كتابة النص: الأستاذ الدكتور يوسف أبو العدوس - جامعة جرش قراءة النص: الدكتور أحمد أبو دلو - جامعة اليرموك مونتاج وإخراج : الدكتور محمد أبوشقير، حمزة الناطور، علي ميّاس تصوير : الأستاذ أحمد الصمادي الإشراف العام: الأستاذ الدكتور يوسف أبو العدوس
فيديو بمناسبة الإسراء والمعراج - إحتفال كلية الشريعة بجامعة جرش 2019 - 1440
فيديو بمناسبة ذكرى المولد النبوي الشريف- مونتاج وإخراج الدكتور محمد أبوشقير- كلية تكنولوجيا المعلومات
التميز في مجالات التعليم والبحث العلمي، وخدمة المجتمع، والارتقاء لمصاف الجامعات المرموقة
محليا واقليميا وعالميا.
المساهمة في بناء مجتمع المعرفة وتطوره من خلال إيجاد بيئة جامعية، وشراكة مجتمعية محفزة للابداع،
وحرية الفكر والتعبير، ومواكبة التطورات التقنية في مجال التعليم، ومن ثم رفد المجتمع بما يحتاجه من
موارد بشرية مؤهلة وملائمة لاحتياجات سوق العمل.
تلتزم الجامعة بترسيخ القيم الجوهرية التالية:
الإلتزام الإجتماعي والأخلاقي، الإنتماء،العدالة والمساواة، الإبداع، الجودة والتميّز، الشفافية والمحاسبة، الحرية المنظبطة والمستقبلية.
دكتوراة نظم المعلومات الحاسوبية
PH.D Degree in Computer Information Systems, University of Banking and Financial Sciences, Jordan-2011
Master’s Degree in Information Systems, Arab Academy for Banking and Financial Sciences, Jordan -2000
High Diploma in Information Systems, Arab Academy for Banking and Financial Sciences, Jordan -1999
2010-2011 Manager Department of Computer, Amaken Plaza Hotel, Jordan Amman.
2008-2012 Head of Applied Arts and Information Technology Dept, Al-Andalus Collage.
2000-2011 lecturer, The Arab Collage, Jordan Amman.
2008 Part-time lecturer, NewHorizons , Jordan Amman .
2000-2003 Part-time lecturer, The Arab Academy For Banking And Financial Sciences “AABFS”,Jordan Amman .
2002-2003 Part-time lecturer, Alisra Private UNV, Jordan Amman .
2002-2007 Part-time lecturer, Princess Alia UNV Collage, Jordan Amman.
2001-2002 Part-time lecturer, Al-Fashir UNV, Jordan Amman.
2001-2004 Part-time lecturer, The Kadecy Collage, Jordan Amman.
1999-2000 MIS officer, Multi Base Systems company, Jordan Amman.
In an era characterized by the relentless evolution of Internet of Things (IoT) technologies, marked by the pervasive adoption of smart devices and the ever-expanding realm of Internet connectivity, the IoT has seamlessly integrated itself into our daily lives. This integration has ushered in a new era for manufacturing companies, enabling them to conduct real-time monitoring of their machinery, supervise product quality, and closely monitor environmental variables within their facilities. In addition to the immediate benefits of risk mitigation and loss prevention, this multifaceted approach has provided decision-makers with a comprehensive perspective for making informed decisions. People are now more dependent than ever in IoT devices and services. However, anomalies within IoT networks pose a critical concern despite the IoT's immense potential. These anomalies can pose significant security and safety risks if they go undetected. Identifying and alerting users of these anomalies on time has become crucial for preventing potential damages and losses. In response to this imperative, our research endeavors to utilize the power of Machine Learning and Deep Learning techniques to detect anomalies in IoT networks. We undertake exhaustive experiments with the IoT-23 dataset to validate our methodology empirically. Our research examines an exhaustive comparison of numerous models, assessing their performance and time efficiency to determine the optimal algorithm for achieving high detection accuracy under strict time constraints. This research represents an important step towards enhancing the security of Industrial IoT environments, thereby protecting vital infrastructure and ensuring the integrity of industrial operations in our increasingly interconnected world.
The detection of cephalometric landmarks in radiographic imagery is pivotal to an extensivearray of medical applications, notably within orthodontics and maxillofacial surgery.Manual annotation of these landmarks, however, is not only labour-intensive but also subjectto potential inaccuracies. To address these challenges, we propose a robust, fully automatedmethod for detecting soft-tissue landmarks. This innovative method effectively integratestwo disparate types of descriptors: Haar-like features, which are primarily employed tocapture local edges and lines, and spatial features, designed to encapsulate the spatialinformation of landmarks. The integration of these descriptors facilitates the construction ofa potent classifier using the AdaBoost technique. To validate the efficacy of the proposedmethod, a novel dataset for the task of soft-tissue landmark detection is introduced,accompanied by two distinct evaluation protocols to determine the detection rate. The firstprotocol quantifies the detection rate within the Mean Radial Error (MRE), while the secondprotocol measures the detection rate within a predefined confidence region R. The conductedexperiments demonstrated the proposed method's superiority over existing state-of-the-arttechniques, yielding average detection rates of 76.7% and 94% within a 2mm radial distanceand within the confidence region R, respectively. This study's findings underscore thepotential of this innovative approach in enhancing the accuracy and efficiency ofcephalometric landmark detection.
