Handwriting recognition is one of the compelling research works going on because every individual in this world has their own style of writing. It is the capability of the computer to identify and understand handwritten digits automatically. Because of the progress in the field of science and technology, everything is being digitalised to reduce human effort. Hence, there comes a need for handwritten digit recognition in many real-time applications. Many Machine Learning and Deep Learning Algorithms are developed which can be used for this digit classification. This project performs Digit Recognition and the analysis of accuracy of algorithms Deep Learning Algorithm.
Preserving the attendance is very crucial in all the institutes for checking the overall performance of students. Each institute has its very own method in this regard. A few are taking attendance manually using the old paper or document based approach and some have adopted techniques of automated attendance the use of few biometric techniques. There are many computerised methods to be had for this reason i.e. biometric attendance. All these methods additionally waste time due to the fact that college students or employees have to make a queue to contact their thumb on the scanning device. This gadget makes use of the face recognition approach for the computerised attendance of students in the study room environment without lectures intervention or the employee .This attendance is recorded with the aid of usage of a digital camera connected in the study room or the working environment i.e. constantly shooting photos of students or employees, discover the faces in pix and examine the detected faces with the database and mark the attendance.
Steganography is one of the best data hiding technique in the world which can be used to hide data without its presence felt. In today’s digital world most of us communicate via use of electronic media or internet. Most people among us remain unaware about the data loss or data theft which can happen on online transmission of data or message. Valuable information including personal data, messages transmitted through internet is vulnerable to hackers who may steal or decrypt our data or messages. This poject is about enhancing the data or message security with use of Audio Steganography using LSB algorithm to hide the message into multiple audio files. The message hidden by this application is less vulnerable to be stolen than other similar applications. This is due to following reasons: Firstly files are taken to hide high amount of message which enhance information hiding capacity. Secondly before being hidden, the message is broken into parts and shuffled randomly based on permutation generated at runtime so even if the LSB gets encountered the message is still unarranged and meaningless which enhances its security. This application is capable to carry large amount of information with greater security.
Liver cancer is one of the most severe types ofcancerous diseases which is responsible for the death of many patients. Liver cancer images have more noises which is difficult to diagnose the level of the tumor. It is a challenging task to automatically identify the tumor from images because of several anatomical changes in different patients. The cancer is difficult to find because of the presence of objects with same intensity level. In this proposed system, fully automated machine learning is used to detect the liver tumor from input image. Region growing technique is used to segment the region of interest. The textural feature are extracted from Gray level co-occurrence matrix (GLCM) of the segmented image. Extracted textural features are given as input to the designed support vector machine classifier system. Performance analysis of SVM classification of liver cancer image is studied. This will be useful for physician in better automatic diagnosis of liver cancer from input images.
Message security is an utmost concern in the web attacks are become more serious. The Message encryption and decryption has applications in internet communication, military communication, medical imaging, multimedia systems, tele-medicine, etc. To make the data secure from various attacks the data must be encrypted before it is transmitting. Absolute protection is a difficult issue to maintain the confidentiality of Message through their transmission over open channels such as internet or networks and is a major concern in the media, so Data Cryptography becomes an area of attraction and interest of research in the field of information security. The project offer proposed system that provides a special kinds of image Encryption data security, Cryptography using RSA algorithm for encrypted Message to extract using RSA algorithm. This approach provides high security and it will be suitable for secured transmission of data over the networks or Internet.
Steganography is the process of hiding a secret message within a larger one in such a way that someone can not know the presence or contents of the hidden message. The purpose of Steganography is to maintain secret communication between two parties. Unlike cryptography, which conceals the contents of a secret message, steganography conceals the very fact that a message is communicated. In this project secret message is embedding in cover image while sending to receiver at extraction part secret message is extracted from cover image which is called as message hiding in image. Although steganography differs from cryptography, there are many analogies between the two, and some authors classify steganography as a form of cryptography since hidden communication is a type of secret message.
Currently over the millions of digital audio files such as digital songs are copied illegally during file-sharing over the networks. It has resulted as the loss of revenue for music and broadcasting industries. The traditional protection schemes are no longer useful to protect copyright and ownership of multimedia objects. These challenges have prompted significant research in digital audio watermarking for protection and authentication. Watermarking is a technique, which is used in protecting digital information like text, images, videos and audio as it provides copyrights and ownership. The identity of the owner of the audio file can be hidden in the audio file which is called Watermark. Therefore, digital audio watermarking is the process of hiding some information into the audio file in such a way that the quality and the audibility of the audio is not affected. It helps to prevent forgery and impersonation of audio signal. Audio watermarking is more challenging than image watermarking due to the dynamic supremacy of hearing capacity over the visual field. This paper presents a novel audio watermarking using wavelet transformation. The proposed method involves Embedding and extraction of audio signal using wavelet transformation. The audio signal which is in .Wave or Mp3 format undergoes segmentation, transformation and embedding the watermarked data and at the last inverse transformation will be carried out. We attempt to develop an efficient method for hiding the information in the audio file such that the copyright information will be protected from illegal copying of the information
Steganography is the process of hiding a secret message within a larger one in such a way that someone can not know the presence or contents of the hidden message. The purpose of Steganography is to maintain secret communication between two parties. Unlike cryptography, which conceals the contents of a secret message, steganography conceals the very fact that a message is communicated. In this project secret message is embedding in cover image while sending to receiver at extraction part secret message is extracted from cover image which is called as message hiding in image. Although steganography differs from cryptography, there are many analogies between the two, and some authors classify steganography as a form of cryptography since hidden communication is a type of secret message.
