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Finding a Practical IT Solution-Open Source Accounting Software

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通讯和计算机（中英文版） 2012年第4期9卷 406-413页

作者： manar abu talib Adel Khelifi Osama El-Temtamy Fatima Ismaeel Mahra Rashed Najah Hasan Summaya Khaled College of Information Technology Zayed University Abu Dhabi P.O. BOX4783 UAE Department of Software Engineering AI Hosn University Abu Dhabi P.O. BOX38772 UAE College of Business Zayed University Abu Dhabi P.O. BOX4783 UAE

Companies, both large and small, are currently emerging in the UAE, and an important step for these firms, and a challenging one, is choosing the right accounting software. Open-source software is gaining acceptance and has become a prominent software paradigm today, which, unlike proprietary off-the-shelf software with its restrictive copyright licenses, is available free of charge. A better understanding of the role of open-source accounting software will result in higher performance accounting and finance departments. With this kind of software, companies can access the source code, change it, and customize it to suit their needs, as well as redistribute it in either modified or unmodified form. There are some companies in the UAE using proprietary accounting software who are not aware of the availability of such an alternative. This research paper studies the state of art of accounting software used in the UAE, and then demonstrates how open-source accounting software could provide a valuable solution for the business sector.

关键词：会计软件开放源码 IT 小型企业开源软件阿联酋研究论文挑战性

Parallel power system restoration planning using heuristic initialization and discrete evolutionary programming

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Journal of Modern Power Systems and Clean Energy 2017年第6期5卷 991-1003页

作者： Dian Najihah abu talib Hazlie MOKHLIS Mohamad Sofian abu TALIP Kanendra NAIDU Department of Electrical Engineering University of Malaya50603 Kuala LumpurMalaysia Electrical Technology Section University of Kuala LumpurBritish Malaysian Institute53100 GombakMalaysia

This paper proposes a sectionalizing planning for parallel power system restoration after a complete system *** restoration is conducted in order to reduce the total restoration process *** and operation knowledge of the system,operating personnel experience,and computer simulation are combined in this planning to improve the system restoration and serve as a guidance for system operators/*** planning is obtained using discrete evolutionary programming optimization method assisted by heuristic initialization and graph theory *** of transmission lines that should not be restored during parallel restoration process(cut set)is determined in order to sectionalize the system into subsystems or *** island with almost similar restoration time is set as an objective function so as to speed up the resynchronization of the *** operation and constraints(black start generator availability,load-generation balance and maintaining acceptable voltage magnitude within each island)is also takeninto account in the course of this *** method is validated using the IEEE 39-bus and 118-bus *** results in terms of restoration time was compared to other methods reported in the literature.

关键词： Sectionalizing planning Parallel power system restoration Graph theory Heuristic initialization Discrete evolutionary programming(DEP)optimization method

Deep Consensus Network for Recycling Waste Detection in Smart Cities

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Computers, Materials & Continua 2023年第5期75卷 4191-4205页

作者： manar Ahmed Hamza Hanan Abdullah Mengash Noha Negm Radwa Marzouk Abdelwahed Motwakel abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278AlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia

Recently,urbanization becomes a major concern for developing as well as developed *** to the increased urbanization,one of the important challenging issues in smart cities is waste ***,automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste *** recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw *** study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection(DCNWORWOD)in Smart *** goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable *** proposed DCNWO-RWOD technique involves the design of deep consensus network(DCN)to detect waste objects in the input *** improving the overall object detection performance of the DCN model,the whale optimization algorithm(WOA)is ***,Na飗e Bayes(NB)classifier is used for the classification of detected waste objects into recyclable and non-recyclable *** performance validation of theDCNWO-RWOD technique takes place using the open access *** extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.

关键词： Smart city waste management object detection recycling deep consensus network

Identification and Classification of Crowd Activities

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Computers, Materials & Continua 2022年第7期72卷 815-832页

作者： manar Elshahawy Ahmed O.Aseeri Shaker El-Sappagh Hassan Soliman Mohammed Elmogy Mervat abu-Elkheir Department of Information Technology Faculty of Computers and InformationMansoura UniversityEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityEgypt Faculty of Computer Science and Engineering Galala University435611SuezEgypt Department of Computer Science Faculty of Media Engineering and TechnologyGerman UniversityEgypt

The identification and classification of collective people’s activities are gaining momentum as significant themes in machine learning,with many potential applications *** need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd *** paper investigates the capability of deep neural network(DNN)algorithms to achieve our carefully engineered pipeline for crowd *** includes three principal stages that cover crowd analysis ***,individual’s detection is represented using the You Only Look Once(YOLO)model for human detection and Kalman filter for multiple human tracking;Second,the density map and crowd counting of a certain location are generated using bounding boxes from a human detector;and Finally,in order to classify normal or abnormal crowds,individual activities are identified with pose *** proposed system successfully achieves designing an effective collective representation of the crowd given the individuals in addition to introducing a significant change of crowd in terms of activities *** results onMOT20 and SDHA datasets demonstrate that the proposed system is robust and *** framework achieves an improved performance of recognition and detection peoplewith a mean average precision of 99.0%,a real-time speed of 0.6ms non-maximumsuppression(NMS)per image for the SDHAdataset,and 95.3%mean average precision for MOT20 with 1.5ms NMS per image.

