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Mobile Cloud is providing facilities of storage and remote application hosting. Several mobile applications are too computation intensive so power consumption issue is critical problem in mobile devices. Offloading feature in mobile cloud computing reduced power consumption issues of mobile devices. Existing research works have either used fixed mobile device speed or does not consider mobile device speed in estimation of local execution energy. Speed of mobile device plays a significant role in determination of local execution energy and it is affected by parallel running applications and clock frequency of mobile device. Because when there are applications running in parallel, execution speed of mobile is not fixed. In order to counter these issues, this work exploits Exponential Weighted Mean Moving Average to predict device speed according to load on mobile device. We have compared proposed work with two types of systems: Fixed CPU Speed system where CPU speed of mobile device is fixed throughout all offloading decisions, and Oracle which assumes to know exact speed of mobile device in advance. Evaluation of all systems is carried by using synthetic workloads.

Mobile cloud computing, Offloading, Network Bandwidth, Energy saving, Execution speed

[1] K. Kumar, and Y.H. Lu, “Cloud Computing For Mobile Users: Could Offloading Computation Save Energy?,” in Proc. of IEEE Computer Society, Vol. 43, pp.51-56, 2010.
[2] E. Cuervo, A. Balasubramanian, D. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl, “MAUI: Making Smartphones Last Longer with Code Offload,” in Proc. of MobiSys ’10, 2010.
[3] P. Bahl, R. Y. Han, Li Erran, andM. Satyanarayanan, “Advancing the State of Mobile Cloud Computing,” in Proc. of MCS’12, June 25, 2012.
[4] S. Kosta, A. Aucinas, P. Hui, R. Mortier, and X. Zhang, “Thinkair: Dynamic Resource Allocation and Parallel Execution in the Cloud for Mobile Code Offloading,” in Proc. of IEEE INFOCOM, 2012.
[5] S. Patel, “A Survey of Mobile Cloud Computing: Architecture, Existing Work & Challenges”, International Journal of Advanced Research in Computer Science & Software Engineering,Vol. 3, Issue 6, June 2013
[6] N. Fernando, S. W. Loke, and W. Rahayu, “Mobile Cloud Computing: A survey,” Future Generation Computer Systems, Vol. 29, pp. 84-106, 2013.
[7] M. V. Barbera, S. Kosta, A. Mei, and J. Stefa, “To Offload or Not to Offload? The Bandwidth & Energy Costs of Mobile Cloud Computing,” in Proc. of IEEE INFOCOM, 2013.
[8] N. Kaushik, and J. Kumar, “A Computation Offloading Framework to Optimize Energy Utilisation in Mobile Cloud Computing Environment,” International Journal of Computer Applications & Information Technology, Vol. 5, Issue II, April-May, 2014.
[9] G. Folino, and F.S. PisaniI, “Automatic offloading of mobile applications into the cloud by means of genetic programming,” in Proc. of Applied Soft Computing, Vol. 25, Issue C, pp. 253–265, December 2014.
[10] M. V. Barbera, A. C. Viana, and M. D. de Amorim, “Data offloading in social mobile networks through VIP delegation,” in Proc. of the Ad Hoc Networks 19, pp. 92-110, 2014
[11] C. M. S. Magurawalage, and K. Yang, “Energy-Efficient & Network-Aware Offloading Algorithm for Mobile Cloud Computing,” Journal of Network & Computer Applications, Vol. 74, pp. 22–33, 2014.
[12] N. Kaushik, Gaurav, and J. Kumar, “A Literature Survey on Mobile Cloud Computing: Open Issues & Future Directions,” International Journal of Engineering & Computer Science ISSN: 2319-7242, Vol. 3, Issue 5, May 2014.
[13] G. Orsinia, D. Badea, and W. Lamersdorf, “Context-Aware Computation Offloading for Mobile Cloud Computing: Requirements Analysis, Survey & Design Guideline,” in Proc. of 12th International Conference on Mobile Systems & Pervasive Computing, Vol. 56, pp. 10 – 17, 2015.
