Univrsity Of Tehran PressJournal of Information Technology Management2980-797214420220701Guest editorial: Industry Internet of Things (IIoT) with security compliances, concerns, and application areas1188137ENAhmed A.ElngarFaculty of Computer & Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt; College of Computer Information Technology, American University in the Emirates, United Arab Emirates.0000-0001-6124-7152A B. K.MishraProfessor, Thakur college of Engineering and Technology, Kandivali (East), Mumbai.HemantKasturiwaleAssociate Professor, Thakur college of Engineering and Technology, Kandivali (East), Mumbai.Journal Article20220713Internet of Things (IoT) makes our reality as conceivable as associated together. These days we nearly have a web framework in almost every place and we can utilize it at any point. Installed embedded gadgets would be exposed to web impact. Regular cases for installed embedded gadgets are MP3 players, MRI, traffic signals, microwaves, clothes washers and dishwashers, GPS, and even heart checking inserts or biochips and so on. IoT tries to set up cutting-edge availability (with the guide of the web) among these referenced gadgets or frameworks or administrations to gradually make automation in all regions. It is possible to picture that all things are associated with each other, and all data would be linked to one another over a standard and distinctive convention space and applications. The Industrial Internet of Things (IIoT) or the fourth Industrial insurgency or Industry 4.0 are generally names given to the utilization of IoT innovation in a business setting. The idea is equivalent to implementing IoT gadgets in the home, yet for this situation, the point is to utilize a combination of sensors, remote systems, enormous information, AI, and investigation to quantify and streamline modern procedures. Instead of simply singular organizations, if presented over a whole supply chain, the effect could be significantly more noteworthy with quick conveyance of materials and the administration of creation from start to finish. Expanding workforce efficiency or cost reserve funds are two expected points, yet the IIoT can likewise make new income streams for organizations instead of simply selling an independent item – for instance, like a motor – makers can likewise sell predictive service of the motor.https://jitm.ut.ac.ir/article_88137_fdbd0c82fb3cffd83c8dd0ebd5249992.pdfUnivrsity Of Tehran PressJournal of Information Technology Management2980-797214420220701Breast Cancer Detection based on 3-D Mammography Images using Deep Learning Strategies2188813210.22059/jitm.2022.88132ENK. MartinSagayamDepartment of ECE, Karunya Institute of Technology and Sciences, Coimbatore - 641114, IndiaA. AmirAnton JoneDepartment of ECE, Karunya Institute of Technology and Sciences, Coimbatore - 641114, IndiaKorhanCengizCollege of Information Technology, University of Fujaiah, UAE.L.RajeshDepartment of Electronics Engineering, Madras Institute of Technology, Anna University, Chennai.Ahmed A.ElngarFaculty of Computer & Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt; College of Computer Information Technology, American University in the Emirates, United Arab Emirates.0000-0001-6124-7152Journal Article20220712In recent scenario, women are suffering from breast cancer disease across the world. Mammography is one of the important methods to detect breast cancer early; that to reduce the cost and workload of radiologists. Medical image processing is a tremendous technique used to determine the disease in advance to reduce the risk factor. To predict the disease from 2-D mammography images for diagnosing and detecting based on advanced soft computing paradigm. Still, to get more accuracy in all coordinate axes, 3-D mammography imaging is used to capture depth information from all different angles. After the reconstruction of this process, a better quality of 3D mammography is obtained. It is useful for the experts to identify the disease in well advance. To improve the accuracy of disease findings, deep convolution neural networks (CNN) can be applied for automatic feature learning, and classifier building. This work also presents a comparison of the other state of art methods used in the last decades.https://jitm.ut.ac.ir/article_88132_b5fa3520da1d317b567f63050316231e.pdfUnivrsity Of Tehran PressJournal of Information Technology Management2980-797214420220701Android Malware Category and Family Identification Using Parallel Machine Learning19398813310.22059/jitm.2022.88133ENAhmedHashem El FikyM.Sc. in Systems and Computers Engineering, Department of Systems and Computers Engineering, Faculty of Engineering Al-Azhar University, Cairo, Egypt.Mohamed AshrafMadkourProfessor, Department of Systems and Computers Engineering, Faculty of Engineering Al-Azhar University, Cairo, Egypt.AymanEl ShenawyAssistant Professor, Department of Systems and Computers Engineering, Faculty of Engineering Al-Azhar University, Cairo, Egypt; Software Engineering and Information Technology, Faculty of Engineering and technology, Egyptian Chinese University, Cairo, Egypt.Journal Article20220713Android malware is one of the most dangerous threats on the Internet. It has been on the rise for several years. As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc. One of the most growing attacks is on Android systems due to its use in many devices worldwide. De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance. There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families. Hence, understand the distinct ob-jective of malware after identifying their family and category. This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families. Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families. The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work. The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods. The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware.https://jitm.ut.ac.ir/article_88133_16d42429ea8c150b3d16ef50fe0a21d7.pdfUnivrsity Of Tehran PressJournal of Information Technology Management2980-797214420220701Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition40568813410.22059/jitm.2022.88134ENOmar IbrahimObaidDepartment of Computer Science, College of Education, AL-Iraqia University, Baghdad, Iraq.Mazin AbedMohammedPh.D., College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, IraqAkbal OmranSalmanElectrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Salama A.MostafaFaculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, 86400, Malaysia.Ahmed A.ElngarFaculty of Computer & Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt; College of Computer Information Technology, American University in the Emirates, United Arab Emirates0000-0001-6124-7152Journal Article20220713The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it extracts more objects, so the rise of execution time is sensible. Whereas small size and high percentage probability makes SqueezeNet suitable for portable applications, while reusing features makes DenseNet suitable for applications for object identification.https://jitm.ut.ac.ir/article_88134_0413b598ec2be4d705c24fcab5e0d253.pdfUnivrsity Of Tehran PressJournal of Information Technology Management2980-797214420220701Design, Realization and Measurements of Printed Patch Antenna with Circular Slots for UWB and IoT Applications57688813510.22059/jitm.2022.88135ENAbdelhakimMoutaouakilModelisation of Complex Systems Laboratory, Cadi Ayyad University, Marrakech, MoroccoYounesJabraneModelisation of Complex Systems Laboratory, Cadi Ayyad University, Marrakech, Morocco.AbdelatiRehaLaboratory of Innovation in Management and Engineering for the Enterprise, ISGAM, Morocco.AbdelazizKouminaLIRBEM, Cadi Ayyad University, Ecole Normale Supérieure, Marrakech, Morocco.Journal Article20220713This paper presents the design, simulation and realization of a patch antenna for IOT applications. The patch antenna consists of a radiating element printed on one face of a dielectric substrate, when the ground plane is placed on the other face. In this work, two techniques are used to design a miniaturized patch antenna: the set-up of slots on the radiating element and the use of defective ground plane. Also, the slot’s radius and Length of inset point effects on the performances of the antenna is illustrated. All the simulated results are performed with FEKO, a solver based on a Moments Method and measurement is made using Vector Network Analyzer Anritsu MS2026C. The propose antenna resonates in three frequency bands 3.91, 4.86 and 5.16GHz for different characteristics such as radiation pattern, gain, return loss, which makes it suitable for many wireless communication applications such as IoT applications.https://jitm.ut.ac.ir/article_88135_4f28b8b33fc6da90cf168de2959fb8e7.pdfUnivrsity Of Tehran PressJournal of Information Technology Management2980-797214420220701AI-WSN: Direction of Arrival Estimation Based on Bee Swarm Optimization for Wireless Sensor Networks69868813610.22059/jitm.2022.88136ENDevikaEResearch Scholar, Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, India-642 107.SaravananAAsssociate Professor, Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, India-642 107.Journal Article20220713An Artificial Intelligence (AI) technique plays the most crucial factor to consider in energy utilization in a wireless sensor network (WSN). AI transforms industrial operations by optimizing the energy consumption in sensor nodes. As a result, it is crucial for improving sensor node location accuracy, particularly in unbalanced or Adhoc environments. Because of this, the purpose of this research is to improve the accuracy of the localization process in locations where sensor nodes encounter barriers or obstacles on a regular basis. The Bees Swarm Optimization (BSO) algorithm is used to segment sensor nodes in order to increase the accuracy of the Direction of Arrival (DoA) estimate between the anchor and unknown node pairs. Even in the presence of unbalanced conditions, the proposed DoA- BSO involving three separate bee colonies can identify plausible anchor nodes as well as segment nodes arranged in clusters. In order to obtain the intended result, the objective function is designed to take into consideration the hops, energy, and transmission distance of the anchor and unknown node pairs, among other factors. The studies are carried out in a large-scale WSN using sensor node pairs in order to determine the precision with which the DoA-BSO can be located. When comparing DoA-BSO to conventional approaches, the findings of the meta-heuristic