ORIGINAL_ARTICLE
Exploring Relevance as Truth Criterion on the Web and Classifying Claims in Belief Levels
The Web has become the most important information source for most of us. Unfortunately, there is no guarantee for the correctness of information on the Web. Moreover, different websites often provide conflicting information on a subject. Several truth discovery methods have been proposed for various scenarios, and they have been successfully applied in diverse application domains. In this paper, we have attempted to answer the question whether the truth is relevant. We conducted an experimental study in which we analyzed and compared the results of two different truth discovery methods: Relevance-based sources ranking and Majority vote. We have found that the truth is not always held by the most relevant sources on the web. Sometimes the truth is given by the majority vote of the crowd. In addition, we have proposed a method of presenting the results of truth discovery with gradual degrees of belief. A method that allows to configure and target the desired level of trust.
https://jitm.ut.ac.ir/article_75786_6f3581260c5de5bf2e7865e39a0f9591.pdf
2020-06-01
1
12
10.22059/jitm.2020.75786
Truth discovery
Source ranking
Relevance
Uncertainty
Belief degree
Fairouz
Zendaoui
f_zendaoui@esi.dz
1
Laboratoire de la Communication dans les Systèmes Informatiques, Ecole Nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Alger, Algérie.
LEAD_AUTHOR
Walid Khaled
Hidouci
w_hidouci@esi.dz
2
Laboratoire de la Communication dans les Systèmes Informatiques, Ecole Nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Alger, Algérie.
AUTHOR
Al-Araji, Z. J., Ahmad, S. S. S., Al-Lamy, H. A., Al-Salihi, M. W., Al-Shami, S. A., Mohammed, H., & Al-Taweel, M. H. (2019). Truth Discovery Using the TrustChecker Algorithm on Online Quran Tafseer. In Intelligent and Interactive Computing (pp. 71-80). Springer, Singapore.
1
Dong, X. L., Berti-Equille, L., & Srivastava, D. (2009). Integrating conflicting data: the role of source dependence. Proceedings of the VLDB Endowment, 2(1), 550-561.
2
Dong, X. L., Saha, B., & Srivastava, D. (2012). Less is more: Selecting sources wisely for integration. Proceedings of the VLDB Endowment, 6(2), 37-48.
3
Gurjar, K., & Moon, Y. S. (2016). Comparative Study of Evaluating the Trustworthiness of Data Based on Data Provenance. Journal of Information Processing Systems, 12(2).
4
Jung, W., Kim, Y., & Shim, K. (2019). Crowdsourced Truth Discovery in the Presence of Hierarchies for Knowledge Fusion. arXiv preprint arXiv:1904.10217.
5
Li, Q., Li, Y., Gao, J., Zhao, B., Fan, W., & Han, J. (2014, June). Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data (pp. 1187-1198).
6
Li, X., Dong, X. L., Lyons, K., Meng, W., & Srivastava, D. (2015). Truth finding on the deep web: Is the problem solved? arXiv preprint arXiv:1503.00303.
7
Li, Y., Gao, J., Meng, C., Li, Q., Su, L., Zhao, B., ... & Han, J. (2016). A survey on truth discovery. ACM Sigkdd Explorations Newsletter, 17(2), 1-16.
8
Pasternack, J., & Roth, D. (2010, August). Knowing what to believe (when you already know something). In Proceedings of the 23rd International Conference on Computational Linguistics (pp. 877-885). Association for Computational Linguistics.
9
Roa-Valverde, A. J., & Sicilia, M. A. (2014). A survey of approaches for ranking on the web of data. Information Retrieval, 17(4), 295-325.
10
Yin, X., & Tan, W. (2011, March). Semi-supervised truth discovery. In Proceedings of the 20th international conference on World wide web (pp. 217-226).
11
Yin, X., Han, J., & Philip, S. Y. (2008). Truth discovery with multiple conflicting information providers on the web. IEEE Transactions on Knowledge and Data Engineering, 20(6), 796-808.
12
Zendaoui, F., & Hidouci, W. K. (2019a)
13
ORIGINAL_ARTICLE
Classification of Brain Tumor by Combination of Pre-Trained VGG16 CNN
In recent years, brain tumors become the leading cause of death in the world. Detection and rapid classification of this tumor are very important and may indicate the likely diagnosis and treatment strategy. In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). The scope of this research is the use of gray level of co-occurrence matrix (GLCM) features images and the original images as inputs to CNNs. Two GLCM features images are used (contrast and energy image). Our experiments show that the original image with energy image as input has better distinguishing features than other input combinations; accuracy can achieve average of 96.5% which is higher than accuracy in state-of-the-art classifiers.
https://jitm.ut.ac.ir/article_75788_e36c948ee9258c82b9398f136692f3f5.pdf
2020-06-01
13
25
10.22059/jitm.2020.75788
Brain tumor
Deep learning
VGG16 CNN
GLCM features
Ouiza Nait
Belaid
o_naitbelaid@esi.dz
1
Laboratoire de la Communication dans les Systèmes Informatiques, Ecole Nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Alger, Algérie.
LEAD_AUTHOR
Malik
Loudini
m_loudini@esi.dz
2
Laboratoire de la Communication dans les Systèmes Informatiques (LCSI), École Nationale Supérieure d’Informatique (ESI), BP 68M, 16309, Oued-Smar, Alger, Algérie.
AUTHOR
Abiwinanda, N., Hanif, M., Hesaputra, S.T., Handayani, A., & Mengko, T.R. (2019). Brain tumor classification using convolutional neural network. In: World Congress on Medical Physics and Biomedical Engineering 2018. Springer, (pp. 183–189).
1
Beyer, M.H. (2007). The GLCM Tutorial Home Page, [Online]. Available: http://www.fp.ucalgary.ca/mhallbey, February, 2007 [Accessed: May 20,2014].
2
Cheng, J. (2017). Brain tumor dataset. Distributed by Figshare. [Online]. Available: https://figshare.com/articles/brain_tumor_dataset/1512427/5.
3
Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., &Feng, Q. (2015). Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS One 10, e0140381. doi: 10.1371/journal.pone.0140381.
4
Cheng, J., Yang, W., Huang, M., Huang, W., Jiang, J., Zhou, Y., Yang, R., Zhao, J., Feng, Y., Feng, Q., & Chen, W. (2016). Retrieval of brain tumors by adaptive spatial pooling and Fisher vector representation. PLoS One 11, e0157112. doi:10.1371/journal.pone.0157112.
5
Chollet, F., & al. (2015). Keras. https ://githu b.com/keras -team/keras
6
DiPietro, Rob. (2016). A Friendly Introduction to Cross-Entropy Loss. Retrieved from https://rdipietro.github.io/ friendly-intro-to-cross-entropy-loss/.
7
Haralick, R. M., Shanmugam, K.& al. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, (6):610–621.
8
Javed, U., Riaz, M.M., Ghafoor, A., & Cheema, T.A. (2013). MRI brain classification using texture features, fuzzy weighting and support vector machine. Prog.Electromagn. Res. B 53, 73–88.
9
Jiang, J., Wu, Y., Huang, M., Yang, W., Chen, W., & Feng, Q. (2013). 3D brain tumor segmentation in multimodal MR images based on learning population-and patient-specific feature sets. Comput.Med. Imaging Graph, 37, 512–521.
10
John, P. (2012). Brain Tumor Classification Using Wavelet and Texture Based Neural Network. Int J Sci Eng Res, 3: 85–90.
11
Khan Swati, Z. N., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., & Lu, J. (2019). Brain tumor classification for MR images using transfer learning and fine-tuning. Computerized Medical Imaging and Graphics, 0895-6111/2019, Elsevier
12
Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. (pp 1097–1105)
13
Pan, S.J., & Yang, Q. (2010). A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359.
14
Paul, J.S., Plassard, A.J., Landman, B.A., & Fabbri, D. (2017). Deep learning for brain tumor classification. medical imaging 2017: biomedical applications in molecular, structural, and functional imaging. International Society for Optics and Photonics. p.1013710.
