Mohammad Rashedur Rahman

North South University · Department of Electrical Engineering and Computer Science

Customer feedback is an invaluable source of information for any organization, and crucial business phases such as market research, product development, and post-sale services may greatly benefit from it. Existing methods utilize qualitative data, surveys, and excel-generated reports to analyze customer complaints and feedback. However, it takes a...

In riverine nations like Bangladesh, floods are frequent, bringing disaster that impairs people's lives and the national economy. Due to climate change, uncontrolled growth, rapid urbanization, expansion of agriculture plantations, and other factors influence Land-Use-Land-Cover (LULC) changes which can make flooding conditions more unpredictable....

In recent years, Bangladesh has seen significant development in the digitalization of various healthcare services. Although many mobile applications and social platforms have been developed to automate the services of the healthcare sector, there is still scope to make the process smooth and easily accessible for general people. This paper describe...

The Bangla Language ranks seventh in the list of most spoken languages with 265 native and non-native speakers around the world and the second Indo-Aryan language after Hindi. However, the growth of research for tasks such as sentiment analysis (SA) in Bangla is relatively low compared to SA in the English language. It is because there are not enou...

Floods are one of the most catastrophic natural disasters. Water level forecasting is an essential method of avoiding floods and disaster preparedness. In recent years, models for predicting water levels have been developed using artificial intelligence techniques like the artificial neural network (ANN). It has been demonstrated that more advanced...

The scarcity and diversity of medical data have made it challenging to build an accurate global classification model in the healthcare sector. The prime reason is privacy concerns and legal obstacles which limit data-sharing scope among institutions in healthcare. On the other hand, data from a single source is hardly sufficient to develop a univer...

Traditional voting procedures are non‐remote, time‐consuming, and less secure. While the voter believes their vote was submitted successfully, the authority does not provide evidence that the vote was counted and tallied. In most cases, the anonymity of a voter is also not sure, as the voter's details are included in the ballot papers. Many voters...

The growing popularity of online news has prompted concerns regarding (i) the socio-political influence over news dissemination, (ii) the waning freedom of news media, (iii) and a facile news evaluation process. A piece of news having the power to capture a large audience and sow the seed of bizarre consequences on a national scale should be pruden...

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not bee...

Financial fraud cases are on the rise even with the current technological advancements. Due to the lack of inter-organization synergy and because of privacy concerns, authentic financial transaction data is rarely available. On the other hand, data-driven technologies like machine learning need authentic data to perform precisely in real-world syst...

Food delivery systems are gaining popularity recently due to the expansion of internet connectivity and for the increasing availability of devices. The growing popularity of such systems has raised concerns regarding (i) Information security, (ii) Business to business (B2B) deep discounting race, and (iii) Strict policy enforcement. Sensitive perso...

Food delivery systems are gaining popularity recently due to the expansion of internet connectivity and for the increasing availability of devices. The growing popularity of such systems has raised concerns regarding (i) Information security, (ii) Business to business (B2B) deep discounting race, and (iii) Strict policy enforcement. Sensitive perso...

More than any other literary genre, poetry presents a significant challenge for Natural Language Processing (NLP) algorithms. Small poetries in the Persian language are called ghazal. Ghazal classification by document embedding technique and sequential learning on poetic era is an under-explored area of research till now. Deep learning and document...

Assessing flood risk is challenging due to complex interactions among flood susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel flood risk assessment framework by utilizing a hybridized deep neural network (DNN) and fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as a case study region,...

The volume and availability of satellite image data has greatly increased over the past few years. But, during the transmission and acquisition of these digital images, noise becomes a prevailing term. When preprocessing the data for computer vision tasks, human experts often produce noise in the labels which can downturn the performance of learnin...

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have...

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ([Formula: see text]) compartmental model considering a new dynamic transmissio...

Recently COVID-19 pandemic has affected the whole world quite seriously. The number of new infectious cases and death cases are rapidly increasing over time. In this study, a theoretical linguistic fuzzy rule-based Susceptible-Exposed-Infectious-Isolated-Recovered (SEIIsR) compartmental model has been proposed to predict the dynamics of the transmi...

Financial fraud cases are on the rise even with the current technological advancements. Due to the lack of inter-organization synergy and because of privacy concerns, authentic financial transaction data is rarely available. On the other hand, data-driven technologies like machine learning need authentic data to perform precisely in real-world syst...

