http://dspace.ewubd.edu:8080/handle/123456789/3742 Sat, 04 Apr 2026 11:33:06 GMT 2026-04-04T11:33:06Z http://dspace.ewubd.edu:8080/handle/123456789/3838 Thyroid Disease Analysis and Prediction by Using Machine Learning and Deep Learning: A comparative Approach Tabassum, Sadia; Rumky, Syeda Fahmida Farzana; Shahariar, Md. Farhan The high incidence of thyroid diseases has increased globally. Thyroid disease is a widespread issue affecting huge human populations. Thyroid problems are chronic, and patients with thyroid abnormalities can lead stable, normal lives if their conditions are adequately managed. The thyroid gland is a small, front-of-the-neck organ that wraps around the windpipe (trachea). This thyroid gland is one of the body's most vital organs. The secretions of thyroid hormone releases are responsible for regulating the metabolism. Hyperthyroidism and hypothyroidism are two frequent chronic conditions of the thyroid that regulate the rate of the body's metabolism by releasing thyroid hormones. When your thyroid is not functioning properly, your entire body might be affected. Hyperthyroidism is the medical term for a condition in which the body produces an excessive amount of thyroid hormone. Hypothyroidism can develop if our body produces too little thyroid hormone. In this thesis, we tried to cover the thyroid disease prediction, analysis and prediction is compared to other similar research who worked in the area of prediction of thyroid disease. The thyroid disease prediction was implemented using two approaches, the first one is Machine Learning, and the second approach is Deep Learning. Five algorithms, including logistic regression, decision tree, support vector machine (linear kernel), support vector machine (RBF Kernel), and Random Forest algorithms, were compared from a variety of machine learning methodologies to predict and evaluate their performance in terms of accuracy. Recurrent neural networks (RNN) are one of the most effective Deep Learning algorithms for learning complex structured data. This study illustrated how to implement logistic regression, decision tree, support vector machine (linear kernel), support vector machine (RBF Kernel), and Random Forest in order to predict thyroid disease. Thyroid data set of machine learning and deep learning repositories has been used for this purpose. The performance of Machine Learning algorithms decision tree and Random Forest approach gives a maximum accuracy of 98.16% which is very good as compared to the other existing algorithms. Using the same performance matrices, the performance of the Deep learning algorithm RNN was compared to that of other Machine Learning algorithms such as logistic regression, decision tree, support vector machine (linear kernel), support vector machine (RBF Kernel), and Random Forest. RNN outperforms over the other algorithms Logistic Regression, Support Vector Machine (Linear Kernel) and Support Vector Machine (RBF Kernel) with optimum prediction accuracy of 97%. Many researchers have developed numerous approaches for the disease's diagnosis, as well as a number of disease prediction models. As in various other fields, machine learning plays a crucial part in the process of disease prediction, which aims for near-perfect accuracy to 100%. As a result, there has been a rise through interest in applying machine learning techniques to the modeling of health care issues. The development of artificial intelligence (AI) approaches is also a powerful resource that may help in the awareness of thyroid disorders, but the diagnostic accuracy is questionable. Confusion matrix is a summary of the classification's prediction outcomes that can help identify the types of error our model generates. Performance of prediction the thyroid disease was measured using several metrics such as Accuracy, MSE, MAE, RMSE, and time. All algorithms that we have applied in our thesis are compared with the following metrics: 1. Accuracy: It defines the similarity between the predicted and actual values. For measuring accuracy, the confusion matrix is utilized to determine the precise accuracy in each class based on result distributions. 2. Mean Square Error (MSE): This is a non-negative metric for determining prediction quality. If value is zero, it is perfect. In reality, however, this is impossible, the values that are closer to zero are considered to be more superior. 3. Root Mean Square Error (RMSE): It is the standard deviation of prediction errors. The distance from the regression line to the anticipated point is the prediction error. 4. Time: The amount of time in seconds it takes to achieve the desired level of accuracy for the given dataset. We have applied confusion matrix along classifier models like logistic regression, decision tree, support vector machine (linear kernel), support vector machine (RBF Kernel), and Random Forest. In our thesis paper, we also developed a critical discussion concerning limits, open challenges, some future scopes which is related to this thesis and tried to give a clear knowledge about the thyroid disease based on our investigation and its detection techniques using machine learning. Our analysis of the different methods proposed has been also provided to draw some conclusions. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh Wed, 28 Sep 2022 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3838 2022-09-28T00:00:00Z http://dspace.ewubd.edu:8080/handle/123456789/3835 Study on COVID-19 vs Viral PNEUMONIA Detection Using Convolutional Neural Network Quddus, Md.Shohel; Rafi, Moin Ahmed; Saba, Humayra Akter In fewer than two years, COVID-19, which is widely regarded as the most lethal virus of the twenty-first century, has been responsible for the deaths of millions of people all over the world. The novel coronavirus known as SARS-CoV-2 is the causative agent of the respiratory sickness known as COVID-19. It was first identified in Wuhan, China, in late December of 2019. According to Hopkins’s projections, the virus will have killed over one million people by October 2020 and infected about 40,000,000 individuals by then. This infection has rapidly expanded across China and into other nations since then, creating a global pandemic in 2020 due to its ease of transmission from person to person via respiratory droplets. Another contagious lung