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Fog computing is latest addition in the environment of cloud computing which mainly brings cloud resources closer to the client. The main aim of fog computing is to execute the small tasks of smart devices at the edge devices whereas to put away the main intensive and non-sensitive tasks for the remote execution on the cloud. This overcomes the drawback that the cloud had due to the centralised control and problems of executing the small sensitive task at the remote area. In this paper, we provide the algorithm based on the three parameters time, energy consumption, and network usage on the basis of that, scheduling of task can take place between the two, cloud as well as fog, which distributes the load between them. The results we get, show that there is a significant decrease in time approximately 40%, network usage with 40% and significant decrease in energy consumption also on running tasks on fog than cloud . Finally, we assess the achievement of the task through the experimental simulation which shows significant decrease in the parameter values for local tasks at the fog computing.

Cloud computing, Fog computing, Internet of things,Task scheduling

[1] T.Kleiner, “ Internet of Things – From Research and Innovation to Market Deployment”,River Publishers, pp. 355,2014.
[2] M. Ketel, “Fog-Cloud Services for IoT”, In the Proceedings of the 2017 SouthEast Conference on ACM SE, USA, pp.262-264, 2017.
[3] M.Kaur, A.M.Aslam, “Big Data Analytics on IoT:Challenges,Open Research Issues and Tools”, Isroset-Journal (IJSRCSE), Vol.6, No.3, pp. 81–85, 2018.
[4] W. Ramirez, X. M. Bruin, E.M. Tordera, B.B.C. Souza, and A.jukan, “Evaluating the benefits of combined and continuous Fog-to-Cloud architectures”, Computer Communication, Vol.113, pp.43-52, 2017.
[5] M.Patinder, P.Bansal, “Cloud Forensics:An Overall Research Perspective”, IJSRNSC journal, Vol.6, No.2, pp.5-10,2018.
[6] Q. Zhu, B. Si, F. Yang, Y. Ma, “Task offloading decision in fog computing system”, China Communicatins, Vol.14, Issue.11, pp.59-68, 2017.
[7] B. Paharia, K. Bhushan, “Fog Computing as a defensive approach against Distributed Denial of Service (DDoS): a proposed architecture”, 9th International Conference on Computer Communication and Network Technology, India, pp.1-7, 2018.
[8] R. Mahmud, K. Ramamohanarao, R. Buyya, “Latency-Aware Application Module Management for Fog Computing Environments”, ACM Transanction on Internet Technology, Vol.19, No.1, pp.1–21, 2018.
[9] I. Takouna, R. Rojas-Cessa, K. Sachs, and C. Meinel, “Communication-aware and energy-efficient scheduling for parallel applications in virtualized data centers”, In the Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility Cloud Computing UCC, Germany, pp.251–255, 2013.
[10] M.Q. Tran, D.T. Nguyen, V.A. Le, D.H. Nguyen T.V. Pham, “Task Placement on Fog Computing Made Efficient for IoT Application Provision”, Wireless Communication and Mobile Computing, Vol. 2019, pp.1–1, 2019.
[11] M. Taneja, A. Davy, “Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm”, In the Proceedings of the 2017 IFIP/IEEE Symposium on Integrated Netwwotk and Service Management, Portugal pp.1222–1228, 2017.

T. A. Bhat, J. S. Saini, "Load balancing in Fog-Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.71-77, 2019.

Sensor nodes of WSN have some degree of source of energy whilst they miles deploy in actual moment surroundings. The whole network depend upon this power to detect an event, collect information from surroundings, data aggregation and talk with base station or else sink to supply the collect statistics. The important challenges are how to increases the network lifetime using less power resource. Paper has shown that nodes close to the sink expend their influence energy faster than the nodes because of heavy operating cost messages from nodes that some distance far away from sink node. Sensors nearly sink are mutual by larger sensors to sink path therefore consume extra energy. The problem is known as hotspot problem, ends in a premature disconnection of the network. Hence Mobile sinks help achieving uniform energy-intake and implicitly offer load-balancing all the way through the network and the “Hotspot” trouble is alleviate. As well, they show of network can be better in terms of lifetime higher coverage and short reply time.

