R. R. Keole’s research while affiliated with Department Of Electronics And Information Technology, India and other places

With the furtherance of technology, the frauds and malpractices related to it have been on the verge of happening, and technology has been a kind of savior in so many cases. Facial recognition can be considered as such a savior in terms of numerous malpractices and fraud activities. Not only in the field of fraud prevention or detection, but facial recognition and automated face detection tools and technologies play an important role in the attendance management systems, detection of criminals, etc. Document image analysis is used in detecting frauds, but the proposed model relies on the image or video. In this paper, the implementation of facial recognition techniques along with their features and application has been explained. This paper also explains how facial recognition technology is now getting introduced and applied across numerous aspects of life. This paper also highlights the drawbacks or the limitations of facial recognition technologies, and in addition, it also presents the various methods and ideas using which facial recognition technology, and its performance can be enhanced and the limitations can be overcome. A novel framework for monitoring student attendance has been implemented. A Web application based upon the Django framework has been designed for easy monitoring and maintaining the attendance of the student using the facial landmark algorithm.KeywordsFacialRecognitionDatabaseMethods Attendance management

Monitoring of food plays a significant role in leading health-related issues and tasks. With its multiple applications and features, image processing emerges to be an interesting field in the process of identifying food items. In this paper, a technique has been presented for classifying the food image using the You Only Look Once (YOLO) algorithm. Unlike the conventional artificial neural networks, the YOLO algorithm has more efficiency, and it has been trained on a loss function that corresponds straight to detection, and the complete model is trained with 6000 epochs. Due to the high variance in the alike domain of food images, food classification becomes a difficult task but it has a significant role in lives at the present time as it can be utilized by numerous sources. In this paper, a comparison of the working of the YOLO algorithm with other techniques that are used in image processing such as ResNet-50, VGG-16, ImageNet, and Inception has been elaborated. In this work, the famous dataset from Kaggle is used for implementation purposes. The dataset consists of 4000 Indian Food Image 80 different categories or classes. The proposed model is giving 99% accuracy for classifying the food. KeywordsImage processingFoodClassificationYOLO algorithmDetectionImage pre-processingConvolution neural networks

It is evident that the evolution in technology has surpassed expectations and reached different heights in a shorter span of time and with evolving technology; a lot of changes have been introduced in our lives, and one such change is the replacement of traditional payment methods with the credit card system. Credit card use increases the most during online shopping. With the huge demand for credit cards worldwide, credit card fraud cases to are increasing rapidly. In this paper, four machine learning algorithms that are decision tree, random forest, logistic regression, and Naïve Bayes have been used for training the models. Also, deep neural networks have been implemented for model training which is giving more promising results compared to the machine learning algorithms. The accuracy of each algorithm used in the implementation of the credit card fraud detection has been compared and analyzed.KeywordsCredit cardMachine learningRandom forestFraudsPreventionAlgorithms

With recent advances in technology, modern computer systems are becoming more flexible. Modern computers are capable of processing millions of information per second. In such cases, traditional input devices such as a mouse or keyboard are relatively slow. In this paper we use system that can be overcome by human interaction with the computer. With innovation and development in technology, motion sensors are able to capture the position and natural movements of the human body. This has made possible a new way of communication with computers. So keeping all these in mind we propose a system which is an untouched and fast communication system. This system will be able to capture the movements of the eyeball for which it is responsible cursor control. The system processes the data in the camera feed and calibrates the parameter interface according to the user. The system then performs computer-related algorithms to determine the location of the doll's and use eyes to implement natural eye-computer interactions