Volume-07 , Special Issue-15 , May - 2019 Go Back

Sexual harassment in public places is overwhelmingly experienced by women and girls. Sexual harassment is, in fact, the most common form of violence against women and girls and that young women are particularly targeted. Sexual harassment has significant and widespread impacts, both on individuals as well as on society. Sexual harassment in public reduces women and girls’ freedom to enjoy public life, and can negatively affect feelings of safety, bodily autonomy and mental health.This project proposes a data-driven method to analyze crime data and behavioral patterns using machine learning algorithms and thus predict emerging crime hotspots for additional police attention.Each community has different crime trends in different areas. These trends are analyzed using machine learning principles which help to predict how crimes against women have significantly increased in various areas of a community. It also helps in rapid visualization and identification of communities which are densely affected with crimes. This approach proves to be quite effective and can also be used for analyzing national crime scenario.

K-means Clustering, Random Forest, Google maps GPS, stemming

[1] Mehedee Hassan, Mohammad Zahidur Rahman, “CrimeNews Analysis: Location and Story Detection”,2017 20th International Conference of Computerand Information Technology (ICCIT)
[2] R. Arulanandam, B. T. R. Savarimuthu, and M. A.Purvis, “Extracting crime information from online newspaper articles,” in Proceedings of the Second Australasian Web Conference - Volume 155,AWC ’14, (Darlinghurst, Australia, Australia), pp. 31–38, Australian Computer Society, Inc., 2014.
[3] I. Jayaweera, C. Sajeewa, S. Liyanage, T. Wijewardane, I. Perera, and A. Wijayasiri, “Crime analytics: Analysis of crimes through newspaper articles,” in 2015 Moratuwa Engineering Research Conference (MERCon), pp. 277–282, April 2015.
[4] P. Chamikara, D. Yapa, R. Kodituwakku and J. Gunathilake, “SLSecureNet : intelligent policing using data mining techniques”, International Journal of Soft Computing and Engineering, vol. 2, no. 1, pp. 175-180, 2012.
[5] S. Adhikari and K. Bogahawatte, “Intelligent criminal identification system,” in The 8th International Conference on Computer Science & Education, Colombo, Sri Lanka, 2013, pp. 633-638.
[6] M. Choi, “A selective sampling method for imbalanced data learning on support vector machines,”, 2010.
[7] K.B.S. Al-Janabi, “A Proposed Framework for Analyzing Crime Data Set using Decision Tree and Simple K-Means Mining Algorithm,” in Journal of Kufa for Mathematics and Computer, Vol. 1, No. 3, 2011, pp.8-24.
[8] K. Zhu and J. Zhang, “Predicting the potential locations of the next crime based on data mining,” in International Journal of Digital Content Technology and Its Application, Vol. 6, No. 20, 2012, pp. 574-581.
[9] A. Bogomolov, B. Lepri, J. Staiano, N. Oliver, F. Pianesi and A. Pentland, “Once upon a crime: towards crime prediction from demographics and mobile data,” in Proceedings of the 16th international conference on multimodal interaction, 2014, pp. 427-434.
[10] U. Thongsatapornwatana, “A survey of data mining techniques for analyzing crime patterns,” in 2nd Asian Conference on Defence Technology (ACDT), Jan 2016, pp. 123–128.
[11] G. Yu, S. Shao, and B. Luo, “Mining crime data by using new similarity measure,” in Second International Conference on Genetic and Evolutionary Computing, Sept 2008, pp. 389–392.
[12] Punetha, D.; Mehta, V. “Protection of the child/ elderly/ disabled/ pet by smart and intelligent GSM and GPS based automatic tracking and alert system” ,Advances in Computing,Communications and Informatics (ICACCI, 2014 InternationalConference on Year: 2014 pg2349 – 2354
[13] Bingbing Lu, Huaping Zhang, Bin Liu1, Zhonghua Zhao, “Research on UseR Identification Algorithm Based on Massive Multi-site VPN Log”, 2017 17th IEEE International Conference on Communication Technology
[14] Ubon Thansatapornwatana, A Survey of Data Mining Techniques For Analyzing Crime Patterns Second Asian Conference onDefense Technology ACDT, IEEE, 2016, ISBN: 978-1-5090-2258-8/16
[15] http://r-statistics.co/Linear-Regression.html.
[16] Machine Learning Tool Kit [Online]. Available: https://github. com/yinlou/mltk
[17] Amazon.com, Inc. (2012) Form 10-K for 2011. Filing date: February 1, 2012. U.S. Securities and Exchange Commission,Washington,DC.
[18] Cattani K, Gilland W, Heese H, Swaminathan J (2006) Boiling frogs: Pricing strategies for a manufacturer adding a direct channel that competes with the traditional channel. Production Oper. Management 15(1):40–56.
[19] Forrester Research, Inc. (2014b) European online retail forecast: 2013 to 2018. Report, May 29. Forrester Research, Inc., Cambridge, MA.
[20] Gans N, van Ryzin G (1999) Dynamic vehicle dispatching: Optimal heavy traffic performance and practical insights. Oper. Res. 47(5):675–692.
[21] Kaplan A (1969) Stock rationing. Management Sci. 15(5):260–267.
[22] Lee H (1987) A multi-echelon inventory model for repairable items with emergency lateral transshipments. Management Sci. 33(10):1302–1316.