Wireless sensor networks, Sink, Mobility, Networks, Cell

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[14] L. Buttyán and P. Schaffer, “Position-based aggregator node election in wireless sensor networks,” Int. J. Distrib. Sensor Netw., vol. 2010, pp. 1–15, Jan. 2010.
[15] K. Karenos and V. Kalogeraki, “Traffic management in sensor networks with a mobile sink,” IEEE Trans. Parallel Distrib. Syst., vol. 21, no. 10, pp. 1515–1530, Oct. 2010.
[16] R. Jaichandran, A. Irudhayaraj, and J. Raja, “Effective strategies and optimal solutions for hot spot problem in wireless sensor networks (WSN),” in Proc. 10th Int. Conf. Inf. Sci. Signal Process. Appl., 2010, pp. 389–392.
[17] A. Erman, A. Dilo, and P. Havinga, “A virtual infrastructure based on honeycomb tessellation for data dissemination in multi-sink mobile wireless sensor networks,” EURASIP J. on Wireless Communications and Networking, vol. 2012, no. 1, p. 17, 2012.
[18] T.-S. Chen, H.-W. Tsai, Y.-H. Chang, and T.-C. Chen, “Geographic converge cast using mobile sink in wireless sensor networks,” Comput. Commun., vol. 36, no. 4, p Feb. 2013.
[19] W. Khan, A. H. Abdullah, M. H. Anisi, and J. I. Bangash, “A comprehensive study of data collection schemes using mobile sinks in wireless sensor networks,” Sensors, vol. 14, no. 2, pp. 2510–2548, 2014.
[20] C. Tunca, S. Isik, M. Y. Donmez and C. Ersoy, "Ring Routing: An Energy-Efficient Routing Protocol for Wireless Sensor Networks with a Mobile Sink," in IEEE Transactions on Mobile Computing, vol. 14, no. 9, pp. 1947-1960, Sept. 1 2015.
[21] A. W. Khan, A. H. Abdullah, M. A. Razzaque and J. I. Bangash, "VGDRA: A Virtual Grid-Based Dynamic Routes Adjustment Scheme for Mobile Sink-Based Wireless Sensor Networks," in IEEE Sensors Journal, vol. 15, no. 1, pp. 526-534, Jan. 2015.
[22] Thakshila Wimalajeewa, Member IEEE, and Pramod K. Varshney,A Survey Compressive Sensing Based Signal Processing in Wireless Sensor Networks .2017
[23] Nicolas Primeau, Rafael Falcon, A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks 2018.
[24] Dinesh Kumar Sah,Tarachand Amgoth Parametric survey on cross-layer designs for wireless sensor networks 2018.

Chetna Chhabra, Yudhvir Singh, "Bisection Based Heuristic Technique to Resolve Sink Mobility in WSNs," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.78-87, 2019.

The technique used for mining the vital data from the pre-existent record known as data mining. It is used for diseases detection at an early stage in medical services. In medical issues, diabetes is a major worldwide problem from various deadliest diseases. “Around 422 million people worldwide are suffering from diabetes”. The purpose of this research is to determine a prototype which can prophesy the possibility with a maximum accuracy of diabetes in patients. So to identify pre-diabetes using two (decision tree and naïve bayes) classification algorithms. The next main focus is to analyze the outcomes and ascertain which technique is more effective and superior from both of them. This paper (pinpointed on) is comparing data mining algorithms which are used for diabetes prognosticate.

