Volume-6 , Issue-10 , Oct - 2018 Go Back

Congestion is an unsolvable and challenging issue for real time data such as voice especially in MANET. Since mobile devices support limited bandwidth, limited battery power, dynamic links and frequently changing routing decision, controlling congestion is a challenging and critical task. Also voice transmission requires valid data to be delivered at receiver by supporting less End-End-Delay. Hence there exists a need to apply dynamic and efficient routing schemes which handle congestion and reduce its severity, proactively avoid congestion before its occurrence. The state of storing packet continuously causes router queue to overflow and drops the incoming packet. Packet drop at these router queues also forecasts nearing congestion. Hence router queue uses various queue management approaches to minimize the occurrence of packet drop and congestion. RED based flavors of proactive queue management schemes received a lot of attention in recent years in controlling congestion over MANET. In this paper, we propose a congestion control approach that supports three different interdisciplinary approaches to control congestion on voice data transmission. Finally we evaluate the performance of the proposed approach using E-Model and compare the performance of the three approaches in order to identify the best approach that supports improved quality at user level. Fuzzy logic based congestion control approach shows improved performance.

CLHCC, PEACR, LPWAP, FANT, BANT

Lingfen Sun, Emmanuel C. Ifeachor, “Voice Quality Prediction Models and Their Application in VoIP Networks”, IEEE Transactions on Multimedia, Vol.8, No.4, Aug 2006.
[2] P.G.Kulkarni, S.I.McClean, G.P.Parr, M.Black, “Light weight ProActive Queue MANagement”, IEEE Transactions on Network and Service Management, Vol.3, No.2, Second Quarter 2006.
[3] K.Dinesh Kumar, I.Ramya , M.Roberts Masillamani, “Queue Management In Mobile Adhoc Networks (Manets), School of Computer Science and Engineering”, IEEE/ACM International Conference on Green Computing and Communications, Dec 2010.
[4] M. Belkadi, M. Lalam, A. M’zoughi, N. Tamani, M. Daoui, R. Aoudjit, “Intelligent Routing and Flow Control in MANETs”, Journal of Computing and Information Technology (CIT), Vol.18, No.3, Sep 2010.
[5] Javad Pashae Barbin , Majid Taghipoor, Vahid Hosseini, “A Novel Algorithm for Manets using Ant Colony”, International Journal of Computer Science Issues (IJCSI), Vol.9, No.1, Jan 2012.
[6] Sreenivasa B.C, G.C. Bhanu Prakash, K.V. Ramakrishnan, “Comparative analysis of ADTCP and ADTCP:Congestion Control Techniques for improving TCP performance over Ad-hoc Networks”, International Journal of Mobile Network Communications & Telematics (IJMNCT), Vol.2, No.4, Aug 2012.
[7] Kajal Yadav, Prof. Gaurav Shrivastava,
“Performance Improvement by Dynamic Queue Management in Mobile Ad-hoc Network”, International Journal of Computer Applications(IJCA), Vol.72, No.1, May 2013.
[8] Hariom Soni, Pradeep Kumar Mishra, “Congestion Control Using Predictive Approach in Mobile Ad-hoc Network”, International Journal of Soft Computing and Engineering (IJSCE), Vol.3, No.4, Sep 2013, ISSN:2231-2307.
[9] H. Zare, F. Adibnia, V. Derhami, “A Rate based Congestion Control Mechanism using Fuzzy Controller in MANETs”, International Journal of Computer Communication, Vol 8, No.3, Jun 2013, ISSN:1841-9836.
[10] D. Jinil Persis,T. Paul Robert, “ Ant Based Multi-objective Routing Optimization in Mobile Ad-hoc Network”, Indian Journal of Science and Technology, Vol.8, No.9, May 2015.
[11]Ritika Mehra, Manjula Saluja, “Adaptive Congestion Control Mechanisms in Mobile Ad-hoc Networks”, International Journal of Engineering Development and Research, Vol.5, No.1, March 2017, ISSN:2221-9939.
[12]Vanita Jain, Aarushi Jain , Achin Jain , Arun Kumar Dubey,” Comparative Study between FA, ACO, and PSO Algorithms for Optimizing Quadratic Assignment Problem”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, No.2, April 2018.
[13]Anurag Singh, Rajnesh Singh, Sunil Gupta,”Evaluating the Performance of TCP over Routing Protocols in MANETs Using NS2”, International Journal of Scientific Research in Network Security and Communication, Vol.6, No.4, August 2018.

