Ahmad Hassanat

Ahmad Hassanat
Mutah University

Professor
https://eportal.mutah.edu.jo/etcea2022/

About

106
Publications
410,175
Reads
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1,590
Citations
Introduction
Professor Ahmad B. A Hassanat was born and grew up in Jordan, and received his Ph.D. in Computer Science from the University of Buckingham at Buckingham, UK in 2010, and B.S. and M.S. degrees in Computer Science from Mutah University/Jordan and Al al-Bayt University/Jordan in 1995 and 2004, respectively. He has been a faculty member of Information Technology department at Mutah University since 2010, currently, working with Mutah University, Jordan.
Additional affiliations
September 2019 - present
University of Tabuk
Position
  • Professor
July 2015 - present
Mu’tah University
Position
  • Professor (Associate)
July 2010 - July 2015
Mu’tah University
Position
  • Professor (Assistant)
Education
November 2006 - June 2010
The University of Buckingham
Field of study
  • Computer Science
September 2002 - July 2004
Al al-Bayt University
Field of study
  • Computer science
September 1991 - June 1995
Mu’tah University
Field of study
  • Computer science

Publications

Publications (106)
Data
Description Many relationships important to civil engineering depend on surface roughness (morphology). Examples are the bond strength between concrete layers, the adhesion of a wheel to the pavement, the angle of friction in the soil in contact with a wall surface, and many other cases when we deal with a material with a surface having the charact...
Article
Full-text available
The classic notion of a win–win situation has a key flaw in that it cannot always offer the parties equal amounts of winningsbecause each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We presen...
Article
Full-text available
Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract d...
Article
Full-text available
One of the most difficult problems analysts and decision-makers may face is how to improve the forecasting and predicting of financial time series. However, several efforts were made to develop more accurate and reliable forecasting methods. The main purpose of this study is to use technical analysis methods to forecast Jordanian insurance companie...
Preprint
Full-text available
For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is, models trained on fictitious data may fail spectacularly when put to real-world problems. The...
Article
Full-text available
There are a plethora of invented classifiers in Machine learning literature, however, there is no optimal classifier in terms of accuracy and time taken to build the trained model, especially with the tremendous development and growth of Big data. Hence, there is still room for improvement. In this paper, we propose a new classification method that...
Article
Full-text available
Since most classifiers are biased toward the dominant class, class imbalance is a challenging problem in machine learning. The most popular approaches to solving this problem include oversampling minority examples and undersampling majority examples. Oversampling may increase the probability of overfitting, whereas undersampling eliminates examples...
Article
Full-text available
Since most classifiers are biased toward the dominant class, class imbalance is a challenging problem in machine learning. The most popular approaches to solving this problem include oversampling minority examples and undersampling majority examples. Oversampling may increase the probability of overfitting, whereas undersampling eliminates examples...
Article
Full-text available
For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is, models trained on fictitious data may fail spectacularly when put to real-world problems. The...
Article
Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiled-persons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the sixth and the seventh fully conn...
Preprint
Full-text available
The classic win-win has a key flaw in that it cannot offer the parties with right amounts of winning because each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We present a novel way to measure...
Preprint
Full-text available
The classic win-win has a key flaw in that it cannot offer the parties the right amounts of winning because each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We present a novel way to measure...
Code
This compressed file contains some of my work and some machine learning methods coded using MS VC++ 2017. For more information, and how to use the code, please see Sharing my code.pdf
Article
Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiledpersons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the sixth and the seventh fully conne...
Preprint
Full-text available
Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiled-persons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the sixth and the seventh fully conn...
Article
Full-text available
The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the...
Article
Full-text available
Classifying and modeling texture images, especially those with significant rotation, illumination, scale, and view-point variations, is a hot topic in the computer vision field. Inspired by local graph structure (LGS), local ternary patterns (LTP), and their variants, this paper proposes a novel image feature descriptor for texture and material cla...
