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| 1 | Reelosiug torques of large induction motors with stator trapped flux 显示文摘 | SHALTOUT A ALOMOUSH M | 1996 | IEEE Trans on Energy Coversion1996，111，1： | 1 |
| 2 | Generalized model for fixed transmission fights auction显示文摘 | Alomoush M I Shahidehpour S M | 2000 | Electric Power Systems Research2000，54，3： | 1 |
| 3 | Wheat response phosphogypsum and myc- on'hizal fungi in alkaline soil显示文摘 | Alkaraki G Alomoush M I | 2002 | Joumal of Plant Nutrition2002，25，4： | 1 |
| 4 | Derivation of UPFC DC load flow model with examples of its use in restructured power systems显示文摘 | Muwaffaq I Alomoush | 2003 | IEEE Trans on power systems2003，18，3： | 1 |
| 5 | Fixed transmission rights for zonal congestion management显示文摘 | Alomoush M I Shahidehpour S M | 1999 | IEE Proceedings- Generation Transmission and Distribution1999，146，5： | 1 |
| 6 | Load frequency control andautomatic generation control using fractional -order con-trollers 显示文摘 | Alomoush Muwaffaq Irsheid | 2010 | Electrical Engineering2010，91，7： | 1 |
| 7 | Generalized model for fixed transmission rights auction显示文摘 | Alomoush M I Shahidehpour S M | 2000 | Electric Power Systems Research2000，，54： | 1 |
| 8 | Generalized model for fixed transmission rights auction显示文摘 | Alomoush M I Shahidehpour S M | | 0，，03： | 1 |
| 9 | Fixed transmission rights for zonal congestionmanagement显示文摘 | Alomoush M I Shahidehpour S M | 1999 | IEE Proc-Gener Transm Distrib1999，146，5： | 1 |
| 10 | Reelosiug torques of large induction motors with stator trapped flux 显示文摘 | SHALTOUT A ALOMOUSH M | 1996 | IEEE Trans on Energy Coversion1996，111，1： | 1 |
| 11 | Age and Gender Classification Using Backpropagation and Bagging Algorithms显示文摘Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%. | Ammar Almomani Mohammed Alweshah Waleed Alomoush Mohammad Alauthman Aseel Jabai Anwar Abbass Ghufran Hamad Meral Abdalla Brij B.Gupta | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 12 | Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm显示文摘Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine learning.The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres.Artificial Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of randomness.However,ABC has some weaknesses,such as balancing exploration and exploitation.To improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is used.Furthermore,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC.The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain. | Ayat Alrosan Waleed Alomoush Mohammed Alswaitti Khalid Alissa Shahnorbanun Sahran Sharif Naser Makhadmeh Kamal Alieyan | 2021 | Computers, Materials & Continua2021，，8： | 0 |
| 13 | Blockchain-Based Decentralized Authentication Model for IoT-Based E-Learning and Educational Environments显示文摘In recent times,technology has advanced significantly and is currently being integrated into educational environments to facilitate distance learning and interaction between learners.Integrating the Internet of Things(IoT)into education can facilitate the teaching and learning process and expand the context in which students learn.Nevertheless,learning data is very sensitive and must be protected when transmitted over the network or stored in data centers.Moreover,the identity and the authenticity of interacting students,instructors,and staff need to be verified to mitigate the impact of attacks.However,most of the current security and authentication schemes are centralized,relying on trusted third-party cloud servers,to facilitate continuous secure communication.In addition,most of these schemes are resourceintensive;thus,security and efficiency issues arise when heterogeneous and resource-limited IoT devices are being used.In this paper,we propose a blockchain-based architecture that accurately identifies and authenticates learners and their IoT devices in a decentralized manner and prevents the unauthorized modification of stored learning records in a distributed university network.It allows students and instructors to easily migrate to and join multiple universities within the network using their identity without the need for user re-authentication.The proposed architecture was tested using a simulation tool,and measured to evaluate its performance.The simulation results demonstrate the ability of the proposed architecture to significantly increase the throughput of learning transactions(40%),reduce the communication overhead and response time(26%),improve authentication efficiency(27%),and reduce the IoT power consumption(35%)compared to the centralized authentication mechanisms.In addition,the security analysis proves the effectiveness of the proposed architecture in resisting various attacks and ensuring the security requirements of learning data in the university network. | Osama A.Khashan Sultan Alamri Waleed Alomoush Mutasem K.Alsmadi Samer Atawneh Usama Mir | 2023 | Computers, Materials & Continua2023，，5： | 0 |
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