Browsing by Author "Tahsin, Anika"

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A reflection on Russia’s existential nihilism from Dostoevsky’s crime and punishment and notes from the underground
(BRAC University, 2019-04) Tahsin, Anika; Tahsin, Anika
Crime and Punishment and Notes from the Underground are two remarkable novels written by Dostoevsky during the late nineteenth century. The novels centrally focus on the tale of poverty and suffering of Russia and how it triggered the philosophical theory of Existential Nihilism amongst the people during the nineteenth century. The author uses Rodion Romanovich Raskolnikov and the anonymous narrator from the underground as the representation of the nineteenth-century existential nihilist from Russia, St. Petersburg. Both of the characters radiate an extreme egocentric and arrogant attitude who prefers alienating themselves from society as they denied abiding by society’s requirement due to their disdain attitude towards it. Not only the characters but the city of St. Petersburg as well as supreme element that significantly features the emerging pessimism in Russia. The city is not merely a backdrop to these novels but an embodiment of misleading radical and moral ideas being introduced in Russia. It embodies the intense pessimism that condemned human existence during that time. Thus, the aim of this thesis is to explore and scrutinize the reasons and triggering factors of Existential Nihilism in Russia in the novels Crime and Punishment and Notes from the underground by scrutinizing Dostoevsky’s stand concerning social, political and moral state of affairs in Nineteenth-century Russia, his approach of sketching the characters of Raskolnikov and the underground man through their perceptions, actions and in addition the diction used in these novels.
Alternative assessment to reduce negative backwash effect: exploring the aspects hindering the successful implementation of alternative assessments
(BRAC University, 2024-05) Tahsin, Anika; Sultana, Asifa
Alternative Assessment (AA) is highly regarded worldwide because of its significance in developing the necessary skills of the learners along with making teaching and learning more authentic. Recently, in Bangladesh, AA has been inaugurated as the new assessment method at the secondary level of education which has developed different points of views among teachers and guardians. This study explored the status of AA as a newly implemented assessment system for secondary-level education. The study aimed to look at AA’s role in reducing the negative backwash effect of examinations. Furthermore, it investigated issues that are acting as obstacles to the successful implementation of AA. This study adopted a qualitative method of data collection and analysis. For the study, I used semi-structured interviews where the participants were secondary-level school teachers. I also used a focus group discussion with the parents of the students. The data was analyzed using the thematic data analysis method. The result of the study showed that the majority of the teachers have a positive attitude towards AA and they appreciate it. They agreed on the fact that AA can reduce backwash effects and make learning active and interesting. Many teachers are not ready due to the poor pay scale, lack of feedback on training, lack of resources, lack of adaptive mindset, and so on. The teachers suggested working on these issues for the successful implementation of AA. On the other hand, the majority of the parents did not have a positive attitude towards it. They believe that AA is a great initiative but it's more appropriate for higher levels of education and due to the implementation of AA, secondary-level students are not taking their studies seriously. The current study is significant in developing teaching strategy and filling the gaps of knowledge regarding the implementation of AA at the secondary level of education.
Assessing performance of teachers based on training and individual job satisfaction at Cambrian School and College
(BRAC University, 8/13/2018) Tahsin, Anika; Ahmed, Salehuddin
Cambrian Education Group is one of the largest and renowned institutions in this country. It gained its reputation both nationally and internationally by creating the first digital campus in Bangladesh. The teachers here are regularly being trained up on various aspects so that they can use their skills to provide quality education to students. It has its own teacher’s training institute which is tailored to their own requirements. It gives their teachers a valuable opportunity to gain knowledge which not only helps in their professional life but their personal life as well; they have become successful both inside and outside their respected classrooms. Their appreciation towards their job experience is deeply related to job satisfaction. To measure the effectiveness of trainings, and job satisfaction level, a simple survey has been conducted to collect data with some close ended questions and personal interview. Teachers from primary, high school and college level have been involved in this survey to add variance. The result positively stated that the trainings are very meaningful and valuable, and the level of job satisfaction is very high amongst them. An institution with such skillful and motivated faculties is bound to give qualified education and bring up meritorious students. Finally, some feasible suggestions have been added that can help the current process towards further excellence.
