Vinai George Biju, Prashant CM



The Generalized Additive Model is found to be a convenient framework due of its flexibility in non-linear predictor specification.  It is possible to combine several forms of smooth plus Gaussian random effects and use numerically accurate and wide-ranging fitting smoothness estimates. The Newton interpretation of smoothing provides standardized interval approximations.  The Model assortment through additional selection penalties and p-value estimates is proposed along with bivariate combination of input variables capturing different non-linear relationship. The proposed extension includes, using non-exponential family distribution, orderly categorical models, negative binomial distributions, and multivariate additive models, log-likelihood based on Laplace and Newton models. The general problem is that there is not one particular architecture do everything with an exponential GAM family.


Generalized Additive Model,Newton Approximation, Laplace,Diabetic Retinopathy,


I. Baquero OS, Santana LM, Chiaravalloti-Neto F. Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models. PloS one. 2018;13(4).

II. da Silva Marques D, Costa PG, Souza GM, Cardozo JG, Barcarolli IF, Bianchini A. Selection of biochemical and physiological parameters in the croaker Micropogoniasfurnieri as biomarkers of chemical contamination in estuaries using a generalized additive model (GAM). Science of The Total Environment. 2019 Jan 10;647:1456-67.

III. Diankha O, Thiaw M. Studying the ten years variability of Octopus vulgaris in Senegalese waters using generalized additive model (GAM). International Journal of Fisheries and Aquatic Studies. 2016;2016:61-7.
IV. Falah F, GhorbaniNejad S, Rahmati O, Daneshfar M, Zeinivand H. Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods. Geocarto international. 2017 Oct 3;32(10):1069-89.

V. Gu C. Smoothing spline ANOVA models: R package gss. Journal of Statistical Software. 2014 Jun 30;58(5):1-25.

VI. Hastie T, Tibshirani R. Generalized additive models for medical research. Statistical methods in medical research. 1995 Sep;4(3):187-96.

VII. Jiang Y, Gao WW, Zhao JL, Chen Q, Liang D, Xu C, Huang LS, Ruan LM. Analysis of influencing factors on soil Zn content using generalized additive model. Scientific reports. 2018 Oct 22;8(1):1-8.

VIII. Li S, Zhai L, Zou B, Sang H, Fang X. A generalized additive model combining principal component analysis for PM2. 5 concentration estimation. ISPRS International Journal of Geo-Information. 2017 Aug;6(8):248.

IX. Matsushima S. Statistical learnability of generalized additive models based on total variation regularization. arXiv preprint arXiv:1802.03001. 2018 Feb 8.

X. Pedersen EJ, Miller DL, Simpson GL, Ross N. Hierarchical generalized additive models: an introduction with mgcv. PeerJ Preprints; 2018 Nov.

XI. Ravindra K, Rattan P, Mor S, Aggarwal AN. Generalized additive models: Building evidence of air pollution, climate change and human health. Environment international. 2019 Nov 1;132:104987.

XII. Tanskanen J, Taipale S, Anttila T. Revealing hidden curvilinear relations between work engagement and its predictors: Demonstrating the added value of generalized additive model (GAM). Journal of Happiness Studies. 2016 Feb 1;17(1):367-87.

XIII. Wood SN. Generalized additive models: an introduction with R. Chapman and Hall/CRC; 2017 May 18.

XIV. Yoon H. Effects of particulate matter (PM10) on tourism sales revenue: A generalized additive modeling approach. Tourism Management. 2019 Oct 1;74:358-69.

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Ritika Kumari, R.L Ujjwal, Vishwa Pratap Singh



Name Data Networking is a future Internet architecture and it depends on data. NDN takes advantage of the current Internet Architecture and aims to address the weaknesses. In NDN, interest messages are used to retrieve data. Each data has a name that is embedded inside each interest packet. Routers use these names to forward the messages as NDN does not use source or destination address. For each interest packet, a packet is issued that is called a Data packet or D-packet. D-pkt holds the name of the content and the data itself. In this paper, we propose a data structure which is the hybrid of Cuckoo filter and Trie for the name lookup process in NDN.


NDN model,Cuckoo Filter based Name Lookup, Bloom Filter-Based Name Lookup, NDN forwarding Overview , Routing and Forwarding in Name Data Networking,


