Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Measuring toxicity is an important step in drug development. However, the current experimental meth- ods which are used to estimate the drug toxicity are expensive and need high computational efforts. Therefore, these methods are not suitable for large-scale evaluation of drug toxicity. As a consequence, there is a high demand to implement computational models that can predict drug toxicity risks. In this paper, we used a dataset that consists of 553 drugs that biotransformed in the liver

Optimized superpixel and AdaBoost classifier for human thermal face recognition

Infrared spectrum-based human recognition systems offer straightforward and robust solutions for achieving an excellent performance in uncontrolled illumination. In this paper, a human thermal face recognition model is proposed. The model consists of four main steps. Firstly, the grey wolf optimization algorithm is used to find optimal superpixel parameters of the quick-shift segmentation method. Then, segmentation-based fractal texture analysis algorithm is used for extracting features and the rough set-based methods are used to select the most discriminative features. Finally, the AdaBoost classifier is employed for the classification process. For evaluating our proposed approach, thermal images from the Terravic Facial infrared dataset were used. The experimental results showed that the proposed approach achieved (1) reasonable segmentation results for the indoor and outdoor thermal images, (2) accuracy of the segmented images better than the non-segmented ones, and (3) the entropy-based feature selection method obtained the best classification accuracy. Generally, the classification accuracy of the proposed model reached to 99% which is better than some of the related work with around 5%

Endothelial dysfunction and the risk of atherosclerosis in overt and subclinical hypothyroidism

Hypothyroidism is associated with increased risk of atherosclerosis. We assessed carotid intima-media thickness (CIMT), as a marker of atherosclerosis, and endothelial function in patients with hypothyroidism. We included 70 female patients with hypothyroidism in the study, 40 patients with overt and 30 patients with subclinical hypothyroidism. Forty, age- and sex-matched, subjects with normal thyroid functions were also included as a control group. CIMT was measured using high-resolution color-coded Doppler ultrasonography. Endothelial function was assessed by measuring the percent of change in blood flow following heat-mediated vasodilation using laser Doppler flowmetry. CIMT was significantly higher in patients with overt and subclinical hypothyroidism as compared with the control group (0.7 ± 0.2 and 0.6 ± 0.2 mm respectively vs 0.45 ± 0.07 mm, P < 0.001 for both). The percent of change in blood flow following heat-mediated vasodilation was significantly impaired in patients with overt and subclinical hypothyroidism as compared with the control group (328 ± 17 and 545 ± 406% respectively vs 898 ± 195%, P < 0.001 for both). The impairment was more significant in overt as compared with subclinical hypothyroidism (P = 0.014). CIMT negatively correlated with percent of change in blood flow following heat-mediated vasodilation in patients with overt and subclinical hypothyroidism (P < 0.001 for both). We concluded that CIMT is significantly higher in patients with overt and subclinical hypothyroidism compared with normal control subjects. Impairment of endothelial function is a contributing factor to the increased risk of atherosclerosis in both groups of patients

Personal Identification Using Ears Based on Statistical Features

Advisor/s: Atalla I. Hashad, Gouda I. Salama. Date and location of PhD thesis defense: May 2014, AASTMTBiometrics is an automated method of recognizing a person based on a physiological (e.g. face, iris, or retina) or behavioral (e.g. gait, signature, or dynamic keystrokes) characteristics. Ear recognition is one of the physiological biometrics' types that have been interested in the recent years. Ear recognition, achieves good accuracy and has many advantages such as it doesn't affected by expressions, health, and more stable than many other biometrics. However, it has many challenges such as the pose of the face, lighting variation, occlusion with hair or clothes. In this research, four proposed models are used to identify people using ear images. The first model used single feature extraction method based on single classifier. While, the second model used single feature extraction method based on multi-classifiers. The third model used feature combination techniques (parallel or serial) based on single classifier. Finally, in the fourth model multi-features and multi-classifiers are used. In this research, there are four methods that are used to extract the features, namely, \textit{Principal Component Analysis} (PCA), \textit{Linear Discriminant Analysis} (LDA), \textit{Independent Component Analysis} (ICA), and \textit{Discrete Cousin Transform} (DCT). Neural networks, decision tree, and minimum distance classifiers are used to classify the unknown samples. The occlusion problem with hair or scarves is one of the big challenges of the ear recognition systems. In this research, segmentation technique is proposed to neglect the occluded part and solve the occlusion problem. The idea of the segmentation technique is based on dividing the ear images into different parts. The occluded part/s is neglected and the rest of the parts are used to identify people based on features fusion and classifiers fusion. The segmentation technique consists of two main types, namely, uniform or non-uniform segmentation techniques. In this research, the uniform segmentation technique is used for many experiments (horizontal, vertical, and grid). All the four proposed models are applied to all ear segments to investigate the power of each model and to achieve a high accuracy. In this research, ear database images is used. The ear dataset consists of 102 grayscale images (6 images for each of 17 subjects) in PGM format [1]. The proposed models are achieved good identification rates using ear images. In the first model, the best accuracy achieved using LDA and neural network classifier. The results of the first model ranged from 64.12\% to 100\%. In the second model, many classifiers are fused to increase the recognition rate. In this method, two methods are used, namely, Borda count and majority voting. The results of this model ranged from 94.12\% to 96.08\%. The third model, the features using two different methods, namely serial and parallel are combined. The results of this model prove that the serial combination is more powerful than parallel combination. Finally, in the fourth model, two features and two classifiers are fused to get one decision. The accuracy of this model is approximately the same of the third model, and it does not achieve good results because there is a diversity between different classifiers. Moreover, the proposed segmentation model achieved good results when some parts of the ear images are occluded