V. Savithri, A. Marimuthu, "Assessing the Quality of Voice using E-Model for Optimized Congestion Control approach in Mobile Ad-hoc Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.674-678, 2018.

There has been many changes taking place in the recent arena of Address Resolution Protocol (ARP) Poisoning from the command line interpretation to the graphical interfaces that are developed. The process of ARP Poisoning is one of the famous techniques amongst present Man in The Middle (MITM) attacks. It is applicable to access the various unsecured websites authentication details which can be captured by the attacker and can be visualized on both the command line and graphical user interface (GUI). The entire process or communication takes place through the ethernet or local area network (LAN) and the result of the poisoned address is the physical address of the LAN which acts as a common interface between both the attacker and victim’s machine. This paper, there by explains the entire mechanism of ARP Poisoning that takes place through the LAN by both the command line interpretation (CLI) and the GUI Ettercap which shows the differences amongst both the methods and determines the best method which has the least complexity.

Address Resolution Protocol (ARP), ARP Poisoning, Man in The Middle Attack, Ettercap, Attacker, Victim, Ethernet or Local Area Network (LAN).

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[4] C. Hornig, Standard for the transmission of IP datagrams over Ethernet networks, Internet Engineering Task Force, RFC 826, November 1982.
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[9] S. Jadhav and Mandal,” A survey on network security for open source,” IEEE International Conference of Current Trends in Advanced Computing (ICCTAC), pp. 1-6,2016.

S. Prudhviraj, C. Sudha, "Command line and Graphical interface comparative analysis for ARP Poisoning through Ettercap," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.679-683, 2018.

Big data and its techniques not only help the biomedical and healthcare sectors to forecast the disease prediction but also the patients. It is difficult to meet the doctor at all the times in hospital for minor symptoms. Big data gives necessary information about the diseases based on the symptoms of the patient. Nowadays people wants to know more about their health, diseases and the related treatments for their betterment. However existing health care system gives structured input which lacks in reliable and accurate prediction. Here, Automatic Multi-Disease Prediction (AMDP) technique is proposed which identifies the most accurate disease based on patient’s input which benefits in early detection. Electronic Health Record (EHR) maintains and updates patient health records which facilitate an improved prediction model. Big data uses both structured and unstructured inputs which result in instant guidance to their health issues. The system takes input from the users which checks for various diseases associated with the symptoms based upon analyzing a variety of datasets. If the system is not able to provide suitable results, it intimate the users to go for Clinical Lab Test (CLT) such as blood test, x-ray, and scan so on where the uploaded images are sent for the effective deep learning prediction. The different parameters included in effective automatic multi disease prediction include preprocessing, clustering and predictive analysis. The main objective of the proposed system is to identify the diseases based on the symptoms and give proper guidance for the patients to take treatment quickly without making any further delay in a convenient and efficient manner.