15
Selvaraj, H., Selvi, ST., Selvathi, D., & Gewali, L. (2007). Brain MRI Slices Classification Using Least Squares Support Vector Machine. Int J Intell Comput Med Sci Image Process, 1: 21–33.
16
Simonyan, K., & Zisserman, K. (2014). Very deep convolutional networks for large-scale image recognition. CoRR, vol. abs/1409.1556.
17
ORIGINAL_ARTICLE
Online Buying Behavior among University Students: A Cross Cultural Empirical Analysis
Internet users all over the world are increasing day by day and showing great interest for online shopping. The main reason for the high usage of the internet is the affordable price of mobile gadgets and low internet tariff plans. Consumer behavior is influenced by various factors such as culture, social class, reference groups relationship, family, income level and income independency, age, gender, etc. The purpose of this study was to find out the differences in buying behaviors among the university students. The study was carried out using google survey with a sample size of 236 students randomly selected from the university of India and Saudi Arabia. The study shows that University students of both countries have more online shopping experience because they use the internet more frequently and they have larger internet usage. Nowadays Students are more computer professionals and those who use the internet for their study work and assignments work are more active in online shopping. As per student’s opinion, they said few things remember in our mind when coming to payment options credit card is the safest option or using online services like pay pal and google wallet services also a good way but finally, cash on delivery is the best way of shopping online. The result of this study would contribute marketers who want to penetrate the market in India and in Kingdom of Saudi Arab, who are already present in the market and desire to take care of the loyalty and satisfaction of their customers.
https://jitm.ut.ac.ir/article_75789_12fa952daa829400044dc7a0762ba88b.pdf
2020-06-01
26
39
10.22059/jitm.2020.75789
Consumer behavior
Online buying behavior
Loyalty
Customer Satisfaction
Internet
Maqsood Hussain
Junaidi
mhjunnaidi@uj.edu.sa
1
Assistant Professor, Department of Supply Chain Management, College of Business, University of Jeddah, Jeddah, Kingdom of Saudi Arab.
LEAD_AUTHOR
Mohammad Saleh
Miralam
mmiralam@uj.edu.sa
2
Associate Professor, Vice Dean of Post Graduate Studies and Scientific Research, Department of Business Administration College of Business, University of Jeddah, Jeddah, Kingdom of Saudi Arab.
AUTHOR
Al Karim, R. (2013). Customer Satisfaction in Online Shopping: a study into the reasons for motivations and inhibitions. IOSR Journal of Business and Management (IOSR-JBM),
1
Al-Debei, M. M., Akroush, M. N., & Ashouri, M. I. (2015). Consumer attitudes towards online shopping. Internet Research, 25(5), 707–733.
2
Al-Salamin, H. & Al-Hammad, A. (2014). Attitude of Saudi Consumers Towards Online Shopping with Special Reference to Al-Hassa Region (KSA). Journal of WEI Business and Economics, 3/3, 39-56.
3
Ansari, Z.A. (2016). Online Shopping Behaviour in Saudi Arabia – An Empirical Study. International Journal of Advanced Research, 4/5, 689-697.
4
Ansari. (2016). Online shopping behavior in Saudi Arabia, An International Journal of Advanced Research, vol. 4(5), pp. 689-697.
5
Bourlakis, M., Papagiannidis, S. and Fox, H. (2008). E-consumer behavior: Past, present and future trajectories of an evolving retail revolution, International Journal of E-Business Research, 2008, vol. 4, no. 3, pp.64-67, 69, 71-76.
6
Dai, B., Forsythe, S., & Kwon, W.S. (2014). The Impact of Online Shopping Experience on Risk Perceptions and Online Purchase Intentions: Does Product Category Matter?. Journal of Electronic Commerce Research, 15(11), 13-24.
7
Debois, S. (2016) Advantages and Disadvantages of Questionnaires - Survey Anyplace.
8
Demangeot, C., & Broderick, A. J. (2010). Consumer perceptions of online shopping environments. Psychology & Marketing, 30(6), 461–469.
9
Dentzel, Z. (2017). How the Internet Has Changed Everyday Life – Open Mind.Euromonitor International. Internet Retailing in Saudi Arabia.
10
Fibre2fashion News Desk. (2015). Ecommerce may further grow in Indian market this year. Retrieved from
11
Grunert, K.G. and Ramus, K. (2005). Consumers willingness to buy food through the internet. British Food Journal. Vol. 107(6): pp.381- 403.
12
Gunawan, G., Ellis-Chadwick, F. and King, M. (2008). An empirical study of the uptake of performance measurement by internet retailers. Vol. 18(4): pp. 361-81.
13
Hoque, M. R., Ali, M. A., & Mahfuz, M. A. (2015). An Empirical Investigation on the adoption of e-Commerce in Bangladesh. Asia Pacific Journal of Information Systems, 25(1).
14
Horrigan, J. (2008). Online Shopping | Pew Research Center - Pew Internet.
15
Hsu, C. L., Chuan-Chuan Lin, J., & Chiang, H. S. (2013). The effects of blogger recommendations on customers’ online shopping intentions. Internet Research, 23(1), 69-88.
16
Hsu, M. H., Chuang, L. W., & Hsu, C. S. (2014). Understanding online shopping intention: the roles of four types of trust and their antecedents. Internet Research, 24(3), 332-352.
17
Huseynov, F., & Yıldırım, S. Ö. (2014). Internet users’ attitudes toward business-to-consumer online shopping: A survey. Information Development, 32(3), 452–465.
18
Joshi, P., & Upadhyay, H. (2014). E-Retailing in India: Despite issues, customers satisfied with top retailers. Consumer Voice, 35.
19
Katawetawaraks, C., & Wang, C. L. (2011). Online shopper behavior: Influences of online shopping decision. Asian Journal of Business Research, 1(2).
20
Khushboo M, Khushboo D, and Himanshu B. (2013). A customer perception towards online shopping In India. Altius Shodh Journal of Management and Commerce, 2013. Volume 3, Issue. 4, pp.95-101.
21
Kim, H., & Song, J. (2010). The quality of word-of-mouth in the online shopping mall. Journal of Research in Interactive Marketing, 4(4), 376-390.
22
Kim, J. & Lennon, S. J. (2013). Effects of reputation and website quality on online consumers' emotion, perceived risk and purchase intention: Based on the stimulus-organism-response model. Journal of Research in Interactive Marketing, 7(1), 33-56.
23
Kim, S. H., & Byramjee, F. (2014). Effects of Risks On Online Consumers' Purchasing Behavior: Are They Risk-Averse Or Risk-Taking? Journal of Applied Business Research, 30(1), 161.
24
Kiyici, M. (2012). Internet Shopping Behavior of College of Education Students, the Turkish Online Journal of Educational Technology. 11(3), 202-214.
25
KPMG Advisory Services Private Limited. (2014). Emerging consumer segments in India.
26
Liu, X., He, M., Gao, F., & Xie, P. (2008). An empirical study of online shopping customer satisfaction in China: A holistic perspective, International Journal of Retail & Distribution Management, 36(11), 919–940.
27
Mohanapriya. S & Anusuya. D. (2014). A study on customer preferences and satisfaction towards selected online websites (with special reference to Coimbatore city), Paripex - Indian Journal of Research, 2014.Volume 3, Issue. 11, pp.45-46.
28
Mookerji, N. (2014). E-retail: You ain't seen nothin' yet. Business Standard. Retrieved from http://www.business-standard.com/article/companies/e-retail-you-ain-t- seennothinyet114080700034_1.html
29
Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon. com. MIS Quarterly, 34(1), 185–200.
30
Novak, T. P., Hoffman, D. L., & Yung, Y.-F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22–42.
31
Pi, S. M., & Sangruang, J. (2011). The perceived risks of online shopping in Taiwan. Social Behavior and Personality: an international journal, 39(2), 275-286.
32
Racherla, P. (2008). Factors influencing consumers' trust perceptions of online product reviews: A study of the tourism and hospitality online product review systems. Temple University.
