Probabilistic neural network for vulnerability prediction on a practical power system

Vulnerability prediction of power systems is important so as to determine its ability to continue to provide service in case of any unforeseen catastrophic contingency. It is considered one of the vital concerns due to the continual blackouts in recent years which indicate that the power system today is too vulnerable to withstand a severer disturbance. The objective of this paper is to investigate and compare the performance of two vulnerability indices used for assessing the vulnerability of power systems when subjected to various contingencies. The Probabilistic Neural Network (PNN) based on power system loss and possible loss of load will be used to speed up the assessment technique. In this study, contingency analyses were carried out on a practical 87 bus test system and the vulnerability indices were calculated using the MATLAB program. Results presented show that PSL index is more accurate for analyzing the impact of contingencies on a practical power system from the view point of power system loss considering the loss of power during contingencies

Social media use, collaborative learning and students’ academic performance: a systematic literature review of theoretical models

This research provided a systematic literature review of theoretical models on interaction and collaborations regarding Information system (IS) and Information Technology (IT). This paper conducted an review of studies dedicated to (IS & IT) on the basis of certain dimensions namely, research theories, review of constructivist theories, definitions of constructivism, social constructivism, theoretical of constructivism, active collaborative learning theory, technology acceptance model (TAM), theory of reasoned action, technology acceptance model and Its extensions, and finally research models and frameworks. The discussion of this research obtained revealed that the interest on the topic has shown an increasing trend over recent years that it has ultimately become a well-known topic for academic research in the future via theories use. From review of theoretical models and related theories we recommend to use constructivism, active collaborative learning theory with (TAM) to measurement performance and satisfaction with social media use as the mediator. However, to boost and enhance the IT continuance intention, it is important that future studies apply considerable use of theoretical and methodological approaches like the qualitative methods to examine the IT continuance intention. © 2005 – ongoing JATIT & LLS

Effective early detection of epileptic seizures through EEG signals using classification algorithms based on t-distributed stochastic neighbor embedding and K-means

Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%

Automatic and early detection of Parkinson’s Disease by analyzing acoustic signals using classification algorithms based on recursive feature elimination method

Parkinson’s disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60–80% inability to produce dopamine, an organic chemical responsible for controlling a person’s movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient’s nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person’s voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson’s cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%

Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification

In this paper, a new texture descriptor inspired from Completed Local Ternary Pattern (CLTP) is proposed and investigated for texture image classification task. A wavelet-CLTP (WCLTP) is proposed by integrating the CLTP with the redundant discrete wavelet transform (RDWT). Firstly, the images are decomposed using RDWT into four sub-bands. Then, the CLTP are extracted from the LL sub-bands coefficients of the image. The RDWT is selected due to its advantages. Unlike the other wavelet transform, the RDWT decompose the images into the same size sub-bands. So, the important textures in the image will be at the same spatial location in each sub-band. As a result, more accurate capturing of the local texture within RDWT domain can be done and the exact measure of local texture can be used. The proposed WCLTP is evaluated for rotation invariant texture classification task. The experimental results using CURTex and Outex texture databases show that the proposed WCLTP outperformed the CLBP and CLBC descriptors and achieved an impressive classification accuracy. Furthermore, the WCLTP outperformed the CLTP in Outex and many cases in the CURTex databases

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients’ lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%

In Vitro and In Vivo Evaluation of a Nano-Tool Appended Oilmix (Clove and Tea Tree Oil) Thermosensitive Gel for Vaginal Candidiasis

The main objective of the proposed work was the development of a thermosensitive gel (containing clove and tea tree oil) for the management of vaginal candidiasis. Both oils have been recommended to be used separately in a topical formulation for vaginal candidiasis. Incorporating two natural ingredients (clove and tea tree oil) into a product give it a broad antimicrobial spectrum and analgesic properties. The two oils were mixed together at a 3:1 ratio and converted into o/w nanoemulsion using the aqueous titration method and plotting pseudo ternary phase diagrams. Further transformations resulted in a gel with thermosensitive properties. To determine the final formulation’s potential for further clinical investigation, in vitro analyses (viscosity measurement, MTT assay, mucoadhesion, ex vivo permeation) and in vivo studies (fungal clearance kinetics in an animal model) were conducted. The current effort leveraged the potential of tea tree and clove oils as formulation ingredients and natural therapeutic agents for vaginal infections. Its synergy generated a stable and effective thermosensitive gel that can be utilized for recurrent candidiasis and other infections

Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features

An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying and diagnosing pneumonia and tuberculosis is X-ray imaging. However, early discrimination is difficult for radiologists and doctors because of the similarities between pneumonia and tuberculosis. As a result, patients do not receive the proper care, which in turn does not prevent the disease from spreading. The goal of this study is to extract hybrid features using a variety of techniques in order to achieve promising results in differentiating between pneumonia and tuberculosis. In this study, several approaches for early identification and distinguishing tuberculosis from pneumonia were suggested. The first proposed system for differentiating between pneumonia and tuberculosis uses hybrid techniques, VGG16 + support vector machine (SVM) and ResNet18 + SVM. The second proposed system for distinguishing between pneumonia and tuberculosis uses an artificial neural network (ANN) based on integrating features of VGG16 and ResNet18, before and after reducing the high dimensions using the principal component analysis (PCA) method. The third proposed system for distinguishing between pneumonia and tuberculosis uses ANN based on integrating features of VGG16 and ResNet18 separately with handcrafted features extracted by local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) algorithms. All the proposed systems have achieved superior results in the early differentiation between pneumonia and tuberculosis. An ANN based on the features of VGG16 with LBP, DWT and GLCM (LDG) reached an accuracy of 99.6%, sensitivity of 99.17%, specificity of 99.42%, precision of 99.63%, and an AUC of 99.58%