Browsing by Author "Dr. Tanzilur Rahman"

Now showing 1 - 7 of 7
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    Open Access
    A Portable ECG Machine
    (North South University, 2018-04-30) Sabuj Golder; MD Fatin Rahman Behon; Nasik Monowar; Dr. Tanzilur Rahman; 1330061042; 1320907042; 1320164042
    Electrocardiography commonly known as ECG is the process of recording electric signals generated from the heart’s rhythm. The accurate Electrocardiogram (ECG) and pulse rate information is one of the most important aspects for the various sorts of heart functioning disorder identification. The paper briefly shows how the project was carried towards the aim and demonstrates a solution for acquiring ECG signals in a portable way. The goal was to make a prototype of a portable ECG-monitoring device is for clinical and non-clinical environments as part of a telemedicine system to provide remote and continuous surveillance of patients. The aim was to make a smart, easy to use, less costly, portable device to observe the heart beat signal from anywhere in any emergency case.
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    Open Access
    A Smart Online Medicine Selling System Along With DIMS And BDMS
    (North South University, 2020-06) Muttakin Ahamed; Md. Golam Haider; Monjur A-Elahi Tanmoy; Dr. Tanzilur Rahman; 1610830042; 1620218042; 1620587042
    An Online Medicine Selling System is a platform where the user will be able to buy medicines online. There are some websites available in our country running online medicine selling systems. But the change we have made in ours is to sell not only New Medicines but also Unused Medicines by the user. Pharmaceuticals, generics, and indications will categorize each of the medicines. Pharmaceuticals will help users to know from which company this medicine has been produced, generics will allow users to know which type of drug it is, and indication will help to know for what disease or problem you need to take that medicine. Secondly, we made a Blood Donor Management System from where the user will find out blood donors available in our country for their needs. And finally, we made a Drug Information Management System, which will help users know about medicines in detail like pharmacology, indications, contraindications, side-effects, precautions, dosage & administrations, and so on. We planned to make a platform that will be a complete hospitality system related to the medical sector that will be a great help for the people of our country. From this thought, we collaborated on three important sectors of medical sectors under one single platform.
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    Open Access
    An IoT Based Water Level Sensing and Real Time Monitoring System
    (North South University, 2019) Md. Tanvir Ahmed; Raiyhan Bhuiyan; Md. Sazid Al Emon; Dr. Tanzilur Rahman; 1420810042; 1410100642; 1411535042
    IoT (Internet of Things) based Water Level Sensing & Real Time Monitoring System signifies the reducing of water wastage on regular basis in our home and industry, through the automation system. Water is one of the most valuable and essential elements on earth. We use it in our daily life. We can’t imagine a single moment in our day to day life without water. Dhaka WASA usually serve water to the whole city for 2 or 3 hours and 2 times per day. So, in our residence, office, industries almost everywhere there is a system or process to use this water. We store supply water in a large ground tank and then pull it with water pump to our usable tank. In this process, people manually operate the pump machine and when overflowed the tank and most of the time people usually forget to switch it off. In daily basis, it’s a huge waste of our natural resource. So, we are suggesting a process to automate the water tank, control the water level and no more wasting. This project offers real time monitoring of our regular usable water tank and control the submersible pump (turn on or off) as necessary. Also, the owner can see the live water level with a simple app and can manually handle the motor (on when off and off when on) through button provide on the app. We are very much aware that there are hundreds of project like this but main difference is none of those set up or tested on market usable tank and pump. We did this successfully and we can sell this product in a reasonable price.
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    Analysis Of PPG Signal Of Cardiac Disease
    (North-south University, 2018-11-30) Ayesha Siddiqua; Nowshin Mostafa; Syeda Meem Rameen; Dr. Tanzilur Rahman; 1321214045; 1410858042; 1430477043
    In this thesis paper we are presenting the result of analysis of Photoplethysmogram (PPG) signal of normal subjects and cardiovascular problematic subjects. Photoplethysmogram (PPG) is a non-invasive and inexpensive method for detection of cardiovascular disease. Photoplethysmogram is a technique that measures blood volume changes from skin which is close to arteries. PPG data were taken from 10 subjects and data were taken from subject’s index finger. Amongst 10 subjects 5 subjects were cardiovascular problematic patients and 5 were normal. Primary cardiac problem detection technique is Electrocardiogram (ECG). ECG is also a noninvasive technique but ECG is expensive compare to PPG. ECG machines are big, not portable and very expensive to have individually at home. ECG is more suitable for hospitals. ECG tests are also expensive. It will cost very large for anyone who wants to keep going on tests of cardiac disease detection at early stage. Compared to ECG PPG is very small, portable and inexpensive. All kind of people can afford PPG based system for cardiac detection at home. In this paper we presented two methods based on PPG signal to analyze cardiac disease one is pulse amped sensor and other one is smartphone. This paper presents all the research, techniques, and algorithm development to analyze cardiac disease using pulse amped sensor and smartphone both individually. Pulse sensor is affordable and 2.1 billion or more people already uses smartphone today's time so any system based on both or any of the way will be low cost and great for early detection of cardiac disease at home or anywhere. Pulse sensor and smartphone both can be a worth alternative of ECG. We hope this study will be valuable to create both or any of the monitoring system for cardiovascular disease detection.
