Reetodeep Hazra's repositories
FIFA-19-Players-and-their-Attribute-Analysis
Information Visualization (IST 719) Final Project and Poster
House-Price-Prediction-using-Big-Data
Big Data Analytics (IST 718) Final Project
Comparative-Study-of-Autism-Spectrum-Disorder-Detection-between-Machine-Learning-Algorithms
India, with near about 855,000 positive cases, is the largest contributor of autistic children worldwide. It is said to be influenced by environmental and genetic causes; with no clear answer provided by the researchers till now. Brain imaging of autism affected children revealed that their brain structures are different than other neurotypical children, so it is widely accepted that damaged brain structure caused Autism Spectrum Disorder.
Machine-Learning-for-Breast-Cancer-Classification-With-ANN-and-Decision-Tree
Breast cancer is one of the commonest cause of cancer deaths in women. It starts developing when threatening bumps start forming from the breast cells, and unfortunately most diagnoses happen in later stages, thus resulting in low chances of survival for the patient. So for early detection and prognosis, it is necessary to detect the benign or threatening nature of the bumps. In this paper, Artificial Neural Networks (ANN) and Decision Tree (DT) classifiers are used to develop a machine learning (ML) model using the Wisconsin diagnostic breast cancer (WDBC) dataset, in order to evaluate the attributes of a breast cancer development at beginning phases and classify it as malignant or benign. In the proposed scheme, feature selection and feature extraction are done to extract statistical features from the dataset and comparison between the models is provided based on their performance to identify the most suitable approach for diagnosis. The dataset apportioned into various arrangements of train-test split. The presentation of the framework is estimated, depending on accuracy, sensitivity, specificity, precision, and recall. The binary classification problem achieved a maximum accuracy of 98.55%.
Machine-Learning-Based-News-Validation
The use of machine learning has become widespread in recent years, especially due to the impact of media content on the general population. In particular, the validation of truth in the context of news has become a critical necessity, due to its ready availability from verified and unverified sources, and its ability to influence people majorly. The present work outlines a LSTM (long short-term memory) based approach to news validation. The results obtained by the model are presented in terms of the training data set and contrasted with the results obtained from the test data set. Appreciable accuracy is achieved through the model, seen through the corresponding loss curves and confusion matrix.
Fraudulent-Credit-Card-Transactions-Prediction-Using-2D-Convolutional-Neural-Network
Fraudulent credit card is one of the most alarming concerns in this modern age. With few misuse of credit card, thousands of dollars can be mishandled. This paper focused on detecting fraud credit card transaction using 2D-Convolutional neural network. The paper reports the categorization of two designated categories- Genuine and fraud transactions. In the pre-processing stage we applied under-sampling technique to handle imbalance dataset. For the improvement of the accuracy, we have decreased the number of convolutional layers with a sigmoid layer at the top. The proposed technique achieved an accuracy of 94%.
Multi-class-Heart-Sounds-Classification-Using-2D-Convolutional-Neural-Network
Heart disease is a major concern. To prevent this, it is important to detect cardiovascular diseases at the early stage. Early discovery of heart infections and constant treatment can lessen the death rate. However, the accurate and effective detection method of heart diseases is necessary to uncover this deadly threat at a very early stage, even without the presence of a medical professional. This paper studies the use of 2Dconvolutional neural network to classify heart sounds into normal and abnormal categories. The paper reports a classification of five designated categories of heart sounds such as artifact, extra heart sound, extra systole, murmur, and normal. For the betterment of the accuracy, we have reduced the number of convolutional neural network layers with a softmax layer at the top. Each convolutional layer is followed by a max pooling and a dropout layer which finally leads to a global average pooling layer. The proposed method achieves an accuracy of 83%.
Detecting-Respiratory-Diseases-from-Recorded-Lung-Sounds-by-2D-CNN
Respiratory disease is among the leading causes of deaths around the world. A large amount of population is being affected regularly with some kinds of lung function disorders which eventually lead to respiratory diseases. Prevention and early detection are essential steps in managing respiratory diseases. To decrease the fatality, an efficient detection model is needed. In this paper, 2D convolutional neural network (CNN) is used to detect respiratory diseases from the recorded lung sounds at early stages. The proposed method can detect respiratory diseases like bronchiectasis, pneumonia, bronchiolitis, chronic obstructive pulmonary disease, upper respiratory tract infection, and healthy by using Mel-frequency cepstral co-efficients (MFCC). In the proposed scheme, a data frame is recorded and after extracting the statistical features from the audio clips, the data is loaded in the data frame where further classification is done using 2D CNN. The model is based on 2D CNN architecture where the number of layers is reduced to a certain extent to achieve more accuracy. The proposed model has only 13 CNN layers where each convolution layer is being associated with a pooling layer of max-pooling 2D type. The final convolution layer has a global-average pooling 2D layer. The proposed method obtained an accuracy of over 92.39%.