Liver tumor is one of the most severe types of cancerous diseases which is responsible for the death of many patients. Liver tumor images have more noises which is difficult to diagnose the level of the tumor. It is a challenging task to automatically identify the tumor from images because of several anatomical changes in different patients. The tumor is difficult to find because of the presence of objects with same intensity level. In this proposed system, fully automated machine learning is used to detect the liver tumor from input image. Region growing technique is used to segment the region of interest. The textural feature are extracted from Gray level co-occurrence matrix (GLCM) of the segmented image. Extracted textural features are given as input to the designed SVM classifier system. Performance analysis of SVM classification of liver tumor image is studied. This will be useful for physician in better automatic diagnosis of liver tumor from input images.
Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. An abundant number of researches work has been performed for recognizing faces under different conditions like changing pose or illumination, degraded images, etc. Still, difficulties created by masks are usually disregarded. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions and then face mask. In this project we implement Real Time Face Mask Detection Using Deep Learning.
Steganography is the process of hiding a secret message within a larger one in such a way that someone can not know the presence or contents of the hidden message. The purpose of Steganography is to maintain secret communication between two parties. Unlike cryptography, which conceals the contents of a secret message, steganography conceals the very fact that a message is communicated. In this project secret message is embedding in cover image while sending to receiver at extraction part secret message is extracted from cover image which is called as message hiding in image. Although steganography differs from cryptography, there are many analogies between the two, and some authors classify steganography as a form of cryptography since hidden communication is a type of secret message.
Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. An abundant number of researches work has been performed for recognizing faces under different conditions like changing pose or illumination, degraded images, etc. Still, difficulties created by masks are usually disregarded. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions and then face mask. In this project we implement Real Time Face Mask Detection Using Deep Learning.
Cancer is the second cause of death in the world. 8.8 million patients died due to cancer in 2015. Breast cancer is the leading cause of death among women. Several types of research have been done on early detection of breast cancer to start treatment and increase the chance of survival. Most of the studies concentrated on mammogram images. However, mammogram images sometimes have a risk of false detection that may endanger the patient’s health. It is vital to find alternative methods which are easier to implement and work with different data sets, cheaper and safer, that can produce a more reliable prediction. This project we proposes a model of Machine Learning (ML) algorithms including Support Vector Machine (SVM). Here it also detect types of Breast Cancer in different categories like NORM=Normal, CALC=Calcification, CIRC=Circumscribed Masses, SPIC=Speculated Masses, MISC=ill-defined Masses, ARCH=Architectural Distortion, ASYM=Asymmetry.
Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, a solution for the detection and classification of fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following steps; in the first step K-Means clustering technique is used for the image segmentation, in the second step some features are extracted from the segmented image, and finally images are classified into one of the classes by using Neural Network. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases.
Fruit diseases can cause significant losses in yield and quality appeared in harvesting. For example, soybean rust (a fungal disease in soybeans) has caused a significant economic loss and just by removing 20% of the infection, the farmers may benefit with an approximately 11 million-dollar profit (Roberts et al., 2006). Some fruit diseases also infect other areas of the tree causing diseases of twigs, leaves and branches. An early detection of fruit diseases can aid in decreasing such losses and can stop further spread of diseases. A lot of work has been done to automate the visual inspection of the fruits by machine vision with respect to size and color. However, detection of defects in the fruits using images is still problematic due to the natural variability of skin color in different types of fruits, high variance of defect types, and presence of stem/calyx. To know what control factors to consider next year to overcome similar losses, it is of great significance to analyze what is being observed.
Message security is an utmost concern in the web attacks are become more serious. The Message encryption and decryption has applications in internet communication, military communication, medical imaging, multimedia systems, tele-medicine, etc. To make the data secure from various attacks the data must be encrypted before it is transmitting. Absolute protection is a difficult issue to maintain the confidentiality of Message through their transmission over open channels such as internet or networks and is a major concern in the media, so Data Cryptography becomes an area of attraction and interest of research in the field of information security. The project offer proposed system that provides a special kinds of image Encryption data security, Cryptography using RSA algorithm for encrypted Message to extract using RSA algorithm. This approach provides high security and it will be suitable for secured transmission of data over the networks or Internet.
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