[14] C. Ragona, F. Granelli, C. Fiandrino, D. Kliazovich, and P. Bouvry, “Energy-Efficient Computation Offloading for Wearable Devices & Smartphones in Mobile Cloud Computing,” in Proc. of IEEE Global Communications, pp. 687–694, 2015.
[15] M. Shiraz, and A. Gani, “Energy Efficient Computational Offloading Framework for Mobile Cloud Computing,” Journal of Grid Computing, Vol. 13, Issue 1, DOI: 10.1007/s10723-014-9323-6, pp. 1–18, March 2015.
[16] Q. K. Gill, and K. Kaur, “A Computation Offloading Scheme for Performance Enhancement of Smart Mobile Devices for Mobile Cloud Computing,” in Proc. of International Conference on Next Generation Intelligent Systems, pp. 1-6,Sept 2016.
[17] S. Deshmukh, and R. Shah, “Computation Offloading Frameworks in Mobile Cloud Computing: A Survey,” in Proc. of IEEE Computer Society magazine, Vol. 3, pp. 16 –22, 2016.
[18] N. Idawati, M. Enzai, and M. Tang, “A Heuristic Algorithm for Multi-Site Computation Offloading in Mobile Cloud Computing,” in Proc. of Computer Science Vol. 80, Issue C, pp. 1232–1241, June 2016.
[19] A. Mukherjee, and D. De, “Low power offloading strategy for Femto-cloud mobile network,” Engineering Science & Technology, an International Journal, Vol. 19, Issue 1, pp. 260-270, March (2016).
[20] J. Panneerselvam, J. Hardy, B. Yuan, and N. Antonopoulos, “Mobilouds: An Energy Efficient MCC Collaborative Framework With Extended Mobile Participation for Next Generation Networks,” IEEE, DOI: 10.1109/ACCESS.2016.2602321, Vol. 4, pp. 125, 2016.
[21] M. Goudarzia,, M. Zamania, Abolfazl, and T. Haghighat, “A Fast Hybrid Multi-Site Computation Offloading for Mobile Cloud Computing,” Journal of Network & Computer Applications, Vol. 80, 2017.
[22] D. Mazza, D. Tarchi, and G. E. Corazza, “A Unified Urban Mobile Cloud Computing Offloading Mechanism for Smart Cities,” in Proc. of IEEE Communications Magazine, Vol. 55 Issue 3, pp. 106–115, March 2017.
[23] S. Sthapit, J. R. Hopgood, and J. Thompson, “Distributed Computational Load Balancing for Real-Time Applications,” in Proc. of 25th European Signal Processing Conference, 2017.
[24] S. Saha, and M. S. Hasan, “Effective Task Migration to Reduce Execution Time in Mobile Cloud Computing,” Proceedings of the 23rd International Conference onAutomation & Computing, DOI: 10.23919/IConAC.2017.8081998, pp.7-8, September 2017.
[25] L. Zhang, and Student Member, IEEE, Di Fu, IEEE, and J. Liu, “On Energy-Efficient Offloading in Mobile Cloud for Real-Time Video Applications,” in Proc. of IEEE Transactions on Circuits & Systems for Video Technology, Vol. 27, Issue 1, JANUARY 2017.
[26] P. Nawrocki, and W. Reszelewski, “Resource Usage Optimization in Mobile Cloud Computing,” in Proc. of Computer Communications 99, pp. 1-12, 2017.
[27] G. Shu, X. Zheng, H. Xu, and J. Li, “Cloudlet-assisted Heuristic Offloading for Mobile Interactive Applications,” 5th IEEE International Conference on Mobile Cloud Computing, Services, & Engineering, DOI: 10.1016/j.neucom.2017.09.056, 28 Dec, 2017.

Nikki, J. Kumar, "Predictive Approach for Energy Efficient Computation Offloading In Mobile Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.62-67, 2018.

In this research work, the patient administration system of a hospital using manual and IoT methods are compared with authenticated sample data. The software which is developed for this IoT Environment is user-friendly, reduces the time and minimizes the cost. Patient waiting time for hospital services is identified as one of the key measurement of a responsive health care system. So, this study addresses the issue of long patient waiting time in the outpatient department (OPD). Outpatient administration always takes a long waiting time for a treatment that has a short time of consultation by the physician. Queuing theory formulas are used to predict the waiting time of the patient. The main goal of this research is focused on how the IoT method can able to reduce patient waiting time.

