Year-2024Year-2024http://repository.iiitd.edu.in/xmlui/handle/123456789/17222026-04-10T22:13:54Z2026-04-10T22:13:54ZGrief unveiled: insights into the emotional terrain of college lifeMittal, ShefaliPrince, Deepak (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/17572025-06-23T22:00:51Z2024-04-29T00:00:00ZGrief unveiled: insights into the emotional terrain of college life
Mittal, Shefali; Prince, Deepak (Advisor)
This project investigates the intersection of grief, academic performance, and coping mechanisms among college students. Through thorough research of scholarly papers and influential books, it reveals the complexity of grief experiences within higher education. The study uncovers diverse coping strategies employed by individuals managing grief while balancing academic responsibilities. It highlights the profound impact of grief on academic outcomes, advocating for tailored support systems in colleges and universities. Recommendations are provided for implementing grief support programs, flexible academic policies, and awareness initiatives. By prioritizing the well-being of grieving students, institutions can foster resilience and academic success. This project urges stakeholders to recognize and address the unique needs of grieving college students, promoting compassion and proactive support measures.
2024-04-29T00:00:00ZTest case design generation from SRS using LLMsBhatia, ShreyaGandhi, TarushiJalote, Pankaj (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/17562025-06-23T22:00:51Z2024-04-29T00:00:00ZTest case design generation from SRS using LLMs
Bhatia, Shreya; Gandhi, Tarushi; Jalote, Pankaj (Advisor)
System testing is a crucial phase in software development, ensuring that the final product meets specified requirements and functions correctly. However, creating comprehensive test cases for system testing can be challenging and time-consuming. This paper explores the use of Large Language Models (LLMs) for generating test case designs from Software Requirements Specification (SRS) documents. With the assistance of LLMs, software engineers can save time and effort while ensuring thorough test coverage. In this study, we collected a dataset consisting of five SRS documents from student engineering projects containing both functional and non-functional requirements. We focused on the functional requirements section, particularly the use cases, as the basis for generating test-case designs. Using prompts, we instructed the LLM first to familiarize itself with the SRS and then generate test case designs for each use case. Subsequently, we evaluated the quality of the generated test cases through feedback from the students who authored the SRS documents. Our experimental design allows us to address several research questions, including the effectiveness of LLMs in generating useful and non-redundanttest case designs, the identification of missing test case conditions, and the nature of use cases where LLMs may struggle to provide adequate test coverage. Through this research, we aim to streamline the system testing process and improve the overall quality of software products.
2024-04-29T00:00:00ZGraph structure learning based DL model for ECG anomaly predictionOberoi, RupinGupta, Anubha (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/17542025-06-23T22:00:51Z2024-04-29T00:00:00ZGraph structure learning based DL model for ECG anomaly prediction
Oberoi, Rupin; Gupta, Anubha (Advisor)
Electrocardiography (ECG) is widely used in cardiography as a non-invasive diagnostic tool for providing a graphical representation of the electrical activity in the heart over a duration of time. It captures the electrical impulses generated by cardiac muscles and is used to detect several types of cardiac conditions, such as hypertrophy and arrhythmia. In this study we work on developing a deep learning model which can effectively classify abnormalities from 12 lead ECG data. We use the PTB-XL dataset, the largest publicly available dataset for 12 lead ECGs.In order to harness the inter-relationship from the data from the 12 leads, we model them asa graph, with the graph structure being learned and design a deep learning model consistingof a graph convolution network (GCN) and present a comprehensive quantitative evaluation, demonstrating comparable performance when compared to existing state of the art works.
2024-04-29T00:00:00ZUnderstanding COVID-19 genomic sequences through the lens of strainformer, a transformer modelChilkoti, MansiSethi, Tavpritesh (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/17532025-06-23T22:00:46Z2024-04-26T00:00:00ZUnderstanding COVID-19 genomic sequences through the lens of strainformer, a transformer model
Chilkoti, Mansi; Sethi, Tavpritesh (Advisor)
This project aims to unravel the complex genomic dynamics of COVID-19, which are critical for understanding its virulence and developing targeted therapeutic interventions. Our approachfocuses on meticulously analyzing the genomic sequences of the SARS-Cov-2 virus, which wasaccomplished using a transformer model trained on real-world SARS-CoV-2 sequences. The transformer model was trained on approximately 2 million sequences, which generated attention scores for genomic codons. These 2 million COVID-19 genomic sequences were aligned using the MAFFT tool. The DNA sequences were then divided into codons to facilitate mapping between aligned and real-world sequences. This mapping method carefully examined the distribution of attention scores across the sequences’ mutated and non-mutated regions.
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