http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/35 2026-04-16T23:00:20Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16879 Fast Office: Smart Office Solutions Orthy, Shinthya Hasan Fast Office is an innovative office rental platform designed to meet the evolving needs of businesses by providing fully furnished, ready-to-use office spaces. The platform aims to simplify the process of renting office spaces while offering a wide range of facilities and features to enhance productivity, collaboration, and convenience for clients. Fast Office provides fully equipped office spaces that include essential furniture, meeting rooms, and modern office equipment. These solutions address to startups and large enterprises, ensuring they can focus on their core business without the hassle of managing office logistics. Thesis 2025-01-15T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16877 Computer-aided Chronic Kidney Disease Detection: A Comparative Study of Machine Learning Algorithms Sithi, Ismet Zahan Chronic Kidney Disease (CKD) continues to pose a significant healthcare challenge, especially in rural areas of developing countries like Bangladesh, where access to affordable and effective diagnostic services is extremely limited. Early detection of CKD is crucial for slowing disease progression and improving patient outcomes. However, the diagnostic methods currently available are often expensive and technologically advanced, making them inaccessible to rural populations. To overcome these challenges, this thesis explores the application of machine learning (ML) models to enhance CKD diagnosis in a cost-effective manner, specifically targeting rural communities with limited healthcare resources. The primary objective of this study is to leverage machine learning to improve the accuracy of CKD diagnosis in settings with small and imbalanced datasets. Many existing ML models are trained on datasets that are predominantly composed of healthy individuals, resulting in high accuracy but poor sensitivity, leading to missed diagnoses of CKD patients. To address this, we implemented data balancing techniques and fine-tuned hyperparameters to enhance the performance of the models for accurate CKD detection. After optimizing the Support Vector Classifier (SVC), we achieved an impressive 95% accuracy with an AUC of 0.9952. Logistic Regression also performed well, reaching 97% accuracy and an AUC of 0.9986. The Random Forest classifier outperformed all other models, achieving perfect classification with 100% accuracy and an AUC of 1.0. These results suggest that optimized machine learning models hold great potential as a low-cost, accurate, and accessible strategy for early CKD detection, particularly in rural regions of Bangladesh where healthcare services are scarce. By implementing such models, it is possible to significantly improve patient outcomes while reducing the financial burden on healthcare systems. Thesis 2025-01-08T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16876 Diagnosis Of Dengue Fever Using Machine Learning Algorithms Thakur, Amonika The study offers crucial information to academics and medical experts, directing the choice of the best modeling algorithms for infectious illnesses. The whole outcomes of my "Diagnosis of Dengue Fever Using Machine Learning Algorithms" proposal may be seen here. This article goes into detail about how the idea was transformed into a thesis.The objective of this project was to create a system that can accurately diagnosis a dengue using machine learning algorithms , millions of people world wide are infected with dengue fever every year, another virus carried by mosquitoes. Dengue outbreaks are relentless,control strategies must be innovative and aggressive. In this context, Machine Learning (ML) techniques appear to be a promising avenue for increasing our ability to predict the occurrence and spread of dengue fever. This study examines how well different machine learning techniques predict dengue fever using a well compiled dataset of 1,037 items and 12 attribute. Some assembly algorithms were used in this work: XGBoost, Random Forest, AdaBoost and CatBoost & Decision Tree, Naive Bayes, K-Nearest Neighbors(KNN), Support Vector Machine (SVM) models were used. CatBoost outperforms other methods studied with an amazing accuracy of 96%. This accuracy is a testament to the algorithm's ability to learn complex relationships in multidemonsial data sets, making it an excellent candidate for dengue fever diagnosis. The present study emphasizes the early diagnosis of dengue infection using machine learning techniques applied to a huge clinical dataset comprising 1,037 patient records and several hematological and biochemical characteristics. The major goal of this study was to develop and test a reliable automated model for identifying patients as Dengue Positive or Dengue Negative based on blood test parameters such as Platelet Count, WBC, RBC, HCT, Lymphocyte (%), and Neutrophil (%). We testted and compared eight suppervised machine learning algorithms: CatBoost, XGBoost, Random Forest, Decision Tree, AdaBoost, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes. Each moddel was trrained and then evalluated on preprocessed data in order to juddge the moddels on important mettrics such as accuracy, precision, recall, and the F1-score. The testing results shoowed that the higheest accuracy was by CatBoost at 96.15%, followed by XGBoost with 95.19%, and Random Forest at 94.23%, refllecting that the perfformance of ensemble bassed algorithms is much better in handdling compplicated and nonllinear data patterns. The Decision Tree had an accuracy of 88.94%, while simppler models like Naive Bayes, KNN, and SVM had lower accuracies in here. Thesis 2025-12-08T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16863 Selectify: Selecting Smart Candidates In An Interview System Powered by AI Alam, Mir Rumana Zebin Selectify is an innovative AI-powered interview platform designed to streamline and enhance the recruitment process for companies and candidates. Leveraging advanced technologies, thsystem features a video calling platform that identifies human behaviors and integrates an AI generative scanner to evaluate candidates' capabilities against job requirements. Selectify operates on a subscription model and includes dynamic role management with three primary user roles: candidates (User Account), interviewers (Employee Account), and administrators (Admin Account), allowing flexible role changes as needed. The platform ensures secure and efficient payment processing through SSLcommerz and empowers companies to make data-driven hiring decisions. With a focus on automation, accuracy, and user experience, Selectify redefines how organizations assess and select talent, providing a seamless, intelligent, and efficient recruitment solution. Selectify is a state-of-the-art AI Generative Interview System crafted to simplify and elevate the hiring process for companies while empowering candidates to showcase their potential. By combining innovative AI technologies with an intuitive interface, Selectify ensures an efficient, accurate, and engaging recruitment experience for all stakeholders. Project Report 2025-01-15T00:00:00Z