Browsing by Author "Rahman, Naimur"

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Conceptual design of a low cost flight data acquisition system for analyzing flight behavior of small unmanned aerial vehicles
(IEEE Xplore, 2017-02-23) Sarker, Tonmoy; Hannan, Purnota; Shahed, Shahidul Alam; Rahman, Naimur; Sakib, Syed Nazmus
This paper represents a conceptual design of a flight data acquisition system for small scale unmanned aerial vehicles. The system consists of three components-the unmanned aircraft, data acquisition hardware and display. The acquired data from various sensors of the acquisition system are stored in on board storage device and the data is essential for achieving situational awareness of the control of the UAV and post flight analysis for improved system behavior, accidents investigation, aerodynamics design improvement etc. Full Text Link: http://doi.org/10.1109/ICCITECHN.2016.7860261
Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches
(BRAC University, 2021-06) Shahriar, Farhan; Dey, Amarttya Prasad; Rahman, Naimur; Tasnim, Zarin; Tanvir, Mohammad Zubayer; Parvez, Zavid
Parkinson’s Disease is the second most common neurological disease after Alzheimer’s Disease. The disease is incurable. However, if the disease can be detected earlier, then the consequences of it’s effect can be relieved. The early phase of PD is called by Prodromal Parkinson’s Disease. The symptoms of the Prodromal phase includes hyposmia, constipation, mood disorders, REM sleep behavior disorder, olfaction dis orders etc. RBD or REM sleep behavior disorder is the most common symptom of Prodromal PD. In this study, we used various deep convolutional neural network architectures and trained them to detect Prodromal PD patients. We collected 20 Prodromal patients and 20 healthy control subject data from the PPMI website and applied CNN architecture mobilenet v1, incception v3, vgg19 and inception resnet v2 to achieve our goal. We ensembled inception resnet v2 and mobilenet v1 with the hope of getting a better result as well. However, we successfully carried out our training and with mobilenet v1 we gained the highest classification accuracy of 81.22%. Inception resnet V2, inception v3, vgg19 and ensemble model achieved respectively 75.30%, 62.55% and 63.32% accuracy.
Financial Performance Analysis of Agrani Bank Limited
(Daffodil International University, 2019-12-12) Rahman, Naimur
Agrani Bank Limited is registered by the Bangladesh Bank as a commercial bank. The bank's functions cover a wide range of banking and functional activities for individuals, businesses, corporate entities, and various multinational agencies. I have discussed ABL's analysis of financial performance and general banking activities in this report. Includes Ratio analysis (liquidity ratio, ratio of operation, credit risk ratio, and productivity ratio), Trend analysis, typical size etc. ABL Principal Branch has three departments. These are: General Banking Department, Foreign Exchange Department and Credit Department. Through these three departments they serve their customers. General banking department provides service to the customers by doing the elementary tasks of the bank. This department has three sections: Main Cash with Cash Cell; Deposit Section Savings, Current deposit, FDR, SNTD; Clearing; Bills; Accounts; These elementary tasks include account opening, providing master credit card, internet banking, offering different types of schemes to the customers, different types of bills and fees collection etc. Foreign Exchange department also play vital role by providing service to the customers. This department has three sections. These are: Export section, Import section and Foreign Remittance section.
Leveraging sequential deep learning models for detecting multitude of human action categories
(BRAC University, 2023-09) Pranta, Kazi Al Refat; Islam, Fahad Mohammad Rejwanul; Ahmed, Khandakar Fahim; Saha, Prince; Rahman, Naimur; Reza, Tanzim; Rahman, Rafeed
In today’s world, where science and technology are constantly evolving day by day, people are drawn to tangible experiences and visual representations. There’s a growing effort to teach machines about human movements and postures to enable smart decision-making. This has led to increased interest in the field of human action recognition (HAR) among researchers globally. Our research focuses on implementing advanced technologies to address criminal activities, specifically emphasizing Human Activity Recognition (HAR). Moreover, our dataset includes 1275 videos, covering 20 different actions involving both violent and non-violent behaviors. In addition, we have developed a pipeline that utilizes YOLO-v8 to extract background, followed by models for accurate video classification. two models,conv-lstm and lrcn, were incorporated into our deep learning pipeline. Through our observations, we found that the LRCN model outperformed the other model, achieving an accuracy of 62% and an F1 score of 60% for the 20 classes, for 17 classes an accuracy of 63% and an F1 score of 66%. for binary classification LRCN got accuracy of 88% and an F1 score of 87%Our research focusses the potential of advanced technologies to significantly improve Human Activity Recognition (HAR) in addressing various aspects of criminal activities in real-time scenario. This marks a substantial step forward in intelligent decision-making and public safety.