关键词： Crowd analysis individual detection You Only Look Once(YOLO) multiple object tracking kalman filter pose estimation

Using the Taguchi Method and Grey Relational Analysis to Optimize the Performance of a Solar Air Heater

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Energy Engineering 2021年第5期118卷 1425-1438页

作者： manar B.AL-Hajji Nabeel abu Shaban Shahnaz Al Khalil Ayat Al-Jarrah Department of Mechanical Engineering School of EngineeringThe University of JordanAmmanJordan Department of Mechanical Engineering Faculty of Engineering and Technology Al-Zaytonah University of JordanAmmanJordan Department of Mechatronics Engineering Faculty of EngineeringThe Hashemite UniversityZarqa’Jordan

Solar energy is regarded as one of the promising renewable energy sources in the *** main aim of this study is to use the Taguchi-Grey relational grade analysis to optimize the performance of two Solar Air Heaters(SAHs).A typical Grey–Taguchi method was *** Orthogonal Array,Signal-to-Noise ratio,Grey Relational Grade,and Analysis of Variance were employed to investigate the performance characteristics of *** observations were made in agreement with Jordanian climate 32°00′N latitude and 36°00′E longitude with a solar intensity of 500 W\m^(2).The operating factors selected for optimization are the tilt angle(T)with three levels(0°,22°,45°),inlet velocity(V)with two levels(1.2,1.8 m/s),and absorber plate material(M)with two levels(Aluminum,wood).In this study,the Grey–Taguchi approach is validated by performing 12 individual *** results show that the process factors sequence required for a maximum SAH efficiency(SAHµ)is V>T>*** this approach,we combined the Orthogonal Array design with Grey Relational *** a result of that,the level of each operating conditions which optimizes both process responses(Temperature difference,ΔT and Solar air heater efficiency,SAHµ)can be specified with a minimum number of tests compared with classic Grey Relational *** optimal operating conditions of a SAH for multiple performance characteristics are determined as T2,M2,and V2,respectively,which are in congruence with the experimental results.

关键词： Solar air heater collector efficiency thermal efficiency grey-taguchi method robust design

A Preliminary Genetic Investigation of Rastrelliger Kanagurta Based on Random Amplified Polymorphic DNA and Mitochondrial ND2 Gene

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湿地科学 2008年第4期6卷 518-525页

作者： Siti Azizah Mohd Nor abu talib A Mohd Ghows M A Samsudin B Aquaculture Research Group School of Biological Sciences University Sains Malaysia 11800 Minden Penang Malaysia Fisheries Research Institute 11900 Batu Maung Penang Malaysia

In a preliminary investigation, Random Amplified Polymorphic DNA (RAPD) analysis and partial mitochondrial ND2 gene sequencing were conducted to study the genetic variation of the Indian mackerel, Rastrelliger kanagurta along a 450 km stretch of its distribution on the west coast of Peninsular Malaysia. A total of 53 individuals from 6 populations were analyzed using 4 RAPD primers and a sub-sample of 15 individuals was chosen for sequencing of partial ND2 gene. Comparison between the 2 markers revealed genetic structuring in the RAPD results but genetic homogeneity for ND2 gene. Based on the former there may be at least 2 genetically differentiated groups of Rastrelliger kanagurta along this stretch.

关键词：基因 DNA分析分析方法生物技术水生生物

An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

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Computers, Materials & Continua 2022年第4期71卷 1233-1247页

作者： Mesfer Al Duhayyim Radwa Marzouk Fahd N.Al-Wesabi Maram Alrajhi manar Ahmed Hamza abu Sarwar Zamani Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza 12613Egypt Department of Computer Science Faculty of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems Faculty of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and *** mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the *** the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational ***,artificial intelligence(AI)techniques can be used for mining a large amount of *** the same time,in DM,the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization *** this motivation,this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification(IEAFSS-NFC)for data mining in the education *** presented IEAFSS-NFC model involves data pre-processing,feature selection,and ***,the Chaotic Whale Optimization Algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification ***,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education *** IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI *** simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.

关键词： Educational data mining feature selection whale optimization classification

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

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Computer Systems Science & Engineering 2023年第4期45卷 737-751页

作者： Saud S.Alotaibi Eatedal Alabdulkreem Sami Althahabi manar Ahmed Hamza Mohammed Rizwanullah abu Sarwar Zamani Abdelwahed Motwakel Radwa Marzouk Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social ***,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process *** OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into ***,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification ***,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM *** major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual *** addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction ***,BLSTM model is employed for the effectual detection and classification of ***,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

关键词： Sentiment analysis opinion mining natural language processing artificial fish swarm algorithm deep learning

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

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Computers, Materials & Continua 2022年第12期73卷 6563-6577页

作者： manar Ahmed Hamza Hadeel Alsolai Jaber S.Alzahrani Mohammad Alamgeer Mohamed Mahmoud Sayed abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel 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 UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariDistrict JhnujhunuRajasthanIndia Department of Architectural Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Intelligent Transportation System(ITS)is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic *** the help of big data and communication technologies,ITS offers real-time investigation and highly-effective traffic *** Flow Prediction(TFP)is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past *** Network(NN)and Machine Learning(ML)models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of *** Learning(DL)is a kind of ML technique which yields effective performance on data classification and prediction *** this motivation,the current study introduces a novel Slime Mould Optimization(SMO)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(SMOBGRU-TP)in smart ***,data preprocessing is performed to normalize the input data in the range of[0,1]using minmax normalization ***,BiGRUmodel is employed for effective forecasting of traffic in smart ***,the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU *** proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

关键词： Smart cities traffic flow prediction slime mould optimization algorithm deep learning intelligent models