File Security Using Elliptic Curve Cryptography (ECC) in Cloud


Data security in cloud computing is a mostly researched topic which has various solutions like applying encryption to data and using multi cloud environment. But still there are many issues related to data security. In this project we are using ECC digital signature method to sign signature of user data while uploading to cloud and use same digital signature to download when required.

The Elliptic Curve Cryptography (ECC) is modern family of public-key cryptosystems, you can use an Elliptic Curve algorithm for public/private key cryptography. To be able to use ECC; cryptographic signatures, hash functions and others that help secure the messages or files are to be studied at a deeper level.

It implements all major capabilities of the asymmetric cryptosystems: Encryption, Signatures and Key Exchange The main advantage is that keys are a lot smaller. With RSA you need key servers to distribute public keys. With Elliptic Curves, you can provide your own public key.

In python, the above described method can be implemented using the   ECDSA Algorithm. 


  • Using public key cryptosystems with both public and private key can give security for data compare to single key encryption. In this project ECC algorithm is used for security data to cloud and uploading data to cloud.

Existing system:

  • AES, DES are mostly used crypto graphic algorithms for securing data. These methods are used in most of the applications which use single key for encryption and decryption.


  • These methods are old methods which are used in most of the applications.
  • They use single key for encryption and decryption.

Proposed system:

  • In cloud environment data security is very important as data is stored in third party servers there is need to effective multi key encryption techniques like ECC algorithms. In this project we are using ECC algorithm in python language and using cloud to store encrypted data.


  • Time taken for encryption process is less
  • Multiple keys are used for encryption and decryption process.


Software Requirement: 

  • Operating system           :           Windows XP/7/10
  • Coding Language           :           Html, JavaScript,  
  • Development Kit             :        Flask Framework
  • Database                             :           SQLite
  • IDE                                          :           Anaconda prompt

Crop Yield Prediction using KNN classification


Agriculture is considered as import field all over the world where there are many challenges in solving problems in the process of estimating crops based on the conditions. This has become a challenge for developing countries.  Using latest technologies many companies are using IOT based services and Mechanical technology to reduce manual work. These methods are mostly useful in the case on reducing manual work but not in prediction process. In this project crop yield prediction using Machine learning latest ML technology and KNN classification algorithm is used for prediction crop yield based on soil and temperature factors.  Dataset is prepared with various soil conditions as features and labels for predicting type of each label is related to certain crop. In prediction process user can give input as soil features and result will be type of crop suitable for specific conditions and application also helps in suggesting best crops with yield for hector.


  • In our country large amount of population are depending on agriculture though government is taking financial steps to help farmers still they are facing problems due to lack of data analysis and prediction on crops.


  • Our objective is to develop an application using machine learning for predicting which crop to be used based on soil condition using k nearest neighbor classification.

Existing system:

          Image based analysis was one of the methods which was previously used for detection land type and then analysis was done.


         Process is based on image analysis results are not accurate as in this method soil conditions are not considered.

       Image processing is a time taking process.

Proposed system:

        Machine learning is the latest technology which python programming language gives advantage in using various algorithms for crop yield prediction based on the input data set. In this process KNN classification algorithm is used for prediction. In this project testing training is performed on given text dataset which includes soil and temperature conditions as features and type of crop as labels.


        Crop yield prediction is performed based on textual dataset and any user can check type of crop best suits for conditions and get crop suggestions. 


System Requirement:

  • Operating system         :           Windows XP/7/10
  • Coding Language :           Html, JavaScript, 
  • Development Kit :        Flask Framework
  • Programming language: Python
  • IDE :           Anaconda prompt

Super Market Management System .Net Project


It is a windows application. By using this application, the admin can generate various details about the regular customer’s, product records, etc. In this application, The Supermarket agent will enter the details of the customer and then the agent will provide an ID to the customer and he will maintain the details of the customers, product quality, price, etc in the database.

In this Super Market Management application, we are providing a scheme that, when the customer purchase rate reaches a certain level the application automatically adds some points to the customer’s ID. Once the user points reach a certain level a message will be displayed, saying that he has won a gift.

This Super Market Management System project will lead to an expansion of the Supermarket with open publicity to gain high margins in the market. In the feature, we can develop this project into a web application.


• Operating System Server: Windows
• Database Server: Microsoft SQL Server
• Client: Microsoft Internet Explorer
• Tools: Microsoft Visual Studio .Net
• User Interface: Asp.Net with Ajax
• Code Behind: VC#.Net


• Processor: Intel Pentium or More
•REQUIREMENTSRam and above
• Hard Disk: PC with 20GB and above

An Object-Oriented Graphics Engine CSE Latest Project Abstract

Introduction to An Object-Oriented Graphics Engine CSE Latest Project:

An Object-Oriented Graphics Engine CSE Project is about the graphic engine which is object-oriented. Most of the users focus on the quality output and also the performance in the implementation of the graphics engine system. In this paper we have implemented object-oriented graphics system. And also the architecture of the system along with the modules is also presented. It has experimentally proved that this system provides high stability and also speed.

The paper provides the implementation of the graphics engine of 3D i.e. Gingko is given. The experiment which is conducted says that the Gingko is capable of supporting the extendable architecture and also provides the efficiency in the method.

The architecture of the Gingko includes four layers where Encapsulation layer is capable of encapsulating the graphical interfaces. The Core layer is used for implementing the main framework and also the management of the entire system. The other layer Extension provides more functions related to GUI. And the last layer of user interface is capable of providing the common API to all its users.

The main goal of this implementation of the algorithm is for providing the convenient services to all its developers and also in reduction in terms of the cost and also the time. And even the programmers are capable of developing the algorithms by their own with the help of the plug-in system. The performance of the graphics engine depends on the frame rate. In this paper for reducing the cost many experiments were also conducted. 

We can conclude that the Gingko is capable of supporting the extendable architecture and also provides the efficiency in the method.

An Efficient Image Processing Method Based on Web Services for Mobile Devices Abstract

An Efficient Image Processing Method Based on Web Services for Mobile Devices Project is about an efficient method for the processing of the image in case of the mobile devices. The present system includes the limitations in the resources which results in the degradation of the image processing system. In the existing system the computing model is centralized and implementation is difficult in the mobile devices. 

The solution is given to the above problem with the implementation of the image processing with the help of the web services. Also the processing tasks are shared among the service providers also the registry including the service requesters. 

The web based services are capable of efficient utilization of the resources of the mobile devices and the image processing tasks are distributed accordingly between them. This web based services are more efficient when compared to that of the traditional system for processing of the image. It has a lot of advantages in terms of coupling and also resources utilization in case of the heterogeneous network. 

The experiments which were conducted on the web based services show that there is an increase of around 30% in terms of the memory usage and also the response time was reduced to around 25%.  The processing of the web based image system includes three layers. 

Hence we can conclude that the web based services of image processing system is capable of overcoming the limitations of the existing traditional systems for the processing of the image.

In the existing system the computing model is centralized and implementation is difficult in the mobile devices. The web based services dominate the traditional system in terms of the memory usage and also the response time.