Browsing by Author "Hasan, Mahmudul"

Now showing 1 - 20 of 55

A Comparative study of pension system between USA and Bangladesh
(BRAC University, 2020-09) Hasan, Mahmudul; Ahmed, Riyashad
Data Path Ltd. is a business process outsourcing company which is the only company in Bangladesh who provides 401(k) retirement plan services to USA clients. In this report, I explored different aspects of Retirement plan process in USA and Bangladesh. I have described how effective and well-regulated US retirement plan industry is and how much behind, unstructured Bangladesh pension system are. As a legal binding, all employers in USA are required to offer retirement plan to their employees whereas in Bangladesh only public sector organizations have retirement benefits for their employees. In Bangladesh, Private sector pension system is very unstructured and not all private company has retirement plan for their employees, if any then benefits they provide are much lower compared to the public sector. Bangladesh government need to adopt a Universal Pension Scheme for all private and public service holders. To ensure the transparency of managing the retirement funds, every retirement plan should be audited by a certified auditor. Government may impose legal provision on every company to manage the retirement fund in a proper way. It may only be possible to provide retirement benefits to all private and public sector employees if government pursue a structured policy and impose the law in ‘The Company Act.’ Bangladesh can take the US pension system as a benchmark to start the compulsory pension scheme in private sectors.
A heuristic approach of text summarization for Bengali documentation
(IEEE Xplore, 2017-12-14) Abujar, Sheikh; Hasan, Mahmudul; Shahin, M.S.I; Hossain, Syed Akhter
Automated Text Summarization is a technique of summarizing any document or text automatically. Summarized text is the concise form of the given text. In Natural language processing many text summarization techniques are available for English language, but only a few for Bangla language. Bangla is one of the most taught and used language all over the world. Most of the text summarization techniques are implemented in two different ways, known as abstractive or extractive approach. This paper deal with the summarization of Bangla text based on extractive method. A new efficient extractive summarization method is proposed in this work. The other summarization tools developed for Bangla language seems not much appropriate from application point of view. The proposed analysis models are applicable for Bangla text summarization. In the proposed approach, basic extractive summarization is applied with new proposed model and a set of Bangla text analysis rules derived from the heuristics. Every Bangla sentences and words from original text is analyzed properly with Bangla sentence clustering method. This work proposed a new type of sentence scoring processes for Bangla text summarization. In the evaluation of this technique, the system reflects good accuracy of results, comparing to that of the human generated summarized result and other Bangla text summarization tools. Full Text Link: http://doi.org/10.1109/ICCCNT.2017.8204166
A machine learning approach for analyzing and predicting suicidal thoughts and behaviors
(Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Raihan, Kazi Raine; Shanto, Sayed Rakibul Hasan; Hasan, Mahmudul
In the field of public health, suicide is a problem of the utmost significance that demands immediate attention and successful preventative measures. There has been an increase in interest in using machine learning to predict and identify people who are at a high risk of suicide as society struggles with the tremendous effects suicide has on individuals, families, and communities. In this work, we provide a complete evaluation of the state-of-the-art machine learning algorithms for suicide prediction, with the goal of highlighting the achievements made thus far and outlining potential avenues for future research. Examining the various aspects and data sources used in prior studies is essential if one wants to comprehend the complicated environment of suicide prediction. As people frequently convey their feelings, problems, and distress signals through written communication, researchers have realized the enormous utility of harnessing text-based data from social media sites. Machine learning algorithms can find patterns and signs that can point to a higher risk of suicide by examining these textual data sources. Electronic health records have also proven to be a useful tool since they include important details regarding a person's medical background, mental health diagnoses, and previous interactions with healthcare systems. The use of machine learning techniques is critical in converting a large amount of data into useful insights for suicide prevention. To evaluate the obtained data, a variety of algorithms have been used, with neural networks emerging as a major technique. Neural networks can understand complicated patterns and correlations in data, allowing them to make accurate forecasts and identify people who are suicidal. Other machine learning approaches, such as support vector machines, decision trees, and ensemble methods, have also shown promising results, demonstrating the wide range of tools available for suicide prediction. While machine learning has the potential to significantly improve suicide prevention efforts, it is critical to address the ethical considerations related to putting such models into practice. To secure individuals' sensitive information, privacy and data security problems must be properly managed. Furthermore, the potential for bias and prejudice within machine learning models must be. 5 | P a g e carefully analyzed and reduced to provide fair and equal results. Researchers and practitioners may strive toward establishing responsible and ethical suicide prediction algorithms by actively engaging with these ethical factors. This thesis focuses on the considerable advances achieved in suicide prediction via the use of machine learning techniques. Researchers have made significant progress in detecting patients at high risk of suicide by using multiple data sources such as social media, electronic health records, and demographic information, as well as employing machine learning algorithms such as neural networks. Looking ahead, machine learning has enormous potential to improve suicide prevention efforts, opening new avenues for tailored treatments and support. However, it is critical that these advances be achieved responsibly and ethically, with privacy, fairness, and equity being valued in the creation and implementation of these models.
