Ghazi Al-Naymat

• Ph.D
• Professor (Associate) at Ajman University

Many researchers consider the face’s orientation and illumination while considering the face recognition process to obtain a reasonable recognition rate. They also consider extracting expression-targeted features for expression recognition. Understanding feelings via face and expression recognition would monitor and control office environments effe...

This paper presents a novel Bayesian optimization-based convolutional neural network (CNN) model for non-invasive blood glucose level (BGL) predication using photoplethysmography (PPG) signals. The Bayesian search found the optimal CNN architecture, achieving 92.85% accuracy in Clarke error grid (CEG) zone A with a wide detection range of 50–200 mg...

During the past decade of the big data era, mobile crowdsourcing has emerged as a popular research area, leveraging the collective intelligence and engagement of a vast number of individuals using their mobile devices. Another actively evolving area is machine learning, which has recently been augmented by the mobile crowdsourcing approach, especia...

The emergence of cryptocurrencies has generated enthusiasm and concern in the modern global economy. However, their high volatility, erratic price fluctuations, and tendency to exhibit price bubbles have made investors cautious about investing in them. Consequently, it is essential to develop methods and models to forecast cryptocurrency returns to...

The growing elderly population in smart home environments necessitates increased remote medical support and frequent doctor visits. To address this need, wearable sensor technology plays a crucial role in designing effective healthcare systems for the elderly, facilitating human–machine interaction. However, wearable technology has not been impleme...

Equilibrium optimizer (EO) is a recent optimization method inspired by the physical equation of the mass balance that provides the conservation of mass entering, leaving, and generating in a control volume and the system always reaches an equilibrium point. It is a fast-growing algorithm that has been adopted by several researchers due to its succe...

The implementation of Deep Learning (DL) Prediction techniques for Human Age Prediction (HAP) has been widely researched and studied to prevent, treat, and extend life expectancy. While most algorithms rely on facial images, MRI scans, and DNA methylation for training and testing, they are seldom implemented due to a lack of significant validation...

The deep learning (DL) classification technique is extensively researched and considered for early lung cancer diagnosis. Despite the encouraging performance reported in the literature, DL models face several challenges to be deployed in real-life systems. These include the DL-Models' stability, the nodule structure's complexity, the lack of proper...

Marine Predators Algorithm (MPA) is a recent nature-inspired optimizer stemmed from widespread foraging mechanisms based on Lévy and Brownian movements in ocean predators. Due to its superb features, such as derivative-free, parameter-less, easy-to-use, flexible, and simplicity, MPA is quickly evolved for a wide range of optimization problems in a...

The continually developing Internet generates a considerable amount of text data. When attempting to extract general topics or themes from a massive corpus of documents, dealing with such a large volume of text data in an unstructured format is a big problem. Text document clustering (TDC) is a technique for grouping texts based on their content si...

This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized fo...

In business and industry, forecasting the future values of a time series is an age-old and widely utilized data analysis strategy. IoT temporal data, used in various applications, is one of the most well-known examples of such a time series. The main problem is to forecast a future window of the data using the provided IoT temporal data. Many forec...

Activation functions are essential components in any neural network model; they play a crucial role in determining the network’s expressive power through their introduced non-linearity. Rectified Linear Unit (ReLU) has been the famous and default choice for most deep neural network models because of its simplicity and ability to tackle the vanishin...

Text Document Clustering (TDC) is a challenging optimization problem in unsupervised machine learning and text mining. The Salp Swarm Algorithm (SSA) has been found to be effective in solving complex optimization problems. However, the SSA’s exploitation phase requires improvement to solve the TDC problem effectively. In this paper, we propose a ne...

The volume of data generated, processed, and consumed in the digital world is exponentially increasing. The clustering of such a huge volume of data, known as big data, necessitates the development of highly scalable clustering methods. Density-based algorithms have attracted researchers’ interest because they help to better understand complex patt...

Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, an...