Data Mining, Diabetes, Decision Tree, Naïve Bayes, WEKA

[1] N. Sneha and Tarun Gangil,“Analysis of diabetes mellitus for early prediction using optimal featuresselection”,(2019).https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0175-6
[2] Mrs. P. Laura Juliet1, T. Bhavadharani,“An Improved Prediction Model For Type 2 Diabetes Mellitus Disease Using Clustering And Classification Algorithms”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 02, Feb 2019.
[3] Ramin Ghorbania and Rouzbeh,“Ghousia Predictive data mining approaches in medical diagnosis: A review of some diseases prediction”, International Journal of Data and Network Science 3 (2019) 47–70.
[4] S.R.Surya,“Literature Survey On Diabetes Mellitus Using Predictive Analytics Of Big Data”, International Journal of Advance Engineering and Research Development Volume 5, Issue 02, February -2018.
[5] Clare Martin, Antonio Martinez-Millana, Andrew Stranieri, Klerisson Paixao, Maurice Mulvenna, and Francisco Nuñez-Benjumea , “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review”, J Med Internet Rest 2018 May; 20(5): e10775.Published online 2018 May 30.
[6] S. M. Hasan Mahmud ,Md Altab Hossin,Md. Razu Ahmed, Sheak Rashed Haider Noori and Md Nazirul Islam Sarkar, “Machine Learning Based Unified Framework for Diabetes Prediction”,Proceedings of the 2018 International Conference on Big Data Engineering and Technology Pages 46-50 ISBN: 978-1-4503-6582-6.
[7] Priyanka Indoria,Yogesh Kumar Rathore, “A Survey: Detection and Prediction of Diabetes Using Machine Learning Techniques”, International Journal of Engineering Research & Technology (IJERT) Vol. 7 Issue 03, March-2018.
[8] Dr.D. Asir Antony Gnana Singh, Dr. E. Jebamalar Leavline, B. Shanawaz Baig, “Diabetes Prediction Using Medical Data”, Journal of Computational Intelligence in Bioinformatics ISSN 0973-385X Volume 10,Number 1(2017) pp.1-8.
[9] Saman Hina, Anita Shaikh and Sohail Abul Sattar, “Analyzing Diabetes Datasets using Data Mining”, Journal of Basic & Applied Sciences, 2017, 13, 466-471
[10] Vrushali Balpande, Rakhi Wajgi, “Review on Prediction of Diabetes using Data Mining Technique”,(2017) International Journal of Research and Scientific Innovation (IJRSI) | Volume IV, Issue IA, January 2017 | ISSN 2321–2705.
[11] Nirmal Kaur, Gurpinder Singh, “A Review Paper On Data Mining And Big Data”, International Journal of Advanced Research in Computer Science Volume 8, No. 4, May 2017.
[12] Shuja Mirza, Dr. Sonu Mittal and Dr. Majid Zaman,“A Review of Data Mining Literature”, International Journal of Computer Science and Information Security(IJCSIS), Vol. 14, No. 11, November 2016.
[13] Ashish Kumar Dogra and TanujWala, “A Review Paper on Data Mining Techniques and Algorithms”,International Journal of Advanced Research in Computer Engineering &Technology (IJARCET) Volume 4 Issue 5, May 2015.
[14] GyorgyJ.Simon,Pedro J.Caraballo,Terry M. Therneau,Steven S. Cha, M. Regina Castro and Peter W.Li “Extending Association Rule Summarization Techniques to Assess Risk of Diabetes Mellitus,” IEEE Transanctions on Knowledge and Data Engineering,vol 27, No.1,January 2015.
[15] Sukhdev Singh Ghuman,“A Review of Data Mining Techniques”, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.4, April- 2014, pg. 1401-1406 (2014).
[16]
Dr.Zuber khan, shaifali singh and Krati Sexena,“Diagnosis of Diabetes Mellitus using K- Nearest Neighbor Algorithmin”, proceeding of International Journal of Computer Science Trends and Technology, vol.2 , July-Aug 2014.
[17] Mukesh kumari and Dr. Rajan Vohra,“Prediction of Diabetes Using Bayesia network,” in proceeding of International Journal of Computer Science and Information Technology vol. 5 , 2014.
[18] Dr. Pramanand Perumal and Sankaranarayanan, “Diabetic prognosis through Data Mining Methods and Techniques,” in proceeding of International Conference on Intelligent Computing Applications, vol.2, 2014.
[19] Satyanarayana Gandi and Amarendra Kothalanka,“An Efficient Expert System For Diabetes By Naïve Bayesian Classifier,” in proceeding of International Journal of Engineering Trends and Technology,2013.
[20] Shankaracharya, Devang Odedra, Subir Samanta, and Ambarish S. Vidyarthi1 et.al, “Computational Intelligence in Early Diabetes Diagnosis: A Review”,(2010). The Review of Diabetic Studies • January 2010 DOI: 10.1900/RDS.2010.7.252 • Source: PubMed.
[21] Ramkrishnan Shrikant and Rakesh Agrawal,“Fast Algorithms for mining association rule,”in proceeding of IEEE International Conference on Data Engineering,vol.16,2007.
[22] Chris Fleizach and Satoru Fukushima, “A naïve bayes classifier on 1998 KDD cup”,(1998).
[23] [Online]Available:Machine Learning Group at the University of Waikato. Weka 3: Data Mining software in Java. Retrieved September 4, 2016, from http://www.cs.waikato.ac.nz/ml/weka/.
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[25] [Online]Available:https://www.kaggle.com/uciml /pima-indians-diabetes-database.
[26] [Online]Available:https://www.ndtv.com/health/diabetes-indian-women-at-high-death-risk-from-diabetes-finds-study-2027180.
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[31] [Online]Available:https://courses.cs.washington.edu/courses/csep521/07wi/prj/leonardo_fabricio.pdf