A Novel Low-Query-Budget Active Learner with Pseudo-Labels for Imbalanced Data

Despite the availability of a large amount of free unlabeled data, collecting sufficient training data for supervised learning models is challenging due to the time and cost involved in the labeling process. The active learning technique we present here provides a solution by querying a small but highly informative set of unlabeled data. It ensures high generalizability across space, improving classification performance with test data that we have never seen before. Most active learners query either the most informative or the most representative data to annotate them. These two criteria are combined in the proposed algorithm by using two phases: exploration and exploitation phases. The former aims to explore the instance space by visiting new regions at each iteration. The second phase attempts to select highly informative points in uncertain regions. Without any predefined knowledge, such as initial training data, these two phases improve the search strategy of the proposed algorithm so that it can explore the minority class space with imbalanced data using a small query budget. Further, some pseudo-labeled points geometrically located in trusted explored regions around the new labeled points are added to the training data, but with lower weights than the original labeled points. These pseudo-labeled points play several roles in our model, such as (i) increasing the size of the training data and (ii) decreasing the size of the version space by reducing the number of hypotheses that are consistent with the training data. Experiments on synthetic and real datasets with different imbalance ratios and dimensions show that the proposed algorithm has significant advantages over various well-known active learners

A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions

Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensive and time-consuming labeling process is still an obstacle to labeling a sufficient amount of training data, which is essential for building supervised learning models. Here, with low labeling cost, the active learning (AL) technique could be a solution, whereby a few, high-quality data points are queried by searching for the most informative and representative points within the instance space. This strategy ensures high generalizability across the space and improves classification performance on data we have never seen before. In this paper, we provide a survey of recent studies on active learning in the context of classification. This survey starts with an introduction to the theoretical background of the AL technique, AL scenarios, AL components supported with visual explanations, and illustrative examples to explain how AL simply works and the benefits of using AL. In addition to an overview of the query strategies for the classification scenarios, this survey provides a high-level summary to explain various practical challenges with AL in real-world settings; it also explains how AL can be combined with various research areas. Finally, the most commonly used AL software packages and experimental evaluation metrics with AL are also discussed

Detection of Saprolegnia parasitica in eggs of angelfish Pterophyllum scalare (Cuvier–Valenciennes) with a history of decreased hatchability

Mass mortalities of angelfish eggs accompanied with very low hatchability were reported in a private ornamental fish farm in Egypt. Examined eggs were badly damaged by water mould that was decisively confirmed as Saprolegnia species. Presumptive identification of the ten retrieved isolates was initially suggestive of Saprolegnia species. Mycological investigations have revealed that only 7 out of 10 isolates were capable of producing sexual stages. Therefore, using molecular tools such as PCR coupled with partial sequencing of inter-transcribed spacer (ITS) gene was one of the most important approaches to distinguish Saprolegnia parasitica from other water moulds. The sequences of ITS gene data derived from eight isolates showed 100% similarity with S. parasitica ATCC90312 sequence and the remaining two isolates were different in one nucleotide (99.9%). The phylogenetic analysis of ITS genes grouped the ten isolates with other S. parasitica in one clad. Further, to control such fungal infection, the efficacy of povidone iodine as surface disinfectant for angelfish and their fertilized eggs were tested. By trial, it was obvious that the obtained post-rinsing results were highly suggestive for the efficacy of povidone iodine as an efficient antifungal disinfectant for both fish and eggs

Modes de développement de la théorie de l’esprit d’enfants présentant un retard mental

In this paper, we proposed a new and robust biometric-based approach to identify head of cattle. This approach used the Weber Local Descriptor (WLD) to extract robust features from cattle muzzle print images (images from 31 head of cattle were used). It also employed the AdaBoost classifier to identify head of cattle from their WLD features. To validate the results obtained by this classifier, other two classifiers (k-Nearest Neighbor (k-NN) and Fuzzy-k-Nearest Neighbor (Fk-NN)) were used. The experimental results showed that the proposed approach achieved a promising accuracy result (approximately 99.5%) which is better than existed proposed solutions. Moreover, to evaluate the results of the proposed approach, four different assessment methods (Area Under Curve (AUC), Sensitivity and Specificity, accuracy rate, and Equal Error Rate (EER)) were used. The results of all these methods showed that the WLD along with AdaBoost algorithm gave very promising results compared to both of the k-NN and Fk-NN algorithms.Web of Science122665