In questions datasets there could be several questions that produce duplicates since they are similar questions due to the ability of writing a question in different forms based on the flexibility of Natural language. However, the process of extracting relevant questions is time consuming if it will be done by a human therefore the computational po...

In questions datasets, several questions could produce duplicates since they are similar questions due to the ability to write a question in different forms based on the flexibility of Natural Language. However, extracting relevant questions is time-consuming if it is performed manually. Therefore, the computational power of computers is necessary...

The natural population-based prediction of type 2 diabetes is costly since it needs a high number of resources. Even though much research has used machine learning algorithms to predict type II diabetes, it could not obtain a sufficient sensitivity range due to imbalanced and sparse data. This research aims to utilize noninvasive features from elec...

Past studies have shown the efficacy of flipped classrooms and gamification learning approaches. However, we know little about the blend of these learning approaches. This study compares the effectiveness of gamified flipped classrooms (GFC) to traditional classroom (TC) learning ap-proaches. We study two different undergraduate cohorts over six-we...

Depicting the reason for the mismatch between instructor expectations of students’ performance in advanced courses and their actual performance has been a challenging issue for a long time, which raises the question of why such a mismatch exists. An implicit reason for this mismatch is the student’s weakness in prerequisite course skills. To solve...

Internet of things (IoT) is an essential technology in our life; the importance of IoT is yearly increasing because of the excellent usage value. IoT management can help stakeholders in analyzing and making the right decisions based on previous historical sensed data. However, some challenges emerge while using the IoT that will be more complicated...

Deep learning is increasingly studied in the prediction of cancer yet few deep learning systems have been introduced for daily use for such purpose. The manual scanning, reading, and analysis by radiologists to detect cancer are very time-consuming processes due to their large volume. Although many types of research have been conducted in this area...

Temporal Internet of things (IoT) data is ubiquitous. Many highly accurate prediction models have been proposed in this area, such as Long-Short Term Memory (LSTM), Autoregressive Moving Average Model (ARIMA), and Rolling Window Regression. However, all of these models employ the direct-previous window of data or all previous data in the training p...

Deep learning (DL) is a type of machine learning capable of processing large quantities of data to provide analytic results based on a particular framework’s parameters and aims. DL is widely used in a variety of fields, including medicine. Currently, there are various DL-based prediction models for predicting cancer probability and survival. Howev...

The recommendation problem involves the prediction of a set of items that maximize the utility for users. Numerous factors, such as the filtering method and similarity measure, affect the prediction accuracy. We propose a novel prediction mechanism that can be applied to collaborative filtering recommender systems. This prediction mechanism consist...

Internet of things (IoT) is a useful technology in different aspects, and it is widely used in many applications; however, this technology faces some major challenges which need to be solved, such as data management and energy saving. Sensors generate a huge amount of data that need to be transferred to other IoT layers in an efficient way to save...

The clustering is an essential technique of data analysis that extracts distribution patterns or similar groups within data. Because of the crucial role of clustering in many scientific applications, numerous research is concerned with developing new algorithms for big data clustering. Despite this fact, the clustering remains a challenge in big da...

Arabic datasets that have two or more records for the same world entity (i.e. person, object, etc.) make institutions suffer from low quality and degraded performance due to duplication in their Arabic datasets without having any mechanism for detecting these duplicates. The operation that distinguishes records for the same real-world entity is cal...

A significant amount of data produced by industries must be processed by machine learning algorithms to facilitates the decision-making process. Traditional machine learning platforms cannot handle the data characterized by their volume, variety, and velocity. Several machine learning toolkits have recently been developed to manage big data. This p...

SMS (Short Message Service) is a mean of communication between people which can either be spam or ham. SMS spam is a major concern since it is annoying and many people do not like to receive it. In this paper, deep learning and random forest machine learning algorithms are used to determine the most important features that can be used as input to c...

Clustering is used to extract hidden patterns and similar groups from data. Therefore, clustering as a method of unsupervised learning is a crucial technique for big data analysis owing to the massive number of unlabeled objects involved. Density-based algorithms have attracted research interest, because they help to better understand complex patte...

