Wael Hadi

Wael Hadi
Petra University · Department of Computer Information Systems

PhD

About

59
Publications
47,361
Reads
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725
Citations
Citations since 2016
24 Research Items
556 Citations
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2016201720182019202020212022020406080100120
2016201720182019202020212022020406080100120
2016201720182019202020212022020406080100120
Introduction
Wael Hadi currently works at the Department of Computer Information Systems, Petra University. Wael does research in Computing in Mathematics, Natural Science, Engineering and Medicine, Artificial Neural Network and Artificial Intelligence. Their current project is 'Application of Naïve Bayes, Decision Tree, and K-Nearest Neighbors for Automated Text Classification'.
Additional affiliations
October 2016 - present
Petra University
Position
  • Professor (Associate)
September 2016 - October 2016
Petra University
Position
  • Professor (Assistant)
September 2012 - October 2016
Petra University
Position
  • Professor (Assistant)

Publications

Publications (59)
Article
Stuttering is a neurodevelopmental speech disorder wherein people suffer from disfluency in speech generation. Recent research has applied machine learning and deep learning approaches to stuttering disfluency recognition and classification. However, these studies have focussed on small datasets, generated by a limited number of speakers and within...
Article
Commuting when there is a significant volume of traffic congestion has been acknowledged as one of the key factors causing stress. Significant levels of stress whilst driving are seen to have a profoundly negative effect on the actions and ability of a driver; this has the capacity to result in risks, hazards and accidents. As such, there is a reco...
Article
Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and sometimes harmful, as they can cause decision-making bias. Accordingly, in our pa...
Article
Full-text available
For decades, the use of weights has proven its superior ability to improve dynamic local search weighting algorithms’ overall performance. This paper proposes a new mechanism where the initial clause’s weights are dynamically allocated based on the problem’s structure. The new mechanism starts by examining each clause in terms of its size and the e...
Article
Full-text available
Text classification is a vital process due to the large volume of electronic articles. One of the drawbacks of text classification is the high dimensionality of feature space. Scholars developed several algorithms to choose relevant features from article text such as Chi-square (x 2), Information Gain (IG), and Correlation (CFS). These algorithms h...
Article
Full-text available
In this paper, we investigated the influence of resetting weights in what we refer to as safely satisfied sub areas within the search space. Our work is divided into two main tracks; track one is to search for sub areas within the search space where a group of connected clauses are all satisfied. In track two, a Weight Reset mechanism is designed a...
Article
Full-text available
Indian Premier League (IPL) is one of the more popular cricket world tournaments, and its financial is increasing each season, its viewership has increased markedly and the betting market for IPL is growing significantly every year. With cricket being a very dynamic game, bettors and bookies are incentivised to bet on the match results because it i...
Article
Driving daily through traffic congestion has been recognised as a major cause of stress. High levels of stress while driving negatively impact the driver's decisions which could potentially lead to accidents and other long-term health hazards. Accordingly, there is a great need to determine stress levels for drivers based on measuring and predictin...
Article
Full-text available
Affective understanding is an area of affective computing which is concerned with advancing the ability of a computer to understand the affective state of its user. This area continues to receive attention in order to improve the human-computer interactions of automated systems and services. Systems within this area typically deal with big data fro...
Preprint
In this paper, we investigated the influence of resetting weights in what we refer to as safely satisfied sub areas within the search space. Our work is divided into two main tracks; track one is to search for sub areas within the search space where a group of connected clauses are all satisfied. In track two, a Weight Reset mechanism is designed a...
Article
In this paper, we study the problem of predicting new locations of groundwater in Jordan through the application of a proposed new method, Groundwater Prediction using Associative Classification (GwPAC). We identify features that differentiate locations of groundwater wells according to whether or not they contain water. In addition, we survey inte...
Article
Associative classification (AC) integrates the task of mining association rules with the classification task to increase the efficiency of the classification process. AC algorithms produce accurate classification and generate easy to understand rules. However, AC algorithms suffer from two drawbacks: the large number of classification rules, and us...
Chapter
With the significant advances in Information and Communications Technology (ICT) over the last half a century, the Cloud computing paradigm is one of the most discussed topics in the field of ICT today. Additionally, Cloud computing has a critical role in today's business world. Without risk management processes embedded into innovative technology...