Big Data, AMDP, EHR, Deep Learning Algorithm, CLT

[1]. Min Chen, Yixue Hao, Kai Hwang, Lu Wang, and Lin Wang, “Disease Prediction by Machine Learning over Big Data from Healthcare Communities”, IEEE. 2169-3536, 2017.
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[3]. SujathaR ,Sumathy R and Anitha Nithya R, “A Survey of Health Care Prediction Using Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 5, Issue 8, 2016.
[4]. Pinky Saikia Dutta, Shrabani Medhi, Sunayana Dutta, Tridisha Das and Sweety Buragohain, “Smart Health Care Using Data Mining”, International Journal of Current Engineering And Scientific Research, ISSN : 2393-8374, Vol.-4, Issue-8,2017.
[5]. Ravi Aavula, M.Kruthini, N.Raviteja and K.Shashank, “Smart Health Consulting Android System”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, Issue 3, 2017.
[6]. Priyanka Vijay Pawar, MeghaSakharamWalunj and PallaviChitte, “Estimation based on Data Mining Approach for Health Analysis”, International Journal on Recent and Innovation Trends in Computing and Communication, Volume: 4 , 2017.
[7]. V. Krishnaiah, G. Narsimha and N. SubhashChandra, “A Study On Clinical Prediction Using Data Mining Techniques”, International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), Vol. 3, Issue 1, 2013.
[8]. Rawan Ali AL-jraib, Wafamoqhas, Huda Saeed Salman, AmeeraMohyAdden and Mona Mohmmad, “Biohouse Journal of Computer Science”, International Journal Series, ISSN 2379-1500, 2017.
[9]. Md. Tahmid Rahman Laskar, Md. Tahmid Hossain, Abu RaihanMostofa Kamal and NafiulRashid, “Automated Disease Prediction System (ADPS): A User Input-based Reliable Architecture for Disease Prediction”, International Journal of Computer Applications (0975 – 8887), Vol. 133 – No.15, 2016.
[10]. Aditya Tomar, “An Approach to Devise an Interactive Software Solution for Smart Health Prediction using Data Mining”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 7, 2016.
[11]. VidyaZope, PoojaGhatge, Aaron Cherian, PiyushMantri and KartikJadhav, “Smart Health Prediction using Machine Learning”, IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 12, 2017.
[12]. AakashKhatavkar, PiyushPotpose and PankajkumarPandey, “Smart Health Prediction System”, IJSRD - International Journal for Scientific Research & Development Vol. 5, Issue 02, 2017.
[13]. Prashant Tiwari, AmanJaiswal, NarendraVishwakarm and PushpanjaliPatel, “Smart Health Care - An Android App To Predict Disease On The Basis Of Symptoms”, International Research Journal of Engineering and Technology (IRJET), Vol.: 04 Issue: 04, 2017.
[14]. EvaK.Lee and Tsung-LinWu, “Classification and disease prediction via mathematical programming”, American Institute of Physics, AIP Conference Proceedings, doi: 10.1063/1.2817343, 2007.
[15]. Riccardo Miotto, Li Li, Brian A. Kidd and Joel T. Dudley, “Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records”, DOI: 10.1038 / srep26094, 2016.
[16]. Prasan Kumar Sahoo, Suvendu Kumar Mohapatra and Shih-Lin Wu, “Analyzing Healthcare Big Data with Prediction for Future Health Condition”, IEEE OI 10.1109/ACCESS.2016.2647619, 2016.
[17]. Cheng-HsiungWeng, Tony Cheng-Kui Huang and Ruo-Ping Han, “Disease prediction with different types of neural network Classifiers”, Elsevier Ltd. http://dx.doi.org/10.1016/j.tele.2015.08.006, 2016.
[18]. Xianglin Yang, Yunhai Ton, XiangfengMeng, Shuai Zhao, ZhiXu, YanjunLi,Guozhen Liu and Shaohua Tan, “Online Adaptive Method for Disease Prediction Based on Big Data of Clinical Laboratory Test”, IEEE978-1-4673-9904-3, 2016.
[19]. AjinkyaKunjir, HarshalSawant, NuzhatF.Shaikh, “Data Mining and Visualization for Prediction of Multiple Diseases in Health Care”, IEEE 978-1-5090-6399-4, 2017.

S Manimekalai, R Suguna, S Arulselvarani, "An Intelligent Automatic Multi-Disease Prediction Technique using Data Mining Algorithms and Big Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.684-690, 2018.

With the wide application of Wireless Sensor Network Technology, a large volume of data is generated. For extracting knowledgeful, understandable and valid patterns from this data, data mining techniques are used. This Wireless Sensor Network Data Mining may use Centralized Mining Approach or Distributed Mining approach. Distributed mining, mining is applied on sensor nodes. After that mined data are sent to sink node. But, in centralized approach whole data from sensor nodes are collected at sink node then mining is applied on dataset. This paper focuses on Centralized Data Mining Approach to mine dataset. Here, Classification Techniques, SVM (support Vector Machine) and KNN (K-Nearest Neighbour), are applied on this collected dataset with taking concentration on optimization of CPU cycle as compressible resource. For this execution time to classify data is used here. For this real dataset, it is resulting that KNN is giving better performace than SVM. The dataset is gathered from a real time data acquisition system based on wireless sensor network that is implemented using XBee Digi modules and open source hardware platform Arduino. It is trying to make a hybrid framework, combination of Distributed Approach and Centralized Approach, for this real time deployment of WSN as a future work.