Sentiment analysis is an area of great interest in research because of its importance and its advantages in many different domains. Many supervised methods and techniques are used in the existing literature to analyze the sentiment of texts, which usually needs manual labeling for training data that takes effort and time. In this paper, we propose...

Density-based algorithms have attracted many researchers due to their ability to identify clusters with arbitrary shapes in noisy datasets. DENCLUE is a density-based algorithm that clusters objects based on a density function instead of proximity measurements within data. DENCLUE is efficient in clustering high-dimensional datasets. However, it ha...

One of the main requirements in clustering spatial datasets is the discovery of clusters with arbitrary-shapes. Density-based algorithms satisfy this requirement by forming clusters as dense regions in the space that are separated by sparser regions. DENCLUE is a density-based algorithm that generates a compact mathematical form of arbitrary-shapes...

For financial institutions and the banking industry, it is very crucial to have predictive models for their core financial activities, and especially those activities which play major roles in risk management. Predicting loan default is one of the critical issues that banks and financial institutions focus on, as huge revenue loss could be prevente...

Clustering is one of the main data mining methods for knowledge discovery. The clustering is an exploratory data analysis technique that categorizes different data objects into similar groups, named clusters. Density-based clustering defines clusters as dense regions that are separated by low dense regions. The DENCLUE (DENsity CLUstEring) is a rob...

The sparsity problem is considered as one of the main issues facing the collaborative filtering. This paper presents a new dimensionality reduction mechanism that is applicable to collaborative filtering. The proposed mechanism is a statisticalbased method that exploits the user-item rating matrix and itemfeature matrix to build the User Interest P...

The exponential increase in the number of malicious threats on computer networks and Internet services due to a large number of attacks makes the network security at continuous risk. One of the most prevalent network attacks that threaten networks is Denial of Service (DoS) flooding attack. DoS attacks have recently become the most attractive type...

Sentiment analysis is the process of analyzing people's sentiments, opinions, evaluations and emotions by studying their written text. It attracts the interest of many researchers, since it is useful for many applications, ranging from decision making to product evaluation to mention a few. Sentiment analysis can be conducted using machine-learning...

The digital society is an outcome of the Internet which has nearly made everything connected and accessible no matter where or when. Nevertheless, despite the fact that conventional IP networks are complicated and very hard to manage, they are still widely adopted. The already established policies make the network configuration/reconfiguration a co...

Sentiment Analysis of large-scale data has become increasingly important and has attracted many researchers, urging them to use new platforms and tools that can handle large volumes of data. In this paper, we present new evaluation experiments of sentiment analysis for a large-scale dataset of online customer's reviews under Apache Spark data Proce...

Hospital readmissions increase the healthcare costs and negatively influence hospitals’ reputation. Predicting readmissions in early stages allows prompting great attention to patients with high risk of readmission, which leverages the healthcare system and saves healthcare expenditures. Machine learning helps in providing more accurate predictions...

The amount of cheap memory growing enables all data to be in main memory databases, this adds a critical performance advantage to the main memory databases. In order to keep and retrieve data effectively, indexing schemes/ systems have been proposed. However, existing indexing algorithms are poorly suited for effective search, not just because of t...

The exponential increase in the number of malicious threats on computer networks and Internet services due to a large number of attacks makes the network security at continuous risk. One of the most prevalent network attacks that threaten networks is Denial of Service (DoS) flooding attack. DoS attacks have recently become the most attractive type...

Theexponentialincreaseinthenumberofmalicious threatsoncomputernetworksandInternetservicesduetoalarge number of attacks makes the network security at continuous risk. Oneofthemostprevalentnetworkattacksthatthreatennetworks is Denial of Service (DoS) flooding attack. DoS attacks have recently become the most attractive type of attacks to attackers and...

Trading strategies can be used to exploit certain patterns within the market. The Pairs Trading Strategy exploits the co-movement nature of pairs of stocks to gain profit. This paper introduces a new methodology framework for the Pairs Trading Strategy from mining to monitoring and trading pairs at appropriate times to gain profit. This framework i...

