The Effect of Re-Listing between First Market and Second Market on Dividend Policy in Amman Stock Exchange(ASE)

This study aimed at investigating the impact of re-listing between the first and second market on the dividend policy for the listed companies in the ASE. It also aimed at investigating whether these companies apply a clear dividends policy. The study used data available in the annual reports of the listed companies in the ASE. The study concluded there is a negative significant relationship between re-listing between the first and second market with dividend policy.  The results also indicated there is a strong significant positive relationship between EPS, FIXA and ROA with dividend policy. These results suggest that companies classified in the first market do not prefer adopting a constant dividend policy because it accomplished its goal to reaching the market, hence reducing the percentage of distributed profits. Keywords: Company Re-listing; Dividends Policy; Financial Performance

The State of Academic Research Advancement in Hospitality: A 5- Year Review From 2018 To 2022 of the Jordanian Universities

Scientific research in the field of hospitality occupies a distinguished position in various research aspects, as it constitutes an important proportion of scientific research related to management, especially business administration, as well as an important aspect of practical research related to social and human sciences. For this reason, this study is presented to track the development of scientific research in the field of hospitality in Jordan and the aspects it touched upon. A comprehensive systematic review approach of five years of hospitality-published research on google scholar was carried out by tracking the official website of scholars in Jordanian universities. A total of 73 hospitality-related articles on Google Scholar by Jordanian scholars at public universities over the past five years were collected and analyzed for this review. The results showed that there is an abundance of research products for the year 2021, that researchers at the University of Jordan are the most scientifically productive in hospitality research, and that most of the research interests were related to human resource management in the field of hospitality. This study provides an important theoretical contribution to guide future researchers to future issues of interest to the hospitality sector in Jordan that were not addressed by the researchers

Classification of EEG mental tasks using Multi-Objective Flower Pollination Algorithm for Person Identification

In the modern life, the authentication technique for any system is considered as one of the most important challenges task which must careful consideration. Therefore, many researchers have developed traditional authentication systems to deal with our digital world. Recently, The Biometric techniques have been successfully provided a high level of authentication, such as fingerprint, face recognition, and voice recognition. In this paper, a new authentication system has been proposed which is based on EEG signals with hybridizing wavelet transform and multi-objective flower pollination algorithm (MOFPA-WT). The main task of MOFPA is to find the optimal WT parameters for EEG signal denoising which can extract unique features form the EEG. The proposed method (MOFPA-WT) tested using a standard EEG database which has five different mental tasks, includes baseline, multiplication, rotation, letter composing, and visual counting. To classify the EEG signals using proposed method four classification methods are applied which are, neural network, decision tree, Naive Bayes, and support vector machine. The performance of the (MOFPA-WT) is evaluated using four criteria: (i) accuracy, (ii) sensitivity, (iii) specificity, (v) false acceptance rate. The experimental results show the (MOFPA-WT) can achieve the highest recognition rates up to 85% using neural network classifier based on visual counting task as well as the EEG_Std feature obtained the highest accuracy compared with others EEG features based on visual counting task

A Modified Coronavirus Herd Immunity Optimizer for the Power Scheduling Problem

The Coronavirus herd immunity optimizer (CHIO) is a new human-based optimization algorithm that imitates the herd immunity strategy to eliminate of the COVID-19 disease. In this paper, the coronavirus herd immunity optimizer (CHIO) is modified to tackle a discrete power scheduling problem in a smart home (PSPSH). PSPSH is a combinatorial optimization problem with NP-hard features. It is a highly constrained discrete scheduling problem concerned with assigning the operation time for smart home appliances based on a dynamic pricing scheme(s) and several other constraints. The primary objective when solving PSPSH is to maintain the stability of the power system by reducing the ratio between average and highest power demand (peak-to-average ratio (PAR)) and reducing electricity bill (EB) with considering the comfort level of users (UC). This paper modifies and adapts the CHIO algorithm to deal with such discrete optimization problems, particularly PSPSH. The adaptation and modification include embedding PSPSH problem-specific operators to CHIO operations to meet the discrete search space requirements. PSPSH is modeled as a multi-objective problem considering all objectives, including PAR, EB, and UC. The proposed method is examined using a dataset that contains 36 home appliances and seven consumption scenarios. The main CHIO parameters are tuned to find their best values. These best values are used to evaluate the proposed method by comparing its results with comparative five metaheuristic algorithms. The proposed method shows encouraging results and almost obtains the best results in all consumption scenarios

Lemurs Optimizer: A New Metaheuristic Algorithm for Global Optimization

The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony (ABC), Sine Cosine Algorithm (SCA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and JAYA algorithm. The findings show that the proposed algorithm outperforms these algorithms in fourteen standard optimization functions and proves the LO’s robust performance in managing its exploration and exploitation capabilities, which significantly leads LO towards the global optimum. The real-world experimental findings demonstrate how LO may tackle such challenges competitively

Review on COVID ‐19 diagnosis models based on machine learning and deep learning approaches

International audienceCOVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development