Wireless Sensor Network Data Mining, Centralized Mining Approach, SVM, KNN, Resource Optimization

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survey”, ComputerNetworks, vol. 52,no. 12,pp. 2292–2330, 2008.
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[12] A. Mahmood, K. Shi, S. Khatoon, Mi Xiao, “Data Mining Techniques for Wireless Sensor Networks: A Survey”, International Journal of Distributed Sensor Networks, Vol. 2013, 2013
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[14] P. Singh, “Sensor Association Rules: a Survey”, Vol. 9, Issue 9, pp. 67-71, 2014
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[18] C. Sudha, A. Nagesh, “A Comprehensive Survey on Data Mining Techniques in Wireless Sensor Networks”, International Journal of Computer Sciences and Engineering (IJCSE), Vol. 6, Issue. 6, pp. 1523-1527, 2018.
[19] M. Maksimović, V. Vujović, “Comparative Analysis Of Data Mining Techniques Applied To Wireless Sensor Network Data For Fire Detection”, Journal of Information Technology and Applications, pp. 65-77, 2013
[19] B.-H. Park, H. Kargupta, “Distributed data mining: Algorithms, systems, and applications” Data Mining Handbook, 2002.
[20] S. Sardellitti G. Scutari S. Barbarossa "Joint optimization of radio and computational resources for multicell mobile-edge computing", IEEE Transactions On Signal And Information Processing Over Networks, Vol. 1, no. 2, pp. 89-103, 2015.
[21] B. A. Parbat, R. K. Dhuware, “Real Time Data Acquisition System for WSN Using Arduino for Polyhouse”, International Journal of Computer Sciences and Engineering(IJCSE), Vol. 6, Issue. 8, pp. 608-612, 2018.
[22] L. He, Z. Qiang, W. Zhou, S. o,“A Review of Resource Scheduling in Large-Scale Server Cluster”, International Conference on Knowledge Management in Organizations, pp. 494-505, 2016, ISBN 978-3-319-62698-7.

B. A. Parbat, R. K. Dhuware, "Study of Use of Classification Techniques in WSN Data Mining for Resource Optimization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.691-696, 2018.

The processing of natural languages using Computational Linguistics is an important domain of NLP(Natural Language processing). Many sentences are written in a natural language which may be defined as units, explicitly "linguistic units" which are meaningful and involve one or more words linked together in accordance with a set of predefined rules called ‘Grammar’. Grammar checking is the task of detection and correction of grammatical errors in the text. Though a lot of work is done particularly for English language yet scant work is done for various Asian languages. This paper explores the contribution made by various researchers in this sphere. This paper critically analyses advancements made in relevance to global context. This facilitates better understandability, comparison and evaluation of previous research.

Computational Linguistics, Natural Language Processing, Grammar checking, Grammatical errors

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[20] C. Scarton, M.D. Oliveira, A. Candido , C. Gasperin, & S. Aluísio, "SIMPLIFICA: a tool for authoring simplified texts in Brazilian Portuguese guided by readability assessments", In Proceedings of the NAACL HLT 2010 Demonstration Session Association for Computational Linguistics,pp.41-44, 2010.
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[30] M. Alam, N. UzZaman, & M. Khan, "N-gram based statistical grammar checker for Bangla and English", Ninth International Conference on Computer and Information Technology (ICCIT), December , 2007.
[31] G. Singh, "Development of Punjabi Grammar Checker. Phd. Dissertation", 2008.
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Vikas Verma, S.K. Sharma, "Comparative analysis of Grammar Checkers of various Asian Languages," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.697-700, 2018.

