Mohammed Alweshah

The model of a probabilistic neural network (PNN) is commonly utilized for classification and pattern recognition issues in data mining. An approach frequently used to enhance its effectiveness is the adjustment of PNN classifier parameters through the outcomes of metaheuristic optimization strategies. Since PNN employs a limited set of instruction...

A significant cause of death and long-term disability globally is brain stroke. Stroke falls into one of two categories: (1) ischemic, which accounts for roughly 85% of cases when it is caused by abrupt cessation of blood supply to a particular area of the brain, and (2) hemorrhagic, which refers to bleeding or blood leakage. To provide stroke pati...

Online customer advocacy has developed as a distinctive strategic way to improve organisational performance by fostering favourable reciprocal affinitive customer behaviours between the business and its customers. Intelligent systems that can identify online social advocates based on their social interaction and long-standing conversations with the...

Scientific communities are still motivated to create novel approaches and methodologies for early estimation of software project development efforts and testing efforts in soft computing environments due to scheduling and budgetary concerns. Therefore, the software engineering prediction problems (SEPPs) are formulated as machine learning (ML) mode...

Gene Selection (GS) is a strategy method targeted at reducing redundancy, limited expressiveness, and low informativeness in gene expression datasets obtained by DNA Microarray technology. These datasets contain a plethora of diverse and high-dimensional samples and genes, with a significant discrepancy in the number of samples and genes present. T...

An inherent problem in software engineering is that competing prediction systems have been found to produce conflicting results. Yet accurate prediction is crucial because the complexity and quality of software requirements have dramatically changed in recent years, and consumers have become considerably more demanding in terms of the cost, timefra...

The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. How...

In software engineering, the planning and budgeting stages of a software project are of great importance to all stakeholders, including project managers as well as clients. The estimated costs and scheduling time needed to develop any software project before and/or during startup form the basis of a project’s success. The main objective of soft- wa...

An imbalanced classification problem is one in which the distribution of instances across defined classes is uneven or biased in one direction or another. In data mining, the probabilistic neural network (PNN) classifier is a well-known technology that has been successfully used to solve a variety of classification difficulties. On the other hand,...

Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM has some limitations in terms of its selection of the initial cluster center. It can be easily trap...

Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems. The main aim of the research is to develop a system able to predicate and classify gender, age, and accent. So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed. Backpropa...

In the Internet of Things (IoT), the data that are sent via devices are sometimes unrelated, duplicated, or erroneous, which makes it difficult to perform the required tasks. Hence transmitted data need to be filtered and selected to suit the nature of the problem being dealt with in order to achieve the highest possible level of security. Feature...

In this paper, an enhanced version of the salp swarm algorithm (SSA) for global optimization problems was developed. Two improvements have been proposed: (i) Diversification of the SSA population referred as SSA\(_{std}\), (ii) SSA parameters are tuned using a self-adaptive technique-based genetic algorithm (GA) referred as SSA\(_{GA-tuner}\). The...

Sentiment analysis (SA) is the process of assessing the sentiment and attitude of digital audiences toward a range of topics and subjects. The aim of this research is to propose an effective approach for finding good-quality solutions for dialectal Arabic SA problems by addressing inherent challenges in an optimal way. This is achieved by determini...

Nowadays, attackers are constantly targeting the modern aspects of technology and attempting to abuse these technologies using different attacks types such as the distributed denial of service attack (DDoS). Therefore, protecting web services is not an easy task. There is a critical demand to detect and prevent DDoS attacks. This paper introduces a...

The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. How...

Feature selection (FS) is considered to be a hard optimization problem in data mining and some artificial intelligence fields. It is a process where rather than studying all of the features of a whole dataset, some associated features of a problem are selected, the aim of which is to increase classification accuracy and reduce computational time. I...

In this paper, an enhanced version of the Salp Swarm Algorithm (SSA) for global optimization problems was developed. Two improvements have been proposed: (i) diversification of the SSA population referred as SSAstd, (ii) SSA parameters are tuned using a self-adaptive technique based Genetic Algorithm (GA) referred as SSAGA−tuner. The novelty of dev...

The vehicle routing problem (VRP) is one of the challenging problems in optimization and can be described as combinatorial optimization and NP-hard problem. Researchers have used many artificial intelligence techniques in order to try to solve this problem. Among these techniques, metaheuristic algorithms that can perform random search are the most...

Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus h...

The use of current data management technologies for the Internet of Things (IoT) is not straightforward due to interface heterogeneity, highly complex and potentially unprotected conditions, and the huge scale of the network. A vast number of devices and sensors around the world are connected to wireless networks, making the data transmitted to and...

Software Defect Estimation (SDE) is a fundamental problem solving mechanism in the field of software engineering (SE). SDE is a task that identifies software models that are likely to have defects. In addition, SDE plays a vital overall role in improving software quality, reducing software development costs and accelerating software development pro...

Feature selection (FS) is an important preprocessing step that has been commonly used in several fields to improve the performance of learning algorithms. In the field of medical data mining, a huge number of features are used in diagnosing disease, but these features have a lot of non-relevant weak correlations and redundant characteristics, which...

The importance of medical data and the crucial nature of the decisions that are based on such data, as well as the large increase in its volume, has encouraged researchers to develop feature selection (FS)-based approaches to identify the most relevant data for specific medical problems In this paper, two intelligent wrapper FS approaches based on...

In the field of medicine, has resulted in the need to filter these data to find information that is relevant for specific research problems. However, in the realm of scientific study, the process of selecting the appropriate data or features represents a substantial and challenging problem. Therefore, in this paper, two wrapper feature selection (F...

The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phis...

