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Full-Text Articles in Engineering
Artificial Intelligence (Ai) And Nuclear Features From The Fine Needle Aspirated (Fna) Tissue Samples To Recognize Breast Cancer, Rumana Islam, Mohammed Tarique
Artificial Intelligence (Ai) And Nuclear Features From The Fine Needle Aspirated (Fna) Tissue Samples To Recognize Breast Cancer, Rumana Islam, Mohammed Tarique
Electrical and Computer Engineering Publications
Breast cancer is one of the paramount causes of new cancer cases worldwide annually. It is a malignant neoplasm that develops in the breast cells. The early screening of this disease is essential to prevent its metastasis. A mammogram X-ray image is the most common screening tool practiced currently when this disease is suspected; all the breast lesions identified are not malignant. The invasive fine needle aspiration (FNA) of a breast mass sample is the secondary screening tool to clinically examine cancerous lesions. The visual image analysis of the stained aspirated sample imposes a challenge for the cytologist to identify …
A Novel Convolutional Neural Network Based Dysphonic Voice Detection Algorithm Using Chromagram, Rumana Islam, Mohammed Tarique
A Novel Convolutional Neural Network Based Dysphonic Voice Detection Algorithm Using Chromagram, Rumana Islam, Mohammed Tarique
Electrical and Computer Engineering Publications
This paper presents a convolutional neural network (CNN) based non-invasive pathological voice detection algorithm using signal processing approach. The proposed algorithm extracts an acoustic feature, called chromagram, from voice samples and applies this feature to the input of a CNN for classification. The main advantage of chromagram is that it can mimic the way humans perceive pitch in sounds and hence can be considered useful to detect dysphonic voices, as the pitch in the generated sounds varies depending on the pathological conditions. The simulation results show that classification accuracy of 85% can be achieved with the chromagram. A comparison of …
Case Study Of Tv Spectrum Sensing Model Based On Machine Learning Techniques, Abdalaziz Mohammad, Faroq Ali Awin, Esam Abdel-Raheem
Case Study Of Tv Spectrum Sensing Model Based On Machine Learning Techniques, Abdalaziz Mohammad, Faroq Ali Awin, Esam Abdel-Raheem
Electrical and Computer Engineering Publications
Spectrum sensing is an essential component in cognitive radios (CR). Machine learning (ML) algorithms are powerful techniques for designing a promising spectrum sensing model. In this work, the supervised ML algorithms, support vector machine (SVM), k-nearest neighbor (kNN), and decision tree (DT) are applied to detect the existence of primary users (PU) over the TV band. Moreover, the Principal Component Analysis (PCA) is incorporated to speed up the learning of the classifiers. Furthermore, the ensemble classification-based approach is employed to enhance the classifier predictivity and performance. Simulation results have shown that the highest performance is achieved by the ensemble classifier. …
Short-Term Load Forecasting Of Microgrid Via Hybrid Support Vector Regression And Long Short-Term Memory Algorithms, Arash Moradzadeh, Sahar Zakeri, Maryam Shoaran, Behnam Mohammadi-Ivatloo, Fazel Mohammadi
Short-Term Load Forecasting Of Microgrid Via Hybrid Support Vector Regression And Long Short-Term Memory Algorithms, Arash Moradzadeh, Sahar Zakeri, Maryam Shoaran, Behnam Mohammadi-Ivatloo, Fazel Mohammadi
Electrical and Computer Engineering Publications
© 2020 by the authors. Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target …