Article
Bankruptcy is an issue of interest in the business world since decades. It is a crucial endeavor for survival to predict this phenomenon in periods of economic turmoil and recession. In fact, bankruptcy modeling is challenging due to the complexity of contributing factors and the highly imbalanced distribution of available data sets. This work aims...
Article
Full-text available
Genetic algorithms (GAs) are powerful heuristic search techniques that are used successfully to solve problems for many different applications. Seeding the initial population is considered as the first step of the GAs. In this work, a new method is proposed, for the initial population seeding called the Multi Linear Regression Based Technique (MLRB...
Article
Full-text available
In March 2020, Saudi Arabia reported that the Coronavirus disease (COVID-19) spread to its territory, originating from China. In this study, a new simulation model estimates and forecasts the number of infected subjects with COVID-19 in the upcoming weeks, based on different parameters, in two major cities in Saudi Arabia, namely Riyadh (the capita...
Article
Full-text available
Genetic algorithm (GA) is an efficient tool for solving optimization problems by evolving solutions, as it mimics the Darwinian theory of natural evolution. The mutation operator is one of the key success factors in GA, as it is considered the exploration operator of GA. Various mutation operators exist to solve hard combinatorial problems such as...
Article
Full-text available
Class imbalance occurs in classification problems in which the "normal" cases, or instances, significantly outnumber the "abnormal" instances. Training a standard classifier on imbalanced data leads to predictive biases which cause poor performance on the class(es) with lower prior probabilities. The less frequent classes are often critically impor...
Article
Full-text available
Research on content based image retrieval (CBIR) has been developing for decades, and numerous methods have been competing to extract the most discriminative features for a better representation of the images' content. Recently, deep learning (DL) methods have gained attention in computer vision including CBIR. In this paper, we present a comparati...
Article
Full-text available
Genetic algorithm (GA) is an artificial intelligence search method, that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include m...
Article
Full-text available
Predicting the compressive strength of cement-stabilized rammed earth (CSRE) using current testing machines is time-consuming and costly and may harm the environment due to the samples' waste. This paper presents an automatic method using computer vision and deep learning to solve the problem. For this purpose, a deep convolutional neural network (...
Article
Full-text available
In this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted using a set of statistical tests. Among the many existing tests, those fit with processed data and for...
Article
Full-text available
The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested examples and the training examples. This raises a major question about whi...
Conference Paper
Full-text available
Due to their large number of applications, eye-tracking systems have gain attention recently. In this work, we propose 4 new features to support the most used feature by these systems, which is the location (x, y). These features are based on the white areas in the four corners of the sclera; the ratio of the whites area (after segmentation) to the...
Conference Paper
Facial image retrieval is a challenging task since faces have many similar features (areas), which makes it difficult for the retrieval systems to distinguish faces of different people. With the advent of deep learning, deep networks are often applied to extract powerful features that are used in many areas of computer vision. This paper investigat...
Conference Paper
Full-text available
Word-based writer identification and authentication have been under investigation for years. In this paper, we present a new Arabic online/offline handwriting dataset for writer authentication and identification. The created dataset includes two parts: AHWDB1 and AHWDB2 which are made freely available for the research community. Each part of the da...
Conference Paper
Full-text available
Invoices are issued by companies, banks and different organizations in different forms including handwritten and machine-printed ones; sometimes, receipts are included as a separated form of invoices. In current practice, normally, classifying these types is done manually, since each needs a special kind of processing such as making them suitable f...
Data
Big Data Classification
Preprint
Full-text available
Facial image retrieval is a challenging task since faces have many similar features (areas), which makes it difficult for the retrieval systems to distinguish faces of different people. With the advent of deep learning, deep networks are often applied to extract powerful features that are used in many areas of computer vision. This paper investigat...
Article
Full-text available
Big data classification is very slow when using traditional machine learning classifiers, particularly when using a lazy and slow-by-nature classifier such as the k-nearest neighbors algorithm (KNN). This paper proposes a new approach which is based on sorting the feature vectors of training data in a binary search tree to accelerate big data class...