Bhavana M S, Bindu B K, Bindushree K, Chethana D N, Kiran Mensinkai, "Prevention of Harassment of Women by Crime Detection, Analysis and Prediction", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.1-5, 2019.

Internet of Things (IoT) plays an effective role in connecting the surrounding environmental things to the network and made easy access things from any remote location. Driving the vehicle which is blocked in the random parking areas is very difficult and time taking. The vehicle can be towed from anyone that is unknown to the driver until him/her return to the parking area. Structured modular concept is used to design the system. The system implemented using ultrasonic sensors, renesas microcontroller, GSM module, IR sensor and MATLAB. The proposed system gives the solution to drive out the blocked car and also recognized that car is towed.

Structured modular design, MATLAB

[1] ZakariaHamidi-Alaoui, AbdelbakiElbelrhitiElalaoui, “A WSN-based approach for prioritising emergency vehicles: Performance analysis of Medium Access Control” IEEE 2018
[2] Yuanyuan Wang , Junji Yao, Danping Li, Dandi Dong, Ying Zhou “A Selection Parking BehaviorLogit Model in Tourist Cities” IEEE 2018
[3] SagarRane, AmanDubey, TejismanParida “Design of IoT Based Intelligent Parking System Using Image Processing Algorithms” IEEE 2017
[4] ToorajRajabioun and Petros A. Ioannou “On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models” IEEE 2015
[5]Ms.SayantiBanerjee ,Ms.PallaviChoudekar,Prof .M.K.Muju “Real Time Car Parking System Using Image Processing” IEEE 2011
[6] W. Xiang, Y. Huang, and S. Majhi, “The design of a wireless access for vehicular environment (WAVE) prototype for intelligent transportation system (ITS) and vehicular infrastructure integration (VII),” in Proc. 68th IEEE VTC Fall, 2008, pp. 1–2.
[7] C. D. Nugent, X. Hong, J. Hallberg, D. Finlay, and K. Synnes, “Assessingthe impact of individual sensor reliability within smart living environments”, in Proc. IEEE Int. Conf. CASE, 2008, pp. 685–690.
[8] D. C. Shoupet al., The High Cost of Free Parking, vol. 7. Washington, DC, USA: Planners Press, American Planning Association, 2005.
[9] D. Teodorovic and P. Lucic, “Intelligent parking systems,” Eur. J. Oper. Res., vol. 175, no. 3, pp. 1666–1681, Dec. 2006.
[10] F. Caicedo, “The use of space availability information in ‘PARC’ systems to reduce search time in parking availability”, Transp. Res. C, Emerging Technol., vol. 17, no. 1, pp. 56–68, Feb. 2009.

Anusha B.M, Anusha S.B, Deekshitha D, Deepa D, Hemanth Y K, "Aid for Blocked Car and Towed Car Using Interent of Things Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.6-10, 2019.

An ever-increasing number of installed surveillance cameras, higher network bandwidth requirements and larger storage space consumption are major considerations when designing a video surveillance storage system. Traditional file systems are not tailored for receiving and storage of large scale video streams. This project attempts to present vsStor, a PB-scale, network-based video surveillance storage system which is used for storing thousands of streams. Another major characteristics of vsStor is the scale-out architecture design which allows the performance to grow by adding more hardware. We make use of clustered architecture for connecting various machines and rely on the web service mechanism for communication between them.

