes training, culminating in achievements of a 43% BLEU-4 score, a 39% METEOR score, and a 47% ROUGE score. Compared to all preceding endeavors in Bengali image captioning, these outcomes signify the pinnacle of current attainable standards.

The pandemic situation due to covid-19 has disrupted routine activities such as attending classes physically in educational institutions, which insisted on moving towards online education with the help of advent and increased uses of new telecommunication services. Service quality is the prerequisite for customer satisfaction. Service quality assessment is crucial to ensure increased customer satisfaction in any service. Typically, it is not easy to evaluate service quality because of opacity in the information, and incompleteness characteristics of problems. With the collected data through an online survey, this study aims to analyze the facts that influence the students' perception regarding the impact of telecommunication service quality on online education during the pandemic situation. Initially, some relevant criteria are derived from literature reviews. The proposed model is exerted to evaluate the quality of the online education and telecommunication service in Bangladesh during the covid-19 pandemic with the participation of 350 students answering 39 questions. The collected data is analyzed to assess the current state of service quality by evaluating the students' satisfaction using the entropy technique. The findings of the study suggest that the online education system in Bangladesh is not interactive enough, and the telecommunication service quality here is not sufficient for this purpose. Telecommunication challenges such as poor network quality, overpricing structure of telecommunication services and slow connection speed must be resolved to ensure satisfactory quality of online education.

This study addresses the impact of movement restrictions, particularly during pandemics, on global consumer behavior, with a focus on grocery shopping. Despite the widespread effects, research on these changes remains limited, prompting the need to investigate hidden purchase patterns contributing to economic growth. Employing secondary data analysis, the study explores the effects of movement restrictions on customer consumption patterns, using K-means cluster analysis to identify distinct consumer segments. The findings highlight significant impacts on purchasing power, item prices, and consumption behavior, with a notable increase in item prices during the post-restriction period followed by a subsequent decline influenced by economic factors such as financial uncertainty and shifting priorities. This research contributes by shedding light on the dynamic nature of consumer behavior during and after movement restrictions, offering valuable insights for policymakers and businesses navigating the post-restriction economic landscape and informing strategies for economic recovery and growth.

In today's fast-paced tech industry, there is a growing need for tools that evaluate a programmer's job readiness based on their coding performance. This study focuses on predicting the potential of Codeforces users to secure various levels of software engineering jobs. The primary objective is to analyze how a user's competitive programming activity correlates with their chances of obtaining positions, ranging from entry-level roles to jobs at major tech companies. We collect user data using the Codeforces API, process key performance metrics, and build a prediction model using a Random Forest classifier. The model categorizes users into four levels of employability, ranging from those needing further development to those ready for top-tier tech jobs. The system is implemented using Flask and deployed on Render for real-time predictions. Our evaluation demonstrates that the approach effectively distinguishes between different skill levels based on coding proficiency and participation. This work lays a foundation for the use of machine learning in career assessment and could be extended to predict job readiness in broader technical fields.

Academic performance depends on a multivariable nexus of socio-academic and financial factors. This study investigates these influences to develop effective strategies for optimizing students' CGPA. To achieve this, we reviewed various literature to identify key influencing factors and constructed an initial hypothetical causal graph based on the findings. Additionally, an online survey was conducted, where 1,050 students participated, providing comprehensive data for analysis. Rigorous data preprocessing techniques, including cleaning and visualization, ensured data quality before analysis. Causal analysis validated the relationships among variables, offering deeper insights into their direct and indirect effects on CGPA. Regression models were implemented for CGPA prediction, while classification models categorized students based on performance levels. Ridge Regression demonstrated strong predictive accuracy, achieving a Mean Absolute Error of 0.12 and a Mean Squared Error of 0.023. Random Forest outperformed in classification, attaining an F1-score near perfection and an accuracy of 98.68%. Explainable AI techniques such as SHAP, LIME, and Interpret enhanced model interpretability, highlighting critical factors such as study hours, scholarships, parental education, and prior academic performance. The study culminated in the development of a web-based application that provides students with personalized insights, allowing them to predict academic performance, identify areas for improvement, and make informed decisions to enhance their outcomes.