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanism that used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrit...

Understanding the Hadoop Distributed File System (HDFS) is currently an important issue for forensic investigators because it is the core of most Big Data environments. The HDFS requires more study to understand how forensic investigations should be performed and what artifacts can be extracted from this framework. The HDFS framework encompasses a...

One of the most prevalent network attacks that threaten networks is Denial of Service (DoS) flooding attacks. Hence, there is a need for effective approaches that can efficiently detect any intrusion in a network. This paper presents an efficient mechanism for network attacks detection within MIB data, which is associated with the protocol (SNMP)....

One of the most prevalent network attacks that threaten networks is Denial of Service (DoS) flooding attacks. Hence, there is a need for effective approaches that can efficiently detect any intrusion in a network. This paper presents an efficient mechanism for network attacks detection within MIB data, which is associated with the protocol (SNMP)....

One of the most prevalent network attacks that threaten networks is Denial of Service (DoS) flooding attacks. Hence, there is a need for effective approaches that can efficiently detect any intrusion in a network. This paper presents an efficient mechanism for network attacks detection within MIB data, which is associated with the protocol (SNMP)....

Service providers aim at having low pressure on their traffic systems and this positively influences the provided services; this has made the Arrival Process as a very important factor of the queuing structure. Examples of arrivals comprise, arrival of the costumers to a supermarket and passengers to a train station to mention a few. This paper sug...

-using file carving techniques is one of most recent techniques that is used to retrieve the important data from unallocated space in a corrupted file system. In the traditional operating systems, such as Windows or Linux that have a small size of hard disk to store data, the researchers implemented many file carving techniques to carve a specific...

Entity resolution (ER) is the operation of distinguishing records that return to the same real world entity. It is used to link records among datasets and to match query records in real-time with existing datasets. Indexing is a major step in the ER process that reduces the search space. Most existing indexing techniques that are utilized in the ER...

Many different classification algorithms could be used in order to analyze, classify or predict data. These algorithms differ in their performance and results. Therefore, in order to select the best approach, a comparison studies required to present the most appropriate approach to be used in a certain domain. This paper presents a comparative stud...

The war on terrorism, radicalism and violent extremism is no longer confined to the battlefield; it has become omnipresent in the recent years with militant, terrorists and insurgent groups actively recruiting new technologies as platform to impel their ideologies worldwide. Nonetheless, one thing remains constant in the fight against radicalism eq...

The rapid increase in the magnitude of data produced by industries that need to be processed using Machine Learning algorithms to generate business intelligence has created a dilemma for data scientists. This is due to the fact that traditional machine learning platforms such as Weka and R are not designed to handle data with such Volume, Velocity...

Online reviews become a valuable source of information that indicate the overall opinion about products and services, which affect customer’s decision to purchase a product or service. Since not all online reviews and comments are truthful, it is important to detect fake and poison reviews. Many machine learning techniques could be applied to detec...

SMS spams are one of the concern s and many people do not like to receive them sinc e they are annoying . Many SMS spam detection methods already exist and differ ent classifiers were used, such classifiers depended on Support Vector mac hine, Naïve Bays and many other machine learning algorithms . In this paper, new classifier is proposed...

NA_SNMP Dataset Towards Generating Realistic SNMP-MIB Dataset for Network Anomaly Detection

Enhancing network services and security can be achieved by performing network traffic classification identifying applications, which is one of the primary components of network operations and management. The traditional transport-layer and port-based classification approaches have some limitations in achieving accurate identification. In this paper...

The enormous growth in computer networks and in Internet usage in recent years, combined with the growth in the amount of data exchanged over networks, have shown an exponential increase in the amount of malicious and mysterious threats to computer networks. Among many security issues, network attack is a major one. For example, Denial of Service (...

This paper presents a methodology for improving the security of identification and authentication processes using Keystroke Dynamics (KSD). KSD is considered a behavioral biometric operating as a second level of security along with the login process after inserting user name and password. KSD is mainly about observing the way in which the user type...