Preprint
Full-text available
Research on content-based image retrieval (CBIR) has been under development for decades, and numerous methods have been competing to extract the most discriminative features for improved representation of the image content. Recently, deep learning methods have gained attention in computer vision, including CBIR. In this paper, we present a comparat...
Article
Full-text available
Big Data classification has recently received a great deal of attention due to the main properties of Big Data, which are volume, variety, and velocity. The furthest-pair-based binary search tree (FPBST) shows a great potential for Big Data classification. This work attempts to improve the performance the FPBST in terms of computation time, space c...
Article
Full-text available
In fingerprint recognition systems, feature extraction is an important part because of its impact on the final performance of the overall system, particularly, in the case of low-quality images, which poses significant challenges to traditional fingerprint feature extraction methods. In this work, we make two major contributions: First, a novel fea...
Preprint
Full-text available
Big Data classification has recently received a great deal of attention due to the main properties of Big Data, which are volume, variety, and velocity. The furthest-pair-based binary search tree (FPBST) shows a great potential for Big Data classification. This work attempts to improve the performance the FPBST in terms of computation time, space c...
Article
Full-text available
Due to their large sizes and/or dimensions, the classification of Big Data is a challenging task using traditional machine learning, particularly if it is carried out using the well-known K-nearest neighbors classifier (KNN) classifier, which is a slow and lazy classifier by its nature. In this paper, we propose a new approach to Big Data classific...
Article
Full-text available
In certain cases, the only evidence to identify terrorists, who are seen in digital images or videos is their hands’ shapes, particularly, the victory sign as performed by many of them when they intentionally hide their faces, and/or distort their voices. This paper proposes new methods to identify those persons for the first time from their victor...
Article
Full-text available
Finding the diameter of a dataset in multidimensional Euclidean space is a well-established problem, with well-known algorithms. However, most of the algorithms found in the literature do not scale well with large values of data dimension, so the time complexity grows exponentially in most cases, which makes these algorithms impractical. Therefore,...
Article
Full-text available
The advances in information technology of both hardware and software have allowed big data to emerge recently, classification of such data is extremely slow, particularly when using K-nearest neighbors (KNN) classifier. In this article, we propose a new approach that creates a binary search tree (BST) to be used later by the KNN to speed up the big...
Preprint
Full-text available
Finding the diameter of a dataset in multidimensional Euclidean space is a well-established problem, with well-known algorithms. However, most of the algorithms found in the literature do not scale well with large values of data dimension, so the time complexity grows exponentially in most cases, which makes these algorithms impractical. Therefore,...
Article
Full-text available
Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary computation that plays a significant role in obtaining meaningful solutions to complex problems with large search space. GAs involve three fundamental operations after creating an initial population, namely selection, crossover, and mutation. The first task in...
Article
Full-text available
The ancient game of ṭāb is a war and race game. It is played by two teams, each consisting of at least one player. In addition to presenting the game and its rules, the authors develop three versions of the game: human versus human, human versus computer, and computer versus computer. The authors employ a Genetic Algorithm (GA) to help the computer...
Conference Paper
Full-text available
Deep Convolutional Neural Networks (CNNs) are widespread, efficient tools of visual recognition. In this paper, we present a comparative study of three popular pre-trained CNN models: AlexNet, VGG-16 and VGG-19. We address the problem of palmprint identification in low-quality imagery and apply Support Vector Machines (SVMs) with all of the compare...
Article
Full-text available
Deep Convolutional Neural Networks (CNNs) are widespread, efficient tools of visual recognition. In this paper, we present a comparative study of three popular pre-trained CNN models: AlexNet, VGG-16 and VGG-19. We address the problem of palmprint identification in low-quality imagery and apply Support Vector Machines (SVMs) with all of the compare...
Conference Paper
Abstract—Content-based image retrieval (CBIR) and image classification are challenging problems in computer vision. In both fields, feature extraction plays an important role in ensuring effectiveness and stability of the results. In this paper, we present an experimental study to test the robustness of the features we proposed earlier. We demonstr...