Novel technique based on cascade classifiers for smoke image detection

This article contributes a novel technique based on cascade classifiers for smoke detection by utilizing the image processing method. It has been a difficult issue for ten years or so due to its variety in shape, texture, and color. In this article, a machine learning methodology is represented to tackle this issue and simulated with MATLAB software. The smoke detection issue acted like a classification issue. The solution is demonstrated as a binary classification issue. Hence, the support vector machine (SVM) is represented for classification. In order to train and test the SVM classifier, both samples of positive and negative are gathered. Two SVM classifiers are utilized in the cascade. The first classifier distinguishes the presence of smoke if smoke presents in a provided input image; the second classifier is utilized to find the locale of smoke in a provided input image. The size of the window is set to 32 × 32 and slided across the whole image to identify the smoke in a zone of the window. The novel technique is a training dataset and utilizing linear kernel function. In this manner, the novel technique is tested with a test dataset. The first SVM classifier obtained 100% accuracy in training and 96% accuracy in testing. A training accuracy of 96% and a test accuracy of 93.6% were obtained by the second SVM classifier. This novel technique proved to be more proficient and cost-savvy than the traditional strategies.

Accurate Multilevel Classification for Wildlife Images

The most common approaches for classification rely on the inference of a specific class. However, every category could be naturally organized within a taxonomic tree, from the most general concept to the specific element, and that is how human knowledge works. This representation avoids the necessity of learning roughly the same features for a range of very similar categories, and it is easier to understand and work with and provides a classification for each abstraction level. In this paper, we carry out an exhaustive study of different methods to perform multilevel classification applied to the task of classifying wild animals and plant species. Different convolutional backbones, data setups, and ensembling techniques are explored to find the model which provides the best performance. As our experimentation remarks, in order to achieve the best performance on the datasets that are arranged in a tree-like structure, the classifier must feature an EfficientNetB5 backbone with an input size of 300 × 300 px, followed by a multilevel classifier. In addition, a Multiscale Crop data augmentation process must be carried out. Finally, the accuracy of this setup is a 62% top-1 accuracy and 88% top-5 accuracy. The architecture could benefit for an accuracy boost if it is involved in an ensemble of cascade classifiers, but the computational demand is unbearable for any real application.

Optimization of principal parameters of bulk materials separation in cascade classifiers

The analysis of principal regularities of material distribution in cascade stages has allowed us to substantiate the optimality of some important separation parameters. It is shown that the best separation effect can be achieved by feeding the initial material to the mid stage of the cascade. Here the number of stages can be limited, since the separation effect is weakened with its growth. This analysis has also demonstrated the limited nature of the velocity-based hypothesis in the theory of gravitation processes, although until now this hypothesis ranks among the central ones in this theory.

FACE RECOGNITION THROUGH CASCADE CLASSIFIER USING EIGENFACES ALGORITHMS.

The easiest way to distinguish each person's identity is through the face. Face recognition is included as an inevitable pre-processing step for face recognition. Face recognition itself has to face difficulties and challenges because sometimes some form of issue is quite different from human face recognition. There are two stages used for the human face recognition process, i.e. face detection, where this process is very fast in humans. In the first phase, the person stored the face image in the database from a different angle. The person's face image storage with the help of Eigenvector value depended on components - face coordinates, face index, face angles, eyes, nose, lips, and mouth within certain distances and positions with each other. There are two types of methods that are popular in currently developed face recognition patterns, the Cascade Classifier method and the Eigenface Algorithm. Facial image recognition The Eigenface method is based on the lack of dimensional space of the face, using principal component analysis for facial features. The main purpose of the use of cascade classifiers on facial recognition using the Eigenface Algorithm was made by finding the eigenvectors corresponding to the largest eigenvalues of the facial image

Automated Attendance Monitoring System using Face Detection and RFID Cards

The attendance serves the most important role in the academic life of any student. Most of the colleges follow the traditional approach of attendance in which the professor speaks out student’s name and record attendance. For each lecture, this repetition of attendance calling is actually wastage of time and a time-taken procedure for calculating attendance of each student. Here an automatic process is proposed which is based on image processing with radio-frequency identification to avoid the losses. In this project approach, there is a use of face detection & RFID cards. Firstly, use the pre-processing step for the face detection and RFID receiver for the RFID cards counting and the second step is to detect, recognize and then the face is matched with stored images in the database. In this paper, viola-Jones algorithm is used for face detection, in which first step of integral image is used for feature computation and Adaboost algorithm is used for feature selection in second step. Then for discarding the non-faces, cascade classifiers is used in the third step of algorithm. The working of this project is to detect and recognize the face and RFID cards then mark the attendance for the corresponding face in the database on matching the face and unique number to the stored dataset. Face detection and RFID cards will be used as input and the attendance will be marked as output. This project is being conferred as a clarification for the “Automated attendance monitoring system.” Here a system of automatic face detection and recognition is proposed to mark the attendance automatically in database. This will save the time of person who is using traditional pen & paper based approach for attendance and hence is a solution for the automated attendance monitoring system. RFID cards are very helpful here for tracking or monitoring the student/teacher/employees within the campus. This system can be used in schools, colleges for students as well as for teachers also and it can be also used in companies, hospitals and malls for maintain records of accurate attendance of their employees.