Breast Cancer Classification via Graph Convolutional Networks with Attention Mechanism Utilizing Multi-Omics Data and Feature Selection Methodology
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-07-08) Tahsin, Anika; Hasan, Suraiya; Akter, Syeda Maksuda
This study emphasizes the integration of clinical data, Copy Number Alteration (CNA), and gene expression data to present an impactful methodology for the classification of PAM-50 breast cancer subtypes. Since breast cancer is a diverse disease, identifying its subtypes with precision is essential to developing therapies tailored to individual treatment plans. Given the variety of molecular traits that contribute to the complexity of breast cancer, this work is relevant because it tackles the problem of using multi-omics data to improve subtype classification. We commit to the inclusion of informative features by using Boruta for feature selection on single-omics data. Graph Convolutional Networks (GCN) help us to capture complex relationships and dependencies within the multi-omics dataset by integrating these various data modalities. This work is important not just because of its methodology but also because it advances precision medicine and cancer research in general. By increasing the precision of PAM-50 subtype classification, the suggested method may help physicians make better-informed choices about treatment plans. The integration of multi-omics data for a thorough understanding of breast cancer might have advanced with this work, which emphasizes the significance of taking clinical, genomic, and expression data into account simultaneously when characterizing subtypes.
Customer Churn Prediction with Machine Learning Approaches
(Daffodil International University, 23-02-12) Borson, Prattoy Paul; Tahsin, Anika
Customer churn prediction is a critical task for many industries, such as telecommunications, banking, and e-commerce. This paper presents a comprehensive survey of customer churn prediction methods, which are typically classified into three categories: statistical methods, machine learning-based methods, and deep learning-based methods. The survey focuses on each category, introducing the most relevant approaches of churn prediction, as well as their respective strengths and weaknesses. We also discuss the challenges and open research issues related to this field. Finally, we outline the future research trends in customer churn prediction in order to inspire new research ideas. RandomForestClassifier achieved the highest accuracy of 84.00%, outperforming other machine learning and Deep learning algorithms.
Genotyping of a Clinically Important SNP rs1799853 Present in the CYP2C9 Gene Using Tetra-primer ARMS PCR Method
(BRAC University, 2020-08) Tahsin, Anika; Hossain, M. Mahboob; Akash, Md. Mahmudul Hasan
Pharmacogenetics is the study of how similar drugs affect different people differently according to their unique genetic makeup as an individual. Pharmacogenetics includes a wide variety of research and discovery including essential medication disclosure, genetic research of pharmacokinetics as well as pharmacodynamics, new medication improvement and persistent hereditary testing where the final objective is to find out how a person’s gene is responding to different medications and find the best possible treatment for that individual or that group of individuals. By foreseeing the medication reaction of an individual, it will be conceivable to build the accomplishment of treatments and decrease the rate of unfavorable symptoms.In this research, our primary intent is to develop a SNP genotyping method for a particular drug, which is a sulfonylureas drug in the case of type 1 diabetes, and how it affects the functionality of CYP2C9 gene in human body. Finally, our goal is to identify whether our desired SNP is present in the particular locus using the Tetra primer ARMS PCR technique and design the drug dose of that patient accordingly
Identification of Tuberculosis
(Daffodil International University, 2019-12) Simul, Tanima Afrin; Tahsin, Anika
Here we are writing a report on “Identification of Tuberculosis”. So what is tuberculosis? Tuberculosis is a disease caused by bacteria called Mycobacterium tuberculosis and the bacteria usually attack lungs and the bacteria can also damage other parts of the body. In our project we are identifying which type of tuberculosis is the patient suffering from. This website will help users to instantly identify the reason of their symptom. Normally people don’t go for a checkup to a doctor for a normal cough. That results in severe problem in the long run. In this system user input their symptoms and this website gives a result whether the user have a risk of TB or not. Admin will provide hospital list, doctor list division wise so that users can easily find out their nearest hospital and also get immense information about doctor details. User can contact with the admin if they have any kind of queries or problems. Smokers and children are more affected by tuberculosis than non-smokers. The people who live in countryside area suffer much for distance. As they don’t have doctor’s or hospital near them. This website will be much beneficial for them.