Frequent pattern mining is used to derive association rules. Association rules specify relativity of target class with rest of the feature(s). The Apriori and FP-growth algorithms are the most famous algorithms used for frequent pattern mining. Classification with feature selection approach is also widely used. This paper provides a detailed study of frequent pattern mining using BitApriori algorithm and use mined association rules for performance improvement of unbalanced data classification. We present a model called FPCM which first mine association rules. Mined association rules are than used for features selection. In final phase, selected features are used in unbalanced data classification using decision tree classifier. Our model shows improved accuracy as compare to the past studies.

Frequent pattern mining, Apriori, BitApriori, Unbalanced data classification, machine learning

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[5] Charu C. Aggarwal, Mansurul A. Bhuiyan and Mohammad Al Hasan, "Frequent Pattern Mining Algorithms: A Survey", Switzerland, Springer International Publishing Switzerland, 2014
[6] Abdul Rahaman Wahab Sait, and Dr.T.Meyappan, "Data preprocessing and Transformation Technique to Generate Pattern From the WebLog", Dubai (UAE), ICSIS’2014, Oct 17-18, 2014.
[7] Abdolrashid Rezvani, J Hosseinkhani, "Enhancing the Performance of BitApriori Algorithm in Data Mining using an Effective Data Structure", Switzerland, IJACSIT,Vol. 4, No. 3, 2015, Page: 85-92.
[8] Samiksha Kankane, V garg, "A survey paper on : Frequent Pattern Analysis Algorithm from the Web Log Data", IJCA, Vol-119, June-2015.
[9] D.Usha, Dr.K.Rameshkumar, "A Complete Survey on application of Frequent Pattern Mining and Association Rule Mining on Crime Pattern Mining", International Journal of Advances in Computer Science and Technology, vol-3, April-2014.
[10] Astha Agrawal, Herna L Victor, Eric Paquet, “SCUT: Multi-Class Imbalanced Data Classification using SMOTE and Cluster-based Undersampling”, SCITEPRESS, 2015.
[11] Isaac Triguero, Sara D Rio, V Lopez, J Bacardit, J M Benitez, F Herrera, “ROSEFW-RF: The winner algorithm for the ECBDL’14 big data competition: An extremely imbalanced big data bioinformatics problem”, Knowledge Based System, Elsevier, 28 May 2015.
[12] Sotiris Kotsiantis, Dimitris Kanellopoulos, Panayiotis Pintelas, “Handling imbalanced datasets: A review”, GESTS International Transactions on Computer Science and Engineering, Vol.30, 2006.
[13] Chang Wan, “Test-Cost Sensitive Classification on Data with Missing Values in the Limited Time”, Springer-Verlag Berlin Heidelberg 2010.
[14] K. Rajeswari, “Feature Selection by Mining Optimized Association Rules based on Apriori Algorithm”, IJCA, Vol. 119, Jun-2015
[15] Guangtao Wang, Qinbao Song, “Selecting Feature Subset via Constraint Association Rules”, Springer-Verlag Berlin Heidelberg 2012
[16] UCI repository, http://tunedit.org/repo/UCI/heart-statlog.arff.

Pratik A Barot, H B Jethva, "Unbalanced Data Classification using Feature Selection through BitApriori Algorithm.," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.701-704, 2018.

Popularity of services over cloud can be estimated from the fact that all major mobile and computer system manufacturers are providing free cloud services to their client on purchase of their product. Easy and fast access to internet services have resulted in increased usage of cloud infrastructure. Also, the amount of data being generated and shared over cloud has increased multi-folds. With ever increasing users, resource provider is aware that they cannot afford wastage or misutilization of its resources. Hence, selection of right resource for fulfilling the request of the customer is vital. Scheduling of tasks over cloud is a key research area. Meta-Heuristic algorithms provide efficient solution to this problem. But, each meta-Heuristic algorithm individually suffers from inherent draw backs. So, there is a need to design a scheduling algorithm that does not suffer from the inherent draw backs of any individual meta-heuristic algorithm and is aware of the current utilization of resources in the cloud. In this paper, an improved resource aware hybrid meta-heuristic scheduling algorithm has been designed which reduces the overall Makespan time, Transfer cost and Response time. It also takes into consideration the current utilization of resources in the cloud.