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    Open Access
    Automatic Sleep Stages Detection Using Supervised Machine Learning
    (North South University, 2020) Saidul Islam Tanveer; Tangim Hossain Akash; Rakibul Hoque Foysal; Dr. Tanzilur Rahman; 1611556043; 1530092042; 1431238043
    Sleep is a natural behavior and part and parcel of Human‟s life. Nowadays, sleeping disorder is common question for both man and women. So, sleep related research is accelerated by researcher and heath care community. Sleep research can achieve the better way for diagnosis and treatment of sleeping related complaint. Over the past few decades, sleep classification is introduced. Automatic sleep stages classification is preferable approach for sleep researchers. Manual sleep scoring also visible Sometimes, nowadays. There is lot of difficulty in manual scoring which is very time consuming and prone to Human error. Automatic sleep stages classification using Machine learning model can create a great solution for diagnosis purpose. Different kinds of machine learning algorithms are used by many researchers. Here, in this research we use multiple supervised machine learning model to classify the sleep stages. In this research using EEG patterns of healthy and mild difficulty subjects over 95% of accuracy is obtained by the classifier. Total 31 features (spectral and statistical features) is applied to dataset before that 10 features were taken. For finding the significance nature of features Kruskalwallis anova test is applied. After that using Knn, Decision tree and Bagged tree algorithms evaluated the model accuracy. Bagged tree algorithm take vital role in accuracy which is higher than two other algothoms.So, the model used in this thesis is effective for both healthy and mild difficulty subject.
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    Open Access
    Blockchain, IPFS, and Encryption based framework for disease diagnosis on smartphones
    (North South University, 2020) Hasib Mahmud; Dr. Tanzilur Rahman; 1621065042
    People are using many wearable sensors and medical IOT(Internet of Things) devices nowadays to measure and to diagnose their personal health status. Measuring heartbeat is one of them. Irregular heartbeat rate can indicate many potential or existing illnesses. But these sensors and IOT devices can be expensive and may not be easily available in the market. At present, we mostly use smartphones which need not to be very expensive and are easily available. We can offer rich reports using machine learning based on patient’s data. So, we have built a cloud based smartphone application framework that collects data (primarily heartbeat rate) from users using available sensors (e.g.: microphone), and processes the data with the help of machine learning scripts and maintains data integrity. Personal Health Record (PHR) service is becoming a valuable source of data for hospitals and doctors. It has become necessary that the patient keeps the ownership of the data, securely organises, shares these medical data and can remain anonymous in this system. In order to achieve these goals, we are going to implement Blockchain, IPFS, and Encryption technologies in a virtual private network, applications of which are getting very popular in health care services. This implementation complies with General Data Protection Regulation (GDPR). There are already some papers written on how we can implement Blockchain in the health care system but very few have been implemented till now. Besides, the use of cryptocurrencies for each transaction in Blockchain increases the overall cost and Blo
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    Open Access
    Major Crops Yield Prediction For Bangladesh
    (North South University, 2020-09) Akhtaruzzaman Khan; Md. Muktadir Hossain; Nurun Naima Tuly; Dr. Tanzilur Rahman; 1611319042; 1610597042; 1620617042
    Bangladesh is predominantly an agricultural country where agriculture sector plays a vital role in accelerating the economic growth. Climate and other environmental changes has become a major threat in the agriculture field. In the present paper, we have considered Max-Temp, Min-Temp, Rainfall, Humidity, Wind Speed, Bright Sunshine, Cloud Coverage and Altitude from the weather dataset and Districts Name, Crop Name, Crop Category, Area, Production and Year from Crop dataset for 18 districts of Bangladesh and combined these two into one for 45 years from 1969 to 2013. Crop production environment consists of inherent sources of heterogeneity and their non-linear behavior. The proposed research work pursues to produce prediction model using machine learning algorithms on 6 types of crops ( Aman, Aus, Boro, Jute, Potato and Wheat ) based on weather data. For crop yield assessment and prediction 9 algorithms( Linear Regression, Lasso Regression, Ridge Regression, Bayesian Ridge, Random Forest, K-Nearest Neighbor, Decision Tree, SVR, Artificial Neural Network) these algorithms provided acceptable values and higher accuracy rate. Linear Regression gave highest score for Aman (R2= 0.79), Aus (R2= 0.88), Boro (R2= 0.95), Jute (R2= 0.96) and Wheat crop (R2= 0.93) and for Potato Random Forest(R2= 0.87). The main purpose of this research work for helping to the farmer to predict the yield of the crop before cultivating onto the agriculture field.The crop yield prediction model discussed in the present paper will further improve in future with the use of long period dataset. Similar model can be developed for different crops of other locations. Keywords: Crop yield, Aman, Aus, Boro, Jute, Potato, Wheat, Prediction, Linear Regression, Random Forest, K-Nearest Neighbor, Decision Tree, SVR, Artificial Neural Network