Isolation of Klebsiella pneumoniae from chicken cloacal samples and analysis of their antibiotic susceptibility pattern from Dhaka City
(BRAC University, 2024-12) Rahman, Anamika; Debnath, Arpita; Tahsin, Anika; Haque, Fahim Kabir Monjurul; Ahmed, Akash
"Background: Zoonotic pathogens can be transmitted from animals to humans, which represent a significant threat to human health due to the possibility of triggering infectious disease outbreaks. This study aimed to detect the prevalence of zoonotic bacteria Klebsiella pneumoniae (K. pneumoniae) in different types of chicken from Dhaka city. Materials and Methods: In this study, 82 chicken cloacal swabs were collected from nine well-known wet marketplaces around Dhaka city from February 2024 to June 2024. These chickens were randomly selected from four different types of chicken, which were processed with saline water under aseptic conditions and inoculated by spreading on HiCrome KPC agar medium for isolation and identification of K. pneumoniae. Metallic blue-colored colonies were considered presumptive K. pneumoniae. Then, PCR was used to confirm K. pneumoniae by targeting the ""16S–23S internal transcribed spacer"" gene. Following that, the Kirby-Bauer disk diffusion method was then used to test for antibiotic susceptibility, and the Clinical and Laboratory Standards Institute (CLSI, 2023) guidelines were followed to interpret the antibiotic susceptibility pattern. Results: Among the 82 samples analyzed, K. pneumoniae was detected in 41 (50%) cases. Randomly selected 50 isolates underwent Antimicrobial Susceptibility Testing, where 80% of the isolates were Multiple Drug-Resistant and 50% were Extensively Drug-Resistant. Isolates showed higher antibiotic resistance to Amoxicillin, Tetracycline, Piperacillin/Tazobactam, and Ciprofloxacin, with resistance rates ranging from 55% to 95% and higher sensitivity to Meropenem and Azithromycin, ranging from 45% to 75%. Conclusion: Findings in this study showed a high occurrence of K. pneumoniae in chickens, indicating that these chickens might be an important reservoir for human and animal infections and suggesting their potential threat to food safety. So, preventive measures, including enhanced biosecurity and public education, must be strengthened to mitigate the spread of zoonotic illnesses."
Leveling and Sectoring based Localization of Primary User for preventing Interference in Cognitive Radio Network
(East West University, 9/23/2018) Sultana, Irin; Tahsin, Anika; Ahmed, Zubair
radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The whole framework of CR is generally a mapping from spectrum sensing to spectrum utilization. In cognitive networks, a cognitive user can interfere with the primary user, cognitive user must be aware of the presence of primary user. If primary user is passive listening, it is not possible to identify the presence of this passive listening user through spectrum sensing. When a user is passive listening, it does not acknowledge or respond to any request. In this situation, hidden node problem arises in cognitive networks. In another situation two cognitive user transmitting to each other makes passive listening primary user wait to communicate though it can communicate easily without any interruption here exposed node problem arise. Cooperative sensing can identify the presence of primary user multiple cognitive user cooperate to reach an optimal global solution. Sharing the information among each other will reduce time and will increase detection efficiency. Localization technique can be done where GPS is not available. Localization of primary users can be done by leveling and sectoring approach. Each CU identified by two coordinate level id and sector id. Base station calculates the relative position of PU based on signal strength. For cooperative sensing here is used centralized cooperative spectrum sensing with relay-assisted schema where sensing channel and report channel can complement and cooperate with each other to improve the performance of cooperative sensing. Count for wrongly enabled CUs is 0 for all the scenarios, which shows that the interference (hidden node problem) to the primary users is prevented. Lastly error percentage is evaluated in MATLAB for the five scenarios such as leveling localization, sectoring localization, varying PU, varying CU, and varying network area in forms of grid size for performance evaluation and hence the desired value will be generated.
