Jubayer Ahamed

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.

In recent times, breast cancer has become one of the major health concerns for women which can have a severe impact on the overall well-being of the affected and may even result in death. Women, being an essential part of today’s world, require a top-notch healthcare system that can ensure their overall well-being and address even sensitive issues like breast cancer. So, it is essential to develop a breast cancer detection system that can detect breast cancer effectively and discriminate between dangerous malignant and benign tumors incorporating advanced machine learning techniques. Notably, promising results have been shown by algorithms like K-Nearest Neighbors, Decision Trees, and Naïve Bayes. Nevertheless, previous studies have only examined the model’s overall performance. The models contain no information regarding the multiclass performances of the evaluation matrices. To address these gaps, this study aims to identify the most efficient algorithm for classifying and predicting breast cancer. The analysis comprises a dataset that encompasses two distinct kinds of breast tumors: Malignant and Benign. This study evaluates the performance of the three mentioned methods by conducting thorough experiments employing k-fold cross-validation. Furthermore, the multiclass classification of various tumors showed superior performance compared to the K-Nearest Neighbors technique and other algorithms. This approach for evaluating the performance of several categories would provide important findings and an in-depth understanding of the overall model's performance which can be useful for future research.

A reproducible solution to a frequently occurring problem in software design was described as a software pattern, also known as a design pattern. It was a tested method for resolving kinds of software design issues that had been classified and identified by several years of experience and best practices. The key to making safe and accessible software was good software design. There were different types of design problems. Solving those problems was very important to produce good and efficient software. There were some tools, techniques, and patterns to solve these problems. Software design knowledge could be effectively captured and communicated using design patterns. Within a particular context of software design, a software design pattern was a standardized, repeatable solution to a frequently occurring problem. The patterns were effective for software design on several levels. In this paper, we identified the major design problems. Then we have analyzed those problems and present design patterns to develop an effective software design.

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.