Browsing by Author "Akter, Shamima"

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AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images
(IEEE, 2023-02-14) Shamrat, F M Javed Mehedi; Akter, Shamima; Azam, Sami; Karim, Asif; Ghosh, Pronab; Hasib, Khan Md.; Boer, Frisode; Ahmed, Kawsar
Alzheimer’s disease is largely the underlying cause of dementia due to its progressive neurodegenerative nature among the elderly. The disease can be divided into five stages: Subjective Memory Concern (SMC), Mild Cognitive Impairment (MCI), Early MCI (EMCI), Late MCI (LMCI), and Alzheimer’s Disease (AD). Alzheimer’s disease is conventionally diagnosed using an MRI scan of the brain. In this research, we propose a fine-tuned convolutional neural network (CNN) classifier called AlzheimerNet, which can identify all five stages of Alzheimer’s disease and the Normal Control (NC) class. The ADNI database’s MRI scan dataset is obtained for use in training and testing the proposed model. To prepare the raw data for analysis, we applied the CLAHE image enhancement method. Data augmentation was used to remedy the unbalanced nature of the dataset and the resultant dataset consisted of 60000 image data on the 6 classes. Initially, five existing models including VGG16, MobileNetV2, AlexNet, ResNet50 and InceptionV3 were trained and tested to achieve test accuracies of 78.84%, 86.85%, 78.87%, 80.98% and 96.31% respectively. Since InceptionV3 provides the highest accuracy, this model is later modified to design the AlzheimerNet using RMSprop optimizer and learning rate 0.00001 to achieve the highest test accuracy of 98.67%. The five pre-trained models and the proposed fine-tuned model were compared in terms of various performance matrices to demonstrate whether the AlzheimerNet model is in fact performing better in classifying and detecting the six classes. An ablation study shows the hyperparameters used in the experiment. The suggested model outperforms the traditional methods for classifying Alzheimer’s disease stages from brain MRI, as measured by a two-tailed Wilcoxon signed-rank test, with a significance of < 0.05.
An insight into foreign direct investment in Bangladesh
(University of Dhaka, 2016-10-03) Akter, Shamima
Foreign Direct Investment (FDI) plays a crucial role in accelerating the development and economic growth of a country. Most of the developing countries rely on FDI to promote their economy as they face capital shortage for their development process. FDI can enable a country to build up capital, developed productive capacity, reduce unemployment and ensure overall economic development. With this background in mind this study was undertaken to give an insight into the determinants and role of FDI for the economic development process of Bangladesh. The present study examines the factors that potentially affect the Foreign Direct Investment (FDI) of a country and identifies the key determinants of the FDI in Bangladesh. This study also explores the FDI theories and how they explain FDI decisions of a developing country like Bangladesh. Based on the data 1999–2013 of FDI factors, this study uses the statistical estimation method to identify the determinants of FDI. In this study we have identified the potential determinants of FDI in Bangladesh. For the empirical analysis, eleven independent variables have been taken. Which are market size, gross national income, inflation, openness, corporate tax rate, domestic investment, external debt, labor force, average exchange rate, average wage in manufacturing and urbanization. As for the estimates out of eleven variables nine variables were significant and having the expected positive sign. On the basis of the correlation and regression analysis it is observed that market size, gross national income, inflation, openness, external debt, labor force, average exchange rate, average wage in manufacturing and urbanization have positive relation and relevant factors of FDI. The other factors corporate tax rate and domestic investment have negative sign and irrelevant factors in determining FDI inflow in Bangladesh. It is observed that there are some administrative loopholes and policy issues that hinder the inflow of FDI in Bangladesh. It may be argued that addressing those issues and making favorable environment, rules and regulation are to be enacted to overcome those problem and to build up confidence for existing and new investors. We are optimistic that Bangladesh will undertake positive move to reduce these barriers and take appropriate measures to attract sizable FDI in Bangladesh to maintain the development wheel of the economy in the years to come.
