Browsing by Author "Mahmud, Hasan"

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A Golden Age
(East West University, 4/28/2011) Mahmud, Hasan
The paper tries to understand Tahmima Anam’s project of writing history of the Liberation War of 1971. Based on E.H. Carr’s idea of history, I have tried to explain the selective and interpretive mode of Anam’s subjective historiography. In particular, I am interested to understand how her writing is different from objective news reporting on liberation war and subjective and overtly passionate war literature. I shall argue that as a post-war generation writer, Anam has tried to pursue a moderate middle path in order to avoid many historical controversies.
AN OVERVIEW ON KLAIM FAMILY As A MODEL OF COMPUTATION
(Daffodil International University, 2012-07) Mahmud, Hasan; Haque, Mohammad Obaidul; Hasan, Md. Kamrul; Ahsan, A.H.M. Amimul; Rahman, SM. Mostafizur
In a mobile computing environment, processes in mobile agents need to communicate with each other, execute processes or spawn new processes locally or in a remote location, share data among processes. By studying the concurrency theory we can deﬁne model for mobile computation based on the idea of concurrent execution ofprocesses. In this paper, we have addressed the requirements of kernel programming language for migratory mobile processes, investigated KIAIM (Kernel Language for Agents Interaction and Mobility) family as a model of computation, the main results of the research, applications and future trends.
Comparative Analysis of Feature Selection Algorithms for Computational Personality Prediction from Social Media
(IEEE Transactions on Computational Social Systems, IEEE, 2020-02-19) Marouf, Ahmed Al; Mahmud, Hasan; Hasan, Md. Kamrul
With the rapid growth of social media, users are getting involved in virtual socialism, generating a huge volume of textual and image contents. Considering the contents such as status updates/tweets and shared posts/retweets, liking other posts is reflecting the online behavior of the users. Predicting personality of a user from these digital footprints has become a computationally challenging problem. In a profile-based approach, utilizing the user-generated textual contents could be useful to reflect the personality in social media. Using huge number of features of different categories, such as traditional linguistic features (character-level, word-level, structural, and so on), psycholinguistic features (emotional affects, perceptions, social relationships, and so on) or social network features (network size, betweenness, and so on) could be useful to predict personality traits from social media. According to a widely popular personality model, namely, big-five-factor model (BFFM), the five factors are openness-to-experience, conscientiousness, extraversion, agreeableness, and neuroticism. Predicting personality is redefined as predicting each of these traits separately from the extracted features. Traditionally, it takes huge number of features to get better accuracy on any prediction task although applying feature selection algorithms may improve the performance of the model. In this article, we have compared the performance of five feature selection algorithms, namely the Pearson correlation coefficient (PCC), correlation-based feature subset (CFS), information gain (IG), symmetric uncertainly (SU) evaluator, and chi-squared (CHI) method. The performance is evaluated using the classic metrics, namely, precision, recall, f-measure, and accuracy as evaluation matrices.
Competitors analysis and financial analysis of Maximus mobile
(BRAC University, 2015-08) Mahmud, Hasan; Ahmed, Ms Syeda Shaherbanu Shahbazi
Now a day, everyone likes to have more comfort in life. As progress insistently towards this it has been appreciated that this is the time to disburse increasing concentration to the subject of human console and entertainment. That’s why there is a significant choice of modish goods in this world and a lot of companies are paying their awareness in this concern. In spite of its authentic efforts, exceptional image and genuineness, the company is speedy by the increasing and insistent competition in industry. Faced with growing competition in the market and vast sales target Maximus realized the fact that there is no way but to be further aggressive in marketing system. Though happy with the accessible marketing system yet Quartel Infotech Limited (QIL) wants to remain their eyes open for unnoticed days and desires to develop their existing financial situation and marketing system to manage with changing surroundings. They are of very important significance for every company because they make a huge input in marketing and promoting of mobile products. So the more helpful and well-organized the sale forces are the more profit they can put together for their own as well as the company. This report is undertaken particularly to evaluate the market position of Maximus. There has some problems which are using as findings for The Maximus Mobile; the customer service given by the company is not successful and well planned, lack of problem of marketing and finance sector. Because of some problems, Maximus cannot come up with their competitors. All through this competitive market there have various competitor companies who provides better service than Maximus. To solve the problems Maximus Mobile should keep an eye on the phones quality; maintain a good relationship with suppliers, dealers and retailers, appointing skillful employees and make a good marketing system. Therefore this paper on “Competitors Analysis and Financial Analysis of Maximus Mobile” shows a clear picture of different competitors market position, tries to explore the field forces sale performance, excellence of the products, financial condition, advertising usefulness, consumer service, quality of goods, communication, transportation, dealer’s satisfaction and so on.
