The success of a software system mostly depends on how effectively and carefully the requirements are understood, prioritized, and handled. Defects, cost escalation and project failure often occur because of misconception of requirements. While current requirement engineering methods have tried to solve these problems, still there are many that struggle to classify perfectly and prioritize needs. This is especially applied for non-functional requirements, where systematic validation remains as a notable gap in the process. This study suggests a structured FR/NFR Defect Decision Ontology Framework that combines ontology-based thinking including machine learning techniques. The framework is performed on a dataset consisting of 6,086 requirements (3,964 FRs and 2,122 NFRs). Defects are automatically identified by using ontology-driven rules, which leads to 458 defective occurrences. Evaluation has been done with a layered 80/20 train–test split with 10-fold cross-validation. FR/NFR classification, defect detection, defect type classification, and severity classification are four classification tasks which are performed by using models such as Naive Bayes, Support Vector Machine (SVM), Logistic Regression, and Random Forest. The result shows strong performance with the highest accuracy, 87.68% for FR/NFR classification, 97.29% for defect detection, 88.76% for defect type classification, and 82.02% for severity classification. The findings indicate that NFR defects are more complex and less traceable than FR defects. The framework will help to improve both accuracy and understandability that supports more effective requirement analysis and decision-making in software engineering.

One of the biggest causes of cancer-related fatalities among women is still Cervical cancer, especially in low and middle-income nations where access to broad screening and early detection may be limited. Cervical cancer is curable if detected in its early stages, but asymptomatic progression frequently results in late diagnosis, which makes treatment more difficult and lowers survival chances. Even though they work well, current screening methods including liquid-based cytology and Pap smears have drawbacks in terms of consistency, sensitivity, and specificity. Recent developments in Deep Learning and Artificial Intelligence have shown promise for greatly improving Cervical cancer detection and diagnosis. In this work, we have introduced CervixCan-Net, a novel Deep Learning based model created for the precise classification of Cervical cancer from histopathology images. Our approach offers a solid and dependable classification solution by addressing common problems like overfitting and computational inefficiency. CervixCan-Net performs better than many state-of-the-art models according to a comparison investigation. CervixCan-Net, with an impressive test accuracy of 99.83%, provides a scalable, automated Cervical cancer classification solution that has great promise for improving patient outcomes and diagnostic accuracy.

This study introduces an improved BERT-based model for sentiment analysis in several languages, specifically focusing on analyzing e-commerce evaluations written in English and Bengali. Conventional sentiment analysis techniques frequently face difficulties in dealing with the subtle linguistic differences and cultural diversities present in datasets containing multiple languages. The model we propose integrates sophisticated methodologies and utilizes Local Interpretable Model-agnostic Explanations (LIME) to enhance the accuracy, interpretability, and dependability of sentiment assessments in various language situations. To tackle the challenges of sentiment categorization in a multilingual setting, we enhance the pre-trained BERT architecture by incorporating extra neural network layers. Compared to traditional machine learning and current deep learning methods, the model underwent a thorough evaluation, showcasing its superior capabilities with accuracy, precision, recall, and F1-score of 0.92. Including LIME improves the model’s transparency, allowing for a better understanding of the decision-making process and increasing user confidence. This research highlights the potential of utilizing advanced deep learning models to address the difficulties of sentiment analysis in global e-commerce environments, providing major implications for both academic research and practical applications in industry.

The process of making decisions on software architecture is the greatest significance for the achievement of a software system's success. Software architecture establishes the framework of the system, specifies its characteristics, and has significant and major effects across the whole life cycle of the system. The complicated characteristics of the software development context and the significance of the problem have caused the research community to build various methodologies focused on supporting software architects to improve their decision-making abilities. With these efforts, the implementation of such systematic methodologies looks to be somewhat constrained in practical application. Moreover, the decision-makers must overcome unexpected difficulties due to the varying software development processes that propose distinct approaches for architecture design. The understanding of these design approaches helps to develop the architectural design framework. In the area of software architecture, a significant change has occurred wherein the focus has shifted from primarily identifying the result of the architecting process, which was primarily expressed through the representation of components and connectors, to the documentation of architectural design decisions and the underlying reasoning behind them. This shift finally concludes in the creation of an architectural design framework. So, a correct decision- making approach is needed to design the software architecture. The present study analyzes the design decisions and proposes a new design decision model for the software architecture. This study introduces a new approach to the decision-making model, wherein software architecture design is viewed based on specific decisions.