Birth Spacing, Breastfeeding Pattern and Child Survival in Bangladesh
(University of Rajshahi, 2009) Akter, Shamima; Rahman, J.A.M. Shoquilur; Abedin, Samad
Birth spacing is a major determinant for evaluating family building process due to its direct relationship with fertility. The mean duration of successive birth intervals is obviously related to the fertility rate, the longer the interval consequently the lower the fertility. Breast feeding and post-partum amenorrhea (PPA) have also been a matter of rapidly growing interest, as they are important not only for maternal and child health but also for its fertility reducing effect through PPA. Hence for a complete understanding of the process of family building, in Bangladesh, it is imperative to analyze the birth spacing, breastfeeding pattern and PPA as well as their differentials and determinants in context of female population of Bangladesh. This study also examines the influence of birth spacing on child survival. The data used for the completion of this work, is extracted from the Bangladesh Demographic and Health Survey (BDHS) conducted in 2003-2004. The BDHS recorded enormous data on complete birth history of 11440 ever married women of the age group 10-49, which are very useful for studying birth interval. The study considers first to fifth birth intervals as it covers most of the range of fertility experience of Bangladeshi women. Since data on breastfeeding were available only for the last child, the study of breastfeeding and post partum amenorrhea is based on information for last birth. The study reveals that the distribution of first to fifth birth interval is largely positively skewed lying somewhere between 12 to 23 months for first births and for subsequent birth in the duration 24 to 35 months. Both Chi-square statistic and Cox proportional hazard model demonstrate that age, women age at marriage, couples education, respondent work status, residence, socio-economic status, contraceptive use and watch TV have significant influence on first birth interval but subsequent birth showed miscellaneous results. Moreover, survival status of previous child showed highly significant effect on second to fifth birth intervals. But the variable religion has no significant orientation at all. To study the quantum and tempo of fertility life table technique is employed and demonstrates that the mean birth interval for marriage to first birth is 24 months but for subsequent birth the interval lies between ranges 30 to 32 months. The differential analysis of quantum and tempo of fertility reveals that educated women have shorter first but longer subsequent birth interval. Urban mother have shorter first birth interval is lower but longer subsequent birth interval is higher than rural mother. The birth interval is higher (25 months) when age at marriage is less than or equal to 15 years, but lower (21 months) when age at marriage is 15 years and above. But for higher order births age at marriage has no differential effect. The summarized results speculate that child survival status affects timing of birth not only for first two or three orders but also for higher orders, but at a smaller pace, for women whose previous child is death birth interval is much lower than those whose previous child is alive. Determinants and differential of breastfeeding and post partum amenorrhea are also performed using life table analysis and Cox Proportional Hazard model. The results indicates that breastfeeding is virtually universal (98.3 percent) and homogeneously prolonged in Bangladesh. The mean duration of breastfeeding is about 32 months. To see the effects of socio-demographic variables on breastfeeding we fitted three model- the first one to see the effect of demographic variables, second one to see the effects of socioeconomic variables and lastly to see the combined effect of these two variables. The study results divulge that demographic variables have more influence than other variables. Finally the proportional hazard analysis has identified that administrative division, Religion, maternal education, working status, current age, age at marriage, parity; Contraceptive use and place of delivery have significant effect on the duration of breast feeding. The overall mean duration of PPA was found to be 8.51 months and the length increases with increase in the parity. The mean length of PPA significantly varies by place of residence, region, mother's education, work status, sex of previous child, breastfeeding status, parity, delivery status and age of mother. The Cox proportional hazard model suggested that duration of breastfeeding has strong positive influence on duration of PPA among all included explanatory variables. The study also analyzed the relationship between the length of preceding birth interval and child survival and their effect on age specific probability of death of index child. The preceding birth interval and child survival are significantly correlated and probability of survival is much lower for less preceding birth interval (<12 months) and also a lesser extent at higher birth interval (84+ months). To see the effect of preceding birth interval and socio-demographic factors on child survival status four separate logistic regression models (neonatal, post neonatal, mortality between 12-35 months and child mortality) are fitted. These models reveal that among the socio-demographic variables, preceding birth interval, breast feeding status and mother's education has strong significant effect on mortality but the variables birth order and mother's age at birth has little or no significant effect. Therefore, the study results emphasize that it is need to encourage women to have longer birth interval, not only to limit family size, but also to guarantee good health of mother and the child. Education is a consistently a dominant factor and such formal and informal education should encourage women to differ marriage and prolong breast feeding.