Medical Image Synthesis using Generative Adversarial Network
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Risha, Antara; Islam, Shaira Saiyara; Tahsin, Anika
Medical image synthesis has emerged as a promising technique in the field of healthcare, enabling the generation of realistic medical images for various applica tions. This study focuses on medical image synthesis using Generative Adversarial Networks (GANs) applied to the IDRID dataset, which contains retinal images for diabetic retinopathy analysis. The objective of this research is to explore the potential of GANs in generating synthetic retinal images that closely resemble real patient data. The IDRID dataset provides a valuable resource for training and evaluating the GAN model. By leveraging the power of GANs, the proposed framework aims to generate high-quality synthetic retinal images with similar char acteristics and visual appearance to real patient images. This has the potential to augment the existing dataset, expand its diversity, and improve the performance of diagnostic and treatment algorithms. The methodology involves training a GAN architecture consisting of a generator and a discriminator network. The generator network learns to generate synthetic retinal images from random noise, while the discriminator network evaluates the authenticity of the generated images. The two networks engage in an adversarial training process, where the generator aims to fool the discriminator into classifying the synthetic images as real. Evaluation of the synthesized retinal images includes quantitative metrics such as structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and analysis to as sess the similarity and quality of the generated images compared to real IDRID dataset images. The outcomes of this research provide insights into the capabili ties of GANs in generating realistic retinal images from the IDRID dataset. The generated images have the potential to enhance the limited availability of labeled medical data, facilitate algorithm development, and support computer-aided di agnosis systems. The findings contribute to the broader field of medical image synthesis, showcasing the potential of GANs in improving healthcare outcomes through enhanced image data availability and diversity
Medical Image Synthesis using Generative Adversarial Network
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Risha, Antara; Islam, Shaira Saiyara; Tahsin, Anika
Medical Image Synthesis using Generative Adversarial Network
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Risha, Antara; Islam, Shaira Saiyara; Tahsin, Anika
Online Class Observation and Conduction in English During the Outbreak of COVID-19
(Daffodil International University, 2020-12-07) Tahsin, Anika
This report on online class observation and conduction in English is created during the pandemic of COVID-19. I had to take class in online platform on Google Meet at my university. I also had to observe two online classes. I conducted my class for a tertiary level class at Daffodil International University, Permanent Campus in Ashulia, Dhaka city. During this internship process, I worked through my knowledge, skills, teaching-learning style, challenges, observation, assessment, feedback and evaluation etc. This was all recorded by our teachers when I was taking the course teacher’s respective class and observing my peers’ classes. Moreover, I filled up the class observation checklists during my class observation to find out the strengths and weaknesses of my peers. I focused on teaching English language through literature.
Phytochemical Screening, Determination of Total Tannin Content & Evaluation of Antioxidant & Thrombolytic Activities of Leaves Extract of Symplocos Macrophylla
(Daffodil International University, 2021) Tahsin, Anika
In this present study, the leaves extract of Symplocos macrophylla Wall. were subjected to a phytochemical screening, determination of total Tannin content and evaluation of Antioxidant and Thrombolytic Activity. Preliminary phytochemical screening shows the presence of alkaloids, glycosides, tannins, saponins, gums and phenols. Total tannin contents in methanol extract of Symplocos macrophylla Wall. leaves were found 0.6367 mg of QE/gm. The % of inhibition of leaves extract for DPPH test of antioxidant activity is 78.06%. and it shows that Symplocos macrophylla Wall. has some antioxidant activity. In the evaluation of thrombolytic activity, leaves extract showed 55.37% of clot lysis.
