Volume-07 , Special Issue-14 , May - 2019 Go Back

Now a days Cyber threats are the costliest threats happening globally. Lets assume a scenario where a user surfs the internet, share files, download files, upload files without any basic security precautions. In this case he/she can get infected with virus and also shares it in form of physical drives or any upload of file, this will also infect other end users. To prevent this stuff in the organisations they conduct a monthly or quarterly security audit which will help them to maintain their systems secure.

Information Gathering, Penetration Testing, Automation, Footprinting, Ethical Hacking

[1] Andress, Mandy. ”Network scanners pinpoint problems.” Network World (2002).
[2] O. Arkin, ”ICMP Usage In Scanning”, The SysSecurity Group, June 2001.
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[4] Smith, Yurick, Doss Ethical Hacking IEEE Conference Publication, DOI: 10.1147/sj. 403.0769, pp. 769-780 - 2014.
[5] Behera, Dash Ethical Hacking: A Security Assessment Tool to Uncover Loopholes and Vulnerabilities in Network and to Ensure Protection to the System , International Journal of Innovations & Advancement in Computer Science, Vol 4, pp. 54-61 - 2015.
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[7] Hall, Gary, and Erin Watson. Hacking: Computer Hacking, Security Testing, Penetration Testing and Basic Security. CreateSpace Independent Publishing Platform, 2016.
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Gopichand D, Lakshmikar B, Siva Teja G, Chaitanya Sai G, Raghavendra Reddy, "Information Gathering and FootPrinting Framework for Penetration Testing using Shell Script", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.239-244, 2019.

Cloud computing is increasing its importance due to the services and IT resources provided by the cloud service providers. There are numerous advantages in cloud computing like highly scalable, low cost and high availability. But the data security and privacy is a major concern in cloud computing. Sharing the resources and storing the data in cloud is the major application in cloud computing. To protect the data in cloud, against unauthorized access, modification and denial of service etc. The biggest challenge in cloud is storing and sharing the sensitive data. In this paper, we have discussed symmetric algorithms, asymmetric algorithms, hash algorithms for security purpose and comparison of algorithms for securing data in different deployment models of cloud. In symmetric we have discussed AES, Fully Homomorphic Encryption, Blowfish, GLEnc, and asymmetric algorithms are DIFFE-HELLMAN, QKD-NAE Technique, RSA Cryptosystem, Identity Based Encryption. In hash few of the algorithms that has been discussed in this paper are MD5, SHA.

Cloud Computing, Data Security, Symmetric Encryption, Asymmetric Encryption, Hash function

