Browsing by Author "Hossain, Afzal"

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Application of lean tools in Hams Garments Ltd. of Hams Group
(BRAC University, 2022-05) Hasan, Md. Mehedi; Uddin, Md. Muslim; Hossain, Afzal
A 100% export oriented leading garments industry starts journey in 2001 with 2 sewing lines and total of 350 workforces at Sreepur area in Gazipur city in Bangladesh. Now it's quite 12,000 workforces with 153 sewing lines within different project of HAMS Group in Gazipur. It expands business into knit, woven, and lingerie products. Industrial Engineering (IE) is a branch of engineering which deals with man, machine and material to illustrate maximum efficiency. IE team provide SMV by using IE tools which is used to calculating CM, minute cost of factory, efficiency of the factory. Lean manufacturing is a Japanese technology and KAIZEN is a lean tools which works for continuous improvement. Basically, lean identify non value added work from process and suggest to eliminate that added more value for the company.
Arsenic poisoning in Bangladesh: review of current situation[book chapter]
(199) Rabbani, G.H; Anwer, Kazi Selim; Hossain, Afzal; Das, H.K.; Gupta, P.K. Sen; Alam, M.S.; Siddique, A.K.M.; Islam, Sufia
Effects of arsenic poisoning on health-an overview[book chapter]
(1999) SenGupta, P.K.; Anwar, K. Selim; Das, H.K.; Hossain, Afzal; Rabbani, G.H.
Ensemble Learning Algorithms for Classification Tasks in Natural Language Processing (NLP)
(© University of Dhaka, 2025-04-20) Hossain, Afzal
Natural Language Processing (NLP) encompasses a multitude of practical applications, including Information Retrieval, Information Extraction, Machine Translation, Text Simplification, Sentiment Analysis, Text Summarization, Spam Filtering, Auto-prediction, Auto-correction, Speech Recognition, Question Answering, and Natural Language Generation. Many of these applications are essentially classification tasks, which can be performed by machine learning models. Ensemble techniques within machine learning involve combining multiple models to improve predictive performance compared to individual models. This thesis explores the application of ensemble learning techniques to improve classification performance in NLP tasks. Various ensemble learning techniques, including bagging, boosting, random forest, and voting, are explored and experimented with. For each ensemble method, common base models, such as Support Vector Machines (SVM), Naive Bayes, Decision Trees, and K-Nearest Neighbor (KNN), are employed. Various evaluation metrics commonly used in NLP classification tasks are used, including accuracy, precision, recall, F1-score, and time complexity of the algorithms. The findings of the thesis suggest that ensemble methods, especially boosting, generally perform better than traditional machine learning methods for NLP classification tasks. The thesis also describes the modification of two ensemble models – firstly, majority voting is modified for the situation when a tie occurs, and secondly, bagging is modified with a different type of sampling. Both of these methods result in improved performances in the datasets. Overall, the research work provides a comprehensive overview of ensemble learning algorithms and their applications in improving classification performance in NLP tasks, backed by theoretical discussions, case studies, and experimental results.
Environment pollution with arsenic in drinking water: an emerging public health problem in Bangladesh[book]
(1999) Anwar, K. Selim; Hossain, Afzal; Siddique, A.K.; SenGupta, P.K.; Rabbani, G.H.
Prevalence and continuation of injectable contraceptives
(Dhaka; International Centre for Diarrhoeal Diseases Research Bangladesh, 1996) Rahman, Md. Mafizur; Hossain, Mian Bazle; Hossain, Afzal; Das, Subas Chandra
Stock market prediction using time series analysis
(BRAC University, 2018-12) Hira, Farhan Islam; Maruf, Mazharul Ferdous; Hossain, Afzal; Arif, Hossain
Stock market, a very unpredictable sector of finance, involves a large number of investors, buyers and sellers. Stock prediction has been a phenomenon since machine learning was introduced. But very few techniques became useful for forecasting the stock market as it changes with the passage of time. As time is playing a crucial rule here, Time Series (TS) analysis is used in this paper to predict short-term stock market. The first step for analyzing TS is to check whether historical stock market data is stationary using Plotting Rolling Statistics and Dickey-Fuller Test. Secondly, Trend and Seasonality is eliminated from the series to make the data a stationary series. Then, TS stochastic model known as Autoregressive Integrated Moving Average (ARIMA) is used as it has been broadly applied in financial and economic sectors for its efficiency and great potentiality for short-term stock market prediction. For comparing the performance, the three subclasses of ARIMA such as: Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA) are also applied. Finally, the forecasted values are converted to the original scale by applying Trend and Seasonality constraints back. KEYWORDS: Stock Prediction, Machine Learning, Time Series, ARMA, ARIMA.
Trends in contraception and gender composition of surviving children: examples from two rural areas of Bangladesh
(1995-03) Mozumder, A.B.M. Khorshed Sarkar Alam; Rahman, Dewan Mizanur; Hossain, Afzal