Aisha, K. Solanki, S. Dalal, A. Dhankhar, "Comparative Performance Analysis of Data Mining in Diabetes," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.88-94, 2019.

In the present days, cancer has become a menacing disease. Lung cancer is the foremost cancer affecting both men and women throughout the world. In this regard, biomedical imaging is a technology that aids fundamental medical investigations. Some of the widely applied biomedical imaging techniques are Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc. Among the imaging techniques, CT images are generally used for detecting life frightening pathologies. CT images present high spatial resolution including contrast deviation in tissue. However, CT images are prone to Gaussian noise due to thermal energy fluctuations. Also CT images get affected by artifact and structural noise which hamper correct diagnosis. To overcome this problem, different de-noising filters like Median filter, Gaussian filter, Box filter, Average filter, X-filter are applied on CT images before further processing. In order to identify the superlative filter metrics like SNR (Signal to Noise Ratio) and PSNR (Peak Signal to Noise Ratio) are used. The CT image dataset in (Digital Imaging and Communications in Medicine) DICOM format provided by the (Lung Image Database Consortium) LIDC has been utilized to perform the analysis in the present work.

CT, SNR, PSNR , Filter, DICOM

[1] Suren Makaju, P.W.C. Prasad, Abeer Alsadoon, A. K. Singh, A Elchouemi, “Lung Cancer Detection using CT Scan Images ”, 6th International Conference on Smart Computing and ommunications, ICSCC 2017, 7-8 December 2017, Kurukshetra, India.
[2] Kamil Dimililer, Buse Ugur, Yoney K. Ever , “Tumor Detection On CT Lung Images Using Image Enhancement”, The Online Journal of Science and Technology,Volume 7, Issue 1, January 2017.
[3] Aarthi poornima Elangovan1, Jeyaseelan.T, “Medical Imaging Modalities: A Survey”, IEEE, 2017.
[4] Hasan Koyuncu, Rahime Ceylan, “A Hybrid Tool on Denoising and
Enhancement of Abdominal CT Images before Organ & Tumour segmentation”, IEEE 37th International Conference on Electronics and Nanotechnology,2017.
[5] Brij Bhan Singh, Shailendra Patel, “Efficient Medical Image Enhancement using CLAHE Enhancement and Wavelet Fusion”,International Journal of Computer Applications (0975 – 8887) Volume 167 – No.5, June 2017.
[6] M.Jayanthi, “Comparative Study of Different Techniques Used for medical image Segmentation of Liver from Abdominal CT Scan”, IEEE WiSPNET 2016 conference.
[7] Khobragade, S., Tiwari, A., Patil, C., Narke, V., “Automatic detection of major lung diseases using Chest Radiographs and classification by feed-forward artificial neural network.”, IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2016.
[8] Ciompi F, Jacobs C, Scholten E.T, Wille M.M.W de Jong, P.A., Prokop, M., van Ginneken, B, “Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images”, Medical Imaging, IEEE Transactions on , vol.34, no.4, pp.962,973, April 2015.
[9] Md. Badrul Alam Miah, Mohammad Abu Yousuf, “Detection of lung cancer from CT image using image processing and neural network”, International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2015.