I. Amadeo M, Campolo C, Molinaro A. Forwarding strategies in named data wireless ad hoc networks: Design and evaluation. Journal of Network and Computer Applications. 2015 Apr 1;50:148-58.
II. Bacanin N. An object-oriented software implementation of a novel cuckoo search algorithm. InProc. of the 5th European Conference on European Computing Conference (ECC’11) 2011 Apr 28 (pp. 245-250).
III. DiBenedetto S, Papadopoulos C, Massey D. Routing policies in named data networking. InProceedings of the ACM SIGCOMM workshop on Information-centric networking 2011 Aug 19 (pp. 38-43).
IV. Ding W, Yan Z, Deng RH. A survey on future Internet security architectures. IEEE Access. 2016 Jul 29;4:4374-93.
V. Fan B, Andersen DG, Kaminsky M, Mitzenmacher MD. Cuckoo filter: Practically better than bloom. InProceedings of the 10th ACM International on Conference on emerging Networking Experiments and Technologies 2014 Dec 2 (pp. 75-88).
VI. Massawe EA, Du S, Zhu H. A scalable and privacy-preserving named data networking architecture based on Bloom filters. In2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops 2013 Jul 8 (pp. 22-26). IEEE.
VII. Mun JH, Lim H. Cache sharing using bloom filters in named data networking. Journal of Network and Computer Applications. 2017 Jul 15;90:74-82.
VIII. Najafimehr M, Ahmadi M. SLCF: Single-hash lookup cuckoo filter. Journal of High Speed Networks. 2019(Preprint):1-2.
IX. Pan J, Paul S, Jain R. A survey of the research on future internet architectures. IEEE Communications Magazine. 2011 Jun 30;49(7):26-36.
X. Quan W, Xu C, Guan J, Zhang H, Grieco LA. Scalable name lookup with adaptive prefix bloom filter for named data networking. IEEE Communications Letters. 2013 Dec 6;18(1):102-5.
XI. Saxena, D., Raychoudhury, V., Suri, N., Becker, C. and Cao, J., 2016. Named data networking: a survey. Computer Science Review, 19, pp.15-55.
XII. WangL, Hoque AK, Yi C, Alyyan A, Zhang B. OSPFN: An OSPF based routing protocol for named data networking. Technical Report NDN-0003; 2012 Jul 25.
XIII. Wang L, Lehman V, Hoque AM, Zhang B, Yu Y, Zhang L. A secure link state routing protocol for NDN. IEEE Access. 2018 Jan 4;6:10470-82.
XIV. Yi C, Afanasyev A, Moiseenko I, Wang L, Zhang B, Zhang L. A case for stateful forwarding plane. Computer Communications. 2013 Apr 1;36(7):779-91.
XV. Yi C, Afanasyev A, Wang L, Zhang B, Zhang L. Adaptive forwarding in named data networking. ACM SIGCOMM computer communication review. 2012 Jun 26;42(3):62-7.
XVI. Yi C, Abraham J, Afanasyev A, Wang L, Zhang B, Zhang L. On the role of routing in named data networking. InProceedings of the 1st ACM Conference on Information-Centric Networking 2014 Sep 24 (pp. 27-36).
XVII. Yuan H, Song T, Crowley P. Scalable NDN forwarding: Concepts, issues and principles. In2012 21st International Conference on computer communications and networks (ICCCN) 2012 Jul 30 (pp. 1-9). IEEE.

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Mohammed Ali Shaik , P. Praveen



K-means clustering algorithm and its variants have many drawbacks and one of the major one is getting stuck at local optima while calculating centroids over random values. Algorithms that optimize computation are iterative in nature for speeding up the process of creation or search of data by multiple search agents. Swarm intelligence (SI), is a primary aspect of artificial intelligence that comprises of high complexity problems and proposed solutions that are sub-optimal and achievable in a given time span. SI adopts cooperative character of an organized group of animals that are formed on the phrase: strive to survive and in this paper we provide a detailed survey of eight different SI algorithms that are related to insect and animal based algorithms and provides initial understanding and exploring of technical aspects of algorithms.


Swarm intelligence,Machine learning, K-means,Bio-inspired algorithms,Intelligent algorithms, Literature review,Nature-inspired computing,