The vehicle routing problem (VRP) is one of the challenging problems in optimization and can be described as combinatorial optimization and NP-hard problem. Researchers have used many artificial intelligence techniques in order to try to solve this problem. Among these techniques, metaheuristic algorithms that can perform random search are the most...

In this paper, software effort prediction (SEP) and software test prediction (STP) (i.e., software reliability problems) are tackled by integrating the salp swarm algorithm (SSA) with a backpropagation neural network (BPNN). Software effort and test prediction problems are common in software engineering and arise when seeking to determine the actua...

Feature selection (FS) is used to solve hard optimization problems in artificial intelligence and data mining. In the FS process, some, rather than all of the features of a dataset are selected in order to both maximize classification accuracy and minimize the time required for computation. In this paper a FS wrapper method that uses K-nearest Neig...

In this paper, the economic load dispatch (ELD) problem with valve point effect is tackled using a hybridization between salp swarm algorithm (SSA) as a population-based algorithm and \(\beta \)-hill climbing optimizer as a single point-based algorithm. The proposed hybrid SSA is abbreviated as HSSA. This is to achieve the right balance between the...

This paper proposes the salp swarm algorithm (SSA) combined with a backpropagation neural network (BPNN) to solve the software fault prediction (SFP) problem. The SFP problem is one of the well-known software engineering problems that are concerned with anticipating the software defects that are likely to appear during a software project or thereaf...

In the medical field, image segmentation provides important information for surgical planning and registration, and thus demands accurate segmentation. In order to improve the effectiveness and the threshold accuracy of segmentation, researchers have tended to use a metaheuristic algorithm as the operational algorithm to achieve better exploitation...

Feature selection (FS) is the process of finding the least possible number of features that are able to describe a dataset in the same way as the original features. Feature selection is a crucial preprocessing step for data mining techniques as it improves the performance of the prediction process in terms of speed and accuracy and also provides a...

Classification is achieved through the categorisation of objects into predefined categories or classes, where the categories or classes are created based on a similar set of attributes of the object. This is referred to as supervised learning. Numerous methodologies have been formulated by researchers in order to solve classification problems effec...

In recent years, classifier ensemble techniques have drawn the attention of many researchers in the machine learning research community. The ultimate goal of these researches is to improve the accuracy of the ensemble compared to the individual classifiers. In this paper, a novel algorithm for building ensembles called dynamic programming-based ens...

Abstract Classification is a crucial step in the data mining field. The probabilistic neural network (PNN) is an efficient method devel- oped for classification problems. The success factor of using PNN for classification problems implies in finding the proper weight during classification process. The main goal of this paper is to improve the perf...

Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective m...

Classification remains as a most significant area in data mining. Probabilistic Neural Network (PNN) is repeatedly used for classification problems. The main aims of this paper are to fine-tune the neural networks weights to increase the classification accuracy and to achieve speed convergence. To achieve this aim, created a hybrid model that inves...

Classification is a crucial step in the data mining field. The probabilistic neural network (PNN) is an efficient method developed for classification problems. The success factor of using PNN for classification problems implies in finding the proper weight during classification process. The main goal of this paper is to improve the performance of P...

Classification is a data mining task that assigns items in a collection to predefined categories or classes, also referred to as supervised learning. The goal of classification is to accurately predict the target class for each case in the data. A review of the literature shows that many algorithms, including statistical and machine learning algori...

Leader election is an important issue in distributed systems and communication networks. Many protocols and algorithms that are running on distributed systems need a leader to ensure smooth execution; the leader has the responsibility to synchronize and coordinate the system processes. The absence of the leader makes the system inconsistent, and th...

Spiking neural networks (SNN) represents the third generation of neural network models, it differs significantly from the early neural network generation. The time is becoming the most important input. The presence and precise timing of spikes encapsulate have a meaning such as human brain behavior. However, deferent techniques are therefore requir...

One of the major objectives of any classification technique is to categorise the incoming input values based on their various attributes. Many techniques have been described in the literature, one of them being the probabilistic neural network (PNN). There were many comparisons made between the various published techniques depending on their precis...

Automated classification of prostate histopathology images includes the identification of multiple classes, such as benign and cancerous (grades 3 & 4). To address the multiclass classification problem in prostate histopathology images, breakdown approaches are utilized, such as one-versus-one (OVO) and one-versus-all (Ovall). In these approaches,...

Automated classification of prostate histopathology images includes the identification of multiple classes, such as benign and cancerous (grades 3 & 4). To address the multiclass classification problem in prostate histopathology images, breakdown approaches are utilized, such as one-versus-one (OVO) and one- versus-all (Ovall). In these approaches,...

One of the early tasks in a handwriting recognition system is the segmentation of a handwritten document that is imaged into text lines, which is the process of defining the region of every text line on a document’s image. In this paper, the focus is in the text-line segmentation of mushaf holy Quran. Thus, Segmentation of the mushaf holy Quran is...

The major aim of classification is to extract categories of inputs according to their characteristics. The literature contains several methods that aim to solve the time series classification problem, such as the artificial neural network (ANN) and the support vector machine (SVM). Time series classification is a supervised learning method that map...

Classification is a task of supervised learning whose aim is to identify to which of a set of categories a new input element belongs. Probabilistic neural network is a variant of artificial neural network, which is simple in structure, easy for training and often used in classification problems. In this paper, the authors propose an improved probab...

This study derives its work from aninterest in the development of an automated approach to tackle highly constrained Patient Admission Scheduling Problems (PASP). It is concerned with an assignment of patients to bed in an appropriate department in such away it can maximize medical treatment effectiveness andpatient’s comfort. In this study we have...