ACO, PSO, VM, Data Centre, Cloud Computing, Cloud, Meta-Heuristic, NIC, Makespan, Response time, Transfer cost

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D. Gupta, H.J.S. Sidhu, "IMPROVED RESOURCE AWARE HYBRID META-HEURISTIC ALGORITHM FOR TASK SCHEDULING IN CLOUD ENVIRONMENT," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.705-711, 2018.

In digital world where data grows rapidly because of more use of internet and number of devices like, smart phones, laptops, personal and machines at a very increase rate. Big data is a pool of large amounts of data collected from various sources, such as social working sites like facebook and transactional data like online shopping sites. Big data is stored in distributed architecture framework. Hadoop is an open source framework for creating distributed applications that process huge amount of data. Hadoop keeps them all together under a single roof with their functionalities. Preparing of information can incorporate different activities like highlighting, indexing and searching. It is highly difficult to process in single machine, with huge amount of data. This paper focused on Concepts and Techniques in Big Data processing.

Big Data , Distributed architecture,Problem, Hadoop,Distributed Processing

[1]. Patel, Aditya B., Manashvi Birla, and Ushma Nair. "Addressing big data problem using Hadoop and Map Reduce." Engineering (NUiCONE), 2012 Nirma University International Conference on. IEEE, 2012.
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B. Suvarnamukhi, M. Seshashayee, "Big Data Concepts and Techniques in Data Processing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.712-714, 2018.

There is a growing interest for mobile ad hoc network (MANET) in the recent years for many time-critical applications, such as military applications or during a disaster recovery scenario in a collaborative manner. In this paper, we proposed a Gwet Kappa Trust Factor-Based Repeated Node Taxonomy Scheme (GKRNTS) for malicious adversaries node detection which focuses on the discrimination of mobile nodes into malicious and benevolent nodes. The interactions between the mobile nodes are periodically monitored and the elucidated data are useful for determining the degree of collaboration between the mobile nodes through the computation of Gwet Kappa. The Gwet Kappa parameter used in this Repeated Node Taxonomy Scheme is stored with each node as an adjacency matrix that stores the interaction activity between the nodes of the network. This adjacency matrix quantifies the extent of cooperation existing between the mobile nodes of the network and they are considered as the taxonomy of the mobile nodes during data communication. The proposed GKRNTS is compared against the TPFPPDM and NPDRDS techniques by simulation using NS2 network simulator has led to promising results in terms of reduced packet rate, energy consumption and computational cost.

MANETs, Node Taxonomy, Gwet Kappa, Malicious Nodes

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R. Saravanan, E. Ilavarasan, "Gwet Kappa Trust Factor-Based Repeated Node Taxonomy Scheme for Malicious Adversaries Detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.715-721, 2018.

A graph is made up of nodes; just like that a social media is a kind of a social network, where each person or organization represents a node. These nodes in a social media are interdependent on each other via common interests, relations, mutual friends, knowledge, common dislikes, beliefs etc. The overall graphical structure of a social media can be very complex with millions of nodes and thousands of interconnections amongst them based upon various grounds. Many researchers have revealed that social network works on various levels and helps in understanding many things such as how an entire organization is run. It helps to solve and understand many critical problems. The analysis of the social media is a very useful tool for extracting knowledge from unstructured data. The knowledge obtained from this field provides a vivid knowledge of various kinds interactions and relations amongst various individuals on social media. The authors have elaborated on the various applications of graph theory on social media and how it is represented viz. strong and weak ties. [1]

Graph, Nodes, Social Media, Graphical structure, Unstructures data, Strong and Weak ties

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[8]. Donglei Du, “Strength of weak ties paradox”.
[9]. Sosial nettverksteori in blog tilhorende kurset INFO207/INF207 pa Universitetet i Bergen, innlegg skrevet av studentene med’
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Anwesha Chakraborty, Trina Dutta, Sushmita Mondal, Asoke Nath, "Application of Graph Theory in Social Media," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.722-729, 2018.