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Achieving excellence in the fields of education, scientific research, and community service, and promote the university to the level the prestigious universities locally, regionally and globally.
Contributing to building and developing the knowledge community by creating university environment and community partnership that stimulate creativity as well as freedom of thought and expression. Also, keeping abreast of technological developments in the field of education, thus providing the society with the qualified human resources that can meet the needs of the labor market.
The University is committed to consolidating the following fundamental values: 1. Social and moral commitment. 2. Sense of belonging. 3. Justice and equality. 4. Creativity. 5. Quality and Excellence. 6. Transparency and accountability. 7. Responsible freedom. 8.Futurity.
Prof. Ahmad KH. Habboush Doctorate / Full Professor
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professor:Computer Science
Ph.D. Kharkov State Poly-technical ,University, Kharkov Ukraine, 2001
Period
University
Work Description
Assistant professor in department of Management Information System as a member of the teaching stuff, (full time) assistant professor.
Assistant Professor , Information Technology Faculty.
Associate Professor , Information Technology Faculty.
No.
Article Title
Authors
Conference
Organizers
Year
1
Advanced Object Monitoring Using Wireless Sensors Network
Mohammad alrawajbeh and Ahmad Haboush
International Conference on Communication Management an Information Technology
ICCMIT
2015
2
BioCloud Anetwork Cloud-Computing Method for Predicting RNA Secondary Structure
Ahmad Haboush
International Center of Econmics ,Humanities & Managment
ICEHM
2016
Recent Research Publications 1- PREMORBID BRAIN VOLUME AGAINST ALZHEIMER'S DISEASE USING MULTIMODAL BIG MEDICAL DATA ANALYSIS Bassam Elzaghmouri3 and Ahmad Khader Habboush3 Padmini Mansingh1, Binod Kumar Pattanayak1, Bibudhend ARPN Journal of Engineering and Applied Sciences Download × PREMORBID BRAIN VOLUME AGAINST ALZHEIMER'S DISEASE USING MULTIMODAL BIG MEDICAL DATA ANALYSIS Abstract Alzheimer’s disease is a neurological ailment in which memory loss and cognitive impairment are brought on by the death of brain cells. Alzheimer's disease is the most prevalent kind of dementia affecting people aged 60 and above. It is a neurodegenerative type of dementia that starts in the middle and gets worse over time. Alzheimer's disease, epilepsy, multiple sclerosis, cancer, depression, and other brain illnesses can all be diagnosed using hippocampus segmentation. Medical pictures have had a significant impact on medicine, diagnosis, and treatment. One of the most crucial image processing techniques is called image segmentation. Our research focuses on measuring the volume concerning typical size by utilizing segmentation techniques. To comprehend the severity of progression in demented people, this study will look at the whole brain (WB), grey matter (GM), and hippocampal (HC) morphological variation and identify the significant biomarkers in MRI brain images. Pre-trained models can demonstrate hippocampal regions with significant severity differences for the considered classes of CN and AD. It is determined that the CNN model for the HC region produces better categorization for CN and AD with 98.2 percent accuracy each. The primary goal of the research was to identify size anomalies using several biochemical features of big medical data analysis. Bassam Elzaghmouri3 and Ahmad Khader Habboush3 Padmini Mansingh1, Binod Kumar Pattanayak1, Bibudhend Journal: 2- Security Life Cycle framework for Exploring & Prevention of Zero day attacks in Cyberterrorism Bassam Mohammad Elzaghmouri 1, Ahmad khader Habboush 2 International Journal of Computer and Communication Technology Download × Security Life Cycle framework for Exploring & Prevention of Zero day attacks in Cyberterrorism The rise of cyber terrorism poses a significant threat to governments, businesses, and individuals worldwide. Cyber terrorists use information technology to carry out attacks that range from simple hacking attempts to more sophisticated attacks involving malware, ransomware, and zero-day exploits. This paper aims to provide an in-depth understanding of cyber terrorism, with a special focus on zero-day attacks. As the