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Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

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Computers, Materials & Continua 2022年第3期70卷 6289-6304页

作者： Anwer Mustafa Hilal Hadeel Alsolai Fahd NAl-Wesabi mohammed Abdullah Al-Hagery Manar Ahmed Hamza Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their *** science integrates the conventions of econometrics with the technological elements of data *** make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related *** technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient *** the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial *** becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time *** this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model *** proposed model involves preprocessing,feature selection,classification,and parameter *** the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the ***,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of ***,functional link neural network(FLNN)model is employed for the classification of the feature reduced ***,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented *** experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.

关键词： Data science small and medium-sized enterprises business sectors financial crisis prediction intelligent systems artificial intelligence decision making machine learning

Fatty gut needs low-fat formula

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Chinese Medical Journal 2019年第9期132卷 1133-1134页

作者： mohammed Hasosah Department of Pediatric Gastroenterology King Saud Bin Abdulaziz University for Health SciencesNational Guard Health AffairsJeddahKingdom of Saudi Arabia

To the Editor:Primary intestinal lymphangiectasia (PIL) is a rare disorder characterized by dilated intestinal lacteals resulting in lymph leakage into the small bowel lumen and responsible for protein-losing enteropa... 详细信息

关键词： Editor PIL hypogammaglobulinemia

Incidence &Risk Factors Associated with Carotid Disease in Patients Undergoing Coronary Artery Bypass Grafting Surgery

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Open Journal of Thoracic Surgery 2014年第2期4卷 17-20页

作者： Hamoud Obied Asim Miari mohammed Alreshidan Fahad Alghofaili Mohammad Ibrahim abdulaziz Albaradai mohammed Koudieh Cardiac Surgery Department Prince Salman Heart Center Riyadh KSA

Objective: The purpose of this study was to evaluate the true incidence and the risk factors associated with carotid disease in the sitting of high risk patients undergoing coronary artery bypass graft (CABG) using carotid duplex scan and to find out if routine preoperative carotid duplex scan is needed among all these patients. Methods: This retrospective study included 402 consecutive patients who underwent bilateral carotid duplex scan admitted for CABG during the period from January 2006 to December 2008. We excluded patients in cardiogenic shock who were taken to operating room emergently. Results: The prevalence of associated risk factors showed diabetes mellitus recorded the highest (93.3%) whereas peripheral vascular disease the lowest (1.7%), hypertension (89.3%), dyslipidemia (72.6%), smoker (21.1%), left main disease (4.7%), and previous stroke (3%). Patients undergoing CABG has high incidence of carotid disease (68.7%) and severe stenosis is more in patients aged 60 and above (13.5%) versus (2.3%) in age 60, previous stroke and left main disease). Conclusion: This study showed that carotid screening is recommended for all patients who are undergoing CABG due to high incidence of carotid disease.

关键词： Carotid Artery Disease Coronary Artery Bypass Grafting Incidence Risk Factors Carotid Duplex Scan

Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion

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Computers, Materials & Continua 2022年第3期70卷 4691-4705页

作者： Ibrahim M.Mehedi Mohd Heidir Mohd Shah Soon Xin Ng Abdulah Jeza Aljohani mohammed El-Hajjar Muhammad Moinuddin Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Next Generation Wireless Research Group University of SouthamptonUK

This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion(AGDI)to track the position of a Linear Flexible Joint Cart(LFJC)system along with vibration suppression of the flexible *** proposed AGDI control law will be comprised of two control *** baseline(continuous)control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control *** control law is realized by inverting the prescribed dynamics using dynamically scaledMoore-Penrose generalized *** boost the robust attributes against system nonlinearities,parametric uncertainties and external perturbations,a discontinuous control law will be augmented which is based on the concept of sliding mode *** discontinuous control law,the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering *** closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is *** computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.

关键词： Adaptive control generalized dynamic inversion moore-penrose generalized inverse sliding mode control lyapunov stability semi-global asymptotic stability

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

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Computers, Materials & Continua 2022年第4期71卷 825-842页

作者： Mesfer Al Duhayyim Marwa Obayya Fahd N.Al-Wesabi Anwer Mustafa Hilal mohammed Rizwanullah Majdy M.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV ***-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different *** experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters.

