A Study to Examine the Attitudes and Perceptions of Saudi Women in the United States and Canada Toward Exercise and Physical Activity

This study is an examination of the knowledge and perceptions of Saudi women in the United States and Canada in regard to exercise and physical activity. No studies on exercise, physical activity or sport for women in Saudi Arabia were found in the literature. This study focused on ascertaining knowledge of Saudi women regarding physical and psychological benefits of exercise and physical activity. Furthermore, this study investigated the barriers that limit Saudi women from participating in exercise and physical activity. The population for this study included Saudi women who are associated with Saudi clubs and student organizations in the United States and Canada. A survey instrument was developed and distributed by mail to the fifteen Saudi clubs listed in the address book provided by the Cultural Mission of Saudi Arabia. All Saudi women members of these clubs were asked to participate in this study. A MANOVA was run to determine if there were significant differences among Saudi women\u27s attitudes and perceptions toward exercise and physical activity based on various demographic variables. Analysis of barriers to participation involved tabulating and calculating frequencies. The results of this study showed that there were no significant differences between the independent variables (i.e. age, marital status, number of children, level of education, occupation, years in the United States or Canada, and level of participation), and the dependent variables (i.e. the importance of exercise for them and their interest in participation and their knowledge about the benefits of exercise and physical activity). Implications of the results are discussed and recommendations for future research are suggested

Enhanced Levenshtein Edit Distance Method functioning as a String-to-String Similarity Measure

Levenshtein is a Minimum Edit Distance method; it is usually used in spell checking applications for generatingcandidates. The method computes the number of the required edit operations to transform one string to another and it canrecognize three types of edit operations: deletion, insertion, and substitution of one letter. Damerau modified the Levenshteinmethod to consider another type of edit operations, the transposition of two adjacent letters, in addition to theconsidered three types. However, the modification suffers from the time complexity which was added to the original quadratictime complexity of the original method. In this paper, we proposed a modification for the original Levenshtein toconsider the same four types using very small number of matching operations which resulted in a shorter execution timeand a similarity measure is also achieved to exploit the resulted distance from any Edit Distance method for finding the amountof similarity between two given strings

A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

This research provides a system that integrates the work of data mining and expert system for different tasks in the process of medical diagnosis, and provides detailed steps to the process of reaching a diagnosis based on the described symptoms and mapping them with existing diagnosis available on the web or on a cloud of medical knowledge based, aggregate these data in a fuzzy manner and produce a satisfactory diagnosis of the persisting problem. The mobile phone interface would make the system user-friendly and provides mobility and accessibility to the user, while posting updates and reading in details the steps that led to the decision or diagnosis that is reached by the K-mean and the fuzzy logic inference engine. The achieved results indicate a promising diagnosis performance of the system as it achieved 90% accuracy and 92.9% F-Score

Big Data Analytics: A Survey

Internet-based programs and communication techniques have become widely used and respected in the IT industry recently. A persistent source of "big data," or data that is enormous in volume, diverse in type, and has a complicated multidimensional structure, is internet applications and communications. Today, several measures are routinely performed with no assurance that any of them will be helpful in understanding the phenomenon of interest in an era of automatic, large-scale data collection. Online transactions that involve buying, selling, or even investing are all examples of e-commerce. As a result, they generate data that has a complex structure and a high dimension. The usual data storage techniques cannot handle those enormous volumes of data. There is a lot of work being done to find ways to minimize the dimensionality of big data in order to provide analytics reports that are even more accurate and data visualizations that are more interesting. As a result, the purpose of this survey study is to give an overview of big data analytics along with related problems and issues that go beyond technology

DIAGNOSE EYES DISEASES USING VARIOUS FEATURES EXTRACTION APPROACHES AND MACHINE LEARNING ALGORITHMS

Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms' advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm)

High-Level Synthesis of the MET Module for Upgrades to the CMS Experiment at the Large Hadron Collider

The phase 2 upgrade at the LHC is expected to increase the data rate in all experiments including the CMS detector. This upgrade is a part of the search for dark matter particles. However, due to the high data rate, an enhanced Level-1 trigger is required. The MET module will be a part of this trigger. The MET module calculates the Missing Transverse Energy of the CMS events. Events with high MET indicate a possibility of containing dark matter particle, and they will be agged for closer inspection. This process should reduce the strain on the software where a more complex analysis is performed. The MET module is implemented in hardware and performs preliminary �lters and calculations. The resource utilization and timing of the module is reasonable and can �t with the other L1 trigger algorithms. More testing and optimization is still possible.</p