Article
Full-text available
Stochastic Local Search (SLS) algorithms are of great importance to many fields of Computer Sciences and Artificial Intelligence. This is due to their efficient performance when applied for solving randomly generated satisfiability problems (SAT). Our focus in the current work is on one of the SLS dynamic weighting approaches known as multi-level w...
Conference Paper
Full-text available
A survey in Jordan was conducted. It investigated factors that may cause increase in stress levels, e.g. time of driving, other drivers’ behavior, weather conditions, and road types. Results of survey are compared with similar studies reported in literature. The idea of a novel computerized system is presented that detects stress level of drivers...
Conference Paper
Full-text available
presently, the planning for sustainable development and enhancement of energy applications has turn into complex due to the contribution of various involvement procedures, criteria's and indicators for environmental constraints, communal culture, Stakeholders views, practical issues and cost-effective measures. This in turns sets key obstacles for...
Conference Paper
Full-text available
The International Software Benchmarking Standards Group (ISBSG) development and enhancement dataset is a source of data using by academia and industry over around the world. It contains several software projects developed and/or enhanced in different countries for many industrial types or to be used by academia for a systematic empirical studies. T...
Article
Knowledge base is becoming a key factor within organisations, since it can maximise the probability and impact of customer satisfaction in, for example, an airline company. Therefore, phases of decision-making and knowledge base are widely used in airline companies to improve the degree of customer satisfaction. The objective of this paper is to de...
Article
Associative classification (AC) is an integration between association rules and classification tasks that aim to predict unseen samples. Several studies indicate that the AC algorithms produce more accurate results than classical data mining algorithms. However, current AC algorithms inherit from association rules two major drawbacks resulting in a...
Article
Full-text available
With the significant advances in Information and Communications Technology (ICT) over the last half a century, the Cloud computing paradigm is one of the most discussed topics in the field of ICT today. Additionally, Cloud computing has a critical role in today's business world. Without risk management processes embedded into innovative technology...
Article
Associative classification (AC) is a new, effective supervised learning approach that aims to predict unseen instances. AC effectively integrates association rule mining and classification, and produces more accurate results than other traditional data mining classification algorithms. In this paper, we propose a new AC algorithm called the Fast As...
Conference Paper
Full-text available
Decision making can be a problem for businesses and decision makers these days; therefore, knowledge management (KM) activities play a significant role in any organization. The value of knowledge depends on its application and use. The success of an organization largely depends on the quality of the knowledge that it generates. An extensive literat...
Article
Full-text available
Electronic Knowledge Repository in electronic customer relationship management (eCRM) is an important topic for improving Electronic Customer Retention. Also, In spite of several previous studies of eCRM, none has adequately examined the influences of Electronic Customer Processes and Electronic Knowledge Repository on Electronic Customer Retention...
Article
Full-text available
Due to the fast growth of the Customer Relationship Management (CRM) strategies revolution, the organizations begin thinking of how to develop their process. In this way, CRM is a business strategy, which aims at attraction, acquiring, withholding and expanding the relationship with the customers. In this present business situation, the Jordanian t...
Article
Full-text available
Associative Classification (AC) is a promising supervised learning method that uses association rule mining to build classification systems (Classifiers). Several studies shows that the AC produce results more accurate than traditional supervised learning methods such as Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB) and...
Article
Full-text available
Arabic sentiment analysis is a process for classifying unseen Arabic text to their predefined classes (i.e. Positive, Negative and Neutral). This paper investigates Decision tree, Naïve Bayesian (NB), K Nearest Neighbor (KNN) and Support Vector Machines (SVMs) on Arabic Twitter corpus. Our Arabic Twitter corpus is collected from twitter ARCHIVISIT....
Article
Full-text available
Current associative classification (AC) algorithms generate only the most obvious class linked with a rule in the training data set and ignore all other classes. We handle this problem by proposing a learning algorithm based on AC called Multi-label Classifiers based Associative Classification (MCAC) that learns rules associated with multiple class...
Article
Full-text available