Yara AlHumaidan, Lama AlAjmi, Moudhi Aljamea, Maqsood Mahmud, ”Analysis of cloud computing security in perspective of saudi arabia”, published in IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), 2018.
[2] T.Ramaporkalai, ”Comparitive study of security algorithms in cloud computing”, pubished in International Journal of Computer Engineering and Applications, volume XII, Issue I, pp.124-121, Jan 2018.
[3] Bih-Hwang Lee, Ervin Kusuma Dewi, Muhammad Farid Wajdi, ”Data Security in Cloud Computing Using AES Under HEROKU Cloud”, published in the 27th wireless and optical communications conference (WOCC2018), 2018.
[4] Ahmed EL-Yahyaoui, Mohamed Dafir Ech-Chrif ELKettani, ”Data Privacy in Cloud Computing”, published in 4th International Conference on Computer and Technology Applications, pp.25-28, 2018.
[5] M. Thangapandiyan, P. M. Rubesh Anand and K. Sakthidasan @ Sankaran, ”Enhanced Cloud Security Implementation using Modified ECC Algorithm”, published in International Conference on Communication and Signal Processing, pp.1019-1022, India, April 3-5, 2018.
[6] M. Thangapandiyan, P. M. Rubesh Anand, and K. Sakthidasan @ Sankaran, ”Quantum Key Distribution and Cryptography Mechanisms for Cloud Data Security”, published in International Conference on Communication and Signal Processing, pp.1030-1035, India, April 35, 2018.
[7] Remi Sahl, Paco Dupont, Christophe Messager, Marc Honnorat, Tran Vu La, ”High-resolution ocean winds: Hybrid-cloud infrastructure for satellite imagery processing”, published in IEEE 11th International Conference on Cloud Computing, pp.883-886, 2018.
[8] Abhishek Mahalle, Jianming Yong Xiaohui, Tao Jun Shen, ”Data Privacy and System Security for Banking and Financial Services Industry based on Cloud Computing Infrastructure”, IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, 2018.
[9] Mennan Selimi, Felix Freitag, Roger Pueyo Centelles, Agust Moll, ”Distributed Storage and Service Discovery for Heterogeneous Community Network Clouds”, IEEE/ACM 7th International Conference on Utility and Cloud Computing, 2014.
[10] Sang-Hyun Lee, Kyung-Wook Shin, ”An Efficient Implementation of SHA processor Including Three Hash Algorithms(SHA-512, SHA512/224, SHA-512/256)”, 2018.
[11] Lalu P, George, Dr.D.I.George Amalarethinam Bursar, Dr.Anjana S.Chandran, ”GLEnc Algorithm to Secure Data in Public Cloud Environment”, 2018.
[12] Rishav Chatterjee, Sharmistha Roy, ”Cryptography in Cloud Computing: A Basic Approach to Ensure Security in Cloud”, 2017.
[13] Celia Li, Cungang Yang, ”A Novice Group Sharing Method for Public Cloud”, published in IEEE 11th International Conference on Cloud Computing, pp.966-969, 2018.
[14] Changhee Hahn,Hyunsoo Kwon, Junbeom Hur, ”Toward Trustworthy Delegation: Verifiable Outsourced Decryption with Temper-Resistance in Pubic Cloud Storage”, published in IEEE 11th International Conference on Cloud Computing, pp.920-923, 2018.
[15] Deepanshi Nanda, Sonia Sharma, ”Security in Cloud Computing using Cryptographic Techniques”, published in International Journal of Computer science and technology, Vol.8, Issue 2, pp.66-69, April - June 2017.
[16] Gary c. Kersler, ”An Overview of Cryptography”, https://www.garykersler.net/library/crypto.html, 31 march 2019.

Shaziya Banu, Gopal K Shyam, "Data Security in Cloud Computing: A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.245-251, 2019.

Reversible information concealing in scrambled pictures has achieved more consideration as of late in research network. Security assurance of extra information alluring for crime scene investigation. In this paper, another technique for reversible information stowing away in scrambled pictures. Our strategy embraces the methodology of holding adequate space for the extra information before encoding the spread picture. First we recognize reasonable squares for concealing information from different pieces of the picture. Before scrambling the picture, at least one LSB-plane of these squares are upheld up into residual pieces of the picture utilizing a high-performing customary RDH strategy that chips away at decoded pictures. In the wake of scrambling the picture, those least significant bits are utilized to conceal extra information. Recuperation of unique spread picture and blunder free extraction of extra information is ensured dependably. Also, the proposed technique is straightforward and instinctive. Tentatively outcomes demonstrate that our technique outflanks the cutting edge strategies for reversible information covering up in scrambled pictures.

Reversibledatahiding;interpolation;encryption;histogram;reservation

[1] L. Zhou, V. Varadharajan, M. Hitchens, Achieving secure role-based access control on encrypted data in cloud storage, IEEE Trans. Inf. Forensics Secur. vol. 8, no.12, pp.1947– 1960, 2013.
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[5] C. Munuera, Steganography and error-correcting codes, Signal Process. vol. 87, no. 6, pp. 1528–1533, 2007.
[6] J. Mielikainen, LSB matching revisited, IEEE Signal Process. Lett. vol. 13, no. 5, pp. 285–287, 2006. [7]. X. Zhang, S. Wang, Efficient steganographic embedding by exploiting modification direction, IEEE Commun. Lett. vol. 10, no. 11, pp. 781–783, 2006.
[7] J. Tian, Reversible data embedding using a difference expansion, IEEE Trans. Circuits Syst. Video Technol. vol. 13, no. 8, pp. 890–896, 2003.