Computer Networking
(Daffodil International University, 2022-01-05) Mahmud, Hasan
In this report, I'm looking to highlight what I've done and what I've learned from doing an internship. In internship as a Computer Networking, my essential recognition become to growing my ability on networking and knowing about IP addressing, switching, different kind of routing and their protocols. At present, there are plenty of network administrators operating on new technology. I want to learn the difference routing and switching protocols also internet protocol. I could want to find out about actual lifestyles task. I want to find out how a real task will operate. I'm very curious to start my service as a network engineer. That's why I pick out the internship as "Computer Networking". Working in Studio Mason Limited delivered large reviews in my forthcoming careers. Working along the real troubles of the customers, changed into some other key factor to advantage reviews.
Depth-aware Hand Gesture Recognition for Human-Computer Interaction
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 1-06-22) Mahmud, Hasan
Hand gestures can be dened as the movement of the hands and ngers in particular orientations to convey some meaningful information. Recently, inexpensive depth cameras have opened ample research opportunities to work with depth-based features in parallel to image-based features. Existing computer vision-based approaches have limitations in capturing depth variations present in the fine-grained gestures and also in the coarse grained. Hence, we got a scope to exploit depth information and use them in the machine learning models to distinguish those hand gestures correctly. In this thesis, we propose a unique depth quantization technique that can effectively distinguish different hand gestures. Using the technique first, we generate contrast varying depth images that can help to extract salient features from gestural images of static gestures. Second, we use depth values to capture hand nger movement information in the Z-direction to discriminate on-air writing tasks of English Capital Alphabets (ECAs). We have used depth-based features, like raw depth values, quantized depth values, and non-depth features like finger joint points in 2D, ngertip coordinates, other derived features from them, then merge these features to generate a unique dataset for testing the signicance of depth features in terms of recognition accuracy. Experiments on both static and dynamic hand gestures showed that the proposed approach gives higher recognition accuracies. Third, to test our proposed method in deep learning settings, we design a depth-aware CNN-LSTM-based deep-learning model to recognize 14 and 28 dynamic hand gestures. The model takes gray-scale varying depth images and 2D hand skeleton joint points as multimodal input. We achieve better recognition accuracies by performing feature-level and score-level fusion techniques in the benchmark dataset.
Determinants of haemoglobin level during pregnancy and relationship with pregnancy outcome in Bangladeshi urban poor
(2007-11-19T04:23:30Z) Osendarp, Saskia; Wahed, MA; Baqui, A.H; Arifeen, S.; Mahmud, Hasan; van Raaij, Joop
Identifying Neuroticism from User Generated Content of Social Media Based on Psycholinguistic Cues
(2nd International Conference on Electrical, Computer and Communication Engineering, IEEE, 2019-02-09) Marouf, Ahmed Al; Hasan, Md. Kamrul; Mahmud, Hasan
Social media has become a huge repository of textual data and images as each of the users' are creating posts, sharing views or news, capturing the moments via photos etc. Sharing or posting statuses/tweets could be considered as a common feature among the popular social networking sites like Facebook, Twitter, and Google+ etc. User generated textual data such as statuses or tweets could be considered as the essential language to communicate in social media with others. This paper investigates the possibilities of identifying negative personality trait based on the psycholinguistic cues extracted from the language used in social media. Predicting personality traits based on widely accepted framework of Big Five Factor Model (BFFM) is a challenging task. According to the model, there are four positive traits namely openness to experience, conscientiousness, agreeableness and extraversion, while there is only one negative trait neuroticism. The tendency of experiencing negative emotions such as anger, sad, anxiety, depression, instability are referred as neuroticism. We have used psycholinguistic cues extracted using linguistic enquiry and word count (LIWC) for predicting neuroticism. We have applied five different classifiers to evaluate the prediction model.
