Browsing by Author "Islam, Md. Mozahidul"

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Machine Learning Based Depression Detection
(Daffodil International University, 2022-01-04) Islam, Md. Mozahidul; Biswas, Saikat; Sarkar, Utpaul
Depression is a common disorder that causes constant mood swings and feelings of sadness. Nowadays It is considered to be a deadly disorder in the world. At present, everyone from young to old is suffering from depression but most of them do not have the right idea about their mental state. It is very important for everyone to have the right idea about their mental state. We will detect depression through machine learning. First, we study some related papers, journals, and online articles then we talk to psychologists and depressed people and then we find some common factors that are related to becoming depressed. Then we collect data based on those factors, such as age, gender, profession, marital status, life satisfaction, feelings, interests, etc. We collect data from both depressed and non-depressed people. We have two outcomes. One is ‘Yes’ which means depressed and another is ‘No’ means not depressed. After data collection, we processed all the data and created a processed dataset. Then we applied machine-learning algorithms to our processed dataset. Machine learning, deep learning, and artificial intelligence are used in various predictions, detection, and recognition systems. We use k-nearest neighbor (kNN), logistic regression, Support Vector Classifier (SVC) Linear, naïve Bayes, random forest, adaptive boosting (ADA boosting), decision tree, and Linear Discriminant Analysis (LDA) Classifier. In our work, logistics regression gave the best performance based on accuracy and the accuracy of logistic regression was 93.50%.
Machine Learning Based Depression Detection
(Daffodil International University, 2022-01-04) Islam, Md. Mozahidul; Biswas, Saikat; Sarkar, Utpaul
Depression is a common disorder that causes constant mood swings and feelings of sadness. Nowadays It is considered to be a deadly disorder in the world. At present, everyone from young to old is suffering from depression but most of them do not have the right idea about their mental state. It is very important for everyone to have the right idea about their mental state. We will detect depression through machine learning. First, we study some related papers, journals, and online articles then we talk to psychologists and depressed people and then we find some common factors that are related to becoming depressed. Then we collect data based on those factors, such as age, gender, profession, marital status, life satisfaction, feelings, interests, etc. We collect data from both depressed and non-depressed people. We have two outcomes. One is ‘Yes’ which means depressed and another is ‘No’ means not depressed. After data collection, we processed all the data and created a processed dataset. Then we applied machine-learning algorithms to our processed dataset. Machine learning, deep learning, and artificial intelligence are used in various predictions, detection, and recognition systems. We use k-nearest neighbor (kNN), logistic regression, Support Vector Classifier (SVC) Linear, naïve Bayes, random forest, adaptive boosting (ADA boosting), decision tree, and Linear Discriminant Analysis (LDA) Classifier. In our work, logistics regression gave the best performance based on accuracy and the accuracy of logistic regression was 93.50%.
Machine Learning Based Depression Detection
(Daffodil International University, 2022-01-04) Islam, Md. Mozahidul; Biswas, Saikat; Sarkar, Utpaul
Depression is a common disorder that causes constant mood swings and feelings of sadness. Nowadays It is considered to be a deadly disorder in the world. At present, everyone from young to old is suffering from depression but most of them do not have the right idea about their mental state. It is very important for everyone to have the right idea about their mental state. We will detect depression through machine learning. First, we study some related papers, journals, and online articles then we talk to psychologists and depressed people and then we find some common factors that are related to becoming depressed. Then we collect data based on those factors, such as age, gender, profession, marital status, life satisfaction, feelings, interests, etc. We collect data from both depressed and non-depressed people. We have two outcomes. One is ‘Yes’ which means depressed and another is ‘No’ means not depressed. After data collection, we processed all the data and created a processed dataset. Then we applied machine-learning algorithms to our processed dataset. Machine learning, deep learning, and artificial intelligence are used in various predictions, detection, and recognition systems. We use k-nearest neighbor (kNN), logistic regression, Support Vector Classifier (SVC) Linear, naïve Bayes, random forest, adaptive boosting (ADA boosting), decision tree, and Linear Discriminant Analysis (LDA) Classifier. In our work, logistics regression gave the best performance based on accuracy and the accuracy of logistic regression was 93.50%.