Text classification is one of the most important tasks in data mining. This paper investigates different variations of vector space models (VSMs) using KNN algorithm. The bases of our comparison are the most popular text evaluation measures. The Experimental results against the Saudi data sets reveal that Cosine outperformed Dice and Jaccard coeffi...
Article
Full-text available
It is generally accepted that due to globalization, competition is becoming more intense and organizations are now able to or forced to open newer markets with different paradigms. As a result, Knowledge Management (KM) has been increasingly recognized as the most important and valuable asset in organizations and as a key differentiating factor in...
Article
Full-text available
Phishing website is a very complex issue to understand and to analyze, since it is joining technical and social problem with each other for which there is no known single silver bullet to solve it entirely. That's why we will try to quantify and qualify all the website phishing characteristics and factors in order to understand where to focus prote...
Article
Full-text available
According to Information and Communication Technology (ICT) revolution; governments too have realized the benefits of information sharing thus the concept of e-Government has emerged and has been implemented in many developed and developing countries. Subsequently, it is becoming the topic of studies that is discussing and identifying the main stre...
Article
Full-text available
Associative classi ̄cation (AC) is a data mining approach that uses association rule discovery methods to build classi ̄cation systems (classi ̄ers). Several research studies reveal that AC normally generates higher accurate classi ̄ers than classic classi ̄cation data mining approaches such as rule in- duction, probabilistic and decision trees. Th...
Article
Full-text available
In recent decades, our computers tolerate multidimensional data to be stored and maintained depending on the high computational strength of these computers. As a result, the two-dimensional pattern matching considered as one of the hot research areas. In this paper, we investigate the exact two dimensional pattern matching problem. A new algorithm...
Article
Full-text available
This paper presents a new approach in cost estimation process that enhances a software cost estimation method called (Use Case Point method). Our promotion was a suggestion to use a set of sixteen factors which considered as the most influential factor in Jordan environment that are used to promote a COCOMO II model.
Chapter
Full-text available
Congestion in networks is considered a serious problem; in order to manage and control this phenomena in early stages before it occurs, a derivation of a new discrete-time queuing network analytical model based on dynamic random early drop (DRED) algorithm is derived to present analytical expressions to calculate three performance measures: average...
Article
Full-text available
Associative classification (AC) is an important data mining approach which effectively integrates association rule mining and classification. Prediction of test data is a fundamental step in classification that impacts the outputted system accuracy. In this paper, we present three new prediction methods (Dominant Class Label, Highest Average Confid...
Chapter
Full-text available
Congestion in networks is considered a serious problem; in order to manage and control this phenomena in early stages before it occurs, a derivation of a new discrete-time queuing network analytical model based on dynamic random early drop (DRED) algorithm is derived to present analytical expressions to calculate three performance measures: average...
Conference Paper
Full-text available
Associative classification (AC) is a promising data mining approach which builds more accurate classifiers than traditional classification technique such as decision trees and rule induction. By integrating association rules mining with classification, AC has two main phases which are rule generation and classifier building. In this paper, we inves...
Conference Paper
Full-text available
Associative classification integrates association rule and classification in data mining to build classifiers that are highly accurate than that of traditional classification approaches such as greedy and decision tree. However, the size of the classifiers produced by associative classification algorithms is usually large and contains insignificant...
Conference Paper
Full-text available
Obviously, there is a strong competition among organizations and fast changes in the business environment. In this way, Customer Relationship Management (CRM) has become the main interest of researchers and practitioners particularly in the domains of Marketing and Information Systems (IS). In view of this, Organizations from different fields of bu...
Conference Paper
Full-text available
In order to manage and control the congestion phenomena in early stages before it occurs, which is one of the main problems in networks, a derivation of a new discrete-time queuing network analytical model based on dynamic random early drop (DRED) algorithm is derived to present analytical expressions to calculate three performance measures; averag...
Article
Full-text available
Associative classification is the integration of classification and association rule discovery in data mining. This approach often derives higher quality classifiers with reference to classification accuracy than traditional classification approaches such as decision trees and rule induction. In this paper, the problem of rule pruning in associativ...