The Australian stock market's reaction to the first wave of the COVID-19 pandemic and Black Summer Bushfires : a sectoral analysis

In this study, we investigated the impact of the first wave of the COVID-19 pandemic on various sectors of the Australian stock market. Market capitalization and equally weighted indices were formed for eleven Australian sectors to examine the influence of the pandemic on them. First, we examined the financial contagion between the Chinese stock market and Australian sector indices through the dynamic conditional correlation fractionally integrated generalized autoregressive conditional heteroskedasticity (DCC-FIGARCH) model. We found high time-varying correlations between the Chinese stock market and most of the Australian sector indices, with the financial, health care, information technology, and utility sectors displaying a decrease in co-movements during the pandemic. The Modified Iterative Cumulative Sum of Squares (MICSS) analysis results indicated the presence of structural breaks in the volatilities of most of the sector indices around the end of February 2020, but consumer staples, industry, information technology and real estate indices did not display any break. Markov regime-switching regression analysis depicted that the pandemic has mainly affected three sectors: consumer staples, industry, and real estate. When we considered the firm size, we found that smaller companies in the energy sector exhibited gradual deterioration, whereas small firms in the consumer staples sector experienced the largest positive impact from the pandemic

The Impact of Ukrainian Crisis on the Connectedness of Stock Index in Asian Economies

The main aim of this study is to measure the dynamic connectedness and spillover effects among emerging stock markets in Asia and the developed stock markets of the US and Europe in the ongoing Ukrainian crisis. The paper also aims to provide a comparative analysis of return and volatility spillovers during the global financial crisis in 2008, the COVID-19 pandemic, and the Ukrainian crisis. This paper utilizes the multiple structural beak test of Bai & Perron (2003) and also depicts the risk and return transmissions among these markets using the Diebold & Yilmaz (2012) method. The main outcomes of this study indicate that the stock markets in Asia are less affected by the political crisis in Ukraine as compared to the previous effects during the GFC and COVID-19 periods. The results also show that sensitivity of Asian financial markets to global shocks has been weakened in the wake of the Ukrainian crisis in favour of increased resilience of Asian stock indices to external shocks. These results carry an important implication for international and local investors as well as for policy makers in Asia, where investors have greater potentials for portfolio diversify and risk reduction across Asian markets. Doi: 10.28991/ESJ-2023-07-02-04 Full Text: PD

Potential impacts of chitosan on growth, yield, endogenous phytohormones, and antioxidants of wheat plant grown under sandy soil conditions

Received: January 15th, 2022 ; Accepted: April 1st, 2023 ; Published: April 17th, 2022 ; Corresponding author: [email protected] field experiment was carried out in sandy soil, during two winter successive seasons to study the impacts of different concentrations of chitosan (50, 100 & 150 mg L-1 ) on several growth parameters and biochemical changes as well as quantitative and qualitative grain yield. Foliar treatment of chitosan significantly increased the growth parameters concurrently with an increment in the photosynthetic pigments, total soluble sugar, proline, free amino acid total carbohydrates, antioxidant activities, phenol, flavonoids, and some minerals nutrition of wheat plant. Wheat plants treated with chitosan at different concentrations significantly increased different endogenous phytohormones auxins (IAA), abscisic acid (ABA), gibberellins (GAs), and cytokinins (Cyt), as compared with the untreated plants. Moreover, chitosan concentrations induced significantly increments in grains yield, nutritive values, carbohydrates %, proteins %, antioxidant compounds and macronutrients of the grain yield. Cultivation of wheat plants under sandy soil conditions and treated with foliar application of 100 mg-1 chitosan gave the higher values of the grain yieldas well as the nutritional values contents

A Survey on Cybercrime Using Social Media

There is growing interest in automating crime detection and prevention for large populations as a result of the increased usage of social media for victimization and criminal activities. This area is frequently researched due to its potential for enabling criminals to reach a large audience. While several studies have investigated specific crimes on social media, a comprehensive review paper that examines all types of social media crimes, their similarities, and detection methods is still lacking. The identification of similarities among crimes and detection methods can facilitate knowledge and data transfer across domains. The goal of this study is to collect a library of social media crimes and establish their connections using a crime taxonomy. The survey also identifies publicly accessible datasets and offers areas for additional study in this area