E-commerce platforms have made our life easier and bought plenty of advantages too. However, due to fraud and scams, trust is a concern while buying products online. In this study, we proposed a blockchain-based architecture for the e-commerce sector where data mining technology is used to detect fraudulent users by generating precise and effective...

Due to the internet and social media advances, people now communicate their ideas and opinions on many topics freely through several channels. However, because of its ethnicity, religion, genders, etc., this freedom of expression is exploited to direct hate to individuals or groups of people. The increase in hate speech led to disputes and cyberbul...

The goal of our research is to design and implement a microcontroller-based smart fire detection and security system with real-time web integration. Most of the available fire alarms depend on the gas sensor only, which is not efficient enough to secure life and property. To minimize the issue, we bring a multisensor fire detection and notification...

In recent years, visual pollution has become a major concern in rapidly rising cities. This research deals with detecting visual pollutants from the street images collected using Google Street View. For this experiment, we chose the streets of Dhaka, the capital city of Bangladesh, to build our image dataset, mainly because Dhaka was ranked recentl...

Human activity recognition (HAR) is one of the leading research fields in ubiquitous computing working to integrate seamless technologies in our daily lives. The researches in this field focus on the technological advancement of fine activity recognition through using minimal technological deployments along with consideration of human factors. The...

Spatial flood susceptibility mapping (FSM) is one of the key components of flood risk assessment. Recent studies showed the efficacy of hybridized machine learning (ML)-based models in predicting flood susceptibility. The quality of this prediction depends on the presence or absence of label noise in the training data collected from real world floo...

Large-scale management of surface water resources in urban areas can be difficult, especially if the region is subject to monsoonal waterlogging. Deep learning-based methods for computer vision tasks, such as image segmentation, can effectively be applied to remote sensing data for generating water body maps of large cities, aiding managerial entit...

Freely available building maps of rapidly changing built and semi-built environments may contain label noise. When temporal correspondence between images and labels does not hold, the labels may be subject to incorrectly observed building instances. For example, in most growing semi-built environments, such as the Kutupalong mega-camp in Bangladesh...

Label noise is a commonly encountered problem in learning building extraction tasks; its presence can reduce performance and increase learning complexity. This is especially true for cases where high resolution aerial drone imagery is used, as the labels may not perfectly correspond/align with the actual objects in the imagery. In general machine l...

Label noise is a commonly encountered problem in learning building extraction tasks; its presence can reduce performance and increase learning complexity. This is especially true for cases where high resolution aerial drone imagery is used, as the labels may not perfectly correspond/align with the actual objects in the imagery. In general machine l...

One of the challenges of training artificial intelligence models for classifying satellite images is the presence of label noise in the datasets that are sometimes crowd-source labeled and as a result, somewhat error prone. In our work, we have utilized three labeled satellite image datasets namely, SAT-6, SAT-4, and EuroSAT. The combined dataset c...

According to the Global Burden of Disease project, skin diseases are the fourth leading cause of benign sickness throughout the world. Diagnosis of dermatological diseases presents a challenge alongside the absence of trained dermatologists and access to formal medical care. This presents a critical challenge, especially in countries with a large r...

In recent times, satellite data availability has increased significantly, helping researchers worldwide to explore, analyze and approach different problems using the most recent techniques. The segmentation of sediment load in coastal areas using satellite imagery can be considered as a cost-efficient process as sediment load analysis can be costly...

The purpose of our research work is to understand the efficiency and advantage of applying machine learning technique on remote sensing data collected from one of the largest mangrove forests in the world, named Sundarbans. Our study area was Sundarbans mangrove forest, and we have detected land cover changes in this area. The images we have used w...

Flooding has become an exceedingly complex problem in many developing countries of the world including Bangladesh. Currently, Bangladesh is using MIKE 11 hydrodynamic model for flood forecasting. Previous studies indicated that hybridized machine learning models, especially support vector regression (SVR) models outperform standalone machine learni...

Bangladesh is a country in South Asia with full of natural beauty, from beaches to hilly regions, forests, and waterfalls. The tourism sector of the country has been emerging over the past few years and creating employment, innovation, and new infrastructures. The tourist spots of Bangladesh are scattered all over the country. This paper focuses ma...

Since December 2019, the novel coronavirus (COVID-19) has become one of the most contagious diseases to have hit the world for several decades. From December 2019 till May 2020, this respiratory syndrome-like disease has quickly spread to all countries around the world and has taken more than 400 thousand lives. The WHO declared a global pandemic s...

