Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

The research aims to investigate the effects of GMAW or MIG welding process on the mechanical properties of dissimilar aluminum alloys 2024-T351 and AA 6061- T651. A series of experimental techniques have been conducted to evaluate mechanical properties of the alloys, by carrying out hardness, tensile and bending tests for welded and un-welded specimens.

Metal inert gas (MIG) has been carried out on sheet metal using ER- 4043(AlSi₅) as a filler metal and argon as shielded gas. The welded joints were tested by X-ray radiography and Faulty pieces were excluded.

Welding joints without defects are subjected to heat treatment including heating the joints in furnace to 170 °C for half an hour then air cooling to rel

Recently a large number of extensive studies have amassed that describe the removal of dyes from water and wastewater using natural adsorbents and modified materials. Methyl orange dye is found in wastewater streams from various industries that include textiles, plastics, printing and paper among other sources. This article reviews methyl orange adsorption onto natural and modified materials. Despite many techniques available, adsorption stands out for efficient water and wastewater treatment for its ease of operation, flexibility and large-scale removal of colorants. It also has a significant potential for regeneration recovery and recycling of adsorbents in comparison to other water treatment methods. The adsorbents described herein were

Abstract

                 The research dealt with a studying the impact of oil price fluctuations on one of the rules of financial discipline, which is the rule of budget deficit in the Iraqi economy for the period (2003-2020) as it is one of the quarterly economies that rely mainly on volatile oil revenues that fluctuate with oil prices in global markets, and therefore the general budget suffers. from The state of instability and then the government resorts to borrowing for a long time . this deficit in the general budget and increase the debt burden in the public debt.The research aim to measure and study the impact of oil price flu

Leap Motion Controller (LMC) is a gesture sensor consists of three infrared light emitters and two infrared stereo cameras as tracking sensors. LMC translates hand movements into graphical data that are used in a variety of applications such as virtual/augmented reality and object movements control. In this work, we intend to control the movements of a prosthetic hand via (LMC) in which fingers are flexed or extended in response to hand movements. This will be carried out by passing in the data from the Leap Motion to a processing unit that processes the raw data by an open-source package (Processing i3) in order to control five servo motors using a micro-controller board. In addition, haptic setup is proposed using force sensors (F

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

Aspect categorisation and its utmost importance in the eld of Aspectbased Sentiment Analysis (ABSA) has encouraged researchers to improve topic model performance for modelling the aspects into categories. In general, a majority of its current methods implement parametric models requiring a pre-determined number of topics beforehand. However, this is not e ciently undertaken with unannotated text data as they lack any class label. Therefore, the current work presented a novel non-parametric model drawing a number of topics based on the semantic association present between opinion-targets (i.e., aspects) and their respective expressed sentiments. The model incorporated the Semantic Association Rules (SAR) into the Hierarchical Dirichlet Proce