I. B. Ozden, S. Ramaswamy, A. Silberschatz. Cyclic associ-ation rules. Proceedings of the 15 th International Conference on Data Engineering. 1998, 412-421.
II. D. R. Li, S. L. Wang, W. Z. Shi et al. On Spatial Data Mining and Knowledge Discovery [J]. Geomatics and Information Science of Wuhan University, 2001, 26(6): 491-499.
III. Dr. Seena Naik, “An Effective use of Data Mining Techniques to Creation”, International Journal of Advancement in Engineering, OCT, 2016, volume:3, Edition:10, pp.157-163, ISSN:2349-3224.
IV. D. Ramesh, Syed Nawaz Pasha, G.Roopa,”A Comparative Analysis of Classification Algorithms on Weather Dataset Using Data Mining Tool”, Oriental Journal of Computer Science and Technology, DEC, 2017, Volume:10, Issue:4, Pp.1-5, ISSN:0974-6471.
V. Forsyth P, Wren A (1997) an ant system for bus driver scheduling. Research Report 97.25, University of Leeds School of Computer Studies
VI. J. Han, G. Dong, Y. Yin. Efficient mining of partial periodic patterns in time series database. In Proc. 1999 Int. Conf. Data Engineering (ICDE’99), pages 106-115, Sydney, Australia, April 1999.
VII. Ji, Xue, et al. “PRACTISE: Robust prediction of data center time series.” International Conference on Network & Service Management 2015. G. Box, G. M. Jenkins. Time series analysis: Forecasting and control, Holden Day Inc., 1976.
VIII. Kuo RJ, Chiu CY, Lin YJ (2004) Integration of fuzzy theory and ant algorithm for vehicle routing problem with time window. In: IEEE annual meeting of the fuzzy information, 2004. Processing NAFIPS’04, vol 2, pp 925–930. IEEE
IX. Mishra A, Agarwal C, Sharma A, Bedi P (2014) Optimized gray-scale image watermarking using DWT-SVD and firefly algorithm. expert syst appl 41(17):7858–7867
X. Mohammed Ali Shaik, “A Survey on Text Classification methods through Machine Learning Methods”, International Journal of Control and Automation, Vol. 12, No.6, (2019), pp. 390 – 396.
XI. Mohammed Ali Shiak, “A Survey of Multi-Agent Management Systems for Time Series Data Prediction”, International Journal of Grid and Distributed Computing, Vol. 12, No. 3, (2019), pp. 166-171.
XII. Mohammed Ali Shaik, “Time Series Forecasting using Vector quantization”, International Journal of Advanced Science and Technology, Vol. 29, No. 4, (2020), pp. 169-175.
XIII. Mohammed Ali Shaik, S Narsimha Rao, Abdul Rahim, “A SURVEY OF TIME SERIES DATA PREDICTION ON SHOPPING MALL”, Indian Journal of Computer Science and Engineering (IJCSE),Vol. 4 No.2 Apr-May 2013, ISSN : 0976-5166, pp. 174-184.
XIV. Mohammed Ali Shaik, P.Praveen, Dr.R.Vijaya Prakash, “Novel Classification Scheme for Multi Agents”, Asian Journal of Computer Science and Technology, ISSN: 2249-0701 Vol.8 No.S3, 2019, pp. 54-58.
XV. Nakamura RY, Pereira LA, Costa KA, Rodrigues D, Papa JP, Yang XS (2012) BBA: a binary bat algorithm for feature selection. In 2012 25th SIBGRAPI conference on graphics, patterns and images. IEEE, pp 291–297
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XVII. Praveen P., Rama B. (2018) A Novel Approach to Improve the Performance of Divisive Clustering- BST. In: Satapathy S., Bhateja V., Raju K., Janakiramaiah B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542. Springer, Singapore.
XVIII. R. Ravi Kumar, M. Babu Reddy and P. Praveen, “A review of feature subset selection on unsupervised learning,” 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, 2017, pp. 163-167.doi: 10.1109/AEEICB.2017.7972404.
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XX. Shekhar, P. Zhang, Y. Huang et al. Trends in Spatial Data Mining. In: H.Kargupta, A.Joshi(Eds.), Data Mining: Next Generation Challenges and Future Directions[C]. AAAI/MIT Press, 2003, 357-380.
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XXII. T. Sampath Kumar,B. Manjula, D. Srinivas,”A New Technique to Secure Data Over Cloud”, Jour of Adv Research in Dynamical & Control Systems, 11-Special Issue, July 2017.
XXIII. T. Sampath Kumar, B. Manjula, Mohammed Ali Shaik, Dr. P. Praveen, “A Comprehensive Study on Single Sign on Technique”, International Journal of Advanced Science and Technology (IJAST), ISSN:2005-4238E-ISSN:2207-6360, Vol-127-June-2019

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Ameena Karem Essa, Haifa Taha Abd



This study aims to test a new treatment that has been developed for type 2 diabetes, by estimating the response of diabetics by experimenting number of mixed linear models, non-parametric, where they were compared by relying on the coefficient of determination and the standard error for the random errors in order to determine the appropriate model and then measure the effectiveness This new treatment is for type 2 diabetes. Therefore, some non-parametric methods were used in estimating the average response for the mixed linear model. The method of the kernel smoothing function was used by employing the Gaussian and Epanchnikov family functions, as well as some formulas of the Cross Validation method. To estimate Bandwidth as Scott and Silverman. An experiment for a new treatment for type 2 diabetes was chosen as an application of the mixed linear model, by experimenting with this drug on a sample of patients who were divided into three different age groups and performing laboratory tests for a period of three months, and then estimating their response rates to the new drug through four models Different. The results demonstrated that the A mixed non-parametric linear model with (Gaussian) function and the (Scott) package was the best fit model for this study, as it gave the largest determination coefficient and the lowest standard deviation of the error, as well as the new drug, was not effective in regulating blood sugar level for all age groups of patients.


