This papers main aim would be to put forth the implementation of cryptography and steganography to secure files and folders in windows platform. It is a windows desktop application. Here the users can lock their various folders in a different format such as recycle bin, help and support, etc. Once it is locked, for example as recycle bin, the original contents of the locked folder will be hidden and replaced with recycle bin's contents. The locking and unlocking will be done with the help of passwords, which will be encrypted and stored in the database. Similarly, this application would also help us to lock an individual text file. By implementing steganography, the text file, pdf, PowerPoint slide etc can be converted to image and stored safely. The algorithm used for converting the text file to image and encrypting-decrypting the password is AES Algorithm.

Keyword : Asp.net with C#, Cryptography, Files, Folders, MySQL database, AES Algorithm, Steganography, Windows Desktop Application, Windows Operating System.

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India is an agriculture based country and its development lies in the advancement of agricultural techniques. This paper discusses the framework for next gen framing based on a combination of precision agriculture and robotic systems. The work focuses on making agriculture domain tech savvy using robo-bees leading to improvement in the yield. Robo-bees are advancement in the field of drones. These Robo-bees are being used to monitor and take necessary decision based on the input provided by the sensor based fields. Robobees are of two types; Queen Bee and the Worker bees. The queen bee plays an important role in the decision making process for next gen farming..............

Keywords: Agricultural technology, data analysis, decision making, robo-bees, sensor based system.

[1]. Percentage of land under agriculture in India, https://data.worldbank.org/indicator/AG.LND.AGRI.ZS
[2]. Total Area under principal crops, average yield under principal crops http://www.mospi.gov.in/statistical-year-book-india/2016/177
[3]. Total population of India 2022, https://www.statista.com/statistics/263766/total-population-of-india/
[4]. Yongxian Song, Juanli Ma, Xianjin Zhang, Yuan Feng, "Design of Wireless Sensor Network-Based Greenhouse Environment monitoring and Automatic Control System", Journal Of Networks, Vol. 7, No. 5, May 2012.
[5]. N.Sakthipriya, "An Effective Method for Crop Monitoring Using Wireless Sensor Network", MiddleEast Journal of Scientific Research 20(9):1127- 1132, 2014 ISSN 1990-9233.

This paper talks about data security, when confidential information is revealed into an undesirable environment intentionally or unintentionally, it is known as a data breach. This paper concern regarding stored data, the way it is encrypted and the problems faced during decryption. This paper aims to increase the time complexity to decrypt data using the RSA algorithm over various other algorithms like DES, AES, and IDEA along with concepts of factorization.

Keywords: R.S.A. algorithm, D.E.S., A.E.S., I.D.E.A.

[1]. Dr. D.I. George Amalarethinam, H. M. Leena, Enhanced RSA Algorithm with varying Key Sizes for Data Security in Cloud
[2]. Ying-yu Cao, Chong Fu ,An Efficient Implementation of RSA Digital Signature Algorithm
[3]. PachipalaYellamma, ChallaNarasimham, VelagapudiSreenivas; Data Security in cloud using RSA
[4]. Pradeep Semwal, Mahesh Kumar Sharma, Comparative Study of Different Cryptographic Algorithms for Data Security in Cloud Computing
[5]. Krishna KeerthiChennam, Lakshmi Muddana, RajaniKanth, Aluvali, Performance Analysis of various Encryption Algorithms for usage in Multistage Encryption for Securing Data in Cloud.
[6]. Zalak Bhatt, Prof. Vinit Gupta, Advanced Security Technique for Format Preserving Encryption

Nowadays, there is a tremendous advancement in areas of automation and computer vision. Object identification is an essential process in these technologies. It identifies any specific object from an image or video sequence and the action is taken accordingly. Machine learning algorithms are extensively used for object identification in various applications. The necessary features are extracted from the images and are trained using various classifiers. This paper proposes an object identification technique using Support Vector Machines (SVM). The proposed system is compared with Decision Tree (DT) and K-Nearest Neighbor (KNN) classification algorithms. The object identification system is assessed on identification accuracy, prevision and recall.