33
Rasooldeen, M., and Taha S. (2014). Online shopping thrives with 60% annual growth, Retail Leadership Summit 2014, 21.
34
Shergill, G. S., & Chen, Z. (2005). Web-based shopping : Consumers’ attitudes towards online shopping in New Zealand. Journal of Electronic Commerce Research, 6(2), 79–94.
35
Sorce, P., Perotti, V., & Widrick, S. (2005). International journal of retail & distribution management, Journal of Consumer Marketing International Journal of Retail & Distribution Management, 33(1), 122–132.
36
Upasan K. (2015). A study of online purchase behavior of Consumers in India, ICTACT, Journal of management Studies, Vol-01, Issue-03.
37
Vinod, A., Subhash, D. A., Kumar, T. S., & Shameem, M. (2015). Examining role of perceived customer value in online shopping. Indian Journal of Economics and Business, 14(2), 235-244.
38
Zuroni, M. J., & Goh, H. L. (2012). Factors influencing consumers’ attitude towards e-commerce purchases through online shopping. International Journal of Humanities and Social Science, 2(4), 223–230.
39
ORIGINAL_ARTICLE
Implementation of Face Recognition Algorithm on Fields Programmable Gate Array Card
The evolution of today's application technologies requires a certain level of robustness, reliability and ease of integration. We choose the Fields Programmable Gate Array (FPGA) hardware description language to implement the facial recognition algorithm based on "Eigen faces" using Principal Component Analysis. In this paper, we first present an overview of the PCA used for facial recognition, then use a VHSIC Hardware Description Language (VHDL) simulation and design platform, which is the ISE. We describe the operation of each block and implement, thereafter, the computation of the global centered images. This corresponds to the first step of the PCA algorithm to assess its performance. The comparison of the results of this implementation with that of MATLAB confirmed the operability and effectiveness of this method for centralizing images. We also implemented the last part of this algorithm which is the computation of the Manhattan distance. The tests have given very satisfactory results.
https://jitm.ut.ac.ir/article_75790_9e26283627419afb6c2c32b7fa0b2427.pdf
2020-06-01
40
58
10.22059/jitm.2020.75790
Fields programmable gate array
VHSIC Hardware description language
Principal component analysis
Manhattan Distance
Fatima
Zohra Allam
fatima_zohra.allam@g.enp.edu.dz
1
Signal and Communication Laboratory, Department of Electronics, National Polytechnic School, Algeria.
AUTHOR
Latifa
Hamami-Mitiche
latifa.hamami@g.enp.edu.dz
2
Signal and Communication Laboratory, Department of Electronics, National Polytechnic School, Algeria.
AUTHOR
Hicham
Bousbia-Salah
hicham.bousbia-salah@g.enp.edu.dz
3
Signal and Communication Laboratory, Department of Electronics, National Polytechnic School, Algeria.
AUTHOR
Barnouti, N.H., Al-Dabbagh, S.S.M, Matti, W.E, & Naser, M.A.S. (2016). Face detection and recognition using Viola-Jones with PCA-LDA and square euclidean distance. International Journal of Advanced Computer Science and Applications, 7(5), (pp. 371-377)
1
Betz, V. & Rose, J. (1997). VPR: A new packing, placement and routing tool for FPGA. In International Workshop on Field Programmable Logic and Applications. London, (pp. 213-222).
2
Chen, S.C. & Chang, Y.W. (2017). FPGA placement and routing. IEEE ACM International Conference on Computer-Aided Design (ICCAD).
3
Clarke J.A., Gaffar, A.A, Constantinides, G.A., & Cheung, P.Y.K. (2006). Fast word-level power models for synthesis of FPGA-based arithmetic. IEEE International Symposium on Circuits and Systems.
4
Das P., Chatterji B. (1990). Orthogonal distances for digital pictures. Information Sciences, 50, (pp. 123-150).
5
Deschamps, J.P, Bioul, G. (2006). Synthesis of arithmetic circuits FPGA, ASIC, and embedded systems. John WILEY & SONS.
6
Dossis, M. (2011). Formal generation of synthesizable RTL from regular programs. 6th International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS).
7
Feng, Q., Yuan, C., Pan, J.S., Yang, J.F., Chou, Y.T., & Zhou, Y. (2017, February). Superimposed Sparse Parameter Classifiers for Face Recognition. IEEE Transactions on Cybernetics. 47(2), (pp. 378-390)
8
Jaafar, A. Soin, N. and Hatta, N. (2017). An educational FPGA design process flow using Xilinx ISE 13.3 project navigator for students. IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA)
9
Lyalin, A. (2007). Formal methods of algorithm analysis for decreasing RTL verification complexity. 9th International Conference - The Experience of Designing and Applications of CAD Systems in Microelectronics.
10
Melter R. (1991). A survey of digital metrics. Vision Geometry Contemporary Mathematics. Contemporary Mathematics, 119, (pp. 95-106).
11
Masato-Inagi, M., Takashima, Y., Nakamura, Y. (2010). Globally optimal time-multiplexing of inter-FPGA connections for multi-FPGA prototyping systems. IPSJ Transactions on System LSI Design Methodology.
12
Pistorius, J. & Hutton. M. (2003). Placement rent exponent calculation methods, temporal behaviour and fpga architecture evaluation. SLIP’03: Procedings of the 2003 International Worhshop on System Level Interconnect Prediction, (pp. 31-38).
13
Rodrequez-Andina, J.J., Moure, M.J., & Valdes, M.D. (2007). Features, design tools, and application domains of FPGA. IEEE Transactions on Industrial Electronics, 54(4).
14
Salcic, Z. (2001). Synthesizing Logic From VHDL Description. VHDL and FPLDs in Digital Systems Design, Prototyping and Customization.
15
Siguenza-Tortosa, D. and Nurmi, J. (2002). VHDL Based simulation environment for proteo NoC. Seventh IEEE International. High-Level Design Validation and Test Workshop, 1(6), (pp. 27-29).
16
Skliarova, I. & Ferrari, A.B. (2000). Exploiting FPGA-based architectures and design tools for problems of reconfigurable computations. Proceedings 13th Symposium on Integrated Circuits and Systems Design. Computer Science.
17
Taraate, V. (2017). Design and simulation using VHDL constructs. PLD Based Design with VHDL – RTL Design, Synthesis and Implementation. Springer Edition.
18
Tang, Q. Mehrez, H. and Tuna, M. (2013). Routing algorithm for Multi-FPGA based systems using multi-point physical tracks. International Symposium on Rapid System Prototyping (RSP).
19
Yuhui, Z., Byeungwoo, J., Danhua, X., Jonathan, W.Q.M., & Hui, Z. (2015). Image segmentation by generalized hierarchical fuzzy C-means algorithm. Journal of Intelligent & Fuzzy Systems, 28(2), (pp. 961-973).
20
Pentland, A., Moghaddam, B. and Starner, T. (1994, Jun). View-based and modular eigenspaces for face recognition. Computer Vision and Pattern Recongnition. Proceedings CVPR’94, IEEE Computer Society Conference, (pp. 84-91).
21
Jian, Y., Zhang, D., Frangi, A. and Y. Yang, J. (2004, January). Two-Dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(1), (pp. 131–137).
22
Monmasson, E., & Cirstea, M. N. (2007, August). FPGA design methodology for industriel control systems. A review IEEE Transactions on Industrial Electronics, 54(4).
23
Morizet, N. (2009, March). Biometric Recognition by Multimodal Fusion of Face and Iris. PhD thesis, specialty: Signal and Images. National School of Telecommunications. Paris, France.
24
Alfke, P. (2009, August). Xilinx Virtex-6 and SPARTAN-6 FPGA families. IEEE Hot Chips 21 Symposium (HCS).
25
Li Jun, L. & Wei, W. (2010, November). PCI express interface design and verification based on SPARTAN-6 FPGA. IEEE 12th International Conference on Communication Technology.