IoT, Healthcare , Waiting time, Queuing theory, Patient

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K. Mohan Kumar, K. Thiyagarajan, K. Geethanjali, "Effectiveness of IoT with Reference To Patient Waiting Time," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.68-76, 2018.

In this paper, K-Nearest Neighbor (KNN) algorithm as a classifier is implemented with slope as feature for detection of QRS-complex in ECG, the detection rate of 99.32% is achieved. The proposed algorithm is evaluated on standard databases CSE dataset-3.

K-NN Alogorithm, QRS detection

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P. Mathur, V.S. Chouhan, "Implementation of K-Nearest Neighbor (KNN) algorithm for detection of QRS Complexes," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.77-79, 2018.

The Novel approach can yield high levels of performance and nicely complements traditional question answering techniques driven by information extraction. In order for question answering systems to benet from this vast store of useful knowledge, they must copy with large volumes of useless data. Question Answering systems (QA) uses similarity in questions and ranking the relevant answer to user. The web gives large data and that require more time as well as no relevancy in answers. To solve this problem proposed system proposed novel Pair wise Learning to rANk model i.e PLANE which can quantitatively rank answer candidates from the relevant question pool. Specially, it uses two components i.e online learning component and one online search component. Our model is effective as well as achieves better performance than several existing questions answer selection system. User gets recommendation based on his profile. User recommend the new question to his friend and this is trust analysis so user can get top recommendation of newly arrived question of languages.

Answer Selection, Community-based Question Answering, Question-Answer pairs, Pair wise learning technique

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[8] A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification Duyu Tang, Bing Qin, Furu Wei, Li Dong, Ting Liu, and Ming Zhou. EEE/ACM transjectiom
on audio search data, language processing, no. 11,november 015
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S.S. Pawar, R.H. Kulkarni, "A Advanced Approach To Construct E-Learning QA System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.80-83, 2018.

Research work in the biomedical domain has been increasing at fast pace. Hence, the knowledge in the field of biomedical domain is growing exponentially. Consequently, the number of text documents containing the knowledge in this field is growing very rapidly. It is often very difficult for researchers to track the knowledge and assimilate it for generating new ideas. Therefore, it is highly desirable to organize such documents for extracting useful information from textual literature and store them in a structured form. As this information is embedded within text, so it is a challenging task to extract them. This paper presents a rule based system to extract biomedical relations along with biomedical entities from biomedical literatures. The system first generates a dependency tree of each sentence of a given literature, and then the rules are applied to extract the information components. The biomedical relations are embedded within these information components. Further, these information components are used to get feasible biomedical relations from a set of abstracts of biomedical literature. Furthermore, the system has been validated on a corpus of 500 abstracts downloaded from PubMed database on Alzheimer key word.

