Browsing by Author "Hassan, Mocksidul"

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Catering service management system
(Daffodil International University, 2024-01-25) Hassan, Mocksidul
This report offers a comprehensive overview of the Catering Service Management System, a mobile application created to enhance the efficiency and management of catering services. Simplifying the operational procedures unique to catering enterprises is the aim of the application. Firebase was used for the back end and Flutter-dart (flutter is a framework and Dart is a programming language) for the front end. The primary objective of this system is to ensure real-time data synchronization across several user interfaces and to simplify order administration and service modification. The program offers consumers an intuitive customer service system and an easy-to-use interface using Flutter's robust features. Firebase's backend integration ensures data consistency, scalability, and secure authentication processes. A battery of tests validated the system's efficacy, showing it could manage many user requests concurrently, preserve data consistency, and deliver a responsive user experience. The scalable, efficient, and userfriendly platform provided by My Catering Service Management System may be tailored to meet the evolving requirements of both catering service providers and their clients. It represents a significant development in digital catering solutions. The system's advanced features, such as its adjustable menus and integration, make it more valuable in the fastpaced catering sector. The Catering Service Management System sets itself apart as a comprehensive solution prepared to elevate the standard for online catering services. This endeavor aids in the digital transformation of the catering industry by staying abreast of emerging technologies and evolving client demands.
Catering service management system
(2024-01-24) Hassan, Mocksidul
This report offers a comprehensive overview of the Catering Service Management System, a mobile application created to enhance the efficiency and management of catering services. Simplifying the operational procedures unique to catering enterprises is the aim of the application. Firebase was used for the back end and Flutter-dart (flutter is a framework and Dart is a programming language) for the front end. The primary objective of this system is to ensure real-time data synchronization across several user interfaces and to simplify order administration and service modification. The program offers consumers an intuitive customer service system and an easy-to-use interface using Flutter's robust features. Firebase's backend integration ensures data consistency, scalability, and secure authentication processes. A battery of tests validated the system's efficacy, showing it could manage many user requests concurrently, preserve data consistency, and deliver a responsive user experience. The scalable, efficient, and userfriendly platform provided by My Catering Service Management System may be tailored to meet the evolving requirements of both catering service providers and their clients. It represents a significant development in digital catering solutions. The system's advanced features, such as its adjustable menus and integration, make it more valuable in the fastpaced catering sector. The Catering Service Management System sets itself apart as a comprehensive solution prepared to elevate the standard for online catering services. This endeavor aids in the digital transformation of the catering industry by staying abreast of emerging technologies and evolving client demands.
Sentiment Analysis in Multilingual Context: Comparative Analysis of Machine Learning and Hybrid Deep Learning Models
(Elsevier, 2023-09-19) Das, Rajesh Kumar; Islam, Mirajul; Hasan, Md Mahmudul; Razia, Sultana; Hassan, Mocksidul; Khushbu, Sharun Akter
This research paper investigates the efficacy of various machine learning models, including deep learning and hybrid models, for text classification in the English and Bangla languages. The study focuses on sentiment analysis of comments from a popular Bengali e-commerce site, "DARAZ," which comprises both Bangla and translated English reviews. The primary objective of this study is to conduct a comparative analysis of various models, evaluating their efficacy in the domain of sentiment analysis. The research methodology includes implementing seven machine learning models and deep learning models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional 1D (Conv1D), and a combined Conv1D-LSTM. Preprocessing techniques are applied to a modified text set to enhance model accuracy. The major conclusion of the study is that Support Vector Machine (SVM) models exhibit superior performance compared to other models, achieving an accuracy of 82.56% for English text sentiment analysis and 86.43% for Bangla text sentiment analysis using the porter stemming algorithm. Additionally, the Bi-LSTM Based Model demonstrates the best performance among the deep learning models, achieving an accuracy of 78.10% for English text and 83.72% for Bangla text using porter stemming. This study signifies significant progress in natural language processing research, particularly for Bangla, by enhancing improved text classification models and methodologies. The results of this research make a significant contribution to the field of sentiment analysis and offer valuable insights for future research and practical applications.