26
Bezerra, E. & Lettnin, D. (2013, October). Writing Synthesizable VHDL Code for FPGA. Synthesizable VHDL Design for FPGA.
27
Komulainen, J., Hadid, A., & Pietikäinen, M. (2014, January). Context based face anti-spoofing. 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), Arlington, VA, USA.
28
Boulkenafet, Z., Komulainen, J., & Hadid, A. (2016, August). Face Spoofing Detection Using Colour Texture Analysis. IEEE Transactions on Information Forensics and Security, IEEE Biometrics Compendium, 11(8), (pp. 1818-1830)
29
Alaslani, M.G., & Elrefaei, L.A. (2018, April). Convolutional neural network based feature extraction for iris recognition. International Journal of Computer Science and Information Technology (IJCSIT), 10(2), (pp. 65-78).
30
Challouf, M. and Hicham, M. (2007). Tutorial Xilinx ISE 9.1. INSAT Tunis.
31
ORIGINAL_ARTICLE
Revolution of Artificial Intelligence and the Internet of Objects in the Customer Journey and the Air Sector
Artificial intelligence (AI) is a discipline interested in the processes and methods that allow a machine to perform tasks related to human intelligence. It offers many opportunities related to problem solving, quick decision-making, increasing efficiency and reducing costs. Because of its so various fields of application, artificial intelligence is at the heart of the new industrial revolution. Algeria aims to present its AI strategy by 2020. In this paper, we are interested in defining AI, its potential fields of application, and in particular, its influence in the customer journey and position of RFID (Radio-Frequency Identification) in the chain; application in the aviation sector and its relationship to the Internet of Things are also described through examples.
https://jitm.ut.ac.ir/article_75791_7ff08f9f143d3795ea40ab59ad734265.pdf
2020-06-01
59
69
10.22059/jitm.2020.75791
Artificial Intelligence
RFID
Browses customer
Airline industry, IoT
Hadjer
Saadi
hadjer_saadi@yahoo.fr
1
Lecturer, Instrumentation Laboratory (LNS), Faculty of Electronics and Informatics, USTHB, Algiers.
LEAD_AUTHOR
Rachida
Touhami
rachida.touhami@gmail.com
2
Professor, Instrumentation Laboratory (LNS), Faculty of Electronics and Informatics, USTHB, Algiers.
AUTHOR
Mustapha C.E.
Yagoub
myagoub@uottawa.ca
3
Professor, School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON Canada K1N 6N5.
AUTHOR
Airports Using Beacons to Take Passenger Experience to the Next Level. (n.d.). Retrieved from https://blog.beaconstac.com/2016/03/10-airports-using-beacons-to-take-passenger-experience-to-the-next-level/.
1
D'abzac, E., & D'abzac, E. (2018, August 8). L'aéroport de San José adopte la reconnaissance faciale pour les vols internationaux. Retrieved from https://www.deplacementspros.com/L-aeroport-de-San-Jose-adopte-la- reconnaissance-faciale-pour-les-vols-internationaux_a49620.html.
2
Dugelay. (1970, January 1). Real-time 3D face identification from a depth camera. Retrieved from http://www.eurecom.fr/fr/publication/3764/detail/real-time-3d-face-identification-from-a-depth-camera.
3
Etude Deloitte Tech Trends 2017 : L'entreprise cinétique. (n.d.). Retrieved from http://www.mtom- mag.com/article3997.html.
4
Laurière Jean-Louis. (1988). Intelligence artificielle. Paris: Eyrolles.
5
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Lomas, N. (2017, May 25). Gatwick Airport now has 2,000 beacons for indoor navigation. Retrieved from https://techcrunch.com/2017/05/25/gatwick-airport-now-has-2000-beacons-for-indoor-navigation/.
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Outsourcing Custom Software Development company in Ukraine. (2017, November 17). Retrieved from https://binariks.com/blog/tips/beacon-sensor-use-business-app/.
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Pomian, J. (1987). Aux origines de lIntelligence Artificielle: H. A. Simon en père fondateur. Quaderni, 1(1), 9–25. doi: 10.3406/quad.1987.2093.
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Proximity Marketing In Airports & Transportation The Proxbook Report The State Of The Proximity Industry Q3 2016. (2016).
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Radio-identification. (2020, March 11). Retrieved from https://fr.wikipedia.org/wiki/Radio-identification.
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Rahul, A., G, G. K., H, U. K., & Rao, S. (2015). Near Field Communication (NFC) Technology: A Survey. International Journal on Cybernetics & Informatics, 4(2), 133–144. doi: 10.5121/ijci.2015.4213.
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Rapport de synthèse, Atelier de préparation du projet du Plan stratégique national de l’intelligence artificielle 2020-2030, Algerian Artificial Intelligence Strategic Plan 2020-2030. (n.d.).
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Russell, S.J., and Norvig, P. (2009) Artificial Intelligence: A Modern Approach, Prentice Hall, 3rd Edition.
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Singh, S. (2013, January 26). What are Proximity Sensors, How They Work And Types? Retrieved from https://thegadgetsquare.com/what-are-proximity-sensors-types-and-how-it-works/.
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Turing, A. M. (1950). I.—Computing Machinery And Intelligence. Mind, LIX(236), 433–460. doi: 10.1093/mind/lix.236.433.
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17
ORIGINAL_ARTICLE
Improving LoRaWAN Performance Using Reservation ALOHA
LoRaWAN is one of the new and updated standards for IoT applications. However, the expected high density of peripheral devices for each gateway, and the absence of an operative synchronization mechanism between the gateway and peripherals, all of which challenges the networks scalability. In this paper, we propose to normalize the communication of LoRaWAN networks using a Reservation-ALOHA (R-ALOHA) instead of the standard ALOHA approach used by LoRa. The implementation is a library package placed on top of the standard LoRaWAN; thus, no modification in pre-existing LoRaWAN structure and libraries is required. Our proposed approach is based on a distributed synchronization service that is suitable for low-cost IoT end-nodes. R-ALOHA LoRaWAN gives better performance in comparison with the previous models; Pure-ALOHA LoRaWAN, Slotted-ALOHA LoRaWAN, and TDMA LoRaWAN. It significantly improves the performance of network regarding the probability of collision, the maximum throughput, and the maximum duty cycle.
https://jitm.ut.ac.ir/article_75792_c914abe3476b0d83dc9de2b587ac44da.pdf
2020-06-01
70
78
10.22059/jitm.2020.75792
Wireless networks, LoRaWAN
Reservation ALOHA, Synchronization
Dina M.
Ibrahim
d.hussein@qu.edu.sa
1
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Adelantado, F., Vilajosana, X., Tuset-Peiro, P., Martinez, B., Melia-Segui, J., & Watteyne, T. (2017). Understanding the limits of LoRaWAN. IEEE Communications magazine, 55(9), 34-40.
1
Alliance, L. (2016). LoRaWAN Specification v1. 0.2. Date of retrieval, 13, 2018.
2
Augustin, A., Yi, J., Clausen, T., & Townsley, W. M. (2016). A study of LoRa: Long range & low power networks for the internet of things. Sensors, 16(9), 1466.
3
Bouguera, T., Diouris, J. F., Chaillout, J. J., Jaouadi, R., & Andrieux, G. (2018). Energy consumption model for sensor nodes based on LoRa and LoRaWAN. Sensors, 18(7), 2104.
4
Casares-Giner, V., Martinez-Bauset, J., & Portillo, C. (2019). Performance evaluation of framed slotted ALOHA with reservation packets and successive interference cancelation for M2M networks. Computer Networks, 155, 15-30.
5
Frenzel, L. (2013). Fundamentals of Communications Access Technologies: FDMA, TDMA, CDMA, OFDMA, AND SDMA. Electronic Design. Last modified on Jan, 22.
6
Ibrahim, D. M. (2019, June). Internet of Things Technology based on LoRaWAN Revolution. In 2019 10th International Conference on Information and Communication Systems (ICICS) (pp. 234-237). IEEE.