The Internet of Things (IoT) technology has recently emerged as a potential global communication medium that efficiently facilitates human-to-human, human-to-machine, and machine-to-machine communications. Most importantly, unlike the traditional Internet, it supports machine-to-machine communication without human intervention. However, billions of devices connected to the IoT environment are mostly wireless, small, hand-held, and resourced-constrained devices with limited storage capacities. Such devices are highly prone to external attacks. These days, cybercriminals often attempt to launch attacks on these devices, which imposes the major challenge of efficiently implementing communications across the IoT environment. In this paper, the issue of cyber-attacks in the IoT environment is addressed. An end-to-end encryption scheme was proposed to protect IoT devices from cyber-attacks.
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.
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.
some of the benefits that AI and the IoT can bring to the education system. In this regard, this research paper aims to investigate how AI and the IoT can be integrated into sustainable education in order to provide students with personalized and immersive learning experiences during pandemics, such as COVID-19, for smart cities. The study’s key findings report that AI can be employed in sustainable education through personalized learning. AI-powered algorithms can be used to analyze student data and create personalized learning experiences for each student. This includes providing students with tailored content, assessments, and feedback that align with their unique learning style and pace. Additionally, AI can be used to communicate with students in a more natural and human-like way, making the learning experience more engaging and interactive. Another key aspect of the integration of AI and the IoT in education obtained from this research is the ability to provide real-time feedback and support. IoT-enabled devices, such as smart cameras and microphones, can be used to monitor student engagement and provide real-time feedback. AI algorithms can then use these data to adapt the learning experience in real time. IoT-enabled devices, such as tablets and laptops, can be used to collect and process student work, allowing for the automatic grading of assignments and assessments. Additionally, IoT technology can facilitate remote monitoring and grading of student work, which would be particularly useful for students who cannot attend traditional classroom settings. Furthermore, AI and the IoT can also be used to create intelligent personal learning environments (PLEs) that provide students with personalized, adaptive, and engaging learning experiences. IoT-enabled devices, such as smart cameras and microphones, combined with AI-powered algorithms, can provide real-time feedback and support, allowing the PLE to adapt to the student’s needs and preferences. It is concluded that integrating AI and the IoT in sustainable education can revolutionize the way people learn, providing students with personalized, real-time feedback and support and opening up new opportunities for remote and disadvantaged students. However, it will be important to ensure that the use of AI and the IoT in education is ethical and responsible to ensure that all students have equal access to the benefits of these technologies.
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 bandwidth-sharing 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
Speech recognition is an important part of human-machine interaction which represents a hot area of researches in the field of computer systems, electronic engineering, communications, and artificial intelligence. While speech signal is very complex and contains huge number of sampling points, the extraction of features from its time and frequency domain is very complex by analytical methods. The neural network capabilities to estimate the complex functions make it very reliable in such applications. This paper presents speech recognizer based on feed forward neural network with multi layer perceptron structure. The speech is preprocessed by two methods; discrete wavelet transformation (DWT) and principal component analysis (PCA). The results and structure are presented and comparison is make over them.
Many clustering algorithms with different methodologies are subjected to be common techniques and main step in many applications in the computer science world. The need of adapting efficient clustering algorithm increases in critical applications (i.e. wireless sensors networks). Utilizing the Fuzzy Logic power; Fuzzy C-mean (FCM) clustering has a major role in most clustering applications. But in many cases, the result of FCM is considered to be non-complete clustering strategy. This paper adapted the FCM algorithm to enable of generating clusters with equal sizes. Also, scattered points that are located far away from all clusters are grouped out of clusters. Another modification is to localize specific points that have ability to locate in more than one cluster; hence this has a non-negligible importance in some fields such as cellular communications.
Clustering is a type of classification under optimization problems, which is considered as a critical area of data mining. Medical clustering problem is a type of unsupervised learning in data mining. This work present a hybridization between our previous proposed Iterative Simulated Annealing (ISA) and Modified Great Deluge (MGD) algorithms for medical clustering problems. The aim of this work is to produce an effective algorithm for partitioning N objects into K clusters. The idea of the hybridization between MGD and ISA is to incorporate the strength of one approach with the strength of the other hoping a more promising algorithm. Also this combination can help to diverse the search space. Experimental results obtained two way of calculating the minimal distance that have been tested on six benchmark medical datasets show that, ISA-MGD is able to outperform some instances of MGD and ISA algorithms.
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All Rights Reseved © 2023 - Developed by: Prof. Mohammed M. Abu Shquier Editor: Ali Mayyas