This study proposed a Project-Based Learning (PBL) Framework in software engineering education by shifting from traditional, exam-heavy assessments to a project-driven evaluation.By increasing the CLO3 weightage from $40 \%$ to $60 \%$,this framework emphasized on industry relevant skills rather than theoretical.This study conducted over two semesters with 100 students(Each Semester,n=50 Students) at Independent University Bangladesh, the study demonstrates significant improvements in learning outcomes, with overall CLO achievement rising from $28 \%$ to $96 \%$.Notably,CLO3, measuring application and analysis skills, improved by $54 \%$, indicating enhanced industry preparedness.A statistical analysis, including ANOVA and regression, confirms a strong correlation between PBL and skill development.The transition to a $60 \%$ project-based assessment enhances student engagement and critical competencies for the industry while fostering research contributions.The new PBL framework fosters lifelong learning, adaptability, and interdisciplinary collaboration which aligns with sustainable education principles.By bridging theory and practice,this approach enhances student engagement and contributes to longterm productivity, making PBL a sustainable and impactful model for engineering education.

This study investigates the integration of Agile methodologies with the Stage-Gate model to enhance New Product Development (NPD) performance, focusing on Bangladesh’s software development sector. By surveying 125 participants across 21 software firms, the study examines the impact of key variables such as team size, speed, and quality on project outcomes. Findings indicate that while this hybrid approach improves efficiency and quality, challenges such as budget constraints, coordination inefficiencies, and cultural resistance persist.The proposed Parallel Sprint Planning approach offers a practical solution by aligning Agile sprints with Stage-Gate milestones, mitigating coordination inefficiencies and integration conflicts, and ensuring that iterative development cycles align with structured decision-making checkpoints. By embedding Agile principles within Stage-Gate’s framework, Parallel Sprint Planning (PSP) enhances flexibility while ensuring governance, providing a comprehensive strategy to improve NPD performance.

Managing diabetes is a major challenge for diabetic patients, especially in Bangladesh, where monitoring and recordkeeping often fall short. To address such issues, the proposed study introduces Diawellness, a web-based system that aims to consolidate and streamline diabetes treatment. The offered system combines demographic data from the patient, medical history, health metrics, and treatment data to provide better health tracking and personalized diet recommendations. Furthermore, we discovered that an integrated, interactive system can dramatically improve diabetes care by encouraging improved health monitoring, timely interventions, and patient autonomy.

A lot of information is spread by people in the social media to update their status and share crucial news with others. But the majority of these platforms don’t promptly validate the individuals or their posts and people aren’t able to identify the fake news manually. Therefore, there is a need for an automated system capable of detecting fake news. This research has proposed to build a model using four machine learning algorithms. The dataset employed in the experiment is a composite of two datasets containing almost equal amounts of true and fake news articles on politics. The preprocessing stages begin with cleaning the data by removing punctuation, tokenization, special characters, white spaces, redundant word elimination, numerals, and English letters followed by stemming and stop with data discretization. Then, we analyzed the collected data and 80% of the data has been used to train each model initially. After that, the four manifested classification algorithms are applied. For identifying fake news from news articles, meth-ods like Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Classifier were used. The trained classifiers’ accuracy has been evaluated using the remaining 20% of the data. The results show that the decision tree model produces the best accuracy of 99.60% and gradient boosting of 99.55%. Besides, the random forest shows 99.10% along with the logistic regression 98.99%. Moreover, we have explored the best model to achieve the highest precision, recall, F1-score based on the confusion matrix’s outcome.

Software testing is a crucial component of software development. With the increasing complexity of software systems, traditional manual testing methods are becoming less feasible. Artificial Intelligence (AI) has emerged as a promising approach to software testing in recent years. This review paper aims to provide an in-depth understanding of the current state of software testing using AI. The review will examine the various approaches, techniques, and tools used in this area and assess their effectiveness. The selected articles for this study have been extracted from different research databases using the advanced search string strategy. Initially, 40 articles have been extracted from different research libraries. After gradual filtering finally, 20 articles have been selected for the study. After studying all the selected papers, we find that various testing tasks can be automated successfully using AI (Machine Learning and Deep Learning) such as Test Case Generation, Defect Prediction, Test Case Prioritization Metamorphic Testing, Android Testing, Test Case Validation, and White Box Testing. This study also finds that the integration of AI in software testing is making software testing activities easier along with better performance. This literature review paper provides a thorough analysis of the impact AI can have on the software testing process.

Sustainability and conservation of energy resource in a developing country such as Bangladesh is a major concern for the people and their government. Massive amount of data is being generated in the energy sector which is untapped and can potentially provide a roadmap to ensure the sustainability of energy resources. We present an approach on how big data can help us find meaning in the data that is available and how analyzing the data can help countries like Bangladesh to set aside and distribute energy in an efficient manner while also providing environmental benefits. We classified the data sources and presented a high-level design that would help us implement such a solution in future. Finally, we discussed the applications of the analysis and how we can move forward towards additional points of interests for achieving energy sustainability.

Although global software industry is growing up rapidly, however surviving in software industry has not become remarkable as earning revenue and profit is below satisfactory in most of cases. Software companies are getting failed to make business by implementing typical software project management. The reasons of failing to earn profit is developing customized software of each client. In this paper, we have proposed a process model which will be followed additionally with typical software project management. By adopting this proposed process model will help the software companies to make profit of software business. This process model will build core and reusable software components based on targeted market segment and will integrate those components while developing software. Thus, software can be built with minimal customization and software companies can save their resources.

The Software Engineering Body of Knowledge (SWEBOK) provides generally accepted knowledge for the software engineering profession. The SWEBOK had been designed to guide the in-house developers and software contractors but the current trend is that it is being used by the innovators. Thus, the SWEBOK has very little guidance for the emerging role. The focus of the innovators will be to improve the business processes as well as study the business practice to learn the concealed requirements of the customer. This paper is an attempt to analyze the existing proposals on the SWEBOK contents, structure, and make a proposal on what kind of contents it should have, and how it should be structured so that the innovators as well as in-house and software contractors can have proper guidance and can benefit from it.

Project management core knowledge areas are known to all. Project Management Body of Knowledge (PMBOK) [6] has prepared these knowledge areas considering all types of projects in mind. Currently, we find that software projects have come with certain level of uncertainty. For many of the software projects which are in the phase of innovation have to user requirements, no budget and no specific time frame. Thus typical project management might not enough for such projects. In this paper, we have proposed additional knowledge area for software project management named Marketing Analysis. We strongly believe that adopting the activities of the proposed knowledge area software firms would be able manage their software project more efficiently and effectively.

This paper presents a framework for building diagnostic imaging teaching and learning facility for entry level medical students of Bangladesh. Initially we demonstrate an X-Ray image analysis and retrieval system that will work as one of the main component in this system. This web based system has three modes. First is the annotation mode where an expert radiologist manually performs annotation of raw x-ray images. To aid the annotation process proposed model proposes a manual and a semi-auto segmentation tool in identifying the region of interests (ROI) in the X-Ray images. Image Retrieval in Medical Applications (IRMA) structure has been used for the annotating the ROIs. In the learning mode, students can retrieve images from the database created by expert radiologists. We proposed information retrieval techniques to find x-ray images of interest. We have used text based and content based search methods which is based on term frequency–inverse document frequency (tf-idf), and Gabor filter respectively.