Narendra P, N. Ganesh Kumar Reddy, N.Ramakrishna, O.Harikrishnareddy, Shashikala N, "Reversible Data Hiding in Encrypted Images", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.252-255, 2019.

Cloud Consumers and Cloud Providers represent the key stakeholders in the Cloud Computing world. While Cloud Providers, provide the infrastructure, service and QoS, Cloud Consumers need to access those services to fulfil their user needs. This paper looks at the challenges Cloud Consumers face w.r.t Managing, Monitoring, Alerts, Penalties, Viewing the Policy Changes w.r.t SLA’s. Currently there are very few tools and mechanisms that help Cloud Consumer to get this data. This Paper proposes different solutions, a. Directly by Cloud Provider, b. By a Agent via Cloud Provider, c. By an Agent with less interaction from Cloud Provider. This paper presents best solution i.e. By an Agent via Cloud Provider and also provides suggestion to develop a web interface tool that covers the entire SLA Life cycle.

Cloud Consumer, Agents, SLA, Dynamic SLA, Cloud Providers, QOS

[1] Zhao, Liang & Sakr, Sherif & Liu, Anna. (2013). Consumer-centric SLA manager for cloud-hosted databases. 2453-2456. 10.1145/2505515.2508196.
[2] Zhao, Liang & Sakr, Sherif & Liu, Anna. (2013). A Framework for Consumer-Centric SLA Management of Cloud-Hosted Databases. IEEE Transactions on Services Computing. 8. 1-1. 10.1109/TSC.2013.5.
[3] M. Wang, X. Wu, W. Zhang, F. Ding, J. Zhou and G. Pei, "A Conceptual Platform of SLA in Cloud Computing," 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, Sydney, NSW, 2011, pp. 1131-1135.
doi: 10.1109/DASC.2011.184
[4] G. J. Mirobi and L. Arockiam, "Service Level Agreement in cloud computing: An overview," 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, 2015, pp. 753-758.
doi: 10.1109/ICCICCT.2015.7475380
[5] H. A. H. Hadi Al Kim and S. Barua, “Service Level Agreement (SLA) for Cloud Computing Compilation with Common and New Formats”, ijsrm, vol. 6, no. 04, pp. EC-2018, Apr. 2018.
[6] Halboob W., Abbas H., Haouam K., Yaseen A. (2014) Dynamically Changing Service Level Agreements (SLAs) Management in Cloud Computing. In: Huang DS., Jo KH., Wang L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science, vol 8589. Springer, Cham

Sachin Kodagali, Gopal Kirshna Shyam, "Dynamic SLA Management from a Cloud Consumer Perspective: Issues, Challenges and Next Steps", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.256-259, 2019.

The recent evolution of IoT being ‘Cognitive Internet of Things’ (CIoT), which is an intelligent automation applicable in a variety of domains. Developing IoT as CIoT can result in as self-reliant without human intervention to function in an intelligent way of decision making to optimize tasks is the sole objective of CIoT aim and purpose. Worldwide urban population is expanding that result in various resources crunch, which hampers sustainable living particularly for urban inhabitants. The urban resources are vital and also critical due to supply and demand mismatch and for sustainable inhabitation, application of IoT/CIoT is relevant as many resources like water, transportation, power, health, housing, education, etc., become scarce for sustainable inhabitation. The paper encompasses by briefing the development and advances of CIoT, specifically its significance in smart cities applications. The CIoT can be trained in taking decisions on par with humans in case of critical situations to find the remedial measures required, which makes the process smart, useful, cognitive for intelligent solution. The properties, characteristics and constraints of CIoT are briefed along with its business applications. Discussion and conclusions are drawn in highlighting the emerging innovation of CIoT, which can become a revolutionary concept in futuristic world of technological novelty in transforming the world as dynamic and smart for the benefit of all the stakeholders.

