Fuzzy C-means (FCM) is a clustering method used for collecting similar data elements within the group according to specific measurements. Tabu is a heuristic algorithm. In this paper, Probabilistic Tabu Search for FCM implemented to find a global clustering based on the minimum value of the Fuzzy objective function. The experiments designed for different networks, and cluster’s number the results show the best performance based on the comparison that is done between the values of the objective function in the case of using standard FCM and Tabu-FCM, for the average of ten runs.

The preliminary test of the compounds N [2– (3,4–dimethoxy nitrobenzene oxazepine– 2,3–dihydro–4,7–dione]–5–mercupto–2–amino–1,3,4–thiadiazol [A] and N [ 2–anthralidene– 5– ( 2–nitrophenyl ) –1,3–oxazepine–4,7–dione–2–d](5–mercapto–1,3,4–thiadiazole–2–amin) [B] , showed that they possess high activity against some positive and negative bacteria , like pseudomonas aeruginosa (pseudo.), Escherichia coli (E-coli), staphylococcus aureus (sta.) and Bacillus subtilis (Ba.) and finally there is a study of the effect of some antibiotics like streptomycin (S), gentamycin (GN), chloramphenicol (C) and Nalitixic acid (NA) in order to compare the differences in effects. In the present study, results

  The preliminary test of the compounds N [2– (3,4–dimethoxy nitrobenzene oxazepine– 2,3–dihydro–4,7–dione]–5–mercupto–2–amino–1,3,4–thiadiazol [A] and N [ 2–anthralidene– 5– ( 2–nitrophenyl ) –1,3–oxazepine–4,7–dione–2–d](5–mercapto–1,3,4–thiadiazole–2–amin) [B] , showed that they possess high activity against some positive and negative bacteria , like pseudomonas aeruginosa (pseudo.), Escherichia coli (E-coli), staphylococcus aureus (sta.) and Bacillus subtilis (Ba.) and finally there is a study of the effect of some antibiotics like streptomyci

Aim: The study designed to evaluate the Geno-protective effect of green tea extract against genotoxicity induced by metronidazole and tinidazole. Methods: Thirty-six mice were used, For each experiment, The animals divided into 6 groups: Group I- Negative control administered distilled water; Group II-Healthy mice treated with metronidazole alone, Group III- Healthy mice treated with tinidazole alone; Group IV- Healthy mice administered green tea extract alone Group V- Healthy mice treated with metronidazole, followed by green tea extract administration, Group VI- Healthy mice treated with tinidazole, followed by administration of green tea extract. Results: treatment with Tinidazole significantly increase total chromosomal aberration (0.18

With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detectors, variable- sized intrusion detection network type misuse where the algorithm generates a set of detectors to distinguish the self-samples. Practica

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D