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Automatic Arabic Document Classification Based on the HRWiTD algorithm

Journal of Software Engineering and Applications 2018年第4期11卷 167-179页

作者： Ehsan Othman ayoub al-hamadi Faculty of Electrical Engineering and Information Technology NIT Otto-von-Guericke-University Magdeburg Germany

The documents contain a large amount of valuable knowledge on various subjects and, more recently, documents on the Internet are available from various sources. Therefore, automatic, rapid and accurate classification of these documents with less human interaction has become necessary. In this paper, we introduce a new algorithm called the highest repetition of words in a text document (HRWiTD) to classify the automatic Arabic text. The corpus is divided into a train set and a test set to be applied to proposed classification technique. The train set is analyzed for learning and the learning data is stored in the Learning Dataset file. The category that contains the highest repetition for each word is assigned as a category for the word in Learning Dataset file. This file includes non-duplicate words with the value of higher repetition and categories and they get from all texts in the train set. For each text in the test set, the category of words is assigned to a specific category by using Learning Dataset file. The category that contains the largest number of words is assigned as the predicted category of the text. To evaluate the classification accuracy of the HRWiTD algorithm, the confusion matrix method is used. The HRWiTD algorithm has been applied to convergent samples from six categories of Arabic news at SPA (Saudi Press Agency). As a result, the accuracy of the HRWiTD algorithm is 86.84%. In addition, we used the same corpus with the most popular machine learning algorithms which are C5.0, KNN, SVM, NB and C4.5, and their results of classification accuracy are 52.86%, 52.38%, 51.90%, 51.90% and 30%, respectively. Thus, the HRWiTD algorithm gives better classification accuracy compared to the most popular machine learning algorithms on the selected domain.

关键词： Automatic Text Classification Confusion Matrix SPA Machine Learning algorithms

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Smart Energy Management System Using Machine Learning

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Computers, Materials & Continua 2024年第1期78卷 959-973页

作者： ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam al hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Face Detection and Localization in Color Images: An Efficient Neural Approach

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Journal of Software Engineering and Applications 2011年第12期4卷 682-687页

作者： Samy Sadek ayoub al-hamadi Bernd Michaelis Usama Sayed 不详

Automatic face detection and localization is a key problem in many computer vision tasks. In this paper, a simple yet effective approach for detecting and locating human faces in color images is proposed. The contribution of this paper is twofold. First, a particular reference to face detection techniques along with a background to neural networks is given. Second, and maybe most importantly, an adaptive cubic-spline neural network is designed to be used to detect and locate human faces in uncontrolled environments. The experimental results conducted on our test set show the effectiveness of the proposed approach and it can compare favorably with other state-of-the-art approaches in the literature.

关键词： Human Face Detection and Localization Spline Activation Function Color Moments Human-Computer Interaction

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A Fast Statistical Approach for Human Activity Recognition

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International Journal of Intelligence Science 2012年第1期2卷 9-15页

作者： Samy Sadek ayoub al-hamadi Bernd Michaelis Usama Sayed 不详

An essential part of any activity recognition system claiming be truly real-time is the ability to perform feature extraction in real-time. We present, in this paper, a quite simple and computationally tractable approach for real-time human activity recognition that is based on simple statistical features. These features are simple and relatively small, accordingly they are easy and fast to be calculated, and further form a relatively low-dimensional feature space in which classification can be carried out robustly. On the Weizmann publicly benchmark dataset, promising results (i.e. 97.8%) have been achieved, showing the effectiveness of the proposed approach compared to the-state-of-the-art. Furthermore, the approach is quite fast and thus can provide timing guarantees to real-time applications.

关键词： Activity Recognition Motion Analysis Statistical Moments Video Interpretation

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A Statistical Framework for Real-Time Traffic Accident Recognition

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Journal of Signal and Information Processing 2010年第1期1卷 77-81页

作者： Samy Sadek ayoub al-hamadi Bernd Michaelis Usama Sayed 不详

Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous Intelligent Transportation Systems (ITS). In this paper, we present a new framework toward a real-time automated recognition of traffic accident based on the Histogram of Flow Gradient (HFG) and statistical logistic regression analysis. First, optical flow is estimated and the HFG is constructed from video shots. Then vehicle patterns are clustered based on the HFG-features. By using logistic regression analysis to fit data to logistic curves, the classifier model is generated. Finally, the trajectory of the vehicle by which the accident was occasioned, is determined and recorded. The experimental results on real video sequences demonstrate the efficiency and the applicability of the framework and show it is of higher robustness and can comfortably provide latency guarantees to real-time surveillance and traffic monitoring applications.

关键词： Activity Pattern Automatic Traffic Accident Recognition Flow Gradient Logistic Model

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On Improvement of Teaching Quality for a Selected Mathematical Topic Using Artificial Neural Networks Modeling(With a Case Study)

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Journal of Literature and Art Studies 2017年第2期7卷 239-246页

作者： Hassan. M. H. Mustafa Fadhel Ben Tourkia ayoub al-hamadi Currently with al-Baha Private College of Sciences Al-BahaKSA On leave from Benha UniversityBanhaEgypt al-Baha Private College of Sciences Al-BahaKingdom of Saudi Arabia Otto-von-Guericke-University MagdeburgGermany