Predictive assessment on landscape and coastal erosion of Bangladesh using geospatial techniques
(Elsevier, 2020-09-20) Islam, Md. Mozahidul; Rahman, Md. Saifur; Kabir, Md. Alamgir; Islam, Md. Nazrul; Chowdhury, Ruhul Mohaiman
Coastal erosion, land use and land cover (LULC) changes analysis using remote sensing is a dynamic, relatively low cost based precise method using now a day. Coastal districts of Bangladesh occupied by naturally grown mangrove forest which are susceptible to rapid land cover (LC) changes and natural erosion. Barguna and Patuakhali districts of Bangladesh deserve special attention for conserving coastal mangrove forest named Tengragiri Wildlife Sanctuary and variety of human forces income. The core objective of this research is to analyze the LULC change along with coastal erosion analysis from 2000 to 2017. Combination of four years Landsat satellite image analysis, primary field data, geo-tag photography, secondary information, utilization of forest carbon inventory 2015 data, and semi-structured questionnaire are the key approaches adopted in the study. K-means cluster based unsupervised and maximum likelihood supervised classification by using ERDAS Imagine 2014 found the total study area is 33,361 ha. Random sampling (40 points/class) based accuracy assessment and verification by google earth pro 7.1 found overall accuracy 88.15% and Kappa coefficient is 0.867. Python coding program and overlay operation tested for conversion analysis any found weighted overlay provide best results. An intensive RS analysis of 33,564 ha mangrove forest and community landscapes generated six (6) distinct land cover class and sub-classes, e.g. Forest, agriculture & grassland, plantation, sandbar, settlement and waterbody. During 2000–2017, agriculture and grassland were decreasing 23 ha/year. Out of 11,831 ha (in 2000) Agri-grass land 9,326 ha remained intact while remaining 2,246 ha converted to settlement mixed with homestead plantation class. This study also presents the landscape erosion-accretion due to natural, quasi-natural and anthropogenic interventions which shows that, along the river flow and at the confluence at the Nishanbaria Union (local name Khouttar Char & Fakir hat) to lower side of the Tengragiri WS locations are susceptible to high trend of land erosion whereas accretions are prominent on the reverse sides named Baliatali Union, Barabagi Union and so on. These results of the study and developed maps will be helpful for the community people, line departments, national and international policy maker and the researchers’ community for monitoring coastal geomorphology including erosion and accretion of this landmass.
Retail banking activities of City Bank Ltd. Gulshan branch of the year 2009, Dhaka
(BRAC University, 12/20/2009) Islam, Md. Mozahidul; Ahmed, Syeda Shaharbanu Shahbazi
City Bank Ltd. deals with four different and significant banking divisions which are the driving force of the bank such as Retail Banking, Corporate and Investment Banking, SME Banking and Treasury Banking. Here the report has been prepared on Retail banking of CBL and it covers detail about retail banking activities performs by the bank. Besides it also consist of general information of CBL, financial information, learning points, findings, and recommendation. Retail banking refers to banking in which banking institutions execute transactions directly with consumers, rather than corporations or other banks. Services offered include: savings and checking accounts, mortgages as well as personal loans, debit cards, credit cards, and so forth. Under retail banking City bank provides three different types of loan for better convenience of the customers such as City Drive, City Express and another one is City Solution. There are some certain benefits and positive effects of the loan; again it also has some problems as well, which are listed in findings part of the report. This report will be helpful for the person who has intention to learn about retail banking and some other banking institute who wants to launch retail banking beside this it may help the management of City Bank ltd. I have made this report on the basis of retail banking that is correlated to General Banking, different part of Foreign Exchange, Business Department etc; I have worked on retail banking activities and seen the financial dealings of the bank. This report contains retail banking activities of City Bank of Gulshan Branch. Under retail banking, general banking department contain all deposits name, its activity, interest rate etc., cash, foreign remittance/ bills, money laundering, prevention of money laundering etc., dispatch, public service.