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Öğe Automatic arrhythmia detection based on the probabilistic neural network with FPGA implementation(Hindawi Ltd, 2022) Srivastava, Rohini; Kumar, Basant; Alenezi, Fayadh; Alhudhaif, Adi; Althubiti, Sara A.; Polat, KemalThis paper presents a prototype implementation of arrhythmia classification using Probabilistic neural network (PNN). Arrhythmia is an irregular heartbeat, resulting in severe heart problems if not diagnosed early. Therefore, accurate and robust arrhythmia classification is a vital task for cardiac patients. The classification of ECG has been performed using PNN into eight ECG classes using a unique combination of six ECG features: heart rate, spectral entropy, and 4th order of autoregressive coefficients. In addition, FPGA implementation has been proposed to prototype the complete system of arrhythmia classification. Artix-7 board has been used for the FPGA implementation for easy and fast execution of the proposed arrhythmia classification. As a result, the average accuracy for ECG classification is found to be 98.27%, and the time consumed in the classification is found to be 17 seconds.Öğe Ensemble learning framework with GLCM texture extraction for early detection of lung cancer on CT images(Hindawi Ltd, 2022) Althubiti, Sara A.; Paul, Sanchita; Mohanty, Rajanikanta; Mohanty, Sachi Nandan; Alenezi, Fayadh; Polat, KemalLung cancer has emerged as a major cause of death among all demographics worldwide, largely caused by a proliferation of smoking habits. However, early detection and diagnosis of lung cancer through technological improvements can save the lives of millions of individuals affected globally. Computerized tomography (CT) scan imaging is a proven and popular technique in the medical field, but diagnosing cancer with only CT scans is a difficult task even for doctors and experts. This is why computer-assisted diagnosis has revolutionized disease diagnosis, especially cancer detection. This study looks at 20 CT scan images of lungs. In a preprocessing step, we chose the best filter to be applied to medical CT images between median, Gaussian, 2D convolution, and mean. From there, it was established that the median filter is the most appropriate. Next, we improved image contrast by applying adaptive histogram equalization. Finally, the preprocessed image with better quality is subjected to two optimization algorithms, fuzzy c-means and k-means clustering. The performance of these algorithms was then compared. Fuzzy c-means showed the highest accuracy of 98%. The feature was extracted using Gray Level Cooccurrence Matrix (GLCM). In classification, a comparison between three algorithms-bagging, gradient boosting, and ensemble (SVM, MLPNN, DT, logistic regression, and KNN)-was performed. Gradient boosting performed the best among these three, having an accuracy of 90.9%.Öğe Graph-based link prediction between human phenotypes and genes(Hindawi Ltd, 2022) Patel, Rushabh; Guo, Yanhui; Alhudhaif, Adi; Alenezi, Fayadh; Althubiti, Sara A.; Polat, KemalDeep phenotyping is defined as learning about genotype-phenotype associations and the history of human illness by analyzing phenotypic anomalies. It is significant to investigate the association between phenotype and genotype. Machine learning approaches are good at predicting the associations between abnormal human phenotypes and genes. A novel framework based on machine learning is proposed to estimate the links between human phenotype ontology (HPO) and genes. The Orphanet's annotation parses the human phenotype-gene associations. An algorithm node2vec generates the embeddings for the nodes (HPO and genes). It performs node sampling on the graph using random walks and learns features on these sampled nodes for embedding. These embeddings were used downstream to predict the link between these nodes by supervised classifiers. Results show the gradient boosting decision tree model (LightGBM) has achieved an optimal AUROC of 0.904 and an AUCPR of 0.784, an optimal weighted F1 score of 0.87. LightGBM can detect more accurate interactions and links between human phenotypes and gene pairs.Öğe Wi-Fi signal-based human action acknowledgement using channel state information with CNN-LSTM: A device less approach(Springer London Ltd, 2022) Kumar, V. Dhilip; Rajesh, P.; Polat, Kemal; Alenezi, Fayadh; Althubiti, Sara A.Human action acknowledgment is an abundant and significant area for machine learning-based researchers due to the level of accuracy in identifying human actions. Due to the rapid growth of technologies in the machine and deep learning techniques, wireless sensors, handy Internet of Things (IoT) devices, and Wireless Fidelity (Wi-Fi), the activity recognition process is made effective with higher accuracy. By using those booming technologies and preserving the privacy of the test person we propose a novel human action recognition model that uses the channel state information (CSI) from Wi-Fi and the most prominent machine learning model, CNN with LSTM. Initially, CSI is introduced, the changes in CSI signals are assessed, and the obtained data samples are made as input to the CNN-LSTM model. To make the recognition more accurate, we also incorporated Kalman filters for noise removal and smoothed the data sample. Furthermore, we have used an image segmentation procedure to identify the initial and end times of all the activities considered and to fragment the image obtained, which is further fed as input to the CNN-LSTM model. Getting a dataset for the experiment is a herculean task. Hence a self-collected dataset is used to assess, or model proposed. Finally, the results obtained are verified and validated for their correctness with appropriate machine learning metrics and parameters like accuracy, F1 score, etc. Our proposed model affords the accuracy of 98.96% for all the considered activities. The model can adapt itself even for a minimum sampling rate and subcarriers found in the test bed.