This paper motivated and inspired by an interdisciplinary critical educational issue adopted for a research work approach. It concerned with application of realistic Artificial Neural Networks (ANNs) models integrating reading brain function with multi-sensory cognitive learning theory. Specifically, these models adopted to improve tutoring quality (academic achievement) while teaching children “how to read?” considering the analysis and evaluation of phonics methodology. Herein, quantitative analysis and evaluation of this issue performed by considering two computer aided learning (Cal) packages concerned with a specific selected mathematical topic namely: long division process. Via realistic modeling of packages using (ANNs) based upon associative memory learning paradigm. In more details, at educational field practice; both Cal packages have been applied for teaching children algorithmic steps performing long division processes. Moreover, learning performance evaluation of presented packages considers children outcomes’ achievement after tutoring for suggested Mathematical Topic either with or without associated tutor’s voice. Interestingly, statistical analysis of obtained educational case study results at children classrooms (for both applied packages) versus classical tutoring proved to be in well agreement with obtained after ANNs computer simulation results.

关键词： artificial neural networks learning performance evaluation computer aided learning long division process associative memory

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Image-Based Methods for Interaction with Head-Worn Worker-Assistance Systems

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Journal of Intelligent Learning Systems and Applications 2014年第3期6卷 141-152页

作者： Frerk Saxen Omer Rashid ayoub al-hamadi Simon Adler alexa Kernchen Rüdiger Mecke Fraunhofer Institute for Factory Operation and Automation (IFF) Magdeburg Germany Institute for Information Technology and Communications Otto-von-Guericke-University Magdeburg Germany

In this paper, a mobile assistance-system is described which supports users in performing manual working tasks in the context of assembling complex products. The assistance system contains a head-worn display for the visualization of information relevant for the workflow as well as a video camera to acquire the scene. This paper is focused on the interaction of the user with this system and describes work in progress and initial results from an industrial application scenario. We present image-based methods for robust recognition of static and dynamic hand gestures in realtime. These methods are used for an intuitive interaction with the assistance-system. The segmentation of the hand based on color information builds the basis of feature extraction for static and dynamic gestures. For the static gestures, the activation of particular sensitive regions in the camera image by the user’s hand is used for interaction. An HMM classifier is used to extract dynamic gestures depending on motion parameters determined based on the optical flow in the camera image.

关键词： Head Mounted Display Gesture Recognition Mobile Assistance System

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Childhood Papillary Thyroid Carcinoma: A Case Report

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Case Reports in Clinical Medicine 2024年第4期13卷 108-114页

作者： alma al Mansour Hayder Makki hamadi Mahmoud Elshafey Mohamed Maryam alkhatry alqassemi Hospital Emirates Health Services Sharjah UAE Ibrahim Bin Hamad Obaidulla Hospital Ras Al Khaimah UAE

Background: Thyroid cancer is a rare disease yet the most common endocrine malignancy in pediatrics. Unlike adult patients, children with thyroid nodules typically don’t complain of pain, soreness, or difficulty swallowing. Additionally, using the recommended therapy for adults to treat paediatrics is not appropriate. There is an unmet need for updated unique guidelines for the management of papillary thyroid carcinoma (PTC) in paediatrics and adolescents. Case Report: A 12-year-old girl had an atypical presentation of metastatic PTC in lymph nodes. She was treated initially with hemi-thyroidectomy, followed by total thyroidectomy. A multidisciplinary team followed her up till successful results were found. Conclusion: Due to the difference in pathophysiology between thyroid tumors in children and adults, a unique approach to PTC management is to be implemented. Further trials are required for a better understanding of risk factors, the likelihood of recurrence, and the long-term side effects of the chosen management plan.

关键词： Papillary Thyroid Carcinoma Childhood Thyroidectomy Case Report

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IoMT-Based Smart Healthcare of Elderly People Using Deep Extreme Learning Machine

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Computers, Materials & Continua 2023年第7期76卷 19-33页

作者： Muath Jarrah Hussam al hamadi Ahmed Abu-Khadrah Taher M.Ghazal School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)43600BangiSelangorMalaysia Center for Cyber-Physical Systems Khalifa University127788Abu DhabiUAE

The Internet of Medical Things(IoMT)enables digital devices to gather,infer,and broadcast health data via the cloud *** phenomenal growth of the IoMT is fueled by many factors,including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based *** is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising *** IoMT is a novel reality transforming our daily *** can renovate modern healthcare by delivering a more personalized,protective,and collaborative approach to ***,the current healthcare system for outdoor senior citizens faces new *** healthcare systems are inefficient and lack user-friendly technologies and interfaces appropriate for elderly people in an outdoor ***,in this research work,a IoMT based Smart Healthcare of Elderly people using Deep Extreme Learning Machine(SH-EDELM)is proposed to monitor the senior citizens’*** performance of the proposed SH-EDELM technique gives better results in terms of 0.9301 accuracy and 0.0699 miss rate,respectively.

关键词： ICT ML FN DELM SH-EDELM

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Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

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Computers, Materials & Continua 2023年第6期75卷 6083-6100页

作者： Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam al hamadi Chan Yeob Yeun School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKMBangiSelangor43600Malaysia College of Engineering and IT University of Dubai14143UAE EECS Department Center for Cyber Physical SystemsKhalifa UniversityAbu Dhabi127788UAE

The software engineering field has long focused on creating high-quality software despite limited *** defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the *** approach can enhance the quality of the final product while lowering development *** defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development *** process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and *** study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high *** the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty *** second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and ***,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning *** experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse *** NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and *** to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

关键词： Ensemble machine learning fusion software defect prediction fuzzy logic

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