Video surveillance, storage system,Distributed file system, Big Data

[1] Video Surveillance Market worth 75.64 Billion USD by 2022.MarketsandMarkets,2017, https://www.marketsandmarkets.com/PressReleases/global-videosurveillance- market.asp.
[2] D. Rodriguez-Silva, L. Adkinson-Orellana, F. Gonz`lez-Castano, I. Armino-Franco and D. Gonz`lez-Martinez, "Video Surveillance Based on Cloud Storage," 2012 IEEE Fifth International Conference on Cloud Computing(CLOUD), IEEE, Jun. 2012, pp. 991-992, doi:10.1109/CLOUD.2012.44.
[3] M.R. Palankar, A. Iamnitchi, M. Ripeanu, and S. Garfinkel, "Amazon S3 for science grids: a viable solution?," Proc. 2008 international workshop on Data-aware distributed computing, ACM, Jun. 2008, pp. 55-64, doi:10.1145/1383519.1383526.
[4] S. Wang, J. Yang, Y. Zhao, A. Cai, and S.Z. Li, "A surveillance video analysis and storage scheme for scalable synopsis browsing," In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, IEEE, Nov. 2011, pp. 1947-1954, doi: 10.1109/ICCVW.2011.6130487.
[5] E. Jaspers, R. Wijnhoven, R. Albers, J. Nesvadba, J. Lukkien, A. Sinitsyn, et al., "CANDELA–storage, analysis and retrieval of video content in distributed systems," In International Workshop on Adaptive Multimedia Retrieval, Springer, 2005, pp. 112-127. doi: 10.1007/11670834_10.
[6] Z. Sun, Q. Zhang, Y. Li, and Y. Tan, "Dppdl: a dynamic partialparallel data layout for green video surveillance storage," IEEE Transactions on Circuits and Systems for Video Technology, IEEE, 2016, doi: 10.1109/TCSVT.2016.2605045.
[7] P. Pillai, Y. Ke, and J. Campbell, "Multi-fidelity storage," Proc. ACM 2nd international workshop on Video surveillance & sensor networks, ACM, Oct. 2004, pp. 72-79, doi: 10.1145/1026799.1026812.
[8] T. Thomas, A.K. Rajan, T. Johnson, S. Anjana, and A. Bithin, "Policy based storage abstraction for video surveillance systems," In Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on, IEEE, Dec. 2016, pp. 1-4, doi: 10.1109/ICCIC.2016.7919565.
[9] T. Zhang, A. Chowdhery, P.V. Bahl, K. Jamieson, and S. Banerjee, "The design and implementation of a wireless video surveillance system," Proc. 21st Annual International Conference on Mobile Computing and Networking, ACM, Sep. 2015, pp. 426-438, doi: 10.1145/2789168.2790123.
[10] D. Borthakur, "HDFS architecture guide," Hadoop Apache Project 53 (2008).
[11] Smart, Safe, Secure Surveillance Hard Drives Data Sheet. Seagate, 2016, http://www.seagate.com/www-content/productcontent/ skyhawk/files/skyhawk-ds-1902-3-1608us.pdf.
[12] J. Tai, D. Liu, Z. Yang, X. Zhu, J. Lo, and N. Mi, "Improving flash resource utilization at minimal management cost in virtualized flashbased storage systems," IEEE Transactions on Cloud Computing 5(3), IEEE, Jul. 2017, pp. 537-549, doi:10.1109/TCC.2015.2424886.

Amrutha B V, Ananya Jeevan, Bhumika R, Chaitra P, Dhanraj S, "An Efficient Implementation of Distributed Storage Protocol for Large Number of Video Streams", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.11-15, 2019.

The Internet of Things (IoT) has been growing rapidly in recent years and widely used in variety of applications such as military, marine, smart home, intelligent transportation, smart health, smart grid and smart city domains. Due to the advancement in technology and increasing traffic, road accidents and traffic hazards have increased, causing more chances of loss of life due to lack of timely help facilities. This project is an attempt towards finding solutions for timely accident notification. The proposed project records the parameters of vehicle at regular intervals of time, through a smart device installed in the vehicle and sends these values onto the Android App, vehicle owner or a third party. The system will facilitate the users in a number of ways such as notification for immediate aid in case of accident, tracking the vehicle conditions in cases of accident and disabling the vehicle remotely. The hardware components include the smart device installed in the vehicle and a mobile phone for user interaction. The smart device installed in the vehicle does not interfere with the normal functioning of the vehicle or cause overheads.

Internet of Things (IoT), Renesas Microcontroller, Global Positioning System (GPS), Accelerometer, Analog to Digital Converter (ADC), Android application, Global System for Mobile (GSM), Liquid Crystal Display (LCD)

[1] Sheheryar Arshad, Chunhai Feng, Israel Elujide, Siwang Zhou, Yonghe Liu, “SafeDrive-Fi: A Multimodal and Device Free Dangerous Driving Recognition System Using WiFi,”2018
[2] Chi-Yu Li, Guan-Hua Tu, Giovanni Salinas, Guo-Huang Hsu, Tien-Yuan Hsieh, Po-Hao Huang, “V2PSense: Enabling Cellular-based V2P Collision Warning Service Through Mobile Sensing,”2018
[3] Sabeen Javaid, Ali Sufian, Saima Pervaiz, Mehak Tanveer ,”Smart Traffic Management System Using Internet of Things” February 2018.
[4] ]Md. Syedul Amin, JubayerJalil, M.B.I. Reaz- Accident Detection and Reporting System using GPS, GPRS and GSM Technology - IEEE/OSA/IAPR International Conference on Informatics.
[5] AmitMeena, SrikrishnaIyer, Monika Nimje, SaketJoglekar, Sachin Jagtap, MujeebRahman - Automatic Accident Detection and Reporting Framework for Two Wheelers - 2014 IEEE International Conference on Advanced Communication Control and Computing Technologies
[6] Elie Nasr, ElieKfoury, David Khoury - An IoT Approach to Vehicle Accident Detection, Reporting, and Navigation - 2016 IEEE International Multidisciplinary Conference on EngineeringTechnology.
[7] Anita Kulkarni, T. Ravi Teja - Automated System for Air Pollution Detection and Control in Vehicles - International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering(An ISO 3297: 2007 Certified Organization)Vol. 3, Issue 9, September
[8] Prof. NitinR.Chopde, Mr.Mangesh K. Nichat - Landmark Based Shortest Path Detection by Using A* and Haversine Formula - International Journal of Innovative Research in Computer and Communication Engineering Vol. 1, Issue 2, April 2013
[9] Michael Miller - The Internet Of Things - Pearson Publications, Edition 2015.
[10] Asoke K Talukder, Hasan Ahmed, Roopa R Yavagal - Mobile Computing : Technology, Applications and Service Creation - MC Graw Hill Publicaitons, Second Edition.
[11] Rahul Gautam, ShubhamChoudhary, Surabhi, InderjeetKaur, MamtaBhusry: Cloud Based Automatic Accident Detection and Vehicle Management System - 2nd International Conference on Science, Technology and Management, University of Delhi(DU), Conference Centre, New Delhi(India), 27 September 2015, www.conferenceworld.in, www.icstmdu.com.