A novel approach to forecast traffic congestion using CMTF and machine learning
(BRAC University, 2018-04) Chowdhury, Md. Mohiuddin; Hasan, Mahmudul; Safait, Saimoom; Uddin, Jia; Chaki, Dipankar
Traffic congestion severely affects many cities around the world causing various problems like fuel wastage, increased stress levels, delayed deliveries and monetary losses. Therefore, it is urgent to make an accurate prediction of traffic jams to minimize these losses. But forecasting is a real challenge to obtain promising results for vibrant and ambiguous traffic flows in urban networks. This paper proposes a new traffic congestion model using pre-calculated density from node information table based on previous traffic data. In this model, we predicted traffic congestion of an intersection according to its adjacent road's node information table, where node information table contains the traffic density of all incoming lanes of an intersection (node). Besides, for this model, we consider all intersections of a city as individual nodes, and we prepare node information table for each node. Our work can be divided into two parts: (1) we perform time series analysis on previous data of a node and its adjacent nodes, and (2) then apply those calculated values to this model and make the prediction based on it. The forecasted value will always be between 0 and 1. Where 0 means no traffic congestion, close to 0 means low traffic congestion and 1 means heavy traffic or close to 1 means congested traffic lane accordingly.
A study on Knit Concern Group
(BRAC University, 2023-10) Hasan, Mahmudul; Hossain, Saif
The Knit Concern Group (KCL), established in 1990, specializes in knitting, dyeing, and garment production. With high-quality products and endorsements from international companies like Oeko Tex, SGS, and WRAP, KCL has gained a reputation for its high-quality products. The company is certified by GOTS, Oeko-Tex, RCS, and OCS. KCL's turnover has grown from 6 million US dollars in 2003 to 149 million US dollars today. Due to its highquality products and the endorsement of numerous international inspection companies, including Oeko Tex, SGS, and WRAP, as well as prestigious purchasers, including MARKS & SPENCER, S.OLIVER, PUMA, H&M, and others, it has gained a great deal of reputation since it started production. KCL has won numerous national and international awards for inclusive skill development and environmental performance.
Assessment of Biological Contaminants in Energy Stimulating Herbal Medicines Collected from Dhaka City, Bangladesh
(Asian Journal of Chemistry, 2020) Zamir, Rausan; Islam, Nazmul; Hasan, Mehdi; Hasan, Mahmudul; Asraf, Ali; Zakaria, M.; Howlader, M.B.H.
Ubiquitous nature of erectile dysfunction (ED) has placed it as one of the most rampant health care problem and therefore consumption of energy stimulating herbal medicines (ESHMs) has increased in Bangladesh. However, these herbal medicines reaching consumers without maintaining proper screening procedure, which bring a threat to public health safety. An analysis of biological contaminants (microbial load) of these herbal medicines available in Bangladesh was investigated. In most of samples, the total bacterial counts (TBC) 6 × 107 − 32 × 1011 cfu/mL and lactobacillus count 6 × 108 − 12 × 1011 cfu/mL exceed the maximum value as percribed by WHO.