Linear mixed model Non-parametric ,Kernel Smoothing,Bandwidth,


I. Carroll, R.J., Delaigle, A. and Hall, P. (2007). “Nonparametric Regression Estimation from Data Contaminated by a Mixture of Berkson and Classical Errors”. Journal of the Royal Statistical Society, 69, 859-878.
II. Czado, C, (2007). “Linear Models with Random Effects”. Lecture notes, web paper.
III Hardle, W., (1994), “Applied Nonparametric Regression”. Humboldt – University, Berlin, Germany.
IV Heather, T., (2008). “Introduction to Generalized Linear Models”. ESRC National Centre for Research Methods, University of Warwick, UK.
V Jiang, J. (2007).” Linear and Generalized Linear Mixed Models and Their Applications”. Springer, New York.
VI McCullagh, P., and Nelder, J. A‖ Generalized Linear Models‖, (1989). 2nd ed. London: Chapman& Hall.
VII Nelder, J., Wedderburn.W., (1972).” Generalized Linear Models‖, Journal of the Royal Statistical Society. Series A (General), 135(3), 370-384.
VIII Racine J, Li, Q., (2004). “Nonparametric Estimation of Regression Functions with both Categorical and Continuous Data.” Journal of Econometrics, 119(1), 99–130.
IX Racine JS, Li Q, Zhu X (2004). “Kernel Estimation of Multivariate Conditional Distributions.”. Annals of Economics and Finance, 5(2), 211–235.
X – Wand M, Ripley, B., (2008).” Kern Smooth Functions for Kernel Smoothing R package”, version 2.22-22, URL
XI – Watson, G., (1964). “Smooth Regression Analysis.” Sankhya, 26(15), 359–372.
XII- Yin, Z., Liu, F.& Xie, Y., (2016). “Nonparametric Regression Estimation with Mixed Measurement Errors “. Applied Mathematics, (7), 2269-2284.

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Yousef A.Baker El-Ebiary, Salameh A. Mjlae, Waheeb Abu-Ulbeh, Ahmed Hassan Hassan, Samer Bamansoor , Syarilla Iryani A. Saany



In a very competitive and fast emerging IT and wireless technology need a company to move fast and it demands the company to have the correct decision support system in choosing suppliers. The right system helps company to gain useful and meaningful data in making the right decision of selecting the right suppliers which helps them to improve their performance and sustainable in the industry that they are involve in. In making the right decision of selecting suppliers the factors of efficiency and effectiveness of the decision support system used have to be concerned.In this paper, different selection methods considering their effectiveness and efficiency systems used in choosing suppliers is discussed.


Support System (DSS), System Enterprise, Information Systems,Supplier selection,Business Sustainable,


I Agnieszka Konys 2019, Methods Supporting Supplier Selection Processes – Knowledge-based Approach,Procedia Computer Science, Volume 159, Pages 1629-1641

II El-Ebiary, Y. A. B., Al-Sammarraie, N. A. & Saany, S. I. A. (2019). “Analysis of Management Information Systems Reports for Decision-Making”. (IJRTE), 8(IC2), pp. 1150-1153.

III El-Ebiary, Y. A. B., Al-Sammarraie, N. A., & Saany, S. I. A. (2019). “The Implementation of M-Commerce in Supply Chain Management System”. 3C Tecnologia, May, pp. 223-240.

IV El-Ebiary, Y.; Najam, I.; Abu-Ulbeh, W. (2018). The Influence of Management Information System (MIS) in Malaysian’s Organisational Processes—Education Sector, Advanced Science Letters, 24(6), pp. 4129-4131(3).

V Galankashi, M. R., Helmi, S. A., & Hashemzahi, P. (2016). Supplier selection in automobile industry: A mixed balanced scorecard–fuzzy AHP approach. Alexandria Engineering Journal, 55(1), 93-100.

VI Kar, A. K. (2015). A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network. Journal of Computational Science, 6, 23–33. doi:10.1016/j.jocs.2014.11.002

VII Konys, A. (2018). An Ontology-Based Knowledge Modelling for a Sustainability Assessment Domain. Sustainability, 10(2), 300. doi:10.3390/su10020300

VIII Li, J., Sun, M., Han, D., Wu, X., Yang, B., Mao, X., & Zhou, Q. (2018). Semantic multi-agent system to assist business integration: An application on supplier selection for shipbuilding yards. Computers in Industry, 96, 10–26. doi:10.1016/j.compind.2018.01.001

IX Mirchandani, D., & Pakath, R. (1999). Four models for a decision support system. Information & Management, 35(1), 31–42. doi:10.1016/s0378-7206(98)00074-3

X Polat, G., & Eray, E. (2015). An integrated approach using AHP-ER to supplier selection in railway projects. Procedia Engineering, 123, 415-422.

XI Scott, J., Ho, W., Dey, P. K., & Talluri, S. (2015). A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments. International Journal of Production Economics, 166, 226-237.

XII Shi, P., Yan, B., Shi, S., & Ke, C. (2015). A decision support system to select suppliers for a sustainable supply chain based on a systematic DEA approach. Information Technology and Management, 16(1), 39-49.