G Gouthami, Kavya B V, Kavya B R, Kavita, Sagar B, "Intelligent Accident Detection With Mobile Phone Using Internet of Things", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.16-20, 2019.

Cloud storage auditing schemes for shared facts refer with checking the integrity of cloud facts shared via a collection of customers.User revocation is commonly supported in such schemes, as customers may be issue to organization or may misbehave.Previously, the computational overhead for consumer revocation in such schemes was linear with the entire quantity of document blocks possessed by a revoked consumer. In this paper, we advise a singular storage auditing scheme that achieves consumer revocation unbiased of the full variety of file blocks possessed via the revoked consumer in the cloud. This is carried out through exploring a novel approach for key era and a brand new personal key replace method. By using this approach, we realize consumer revocation via simply updating the non revoked customers’ personal keys rather than authenticators of the revoked consumer. The integrity auditing of the revoked consumer’s data can nonetheless be efficaciously achieved when the authenticators aren`t updated. Meanwhile, the proposed scheme is based totally on identification-base cryptography, which gets rid of the complex certificate control in conventional Public Key Infrastructure (PKI) structures. the safety and efficiency of the proposed scheme are confirmed through both evaluation and experimental consequences.

Revocation,Key Generation,Cloud Computing

[1]. Hui Cui, Robert H Deng, Joseph K Liu, Xui Yi, “ Server-Aided Attribute-Based Signature With Revocation for Resource-Constrained Industrial-Internet-of-Things Devices” IEEE Transactions on Industrial Informatics, Volume: 14 , Issue: 8 , Aug. 2018
[2]. Suzuki T, Emura K, Ohigashi T, ”A Generic Construction of Integrated Secure-Channel Free PEKS and PKE and its Application to EMRs in Cloud Storage” J Med Syst. 2019 Mar 28, vol 43(5):128.
[3]. Xing Q, Wang B, Wang X, Tao J, “Unbounded and revocable hierarchical identity-based encryption with adaptive security, decryption key exposure resistant, and short public parameters”, 12 Apr 2018, vol 13(4), /journal pone.
[4]. G. Yang, J. Yu, W. Shen, Q. Su, F.Zhang, R Hao ,”Enabling Public Auditing for Shared Data in Cloud Storage Supporting Identity Privacy and Traceability” ,April 2016
[5]. J. Yu, K.Ren, C. Wang,“Enabling Cloud Storage Auditing with Verifiable Outsourcing of Key Updates” , August 2016.
[6]. H. Wang , “Proxy provable data possession in public clouds” , vol. 113, pp. 130-139, 2016.
[7]. J. Yu, K. Ren, C. Wang, V. Varadharajan ,”Enabling cloud storage auditing with key-exposure resistance”, 39(10), 9359–9366, Sept 2018.
[8]. Y. Luo, M. Xu, S. Fu, D. Wang, and J. Deng, “Efficient Integrity Auditing for Shared Data in the Cloud with Secure User Revocation,” IEEE Trust com/Big DataSE /ISPA,pp. 434-442, 2015.
[9]. J. Yu, K. Ren, and C. Wang, “Enabling Cloud StorageAuditing with Verifiable Outsourcing of Key Updates,”IEEE Transactions on Information Forensics and Security, vol. 11, no.5, pp. 1362-1375, 2016.
[10]. J. Yu and H. Wang, “Strong Key-Exposure ResilientAuditing for Secure Cloud Storage,” IEEE Transactionson Information Forensics and Security, vol. 12, no.8, pp.1931-1940, 2017.
[11]. J. Yu, H. Rong, H. Xia, H. Zhang, X. Cheng, and F.Kong, “Intrusion-resilient identity-based signatures: Concrete scheme in the standard model and generic construction,” Information Sciences, vol. 442-443, pp. 158-172, 2018.
[12]. J. Yu, R. Hao, H. Zhao, M. Shu, and J. Fan, “IRIBE:Intrusion-resilient identity-based encryption,”InformationSciences, vol. 329, pp. 90-104, 2016.
[13]. W. Shen, G. Yang, J. Yu , H. Zhang, F. Kong, and R. Hao, “Remote data possession checking with privacy-preserving authenticators for cloud storage,” Future Generation Computer Systems, vol. 76, pp. 136-145, 2017.
[14]. F. FatemiMoghaddam, P. Wieder, and R. Yahyapour, “Federated PolicyManagement Engine for Reliable Cloud Computing,” in IEEEInternational Conference on Ubiquitous and Future Networks (ICUFN2017), 2017.
[15]. F. FatemiMoghaddam, P. Wieder, and R. Yahyapour, “PolicyManagement Engine (PME) - A Policy-Based Schema to Classify andManage Sensitive Data in Cloud Storages,” J. Inf. Secur.Appl., vol. 36,pp. 11–19, 2017.
[16]. W. Shen, J. Yu, H. Xia, H. Zhang, X. Lu and R. Hao, “Light-weight and Privacy-preserving Secure Cloud Auditing Scheme for Group Users via the Third Party Medium,” Journal of Network and Computer Applications,vol. 82, pp.56-64, 2017.
[17]. M. Sookhak, A. Gania, M. K. Khanb, and R. Buyyac, “Dynamic Remote Data Auditing for Securing Big Data Storage in Cloud Computing,” Information Science, vol.380, pp. 101-116, 2017.
[18]. L. Rao, H. Zhang, and T. Tu, “Dynamic Outsourced Auditing Services for Cloud Storage Based on Batch-Leaves- Authenticated Merkle Hash Tree,” IEEE Transactions on Services Computing, Available online 26 May 2017 DOI:10.1109/TSC.2017.2708116.
[19]. J. Yu and H. Wang, “Strong Key-Exposure Resilient Auditing for Secure Cloud Storage,” IEEE Transactions on Information Forensics and Security, vol. 12, no.8, pp. 1931-1940, 2017.