Audio Watermarking: A Comprehensive Review
(Scopus, 2024) Uddin, Mohammad Shorif; jaman, Ohiduj; Hasan, Mahmudul; Shimamura, Tetsuya
Audio watermarking has emerged as a potent tech nology for copyright protection, content authentication, content monitoring, and tracking in the digital age. This paper offers a comprehensive exploration of audio watermarking principles, techniques, applications, and challenges. Initially, it presents the fundamental concepts of digital watermarking, elucidating its key characteristics and functionalities. After that, different audio watermarking methods in both the time and transform domains are explained, such as feature-based, parametric, and spread spectrum methods, along with how they work, and their pros and cons. The paper further addresses critical challenges in maintaining key criteria such as imperceptibility, robustness, and payload capacity associated with audio watermarking. Addi tionally, it examines watermarking evaluation metrics, datasets, and performance findings under diverse signal-processing attacks. Finally, the review concludes by discussing future directions in audio watermarking research, emphasizing advancements in deep learning-based approaches and emerging applications..
Bengali Text Generation Using Bi-directional RNN
(10th International Conference on Computing, Communication and Networking Technologies, IEEE, 2019-07-06) Abujar, Sheikh; Masum, Abu Kaisar Mohammad; Hossain, Syed Akhter; Hasan, Mahmudul; Chowdhury, S. M. Mazharul Hoque
Current world is growing so fast and communication between nation and different type of people with different language became part of our life. Even from buying product to our social life everything is dependent on communication. Therefore language is the most important part of human life. Though still now there is a language barrier for communication between people. But very soon language will be universal and everyone will be able to communicate in any language worldwide using the NLP technology. For that it is necessary to understand each language individually. This research proposes a new type of text generation of Bangla language using the bi-directional RNN. This technique is used to predict the next possible word in a Bangla text.
Bubble departure phenomena on modified surfaces for enhanced pool boiling
(Department of Mechanical Engineering, 2018-06-02) Hasan, Mahmudul; Mozumder, Dr. Aloke Kumar
This research work explores bubble departure phenomena on different modified surfaces for enhanced pool boiling of water. Experiments were conducted for understanding bubble interaction with surfaces of different topography. The experiments were conducted in a controlled environment. Important parameters of pool boiling i.e. bubble departure diameter and bubble departure frequency were measured and analyzed to understand the mechanism of pool boiling and associated heat transfer. High speed video camera was employed to capture bubble phenomena on boiling surface. Three different surfaces have been used for experimentation as plain surface, pitted surface and finned surface. Copper is used as boiling surface and fin material. The setup was designed in such a way that the effect of surface topography can be precisely measured. It has been observed that bubble departure phenomena not only depends on the supplied heat flux, but also on the surface topography, bubble merging and nucleation site density. Here it is revealed that with higher heat flux bubble departure diameter and bubble departure frequency generally increases along with heat transfer coefficient. But the increment is not linear as it seems. Although with the increase of heat flux, bubble departure diameter increases, there are other factors like surface tension, acting forces due to fluid motion, drag force and surface topography that affects the phenomena. These factors also affects the heat transfer coefficient of the system. From the visual observation of the images of high speed video camera and analysis of obtained data, an empirical correlation has been proposed that can well predict the bubble departure diameter in pool boiling for different modified surfaces.
Characterization of Carbon Nanotube field effect transistor
(BRAC University, 9/4/2012) Khan, Sabbir Ahmed; Hasan, Mahmudul; Mominuzzaman, Sharif Mohammad
From the concept of material science, any materials haying an individual structure And characteristics have their own limitations. Due to the call for technological advancement, silicon-based integrated circuits and the scaling of’ silicon MOSFET design faces highcomplications like tunneling effect, short channel effect, gate oxide thickness effect etc. To solve these problems, new material alternatives are needed with such characteristics. Recently, carbon nanotube has caught the attentions with promising future to replace silicon-based materials due to its superior electrical properties and characteristics. Simulation studies of carbon nanotube field-effect transistors ((CNFETs) are presented using models of increasing rigor and versatility that have been systematically developed. The studies and modeling of carbon nanotube, which includes band structures and current-voltage graphical plots, are covered in this thesis Also, analysis has been made to see the effect of gate oxide thickness change, temperature change, dielectric constant change, gate control coefficient, drain control coefficient and chirality changing effect on the device performance, in particular on the drain current. "The purpose of this paper is to study the behavior of CNFET and the twain focus is on the simulation of its current.-voltage (I-V) characteristic and observes the parameter changing effect on it. The simulation study is carried out using MATLAB program and the result obtained is used to compare the device performance with MOSFET. Resides, further analysis has been done through the comparison of' the simulation result of the other groups to justify ' result.