CIoT, Smart Cities, WSN, Machine Learning, Automation, Data Analytics, RFID Technology

[1]https://www.ibmbigdatahub.com/blog/what-cognitive-iot, “What is cognitive IoT?” by Sky Matthews, CTO, Internet of Things, IBM, March 24, 2016.
[2] https://www.iotforall.com/cloud-fog-computing-iot/, “The Role of Cloud Computing and Fog Computing in IoT” by Brian Ray, June 14, 2017.
[3] Qihui Wu et al., “Cognitive Internet of Things: A New Paradigm beyond Connection” IEEE-arXiv: 1403.2498v1, 11th March 2011.
[4] Pijush Kanti Dutta Pramanik and Prasenjit Choudahury, “Beyond Automation: The Cognitive IoT. Artificial Intelligence Brings Sense to the Internet of Things”, Chapter, January 2018. www.researchgate.net/publication/322158474,
[5] Zhang, et al. “Cognitive internet of things: concepts and application example”, Int. J. Comput. Sci. Issues (IJCSI) 9(6), 151–158, 2012.
[6] Quhui Wu, et al. “Cognitive internet of things: a new paradigm beyond connection”. IEEE J. Int. Things 2014.
[7] Garrett, M, “Big data analytics and cognitive computing—future opportunities for astronomical research”, 2014.
[8] Millman R, “Artificial intelligence needed to make sense of IoT data”, 6 Sep 2016. https://
internetofbusiness.com/artificial-intelligence-needed-make-sense-iot-data/.
[9] “Cognitive computing defined, cognitive computing consortium”. https://cognitivecompu
tingconsortium.com/resources/cognitive-computing-defined/#1467829079735-c0934399–599a.
[10] www.coseer.com, “How is cognitive computing different from big data and NLP?”
[11] “Prolim: The internet of things—bringing greater business benefits”, 10 Sep 2015. https://
prolim.com/the-internet-of-things-bringing-greater-business-benefits/.
[12] Panagiotis Vlacheas et al., “Enabling Smart Cities through a Cognitive Management Framework for the Internet of Things”, IEEE Communication Magazine, June 2013, pp. 102-111.

K. Sukumaran, "Developments in Cognitive Internet of Things and its Application in Smart Cities", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.260-265, 2019.

The rapid growth of the online information and textual resources has made the text summarization more favourite domain to emphasise the importance and intention of textual information. Manual summarization of large source documents is arduous. Text summarization is automatic text summarization which shortens and condenses the original text document without any loss of original content in an efficient way. In recent years text summarization is one of the most favourite research domains in natural language processing and could attracted more attention of NLP researchers. The intact relationship exists between text mining and text Summarization. In this work, topic of text mining and text summarization considered in the beginning. There after a model has been designed on some of the summarization approaches and essential parameters for exerptting predominant sentences, found the main steps of the summarizing process, and the most significant extraction criteria are presented.

Text summarization, manual summarization, summary, text ranking

[1] Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modelling. arXiv preprint arXiv:1412.3555. (2014).
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[3] Facial feature extraction Using Hierarchical MAX(HMAX) Method
Akshaya Pisal ; Ravindra Sor ; K. S. Kinage.2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA).(2017).
[4] Graves, A., Mohamed, A.-r., & Hinton, G. Speech recognition with deep recurrent neural networks. Paper presented at the Acoustics, speech and signal processing(ices), 2013 ieee international conference on. (2013).
[5] Hinton, G. E., & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. science,313(5786), 504-507. (2006).
[6] Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., &Salakhutdinov, R. R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580. (2012).
[7] Feature extraction for co-occurrence-based cosine similarity score of text documents Ammar Ismael Kadhim ; Yu-N Cheah ; Nurul Hashimah Ahamed ; Lubab A. Salman 2014 IEEE Student Conference on Research and Development.(2014).
[8] LeCun, Y., Bengio, Y., & Hinton, G. Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539. (2015).
[9]Sentence Ranking with the Semantic Link Network in Scientific Paper Jiao Tian ; Mengyun Cao ; Jin Liu ; Xiaoping Sun ; Hai Zhuge 2015 11th International Conference on Semantics, Knowledge and Grids (SKG).(2015).
[10] Lin, C.-Y. (2004). Rouge: A package for automatic evaluation of summaries. Paper presented at the Text summarization branches out: Proceedings of the ACL-04 workshop.