world becomes more digitized and automated, it brings convenience to everyone's lives. However, it also leads to growing concerns about security threats, including data leakage, website hacking, attacks, phishing, and zero-day attacks. These concerns are not only for organizations, businesses, and society, but also for governments worldwide. This paper aims to provide an introductory literature review on the basics of cyber-terrorism, focusing on zero-day attacks. The paper explores the economic and financial destruction caused by zero-day attacks and examines various types of zero-day attacks. It also looks at the steps taken by international organizations to address these issues and the recommendations they have made. Additionally, the paper examines the impact of these externalities on policymaking and society. As cyber-security becomes increasingly important for businesses and policymakers, the paper aims to delve deeper into this aspect, which has the potential to threaten national security, public life, and the economic and financial stability of developed, developing, and underdeveloped economies. Bassam Mohammad Elzaghmouri 1, Ahmad khader Habboush 2 Journal: 3- ADAPTIVE BANDWIDTH MANAGEMENT MODEL FOR WIRELESS MOBILE AD-HOC NETWORK Nibedita Jagadev1,Binod Kumar Pattanayak1,Ahmadkhader habboush2, Bassam Mohammad Elzaghmouri3, Mahmo International Journal of Computer Networks and Communications Download × ADAPTIVE BANDWIDTH MANAGEMENT MODEL FOR WIRELESS MOBILE AD-HOC NETWORK The quality of service (QoS) component in a mobile ad-hoc network has an active role in the current network scenario. In a dynamic mobile ad hoc network, ensuring optimum QoS with a scarce network resource is a significant challenge. To achieve QoS, it is essential to adopt some effective and efficient mechanisms. We have proposed an adaptive bandwidth manager model (ABMM) which uses a bandwidthsharing concept along with the flexible bandwidth reservation algorithm (FBRA) for an effective, quick and authentic data transfer. During real-time data transfer, to make communication effective, we make use of bandwidth-sharing network design problems and the concept of reserving bandwidth in high-performance networks. In our proposed model we are concentrating on the maximum utilization of resources, and using the scheduling concept to provide the minimum required bandwidth guarantee to QoS flows. Our goal is to reduce the delay in data transfer and enhance the throughput while properly utilizing the system resources. Our simulation result also shows that our model improves the network performance. Nibedita Jagadev1,Binod Kumar Pattanayak1,Ahmadkhader habboush2, Bassam Mohammad Elzaghmouri3, Mahmo Journal: 4- Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification Mohammad Alnabhan, Ahmad Khader Habboush, Qasem Abu Al-Haija, Arup Kumar Mohanty, Saumendra Pattnaik Mobile Information Systems Download × Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification This research utilizes metaheuristic optimization inspired by the Egyptian Vulture Optimization (EVO) technique. Biomedical image segregation is developed to reduce the complex association of hyperparameters of Convolutional Neural networks (CNN). The complex attributes of CNN include the type of kernel, size of the kernel, size of the batch, epoch counts, momentum, learning rate, activation function, convolution layer, and dropout. However, the life cycle of an Egyptian vulture influences the optimization technique to resolve complexity and increase the accuracy of CNN. The proposed CNN-based EVO model was evaluated in comparison to ANN-based and deep learning-based classifiers utilizing brain MRI image datasets. The results achieved have confirmed the efficiency and performance of the proposed CNN-based EVO model, in which the average detection accuracy and precision were 93% and 95%, respectively. Mohammad Alnabhan, Ahmad Khader Habboush, Qasem Abu Al-Haija, Arup Kumar Mohanty, Saumendra Pattnaik Journal: 5- An IoT-Fog-Cloud Integrated Framework for Real-Time Remote Cardiovascular Disease Diagnosis Abhilash Pati, Manoranjan Parhi, Mohammad Alnabhan, Binod Kumar Pattanayak, Ahmad Khader Habboush, M Informatics Download × An IoT-Fog-Cloud Integrated Framework for Real-Time Remote Cardiovascular Disease Diagnosis Recently, it has proven difficult to make an immediate remote diagnosis of any