Alzheimer’s disease (AD) is an age-related progressive and neurodegenerative disorder, which is characterized by loss of memory and cognitive decline. It is the main cause of disability among older people. The rapid increase in the number of people living with AD and other forms of dementia due to the aging population represents a major challenge to health and social care systems worldwide. Degeneration of brain cells due to AD starts many years before the clinical manifestations become clear. Early diagnosis of AD will contribute to the development of effective treatments that could slow, stop, or prevent significant cognitive decline. Consequently, early diagnosis of AD may also be valuable in detecting patients with dementia who have n

Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte

This study involved preparation of Graphene oxide (GO) and reduced graphene oxide (RGO) using Hummer method and chemical method respectively. These carbon nanomaterials were used as starting material to make novel functionalize with thiocarbohydrazide (TCH) which was prepared by reacting CS2 with hydrazine to form GO or RGO- 4-amino,5-substituted 1H,1,2,4 Triazole 5(4H) thion (ASTT) ,(GOT) and( RGOT) respectively via cyclocondensation reaction. Also MnO2 nanorod was prepared to form hybridized with GOT and RGOT. A commercial multiwall carbon nanotube (MWCNT) and functionalization with carboxylic groups' (f-MWCNT) and its nanocomposite with GOT were also prepared. All carbon nanomaterials were characterized with different techniques such as

Abstract: The development of highly sensitive sensors has become an efficient field of research. In this work, an ArF Excimer laser of 193 nm with a maximum pulse energy of 275 mJ, 15 ns pulse duration and a repetition rate of 1 Hz is utilized to form a Laser Induced Periodic Surface Structures (LIPSS) of three different morphologies (nanochains, contours, grooves) on surface of CR39 polymer at a fluence range above the ablation threshold (250 mJ/cm2). The laser ablated polymer surface is then Surface Enhanced Raman Scattering (SERS) activated by deposition of a gold layer of 30 nm thickness. The capability of the produced substrate for surface enhanced Raman scattering is evaluated through thiophenol as an analyte molecule. It is observ

The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. T