Multihead Text Mining from COVID-19 Feedback Using Machine Learning, Deep Learning, and Hybrid Deep Learning Approaches
(2024-08-24) Kobra, Khadijatul; Sammi, Samrina Sarkar; Rahman, Naimur; Khushbu, Sharun Akter; Islam, Mirajul
This study examines the impact of the COVID-19 epidemic on students in Bangladesh through text classification using various machine learning (ML) algorithms and deep learning (DL) models. The pandemic led to emergency crisis protocols in the country, including self-quarantine and the closure of educational and governmental institutions, resulting in significant negative impacts on individuals’ physical and mental health, including anxiety, sadness, and terror. To better understand the psychological effects of the epidemic, the authors collected survey data from 400 students in various divisions of Bangladesh using self-administered questionnaires through Google Forms. Preprocessing techniques such as tokenization, filtering, and n-gram modeling were used in the analysis. The study deployed eight different ML algorithms and DL models, including LSTM, BiLSTM, and CNN, to classify the effects on students’ academic, mental, and social lives. The results show that the ML classifier algorithms were highly effective, achieving accuracies of 95.00%, 93.75%, and 95.00% for academic, mental, and social life impact, respectively. Furthermore, hybrid DL models, such as CNN-LSTM and CNN-BiLSTM, produced good scores in predicting the impacts on students’ lives. Overall, this study provides valuable insights into the impacts of the COVID-19 epidemic on students’ academic, mental, and social well-being in Bangladesh.
Online Shopping
(Daffodil International University, 2021-06-01) Islam, Shariful; Rahman, Naimur; Munna, Masturat Monjure
The Web Application of online shopping is accustomed to bring out information from web based business. The web application has become the wellspring of seem business or business sites. The developing number of online business sites has placed clients in uncertainty to look for destinations to get a specific result of interest with the best cost and quality the record report a layout of the web framework which develop client occasion. The site license online clients to review item explanation and differentiation the costs of the specific item available on other online shopping destinations. This project was develop with Front-end: HTML, CSS, JAVASCRIPT, Back-end: PHP, Ajax, MY SQL. Produce and managing condition is a dare for IT, system, and Online Shopping projects or for any activity where you need to manage the promised Connection. Requirements good organic product survey and price compare are an activity that can deliver a high, quick return on the outcome. The project survey the system condition and then creates the condition description. It studies other connected systems and then design system description. The system is then create according to the statement to meet the condition. The system is created as an analysis and compare e-commerce system. The web application system distribute with data entry, confirmation, update, and deletion, while the collective system deals with system interconnection with management and users. There is also a community system where users can post knowledge there. Thus, the more than functions of this project will save the user time, and in consequence the user will also save money on the organization of the system
Optimizing abstractive summarization with fine-tuned PEGASUS
(BRAC University, 2023-09) Rafi, Sadiul Arefin; Rahman, Naimur; Islam, Kazi Nazibul; Ahmad, Ha-mim; Sadeque, Farig Yousuf
Abstractive text summarization is the technique of generating a short and concise summary comprising the salient ideas of a source text without making a subset of the salient sentences from the source text. The introduction of transformer models such as BART, T5, and PEGASUS has made this sort of summarization process more efficient and accurate. The objective of this paper is to analyze the performance of different transformer models, compare them to find an efficient model and fine-tune the model on csebuetnlp/xlsum English corpus. The performance of the generated summaries from the fine-tuned PEGASUS models is evaluated using the ROUGE metric, which basically compares the auto-generated summaries with human-created summaries. The fine-tuned PEGASUS model gives a state-of-the-art performance on the XLSum English Corpus.
Regional-Speech: A Bengali Speech Recognition Dataset for Benchmarking Models Under Dialect Variation in Puran Dhaka.
(Daffodil International University, 2025-01-18) Rahman, Naimur
Regional dialects of the Bengali language, which is spoken throughout South Asia and among the Bengali diaspora, are influenced by historical, cultural, and geographic factors. Eastern Bengali, Manbhumi, Rangpuri, Varendri, and Rarhi are the five main dialects of Bengali based on phonology and pronunciation. There are additional differences in vocabulary, pronunciation, syntax, and morphology within Bangladesh. The distinctive characteristics of dialects from areas such as Dhaka, Chittagong, Sylhet, Rangpur, Rajshahi, Noakhali, and Barishal set them apart from both standard Bengali and from each other. Notwithstanding this linguistic diversity, there is still a dearth of resources and research devoted to comprehending regional Bengali dialects and incorporating them into natural language processing (NLP) systems. By examining these dialects using thorough, datadriven linguistic analyses, such as phonetic and morphological studies, this study seeks to close this gap. We also evaluate the viability of creating computer models customized for these dialects, such as Automatic Speech Recognition (ASR) systems. Applications like virtual voice assistants and other Bengali language tools might be made possible by such models. Our research aims to promote inclusivity and effective communication while advancing knowledge and supporting the preservation of regional Bengali dialects (Puran Dhaka “Dhakaiya”). In order to ensure the Bengali language's continued relevance in contemporary computational applications, this research helps to develop language technologies that respect the language's cultural and linguistic heritage by attending to the linguistic needs of Bengali-speaking communities.
Security Operations (VAPT)
(Daffodil International University, 22-11-25) Rahman, Naimur
Described in this report is my role as an Engineer, Cyber Security at Enterprise InfoSec Consultants (EIC). I am doing my internship under Md. Jahangir Alam, CISA. I finished my 4-month internship with Enterprise InfoSec Consultants (EIC) on November 25, 2022, having started it on July 25, 2022. In this report, I'll go over each task I put into practice during my internship.