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The role of e-readiness assessment criteria in accepting e-commerce for small and medium size enterprisesmDevelopment of technology acceptance model

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Chinese Business Review 2010年第2期9卷 21-26页

作者： nour-Mohammad Yaghoubi Roya Shakeri Department of Management School of Management and Accounting University of Sistan and Baluchestan Zahedan *** Iran

This paper develops Davis technology acceptance model by adding two criteria of e-readiness assessment. For small and medium size enterprises, the paper evaluates the credit of developed model in studying the efficient factors in internet technology acceptance, which is one of the most applicable communication tools in e-commerce. The added factors, taken from models and means of e-readiness assessment, are ＂existing information technology infrastructures＂ as well as ＂organization＇s training＂. The authors study 45 small and medium size enterprises in the city of Tehran, which are active in information technology products and services. Using Lisrel structural equation modelling, it is shown that there is a meaningful relationship between the added factors and the variables indicating internet network usage. Therefore, the better factors of e-readiness assessment lead to the higher acceptance level of electronic communication tools in e-commerce.

关键词： information technology e-readiness e-commerce technology acceptance small and medium size enterprises

Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

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Computers, Materials & Continua 2022年第11期73卷 3367-3382页

作者： Hadil Shaiba Radwa Marzouk Mohamed K nour Noha Negm Anwer Mustafa Hilal Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the ***,weather constitutes a key role in all regular human *** forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from ***,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather *** research works have been conducted on weather *** drawbacks of existing approaches are that they are less effective,inaccurate,and *** overcome these issues,this paper proposes an enhanced and reliable weather forecasting *** well as developing weather forecasting in remote *** data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather *** aim of this study is to demonstrate its ability to predict weather forecasts as soon as *** evaluation shows our ensemble technique achieves 95%prediction ***,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.

关键词： Weather forecasting KNN random forest gradient boosting decision tree naive bayes bernoulli

Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

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Computers, Materials & Continua 2022年第11期73卷 4123-4138页

作者： Areej A.Malibari Marwa Obayya Mohamed K.nour Amal S.Mehanna Manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease *** cancer is the most widely affected cancer worldwide,with an increased death rate *** to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast *** detection of breast cancer will reduce the death rate *** early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive *** paper proposed an efficient and optimized deep learning-based feature selection approach with this *** model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm *** learning is used in the extraction of features *** ext,a convolution neural network,is used to extract the *** two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant *** optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant *** feature extraction and classification process used two datasets,breast,and *** to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAS dataset.

关键词： Breast cancer detection computer-aided diagnosis(CAD) deep learning CNN entropy butterfly optimization

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

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Computers, Materials & Continua 2022年第9期72卷 5499-5513页

作者： Masoud Alajmi Mohamed K.nour Siwar Ben Haj Hassine Mimouna Abdullah Alkhonaini Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical *** greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of *** security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion *** EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion ***,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)*** extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying *** simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

关键词： Intrusion detection system metaheuristics stacked autoencoder deep learning cyber physical energy systems clustering wsn

Relations between Low-Frequency Modes of Climate Variability and Air-Sea Heat Flux at the Mediterranean Interface

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Journal of Earth Science and Engineering 2014年第2期4卷 101-106页

作者： Lamri Nacef nour El Islam Bachari Department of Ecology and Environment Faculty of Biological Sciences Houari Boumediene University of Science and TechnologyAlgiers 16111 Algeria

The main objective of this work is to examine statistical causality relationships between low-frequency modes of climate variability and winter （December to February） anomaly of net heat flux at the Mediterranean air-sea interface. The introduction of the concept of Granger causality allowed us to examine the influence of these climates indices on the net heat flux anomaly and to select Mediterranean surface regions that really influenced by each index. Results show that the winter anomaly of the net heat flux in the Algerian basin south and the gulf of Lion is mainly caused by the Arctic Oscillation. El Nifio-Southern Oscillation influences much more the Algerian basin north and the northern lonian Sea. The Quasi-Biennial Oscillation affects only the Alboran and the Tyrrhenian Seas. But the Adriatic and Levantine basin are impacted by any climate index. They also show that these climate indices can increase explained variance in winter variations of air-sea net heat flux by 10% to 15%, with a lag of three seasons. These relationships are less persistent and spatially limited.

