Friday, 26 June 2026

Image Encryption Using ECC Elliptic Curve Cryptography | Image Encryption Using Matlab Project With Source Code

 ABSTRACT

            During the last decade information security has become the major issue. The encrypting and decrypting of the data has been widely investigated because the demand for the better encryption and decryption of the data is gradually increased for getting the better security for the communication between the devices more privately. The Image Encryption Decryption play a major role for the fulfillment for this demand. A lot of information is perceived when we observe an image. Images have become an inevitable source of information. Everyday we come across various image from various sources. When images are confidential and we want the image to be transferred safe and securely, cryptography comes into play. The cryptographic technique which we have implemented in this project is the Elliptic Curve Cryptography (ECC). Various study on ECC has concluded that the difficultly to solve an Elliptic Curve Discrete Logarithmic Problem is exponentially hard with respect to the key size used. This property makes ECC a very good choice for encryption/decryption process compared to other cryptographic techniques which are linearly difficult or sub exponentially difficult. This project is developed in matlab.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com

Thursday, 25 June 2026

Plant Leaf Disease Detection Using Matlab Project Source Code || Plant Disease Detection Using Image Processing

ABSTRACT

            The detection and classification of plant diseases are the crucial factors in plant production and the reduction of losses in crop yield. This project proposes an approach for plant leaf disease detection and classification on plants using image processing. The algorithm presented has three basic steps: Image Pre-processing and analysis Recognition of plant disease. The plant disease diagnosis is restricted by person’s visual capabilities as it is microscopic in nature. Due to optical nature of plant monitoring task, computer visualization methods are adopted in plant disease recognition. The aim is to detect the symptoms of the disease occurred in leaves in an accurate way. Once the captured image is pre-processed, the various properties of the plant leaf such as intensity, color and size are extracted and sent to with Convolutional  Neural Network CNN for classification. This project is developed in Matlab.

PROJECT OUTPUT


PROJECT DEMO VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com