Broca’s Area of Brain to Analyze the Language Impairment Problem and Behavior Analysis of Autism
(Springer, 2022-01-01) Islam, Md Ashiqul; Karim, Rafat; Ahmed, Faruq; Maksuda; Hossen, Md Sagar; Akter, Shamima
The brain is a tremendous three-pound organ that controls all functions of the body, interprets data from the surface world. The human brain may be a hub for specific primary tasks. When our brain does not work properly or fails to complete his tasks our science contemplates it as an unfit brain. In our trendy science day by day we wish to understand a couple of human brain and behavior. During this continuation, we tend to see that the ordinary brain and unfit brain have some variations. The spectrum disorder syndrome people face some difficulties [1]. They cannot socially interact with people very well, the communication gap, and abnormal behaviors are facing in their faces. In the frontal lobe, Broca’s area is one of the reasons that play an important role in language production. Though its precise linguistic functions are still a bit unclear. It is named by physician Paul Broca. In this problem with aphasia reading and writing are also impaired but language comprehension is typically relatively preserved. Some symptoms are involved with producing movements like the tongue and mouth that help speech to be produced [2]. And also some other symptoms are that is involved producing grammar, verbal memory, syntax. Broca’s area also has some linguistic and non-linguistic functions. It plays a role in language comprehension,
COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images
(Daffodil International University, 2021-11-13) Akter, Shamima; Shamrat, F. M. Javed Mehedi; Chakraborty, Sovon; Karim, Asif; Azam, Sami
COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, theWilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.
Covid-19 Detection Using Deep Learning Algorithm on Chest X-ray Images
(Biology, 2021-11) Akter, Shamima; Shamrat, F M Javed Mehedi; Chakraborty, Sovon; Karim, Asif; Azam, Sami
COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, Dense Net, EfficientNetB7, Alex Net, and Google Net. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.
Cyber Security Industry Based
(Daffodil International University, 2025-09-19) Akter, Shamima
This report details the professional experience acquired during an internship at Backdoor Private Limited, a leading cybersecurity company focused on Vulnerability Assessment and Penetration Testing (VAPT), Digital Forensics, and Security Operations Center (SOC) activities. Throughout the internship, significant contributions involved sandboxing and simulating hacking following Cyber Kill Chain Methodology, performing Vulnerability Assessment & Penetration Testing to uncover and address system weaknesses, aiding in Digital Forensic analyses to investigate cyber incidents, and supporting SOC efforts to monitor and counter real-time security threats. Practical use of industry-standard tools, including forensic applications and penetration testing platforms, bolstered technical skills in cybersecurity practices. Working alongside seasoned experts offered valuable perspectives on proactive threat prevention and effective security strategies. The internship enhanced proficiency in VAPT, digital forensics, and SOC operations, underscoring the need for adaptability and diligence in protecting against dynamic cyber risks
Design and Implementation of Arduino Based Home Security System
(Daffodil International University, 2018-12) Tanjin, Taranna; Akter, Shamima
Safety of home or other establishment is a prerequisite of a peaceful life. Sometimes security cannot be guaranteed with security guard as they also get involved in crimes. On the other hand, very few can afford security guard in our economic condition. In our project titled “Design and implementation of Arduino based Home Security System,” we proposed and developed a security solution that is affordable as well as usable in residence or other establishments. As a part of access control, main door is interfaced with electric locking system that can be open or closed upon successful authentication. To verify legitimate user, RFID authentication is integrated with the system. Each user of the system will have an RFID card by which he can verify his identity to the system. In addition to access control, various sensors are interfaced to detect unwanted situation to avoid accident or reduce possibility of loss by taking immediate action. All incidents including entry, exit, gas leak, presence of unwanted person inside room will be sent to the owner’s mobile phone immediately. Thus, the system will provide a security and emergency solution for an establishment.