[10] Ritika Agarwal, Ankit Shankhadhar, Raj Kumar Sagar, “Detection of Lung Cancer Using Content Based Medical Image Retrieval”, Fifth International Conference on Advanced Computing & Communication Technologies, IEEE, 2015.
[11] V. Vijaya Kishore, R.V.S.Satyanarayana, “Performance Evaluation of Edge Detectors -Morphology Based ROI Segmentation and Nodule Detection from DICOM Lung Images in the Noisy Environment”, IEEE,2012.
[12] Yang Yu, Hong Zhao, “A Texture-based Morphologic Enhancement Filter in Two-dimensional Thoracic CT scans”, IEEE,2006.
[13] Kshipra Singh, Jijo S Nair, “A Literature Review On Satellite Image Data Enhancement Using Digital Image Processing”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1114-1119, 2018.
[14] M. Rathika, R. Shenbagavalli, "Image Restoration of Damaged Mural images based on Image Decomposition", International Journal of Computer Sciences and Engineering, Vol.07, Special Issue.08, pp.32-37, 2019.

Bhavani K, M T Gopalakrishna, "Analysis of Pre-processing Techniques on CT DICOM Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.95-98, 2019.

The distributed system protocol based on single password is old fascinated, nowadays security issues are important aspects for any kind or sort of system protocol. As per the security need key exchange protocol which shows better security and provide greater convenience i.e. smart card and key security password for the key exchange system protocol in data sharing distributed systems. The present study proposes a general architecture construction of smart card and key security password for the key exchange system protocol in data sharing distributed systems. The present study introduces the combinatorial method of password authentication key exchange (PAKE) without public key. This constructed architecture has additional exchange phase as compare to the scheme for the public encryption (original). As compared with the protocols used in distributed system, the proposed architecture construction shows great properties in term of security and quite better computational efficiency it means operation time is less and low cost at storage.

Smart Card, PAKE, TWO-PAKE, General Architecture Construction, Distributed System

[1] ZHANG Gefei, FAN Dan, ZHANG Yuqing and LI Xiaowei, “A Provably Secure General Construction for Key Exchange Protocols Using Smart Card and Password”, Chinese. Journal of Electronics 2017.
[2] Qi Xie, Duncan S. Wong, Guilin Wang, Xiao Tan, Kefei Chen, Liming Fang,” Provably Secure Dynamic ID-based Anonymous Two-factor Authenticated Key Exchange Protocol with Extended Security Model”, IEEE Transaction 2016.
[3] Hung-Min Sun, Shiuan-Tung Chen, Jyh-Haw Yeh and Chia-Yun Cheng, “A Shoulder Surfing Resistant Graphical Authentication System”, IEEE Transaction 2016.
[4] R. Madhusudhan and Manjunath Hegde, “Cryptanalysis and Improvement of Remote User Authentication Scheme Using Smart Card”, IEEE 2016.
[5] Zheng xianGao, ShouHsuan Stephen Huang, Wei Ding, “Cryptanalysis of Three Dynamic ID-Based Remote User Authentication Schemes Using Smart Cards”, IEEE 2016

Pritaj Yadav, Sitesh Kumar Sinha, S. Veenadhari, "Smart Card Based Password Authenticated Key Agreement in Distributed Systems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.99-104, 2019.

