Browsing by Author "Rahman, Tasmiah"

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Conductive Ternary Nitride as an Alternative Material in the Design of Plasmonic Refractive Index Sensor
(Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-06-25) Habiba, Anika Rahman; Rahman, Tasmiah; Prapti, Sumaiya Tasnim
Traditional plasmonic materials such as gold and silver have been widely used in Metal- Insulator-Metal (MIM) plasmonic sensors. However, these materials face significant challenges, including high optical losses, chemical reactivity and compatibility issues with standard fabrication processes. This thesis investigates the use of conductive ternary nitrides, specifically 𝑇𝑖(𝑥)𝑌(1−𝑥)𝑁, as a promising alternative for designing plasmonic refractive index sensors. Employing the Finite Element Method (FEM), the study examines the plasmonic properties of 𝑇𝑖(𝑥)𝑌(1−𝑥)𝑁, aiming to overcome the drawbacks of conventional materials. The initial parameters of the proposed sensor achieved a sensitivity of 513.3085 nm/RIU. After optimization, the sensor demonstrated enhanced sensitivity of 819 nm/RIU and an improved FOM of 32 RIU^(-1). The proposed plasmonic refractive index sensor, utilizing 𝑇𝑖(𝑥)𝑌(1−𝑥)𝑁, has demonstrated exceptional sensitivity in air pressure sensing applications, enabling precise detection of minute pressure changes. This capability is particularly beneficial for environmental monitoring and industrial applications where accurate air pressure measurements are crucial. The research provides a comprehensive comparison between 𝑇𝑖(𝑥)𝑌(1−𝑥)𝑁 and traditional plasmonic materials, underscoring the advantages of 𝑇𝑖(𝑥)𝑌(1−𝑥)𝑁, such as lower optical losses, higher chemical stability, and better compatibility with existing manufacturing technologies. The findings of this study pave the way for the development of efficient, high-performing plasmonic devices, advancing the fields of nanophotonics and plasmonic sensor technology.
Evaluation of Machine Learning Approaches for Prediction of Dengue Fever
(Springer, 2022-10-14) Rahman, Tasmiah; Rahman, Md. Mahmudur
Dengue is a mosquito-borne, deadly viral disease that is a major threat to public health all over the world. Dengue and covid-19 symptoms are almost same, and sometimes, people are confused about which disease they are infected with. This year in Bangladesh dengue and covid-19 patients have been increasing at an alarming rate, and most of the time people didn’t properly recognize the disease. A developing country like Bangladesh has faced many difficulties to handle this situation. The target of this research work is to analyze the symptoms and predict the chances to get infected with dengue fever. Machine learning techniques are widely utilized in the health industry to detect fraud in treatment at lower cost, predictive analysis, cure the disease. Four machine learning algorithms are used which are support vector machine, decision tree, K-nearest neighbor, random forest to predict dengue fever based on symptoms. The results were compared for percentage split and K-fold cross-validation method for before and after applying principal component analysis. The experimental result shows that the support vector machine algorithm provides the highest performance compared to others algorithms.
Prediction of Type 2 Diabetes Using Different Machine Learning Algorithms
(Daffodil International University, 2021-01-28) Rahman, Tasmiah; Azad, Anamika
Diabetes is a major threat for all over the world. It is rapidly getting worse day by day. It is a big challenge to determine diabetes properly and give proper treatment at a right time. Now in this era of technology many machine learning algorithms are used to develop software to predict diabetes disease more accurately so that doctor can give patients proper advice and medicine which can reduce the risk of death. The purpose of this paper is to analyzing different Machine Learning algorithms for finding an efficient way to predict diabetes. In this thesis, we analyze 10 different machine learning algorithms which are Decision tree, Logistic regression, Multinomial Naïve Bayes, Gaussian Naïve Bayes, KNN, Support vector Classifier, Random Forest, Gradient Boosting, AdaBoost and Bagging by using a proper dataset. In our dataset there is 8 features and 2000 patients information. Here we find out the correlation of each attribute by using standard data mining technique. Dataset was preprocessed by using different preprocess method. We apply percentage split,10-fold and 15-fold cross validation technique on individual 10 different algorithms. In the end of our implementation, we find the highest accuracy in Decision tree which is 84.3% for percentage split,87% for 10-fold and 87.8% for 15-fold cross validation. Machine learning technique take less time for predict disease.