Effect of Elastane and Thread Density on Mechanical Attributes of Stretch Woven Fabric
(AATCC Journal of Research, 2020-01-01) Siddiqa, Fahmida; Haque, Md; Smriti, Mahbubul; Akter, Shamima; Farzana, Nawshin; Haque, Ahsanul; Naser, Md Abu
Stretch woven fabrics continue to grow in popularity, offering superior elastic properties and comfort. However, there are a number of factors (e.g., elongation, recovery, growth, tensile strength, tearing strength, and shrinkage) that can affect the attributes and performance of stretch woven fabric. These were investigated in the present study in relation to different elastane content and thread density. Blended cotton woven fabrics containing an increased elastane content gave enhanced elongation and recovery, despite a decrease in thread density. The tensile strength, tearing strength, shrinkage, and fabric growth decreased when the elastane ratio increased, regardless of the decline in warp thread density.
Effects of air pollution on the neuronal activities of rickshaw-pullers and drivers of Dhaka city
(© University of Dhaka, 2025-04-22) Akter, Shamima
Find-A-Doctor
(Daffodil International University, 2019-05-01) Akter, Shamima
Find-A-Doctor is a system that provides successful help to manage the medical doctor easier for the user. In our society, every family haveat least one sick person. Almost in every three months, we need to deal appointments with consultants for various kind of diseases. For this system we can easily find a specialist doctor.
Hydrodynamic Analysis of Wave Slamming on a Horizontal Circular Cylinder
(Department of Naval Architecture and Marine Engineering, 2011-01-11) Akter, Shamima; Khalil, Dr. Gazi Md
Information about the forces acting on cylindrical structures subjected to wave impact is of significant importance in ocean engineering and naval architecture. The design of structures that must survive in a wave environment depends on knowledge of the forces that occur at impact. Impact loads due to wave slamming on horizontal members of an offshore structure are of considerable interest in the context of offshore design, particularly because such loads can give rise to structural failure. Predictions of the wave slamming force generally involve the use of a slamming coefficient. The purpose of this thesis is to analyse the wave impact forces acting on a fixed, slender, horizontal circular cylinder in the vicinity of the free surface, taking account of the intermittent submergence and wave slamming. It presents a new mathematical model for the impact forces acting on a horizontal circular cylinder from the instant of impact to full immersion. Two new expressions are derived for the slamming coefficient, the first expression as a function of the Froude number and the second expression as a function of the Keulegan-Carpenter number. A computer program is developed on the basis of the aforesaid theoretical analysis. The program is written in Fortran 90/95 and executed on the personal computer. The computational results are plotted to show the variation of the slamming coefficient with the Froude number, Keulegan-Carpenter number, wave amplitude, cylinder diameter, depth of cylinder immersion, instantaneous height of the wave surface above the mean water level and the added mass per unit length of the cylinder. In order to check the validity of the mathematical model developed, the present results are compared with the theoretical and experimental results of other investigators.
Recognition of Leaf Blight and Fruit Rot Diseases of Eggplant Using Transfer Learning
(Daffodil International University, 2020-12-05) Haque, Imdadul; Akter, Shamima; Hasam, Md Lakibul
Eggplant is an essential food in Bangladesh because the majority of the population still live below the poverty line. Eggplant is so inexpensive that it helps poor people to meet the demand of food. In other words, we can say eggplant helps to fulfill one of the basic needs (food) of humans. But a large quantity of eggplant production faces a huge loss because of eggplant’s leaf blight and fruit rot diseases. Many types of leaf blight and fruit rot diseases reduce the healthy growth of eggplant seedlings and fruits. Due to lack of technology and education in Bangladesh, many farmers are still unable to diagnose the disease properly. As a result, it is harmful for both economic development and poor people. So, we proposed our work that can detect the leaf blight and fruit rot diseases of Eggplant very accurately. We use MobileNet, CNN, Multilayer Perceptron & VGG16 for our work. MobileNet, CNN, Multilayer Perceptron, VGG16 help us to enhance the object detection result. These methods are very helpful to detect eggplant disease. MobileNet gives accuracy 95%, CNN gives accuracy 81%, Multilayer Perceptron gives accuracy 77% & VGG16 gives accuracy 70%. The accuracies are pretty good, MobileNet gives the best accuracy among the other methods. Hence this work will help to increase the economic development and eggplant production, reduce the food shortage of the poor and the grief of the farmer.