DC MOTOR CONTROL USING REMOTE
(Daffodil International University, 2018-12-01) Hasan, Mahmudul
The aim of development of this project is towards providing efficient and simple method for control of DC motor using IR remote. DC motor is the most common type of motor. DC motors normally have just two leads. One positive and one negative, If the leads are switched the motor will rotate in opposite direction. The main objective of this project is to make a DC motor speed control system, (without changing the way that is the leads are connected of the motor), with low cost and easy to control any industry. The project has four main system: 1) Forward direction 2) Reverse direction 3) forward and reverse direction 4) High and low variable speed. This paper discusses the design and implementation of a DC motor speed control system using IR remote. The Microcontroller is programmed by using C-Programming Language so that it can be easily controlled.
Department of Textile Engineering Study on Photo Fading and Color Fastness Evaluation of Disperse Dye on Polyester Fabric
(Daffodil International University, 23-04-01) Hasan, Mahmudul
In this study, dyeing of polyester fibre in different shade ( 1%, 2%, 3%) with disperse dye is carried out. Here high temperature method is used in dyeing of polyester fiber with disperse dye. The main purpose of this study is to find out the evaluation of the photo fading of the difference shade% of the disperse dye with polyester fiber. The dyeing is carried out at 130 ℃ where we use different chemical for dyeing. I also find out the color fastness rating for the different shade percentage of the disperse dye. I have measured the color fastness to wash, color fastness to rubbing, color fastness to perspiration. these testing is done according to the ISO method. Color fastness to washing rating is excellent for each shade. Color fastness to rubbing is excellent for each shade. Also, color fastness to perspiration is excellent for each shade.
Design and Implementation of an IoT Based Indoor Air Quality Monitoring System
(2021-09) Alam, Didarul; Hasan, Mahmudul
Smart cities follow different strategies to face public health challenges associated with socio-economic objectives. Buildings play a crucial role in smart cities and are closely related to people’s health. Moreover, they are equally essential to meet sustainable objectives. People spend most of their time indoors. Therefore, indoor air quality has a critical impact on health and well-being. With the increasing population of elders, ambient-assisted living systems are required to promote occupational health and well being. Furthermore, living environments must incorporate monitoring systems to detect unfavorable indoor quality scenarios in useful time. This paper reviews the current state of the art on indoor air quality monitoring systems based on Internet of Things and wireless sensor networks in the last five years (2014–2019). This document focuses on the architecture, microcontrollers, connectivity, and sensors used by these systems. The main contribution is to synthesize the existing body of knowledge and identify common threads and gaps that open up new significant and challenging future research directions.Air quality monitoring provides raw measurement of gases and pollutant concentrations, which can then be analyzed and interpreted. Air pollution is a concern in many urban areas and be the major reason for respiratory problems among many people, monitoring the air quality may help many distress from respiratory problems and diseases, and thereafter informing engineering and policy decision makers to recover the quality of air. Major contributor’s air causing respiratory problems are Fine particles produced by the burning of fossil fuel, noxious gases, Ground-level ozone g), Volatile organic compounds. A prototype for air pollution monitoring device has been developed to measure the concentration of CO2 and gases, monitoring at a specified rate and communicating, to notify to any wireless device when the threshold of these gases is reached. Though the prototype can be extended across regions for high-fidelity emissions monitoring to explore the effects of environmental factors on intra-hour air quality.