disease, pneumonia is typically brought on by a bacterial infection of the alveoli. Pus occurs when infected lung tissue becomes irritated. Patients typically feel the effects of the virus in their lungs first, thus chest X-rays can help doctors diagnose the disease. Experts perform physical exams and use diagnostic tools including chest X-rays, ultrasounds, and lung biopsies to identify whether or not a patient has these illnesses. In this analysis, we recommend using a chest X-ray to prioritize people for subsequent RT-PCR testing. It would also aid in the identification of patients with a high chance of COVID and a false-negative RT-PCR who require additional testing. It is urgent to create automated technologies that could diagnose this disease in its early stages, in a non-invasive manner, and in a shorter amount of time. However, selecting the most accurate models to characterize COVID-19 patients is challenging due to the inability to compare the outputs of diverse data types and gathering methods. This is the only way to remedy the issue. As a result, much research has been conducted to establish an appropriate method for diagnosing and classifying people as COVID-19-positive, healthy, or affected by other pulmonary lung illnesses. In a few earlier scholarly works, semiautomatic machine learning techniques with limited precision were proposed. In this study, we wanted to develop reliable deep learning approaches, which are a subset of machine learning and AI that model the way humans acquire knowledge. Data science encompasses fields like statistics and predictive modeling, two of which benefit greatly from deep learning. One component of this is what are known as convolutional neural networks (CNN). Any automatic, reliable, and accurate screening strategy for COVID-19 infection would be helpful for rapid diagnosis and reducing exposure to the virus for medical or healthcare personnel. The work takes advantage of a versatile and successful deep learning approach by employing the CNN model to predict and identify a patient as being unaffected or impacted by the disease using an image from a chest X-ray. In order to prove how well the CNN model was trained, the researchers employed a dataset consisting of 6,000 images with a resolution of 224x224 and 32 batches. Convolutional neural networks (CNNs) were demonstrated to be very effective for medical picture classification. The authors of this piece propose using convolutional neural networks (CNNs) to automatically classify chest X-ray images for signs of COVID-19. Using the dataset, eleven current CNN models—VGG16, VGG19, DenseNet, max poling operation, and SoftMax activation function—that can distinguish between COVID-19, pneumonia, and other lung diseases—were first used to identify the symptoms of COVID-19. To avoid overfitting, we used a stratified 5-fold cross-validation approach, allocating 90 percent of the dataset to training and 10percent to testing (unseen folds), and validating our model on 20 percent of the training data. A 95 percent accuracy rate was achieved during performance training with the trained model. Python’s built-in machine learning functionality utilizes a confusion matrix. The predictions made by a classification issue are recorded in a confusion matrix. For each category, the number of correct and incorrect predictions is represented by a count value. That’s the key to deciphering the matrix of ambiguity. The confusion matrix illustrates how your classification model generates predictions despite the uncertainty it faces. The research study can use chest X-ray pictures to identify and detect COVID-19, normal, and pneumonia infections, according to the results of the tests. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh Wed, 28 Sep 2022 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3835 2022-09-28T00:00:00Z http://dspace.ewubd.edu:8080/handle/123456789/3834 Performance Analysis of Energy Harvesting Scheme using Dual-hop Wireless Link Yesmin, Suriya; Mim, Sabera; Faisal, Md Fahim In recent years most researchers in different sectors of wireless communication  are analysing to find out maximum channel capacity with the optimal solutions of signal energy at the receiving end of the wireless communication system. In our thesis paper, we have tried to solve the major difficulty of obtaining an optimal solution for the "power splitting ratio" of an energy harvesting scheme that utilized a dual-hop wireless link. However, in order to figure out the solution that is most effective for overcoming that difficulty, we will be required to consider the optimal condition of "power supply at source terminal" and "relay gain." This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh Wed, 28 Sep 2022 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3834 2022-09-28T00:00:00Z http://dspace.ewubd.edu:8080/handle/123456789/3766 Automatic Covid-19 Detection Model from Chest X-ray using CNN, VGG16 and Best Fit Accurate Model Shaon, Sheikh Shahed Ahmed This paper describes chest X-Ray images classification like Covid-19 infected chest images or normal chest images using various types of deep learning model. These are VGG16(Transfer learning) and CNN (Convolution Neural Network). Here We made a comparison among VGG16, CNN, SVM models and collected results that which deep learning is more accurate to identify Covid-19 or normal. These models were applied for same dataset and dataset was randomly chosen almost 1350 images (Covid-19 and Normal both) from a website. For this work, at first, we have preprocessed the chest X-Ray image. Then we have extracted the distinct features from the chest X-Ray images. After that, these features have trained into various Deep Learning algorithm and finally classify these images into the category. From the experiment, Convolution Neural Network (CNN) model achieving highest accuracy more than others. The CNN models achieving training accuracy of up to 100% and validation accuracy 94.5% and the VGG16 models achieving training accuracy up to 99.1% and validation accuracy 94.2%. Then validating the CNN model how it detects COVID-19 or normal. After that, best fit accurate model can be easily identified. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh Thu, 05 May 2022 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3766 2022-05-05T00:00:00Z