7
Khater, E. M., & Ibrahim, D. M. (2019). Proposed ST-Slotted-CS-ALOHA Protocol for Time Saving and Collision Avoidance. ISeCure, 11(3).
8
Lavric, A., & Petrariu, A. I. (2018, May). LoRaWAN communication protocol: The new era of IoT. In 2018 International Conference on Development and Application Systems (DAS) (pp. 74-77). IEEE.
9
Muzammir, M. I., Abidin, H. Z., Abdullah, S. A. C., & Zaman, F. H. K. (2019, April). Performance analysis of LoRaWAN for indoor application. In 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 156-159). IEEE.
10
Nolan, K. E., Guibene, W., & Kelly, M. Y. (2016, September). An evaluation of low power wide area network technologies for the Internet of Things. In 2016 international wireless communications and mobile computing conference (IWCMC) (pp. 439-444). IEEE.
11
Petrovic, I., & Thomas, G. (2006). Multi-Frame Reservation (MFR) ALOHA Protocols. In 14. Telekomunikacioni forum TELFOR 2006.
12
Piyare, R., Murphy, A. L., Magno, M., & Benini, L. (2018). On-demand LoRa: Asynchronous TDMA for energy efficient and low latency communication in IoT. Sensors, 18(11), 3718.
13
Polonelli, T., Brunelli, D., & Benini, L. (2018, October). Slotted aloha overlay on lorawan-a distributed synchronization approach. In 2018 IEEE 16th International Conference on Embedded and Ubiquitous Computing (EUC) (pp. 129-132). IEEE.
14
Polonelli, T., Brunelli, D., Marzocchi, A., & Benini, L. (2019). Slotted aloha on lorawan-design, analysis, and deployment. Sensors, 19(4), 838.
15
Rahman, K. A., & Tepe, K. E. (2012). Extended sliding frame R-Aloha: Medium access control (MAC) protocol for mobile networks. Ad Hoc Networks, 10(6), 1017-1027.
16
Sanchez-Iborra, R., Sanchez-Gomez, J., Ballesta-Viñas, J., Cano, M. D., & Skarmeta, A. F. (2018). Performance evaluation of LoRa considering scenario conditions. Sensors, 18(3), 772.
17
Trüb, R., & Thiele, L. (2018, November). Increasing Throughput and Efficiency of LoRaWAN Class A. In UBICOMM 2018. The Twelfth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (pp. 54-64). International Academy, Research, and Industry Association (IARIA).
18
ORIGINAL_ARTICLE
Policy Model for Sharing Network Slices in 5G Core Network
As mobile data traffic increases, and the number of services provided by the mobile network increases, service load flows as well, which requires changing in the principles, models, and strategies for media transmission streams serving to guarantee the given nature of giving a wide scope of services in Flexible and cost-effective. Right now, the fundamental question remains what number of network slices will be cost effective for slice managing and giving the required functionality. So, the aim is to improve the efficiency of mobile network by forming an ideal slice in a multi-service communication network. In this paper, we propose a model to demonstrate network resource allocation system that forms devoted network slices to serve particular types of services independently on shared infrastructure. This model solves the problem of creating a strategy to form multi-service core mobile communication network slices, which allow the providing of a wide scope of services with certain quality indicators according to the effective dynamic configuration of the system. A resource management system model is created, to provide a method that considers costs related with excessive resource allocation, and also reduces the number of network recalculations, allowing for a reasonable proportion of management costs and Qualities of Service.
https://jitm.ut.ac.ir/article_75793_0c9993b930302813d64c19e64143e968.pdf
2020-06-01
79
89
10.22059/jitm.2020.75793
Network functions virtualization
Network slicing
5G
Evolved packet core
Mohammad Ali
Hammoudeh
maah37@qu.edu.sa
1
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Bera, S., Misra, S., & Vasilakos, A. V. (2017). Software-defined networking for internet of things: A survey. IEEE Internet of Things Journal, 4(6), 1994-2008.
1
Foukas, X., Patounas, G., Elmokashfi, A., & Marina, M. K. (2017). Network slicing in 5G: Survey and challenges. IEEE Communications Magazine, 55(5), 94-100.
2
Guan, W., Wen, X., Wang, L., Lu, Z., & Shen, Y. (2018). A service-oriented deployment policy of end-to-end network slicing based on complex network theory. IEEE Access, 6, 19691-19701.
3
Hashim, H. A., & Abido, M. A. (2019). Location management in LTE networks using multi-objective particle swarm optimization. Computer Networks, 157, 78-88.
4
Liu, J., Shen, H., Narman, H. S., Chung, W., & Lin, Z. (2018). A survey of mobile crowdsensing techniques: A critical component for the internet of things. ACM Transactions on Cyber-Physical Systems, 2(3), 1-26.
5
Mijumbi, R., Serrat, J., Gorricho, J. L., Bouten, N., De Turck, F., & Boutaba, R. (2015). Network function virtualization: State-of-the-art and research challenges. IEEE Communications surveys & tutorials, 18(1), 236-262.
6
Narang, S., Nalwa, T., Choudhury, T., & Kashyap, N. (2018, February). An efficient method for security measurement in internet of things. In 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT) (pp. 319-323). IEEE.
7
Shatzkamer, K., Lake, D., Dodd-noble, A. S., & Bosch, P. (2018). U.S. Patent No. 10,057,109. Washington, DC: U.S. Patent and Trademark Office.
8
Shimojo T., Sama M. R., Khan A., Iwashina S. Costefficient method for managing network slices in a multiservice 5G core network, Proceedings of the 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). Lisbon, Portugal, 2017, pp. 1121–1126.
9
Shimojo, T., Sama, M. R., Khan, A., & Iwashina, S. (2017, May). Cost-efficient method for managing network slices in a multi-service 5G core network. In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (pp. 1121-1126). IEEE.
10
Yi, B., Wang, X., Li, K., & Huang, M. (2018). A comprehensive survey of network function virtualization. Computer Networks, 133, 212-262.
11
ORIGINAL_ARTICLE
Comparative Study between Hologram Technology and Augmented Reality
The great development witnessed by our current age has led to the emergence of many diverse modern technologies, one of these advanced technologies is Hologram technology and Augmented reality technology, These two technologies are somewhat similar to somewhat, as it can be said that they perform almost the same purpose, and at the same time, Hologram technology differs from augmented reality in several aspects, as the way in which the three-dimensional images are created and the properties of that images itself. This paper aims to compare and study the similarities and differences between Hologram technology and Augmented reality technology. It is a standard comparison as the comparison takes place according to a number of different aspects of both technologies. Comparing the characteristics of the two technologies showed that there is no one of them excels over the other, but according to different systems and situations, it is maybe better and more appropriate to use one of them than using the other one.
https://jitm.ut.ac.ir/article_75794_e53c558e9317df1c37454a5d1d4e4e07.pdf
2020-06-01
90
106
10.22059/jitm.2020.75794
Hologram
Three-dimensional image
augmented reality
Hologram fan
technology
Doaa M.
Elmahal
361218125@qu.edu.sa
1
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
LEAD_AUTHOR
Asma S.
Ahmad
352220476@qu.edu.sa
2
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Alaa T.
Alomaier
361218093@qu.edu.sa
3
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Reem F.
Abdlfatah
362218948@qu.edu.sa
4
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Dina M.
Hussein
d.hussein@qu.edu.sa
5
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Algarawi, F. K., Alslamah, W. A., Alhabib, A. A., Alfehaid, A. S., & Ibrahim, D. M. (2018). Applying Augmented Reality Technology for an E-Learning System. International Journal of Computer and Information Engineering, 12(3), 182-187.
1
Amazon. (2020, January). 3pcs Smartphone Hologram Projector, Mini 3D Holographic Projection Pyramid with Suction Cup for Any Smartphone or Tablet 360 Virtual Reality (As Pictures Shown): Amazon.co.uk: DIY & Tools. www.Amazon.Co.Uk. https://www.amazon.co.uk/ Smartphone-Hologram-Projector-Holographic-Projection/dp/B07K21G92Y.
