School of Data and Sciences (SDS)
http://hdl.handle.net/10361/15988
2024-03-28T19:19:40ZIsolation and Characterization of E. coli Bacteriophage from Raw Meat and Environment Water
http://hdl.handle.net/10361/22482
Isolation and Characterization of E. coli Bacteriophage from Raw Meat and Environment Water
Islam, Mahbuba; Siddik, Md Abu Bakar; Mondol, Sudorshon; Hossain, Md Shahriar
Several diseases such as stomach cramps, urinary tract infections, and diarrhea are caused by the bacterium Escherichia coli. One of the five subtypes of E. coli which are ETEC, EPEC, EAEC, EHEC/STEC, and EIEC are responsible for intestinal infections. E. coli outbreak is a major public health concern. One of the key elements that can put an end to outbreaks could be bacteriophages. Since their discovery in 1915, they have been beneficial to humans in a variety of ways, including phage therapy, genetic screening tools, diagnostic weaponry, pathogenic bacteria detectors, therapeutic agents, and more. Phage research has gained attention because of the recent rise in antibiotic resistance because of their capacity to infect and destroy bacteria without endangering humans. This experiment is designed to isolate and characterize bacteriophage specific to E. coli. It includes procedures that have been used in the lab to check for the presence of phages and the results of the experiments. In this research, E. coli bacteria and bacteriophage was isolated from the Raw meat. Unfortunately, only one bacteriophage was possible to isolate from Raw meat and which is why water samples are collected from various regions of Mohakhali, Gulshan and Hatirjheel and from the samples, four more bacteriophages were successfully isolated. Phage ECPW4.4, ECPW5.1 and ECPW12.3 were chosen for further analysis and characterization. In characterization, three tests were done, which were; Temperature test, salinity test and pH test. On these three tests, the three bacteriophages showed better results overall. Furthermore, more tests and analysis are needed to be done but because of the closure of BRAC University Lab, further research was not possible.
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Biotechnology and Bachelor of Science in Microbiology, 2023.; Catalogued from PDF version of thesis.; Includes bibliographical references (pages 62-65).
2023-12-01T00:00:00ZEnhancing underwater object detection through water artifact removal and using ensemble transfer learning
http://hdl.handle.net/10361/22189
Enhancing underwater object detection through water artifact removal and using ensemble transfer learning
Saikat, Nayem Hossain; Jahan, Sarowar; Abrar, Fahim; Rahman, Md. Motaqabbir; Rahman, Md. Ashikur
The utilization and exploration of deep-sea resources has made underwater autonomous operation increasingly important to mitigate the dangers of the highpressure deep-sea environment. Intelligent computer vision plays a crucial role in underwater autonomous operation, and pre-processing procedures such as weak illumination and low-quality image enhancement are necessary for underwater vision. Underwater object detection plays a critical role in various domains such as marine biology, environmental monitoring, and underwater robotics. However, it is a challenging task due to the complexities of the underwater environment, including poor visibility, light attenuation, and color distortion. In this research paper, we propose a comprehensive methodology for underwater object detection using transfer learning with PyTorch and Jetson Inference. The contributions of this research paper include advancements in underwater object detection through the combination of transfer learning, fine-tuning, and optimization techniques. The utilization of PyTorch and Jetson Inference frameworks provides a powerful and efficient platform for implementing and deploying the model. Additionally, the incorporation of image-clearing techniques ensures the quality of the dataset and improves the model’s performance in challenging underwater conditions. The results of this research have practical implications for a variety of underwater applications, including marine environment monitoring, underwater exploration, and underwater autonomous robots for visual data collection in complex scenarios. By accurately detecting and classifying underwater objects, our methodology contributes to the understanding and preservation of underwater ecosystems, enhancing the capabilities of underwater systems and facilitating decision-making processes. Future work in this field may involve exploring different architectures, incorporating additional data augmentation techniques, and further fine-tuning the model with larger and more diverse underwater datasets. These efforts will contribute to advancing the state-of-the-art in underwater object detection, enabling more robust and efficient solutions for a wide range of underwater applications. .
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 45-46).
2023-05-01T00:00:00ZOperating and analyzing of business data with digital enterprise division of BanglaCAT
http://hdl.handle.net/10361/22188
Operating and analyzing of business data with digital enterprise division of BanglaCAT
Mitra, Mithila
This internship report provides a comprehensive overview of the experiences, learn- ing, and contributions made during the internship period at Bangla CAT. The re- port encompasses a tailed description of the objectives, scope, methodology, and outcomes of the internship. It also highlights the key projects and tasks under- taken, along with the skills acquired and professional growth achieved during the internship.
This internship report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.; Cataloged from PDF version of thesis.; Includes bibliographical references (page 17).
2023-05-01T00:00:00ZRipple down rule based decision intelligence for mental disorder diagnosis
http://hdl.handle.net/10361/22187
Ripple down rule based decision intelligence for mental disorder diagnosis
Rahman, G.M. Arafat; Ibnul, Tahmid Nizam; Mia, MD. Shamim; Akash, Abid Mahmood; Banerjee, Avinandan
Ripple Down Rule (RDR), a rule-based incremental system, enables knowledge acquisition from human experts to knowledge-based systems (KBS). The majority of modern decision intelligence systems rely on machine learning algorithms, despite the fact that most machine learning algorithms have their own limitations, such as a lack of explainability, an inability to provide multiple outputs, and poor performance with imbalanced or unbalanced data. In addition, RDR still needs to be implemented in the mental health field, and most of the current screening tests cannot diagnose multiple mental disorders at a time. Because of these issues, this paper presents an RDR-based approach for diagnosing mental disorders based on data gathered from primary sources. Since RDR is both a knowledge-based system and an inference engine where domain experts provide rules and conclusions, it can correctly explain its conclusion and provide multiple outputs using the Multiple Classification Ripple Down Rule (MCRDR). In addition, a version of the XGBoost classification algorithm called 'XGBoost Binary Classification Block' has been presented to produce multiple outputs. Comparing the experimental outcomes of three classifier models, we find that XGBoost multi-class classification has 49% accuracy, XGB Binary Classification Block has 96% accuracy, and RDR outperforms the other two by accurately predicting all outputs.
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 59-61).
2023-01-01T00:00:00Z