Abstract—Users and organizations find it continuously challenging to deal with distributed denial of service (DDoS) attacks. . The security engineer works to keep a service available at all times by dealing with intruder attacks. The intrusiondetection system (IDS) is one of the solutions to detecting and classifying any anomalous behavior. The IDS...

Users and organizations find it continuously challenging to deal with distributed denial of service (DDoS) attacks. . The security engineer works to keep a service available at all times by dealing with intruder attacks. The intrusion-detection system (IDS) is one of the solutions to detecting and classifying any anomalous behavior. The IDS system...

We refer to the problem of constructing broadcast trees with cost and delay constraints in the networks as a delay-constrained minimum spanning tree problem in directed networks. Hence it is necessary determining a spanning tree of minimal cost to connect the source node to all nodes subject to delay constraints on broadcast routing. In this paper,...

Time series are ubiquitous application domains that generate data including GPS, stock market, and ECG. Researchers concentrate on mining time series data to extract important knowledge and insights. Time series similarity search is a data mining technique that is widely used to compare time series data using similarity measurements, such as dynami...

Pairs trading is an investment strategy that depends on the price divergence between a pair of stocks. Essentially, this strategy involves choosing a pair of stocks that historically move together, then taking a long-short position if the pair's prices diverge, and finally reversing the previous position when prices converge. The rationale of the p...

Recent research on pattern discovery has progressed from mining frequent patterns and sequences to mining structured patterns, such as trees and graphs. Graphs as general data structure can model complex relations among data with wide applications in web exploration and social networks. However, the process of mining large graph patterns is a chall...

Solitary polling technique is not a better choice to get rid of multiple queuing problems for getting enhanced performance against single server. Enhanced and reliable performance upon multi queued traffic can be achieved through the utilization of right selection of joint polling schemes. The complexity of polling design is directly proportional t...

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a larg...

We present a new space-efficient approach, (SparseDTW), to compute the Dynamic Time Warping (DTW) distance between two time series that always yields the optimal result. This is in contrast to other known approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the exist...

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a larg...

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks, and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a lar...

The Resource Description Framework (RDF) is a flexible model for representing information about resources in the Web. With the increasing amount of RDF data which is becoming available, efficient and scalable management of RDF data has become a fundamental challenge to achieve the Semantic Web vision. The RDF model has attracted attentions in the d...

Graphs are widely used for modeling complicated data such as social networks, chemical compounds, protein interactions and semantic web. To effectively understand and utilize any collection of graphs, a graph database that efficiently supports elementary querying mechanisms is crucially required. For example, Subgraph and Supergraph queries are imp...

Purpose The purpose of this paper is to provide a detailed discussion for different types of graph queries and a different mechanism for indexing and querying graph databases. Design/methodology/approach The paper reviews the existing approaches and techniques for indexing and querying graph databases. For each approach, the strengths and weakness...

Graphs are widely used for modeling complicated data such as social networks, chemical compounds, protein interactions, XML documents and multimedia databases. To be able to effectively understand and utilize any collection of graphs, a graph database that efficiently supports elementary querying mechanisms is crucially required. Supergraph query i...

Spatial data is essentially different from transactional data in its nature. The objects in a spatial database are distinguished by a spatial (location) and several non-spatial (aspatial) attributes. For example, an astronomy database that contains galaxy data may contain the x, y and z coordinates (spatial features) of each galaxy, their types and...

The Resource Description Framework (RDF) is a flexible model for representing information about resources in the web. With the increasing amount of RDF data which is becoming available, efficient and scalable management of RDF data has become a fundamental challenge to achieve the SemanticWeb vision. The RDF model has attracted the attention of the...

Time Series are ubiquitous, hence, similarity search is one of the biggest challenges in the area of mining time series data. This is due to the vast data size, number of sequences and number of dimensions that lead to a very costly querying process. In this paper, we demonstrate, for the first time, the use of three dimensionality reduction techni...