Strategies for maintaining brand relevance in fast-paced markets- an empirical study on Social Islami Bank Limited
(BRAC University, 2023-07) Tahsin, Anika; Chowdury, Suman Paul
This study examines the tactics used by Social Islami Bank Limited (SIBL) to sustain its brand significance in the ever-changing banking sector. This highlights the bank's capacity to adjust to changing market demands and meet customer expectations. SIBL has successfully used digital transformation, namely targeting online banking services and mobile banking applications, to improve customer accessibility and position itself as a technologically sophisticated institution. In addition, the bank has made investments in customer relationship management by using data analytics. This allows for a deeper comprehension and prediction of client needs, ultimately leading to the provision of more tailored services. SIBL's dedication to social responsibility and ethical banking is in line with the increasing consumer inclination towards socially responsible businesses, hence bolstering its reputation and attractiveness to socially aware customers. The research highlights the significance of ongoing innovation in the products and services provided by the SIBL brand, as well as the value of good brand communication, in maintaining the brand's relevance. These tactics provide useful insights for other financial businesses aiming to stay relevant in constantly changing markets. The study used a qualitative research methodology, which involves conducting interviews with important individuals involved in the subject matter and examining relevant literature. Additionally, it recognizes certain limitations, such as the presence of response bias during interviews and limits related to the availability of data.
The recruitment and selection process of BRAC international
(BRAC University, 2015-08) Tahsin, Anika; Adrita, Ummul Wara
BRAC International started its global journey in 2002 in Afghanistan, and since then it expanded its activities in nine other developing countries in Asia and Africa, making it a global leader in providing opportunities for the world’s poor on a non-profit basis. The total workforce of BRAC International is above 8000+ staff which means there is an enormous scale of human resource activity. In every BRAC International country except Philippines there is a separate Country Head of Human Resources & Training and a small HR team that manage the operational activities of these enormous operations, managing their performance and capacity development. In this particular report, how a HR department of an international non-governmental organization works, how they manage all nine countries HR activities sitting in the head office, how they hire the most suitable candidates and so on are describes from the eye of an intern. The SWOT analysis is done to find out their strong and weak points, and based on those, some recommendations are prepared. Their strongest point is that they have a much enriched HR department with systematic approaches to conduct their activities, and the main drawback they have is that they use insufficient advertisement channels to minimize their cost, but at the same time they are also minimizing the size of potential applicant pool. Before having any final remarks about this report, it is requested to keep in mind that it was prepared in a very short time’s notice, and because of confidentiality, data could not be collected properly to do the analysis. Yet it might be a good enough report to have an in depth idea about a selection and hiring process of world’s largest NGO with over 40 years of experience of working with humans.
Unsupervised semantic segmentation for localization of wetland area fluctuations
(Brac University, 2024-05) Tahsin, Anika; Fairooz, Maisha; Rabbi, Gazi Rehan; Alam, Md. Golam Rabiul
This research delves deeply into the intricate dynamics of wetlands in Bangladesh, with a particular focus on the haors, utilizing continuous monitoring to grasp the nuanced temporal changes that occur. It introduces an innovative unsupervised se mantic segmentation methodology tailored for analyzing the yearly fluctuations in wetlands. Leveraging the rich dataset provided by multi-temporal satellite imagery and cutting-edge unsupervised learning algorithms, this approach stands poised to revolutionize our understanding of wetland dynamics. At the heart of our method ology lies the strategic application of feature extraction and advanced clustering techniques, with a notable inclusion being the decoder model. These techniques enable the segmentation of wetland regions based on discernible patterns of expan sion and contraction. Moreover, our research extends beyond mere segmentation, incorporating time series methods to forecast wetland fluctuations. By integrating predictive analytics into our framework, we strive to provide not just a snapshot of wetland conditions but also insights into their future trajectories. To validate the efficacy of our approach, rigorous comparative analyses with actual data are conducted. This empirical validation serves to enrich our comprehension of river system dynamics and lends support to ongoing wildlife preservation initiatives. Our methodology represents a significant advancement in unsupervised learning meth ods, adept at adapting to dynamic conditions without the constraints of labeled training data. Furthermore, the incorporation of advanced clustering techniques enhances our ability to pinpoint regions undergoing substantial changes, thereby facilitating targeted conservation efforts. Crucially, the journey continues after seg mentation and prediction. Post-processing of segmentation results allows for metic ulous accuracy assessment, ensuring the reliability of our findings. Through a series of meticulously designed experiments, we showcase the robustness and effective ness of our methodology and model. By pushing the boundaries of unsupervised semantic segmentation and environmental research, we aspire to make meaningful contributions to the broader scientific community and pave the way for informed conservation strategies.