Product market demand analysis plays a significant role for originating business strategies due to its noticeable impact on the competitive business field. Furthermore, there are roughly 228 million native Bengali speakers, the majority of whom use Banglish text to interact with one another on social media. Consumers are buying and evaluating items on social media with Banglish text as social media emerges as an online marketplace for entrepreneurs. People use social media to find preferred smartphone brands and models by sharing their positive and bad experiences with them. For this reason, our goal is to gather Banglish text data and use sentiment analysis and named entity identification to assess Bangladeshi market demand for smartphones in order to determine the most popular smartphones by gender. We scraped product related data from social media with instant data scrapers and crawled data from Wikipedia and other sites for product information with python web scrapers. Using Python's Pandas and Seaborn libraries, the raw data is filtered using NLP methods. To train our datasets for named entity recognition, we utilized Spacey's custom NER model, Amazon Comprehend Custom NER. A tensorflow sequential model was deployed with parameter tweaking for sentiment analysis. Meanwhile, we used the Google Cloud Translation API to estimate the gender of the reviewers using the BanglaLinga library. In this article, we use natural language processing (NLP) approaches and several machine learning models to identify the most in-demand items and services in the Bangladeshi market. Our model has an accuracy of 87.99% in Spacy Custom Named Entity recognition, 95.51% in Amazon Comprehend Custom NER, and 87.02% in the Sequential model for demand analysis. After Spacy's study, we were able to manage 80% of mistakes related to misspelled words using a mix of Levenshtein distance and ratio algorithms.
In order to support students in integrating information from the four disciplines, STEM education places a strong emphasis on using real-world contexts. The STEM Framework is a comprehensive STEM educational framework built around challenges that are protracted, persistent, and difficult. The knowledge and abilities needed to tackle those challenges must come from at least one dominant field, with the other disciplines contributing the techniques, procedures, or resources to aid in the process. This method can be used by teachers to create STEM lessons by identifying a central issue whose resolution calls for expertise from two or more STEM fields. The term “STEM education” is not new in the area, but due to pedagogical and curriculum issues, it is still not being taught in the classroom properly. In order to enable teachers to innovate and take charge of STEM education in their classroom, it is crucial to provide them with programs that assist them in applying and implementing STEM pedagogies in the classroom. To integrate STEM into practice, firstly we should identify the key elements of STEM. This study is conducted for addressing the major components of the STEM Education framework in Bangladesh perspective.
The difficulty of allocating a balanced educational syllabus to academic periods of a curriculum, also known as curriculum balancing, has long been a source of consternation for any institution of higher education attempting to connect learners and teachers. The balanced academic curriculum challenge entails assigning courses to academic times while adhering to all load restrictions and prerequisite requirements. The balanced academic curriculum problem (BACP) includes assigning subjects to class hours that fulfill standards even while managing students’ burden in terms of credits, course load, and perquisites that includes subjects covered in the previous semesters/periods. The number of credits every semester corresponds to the academic load. As a result, educational frameworks must be “balanced,” which means the credits for each period should be equivalent in order for students to bear minimum work. As a result, it is desirable to reduce this cost by developing a study plan that employs an algorithm that conducts this work automatically. Using an optimization method, this article provides a solution to the challenge of curricula balancing based on the discrete firefly algorithm (DFA). In research, FA has already been used to solve the BACP problem. However, the basic FA is modified to DFA with a local search mechanism inbuilt that helps to reach optimum solution in less number of iterations. A series of tests on standard and real data instances are done to check the efficiency of the suggested approach, with the objective of producing a platform that would simplify the procedure of building a curriculum system at institutions of higher learning. The results show that the proposed solution obtained a rather rapid solution and hit the recognized optimum in most of the iterations.
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