Text mining; Biomedical text mining, Biomedical relation extraction

[1] M. Habibi, L. Weber, M. Neves, D.L. Wiegandt, U. Leser, “Deep learning with word embeddings improves biomedical named entity recognition”, Bioinformatics, 33(14), pp. i37-i48, 2017.
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[3] T.K. Jenssen,A. Laegreid, J. Komorowski, E. Hovig, “A literature network of human genes for high-throughput analysis of gene expression”, Nature Genetics, 28(1), pp. 21–28, 2001.
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[5] A. Divoli, T.K. Attwood, “BioIE: extracting informative sentences from the biomedical literature”, Bioinformatics, 21, pp. 2138–2139, 2005.
[6] K. Fundel, R. Kuffner, R. Zimmer, “RelEx—Relation extraction using dependency parse trees”, Bioinformatics, 23(3), pp. 365–371, 2007.
[7] Jahiruddin, M. Abulaish, L. Dey, “A concept-driven biomedical knowledge extraction and visualization framework for conceptualization of text corpora”, Journal of Biomedical Informatics, 43, pp. 1020-1035, 2010.
[8] E.S. Chen, G. Hripcsak, H. Xu, M. Markatou, C. Friedman, “Automated acquisition of disease–drug knowledge from biomedical and clinical documents: an initial study”, Journal of the American Medical Informatics Association, 15(1), pp. 87–98, 2008.
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[11] B. Rink, S. Harabagiu, K. Roberts, “Automatic extraction of relations between medical concepts in clinical texts”, Journal of the American Medical Informatics Association, 18(5), pp. 594–600, 2011.
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[14] M.S. Simpson, D. Demner-Fushman, “Biomedical text mining: A survey of recent progress”, Mining Text Data, Springer, pp. 465–517, 2012.
[15] A. Airola, S. Pyysalo, J. Björne, T. Pahikkala, F. Ginter, T. Salakoski, “A graph kernel for protein-protein interaction extraction”, In the Proceedings of the workshop on current trends in biomedical natural language processing. Association for Computational Linguistics, Columbus, Ohio, USA, pp. 1–9, 2008.
[16] M. Miwa, R. Saetre, Y. Miyao, J. Tsujii, “Protein– protein interaction extraction by leveraging multiple kernels and parsers”, International journal of medical informatics, 78(12), pp. e39–e46, 2009.
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[19] P. Thompson, S.A. Iqbal, J. McNaught, S. Ananiadou, “Construction of an annotated corpus to support biomedical information extraction”, BMC bioinformatics, 10(1), pp. 349-367, 2009.

Jahiruddin, "Biomedical Literature Mining for Biomedical Relation Extraction," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.84-93, 2018.

The internet overwhelms us with huge amount of widely extended, well integrated, rich and dynamic hypertext information. It has deeply influenced our lives and daily routine. Billions of websites contains learning related and unrelated contents. It is very difficult to find and maintain the unrelated urls dataset to stop student from accessing the irrelevant sites in browser. Web content filtering is one of the essential tool which helps to filter out unwanted content. The proposed algorithm used to create strong keyword database of learning sites. This database used along with browser extension to analyze every incoming site and then allows browser to display only learning sites. In this extension natural language processing (NLP) plays an important role to find out and block non learning sites. We have measured the accuracy of the tool using precision and recall.

Internet, Techno-Savvy, WWW, Web Mining, Filter, NLP

[1] https://en.wikipedia.org/wiki/Information_and_communications_technology
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Sangita. S. Modi, Sudhir B. Jagtap, "Keyword Based Web Filtering Tool For E-Learning Sites," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.94-97, 2018.

E-commerce deals with selling and purchasing of goods and services through internet and computer networks. E-commerce can enhance economic growth, increase business opportunities, competitiveness, better and profitable access to markets. E-Commerce is emerging as a new way of helping business enterprises to compete in the market and thus contributing to economic success. In this paper, we present and discuss these findings, and identify changes that will be required for broader acceptance and diffusion of e-commerce in India. In this research paper we will discuss about advanced analysis of E-commerce which will comprise of strengths, weaknesses, issues, opportunities and challenges faced by e-commerce in current scenario

E-commerce, E-commerce Challenges, E-commerce Issues, E-commerce Strength

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Raj Kumar, Rakesh kumar Saini, Kuldeep Singh, "Issues and Challenges in E-Commerce," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.98-101, 2018.

Wireless sensor network are autonomous nodes which monitor and communicate the sensor data to central unit through wireless network. There devices are installed in the remote areas and used for communicate sensitive information in different application such as smart grids, MANET. The attackers attack on these nodes to extract information or modify. In this paper, various attacks and its countermeasure technique such as cryptography algorithms are studied and comparative analysis is done for the wireless sensor network. From, the analysis found that symmetric and asymmetric ciphers are hybrid which provide authentication and confidentiality but increased the resources requirement. But, the wireless sensor nodes are battery operated and low memory available. Therefore, we recommend in the future work preferred lightweight cipher and different modes of authentication such as CCM, counter mode to enhance the performance on wireless sensor network.