Structural integrity assessment is aspect of engineering which deals with the ability of a structure to support operating loads (such as weight, engine generated forces, etc.) without failure, and also includes the study of past structural failures in order to prevent failures in future designs. Integrity of a structure is the ability, to hold together as a single or group of structures, under various operating non-operating loads, including its own weight, without deforming excessively. Sometimes earthquake loads might be responsible for the structural failure of the Genset Structure. Primarily Genset is used as emergency power source but in some cases it needs to be used in rescue operations after earthquake events, so its structure needs to be assessed for Earthquake loads, as it can be used in rescue operations after earthquake events.

Genset, Earthquake loads, FEA, Response Spectrum, Structural integrity, Seismicity

[1] Vikas Lingam, Raghupati Dasari, Giridhar Kumar and Mrigendra Nath Ray, “Seismic Qualification by Analysis of Emergency diesel generator in nuclear power plants,” Transactions, SMiRT-23, Manchester, United Kingdom - August 10-14, 2015.
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[3] Vilho Jussila and Ludovic Fülöp, “Seismic Qualification of complex equipment by combined analysis and testing,” VTT-R-06003-14, Finnish research programme on Nuclear Power Plant Safety 2011-2014.
[4] Nicolae Zemtev, “Seismic analysis of a vertical water tank,” SISOM 2011 and Session of the Commission of Acoustics, Bucharest 25-26 May. Electronics, Inc., Core Technology Group, Seoul, Korea.
[5] Byeong Moo Jin, Se Jin Jeon, Seong Woon Kim, Young Jin Kim, and Chul Hun Chun, “Earthquake Response Analysis of LNG Storage tank by Axisymmetric Finite Element Model and Comparison to the Results of the Simple Model,” 13th World Conference on Earthquake Engineering Vancouver, B.C., Canada August 1-6, 2004. Paper No. 394.
[6] Edward D. Johnson, “The Need for Seismic Analysis and Planning as Part of Ongoing archaeological Site Management and Conservation: A Case Study of the Necropolis of Saqqara,” Journal of the American Research Centre in Egypt, Vol. 36 (1999), pp. 135-147.
[7] Hyung-Bin Im, Sewan Kim and Jintai Chung, “Seismic analysis of an axial blower using Ansys,” LG
[8] IS-1893 (Part 1): 2016, Bureau of Indian Standard
[9] IS-1893 (Part 4): 2015, Bureau of Indian Standard
[10] https://nptel.ac.in/courses/105101004/3

Avinash Vibhute, Vaibhav Shinde, Amar Paranjape, "Structural Integrity Assessment of Genset Structure for Earthquake Loads," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.105-109, 2019.

The implementation of GST in India was considered to be major tax reform, since Independence. The research paper focuses on the impact of goods & service tax on the pricing of Indian carpet & flooring industry. The GST has not only provide full set off for input tax but also abolish the burden of several existing taxes viz Central Excise Tax, VAT/Sales Tax, Service tax etc. There was fear among the contributors of tax that the cost of goods might go up, harassment would be more and so on. This study is an attempt to examine the pre and post GST regime and, the pros and cons of old as well as the new tax system on the pricing of carpet & flooring industry. There are mixed opinion & response among the manufacturers & traders of carpet industry. Therefore, it is an attempt to compare past tax structure and current GST regime in India.