coronary illness, including heart disease, diabetes, etc. The drawbacks of cloud computing infrastructures, such as excessive latency, bandwidth, energy consumption, security, and privacy concerns, have lately been addressed by Fog computing with IoT applications. In this study, an IoT-Fog-Cloud integrated system, called a Fog-empowered framework for real-time analysis in heart patients using ENsemble Deep learning (FRIEND), has been introduced that can instantaneously facilitate remote diagnosis of heart patients. The proposed system was trained on the combined dataset of Long-Beach, Cleveland, Switzerland, and Hungarian heart disease datasets. We first tested the model with eight basic ML approaches, including the decision tree, logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, AdaBoost, and XGBoost approaches, and then applied ensemble methods including bagging classifiers, weighted averaging, and soft and hard voting to achieve enhanced outcomes and a deep neural network, a deep learning approach, with the ensemble methods. These models were validated using 16 performance and 9 network parameters to justify this work. The accuracy, PPV, TPR, TNR, and F1 scores of the experiments reached 94.27%, 97.59%, 96.09%, 75.44%, and 96.83%, respectively, which were comparatively higher when the deep neural network was assembled with bagging and hard-voting classifiers. The user-friendliness and the inclusion of Fog computing principles, instantaneous remote cardiac patient … Abhilash Pati, Manoranjan Parhi, Mohammad Alnabhan, Binod Kumar Pattanayak, Ahmad Khader Habboush, M Journal: 6- BioCloud: A Network Cloud-Computing Method for Predicting RNA Secondary Structure Ahmad Habboush, Mohammad Al Rawajbeh, Ahmed M. Manasrah, and Ra’ed M. Al-Khatib International Journal of Humanities and Applied Sciences (IJHAS) × BioCloud: A Network Cloud-Computing Method for Predicting RNA Secondary Structure Predicting RNA secondary structure becomes an important issue, due to the useful functions of RNA in designing antiviral drugs for AIDS and malignant diseases like cancer. Many computational methods have been proposed to predict RNA secondary structure from a given single sequences. In this paper, the BioCloud method is proposed, which is more accurate cloudcomputing method for predicting the secondary structure of RNA. The proposed BioCloud method runs via a global networks as a cloud computing to predict the RNA secondary structure. Practically, the proposed method uses a Minimum Free Energy (MFE) algorithm with the Dynamic Programming (DP) prediction method to predict the needed structure of RNA molecules. The experimental results show that the BioCloud method runs faster and predict more accurate RNA secondary structure compared to the state-of-the-art prediction methods exist in the literature. The proposed BioCloud method performs efficiently in a short running time and available globally in a cloud-computing system. Ahmad Habboush, Mohammad Al Rawajbeh, Ahmed M. Manasrah, and Ra’ed M. Al-Khatib Journal: 7- Enhancing the eGovernment functionality using Knowledge Management Mohammad Al Rawajbeh, Ahmad Habboush World Academy of Science, Engineering and Technology Download × Enhancing the eGovernment functionality using Knowledge Management The primary aim of the e-government applications is the fast citizen service and the accomplishment of governmental functions. This paper discusses the knowledge management for e-government development in the needs and role. The paper focused on analyzing the advantages of using knowledge management by using the existing IT technologies to maximize the government functions efficiency. The proposed new approach of providing government services is based on using Knowledge management as a part of e-government system. Keywords—E-government, knowledge management, e-service, e-tools, governmental functions. I. Mohammad Al Rawajbeh, Ahmad Habboush Journal: 8- Parallel sequential searching algorithm for unsorted array Ahmad Habboush, Sami Qawasmeh Research Journal of Applied Science Download × Parallel sequential searching algorithm for unsorted array Parallel search is a way to increase search speed by using additional processors. Researchers propose a parallel search algorithm that searches an item in unordered array, the searching time obtained is better than that obtained in binary search. That is justified by