Recognition of Leaf Blight and Fruit Rot Diseases of Eggplant using Transfer Learning
(Daffodil International University, 2020-12-31) Haque, Imdadul; Akter, Shamima; Hasam, Md Lakibul
Eggplant is an essential food in Bangladesh because the majority of the population still live below the poverty line. Eggplant is so inexpensive that it helps poor people to meet the demand of food. In other words, we can say eggplant helps to fulfill one of the basic needs (food) of humans. But a large quantity of eggplant production faces a huge loss because of eggplant’s leaf blight and fruit rot diseases. Many types of leaf blight and fruit rot diseases reduce the healthy growth of eggplant seedlings and fruits. Due to lack of technology and education in Bangladesh, many farmers are still unable to diagnose the disease properly. As a result, it is harmful for both economic development and poor people. So, we proposed our work that can detect the leaf blight and fruit rot diseases of Eggplant very accurately. We use MobileNet, CNN, Multilayer Perceptron & VGG16 for our work. MobileNet, CNN, Multilayer Perceptron, VGG16 help us to enhance the object detection result. These methods are very helpful to detect eggplant disease. MobileNet gives accuracy 95%, CNN gives accuracy 81%, Multilayer Perceptron gives accuracy 77% & VGG16 gives accuracy 70%. The accuracies are pretty good, MobileNet gives the best accuracy among the other methods. Hence this work will help to increase the economic development and eggplant production, reduce the food shortage of the poor and the grief of the farmer.
Recognizing Human Emotions from Eyes and Surrounding Features: A Deep Learning Approach
(Scopus, 2021) Shuvo, Md Nymur Rahman; Akter, Shamima; Islam, Md. Ashiqul; Hasan, Shazid; Shamsojjaman, Muhammad; Khatun, Tania
Abstract: The need for an efficient intelligent system to detect human emotions is imperative. In this study, we proposed an automated convolutional neural network-based approach to recognize the human mental state from eyes and their surrounding features. We have applied deep convolutional neural network based Keras applications with the help of transfer learning and fine-tuning. We have worked with six universal emotions (i.e., happiness, disgust, sadness, fear, anger, and surprise) with a dataset containing 588 unique double eye images. In this study, we considered the eyes and their surrounding areas (Upper and lower eyelid, glabella, and brow) to detect the emotional state. The state and movement of the iris and pupil can vary with the various mental states. The common features found within the entire eyes during different mental states can help to capture human expression. The dataset was trained with pre-trained weights and used a confusion matrix to analyze the prediction to achieve better accuracy. The highest accuracy was achieved by DenseNet-201 is 91.78%, whereas VGG-16 and Inception-ResNet-v2 show 90.43% and 89.67%, respectively. This study will provide an insight into the current state of research to obtain better facial recognition.
Risk Factor Prediction of Chronic Kidney Disease Based On Machine Learning Algorithms
(Scopus, 2020) Islam, Md. Ashiqul; Akter, Shamima; Hossen, Md. Sagar; Keya, Sadia Ahmed; Tisha, Sadia Afrin; Hossain, Shahed
Chronic kidney disease (CKD) is an increasing medical issue that declines the productivity of renal capacities and subsequently damages the kidneys. CKD is very common nowadays; cardiovascular infection and end-stage renal illness are two life threatening diseases that can be caused as after-effects of CKD. These are conceivably preventable through early recognizable conditions and treatment of people who are in danger. The expectation of medical problems is a very troublesome assignment. CKD is particularly one of the most lethal diseases in the clinical field. Before it becomes too late to recognize CKD forecast, to get rid of risks, the prediction of risk factor is a major necessary step in the immediate stage. In this research work six algorithms such as Naïve Bayes, Random forest, Simple logistic regression, Decision Stump, Linear regression model, simple linear regression model is used to predict the risk factors of CKD. Considering the orderly execution and investigations of these strategies, six algorithms give a superior and quicker characterization execution. Six individual algorithms are applied to the dataset and the best outcomes have been acquired through the classification of predicting risk factors.