Ayushi P Bohara, Chethan A, Haripriya B, Harshitha V, Arun Biradar, "Enabling Efficient Consumer Revocation for Identity based Cloud Storage Auditing for Shared Big Data Records", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.21-26, 2019.

Using hadoop in big data technologies into agriculture presents a significant challenge; at the same time, this technology contributes effectively in many countries’ economic and social development. In this work, we will study environmental data provided by precision agriculture information technologies, which represents a crucial source of data in need of being wisely managed and analyzed with appropriate methods and tools in order to extract the meaningful information by providing decision making support to the farmers.

big data technology, hadoop, environmental data, decision making

[1] Ritaban Dutta; Ahsan Morshed; Jagannath Aryal; Claire D`Este...Development of an intelligent environmental knowledge system for sustainable agricultural [J]. Environmental Modelling and Software. 2014.
[2] Ştefan Conţiu; Adrian Groza. Improving remote sensing crop classification by argumentation-based conflict resolution in ensemble learning [J]. Expert Systems With Applications,2016.
[3] Safaa Abdelraouf Ahmed; Shadia Ragheb Tewfik; Hala Ahmed Tal. Development and verification of a decision support system for the selection of optimum water reuse schemes [J]. Desalination,2003.
[4] M. Pérez-Ruiz; P. Gonzalez-de-Santos; A. Ribeiro; C. Fernand. Highlights and preliminary results for autonomous crop protection [J]. Computers and Electronics in Agriculture, 2015.
[5] Ranya Elsheikh; Abdul Rashid B. Mohamed Shariff; Fazel Amiri. Agriculture Land Suitability Evaluator (ALSE): A decision and planning support tool for tropical and subtropical crops [J]. Computers and Electronics in Agriculture, 2013.
[6] Wu Wan-sheng; Su Zhong-bin; Li Xiao-ming. Research on Intelligent Decision Support System of Soybean [J]. Journal of Northeast Agricultural University(English Edition), 2013.
[7] Golait, Current Issues in Agriculture Credit in India: An Assessment, Reserve Bank of India Occasional Papers, Vol.28, Issue No.1, pp. 1-2, 2017.
[8] Manes, ICT applications in agriculture from Precision Agriculture to Aml, Presented in MIDRA Consortium, Ambient Intelligence Workshop, Florence 2017.
[9] Pulapre, Ramesh, Pankaj, Agriculture Growth in India Since 1991, Department of Economics Analysis and policy, Reserve Bank of India, Mumbai, Study No. 27, pp 18-19, 2015.
[10] Blackmore, S.(1994). Precision Farming: An Introduction. Outlook on Agriculture 23(4) 4, 275-280.
[11] R. D. Ludena, A. Ahrary et al., “Big data approach in an ict agriculture project,” in Awareness Science and Technology and Ubi-Media Computing (iCAST-UMEDIA), 2013 International Joint Conference on. IEEE, 2016, pp. 261–265.
[12] B. Venkatalakshmi and P. Devi, “Decision support system for precision agriculture,” International Journal of Research in Engineering and Technology, vol. 3, no. 7, pp. 849–852, 2014.
[13] H R Zhang, Z L Li, T F Qu, X Y Wei and G C Yang, “Overview of Agriculture Big Data Research”, Computer Science, vol.41, no.11A, pp.387-392, Nov 2014.
[14] J Q Ren, Z X Chen and Q P Zhou, “MODIS vegetation index data used for estimating corn yield in USA”, Journal of Remote Sensing, vol.19, no.4, pp.568-577, 2015.
[15] F Q Song, Z L Zheng and L C Wang, “Yield Estimation for Winter Wheat of Henan Province Based on CASA Model”, Henan Science vol.30, no.10, pp. 1466-1471, Oct.2014.
[16] W G Li and L H Zhao, “Wheat growth monitoring based on medium and high resolution images”,Jiangsu Journal of Agricultural Sciences, vol.27, no.4, pp.736-739, 2017.
[17] Y L Qian, Y Y Hou and H Yan, “Global crop growth condition monitoring and yield trend prediction with remote sensing”, Transactions of the Chinese Society of Agricultural Engineering, vol.28, no.13, pp.166-171, 2015.
[18] C Yang, J H EVERITT and D MURDEN, “Evaluating high resolution SPOT 5 satellite imagery for crop identification”, Computers & Electronics in Agriculture, vol.7q5, no.2, pp.347-354, 2015.
[19] Pierce, F.J.; Elliott, T.V. Regional and on-Farm Wireless Sensor Networks for Agricultural Systems in Eastern Washington. Comput. Electron. Agric. 61, pp 32-43, 2016.
[20] K Yu, Z M Wang and L Sun. “Crop growth condition monitoring and analyzing in county scale by time series MODIS medium resolution data”, proceedings of the International Conference on Agro-Geo informatics, F, 2017.