XIII Sivakumar, R., Kannan, D., & Murugesan, P. (2015). Green vendor evaluation and selection using AHP and Taguchi loss functions in production outsourcing in mining industry. Resources Policy, 46, 64-75.

XIV YAB EL-EBIARY (2016). Management Information Systems and Their Importance in the Decision-Making. International Journal of Latest Engineering and Management Research (IJLEMR), 1(7) PP. 10-14.

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Yousef A.Baker El-Ebiary, Salameh A. Mjlae, Waheeb Abu-Ulbeh, Ahmed Hassan Hassan, Samer Bamansoor, Syarilla Iryani A. Saany



Management Information System (MIS) is the use of information technology, people, and business processes to record, store and process data to produce data-driven information that helps managers to derive decisions for the organizations.The decision is consciously taken from a variety of alternatives and the consent of many is based on the goal of achieving the desired outcome. MIS can be defined as a collection of systems, hardware, procedures, and people that all work together to process, store, and produce information that is useful to the organization. It is an important system for every organization that needsto have to ensure they remain competitive in the market. However, not all MIS fulfil the requirements from stakeholders. Some have failed to do so due to several factors such as poor requirement design or improper training to the users. Therefore, in this study, the paper focus to identify the key criteria that contribute to effectiveness in developing the “fit” MIS based on previous studies. The criteria discussed in detail by hoping this find out will become major guidelines to create a good MIS.


Management Information System (MIS),Information Systems,Middle Management,Enterprise Systems, Decision-Making,


I Al Shobaki, M. J., & Abu-Naser, S. S. (2017). The Requirements of Computerized Management Information Systems and Their Role in Improving the Quality of Administrative Decisions in the Palestinian Ministry of Education and Higher Education.

II Amuna, Y. M. A., Al Shobaki, M. J., & Naser, S. S. A. (2017). The Role of Knowledge-Based Computerized Management Information Systems in the Administrative Decision-Making Process.

III Babaei, M., & Beikzad, J. (2013). Management information system, challenges, and solutions. European Online Journal of Natural and Social Sciences: Proceedings, 2(3 (s)), pp-374.

IV Bendre, M. P., Murukate, M. P., Desai, M. V., Dhenge, M. D., & Kelkar, M. B. (2017). Management Information System.

V Berisha-Shaqiri, A. (2015). Management Information System and Competitive Advantage. Mediterranean Journal of Social Sciences, 6(1), 204.

VI Chițescu, R. I. (2015). Informational Management System and Its Challenges at Decision Level. SEA–Practical Application of Science, 3(08), 33-38.

VII Djilali, B. (2017). The Effect of Information Systems Efficiency On Effectiveness of Decision Making: A Field Study in Algerian Banks.

VIII El-Ebiary, Y. A. B., Al-Sammarraie, N. A. & Saany, S. I. A. (2019). “Analysis of Management Information Systems Reports for Decision-Making”. (IJRTE), 8(IC2), pp. 1150-1153.

IX El-Ebiary, Y.; Najam, I.; Abu-Ulbeh, W. (2018). The Influence of Management Information System (MIS) in Malaysian’s Organisational Processes—Education Sector, Advanced Science Letters, 24(6), pp. 4129-4131(3).

X Elhadi, O. A., & Quanxiu, L. (2013). Public sector employees’ view (s) of obstacles facing the development of Management Information Systems in the River Nile State-Sudan.

XI Furduescu, B. A. (2017). Management Information Systems. HOLISTICA–Journal of Business and Public Administration, 8(3), 61-70.

XII Hakimpoor, H., &Khairabadi, M. (2018).Management Information Systems, Conceptual Dimensions of Information Quality and Quality of Managerial Decisions: Modelling Artificial Neural Networks.

XIII Ijoema, M. M. (2018). Importance of Management Information System in service Delivery and Paper Work in Nigeria University. IOSR Journal of Business and Management, 20(9), 30-38.

XIV Kalhoro, S., Rahoo, L. A., Kalhoro, M., & Nagar, M. A. K. (2019). The Meaning and Role of Management Information System in the Telecom Companies in Sindh Province.

XV Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.

XVI Mahasneh, M. S. (2015). The Importance of Management Information Systems in Decision-Making Process in Najran University.

XVII Mohammed, A. N. N. A. M., & Hu, W. (2015). Using Management Information Systems (MIS) to Boost Corporate Performance. International Journal of Management Science and Business Administration, 1(11), 55-61.

XVIII Sarveswaran, K., Perera, P., Nanayakkara, S., Perera, A., & Fernando, S. (2006). Challenges in developing MIS–Case from Government sector.