Image processing is crucial in any image analysis to determine the problem. If it is a medical area, a suitable image processing method becomes even more imperative to get as accurate results as possible. Due to the widespread outbreak of coronavirus disease 2019 (COVID-19), an infectious respiratory disease, it has become quite urgent that a relia...

Nowadays, by using different computational system medical sector predict diseases. These systems not only aid medical experts but also normal people. In recent years stroke becomes life threatening deadly cause and it increased at global alarming state. Early detection of stroke disease can be helpful to make decision and to change the lifestyle of...

Neonatal sepsis that is a major threat for maternal and neonatal health worldwide. In this work we design non-invasive, deep learning classification models for predicting accurately and efficiently the early-onset sepsis in neonates in Neonatal Intensive Care Units. By non-invasive, it means that no external instrument or foreign body is introduced...

In this age of natural language processing, most of the sentiment analysis tasks are done by polarization, for example, 0 for negative or 1 for positive of the given context/text. In some work, the tasks are done using fine-grained polarization, such as very negative or very positive. The proposed system of this paper includes the categorization of...

Recent advances in the field of natural language processing has improved state-of-the-art performances on many tasks including question answering for languages like English. Bengali language is ranked seventh and is spoken by about 300 million people all over the world. But due to lack of data and active research on QA similar progress has not been...

In this study, Random Forest Regressor, Linear Regression, Generalized Regression Neural Network (GRNN) and Fully connected Neural Network (FCNN) models are leveraged for predicting unconfined compression coefficient with respect to standard penetration test (N-value), depth and soil type. The study is focused on a particular correlation of undrain...

Road crash is one of the major burning issues for Bangladesh. There are several factors that are responsible for occurring road crashes. If we can understand the causes and predict the severity level of a particular type of accident upfront, we can take necessary steps in the proper time to lessen the damages. In this study, we have built some pred...

Statistical values alone cannot bring the whole scenario of crime occurrences in the city of Dhaka. We need a better way to use these statistical values to predict crime occurrences and make the city a safer place to live. Proper decision-making for the future is key in reducing the rate of criminal offenses in an area or a city. If the law enforce...

Nowadays, the Internet of Things (IoT) is a common word for the people because of its increasing number of users. Statistical results show that the users of IoT devices are dramatically increasing, and in the future, it will be to an ever-increasing extent. Because of the increasing number of users, security experts are now concerned about its secu...

Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex...

Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex...

According to a national survey in Bangladesh, a south asian country, approximately 22.6 percent of the new born babies are born with low birth weight (below 2.5 kg or 2500 grams) [13]. There are some key factors regarding low birth weight which are clinically recognized but apart from the clincical perspective some other health and demographic fact...

In this study, General Regression Neural Network(GRNN), Artificial Neural Network (ANN), Fully Connected Neural Network (FCNN), Support Vector Regression (SVR) and Linear Regression (LR) models have been implemented in order to predict the composition of soil with respect to the Standard Penetration Test (SPT), and soil depth. The primary focus has...

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is the most burning issue all over the world right now. In this study, we have proposed a new fuzzy rule-based Susceptible-Exposed-Infected-Recovered-Death (SEIRD) compartm...

In recent times, the air quality level of Dhaka city has been termed as hazardous. The weather of Dhaka city has gone through some drastic changes because of extreme air pollution. In this paper, we have applied several machine learning models that include deep learning such as Long Short-Term Memory (LSTM) and proposed different techniques to fore...

Building maps have a plethora of applications in government, industry and academia. In most cases, large scale maps can be retrieved from OpenStreetMap vector data. However, for certain rapidly changing built and semi-built environments, corresponding maps are not as accurate and contain label noise such as missing, incorrectly present, shifted lab...

Since the dawn of human civilization, forced migration scenarios have been witnessed in different regions and populations, and is still present in the twenty-first century. The current largest population of stateless refugees in the world, the Rohingya people, reside in the southeastern border region of Bangladesh. Due to rapid expansion of refugee...

This research work introduces and describes a robust method for extracting harmonic color features (HCFs) and verifies its validity by predicting visual aesthetics of a large image dataset against a large human survey on the same dataset. This work is a continuation of our previous research (Firoze et al. [13]) where we demonstrated a machine’s cap...

Convolutional neural networks (CNN) are the most popular class of models for image recognition and classification task nowadays. Most of the superstores and fruit vendors resort to human inspection to check the quality of the fruits stored in their inventory. However, this process can be automated. We propose a system that can be trained with a fru...