Design and Implementation of an IoT Based Indoor Air Quality Monitoring System
(Department of Electronic and Telecommunication Engineering, International Islamic University Chittagong, 2021-09) Toshif, Didarul; Hasan, Mahmudul
Smart cities follow different strategies to face public health challenges associated with socio-economic objectives. Buildings play a crucial role in smart cities and are closely related to people’s health. Moreover, they are equally essential to meet sustainable objectives. People spend most of their time indoors. Therefore, indoor air quality has a critical impact on health and well-being. With the increasing population of elders, ambient-assisted living systems are required to promote occupational health and wellbeing. Furthermore, living environments must incorporate monitoring systems to detect unfavorable indoor quality scenarios in useful time. This paper reviews the current state of the art on indoor air quality monitoring systems based on Internet of Things and wireless sensor networks in the last five years (2014–2019). This document focuses on the architecture, microcontrollers, connectivity, and sensors used by these systems. The main contribution is to synthesize the existing body of knowledge and identify common threads and gaps that open up new significant and challenging future research directions.Air quality monitoring provides raw measurement of gases and pollutant concentrations, which can then be analyzed and interpreted. Air pollution is a concern in many urban areas and be the major reason for respiratory problems among many people, monitoring the air quality may help many distress from respiratory problems and diseases, and thereafter informing engineering and policy decision makers to recover the quality of air. Major contributor’s air causing respiratory problems are Fine particles produced by the burning of fossil fuel, noxious gases, Ground-level ozone g), Volatile organic compounds. A prototype for air pollution monitoring device has been developed to measure the concentration of CO2 and gases, monitoring at a specified rate and communicating, to notify to any wireless device when the threshold of these gases is reached. Though the prototype can be extended across regions for high-fidelity emissions monitoring to explore the effects of environmental factors on intra-hour air quality.
Design of a Power System (Solar-Diesel Generator) for a Garment Industry and Load Optimization
(International Journal of Engineering Applied Sciences and Technology, 2019-12) Nahian, Ahnaf Tahmid; Himel, Md.Tahmid Farhan; Hasan, Mahmudul; Rahman, Nafeez; Hossain, Chowdhury Akram
Due to adverse effect of global warming and environmental pollution, future world is looking for decontaminated green energy resources for power generation. Economy of today’s world is based on commercial activities and rapid industrialization. To ensure sustainable economic activity we need to fulfil the energy demand of equipment as well as to serve the automation technology of industrial sector. This results an excess pressure on electricity demand significantly. In spite of many restrictions and proper technical support Bangladesh is looking forward to extract energy from its available renewable resources like other countries. Hybrid power system is a good choice to serve this purpose. This work mainly emphasis on the design and feasibility study of hybrid power system in the context of a particular garment industry, as the garments are the major source of foreign currency and employment in our country. The system is comprised of solar PV and diesel generator. Cost analysis and load optimization is done by HOMER Pro. System validity and advantages are also discussed in explicit way.
Detection of alzheimer's disease using deep learning
(BRAC University, 2019-12) Hasan, Mahmudul; Hassan, Syed Zafrul; Azmi, Tanzina Hassan; Hossain, Emtiaz; Parvez, Mohammad Zavid
Machine Learning has been on top of its form over the last few years. It covers a vast area of predictive web browsing, email and text classification, object detection, face recognition etc. Among all of the other applications of machine learning, deep learning has gained more popularity over the last several years. It is helping researchers in the field of biomedical problems like detection of different types of diseases such as Cancer, Alzheimer, Malaria, Blood cell detection etc. Deep learning is a subset of machine learning algorithms that is used for classification, image processing etc. by extracting features. In our research, we used Convolutional Neural Network (CNN) for classi cation of Alzheimer patients and healthy patients from Magnetic Resonance Imaging (MRI) data. The dataset (OASIS-1) contains 416 subjects classi ed into non-demented and mild to moderate Alzheimer's disease. The classification of this type of medical data is very significant for creating a prediction model or system to examine the presence of the disease in different subjects or to estimate the phase of the disease. Classification of Alzheimer's disease has always been difficult and selecting the distinctive features is the most complicated part of it. By using different CNN architectures like InceptionV3, Xception, MobileNetV2, VGG16, VGG19 we have classified Alzheimer's patients from healthy subjects by calculating different model accuracy, confusion matrix and ROC curve from their MRI data. Among all the models, the basic CNN and the InceptionV3 provide the best accuracy up to 90.62%. This research shows us how different CNN architectures perform on our MRI data of Alzheimer's subjects and healthy subjects in case of classification and helps us to find the best models for the detection of Alzheimer's disease.
Documentation Multi-Client
(Daffodil International University, 2021-12-30) Hasan, Mahmudul
‘Multi-Client Project’ is basically kind of updated version of this ongoing e-commerce industry. In this project seller, customer and employee will be given enough priority. Seller can sell their product through us by their name and customer will be assured by our employee. Employee will be always there for their help, like delivery and other staff. Also in this project, customer can see seller shop location and contact directly with seller. Product could be anything. There will be no specific product recommended. Any seller can sell anything.