Article
Full-text available
There is an increasing order in digitized technology. This increasing order requires high qualitative document management system which can be used in secure fashion especially for organization with different branches and different location. In this paper we propose a qualitative document management framework which could be used for most organizatio...
Conference Paper
Full-text available
Text categorization is one of the well studied problems in data mining and information retrieval. Given a large quantity of documents in a data set where each document is associated with its corresponding category. Categorization involves building a model from classified documents, in order to classify previously unseen documents as accurately as p...
Conference Paper
Full-text available
This paper proposes a derivation of discrete-time queuing network analytical model based on dynamic random early drop (DRED) algorithm to manage and control congestion in early stages before it occurs, which is referred to as the 3QN model. The proposed model consists of three interconnected queue processing nodes. Expressions were derived to calcu...
Conference Paper
Full-text available
Obviously, there is a strong competition among organizations and fast changes in the business environment. In this way, Customer Relationship Management (CRM) has become the main interest of researchers and practitioners particularly in the domains of Marketing and Information Systems (IS). In view of this, Organizations from different fields of bu...
Article
Full-text available
Text classification is a supervised learning technique that uses labeled training data to derive a classification system (classifier) and then automatically classifies unlabelled text data using the derived classifier. In this paper, we investigate K-Nearest Neighbor method (KNN) and Support Vector Machine algorithm (SVM) on different Arabic data s...
Article
Full-text available
Text classification is a supervised learning technique that uses labelled training data to derive a classification system (classifier) and then automatically classifies unlabelled text data using the derived classifier. This paper investigates Naïve Bayesian method (NB) and Support Vector Machine (SVM) on different Arabic data sets. The bases of ou...
Article
Full-text available
Text classification is a supervised technique that uses labelled training data to learn the classification system and then automatically classifies the remaining text using the learned system. This paper investigates Naïve Bayesian algorithm based on Chi Square features selection method. The base of our comparisons are macro F1, macro recall and ma...
Article
Full-text available
On 20 text categorization data sets, the research investigated different variations of VSM using KNN algorithm and different term weighting approaches compared in term of F1 measure. The experimental results provide evidence that Dice and Jaccard Coefficient outperformed the Cosine Coefficient approach with regards to F1 results and the Dice-based...
Article
Risk management is becoming a key factor within organizations since it ensures a successful execution of projects. In an organization life cycle, there is simply no way to guarantee a completely risk free workplace. Moreover, information is essential to efficient and effective business processes and it is a critical success factor for a competitive...
Article
Full-text available
Text classification is a supervised learning technique that uses labelled training data to derive a classification system (classifier) and then automatically classifies unlabelled text data using the derived classifier. This paper investigates Naive Bayesian method (NB) and K-Nearest Neighbour algorithm (KNN) on different Arabic data sets. The base...
Article
Full-text available
Risk Management (RM) is becoming a key factor within organizations since it ensures a successful execution of projects. Consequently, Knowledge Management (KM) process has turned out to become a strategic resource of organization to the extent in which it is viewed nowadays as the basis of reduces the risk. The aim of this paper is to present a con...
Article
Full-text available
The purpose of this paper is to evaluate customers’ general expectation and perception of insurers in terms of services offered at the insurance service counter (ISC). Other than that, this paper also examines the relationship between the demographic factors and SERVQUAL mean score. The study utilized the survey approach. The sample consisted of 31...
Article
Full-text available
It is evident that there is a strong competition among organizations and a sort of rapid change in the business environment is taking place. Therefore; organizations start thinking of how to improve their processes to stay competent. Knowledge has become a strategic resource and a basis of competitive advantage in the organization. However, many or...

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Projects

Projects (3)
Project
predicting groundwater locations / 2014-2016 / completed This project is funded by the European Union SRTD-II is the second phase of the “Support to Research, Technological Development and Innovation in Jordan” project.
Project
solving the classification issue against SPA datasets; KNN, decision tree, and NB algorithms are utilized