Harshitha P Patil, Gokul D, Dharmatej M, K V Rajashekhar Reddy, Chandan Raj B R, "Agricultural Intelligence Decision System Using Big Data Analysis", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.27-31, 2019.

One of the biggest environmental problems right now is air pollution. Air quality is needed to be consistently monitored and assessed to ensure better living conditions. The U.S. Environmental Protection Agency (EPA) uses the air quality index (AQI) to standardize the air quality. However, AQI requires precise and accurate sensor readings and complex calculations, making it not feasible for portable air quality monitoring devices. The aim of this paper is to find an alternative way of monitoring and characterizing air quality through the use of integrated gas sensors and building predictive models using machine learning algorithms that can be used to obtain data-driven solutions to mitigate the risk of air pollution.

AQI(Air Quality Index),Data Cleaning, Softmax Function

[1] R. Tibshirani, “Regression shrinkage and selection via the lasso,”Journal of the Royal Statistical Society. Series B(Methodological),vol. 58, no. 1, pp. 267–288, 1996.
[2] M. Yuan and Y. Lin, “Model selection and estimation in regressionwith grouped variables,” Journal of the Royal Statistical Society:Series B (Statistical Methodology), vol. 68, pp. 49–67, 2006.
[3] L. Li, X. Zhang, J. Holt, J. Tian, and R. Piltner, “Spatiotemporal interpolation methods for air pollution exposure,” in Symposiumon Abstraction, Reformulation, and Approximation, 2011.
[4] Y. Zheng, F. Liu, and H.-P. Hsieh, “U-air: When urban air quality inference meets big data,” in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’13, 2013, pp. 1436–1444.
[5] World Health Organization (WHO), “7 million premature deaths annually linked to air pollution,” Mar. 2014. [Online]. Available: http://www.who.int/mediacentre/news/releases/2014/airpollution/en
[6] Y.C. Wang and G.W. Chen, “Efficient Data Gathering and Estimationfor Metropolitan Air Quality Monitoring by Using Vehicular Sensor Networks,” IEEE Trans. Veh. Technol., vol. 66, no. 8, pp. 7234–7248,2017.
[7] Y. Li and J. He, “Design of an intelligent indoor air quality monitoringand purification device,” in 2017 IEEE 3rd Information Technology andMechatronics Engineering Conference (ITOEC), 2017, pp. 1147–1150.
[8] G. O. Avendanoet al., “Microcontroller and app-based air quality monitoring system for particulate matter 2.5 (PM2.5) and particulate matter 1 (PM1),” in 2017 IEEE 9th International Conference onHumanoid, Nanotechnology, Information Technology, Communicationand Control, Environment and Management (HNICEM), 2017, vol. 5, pp. 1–4.
[9] J. Molka-Danielsen, P. Engelseth, V. Olesnanikova, P. Sarafin, and R. Zalman, “Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor NetworkTechnologies,” 2017 5th Int. Conf. Enterp. Syst., pp. 38–45, 2017.
[10] Y. Wu et al., “Mobile Microscopy and Machine Learning Provide Accurate and High-throughput Monitoring of Air Quality,” in Conference on Lasers and Electro-Optics, 2017.

Ashok Shah, Prayash Rimal, Dev Bhasker Singh, Jagadeesh B N, "Development of Machine Learning-Based Predictive Models for Air Quality Analysis and Prediction", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.32-35, 2019.

With the approach of distributed computing, verified information de-duplication has picked up a ton of fame. Numerous methods have been proposed in the writing of this continuous research territory. Among these procedures, theMessage Locked Encryption (MLE) conspire is regularly referenced. Analysts have presented MLE based conventions which give verified de-duplication of information, where the information is by and large in content structure. Thus, sight and sound information, for example, pictures and video, which are bigger in size contrasted with content documents, have not been given much consideration. Applying tied down information de-duplication to such information documents could essentially decrease the expense and space required for their capacity. In this paper we present a safe de-duplication conspire for close indistinguishable (CI) pictures utilizing the Dual Integrity Convergent Encryption (DICE) convention, which is a variation of the MLE based plan. In the proposed plan, a picture is disintegrated into squares and the DICE convention is connected on each square independently as opposed to on the whole picture. As a result, the hinders that are normal between at least two CI pictures are put away just once at the cloud.