Cryptography, Wireless SensorNetwork,Attacks,AES,RSA

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[17] Murugan, M. S., Sasilatha, T., & Dean, A. M. E. T. “Design of Hybrid Model Cryptographic Algorithm for Wireless Sensor Network,”International Journal of Pure and Applied Mathematics, vol. 117, pp.171-177, 2017.
[18] Sharmila, R., & Vijayalakshmi, V.,“Hybrid Key Management Scheme for Wireless Sensor Networks,” International Journal of Security and Its Applications, vol. 9, issue 11, pp. 125-132, 2015.
[19] Hwang, T., &Gope, P.,“Robust stream‐cipher mode of authenticated encryption for secure communication in wireless sensor network,” Security and Communication Networks, vol. 9, issue 7, pp. 667-679, 2016.

H. Kaur, K. Kaur, "Review Paper on Cryptography Algorithms Used in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.102-105, 2018.

Since the beginning of courtesy, man has always been motivated by the need to make advancement and better the existing technologies. This has led to remarkable development and progress which has been a launching pad for further evoluation of all the momemtous advances made by society from the beginning till date. Cloud computing is the concept of using outlying services through a network using various resources. It is basically meant to give maximum with the minimum resources i.e. the user end is having the minimum hardware requirement but is using the maximum capability of computing. This is possible only through this technology which requires and utilizes its resources in the best way. But this advantage comes at a cost. Firstly, the data is uploaded insecurely which has a high risk of being hacked by some malicious people. Secondly, the data saved at outlying servers is under the surveillance of unauthorized people who can do anything with our data and Information. So, these data security risks are causing a barrier in the development of the area of cloud computing infrastructure. In this paper we have discussed about the various techniques by which we can enhance the cloud service in relation to security and data privacy and also designed a new effective cloud data security infrastructure.

Cloud Computing, OTP, Data Security, Data Privacy

[1] V. Paranjape, V. Pandey, “An approach towards security in private cloud using otp”, International Journal of Emerging Technology and Advanced Engineering (IJETAE), Vol.3, Issue.3, pp.683-687, 2013.
[2] S. Arora, P. Luthra “Security Storage Model of Data in Cloud”, International Journal of Current Engineering and Scientific Research (IJCESR), Vol.2, Issue.6, pp.99-105, 2015.
[3] H. B. Patel, D. R. Patel, B. Borisaniya, A. Patel, “Data storage security model for cloud computing”, Advances in Communication, Network, and Computing, CNC 2012, pp. 37–45, 2012.
[4] S. Kamara, K. Lauter, “Cryptographic cloud storage,” in Financial Cryptography and Data Security. Springer, pp. 136–149, 2010.
[5] G. Choudhury, J. Abudin, “Modified secure two-way authentication system in cloud computing using encrypted one-time password.” International Journal of Computer Science & Information Technologies, IJCSIT, Vol.5, no. 3, 2014.
[6] S. K. Sood, “A combined approach to ensure data security in cloud computing”, Journal of Network and Computer Applications, Vol.35, no.6, pp.1831–1838, 2012.
[7] R. Manjusha, R. Ramachandran, “Comparative study of attribute-based encryption techniques in cloud computing”, in Embedded Systems (ICES), International Conference on. IEEE, pp.116–120, 2014.
[8] V. Spoorthy, M. Mamatha, B. Santhosh Kumar, "A survey on data storage and security in cloud computing", International Journal of
Computer Science and Mobile Computing, IJCSMC, vol.3, Issue.6, pp. 306-316, 2014.
[9] N. Gowtham Kumar, K. Praveen Kumar Rao, “Hash Based Approach for Providing Privacy and Integrity in Cloud Data Storage using Digital Signatures”, International Journal of Computer Science and Information Technologies, IJCSIT, Vol.5, pp. 8074-8078, 2014.
[10] E. M. Mohamed, H. S. Abdelkader, S. El-Etriby, “Data Security Model for Cloud Computing”, Journal of Communication and Computer, Vol.10, pp.1047-1062, 2013.

Somnath Basak, Somnath Dey, Mrinmay Deb, "Security Approach for Data Storage and Retrival in The Cloud," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.106-110, 2018.