GST, Cascading effect, Excise duty VAT, Carpets, Floorings

[1]. Nayyar, A., & Singh, I. (2018). A Comprehensive Analysis of Goods and Services Tax (GST) in India. Indian Journal of Finance, 12(2), 57-71.
[2]. Tandon, N., & Tandon, D. Analytics of Goods And Services Taxation (GST) Enigma In India–Prospects, implications & Rollout.
[3]. Kumar, A. (2017). Goods and Service Tax in India-Problems and Prospects. International Journal in Management & Social Science, 5(7), 488-495.
[4]. Shanti. S & Murty, A.V.N (2019). | International Journal of Innovative Technology and Exploring Engineering(TM),8(7), 409-413.
[5]. Manoj, S. (2019). Goods and Services Tax (GST) in India–An Overview and impact. Advances in Management, 12(1), 59-61.
[6]. Vasanthagopal, R. (2011). GST in India: A Big Leap in the Indirect Taxation System. International Journal of Trade, Economics and Finance, 2(2), 144.
[7]. Kuruvilla, R. R., Harikumar, P. N., & Alex, L. (2018). A Study on the Implications of GST in Jewellery Business. Asian Journal of Managerial Science, 7(3), 34-36.
[8]. Kumar, N. (2014). Goods and Services Tax in India: A way forward. Global Journal of Multidisciplinary Studies, 3(6).
[9]. http://www.handicrafts.nic.in/
[10]. https://www.ibef.org/exports/carpet-industry-in-india.aspx
[11]. http://www.gstgovt.in
[12]. http://www.cbic.gov.in/resources//htdocs-cbec/gst/01032019-GST
[13]. http://www.cepc.co.in/

Yogesh Garg, Neeta Anand, "A Study on GST & Its Impact on Pricing of Carpets & Floorings Industry in India," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.110-114, 2019.

In mobile wireless networks, path breakage happens frequently due to the movement of mobile nodes, node failure, channel fading and shadowing. It is challenging to combat path breakage at the cost of minimum control overhead, while adapting to topological changes rapidly. We propose a new greedy technique EPFA (Enhanced Perimeter Forwarding Algorithm) for transmitting the mobile nodes from source to destination. The nodes will be communicated and travelled properly by the new technique without a greater loss. Moreover the paper discuss about the cluster or region head, the role of the CH and the subordinate TH node. The algorithm clearly explains about the work flow of the CH and TH. The simulation diagram discusses about the packet delivery ration, collision rate, total delay of the node in the required time and the energy consumption rate of the mobility node.

Clutster region, Control overhead, Transition head

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[5] Raicu, L., et al. "e3D: an energy-efficient routing algorithm for wireless sensor networks." Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004. Proceedings of the 2004. IEEE, 2004.
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S. Hemalatha, E. George Dharma Prakash Raj, "Enhanced Greedy Perimeter Forwarding Algorithm for Mobile Sensor Network in Cluster region," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.115-123, 2019.

In this paper, a comparative analysis of recent techniques for character recognition is done. Our purpose is to identify the impact of machine learning in the domain of character identification. Character recognition has a lot of applications in the fields of banking , healthcare and other fields for searchability , storability, readability, editability, accessibility, etc. to ease up various processes. Traditional machine learning techniques like a neural network, support vector machine, random forest, etc. have been used as classification techniques. Now with the advancement in the field of computer hardware and efficient research in artificial intelligence field have given emergence to deep learning algorithms. Recent articles are using deep learning for character identification. They also depict how various functions improve the performance in the filed of pattern recognition over time. The primary purpose of this paper is to encourage young researchers towards this domain and thus learn and work towards achieving novelty in the field.

Handwritten character recognition, Machine learning, Feature extraction, Deep learning

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Ashay Singh, Ankur Singh Bist, "A Wide Scale Survey on Handwritten Character Recognition using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.124-134, 2019.

In the latest years, the analyst researchers have proposed answers for assistance phones improve execution time and decrease imperativeness use by offloading overpowering regular employment to remote components, of late, moved by the promising eventual outcomes of message sending in shrewd frameworks, various pros have proposed methods for undertaking offloading towards near to phones, delivering the Device-to-Device offloading perspective. None of these techniques, in any case, offers any instrument that considers narrow-minded customers and, specifically, that moves and settles the contribution devices who spend their benefits. In this paper, we address these issues and propose the structure of a system that incorporates a motivator conspire and a notoriety instrument. Our proposition pursues the standards of the Hidden Market Design approach, which enables clients to determine the measure of assets they are eager to forfeit while taking an interest in the offloading framework. The hidden calculation that clients don’t know about depends on an honest closeout procedure and a distributed notoriety trade conspire.

Cryptocurrency,Computation offloading,Device to Device

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Farheen Sultana, Mohd Tajuddin, "A Digital Currency for Computation offloading," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.135-139, 2019.