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Chen, Y., Wang, Q., Chen, H., Song, X., Tang, H., & Tian, M. (2019). An overview of augmented reality technology. Journal of Physics: Conference Series, 1237, 022082. https://doi.org/10.1088/1742-6596/1237/2/022082.
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EDWARDS-Stewart, A., HOYT, T., & REGER, G. (2016). Classifying Different Types of Augmented Reality Technology. Annual Review of CyberTherapy and Telemedicine, 14, 199–202.
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Elmorshidy, A. (2010). Holographic projection technology: The world is changing.
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12
Markgraf, B. (2019, March 2). How Do Holographic Projectors Work? Retrieved from https://sciencing.com/holographic-projectors-work-12226294.html.
13
Nikolov, N. (2016, October). This device can project ’Star Wars’-like holograms in the air. Mashable. https://mashable.com/2016/10/24/holovect-3d-projections-star-wars/.
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Poetker, Bridget. (2019). A Brief History of Augmented Reality (+Future Trends & Impact). G2.Com. https://learn.g2.com/history-of-augmented-reality.
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Rouse, M. (2005, April 5). What is hologram? - Definition from WhatIs.com. Retrieved from https://whatis.techtarget.com/definition/hologram.
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19
Sharma, Kirti. (2016, June 20). US tech giant IBM patents Google Glass-like night vision eyewear. Techiexpert.Com. https://www.techiexpert.com/us-tech-giant-ibm-patents-google-glass-like-night-vision-eyewear/.
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Slinger, C, et al. Computer-Generated Holography as a Generic Display Technology. Vol. 38, Computer, Sept. 2005, pp. 46–53.
21
Spandana. (2019, March 25). Top AR apps with growing adoption. Medium. https://arvrjourney.com/ top-ar-apps-with-growing-adoption-7f1a87868622.
22
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Wong, K., Jamali, S., & Shiratuddin, M. F. (2014). A Review of Augmented Reality and Mobile-Augmented Reality Technology. The International Journal of Learning in Higher Education, 20, 37–54. https://doi.org/10.18848/1447-9494/CGP/v20i02/48690.
27
ORIGINAL_ARTICLE
Top Benefits and Hindrances to Cloud Computing Adoption in Saudi Arabia: A Brief Study
Cloud computing is an emerging concept of information technology that in many countries has an influence on many companies. The research was conducted to evaluate cloud computing adoption in Saudi Arabia; Benefits and hindrances for small and medium-sized enterprises (SMEs). The qualitative research approach is performed by interviews with the management of a variety of SMEs active in the information and communication technology (ICT) industry. This paper illustrates a significant positive correlation between the use of cloud computing and organizational quality performances. The paper concluded that the knowledge level of SMEs on the accessibility of cloud services is below average scale. The greatest challenges about the cloud service are privacy and security in the cloud among providers and users for the Saudi Arabian firms.
https://jitm.ut.ac.ir/article_75795_88df02847a800de247362cb33fba74b6.pdf
2020-06-01
107
122
10.22059/jitm.2020.75795
Cloud Computing
Benefit
Hindrance
Adoption of Technology
SMEs
Saudi Arabia
Arwa
Albelaihi
a.albelaihi@qu.edu.sa
1
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Nabeel
Khan
n.khan@qu.edu.sa
2
Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.
AUTHOR
Alhammadi, A., Stanier, C., & Eardley, A. (2015). The Determinants of Cloud Computing Adoption in Saudi Arabia. Computer Science & Information Technology (CS & IT). doi: 10.5121/csit.2015.51406
1
Alharbi, F., Atkins, A., & Stanier, C. (2016). Understanding the determinants of Cloud Computing adoption in Saudi healthcare organisations. Complex & Intelligent Systems, 2(3), 155–171. doi: 10.1007/s40747-016-0021-9
2
Alkhater, N., Wills, G., & Walters, R. (2014). Factors Influencing an Organisations Intention to Adopt Cloud Computing in Saudi Arabia. 2014 IEEE 6th International Conference on Cloud Computing Technology and Science. doi: 10.1109/cloudcom.2014.95
3
Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2018). Key Issues for Embracing the Cloud Computing to Adopt a Digital Transformation: A study of Saudi Public Sector. Procedia Computer Science, 130, 1037–1043. doi: 10.1016/j.procs.2018.04.145
4
Al-Somali, S. A., Gholami, R., & Clegg, B. (2015). A stage-oriented model (SOM) for e-commerce adoption: a study of Saudi Arabian organizations. Journal of Manufacturing Technology Management, 26(1), 2–35. doi: 10.1108/jmtm-03-2013-0019
5
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D. Vrsajkovic, “Evaluating Determinants of Cloud Computing Acceptance in Croatian SME Organizations”, Thesis: Rochester Institute of Technology, 2016.
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L.P. Willcocks, W. Venters, and A. Edgar, (2012) “Cloud and the future of business: from costs to innovation, London: Accenture in association with The Outsourcing Unit London School of Economics and Political Science.
10
Mikkonen I., Khan I., (2016). Cloud computing: SME company point of view., Management Challenges in the 21st Century: Digitalization of Society, Economy and Market: Current Issues and Challenges.,
11
Nabeel Khan (2016) A cloud computing adoption framework for data migration and cloud adoption by Small and medium-sized enterprises (SMEs). University of Salford, Doctoral Thesis.
12
Noor, T. H. (2016). Usage and Technology Acceptance of Cloud Computing in Saudi Arabian Universities. International Journal of Software Engineering and Its Applications, 10(9), 65–76. doi: 10.14257/ijseia.2016.10.9.07
13
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14
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Shimba. F, “Cloud Computing: Strategies for Cloud Computing Adoption”, Dissertation: Doctoral in Computing, Dublin Institute of Technology, 2010.
16
Singleton, R., & Straits, B. C. (2009). Approaches to social research. New York, NY: Oxford Univ. Press.
17
Stieninger, M., & Nedbal, D. (2014). Diffusion and Acceptance of Cloud Computing in SMEs: Towards a Valence Model of Relevant Factors. 2014 47th Hawaii International Conference on System Sciences. doi: 10.1109/hicss.2014.410
18
Tashkandi, A., & Al-Jabri, I. (2015). Cloud Computing Adoption by Higher Education Institutions in Saudi Arabia: Analysis Based on TOE. 2015 International Conference on Cloud Computing (ICCC). doi: 10.1109/cloudcomp.2015.7149634
19
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24
Yamin, M. and Ammar A. Al Makram. (2015). “Cloud Computing in SMEs: Case of Saudi Arabia.”. BVICAM's International Journal of Information Technology, 7, 853-860.
25
Yang, H. and Tate, M. (2012) A Descriptive Literature Review and Classification of Cloud Computing Research. Communications of the Association for Information Systems, 31, 35-60.
26
ORIGINAL_ARTICLE
Using Machine Learning Algorithms for Automatic Cyber Bullying Detection in Arabic Social Media
Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine learning algorithms. In this paper, we have reviewed algorithms for automatic cyberbullying detection in Arabic of machine learning, and after comparing the highest accuracy of these classifications we will propose the techniques Ridge Regression (RR) and Logistic Regression (LR), which achieved the highest accuracy between the various techniques applied in the automatic cyberbullying detection in English and between the techniques that was used in the sentiment analysis in Arabic text, The purpose of this work is applying these techniques for detecting cyberbullying in Arabic.
https://jitm.ut.ac.ir/article_75796_38e99c269fdd70ebb3d0484afa88f3f2.pdf
2020-06-01
123
130
10.22059/jitm.2020.75796
Cyberbullying
Machine Learning (ML)
Sentiment analysis
Cyberbullying Detection in Arabic
Bedoor Y.
AlHarbi
362218730@qu.edu.sa
1
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Mashael S.
AlHarbi
mashae1.017o@gmail.com
2
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Nouf J.
AlZahrani
noufjamman0@gmail.com
3
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Meshaiel M.