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Yousef A.Baker El-Ebiary, Salameh A. Mjlae, ,Syarilla Iryani A. Saany, Julaily Aida Jusoh, M. Hafiz Yusoff, Muhamad Syafik Izwan



VR technology has begun entering the repertoir of tools used by all parties. Today, sports goods business uses VR for visualization and sales. For example, VR is used as a promotional tool in selling goods to consumers and providing up-to-date information on the right prices and archery items. Very important, VR can help customers communicate better about the proposed plateform. Therefore, the proposed application for the X-10 Store in Virtual Reality is recommended. The X-10 Shop is a shop to provide information and pricing for beginner users on archery tools and they can also learn about archery equipment before buying it. The main objective of this research is to develop applications, to test the use of this application. This app is a platform for users to create users who want to buy archery tools and easy tools to view information and prices of archery equipment without having to go to the store and waste time to get there. This involves the use of Unity application development software to generate X-10 Shop in Realita Maya.


Virtual Reality, VR,Mobile based application,Computer Application ,Electronic Shop,X-10 Shop,


I Andy Hood, (2017, May 6). Archery Star. Grand Millennium Plaza (Lower Block), 181 Queen’s Road Central, Hong Kong: Taprun Studio.

II El-Ebiary, Y. A. M. A., Saany, S. I. A., Rahman, M. N. A., Alwi, E. A. Z. E., Mohamad, M., & Ahmad, M. M. T. (2019). “Using Smartphone Application to Notify Muslim Travelers the Jama’Qasar Pray, Azan Times and Other Facilities”. (IJEAT), 8(2S2), pp. 366-370.

III Matt Foro, (2018, April 11). Archery Kings VR. United Kingdom: Appnori Inc Developer.

IV Saany, S. I. A., El-Ebiary, Y. A. M. A., Rahman, M. N. A., Alwi, E. A. Z. E., Mohamad, M., & Ahmad, M. M. T. (2019). Lactation Mobile Application in Islam Perspective”. (IJEAT), 8(3S), pp. 271-274.

V Sinteza. (2017). Addie Model for Development of E-Courses. Information Technology in Education, 244.

VI Wiki. (6 May, 2019). Wikipedia. Retrieved from Virtual reality:

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Abdul Samad Dahri, Shafiq-ur-Rehman Massan, Liaquat Ali Thebo



Prisons are overcrowded and running out of capacity globally including Indonesia. The Indonesian justice system is claimed for irregularities and prisoner recidivism issues, thus, needs remedy than ever before to monitor prisoners’ actions. To help this situation, Indonesia is enforcing a restorative justice system for post-prison rehabilitation and reintegration of people back in society. This article has addressed the restorative justice system from Big Data perspective. This might face data management issues and techniques to interpret and extract relevant information. Here, Big Data and analytic techniques are introduced for a breakthrough in Indonesian restorative justice system towards a potentially more controlled and meaningful digital era of correctional programming. Potential implications are unearthed, likewise, recommendations are limitless. Similarly, research terrain is vastly unknown which attracts further investigation in both conceptual and empirical field regarding the law, policy, and practice for overall strong Indonesian judicial system.


Restorative Justice,Big Data, Indonesia,Conceptual paper,


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II. Barbier, G., & Liu, H. (2011). Data mining in social media. In Social network data analytics (pp. 327-352). Springer, Boston, MA.

III. Bucher T (2012) ‘Want to be on the top?’ Algorithmic power and the threat of invisibility on Facebook. New Media and Society 14(7): 1164–1180.

IV. Bradshaw, W., Roseborough, D., &Umbreit, M. S. (2006). The effect of victim offender mediation on juvenile offender recidivism: A meta‐analysis. Conflict Resolution Quarterly, 24(1), 87-98.

V. Chang Z., Larsson H., Lichtenstein P., &Fazel S. (2015). Psychiatric disorders and violent reoffending: A national cohort study of convicted prisoners in Sweden. The Lancet Psychiatry, 2, 891–900. 10.1016/S2215-0366(15)00234-5
VI. Chung, W. (2014). BizPro: Extracting and categorizing business intelligence factors from textual news articles. International Journal of Information Management, 34(2), 272-284.

VII. Fazel S., & Seewald K. (2012). Severe mental illness in 33,588 prisoners worldwide: Systematic review and meta-regression analysis. The British Journal of Psychiatry, 200, 364–373. 10.1192/bjp.bp.111.096370

VIII. Feblowitz, J. (2012). The big deal about big data in upstream oil and gas. IDC Energy Insights, 1-11.

IX. Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. Galaway, B. (1995). Victim‐offender mediation by New Zealand probation officers: The possibilities and the reality. Mediation Quarterly, 12(3), 249-262.

X. FriederDünkel, ‘The Rise and Fall of Prison Population Rates in Europe’, Criminology in Europe, 2016,

XI. Goodrum, P. M., McLaren, M. A., &Durfee, A. (2006). The application of active radio frequency identification technology for tool tracking on construction job sites. Automation in Construction, 15(3), 292-302

XII. Goff A., Rose E., Rose S., & Purves D. (2007). Does PTSD occur in sentenced prison populations? A systematic literature review. Criminal Behavior and Mental Health, 17, 152–162. 10.1002/cbm.653

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XIV. Gundecha, P., & Liu, H. (2012). Mining social media: a brief introduction. In New Directions in Informatics, Optimization, Logistics, and Production (pp. 1-17). Informs.