the fact that the binary search requires a variant time for sorting the input array. The speed up of the proposed algorithm is increased linearly with the input size by saving the time spent in sorting input data. In the proposed algorithm, the array to be searched is divided into two subarrays and then, two search threads are created in parallel which required O (n/2) in the worst case (where all the items is scanned), reducing the searching time in the worst case to O (n/2 Hlog n). Log n is the time needed to splitting an array of size n. The efficiency is increased quickly for an input size of 5000-1,000,000 item. However, the efficiency suffers a little variation for an … Ahmad Habboush, Sami Qawasmeh Journal: 9- Acceptance of mobile learning by university students Ahmad Habboush, Ayman Nassuora, Abdel-Rahman Hussein American Journal of Scientific Research Download × Acceptance of mobile learning by university students Resting on the use of mobile device which is increasingly popular around the world, mobile learning in fact extends the reach of education to all social-economic levels independent of location and time, indicating a new opportunity for education industry development. Nonetheless, there is still a lack of a comprehensive understanding regarding the factors affecting the adoption of mobile learning. Based on information systems/mobile commerce acceptance literature, this study developed an integrated model to predict the acceptance of mobile learning by university students. This model hopefully provides a framework for future research, and will serve as a basis for our future survey and analysis of data. Ahmad Habboush, Ayman Nassuora, Abdel-Rahman Hussein Journal: 10- Arabic text summarization model using clustering techniques Ahmad Haboush, Maryam Al-Zoubi, Ahmad Momani, Motassem Tarazi World of Computer Science and Information Technology Journal (WCSIT) Download × Arabic text summarization model using clustering techniques The model is thoroughly illustrated through its different stages. Obviously, the general scheme follows traditional descriptive model of most of the system stages in literature with the exception of the ranking stage. This model with its developed technique has been subjected to a set of experiments. Various Arabic text examples are used for evaluation purposes. The efficiency of the summarization is calculated in terms of Precision and Recall measures. Result obtained actually is considered promising and competitive to the verb/noun categorization ranking method. This enhancement has been detected for Precision 76% and Recall 79% with the analogous values of 62% and 70% obtained in the verb/noun categorization method. The enhancement emerges in this tangible result is attributed to the implicit embedding of semantic capability of the developed model to expand the extract boundaries towards the abstract extremes of the design theme. Ahmad Haboush, Maryam Al-Zoubi, Ahmad Momani, Motassem Tarazi Journal: صفحة 1 من 3123التاليالاخيرة
Abstract Alzheimer’s disease is a neurological ailment in which memory loss and cognitive impairment are brought on by the death of brain cells. Alzheimer's disease is the most prevalent kind of dementia affecting people aged 60 and above. It is a neurodegenerative type of dementia that starts in the middle and gets worse over time. Alzheimer's disease, epilepsy, multiple sclerosis, cancer, depression, and other brain illnesses can all be diagnosed using hippocampus segmentation. Medical pictures have had a significant impact on medicine, diagnosis, and treatment. One of the most crucial image processing techniques is called image segmentation. Our research focuses on measuring the volume concerning typical size by utilizing segmentation techniques. To comprehend the severity of progression in demented people, this study will look at the whole brain (WB), grey matter (GM), and hippocampal (HC) morphological variation and identify the significant biomarkers in MRI brain images. Pre-trained models can demonstrate hippocampal regions with significant severity differences for the considered classes of CN and AD. It is determined that the CNN model for the HC region produces better categorization for CN and AD with 98.2 percent accuracy each. The primary goal of the research was to identify size anomalies using several biochemical features of big medical data analysis.