Aruna Kumara B, Smitha N S, Yashaswini Patil, Shilpa P, Sufiya, "Text Summarization Using Ranking Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.266-269, 2019.

Artificial Intelligence is a multidisciplinary field whose aim is to automate activities that currently require human intelligence. Generally, Artificial Intelligence systems function based on a Knowledge Base of facts and guidelines that characterize the system`s domain of proficiency. The factors of a Knowledge Base consist of independently legitimate (or at least achievable) chunks of records. In computer systems the information is stored historically in form of files. File is considered as a primary entity for keeping the information. The system has to automatically organize and utilize this information to solve the specific problems that it encounters. Devices or modules built on Artificial Intelligence are generally human friendly and easier to use. Building few modules which are interlinked and are accessed through one specific module is the idea behind our proposed system. Each module that is built has its own functionality and specification in generating output. One such module is dedicated for file security purpose.

Access to Folders, File Monitoring, Interactive Quiz, News, Personal Assistant, Speech Recognition

[1] A. Nagdev, J. Panchal, H. Nanwani, H. Pawar, “File System Monitoring for Windows”, International Journal of Computer Science and Mobile Computing, Vol. 7, Issue. 3, pp.88-91, 2018.
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[3] J.M. Boucqueau, “Digital Rights Management”, IEEE, 2006-2012.
[4] M.A. Raba bah, A.A. Marghilani, “Artificial Intelligence Technique for Speech Recognition Based on Neural Networks”, Oriental Journal of Computer Science and Technology, Vol. 7, No. 3, 2014.
[5] T.A. AI Smadi, “An Improved Real-Time Speech In Case of Isolated Word Recognition”, International Journal of Engineering Research and Application, Vol. 3, Issue 5, pp. 1-5, 2013.
[6] Y. Hen Hu, J.N. Hwang, “Handbook of neural network signal processing”, CRC press, London, 2001. – 384c.
[7] A. Hamid, O. Mohamed, A.-R., Jiang, H., and Penn, “Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition”, ICASSP, IEEE, pp. 4277–4280, 2012.
[8] S.K. Gaikwad, B.W. Gawali, P. Yannawar, “A Review on Speech Recognition Technique”, International Journal of Computer Applications, Vol. 10, No. 3, 2010.
[9] M.A. Zissman, “Predicting, diagonosing and improving automatic language identification performance”, Proc.Eurospeech97, Vol. 1, pp. 51-54, 1997.
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Lalitha L A, Meghana A K, Meghana M P, Monisha R, Nayana R, "Laptop Assistant and Alert Notifier", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.270-273, 2019.

Traffic sign location is for empowering self-sufficient vehicle driving frameworks. It requires a unique treatment of information: need a strong and ongoing investigation of a circumstance. It gets increasingly troublesome in the cities like condition where various traffic signs, leaving vehicles, people on foot and other moving or foundation pictures make the acknowledgment much troublesome. The techniques are partitioned into three classifications: shading based, shape-based, and learning based. Our sign location step depends just on shape-discovery (square shapes or circles). Traffic signs identification and acknowledgment (TSR) is a key module for new driving help keen capacities, as it is a prerequisite for the vital dimension of traffic scene understanding. A TSR framework as a rule includes two primary advances: 1/ identification of potential traffic signs in the picture, in view of the normal shape/shading plan of looked for traffic signs; 2/ arrangement of the chose areas of intrigue (ROI) for distinguishing the definite kind of sign, or dismissing the ROI.