XV. Hartmann, A., & von Lampe, K. (2008). The German underworld and the Ringvereine from the 1890s through the 1950s. Global Crime, 9(1-2), 108-135.

XVI. Hawton K., Linsell L., Adeniji T., Sariaslan A., &Fazel S. (2014). Self-harm in prisons in England and Wales: An epidemiological study of prevalence, risk factors, clustering, and subsequent suicide. The Lancet, 383, 1147–1154. 10.1016/S0140-6736(13)62118-2

XVII. Hirschberg, J., Hjalmarsson, A., &Elhadad, N. (2010). “You’re as sick as you sound”: Using computational approaches for modeling speaker state to gauge illness and recovery. In Advances in speech recognition (pp. 305-322). Springer, Boston, MA.

XVIII. Joanna Shapland (July, 1 2008). Restorative justice reduces crime by 27%. Retrieved from on March, 24 2019.

XIX. Kays, R., Crofoot, M. C., Jetz, W., &Wikelski, M. (2015). Terrestrial animal tracking as an eye on life and planet. Science, 348(6240), aaa2478

XX. Kristiansen, S., &Trijono, L. (2005). Authority and law enforcement: local government reforms and security systems in Indonesia. Contemporary Southeast Asia: A Journal of International and Strategic Affairs, 27(2), 236-254.

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Arpita Gupta, Nandhini Swaminathan, Ramadoss Balakrishnan



The image-based Facial Emotion Recognition (FER) aims to classify the image into basic emotions being communicated by it. FER is one of the most prominent research areas in computer vision. Most of the existing works are aimed at high-quality images which are collected in the lab environment. These images are very different from the real-life facial emotion that leads to a lack of wild labeled data. Deep learning using transfer learning has shown promising results in computer vision in solving the problem of lack of labeled data.  In the recent system, there is a great focus to overcome the lack of data issue in FER. Our paper has utilized the deep residual networks with inductive learning and self-attention module to overcome this problem. We have experimented different pretraining settings and datasets for the model, which are ImageNet and VGG face dataset (source datasets). The self-attention block is applied for better visual perspective to the model. Our target dataset is FER-2013, a benchmark dataset in FER. TransFER is a deep residual network based on inductive learning and attention module. Our proposed approach has achieved superior performance than the existing state of the art models in the FER application using transfer learning.


Facial Emotion Recognition, Deep Learning,Deep Residual Networks,Transfer Learning, Inductive Learning, Self-Attention,


I Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. “Imagenet: A large-scale hierarchical image database.” In 2009 IEEE conference on computer vision and pattern recognition, pp. 248-255. Ieee, 2009.

II Devries, Terrance, Kumar Biswaranjan, and Graham W. Taylor. “Multi-task learning of facial landmarks and expression.” In 2014 Canadian Conference on Computer and Robot Vision, pp. 98-103. IEEE, 2014.

III Ekman, Paul, and Wallace V. Friesen. “Constants across cultures in the face and emotion.” Journal of personality and social psychology 17, no. 2 (1971): 124.

IV Geng, Mengyue, Yaowei Wang, Tao Xiang, and Yonghong Tian. “Deep transfer learning for person re-identification.” arXiv preprint arXiv:1611.05244 (2016).

V Goodfellow, Ian J., Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski et al. “Challenges in representation learning: A report on three machine learning contests.” In International Conference on Neural Information Processing, pp. 117-124. Springer, Berlin, Heidelberg, 2013.

VI He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. “Deep residual learning for image recognition.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.

VII Li, Shan, and Weihong Deng. “Deep emotion transfer network for cross-database facial expression recognition.” In 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3092-3099. IEEE, 2018.

VIII Liu, Mengyi, Shaoxin Li, Shiguang Shan, and Xilin Chen. “Au-aware deep networks for facial expression recognition.” In 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1-6. IEEE, 2013.

IX Miao, Yun-Qian, Rodrigo Araujo, and Mohamed S. Kamel. “Cross-domain facial expression recognition using supervised kernel mean matching.” In 2012 11th International Conference on Machine Learning and Applications, vol. 2, pp. 326-332. IEEE, 2012.

X Mollahosseini, Ali, David Chan, and Mohammad H. Mahoor. “Going deeper into facial expression recognition using deep neural networks.” In 2016 IEEE Winter conference on applications of computer vision (WACV), pp. 1-10. IEEE, 2016.

XI Ng, Hong-Wei, Viet Dung Nguyen, Vassilios Vonikakis, and Stefan Winkler. “Deep learning for emotion recognition on small datasets using transfer learning.” In Proceedings of the 2015 ACM on international conference on multimodal interaction, pp. 443-449. 2015.