Keywords: Decision Tree, K-Nearest Neighbor, Support Vector Machines.

[1] Noorpreet Kaur Gill, Anand Sharma, Vehicle Detection from Satellite Images in Digital Image Processing, International Journal of Computational Intelligence Research, 13(5), 2017.
[2] Luigi Di Stefano, Enrico Viarani, Vehicle Detection and Tracking Using the Block Matching Algorithm, Proc. of 3rd IMACS/IEEE, pp. 4491—4496, 1999.
[3] Juan Wu, Bo Peng, Zhenxiang Huang, Jietao Xie, Research on Computer Vision-Based Object Detection and Classification, IFIP International Federation for Information Processing 2013, pp. 183–188, 2013.
[4] R.Muralidharan, Dr.C.Chandrasekar, Object Recognition using SVM-KNN based on Geometric Moment Invariant, International Journal of Computer Trends and Technology, July-Aug 2011.
[5] Khushboo Khurana, Reetu Awasthi, Techniques for Object Recognition in Images and Multi-Object Detection, International Journal of Advanced Research in Computer Engineering & Technology, vol. 2(4), pp. 1383-1388, 2013.

As more and more people are expressing their views and opinions on various microblogging websites there has been a surge of data generated by the users, these websites have people sharing their thoughts daily because of short and simple form of expressions. We can consider such type of data as a resource and perform sentiment analysis on data of various products and services making better data driven decisions. This paper highlights the usefulness of sentiment analysis along with the type of data that is being analyzed, the complex process involved in analyzing the data, the different approaches that can be used, and an experimental observation using the Machine Learning approach

Keywords : Microblogging, Twitter, Sentiment Analysis, API, NLTK, Naïve Bayes Classifier Machine Learning Approach, Lexicon based Approach.

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With increase in the utility of Wireless Sensor Networks in various mission critical fields like environmental monitoring, traffic control, military, medical and healthcare, inventory tracking and smart spaces; secure and reliable sensor networks are a necessity. Majority of sensor networks are deployed in hostile
areas making them difficult to maintain. With limited energy of sensor nodes deploying an efficient Intrusion Detection System which does not consume more resources than the primary function of the network is of paramount importance. The Intrusion Detection System should detect and recover from internal and external attacks. In this paper, analysis of different methodologies is performed to identify their advantages and disadvantages.

Keywords: - WSN, IDS, TPIDS, HIDS, EPID, Cognitive networks.

[1]. Han Zhijie, Wang Ruchuang, Intrusion Detection for Wireless Sensor Network Based on Traffic Prediction Model, International Conference on Solid State Devices and Materials Science, 2012.
[2]. Joseph Rish Simenthy CEng , AMIE, K. Vijayan, Advanced Intrusion Detection System for Wireless Sensor Networks, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2014.
[3]. Khandakar Rashed Ahmed , A.S.M Shihavuddin, Kabir Ahmed, Md. Shirajum Munir and Md Anwar Asad, Abnormal Node Detection in Wireless Sensor Network by Pair Based Approach using IDS Secure Routing Methodology, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.12, December 2008.
[4]. G Sunilkumar , Thriveni J , K R Venugopal , L M Patnaik, Cognitive Approach Based User Node Activity Monitoring for Intrusion Detection in Wireless Networks, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012.
[5]. Ana Paula R. da Silva, Marcelo H.T. Martins, Bruno P.S. Rocha, Antonio A.F. Loureiro, Linnyer B. Ruiz, Hao Chi Wong, Decentralized Intrusion Detection in Wireless Sensor Networks, Proceedings of the First ACM Workshop on Q2S and Security for Wireless and Mobile Networks, Montreal, Quebec, Canada, October 13, 2005.