web cam, image processing, matlab,detection,recognition,traffic signs

[1]. M. Benallal and J. Meunier, J. “Real-time color segmentation of road signs,” in Proceedings of the Electrical and Computer Engineering Conference, Canada, vol. 3, pp.1823-1826, 2003.
[2]. Ching-Hao Lai, Chia-Chen Yu,” An Efficient Real-Time Traffic Sign Recognition System for Intelligent Vehicles with Smart Phones”, International Conference on Technologies and Applications of Artificial Intelligence, pp.195-202, 2010.
[3]. W. J. Kuo and C. C. Lin, “Two-stage road sign detection and recognition,” in IEEE International Conference on Multimedia and Expo, pp. 1427–1430, July 2007.
[4]. A. Lorsakul and J. Suthakorn, “Traffic Sign Recognition for Intelligent Vehicle/Driver Assistance System Using Neural Network on OpenCV,” in 4th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI 2007).
[5]. Huda Noor Dean and Jabir K.V.T “Real Time Detection and Recognition of Indian Traffic Signs using Matlab” International Journal of Scientific & Engineering Research, Volume 4, Issue 5, May-2013.
[6]. Revathi A.S, Sanamdeep Singh Anand, TejaswinGumber “Traffic sign detection and recognition using MATLAB” International Journal of Science and Research (IJSR) 2015.
[7]. 7.Gangyi Wang, Guanghui Ren, Zhilu Wu, Yaqin Zhao, and Lihui Jiang "A robust, coarse-to-fine traffic sign detection method".
[8]. S. Maji, A. C. Berg, and J. Malik, “Classification using intersection kernel support vector machines is efficient,” in IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1–8.
[9]. Chakraborty S, Uddin M N, Deb K. Bangladeshi road sign recognition based on DtBs vector and artificial neural network[C]. Electrical, Computer and Communication Engineering (ECCE), International Conference on. IEEE, 2017: 599-603.
[10]. Zhu Y, Zhang C, Zhou D, et al. Traffic sign detection and recognition using fully convolutional network guided proposals [J]. Neurocomputing, 2016, 214: 758-766.
[11]. Berkaya S K, Gunduz H, Ozsen O, et al. On circular traffic sign detection and recognition [J]. Expert Systems with Applications, 2016, 48: 67-75

Harshavardhan Anil Patil, Vijay Kumar Gupta, Ishaan Poddar, Nikhil Ranjan, Meenakshi Sundarm A., "Traffic Sign Detection and Recognition", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.274-278, 2019.

our idea is to develop a machine learning model which is capable of predicting the quality of soil. Our idea focuses on Agriculture domain. Agriculture is the key to the economy and infrastructure of India. It plays a significant and most strategic role in the progress and financial growth of the nation. As the technology is rapidly advancing, extending it to agricultural domain yields most needed and promising results in achieving precision agriculture. The model we have designed is a works towards achieving it. The model that analyzes the quality of soil thereby predicting the yield of the crop by considering various parameters. Crop yield prediction provides information for decision makers to maximize the crop productivity.Manually testing the quality of soil regularly is a complex task, so there is a need for automating the process that we are currently following, through an ML (Machine Learning) Model. Machine learning approach offers new contingency in the field of agriculture which is very much useful in soil dataset analysis and visualization of various parameters related to soil which would also help in decision making. It is crucial to design and implement a well-planned management system for monitoring various nutrients level by means of soil analysis procedure. In our model, various soil data sample from various regions are classified based on primary and secondary properties.