关键词： Unmanned aerial vehicle 6G networks artificial intelligence energy efficiency clustering route planning data collection metaheuristics

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

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Computers, Materials & Continua 2022年第6期71卷 4871-4894页

作者： Yasmeen Al-Saeed Wael A.Gab-Allah Hassan Soliman Maysoon F.Abulkhair Wafaa M.Shalash mohammed Elmogy Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Faculty of Computers&Artificial Intelligence South Valley UniversityHurghada84511Egypt Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBanha13511Egypt

One of the leading causes of mortality worldwide is liver *** earlier the detection of hepatic tumors,the lower the mortality *** paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign *** hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors *** proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines *** performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and *** proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.

关键词： Liver tumor hepatic tumors diagnosis CT scans analysis liver segmentation tumor segmentation features extraction tumors classification FGFCM CAD system

Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

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Computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

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Computers, Materials & Continua 2022年第11期73卷 2967-2984页

作者： Gulzar Ahmed Tahir Alyas Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science University of South AsiaLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Handwritten character recognition systems are used in every field of life nowadays,including shopping malls,banks,educational institutes,*** is the national language of Pakistan,and it is the fourth spoken language in the ***,it is still challenging to recognize Urdu handwritten characters owing to their cursive *** paper presents a Convolutional Neural Networks(CNN)model to recognize Urdu handwritten alphabet recognition(UHAR)offline and online *** research contributes an Urdu handwritten dataset(aka UHDS)to empower future works in this *** offline systems,optical readers are used for extracting the alphabets,while diagonal-based extraction methods are implemented in online ***,our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text *** this end,we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline ***,we carried out detailed experiments for character recognition,as detailed in the *** proposed CNN model outperformed as compared to previously published approaches.

关键词： Urdu handwritten text recognition handwritten dataset convolutional neural network artificial intelligence machine learning deep learning

New Robust Energy Management Model for Interconnected Power Networks Using Petri Nets Approach

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Smart Grid and Renewable Energy 2016年第1期7卷 46-65页

作者： Mouhammad Alanfaf Mohamed Mladjao El Abbassi ikram Darcherif Abdel-Moumen El Ganaoui mohammed University of Lorraine IUT Henri Poincaré de Longwy LERMAB Longwy Nancy France ECAM-EPMI LR2E-Lab Cergy-Pontoise France ECAM-EPMI Quartz-Lab Cergy-Pontoise France

The problematic of energy management, particularly in terms of resources control and efficiency, has become in the space of a few years an eminently strategic subject. Its implementation is both complex and exciting as the prospects are promising, especially in relation with smart grids technologies. The deregulation of the electricity market, the high cost of storage, and the new laws on energy transition incite some significant users (collectivities, cities, regions, etc.) to form themselves into local producers in order to gain autonomy and reduce their energy bills. Thus, they may have their own sources (classic and/or renewable energy sources) to satisfy their needs and sell their excess production instead of storing it. In this idea, the territorial interconnection principle offers several advantages (energy efficiency, environmental protection, better economic balance). The main challenge of such systems is to ensure good energy management. Therefore, power distribution strategy must be implemented by matching the supply and the demand. Such systems have to be financially viable and environmentally sustainable. This allows among others to reduce the electricity bill and limit the systematic use of the national power network, typically using non-renewable sources, and thereby support sustainable development. This paper presents an original model for aid-decision in terms of grid configurations and control powers exchanged between interconnected territories. The model is based on Petri nets. Therefore, an iterative algorithm for power flow management is based on instantaneous gap between the production capability (photovoltaic, wind) and the demand of each user. So, in order to validate our model, we selected three French regions: the PACA region, the Champagne-Ardenne region and the Lorraine region. Due to their policy, their geographical and climatic features, we opted for two renewable sources: “wind” and “photovoltaic”. The numerical simulations are perform

关键词： Smart Grid Energy Management Interconnection Economic Analysis Pv System Wind Energy Conversion System (WECS)