The Insurance Act 2010 for non-life insurance; problems & prospects of peoples insurance company limited
(BRAC University, 5/20/2012) Akter, Shamima; Rahman, Sharmin Shabnam
The report on “The Insurance Act 2010 (for non-life insurance business) and the problems & prospects of Peoples Insurance Company Limited” has been prepared based on qualitative research as required for completion of BBA degree from BRAC University. In a developing country like Bangladesh, different insurance companies are playing a very crucial role in the economic growth. Though insurance industry has significant prospects in the economy but for some reasons it has failed to achieve its goal to some extent. There are 62 insurance companies in Bangladesh. Among them, Peoples Insurance Company Limited is the second private insurance company and it has been doing insurance business in the country over 27 years since 1985. The company was established and had been operated under the Insurance Act 1938 since its inception until 2010. However, as the need of time, the parliament of Bangladesh passed a new Insurance Act 2010 in March, 2010 in order to reform & modernize the insurance sector in Bangladesh. Since just after passing a new act to replace the old one, the previous Insurance Act 1938 became inactive due to pass Insurance Act 2010. The insurance industry faced a shock suddenly. In this report, the Insurance Act 2010 has been summarized focusing on the additions and changes brought by the new act which are different from the former Insurance Act 1938. A qualitative research has been conducted to gather required information where I have taken interviews of four top level employees of Peoples Insurance Company Limited. The entire report has been categorized into six chapters. In chapter one, the introduction, scope, objective, methodology, and limitations of the report have been described. Then chapter two of the report focuses on the Peoples Insurance Company Limited—the company overview, its services, vision, mission, and future plans. Here, I also have conducted a financial analysis on the company‘s performance since 2002 until 2010 where short descriptions of the performance have been given along with necessary graphical presentations. In chapter three, I have described the internship program under the Department of Finance & Accounts at the corporate office of PICL specifying the departmental responsibilities along with my own responsibilities that I performed as an intern. Here, I have tried to discuss about my work procedures while performing different responsibilities and also my learning from the work experiences. In the report, chapter four and chapter five bear more weight. Chapter four basically works on the topic of this report where a brief description on insurance, Insurance laws and regulatory system, and regulatory conflicts in the insurance industry in Bangladesh along with the emergence of establishing and passing the new insurance act. Hence, the Insurance Act 2010 has been taken into focused in this chapter specially for the non-life insurance sector—why the new act has been passed, how the new regulatory authority is working for the reformation and modernization of the insurance sector in Bangladesh, what are the problems that the non-life insurance companies (specially the Peoples Insurance Company Limited) are facing while trying to follow the new rules and regulations to implement the new act in doing their business. Besides, the huge prospects of the insurance companies while the new act will be fully functional also has been identified and described. Continuously, in chapter five, the findings have been presented along with developing necessary recommendations addressing both the regulatory authorities and the Peoples Insurance Company Limited in order to overcome the conflicting situations as well as to make the prospects come true. Then conclusion has been drawn by expressing hope and expectation on the modernization of the insurance sector in Bangladesh competing with its neighbors. Lastly, in chapter six, appendix of the report has been given which encloses the supporting information & documents of the report & references that might be helpful to understand the whole report more clearly.
Understanding Parent-Child Relationship
(University of Dhaka, 2019-10-10) Akter, Shamima
Although sufficient work on parent-child relationship has been conducted worldwide, the number of studies that focus on parental behavior and perceived meaning are very limited. Additionally, as culture is known to shape human behavior in numerous ways, is necessary to understand this behavior in the context of Bangladesh. The aim of the present study was to understand parent child relationship and their perceived meaning among the children. A qualitative research design using grounded theory approach was adopted in this study to explore the behavior pattern from narrative collected from both parents and children. Purposive sampling technique was employed to select ten participants among them four were child and six were parent, using predefined inclusion and exclusion criteria. In-depth interview was used to collect data which were audio recorded for ensuring accuracy. Data analysis involved verbatim transcription of the interviews followed by open and axial coding. This study found nineteen specific types of parental behavior and eleven types of perceived meaning of those behaviors in relation to parent-child relationship. The findings provided detailed insight and understanding of parent-child relationship along with process of creating healthy environment in parent-child relationship. The findings clearly reflect that healthy environment of parent child relationship is developed by how children perceive their parents behavior. Moreover it was also found that in parent-child relationship reciprocity of taking care is developed through the establishment of cooperativeness and internalization of parental rules. Present study can contribute into this through enhanced knowledge from detailed understanding on parent- child relationship. The results suggest practical implications for clinical intervention as creating healthy environment in parent child relationship for Bangladeshi population.