Machine Learning, Image Dataset, Soil Parameters, Image Processing, Supervised Learning, SVM Image Classifier

[1] P. Vinciya, Dr. A. Valarmathi, “Agriculture Analysis for Next Generation High Tech Farming in Data Mining”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Volume 6, Issue 5, 2016.
[2] Shivnath Ghosh, Santanu Koley, “Machine Learning for Soil Fertility and Plant Nutrient Management using Back Propagation Neural Networks”, International Journal on Recent and Innovation Trends in Computing and Communication(IJRITCC), Volume 2 Issue 2, 2014.
[3] Priya R L, “Prediction of crop yield using machine learning”, International Research Journal of Engineering and Technology (IRJET), Volume 5, Issue 2, 2018.
[4] Karisiddappa, Ramegowda, “Soil characterisation based on digital image analysis”, In the proceedings of 2010 Indian Geotechnical Conference, India.
[5] A. Morellos, “Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy”, Biosystems Engineering, Volume 152, 2016.
[6] Vrushal Milan Dolas, “A Novel Approach for Classification of Soil and Crop Prediction”, International Journal of Computer Science and Mobile Computing, Volume 7, Issue 3, 2018.
[7] Supriya D M, “Analysis of Soil Behavior and Prediction of Crop Yield using Data Mining Approach”, International Journal of Innovative Research in Computer and Communication Engineering, Volume 5, Issue 5, 2017.
[8] Rushika Ghadge, “Prediction of Crop Yield using Machine Learning”, International Research Journal of Engineering and Technology, Volume 5, Issue 2, 2018.
[9] Vaneesbeer Singh,Abid Sarwar, “Analysis of soil and prediction of crop yield (Rice) using Machine Learning approach”, IJARCSE, Volume 5, Issue 8,2017.
[10] P. Vinciya, Dr. A. Valarmathi, “Agriculture Analysis for Next Generation High Tech Farming in Data Mining”, IJARCSSE,vol. 6, Issue 5, 2016.

Ramya R, Ranjitha D, Revathy T, P R Vijeth, Ranjitha U N, "Prediction of Soil Quality using Machine Learning Approach", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.279-283, 2019.

Aim of this paper is to propose a user-centric location recommendation service for the rapidly increasing LBSN (location-based social network). Our idea is to consider three important influencing factors i.e. client predilection, social impacts and distance influence for point-of-interest recommendations. Also, the influence factors i.e. client predilection, social impact are predicted via user-based collaborative filtering and friend-based collaborative filtering, we propose a technique to focus more on distance factor impacts because of the spatial clustering recorded in user visiting locations in LBSNs. Our research shows that the distance influence among locations plays a vital role in user check-in practices which is implemented by power law distribution. Likewise, we build an agglomerative location recommendation system, which combines client predilection to a location with social effect and distance influence. Our result shows that the proposed fusion framework performs better than the already proposed recommendation techniques.

LBSN,point-of-interest recommendation system,power-law

[1]. Fei Yu, Zhijun Li, Shouxu Jiang, Shirong Lin, “Point-of-interest Recommendation for Location Promotion in Location-based Social Networks”,2017 IEEE 18th International Conference on Mobile Data Management.
[2]. H. Ma, H. Yang, M. R. Lyu, and I. King. SoRec: “social recommendation using probabilistic matrix factorization.” In CIKM, pages 931–940, 2008.
[3]. M. Balabanovic and Y. Shoham.” Content-based collaborative recommendation”. CACM, 40(3):66–72, 1997
[4]. V. W. Zheng, B. Cao, Y. Zheng, X. Xie, and Q. Yang. “Collaborative filtering meets mobile recommendation: A user-centered approach”. In AAAI, 2010.
[5]. V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang. “Collaborative location and activity recommendations with gps history data.” In WWW, pages 1029–1038, 2010.
[6]. I. Konstas, V. Stathopoulos, and J. M. Jose. “On social networks and collaborative recommendation.” In SIGIR, pages 195–202, 2009.
[7]. H. Ma, I. King, and M. R. Lyu. “Learning to recommend with social trust ensemble.” In SIGIR, pages 203–210, 2009.
[8]. H. Ma, M. R. Lyu, and I. King. “Learning to recommend with trust and distrust relationships.” In RecSys, pages 189–196, 2009
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Chidanand, Farooque Azam, Chaitanya, Deepanshu, Kiran Kumar, "User Centric Recommendation System for Location Promotion in LBSNs", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.284-287, 2019.