关键词： Mediterranean Sea winter net heat flux air-sea interface climate index causality analysis.

How to Protect Intellectual Property of Foreign Companies in China: Trademark as A Sample

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How to Protect Intellectual Property of Foreign Companies in...

作者：林光明（CHAMKHI nour HOUDA）北京邮电大学

学位级别：硕士

现在,中国是世界上发展最快的市场之一,当然,这种巨大的增长潜力吸引了许多外国公司在中国做生意。这种跨国合作对发展中的市场非常有益,但同时也显现出自己的问题,如知识产权被盗用。因为产权保护方面的漏洞,跨国企业常常遇到产权问题...

现在,中国是世界上发展最快的市场之一,当然,这种巨大的增长潜力吸引了许多外国公司在中国做生意。这种跨国合作对发展中的市场非常有益,但同时也显现出自己的问题,如知识产权被盗用。因为产权保护方面的漏洞,跨国企业常常遇到产权问题,法律术语为:知识产权被侵犯。由于知识产权被侵犯,中国企业成为外资企业的一大顾虑,尤其是商标保护方面。本文的主要目的是介绍公司知识产权保护方法,主要是商标保护。因为中国拥有世界上四分之一的消费者,所以外国品牌的持有者必须小心地掌握如何在该国做生意并取得成功。本文的出发点在于假设在中国没有安全的商标秘密措施。本文拟从中国法律体系的角度分析这种现象的成因,通过回顾中国的知识产权保护法,作者认为中国这方面的法律和西方的法律相当一致,之所以知识产权被盗用主要是因为商标持有者知法犯法,以及相关合同的不严谨。同时,通过近几年发生在中国侵权行为的例子,作者集中讨论商标保护的重要性。通过分析这些例子,作者介绍了在华外资企业常见的几种商标保护方面的问题。同时,文章对司法保护商标的差异和文化差异进行了阐述。每个国家都有其特定的文化,中国的历史悠久,国人有自己的思维定势,商业也受其影响。外国人应该看整体形象,不忽视中国文化。本论文从合同的签订和激励员工这两方面为商标侵权提供解决方案。商标所有者在中国保护商标所面临的困难是多样的,这要求持有者改进保护措施,需要政府、司法系统和商标所有者之间的牢固合作。作者采用经济分析的方式,同时结合法律和经济学知识提出了一些解决方案。研究表明,尽管中国政府尽最大的努力通过改革法律法规和发展中国的商标保护工作,但很大部分侵权行为可以说是:知识产权人缺乏预防性保护,他们自己如果会理解中国的经济,文化和法律制度,那么这些侵权行为呢可能会比较少见。

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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

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Computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

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Computers, Materials & Continua 2022年第11期73卷 3905-3920页

作者： Radwa Marzouk Eatedal Alabdulkreem Sami Dhahbi Mohamed K.nour Mesfer Al Duhayyim Mahmoud Othman Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi 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 UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Oral cancer is the most commonly occurring‘head and neck cancers’across the *** of the oral cancer cases are diagnosed at later stages due to absence of awareness among *** earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this *** this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing *** proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data *** by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep ***,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and ***,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several *** extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.

关键词： Oral cancer detection image classification artificial intelligence machine learning deep learning

Intelligent Deep Convolutional Neural Network Based Object DetectionModel for Visually Challenged People

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Computer Systems Science & Engineering 2023年第9期46卷 3191-3207页

作者： S.Kiruthika Devi Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mohamed K.nour Ahmed Ashour Anwer Mustafa Hilal Department of Computing Technology SRM Institute of Science and TechnologyIndia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Engineering Mathematics and Physics Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)and Computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged *** detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so *** test images include all these variants as *** detection models alert them about their surroundings when they want to walk *** study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition *** on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and *** further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the *** accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization ***,Inception V3 can handle images of different *** makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

关键词： Pre-trained models object detection visually challenged people deep learning Inception V3 DenseNet-121