Criminal Liability for Board of Director’s Members in Public Shareholding Companies in Jordanian Companies Law

This study dealt with criminal liability for board of director’s members of public shareholding companies in Jordanian Companies Law, in addition to Jordanian criminal code. Criminal liability of legal person has been clarified through this study; also boards of director’s crimes were determined by a number of results. The most important of these is the fact that criminal liability applied to the legal person is exactly the same for the natural person, taking into consideration the nature of legal person and the corresponding criminal penalties

An Unsupervised Approach for Sentiment Analysis on Social Media Short Text Classification in Roman Urdu

During the last two decades, sentiment analysis, also known as opinion mining, has become one of the most explored research areas in Natural Language Processing (NIP) and data mining. Sentiment analysis focuses on the sentiments or opinions of consumers expressed over social media or different web sites. Due to exposure on the Internet, sentiment analysis has attracted vast numbers of researchers over the globe. A large amount of research has been conducted in English, Chinese, and other languages used worldwide. However, Roman Urdu has been neglected despite being the third most used language for communication in the world, covering millions of users around the globe. Although some techniques have been proposed for sentiment analysis in Roman Urdu, these techniques are limited to a specific domain or developed incorrectly due to the unavailability of language resources available for Roman Urdu. Therefore, in this article, we are proposing an unsupervised approach for sentiment analysis in Roman Urdu. First, the proposed model normalizes the text to overcome spelling variations of different words. After normalizing text, we have used Roman Urdu and English opinion lexicons to correctly identify users\u27 opinions from the text. We have also incorporated negation terms and stemming to assign polarities to each extracted opinion. Furthermore, our model assigns a score to each sentence on the basis of the polarities of extracted opinions and classifies each sentence as positive, negative, or neutral. In order to verify our approach, we have conducted experiments on two publicly available datasets for Roman Urdu and compared our approach with the existing model. Results have demonstrated that our approach outperforms existing models for sentiment analysis tasks in Roman Urdu. Furthermore, our approach does not suffer from domain dependency

Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach

In modern agriculture, correctly identifying rice leaf diseases is crucial for maintaining crop health and promoting sustainable food production. This study presents a detailed methodology to enhance the accuracy of rice leaf disease classification. We achieve this by employing a Convolutional Neural Network (CNN) model specifically designed for rice leaf images. The proposed method achieved an accuracy of 0.914 during the final epoch, demonstrating highly competitive performance compared to other models, with low loss and minimal overfitting. A comparison was conducted with Transfer Learning Inception-v3 and Transfer Learning EfficientNet-B2 models, and the proposed method showed superior accuracy and performance. With the increasing demand for precision agriculture, models like the proposed one show great potential in accurately detecting and managing diseases, ultimately leading to improved crop yields and ecological sustainability

Dynamic generalized normal distribution optimization for feature selection

High dimensionality of data represents a major problem that affects the accuracy of the classification. This problem related with classification is mainly resulted from the availability of irrelevant features. Feature selection represents a solution to a problem by selecting the most informative features and discard the irrelevant features. Generalized normal distribution optimization (GNDO) represents a newly developed optimization that confirmed its outperformance in comparison with well-known optimization algorithms on parameter extraction for photovoltaic models. As an optimization algorithm, however, GNDO suffers from degraded performance when dealing with a problem with a high dimensionality. The main problems of GNDO include exploitation problem by falling into local optima problem. Also, GNDO has solutions diversity problem when it deals with data with high dimensionality. To alleviate the drawbacks of this algorithm and solve feature selection problems, a local search algorithm (LSA) is used. The new algorithm is called dynamic generalized normal distribution optimization (DGNDO), which includes the following main improvements to GNDO: it can improve the best solution to solve the local optima problem, it can improve solution diversity by improving the randomly selected solution, and it can improve both exploration and exploitation combined. To confirm the outperformance and efficiency of the new DGNDO algorithm, DGNDO algorithm is applied on 20 benchmarked datasets from UCI repository of data. In addition, DGNDO algorithm results are compared with seven well-known optimization algorithms using number of evaluation metrics including classification, accuracy, fitness, the number of selected features, statistical results using Wilcoxon test and convergence curves. The obtained results reveal the superiority of DGNDO algorithm over all other competing algorithms

Improved Reptile Search Optimization Algorithm using Chaotic map and Simulated Annealing for Feature Selection in Medical Filed

The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles’ encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search Algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA; the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization algorithm (GOA) and Slime Mould Optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets

The Image of Jordanian Universities among Arab Students

The study aims to identify the image of the Jordanian universities among Arab students, this study is considered of a descriptive studies that used the survey approach to obtain its results by applying the questionnaire tool to an purposive sample of (366) Arab students studying in the Jordanian universities. The results of the study were as follows: that the level of factors that contribute to the formation of the image of the Jordanian universities was high from the point of view of Arab students, and that the security and safety enjoyed by the Jordanian universities is one of the most important factors that contribute to the formation of the image of the Jordanian universities, in addition to the scientific and academic achievements that achieved by the Jordanian universities and the diversity of specializations in universities. It was also found that the sources of forming the image of the Jordanian universities were average from the Arab students\u27 point of view, and it was found that the university\u27s website and its pages on social media sites are among the most prominent sources of forming the image of Jordanian universities among Arab students, in addition to the presence of an average influence of friends and relatives who received their education in the Jordanian universities, and the researches published by faculty members on scientific research websites and engines

Hybrid feature selection based on principal component analysis and grey wolf optimizer algorithm for Arabic news article classification

The rapid growth of electronic documents has resulted from the expansion and development of internet technologies. Text-documents classification is a key task in natural language processing that converts unstructured data into structured form and then extract knowledge from it. This conversion generates a high dimensional data that needs further analusis using data mining techniques like feature extraction, feature selection, and classification to derive meaningful insights from the data. Feature selection is a technique used for reducing dimensionality in order to prune the feature space and, as a result, lowering the computational cost and enhancing classification accuracy. This work presents a hybrid filter-wrapper method based on Principal Component Analysis (PCA) as a filter approach to select an appropriate and informative subset of features and Grey Wolf Optimizer (GWO) as wrapper approach (PCA-GWO) to select further informative features. Logistic Regression (LR) is used as an elevator to test the classification accuracy of candidate feature subsets produced by GWO. Three Arabic datasets, namely Alkhaleej, Akhbarona, and Arabiya, are used to assess the efficiency of the proposed method. The experimental results confirm that the proposed method based on PCA-GWO outperforms the baseline classifiers with/without feature selection and other feature selection approaches in terms of classification accuracy

An improved dandelion optimizer algorithm for spam detection next-generation email filtering system

Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve DO's ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), Chimp Optimization Algorithm (ChOA), Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO al-gorithm, which represents a promising approach for solving high-dimensional optimization prob-lems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning

Improved sine cosine algorithm with simulated annealing and singer chaotic map for Hadith classification

Feature selection (FS) represents an important task in classification. Hadith represents an example in which we can apply FS on it. Hadiths are the second major source of Islam after the Quran. Thousands of Hadiths are available in Islam, and these Hadiths are grouped into a number of classes. In the literature, there are many studies conducted for Hadiths classification. Sine Cosine Algorithm (SCA) is a new metaheuristic optimization algorithm. SCA algorithm is mainly based on exploring the search space using sine and cosine mathematical formulas to find the optimal solution. However, SCA, like other Optimization Algorithm (OA), suffers from the problem of local optima and solution diversity. In this paper, to overcome SCA problems and use it for the FS problem, two major improvements were introduced to the standard SCA algorithm. The first improvement includes the use of singer chaotic map within SCA to improve solutions diversity. The second improvement includes the use of the Simulated Annealing (SA) algorithm as a local search operator within SCA to improve its exploitation. In addition, the Gini Index (GI) is used to filter the resulted selected features to reduce the number of features to be explored by SCA. Furthermore, three new Hadith datasets were created. To evaluate the proposed Improved SCA (ISCA), the new three Hadiths datasets were used in our experiments. Furthermore, to confirm the generality of ISCA, we also applied it on 14 benchmark datasets from the UCI repository. The ISCA results were compared with the original SCA and the state-of-the-art algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization Algorithm (GOA), and the most recent optimization algorithm, Harris Hawks Optimizer (HHO). The obtained results confirm the clear outperformance of ISCA in comparison with other optimization algorithms and Hadith classification baseline works. From the obtained results, it is inferred that ISCA can simultaneously improve the classification accuracy while it selects the most informative features

Methodical approach to the choice of a business management strategy within the framework of a change in commercial activities

The main purpose of the article is the formation of new strategies in business management with changes in commercial activities. The object of the study is the system of commercial activity and possible changes in it. The scientific task is the definition of a new methodological approach to the formation of new strategies in business management with changes in commercial activity. The research methodology involves the use of a modern method for modeling strategies in business management with changes in business activities. As a result, we presented a methodical approach to the formation of business management strategies in the face of changes in commercial activities. The author’s vision of how the stages of forming the main business management strategy through block-object-oriented modeling should look graphically and schematically was presented.  Each of its blocks involves many processions and actions aimed at changes in commercial activity. The innovative novelty of our study lies in the presented methodological approach to the formation of a business management strategy within the framework of changes in commercial activity