algorithm show that it improves the accuracy and segmentation of nodes significantlyhttps://jitm.ut.ac.ir/article_88136_cc1df04d507c28d10f9cfb6b824ec9ae.pdfUnivrsity Of Tehran PressJournal of Information Technology Management2980-797214420220701Comparison between the Conventional Partial Least Squares (Pls) and the Robust Partial Least Squares (Rpls-Sem) Through Winsorization Approach87948829110.22059/jitm.2022.88291ENGholamRezaZandiAssociate Professor, Universiti Kuala Lumpur (UniKL) Business School, Malaysia.Fadya RamadanShakhimDepartment of Statistics, Faculty of Science, Al-Zawiya University, Al-Zawiya, Libya.ZulkifleyMohamedDepartment of Mathematics, Faculty of Science and Mathematics, University Pendidikan Sultan Idris 35900 Tanjong Malim, Perak, Malaysia.Amel SaadAlshargawiDepartment of Statistics, Faculty of Science, Tripoli University, Tripoli, Libya.Journal Article20220727This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used in this model were based on the electricity generation data, derived from the Al-Zawiya Steam Power Plant, Libya. Furthermore, the researchers compared the novel RPLS-SEM approach with the traditional PLS-SEM approach and noted that the novel RPLS-SEM method was more efficient compared to PLS-SEM.https://jitm.ut.ac.ir/article_88291_16549540c74b2481382a2cd0eaa691ea.pdfUnivrsity Of Tehran PressJournal of Information Technology Management2980-797214420220701Factors Effecting the Adoption of E-Learning: An Empirical Study of Libyan Universities951178829210.22059/jitm.2022.88292ENGholamRezaZandiAssociate Professor, Universiti Kuala Lumpur (UniKL) Business School, Malaysia.Husam A. E.LahrashDepartment of Data Analysis, Faculty of Economics, University of Zawia, Libya.Fadya RamadanShakhimDepartment of Statistics, Faculty of Science, Al-Zawiya University, Al-Zawiya, Libya.Journal Article20220727The main aim of this thesis is to investigate the factors that could affect the students to adopt e-learning system in Libyan universities. This study is quantitative approach, a questionnaire was adopted from previous studies and distributed among the students to collect the data. The sample of the study consists 365 students from Libya. AMOS software was used to analysis the data. The results indicated that Performance Expectancy, Efforts Expectancy, Facilitating Conditions, Habit and Trust have significant impact on behavioural intention. Moreover, the relationship between Behavioural intention and use behaviour is also significantly positive. However, the relationship of social influence and behavioural intention was not found significant. Finally, the moderation effect was significant and supported between social influence, trust, and Habit with behavioural intention.https://jitm.ut.ac.ir/article_88292_2385e9680bd9003e4c3d494058166a72.pdfUnivrsity Of Tehran PressJournal of Information Technology Management2980-797214420220701The relationship between Gamification and Sustainability of small and medium enterprise: Explaining the role of digital transformation in open innovation and value co-creation1181378880510.22059/jitm.2022.340460.3045ENAmir MohammadColabiAssistant Professor, Department of Business Management, Faculty of Management and Economic, Tarbiat Modares University, Tehran, Iran.FatemehSharaeiMaster of Business Management, Department of Business Management, Faculty of Management and Economic, Tarbiat Modares University, Tehran, Iran.0000-0001-5265-870XSaharAlipourMaster of Business Management, Department of Business Management, Faculty of Management and Economic, Tarbiat Modares University, Tehran, Iran.0000-0002-5778-2483Journal Article20220313Accelerating digital transformation and the basic needs of businesses to adapt to environmental transformations and complexities have doubled the necessity for extensive stakeholder interactions. Gamification is a powerful tool that facilitates stakeholder interaction and a common understanding of the vision and contributes to the sustainability of businesses. Businesses' sustainability in the digital transformation age depends on the continuous interaction of stakeholders and a comprehensive understanding of all the business pillars, which will be possible through the flow of ideas inside and outside the workplace and by providing innovative processes. In this regard, this study explores the effect of gamification on corporate sustainability by explaining the role of digital transformation, open innovation, and value co-creation. The statistical population of this study comprises top managers and experts in e-businesses who use gamification in their processes. The statistical sample included 117 managers and experts active in this field, and they were selected through convenience sampling. The data collection tool was a questionnaire reliability of which was 0.763 using Cronbach's alpha. Using Smart PLS software, the gathered data were analyzed by structural equation modeling (SEM). Gamification, with a factor of 74%, has a positive effect on open innovation, while a factor of 85% has a positive effect on digital transformation. Gamification, both directly and indirectly, creates value co-creation in businesses, and finally, value co-creation at 78% affects corporate sustainability.https://jitm.ut.ac.ir/article_88805_b531378d58aa0885c6e4ea0a0bb012eb.pdf