Alsheail
m.alsheail@qu.edu.sa
4
Information Technology Dept., College of Computer, Qassim University, Qassim, Saudi Arabia.
AUTHOR
Dina M.
Ibrahim
d.hussein@qu.edu.sa
5
Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
AUTHOR
Alharbi, B. Y., Alharbi, M. S., Alzahrani, N. J., Alsheail, M. M., Alshobaili, J. F., & Ibrahim, D. M. (2019). Automatic Cyber Bullying Detection in Arabic Social Media. Int J Engineering Research and Technology, 12(12), 2330-2335.
1
Feng, J., Gong, C., Li, X., & Lau, R. Y. (2018). Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews. Wireless Communications and Mobile Computing, 2018.
2
Gamal, D., Alfonse, M., El-Horbaty, E. S. M., & Salem, A. B. M. (2019). Twitter benchmark dataset for Arabic sentiment analysis. Int J Mod Educ Comput Sci, 11(1), 33.
3
Haidar, B., Chamoun, M., & Serhrouchni, A. (2018, September). Arabic Cyberbullying Detection: Using Deep Learning. In 2018 7th International Conference on Computer and Communication Engineering (ICCCE) (pp. 284-289). IEEE.
4
Mangaonkar, A., Hayrapetian, A., & Raje, R. (2015, May). Collaborative detection of cyberbullying behavior in Twitter data. In 2015 IEEE international conference on electro/information technology (EIT) (pp. 611-616). IEEE.
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11
ORIGINAL_ARTICLE
The Impact of Dynamic Balanced Scorecard in Knowledge-Intensive Organizations’ Business Process Management: A New Approach Evidenced by Small and Medium-Size Enterprises in Latin America
Dynamic Balanced Scorecard (DBSC) is an effective business performance management control tool for dealing with business uncertainty, performance monitoring, evaluation and forecasting. DBSC has been proposed and utilized extensively over the years as an effective tool to manage and control the dynamics of business processes (BP) and their performance. However, there is little evidence of its application in knowledge-intensive (KI) organizations and how they can develop and enhance key aspects of their business processes, such as product-service systems innovation, and sustainability, for example. Moreover, the literature does not mention nor does it provide a vision or a DBSC model in cases where business process management (BPM), linked to knowledge creation and organizational transformation initiatives, are factored in the DBSC model. Hence this article explores this vein and aims to demonstrate the advantages of DBSC in this type of scenarios, with stark contrast of failed organizations of the past, particularly in small and medium-size enterprises (SME). Most of the private sector in developing countries like Chile is comprised of SMEs, which thrive and seek to grow sustainably adhering to a global economic trend. The DBSC model being shown here illustrates SMEs strategy, which reveals how intrinsic characteristics of knowledge-intensive organizations can foster sustainability and innovation in BPM.
https://jitm.ut.ac.ir/article_75797_fdfcf27fbc3c6779e9dabc1522c586b1.pdf
2020-06-01
131
152
10.22059/jitm.2020.75797
Dynamic Balanced Scorecard (DBSC)
model
Knowledge-intensive Organizations
Business Process Management
Small and Medium-Size Enterprises (SME)
Fernando
Yanine
fyanine@uft.cl
1
Associate Professor, School of Engineering, Universidad Finis Terrae, Av. Pedro de Valdivia 1509, Providencia; Santiago.
LEAD_AUTHOR
Felisa M.
Cordova
fcordova@uft.cl
2
Professor and Director of the School of Engineering, Universidad Finis Terrae, Av. Pedro de Valdivia 1509, Providencia; Santiago, Chile.
AUTHOR
Claudia
Duran
c.durans@utem.cl
3
Academic; Faculty of Engineering, Department of Industry, Universidad Tecnológica Metropolitana, Av. José Pedro Alessandri 1242, Ñuñoa, Santiago, Chile.
AUTHOR
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53
ORIGINAL_ARTICLE
Green Energy Generation in Buildings: Grid-Tied Distributed Generation Systems (DGS) With Energy Storage Applications to Sustain the Smart Grid Transformation
The challenge of electricity distribution’s upgrade to incorporate new technologies is big, and electric utilities are mandated to work diligently on this agenda, thus making investments to ensure that current networks maintain their electricity supply commitments secure and reliable in face of disruptions and adverse environmental conditions from a variety of sources. The paper presents a new model based on energy homeostasis for power control and energy management using tariffs differentiation as incentive, considered by ENEL, the largest electric utility in Chile. The model optimizes grid-tied distributed generation (DG) systems with energy storage, in line with the utility’s green energy program, part of its Smart Grid Transformation, aimed at installing grid-tied DG systems with solar generation and energy storage in Santiago, Chile. Results present different tariff options, system’s capacity and energy storage alternatives, in order to compare proposed strategies with the actual case, where no green energy is present. The results show the advantage of the proposed tariffs scheme and power-energy management model based on different scenarios, providing a good and safe option for installing DG solutions to the grid.
https://jitm.ut.ac.ir/article_75798_5a184ef024663f4f5af28c647044a030.pdf
2020-06-01
153
162
10.22059/jitm.2020.75798
Electric tariffs
Energy homeostasis
distributed generation
Electric utility
Green energy
Power and energy management
Antonio
Sanchez-Squella
antonio.sanchez@usm.cl
1
Assistant Professor; Dept. of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago, Chile.
AUTHOR
Fernando
Yanine
fyanine@uft.cl
2
Associate Professor, School of Engineering, Universidad Finis Terrae, Av. Pedro de Valdivia 1509, Providencia; Santiago, Chile.
AUTHOR
Aldo
Barrueto
antonio.sanchz@usm.cl
3
Assistant Professor; Dept. of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago, Chile.
AUTHOR
Antonio
Parejo
aparejo@us.es
4
Department of Electronic Technology, Escuela Politécnica Superior, University of Seville, Seville, Spain.
AUTHOR
Caballero, F., Sauma, E., & Yanine, F. (2013). Business optimal design of a grid-connected hybrid PV (photovoltaic)-wind energy system without energy storage for an Easter Island's block. Energy, 61, 248-261.
1
de Faria Jr, H., Trigoso, F. B., & Cavalcanti, J. A. (2017). Review of distributed generation with photovoltaic grid connected systems in Brazil: Challenges and prospects. Renewable and Sustainable Energy Reviews, 75, 469-475.
2
Farhangi, H. (Ed.). (2016). Smart microgrids: lessons from campus microgrid design and implementation. CRC Press.
3
Hanna, R., Ghonima, M., Kleissl, J., Tynan, G., & Victor, D. G. (2017). Evaluating business models for microgrids: Interactions of technology and policy. Energy Policy, 103, 47-61.
4
Minnaar, U. J. (2016). Regulatory practices and Distribution System Cost impact studies for distributed generation: Considerations for South African distribution utilities and regulators. Renewable and Sustainable Energy Reviews, 56, 1139-1149.
5
Paliwal, P., Patidar, N. P., & Nema, R. K. (2014). Planning of grid integrated distributed generators: A review of technology, objectives and techniques. Renewable and sustainable energy reviews, 40, 557-570.
6
Parejo, A., Sanchez-Squella, A., Barraza, R., Yanine, F., Barrueto-Guzman, A., & Leon, C. (2019). Design and simulation of an energy homeostaticity system for electric and thermal power management in a building with smart microgrid. Energies, 12(9), 1806.
7
Ungar, L., Kallakuri, C., & Barrett, J. (2015). 2015 Federal Energy Efficiency Legislation: Projected Impacts. U. S. Senate, “Energy Policy Modernization Act of 2015.” Senate of the United States.
8
Van Koten, S., & Ortmann, A. (2008). The unbundling regime for electricity utilities in the EU: A case of legislative and regulatory capture? Energy Economics, 30(6), 3128-3140.
9
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10
Wood, E. (2016) “US Senate Passes Bill that Supports Grid-Connected and Hybrid Microgrids. Microgrid Knowledge.” Microgrid Knowledge, https://microgridknowledge.com/hybrid-microgrids/ (accessed 29 January 2020).
