Abstract

Business organizations are using the technological innovations like cloud computing (CC) as a developmental platform in order to improve the performance of their information systems. In that context, our paper discusses know-how in employing the public and private CC to serve as platforms to develop the evaluation system of annual employees' performance (ESAEP) at Iraqi universities. Therefore, we ask the paper question which is “Is it possible to adopt the innovative solutions of ICTs (Like: public and private CC) for finding the developmental vision about management information systems at business organizations?”. In addition, the paper aim

Inherent fluctuations in the availability of energy from renewables, particularly solar, remain a substantial impediment to their widespread deployment worldwide. Employing phase-change materials (PCMs) as media, saving energy for later consumption, offers a promising solution for overcoming the problem. However, the heat conductivities of most PCMs are limited, which severely limits the energy storage potential of these materials. This study suggests employing circular fins with staggered distribution to achieve improved thermal response rates of PCM in a vertical triple-tube heat exchanger involving two opposite flow streams of the heat-transfer fluid (HTF). Since heat diffusion is not the same at various portions of the PCM unit,

ABSTRACT: BACKGROUND: Left ventricular hypertrophy is a significant risk factor for cardiovascular complications such as ischemic heart disease, heart failure, sudden death, atrial fibrillation, and stroke. A proper non-expensive tool is required for detection of this pathology. Different electrocardiographic (ECG) criteria were investigated; however, the results were conflicting regarding the accuracy of these criteria. OBJECTIVE: To assess the accuracy of three electrocardiographic criteria in diagnosis of left ventricular hypertrophy in adult patients with hypertension using echocardiography as a reference test. PATIENTS AND METHODS: This is a hospital-based cross sectional observational study which included 340 adult patients with a his

Acute appendicitis is the most common surgical abdominal emergency. Its clinical diagnosis remains a challenge to surgeons, so different imaging options were introduced to improve diagnostic accuracy. Among these imaging modality choices, diagnostic medical sonography (DMS) is a simple, easily available, and cost effective clinical tool. The purpose of this study was to assess the accuracy of DMS, in the diagnosis of acute appendicitis compared to the histopathology report, as a gold standard. Between May 2015 and May 2016, 215 patients with suspected appendicitis were examined with DMS. The DMS findings were recorded as positive and negative for acute appendicitis and compared with the histopathological results, as a gold standard

Plane cubics curves may be classified up to isomorphism or projective equivalence. In this paper, the inequivalent elliptic cubic curves which are non-singular plane cubic curves have been classified projectively over the finite field of order nineteen, and determined if they are complete or incomplete as arcs of degree three. Also, the maximum size of a complete elliptic curve that can be constructed from each incomplete elliptic curve are given.

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s

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

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the  classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an