De-duplication, Storage system, DICE protocol, Cloud storage

[1] T. Jiang, X. Chen, Q. Wu, J. Ma, W. Susilo, and W.Lou, “Towards efficient fully randomized message-locked encryption”, on the 21st Australasian Conference on Information Security and Privacy, Melbourne, VIC, Australia, 2016, pp. 361-375.
[2] D. Koo, J. Hur, and H. Yoon, “Secure and efficient deduplication over encrypted data with dynamic updates in cloud storage,” in Frontier and Innovation in Future Computing and Communication, Dordrecht, 2014, pp. 229-235.
[3] A. Agarwala, P. Singh, and P.K. Atrey, “DICE: A dual integrity convergent encryption protocol for client side secure data deduplication,” in IEEE International Conference on Systems, Man, and Cybernetics, Banff, Canada, 2017, pp.2176-2181.
[4] M. Bellare, S. Keelveedhi, and T. Ristenpart, “Message locked encryption and secure deduplication,” in Advances in Cryptology-32nd Annual International Conference on the Theory and Applications of Cryptographic Techniques, Athens, Greece, 2013, pp. 296-312.
[5] M. Bellare and S. Keelveedhi, “Interactive message locked encryption and secure deduplication,” in Public Key Cryptography- 18th IACR International Conference on Practice and Theory in Public Key Cryptography, Gaithersburg, MD, USA, 2015, pp. 516-538.
[6] J. Stanek, A. Sorniotti, E. Androulaki, and L.Kencl, “A secure data deduplication scheme for cloud storage,” in Financial Cryptography and Data Security, Berlin, Heidelberg, 2014, pp. 99-118.
[7] M. W. Storer, K. Greenan, D. D. Long, and E. L. Miller, “Secure data deduplication,” in Proceedings of the 4th ACM International Workshop on Storage Security and Survivability, Fairfax, Virginia, USA, 2008, pp. 1-10.
[8] J. R. Douceur, A. Adya, W. J. Bolosky, P. Simon, and M. Theimer, “Reclaiming space from duplicate files in a serverless distributed file system,” in the 22nd International Conference on Distributed Computing Systems, Vienna, Austria, 2002, pp. 617-624.
[9] K. Keonwoo, Y. Taek-Young, J. Nam-Su, and C. Ku-Young, “Client-side deduplication to enhance security and reduce communication costs,” ETRI Journal, vol. 39, no. 2, pp. 116-123,2017.
[10] H. Gang, H. Yan, and L. Xu, Secure Image Deduplication in Cloud Storage. Cham: Springer International Publishing, 2015, pp. 243-251.
[11] F. Rashid, A. Miri, and I. Woungang, “Secure image deduplication through image compression,” J. Inf. Secur. Appl., vol. 27, no. C, pp. 54-64, 2016.
[12] D. Li, C. Yang, C. Li, Q. Jiang, X. Chen, J. Ma, and J. Ren, “A client-based secure deduplication of multimedia data,” in IEEE International Conference on Communications, Paris, France, 2017, pp. 1-6.
[13] X. Li, J. Li, and F. Huang, “A secure cloud storage system supporting privacy-preserving fuzzy deduplication,” Soft Computing, vol. 20, no. 4, pp. 1437-1448, 2016.
[14] Ashish Agarwala, Priyanka Singh, Pradeep k, “Client side secure image deduplication using DICE protocol,” Albony, New York, USA.

Akshay R, Ankith A K, Amith B, Jayanth R, Kiran Mensinkai, "Deduplication of Image at Client Side Using DICE Protocol", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.36-42, 2019.

In recent day scenario, the incessant increase in air and sound pollution prove to be an alarming problem. It has become mandatory to control and appropriately monitor the situation so that the required steps to curb the situation can be undertaken. In this project, an IOT-based method to monitor the Air Quality Index and the Noise Intensity of a region, have been proposed. The recommended technology comprises of four modules namely, the Air Quality Index Monitoring Module, the Sound Intensity Detection Module, the Cloud-based Monitoring Module and the Anomaly Notification Module. Firstly, the Air Quality Index is measured considering the presence of the five criteria air pollutants. Then the sound intensity is detected using respective sensor. After that, the Cloud-based Monitoring Module ensures the process of acquiring the data with the help of Wi-fi-module present in Raspberry Pi which fulfils the objective of analysis of information on a periodical basis.