11
Yanine, F. F., Caballero, F. I., Sauma, E. E., & Córdova, F. M. (2014). Building sustainable energy systems: Homeostatic control of grid-connected microgrids, as a means to reconcile power supply and energy demand response management. Renewable and Sustainable Energy Reviews, 40, 1168-1191.
12
Yanine, F. F., Caballero, F. I., Sauma, E. E., & Córdova, F. M. (2014). Homeostatic control, smart metering and efficient energy supply and consumption criteria: A means to building more sustainable hybrid micro-generation systems. Renewable and Sustainable Energy Reviews, 38, 235-258.
13
Yanine, F. F., Córdova, F. M., & Valenzuela, L. (2015). Sustainable hybrid energy systems: an energy and exergy management approach with homeostatic control of microgrids. Procedia Computer Science, 55, 642-649.
14
Yanine, F., & Córdova, F. M. (2013, March). Homeostatic control in grid-connected micro-generation power systems: A means to adapt to changing scenarios while preserving energy sustainability. In 2013 International Renewable and Sustainable Energy Conference (IRSEC) (pp. 525-530). IEEE.
15
Yanine, F., Sanchez-Squella, A., Barrueto, A., Cordova, F., & Sahoo, S. K. (2017). Engineering sustainable energy systems: how reactive and predictive homeostatic control can prepare electric power systems for environmental challenges. Procedia computer science, 122, 439-446.
16
Yanine, F., Sanchez-Squella, A., Barrueto, A., Sahoo, S. K., & Cordova, F. (2018). Smart Energy Systems: The Need to Incorporate Homeostatically Controlled Microgrids to the Electric Power Distribution Industry: An Electric Utilities’ Perspective. Int. J. Eng. Technology 7, 64-73.
17
Yanine, F., Sanchez-Squella, A., Barrueto, A., Sahoo, S. K., Parejo, A., Shah, D., & Cordova, F. M. (2019). Homeostaticity of energy systems: How to engineer grid flexibility and why should electric utilities care. Periodicals of Engineering and Natural Sciences, 7(1), 474-482.
18
Yanine, F., Sanchez-Squella, A., Barrueto, A., Tosso, J., Cordova, F. M., & Rother, H. C. (2018). Reviewing homeostasis of sustainable energy systems: How reactive and predictive homeostasis can enable electric utilities to operate distributed generation as part of their power supply services. Renewable and Sustainable Energy Reviews, 81, 2879-2892.
19
Yanine, F., Sanchez-Squella, A., Parejos, A., Barrueto, A., Rother, H., & Sahoo, S. K. (2019). Grid-tied distributed generation with energy storage to advance renewables in the residential sector: tariff analysis with energy sharing innovations; Part I. Procedia Computer Science, 162, 111-118.
20
ORIGINAL_ARTICLE
The Role of Parental Mediation in the Relationship between Adolescents’ Use of Social Media and Family Relationships in Saudi Arabia
This study aimed to examine the impact of parenting mediation strategies on family relationships and social media use among Saudi adolescents. To achieve the aim, a quantitative research design was used, involving questionnaires with data collected from 393 Saudi students aged 13-18 years. Pearson correlation and hierarchical multiple regression analyses were performed. The key findings of this study showed that Snapchat and Instagram were the most popular social media sites among Saudi adolescents, and parenting mediation strategies were affected by family relationships. Just over a third of participants (35.62%) reported that they spent 3-5 hours per day on social media with another 30.79% spending more than 6 hours per day on social media. Family relationships were found to strongly predict the social integration and social media addiction. The data showed a significant negative correlation between excessive use of social media and two components of family relationships (cohesion and expressiveness). Moreover, the results suggest that lower levels of family expressiveness and higher levels of family conflict were associated with social media addiction. The parenting mediation strategies were shown to predict the cohesiveness and expressiveness of family relationships. Finally, technical and monitoring parenting mediation strategies were found significant associated with the social media use and the family relationships. These results contribute to formulating guidelines for parents and policymakers in developing countries such as Saudi Arabia to protect their children from the social media risks.
https://jitm.ut.ac.ir/article_75799_65e3e454b1a1d7437ec0deb3276a4abd.pdf
2020-06-01
163
183
10.22059/jitm.2020.75799
Parenting mediation strategies
Social media use
family relationships
Adolescents
Saudi Arabia
Najwa
Albeladi
nsha1@le.ac.uk
1
PhD Candidate, Department of Neuroscience, Psychology & Behaviour, University of Leicester, George Davies Centre, Leicester, LE1 7HA, UK,
LEAD_AUTHOR
Emma
Palmer
ejp8@leicester.ac.uk
2
Reader in Forensic Psychology, Department of Neuroscience, Psychology & Behaviour, University of Leicester, George Davies Centre, Leicester, LE1 7HA, UK,
AUTHOR
Age Restrictions on Social Media Services (n.d.) UK Safer Internet Centre wins award. Age Restrictions on Social Media Services. Retrieved February 23, 2019, from https://www.saferinternet.org.uk/blog/age-restrictions-social-media-services.
1
Ali, S., Harbi, H. A. A., & Rahman, S. R. (2018). Relationship between Use of Social Media and Depression among Female Teenagers in Buraidah, AlQassim, Saudi Arabia. Journal of Child and Adolescent Behavior, 06(03). doi: 10.4172/2375-4494.1000374
2
Anderson, M. and Jiang, J. (2018). Teens, social media, & technology 2018. Pew Research Centre. Retrieved January 23, 2020 from http://www.pewinternet.org/2018/05/31/teens-social-media-technology-2018/.
3
Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a Facebook Addiction Scale. Psychological Reports, 110(2), 501–517. doi: 10.2466/02.09.18.pr0.110.2.501-517
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Benrazavi, R., Teimouri, M., & Griffiths, M. D. (2015). Utility of Parental Mediation Model on Youth’s Problematic Online Gaming. International Journal of Mental Health and Addiction, 13(6), 712–727. doi: 10.1007/s11469-015-9561-2
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Bersamin, M., Todd, M., Fisher, D. A., Hill, D. L., Grube, J. W., & Walker, S. (2008). Parenting Practices and Adolescent Sexual Behavior: A Longitudinal Study. Journal of Marriage and Family, 70(1), 97–112. doi: 10.1111/j.1741-3737.2007.00464.x
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Chen, G. M. (2011). Tweet this: A uses and gratifications perspective on how active Twitter use gratifies a need to connect with others. Computers in Human Behavior, 27(2), 755–762. doi: 10.1016/j.chb.2010.10.023
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11
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12
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13
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28
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29
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ORIGINAL_ARTICLE
IRHM: Inclusive Review Helpfulness Model for Review Helpfulness Prediction in E-commerce Platform
Online reviews have become essential aspect in E-commerce platforms due to its role for assisting customers’ buying choices. Furthermore, the most helpful reviews that have some attributes are support customers buying decision; therefore, there is needs for investigating what are the attributes that increase the Review Helpfulness (RH). This research paper proposed novel model called inclusive review helpfulnessmodel (IRHM) can be used to detect the most attributes affecting the RH and build classifier that can predict RH based on these attributes. IRHM is implemented on Amazon.com using collection of reviews from different categories. The results show that IRHM can detect the most important attributes and classify the reviews as helpful or not with accuracy of 94%, precision of 0.20 and had excellent area under curve close to 0.94.
https://jitm.ut.ac.ir/article_75800_0bbe6364b966d46314f4fd6fca4c0657.pdf
2020-06-01
184
197
10.22059/jitm.2020.75800
Review helpfulness
Recommender System
Machine learning
Sentiment analysis
Yasamyian
Almutairi
yalmutairi0033@stu.kau.edu.sa
1
Master student in Information system at king Abduziz University, Jeddah, Saudi Arabia.
AUTHOR
Manal
Abdullah
maaabdullah@kau.edu.sa
2
Associated Professor in king Abuduziz University, Jeddah, Saudi Arabia.
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