Due to the technology innovations, a medical diagnosis has developed as an emerging area in the healthcare systems. Over the past decades, different reliable prediction models have been developed according to the survival analysis method with different degree of success. A survival of patient’s after liver transplantation has been predicted by using Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) model for better diagnosis. Conversely, patients undergoing liver transplantation may have a very poor diagnosis. Also, it depends on the proper selection of attributes and model. Hence in this article, an enhanced model is proposed for prediction of long-term survival of patient’s after liver transplantation. Initially, data are collected and the Principal Component Analysis (PCA) is applied for dimensionality reduction which removes unnecessary attributes of liver patients. Then, the data is trained separately by using Convolutional Neural Network (CNN) model with the suitable selection of data attributes. Finally, the performance of the proposed model is analyzed and compared with the existing MLP-ANN model in terms of sensitivity, specificity and accuracy. The experimental results show that the proposed CNN model achieves high prediction accuracy in survival analysis after liver transplantation.

Liver transplantation, Survival prediction, MLP-ANN, Convolutional neural network, Principal component analysis

[1] A.L. Gleisner, A. Muñoz, A. Brandao, C. Marroni, M.L. Zanotelli, G.G. Cantisani, ... T.M. Pawlik, “Survival benefit of liver transplantation and the effect of underlying liver disease”, Surgery, Vol.147, Issue.3, pp.392-404, 2010.
[2] A. Karapanagiotou, C. Kydona, C. Dimitriadis, S. Papadopoulos, T. Theodoridou, T. Tholioti, ... N. Gritsi-Gerogianni, “Impact of the Model for End-stage Liver Disease (MELD) score on liver transplantation in Greece”, In Transplantation proceedings, Vol.46, Issue.9, pp.3212-3215, 2014.
[3] Priyanka, S. Khan, T. Kour, “Investigation on Smart Health Care Using Data Mining Methods”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.2, pp.31-36, 2016.
[4] V. Davis, S. Devane, “Diagnosis of Brain Hemorrhage Using Artificial Neural Network”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.1, pp.20-23, 2017.
[5] C.G. Raji, S.V. Chandra, “Long-Term Forecasting the Survival in Liver Transplantation Using Multilayer Perceptron Networks”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol.47, Issue.8, pp.2318-2329, 2017.
[6] M. Cruz-Ramirez, C. Hervas-Martinez, J.C. Fernandez, J. Briceno, M. De La Mata, “Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks”, Artificial intelligence in medicine, Vol.58, Issue.1, pp.37-49, 2013.
[7] C.G. Raji, S.V. Chandra, “Artificial neural networks in prediction of patient survival after liver transplantation”, Journal of Health & Medical Informatics, Vol.7, Issue.1, pp.1-7, 2016.
[8] C.G. Raji, S.V. Chandra, “Graft survival prediction in liver transplantation using artificial neural network models”, Journal of computational science, Vol.16, pp.72-78, 2016.
[9] C.G. Raji, S.V. Chandra, “Prediction and survival analysis of patients after liver transplantation using RBF networks”, In International Conference on Data Mining and Big Data, Springer, pp.147-155, 2016.
[10] Q. Ling, H. Dai, R. Zhuang, T. Shen, W. Wang, X. Xu, S. Zheng, S. “Predicting short-term survival after liver transplantation on eight score systems: a national report from China Liver Transplant Registry”, Scientific Reports, Vol.7, Issue.42253, pp.1-8, 2017.
[11] C.G. Raji, H.S. Anand, S.V. Chandra, “Computer based prognosis model with dimensionality reduction and validation of attributes for prolonged survival prediction”, Informatics in Medicine Unlocked, Vol.9, pp.93-106, 2017.
[12] M. Nematollahi, R. Akbari, S. Nikeghbalian, C. Salehnasab, “Classification Models to Predict Survival of Kidney Transplant Recipients Using Two Intelligent Techniques of Data Mining and Logistic Regression”, International Journal of Organ Transplantation Medicine, Vol.8, Issue.2, pp.119-122, 2017.

V. Mubeena, "Long-Term Survival Prediction After Liver Transplantation Using Convolutional Neural Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.111-114, 2018.