MQTT Protocol, Noise Pollution Level

[1] L.Ezhilarasi, K.Sripriya, A .Suganya, K.Vinodhini, “ A System For Monitoring Air And Sound Pollution Using Arduino Controller With Iot Technology.” , International Research Journal in Advanced Engineering and Technology (IRJAET)
[2] Mahantesh B Dalawai, Siva Yellampalli, Pradeep S.V, “IOT Based Air and Noise Pollution Monitoring in Urban and Rural Areas, Important Zones like Schools and Hospitals in Real Time.”, International e-Journal for Technology and Research-2017.
[3] Arushi Singh, Divya Pathak, Prachi Pandit1, Shruti Patil, P Priti. C. Golar , “IOT based Air and Sound Pollution Monitoring System.” International Journal of Advanced Research in Electrical,
[4] A. Sumithra, J.Jane Ida, K. Karthika , S. Gavaskar, “A Smart Environmental Monitoring System Using Internet Of Things.” International Journal of Scientific Engineering”
[5] Mohannad Ibrahim , Abdelghafor Elgamri , Sharief Babiker . Ahmed Mohamed, “Internet of things based smart environmental monitoring using the Raspberry-Pi computer.” Fifth International Conference on Digital Information Processing and Communications (ICDIPC), 2015
[6] Giovanni B. Fioccola , Raffaele Sommese, Imma Tufano, Roberto Canonico, Giorgio Ventre, “ Polluino: An efficient cloud-based management of IoT devices for air quality monitoring.” IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 2016
[7] SRM.ArthiShri, NB.Keerthana, S.Sandhiyaa,P.Deepa, D.Mythili,” Noise and Air Pollution Monitoring System Using IOT.” SSRG International Journal of Electrical and Electronics Engineering– (ICETM-2017) - Special Issue- March 2017.
[8] Seung Ho Kim ; Jong Mun Jeong ; Min Tae Hwang ; Chang Soon Kang, “Development of an IoT-based atmospheric environment monitoring system.” International Conference on Information and Communication Technology Convergence (ICTC)., 2017
[9] Somansh Kumar, Ashish Jasuja,“ Air quality monitoring system based on IoT using Raspberry Pi.”, International Conference on Computing, Communication and Automation (ICCCA), 2017.
[10] Himadri Nath Saha, Nilan Saha, Rohan Ghosh, Sayantan Roychoudhury, “Recent trends in implementation of Internet of Things — A review”, IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2016
[11] Himadri Nath Saha, Abhilasha Mandal, Abhirup Sinha, “ Recent trends in the Internet of Things”, IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), 2017
[12] Himadri Nath Saha, Supratim Auddy, Subrata Pal, Avimita Chatterjee, Shivesh Pandey, Rocky Singh, Rakhee Singh, Debmalya Ghosh, Ankita Maity, Priyanshu Sharan, Swarnadeep Banerjee, “Pollution Control using Internet of
[13] Things(IoT).”, 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), 2017

Aishwarya S, Apoorva M J, Divya Vani M, Indushree S, Prasanna Kumar M, "Implementation of Web Based Environmental Pollution Monitoring System Using Raspberry Pi 3 Model", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.43-48, 2019.

In today’s world, wildlife is an important factor in maintaining natural balance of any nation’s environment. One of the important and vital roles is played by the forest department. There are many concerns regarding the safety of wildlife, so for their security is of main concern for this purpose instrument may be mounted on them to view the present location. Bio-sensor systems comprise various types of small physiological sensors, transmission modules and processing capabilities. Hence, sensor networks can collect, transmit, and store vast volumes of environmental data, which may be used in research or monitoring wildlife.GPS used to log the longitude and latitude so that direction can be known easily. These devices are being added to them will explore the possibility of embedding GPS devices so forest department official can track their animal’s movements in real time.So by using these equipment’s we are trying to implement the basic life- guarding system for wild life in low cost and high reliability.

Global Positioning System,Radio Frequency

[1] M. Abadi, P. Barham, J. Chen, Zhifeng Chen, A. Davis, J. Dean, M. Devin, Sanjay Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng. 2016. TensorFlow: A System for Large-Scale Machine Learning. In Symposium on Operating Systems Design and Implementation (OSDI).
[2] A. Alemi. 2016. Improving Inception and Image Classification in Tensor-Flow.https://research.googleblog.com/2016/08/improving-inception-and-image. html. (2016). [Online; accessed 22-Aug-2016].
[3] David Anthony, William P Bennett, Mehmet C Vuran, Matthew B Dwyer, Sebastian Elbaum, Anne Lacy, Mike Engels, and Walter Wehtje. 2012. Sensing through the continent: towards monitoring migratory birds using cellular sensor networks. In Proceedings of the 11th international conference on Information Processing in Sensor Networks. ACM, 329–340.
[4] Aristotle Cloud Federation 2017. Aristotle Cloud Federation. (2017). https://federatedcloud.org [Online; accessed 22-Aug-2016].
[5] AWS Edge Locations 2016. AWS Edge Locations. (2016). https://aws.amazon.
[6] G. Bradski. 2000. OpenCV. Dr. Dobb’s Journal of Software Tools (2000).
[7] Tilo Burghardt, Janko Calic, and Barry T Thomas. 2004. Tracking Animals inWildlife Videos Using Face Detection.. In EWIMT.
[8]Cisco. 2016. Fog Data Services - Cisco. (2016). http://www.cisco.com/c/en/us/products/cloud-systems-management/fog-data-services/index.html [Online;accessed 22-Aug-2016].
[9] Cloudlet-based Mobile Computing 2016. Cloudlet-based Mobile Computing.(2016). http://elijah.cs.cmu.edu/ [Online; accessed 22-Aug-2016].
[10] Content Distribution Network 2016. Content Distribution Network. https://en.wikipedia.org/wiki/Content delivery network. (2016). [Online; accessed22-Aug-2016].

Anju V, Deeksha A S, Deepika B U, Lochana K, Anusha K L, "Maner: Managed Information Dispersal Plan for GPRS IoT Enabled Wildlife Monitoring System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.49-53, 2019.