“Enhancing Product Customization: Leveraging 3D Modeling and AI-Generated Print Application”
(Daffodil International University, 23-06-25) Hasan, Mahmudul
"Enhancing Product Customization: Leveraging 3D Modeling and AI-Generated Print Application" is a groundbreaking project that explores the integration of 3D modeling and AI to revolutionize the realm of product customization. Traditionally, manual alterations were time-consuming and costly, but with the convergence of 3D modeling and AI, businesses can now offer unprecedented flexibility and efficiency to their customers. This project develops an application that combines user-friendly interfaces, advanced 3D modeling technology, and AI-driven customization options to provide a unique and personalized experience for users. By continuously learning from user interactions and preferences, the AI engine suggests innovative customization options that align with individual customer tastes. The project incorporates credible research findings and insights to ensure accuracy and credibility. Overall, this project sets a new standard for personalized product innovation by harnessing the power of 3D modeling and AI.
Ensemble Based Machine Learning Model for Early Detection of Mother's Delivery Mode
(IEEE, 2023-04-19) Hasan, Mahmudul; Zobair, Md Jakaria; Akter, Sumya; Ashef, Mahir; Akter, Nazrin; Sadia, Nahid Binte
The mother's mode of delivery greatly impacts the relationship between the newborn baby and the mother, as well as the mother's and baby's health. Currently, the cesarean rate is increasing at an alarming rate. The inability to predict the mother's health status and mode of delivery are mainly responsible for this situation. Support Vector Machine (SVM), Decision Tree, Random Forest (RF), Gradient Boosting Classifier(GBC), Logistic Regression, Gaussian Naive Bayes, Stochastic Gradient Descent, CatBoost (CB), Adaptive Boosting (AB), Gaussian Naïve Bayes, Extreme Gradient Boosting(XGB) are used to predict the mother's mode of delivery. This study also proposed an ensemble machine learning algorithm that stacked the SVC, XGB, and RF together and named the ensemble SVXGBRF. To preprocess the dataset, we use a pipeline that basic preprocessing techniques, data balancing and feature selection. Our proposed SVXGBRF classifiers show 95.52% accuracy, 96% precision, recall, f1 score, and 99% AUC score. SVXGBRF shows its superiority, where most models show an accuracy of less than 90% except RF, GBC, CB, and AB. Eventually, this research could be utilized to develop a decision-support system for reducing the number of cesarean sections by trying to extract insights from complex data patterns.
Farm automation system with IoT application
(BRAC University, 2017-12) Hasan, Mahmudul; Hossain, Syed Maksud; Rahman, Mohammad Saad Ur; Ullah, M. M. Sakib; Rhaman, Dr. Md. Khalilur
Agribusiness is the broadest fiscal portion and accepts a basic part in the general monetary change of a country. The world's population is growing additionally; with that improvement we should make greater sustenance. For the immense number of populace it is extremely hard to guarantee the food. From one perspective to guarantee the sustenance of tremendous populace is troublesome then again it is likewise hard to create sustenance thing for food where less measure of individuals and youthful age is additionally losing their interest on cultivating. The extended age has, as it were, originate from incremental changes in development likewise, economies of scale, however that inclination is going to a level. Standard agribusiness procedures are unsustainable and an adjustment in context is required. The main purpose of our project is to make an advanced agricultural system with the help of IoT application, so that we can easily maintain a farm. We are going to create an automation system which can water the plants of a farm without the help of any human hand. Moreover it will have the options of planting seeds, measuring soil moisture etc. To implement these features, we will work with robotic hand which will be controlled by computer numerical control with the help of Arduino and Raspberry pi. The arm will move with the help of motor and wheels. There will be a metal rail through which the arm can be moved. Two motors will help the arm to move on both X and Y axis and a stepper motor will be used for moving the arm on Z axis. As the arm can move on three dimensional spaces, we can make the arm work with our target purposes. The following implementation procedure of Farm Automation with IoT application, Seeding with the help of seeder with a systematic way, Watering, Temperature measuring, Image processing to identify the soils condition, a big data base, 24 hour automatic monitoring, Collecting and sending data to the users, easy to operate. The main goal of this project is to create a new Agricultural revolution