The rise of cyber terrorism poses a significant threat to governments, businesses, and individuals worldwide. Cyber terrorists use information technology to carry out attacks that range from simple hacking attempts to more sophisticated attacks involving malware, ransomware, and zero-day exploits. This paper aims to provide an in-depth understanding of cyber terrorism, with a special focus on zero-day attacks. As the world becomes more digitized and automated, it brings convenience to everyone's lives. However, it also leads to growing concerns about security threats, including data leakage, website hacking, attacks, phishing, and zero-day attacks. These concerns are not only for organizations, businesses, and society, but also for governments worldwide. This paper aims to provide an introductory literature review on the basics of cyber-terrorism, focusing on zero-day attacks. The paper explores the economic and financial destruction caused by zero-day attacks and examines various types of zero-day attacks. It also looks at the steps taken by international organizations to address these issues and the recommendations they have made. Additionally, the paper examines the impact of these externalities on policymaking and society. As cyber-security becomes increasingly important for businesses and policymakers, the paper aims to delve deeper into this aspect, which has the potential to threaten national security, public life, and the economic and financial stability of developed, developing, and underdeveloped economies.
The quality of service (QoS) component in a mobile ad-hoc network has an active role in the current network scenario. In a dynamic mobile ad hoc network, ensuring optimum QoS with a scarce network resource is a significant challenge. To achieve QoS, it is essential to adopt some effective and efficient mechanisms. We have proposed an adaptive bandwidth manager model (ABMM) which uses a bandwidthsharing concept along with the flexible bandwidth reservation algorithm (FBRA) for an effective, quick and authentic data transfer. During real-time data transfer, to make communication effective, we make use of bandwidth-sharing network design problems and the concept of reserving bandwidth in high-performance networks. In our proposed model we are concentrating on the maximum utilization of resources, and using the scheduling concept to provide the minimum required bandwidth guarantee to QoS flows. Our goal is to reduce the delay in data transfer and enhance the throughput while properly utilizing the system resources. Our simulation result also shows that our model improves the network performance.
This research utilizes metaheuristic optimization inspired by the Egyptian Vulture Optimization (EVO) technique. Biomedical image segregation is developed to reduce the complex association of hyperparameters of Convolutional Neural networks (CNN). The complex attributes of CNN include the type of kernel, size of the kernel, size of the batch, epoch counts, momentum, learning rate, activation function, convolution layer, and dropout. However, the life cycle of an Egyptian vulture influences the optimization technique to resolve complexity and increase the accuracy of CNN. The proposed CNN-based EVO model was evaluated in comparison to ANN-based and deep learning-based classifiers utilizing brain MRI image datasets. The results achieved have confirmed the efficiency and performance of the proposed CNN-based EVO model, in which the average detection accuracy and precision were 93% and 95%, respectively.
Recently, it has proven difficult to make an immediate remote diagnosis of any coronary illness, including heart disease, diabetes, etc. The drawbacks of cloud computing infrastructures, such as excessive latency, bandwidth, energy consumption, security, and privacy concerns, have lately been addressed by Fog computing with IoT applications. In this study, an IoT-Fog-Cloud integrated system, called a Fog-empowered framework for real-time analysis in heart patients using ENsemble Deep learning (FRIEND), has been introduced that can instantaneously facilitate remote diagnosis of heart patients. The proposed system was trained on the combined dataset of Long-Beach, Cleveland, Switzerland, and Hungarian heart disease datasets. We first tested the model with eight basic ML approaches, including the decision tree, logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, AdaBoost, and XGBoost approaches, and then applied ensemble methods including bagging classifiers, weighted averaging, and soft and hard voting to achieve enhanced outcomes and a deep neural network, a deep learning approach, with the ensemble methods. These models were validated using 16 performance and 9 network parameters to justify this work. The accuracy, PPV, TPR, TNR, and F1 scores of the experiments reached 94.27%, 97.59%, 96.09%, 75.44%, and 96.83%, respectively, which were comparatively higher when the deep neural network was assembled with bagging and hard-voting classifiers. The user-friendliness and the inclusion of Fog computing principles, instantaneous remote cardiac patient …
Faculty
Time
day
courses
EN-603
8
Sun/Tue
software engineering
EN-719
11
computer organization and design
EN-602
Mon/Wed
computer skills
Email : ahmad_ram2001@jpu.edu.jo or ahmad_ram2001@yahoo.com