Infant growth patterns in the slums of Dhaka in relation to birth weight, intrauterine growth retardation, and prematurity
(2000-10) Arifeen, Shams E.; Black, Robert E.; Caulfield, Laura E.; Antelman, Gretchen; Baqui, Abdullah H.; Nahar, Quamrun; Alamgir, Shamsuddin; Mahmud, Hasan
Parallel optical flow detection using CUDA
(BRAC University, 4/30/2014) Hossen, Khalid; Mahmud, Hasan; Alom, Md. Zahangir; Karim, Risul
The intention of this thesis paper is to deploy a parallel implementation of the optical flow detection algorithm known as the Lucas-Kanade algorithm. As an important algorithm in the field of computer vision, it is believed that it holds much promise and shows much potential for benefiting from techniques used to enhance performance through parallel programming which can be executed with the use of CUDA. Though more techniques of parallel programming exist that can be used to fasten the process, Lucas-Kanade has never been implemented in parallel programming before. The result of the research has shown both serial and parallel implementation of optical flow detection using deferent processing units (CPUs and GPUs). The parallel implementation have lessened 2 to 13 seconds of processing time (depending on the hardware configuration) for the same database compare to serial implementation.
Real Time Heart Disease Monitoring System
(East West University, 2018-05-05) Hosen, Md. Solaiman; Mahmud, Hasan
During the recent decade, rapid advancements in healthcare services and low cost wireless communication have greatly assisted in coping with the problem of fewer medical facilities. The main purpose of this research work is to develop a wireless sensor network system that can continuously monitor and detect cardiovascular disease experienced in patients at remote areas. One of the most prevalent healthcare problems today is the poor survival rate of out-of-hospital sudden cardiac arrests. The Objective of this study is to present a Wearable Body Area Network System to continuously capture and sent the ECG signal to patient’s Mobile Phone. By analyzing the signal critical situation will be identified and alert will be sent to doctor, relatives and Ambulance services using data processing algorithm implemented on patient’s mobile phone. A wireless transmission system is also proposed for continuous data transmitting to a server system where a doctor can monitor the patient Electrocardiography (ECG) from a long distance. In this project we developed a wearable ECG device and a real time Brachycardia, tachycardia, and sinus arrhythmia detection based android mobile application. ECG signals from patient’s body is collected by the mini ECG device and sent through a Bluetooth module to Android Mobile Application. On Android application processed data analysis based Pan Tompkins algorithms to detect complex QRS ECG signal and heart beats. From the number of heart rate can be detected abnormalities. Upon completing the system, we tested the system using signals generated by Fluke PS400 and real data. There are three categories of abnormalities under study: Brachycardia, tachycardia, and sinus arrhythmia. Normal heart signal is also included in the test. We have tested this application in real time by collecting the ECG from the patient in stationary as well as simulated data. In both situations the application fulfills requirements of the proposed system.
Secret Life of Conjunctions
(Scopus, 2020) Marouf, Ahmed Al; Hasan, Md. Kamrul; Mahmud, Hasan
Large amount of textual, visual, and audio data are generating in social networking sites by the users nowadays. Social media users are generating these data in high increasing rate than any other time. Status updates/tweets, likes, comments, and shares/re-tweets are the basic features provided by the online social networking (OSN) sites. This paper utilizes the status updates of users to analyze and extract relevant natural language features to map them into predicting personality traits of those users. It is evident that using more features in a supervised learning system can predict more accurately. However, the linguistic features such as function words, character-level, word-level, structure-level features could be considered as relevant features for this case. While predicting the big five personality traits: openness-to-experience, conscientiousness, extraversion, agreeableness and neuroticism, the highly correlated features are determined applying feature selection algorithms. For experimentation, the research question is “What are the highly correlated features which are commonly found for all five personality traits?” In this paper, we have presented the experimental findings while determining the highly correlated features with the class and found that the percentage of “conjunction words” is always a common feature for each of the personality traits. The underlying (secret) relationship of this feature is analyzed in this paper.