XII Ouellet, Sébastien. “Real-time emotion recognition for gaming using deep convolutional network features.” arXiv preprint arXiv:1408.3750 (2014).

XIII Parkhi, Omkar M., Andrea Vedaldi, and Andrew Zisserman. “Deep face recognition.” (2015).

XIV Sandbach, Georgia, Stefanos Zafeiriou, Maja Pantic, and Daniel Rueckert. “Recognition of 3D facial expression dynamics.” Image and Vision Computing 30, no. 10 (2012): 762-773.

XV Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and IlliaPolosukhin. “Attention is all you need.” In Advances in neural information processing systems, pp. 5998-6008. 2017.

XVI Xu, Mao, Wei Cheng, Qian Zhao, Li Ma, and Fang Xu. “Facial expression recognition based on transfer learning from deep convolutional networks.” In 2015 11th International Conference on Natural Computation (ICNC), pp. 702-708. IEEE, 2015.

XVII Yan, Haibin, Marcelo H. Ang, and AunNeow Poo. “Cross-dataset facial expression recognition.” In 2011 IEEE International Conference on Robotics and Automation, pp. 5985-5990. IEEE, 2011.

XVIII Zhang, Zhanpeng, Ping Luo, Chen-Change Loy, and Xiaoou Tang. “Learning social relation traits from face images.” In Proceedings of the IEEE International Conference on Computer Vision, pp. 3631-3639. 2015.

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Amr Mohammed Sayed Emam Sallam, AllaaEddin Ismaail, Mohammed Ebrahim El Sherbiny Sakr, Mohammed Elsayed Mohammed Mohammed Abdou, Yousef A.Baker El-Ebiary



Abstract With the great development that the world has witnessed with regard to technology, and specifically the emergence of the Internet, there have been a number of negative and positive impacts on the individual and society, perhaps the greatest benefit of the Internet is to provide access to infinite information and knowledge with ease by browsing the websites spread on the Internet and the ease of communication The Internet has contributed greatly to the ease of communication and sharing between individuals regardless of distances, and has provided immediate access to anyone in the world. However, in light of the huge spread of information, it is difficult to choose the correct and accurate information, and among the misconceptions on the Internet are atheism or disbelief in God and inclination through the people of faith, rationality, disbelief in resurrection, heaven, fire, and dedication of all life to the world only, which is what is today called "secular or non-religious" Atheists base their ideas on their denial of the unseen altogether and in detail their mockery of rituals their fierce war against good morals and customs maximizing them for matter and nature. This research aims to clarify the full meaning of atheism in terms and form, and the importance of technology in the spread of information..


Electronic Information,The Internet,Online Data,Atheism, Social Media,


I. Abd al-Rahman Abd al-Khaleq: Atheism, the causes of this phenomenon and methods of treating it, Saudi Ministry of Ifta, 2nd edition, 1404.
II. AllaaEddinIsmaail, Mohammed Elsayed Mohammed MohammedAbdou, Amr Mohammed SayedEmamSallam, Mohd Faizal, A.K., Yousef A.Baker El-Ebiary, MohammedEbrahim El SherbinySakr. “Christian Attitudes towards the Bible through Wikipedia Content” Volume 83, Issue: May – June 2020, P: 9260 – 9269. (TEM).
III. An article in the BBC’s Atheism in the Arab World: Why Have Some People Abandoned Religion? (August 31, 2015).
IV. Arabia Net on Wednesday 06 Safar 1434 AH – December 19, 2012 AD.
V. Interpretation of Al-Tabari – Dar Al-Hadith – Cairo, 2016.
VI. Mohammed Ebrahim El SherbinySakr, Amr Mohammed Sayed EmamSallam, Mohammed Elsayed Mohammed Mohammed Abdou, AllaaEddinIsmaail, Yousef A.Baker El-Ebiary. “A Sample of Orientalist Suspicious Contained in The Internet and The Response” Volume 83, Issue: May – June 2020, P: 7026 – 7032. (TEM).
VII. Muhammad Abdullah Draz – Religion – Hindawi Foundation – Cairo – 2014 edition.
VIII. Muhammad Al-Khader Hussein: Atheism, its causes, its natures, its evils, the reasons for its emergence, its treatment, the research of Muhammad Al-Shaibani, Ibn Taymiyyah Library, Kuwait, first edition, 1406.
IX. Shehata H. M. El Sheikh, A.Ghani Bin Md Din, Rabie I. M. H., M. Hamed M. Said, Shaaban A. Hameed R. M., Yousef A.Baker El-Ebiary, “Electronic Content On the Internet and Its Role in The Intellectual and Ideological Extension of the Kharijites Division and Its Impact On the Islamic World”, (JCR). 2020; 7(15): 293-298, doi: 10.31838/jcr.07.15.37.

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