Deep Learning and Uniform LBP Histograms for Position Recognition of Elderly People with Privacy Preservation

For the elderly population, falls are a vital health problem especially in the current context of home care for COVID-19 patients. Given the saturation of health structures, patients are quarantined, in order to prevent the spread of the disease. Therefore, it is highly desirable to have a dedicated monitoring system to adequately improve their independent living and significantly reduce assistance costs. A fall event is considered as a specific and brutal change of pose. Thus, human poses should be first identified in order to detect abnormal events. Prompted by the great results achieved by the deep neural networks, we proposed a new architecture for image classification based on local binary pattern (LBP) histograms for feature extraction. These features were then saved, instead of saving the whole image in the series of identified poses. We aimed to preserve privacy, which is highly recommended in health informatics. The novelty of this study lies in the recognition of individuals’ positions in video images avoiding the convolution neural networks (CNNs) exorbitant computational cost and Minimizing the number of necessary inputs when learning a recognition model. The obtained numerical results of our approach application are very promising compared to the results of using other complex architectures like the deep CNNs

A Novel Approach for Online Signature Verification Using Fisher Based Probabilistic Neural Network

The rapid advancements in communication, networking and mobility have entailed an urgency to further develop basic biometric capabilities to face security challenges. Online signature authentication is increasingly gaining interest thanks to the advent of high quality signature devices. In this paper, we propose a new approach for automatic authentication using dynamic signature. The key features consist in using a powerful combination of linear discriminant analysis (LDA) and probabibilistic neural network (PNN) model together with an appropriate decision making process. LDA is used to reduce the dimensionality of the feature space while maintining discrimination between users. Based on its results, a PNN model is constructed and used for matching purposes. Then a decision making process relying on an appropriate decision rule is performed to accept or reject a claimed identity. Data sets from SVC 2004 have been used to assess the performance of the proposed system. The results show that the proposed method competes with and even outperforms existing methods

A Novel Approach for Online Signature Verification Using Fisher Based Probabilistic Neural Network

The rapid advancements in communication, networking and mobility have entailed an urgency to further develop basic biometric capabilities to face security challenges. Online signature authentication is increasingly gaining interest thanks to the advent of high quality signature devices. In this paper, we propose a new approach for automatic authentication using dynamic signature. The key features consist in using a powerful combination of linear discriminant analysis (LDA) and probabibilistic neural network (PNN) model together with an appropriate decision making process. LDA is used to reduce the dimensionality of the feature space while maintining discrimination between users. Based on its results, a PNN model is constructed and used for matching purposes. Then a decision making process relying on an appropriate decision rule is performed to accept or reject a claimed identity. Data sets from SVC 2004 have been used to assess the performance of the proposed system. The results show that the proposed method competes with and even outperforms existing methods

A Multilayered Audio Signal Encryption Approach for Secure Voice Communication

In this paper, multilayer cryptosystems for encrypting audio communications are proposed. These cryptosystems combine audio signals with other active concealing signals, such as speech signals, by continuously fusing the audio signal with a speech signal without silent periods. The goal of these cryptosystems is to prevent unauthorized parties from listening to encrypted audio communications. Preprocessing is performed on both the speech signal and the audio signal before they are combined, as this is necessary to get the signals ready for fusion. Instead of encoding and decoding methods, the cryptosystems rely on the values of audio samples, which allows for saving time while increasing their resistance to hackers and environments with a noisy background. The main feature of the proposed approach is to consider three levels of encryption namely fusion, substitution, and permutation where various combinations are considered. The resulting cryptosystems are compared to the one-dimensional logistic map-based encryption techniques and other state-of-the-art methods. The performance of the suggested cryptosystems is evaluated by the use of the histogram, structural similarity index, signal-to-noise ratio (SNR), log-likelihood ratio, spectrum distortion, and correlation coefficient in simulated testing. A comparative analysis in relation to the encryption of logistic maps is given. This research demonstrates that increasing the level of encryption results in increased security. It is obvious that the proposed salting-based encryption method and the multilayer DCT/DST cryptosystem offer better levels of security as they attain the lowest SNR values, −25 dB and −2.5 dB, respectively. In terms of the used evaluation metrics, the proposed multilayer cryptosystem achieved the best results in discrete cosine transform and discrete sine transform, demonstrating a very promising performance

A Novel Quantum Behaved Particle Swarm Optimization Algorithm with Chaotic Search for Image Alignment

In an attempt to improve existing evolutionary metaheuristics quantum computing principles have been used. While some of them focus on the representation scheme adopted others deal with the behavior of the underlying algorithm. In this paper, we propose a search strategy that combines the ideas of use of a chaotic search with a selection operation within a quantum behaved Particle Swarm optimization algorithm. This search strategy is developed in order to achieve image alignment through maximization of an entropic measure: mutual information. The proposed framework is general as it handles any kind of transformation. Experimental results show the effectiveness of the algorithm to achieve good quality alignment for both mono modality and multimodality images. The proposed combination of the two features has lead to better solutions compared to those obtained by using each feature alone

CircleVis: A Visualization Tool for Circular Labeling Arrangements and Overlap Removal

Information visualization refers to the practice of representing data in a meaningful, visual way that users can interpret and easily comprehend. Geometric or visual encoding shapes such as circles, rectangles, and bars have grown in popularity in data visualization research over time. Circles are a common shape used by domain experts to solve real-world problems and analyze data. As a result, data can be encoded using a simple circle with a set of labels associated with an arc or portion of the circle. Labels can then be arranged in various ways based on human perception (easy to read) or by optimizing the available space around the circle. However, overlaps can occur in one or more arrangements. This paper proposes CircleVis, a new visualization tool for label arrangement and overlap removal in circle visual encoding. First, a mathematical model is presented in order to formulate existing arrangements such as angular, path, and linear. Furthermore, based on user interaction, a new arrangement approach is proposed to optimize available space in each circle arc and delete label overlaps. Finally, users test and evaluate the designed tool using the COVID-19 dataset for validation purposes. The obtained results demonstrate the efficacy of the proposed method for label arrangement and overlapping removal in circular layout

Combining Fisher Discriminant Analysis and Probabilistic Neural Network for Effective On-line Signature Recognition

The advent of new technologies enables capturing the dynamic of a signature. This has opened a new perspective for the possible use of signatures as a basis for an authentication system that is accurate and trustworthy enough to be integrated in practical applications. Automatic online signature recognition and verification is one of the biometric techniques being the subject of a growing and intensive research activity. In this paper, we address this problem and we propose a two-stage approach for personal identification. The first stage consists in the use of linear discriminant analysis to reduce the dimensionality of the feature space while maintaining discrimination between user classes. The second stage consists in tailoring a probabilistic neural network for effective classification purposes. Several experiments have been conducted using SVC2004 database. Very high classification rates have been achieved showing the effectiveness of the proposed approach

Female Saudi College students' e-learning experience amidst COVID-19 pandemic: An investigation and analysis

Emergency remote teaching in the immediate wake of the COVID-19 pandemic has created a challenging situation for both students and teachers. The purpose of this research is to identify the perceptions and challenges that university students faced during online classes in a women only university in Saudi Arabia. Data was collected by circulating Google forms among students from different colleges, and a total of 542 students submitted their responses. Apart from gathering the personal information of participants, the survey also collected information on aspects such as educational, financial, internet connectivity and volunteering/donations. Chi-squared test was used to determine whether there was a significant difference in opinion between different groups of students on various questions. Stress was identified as the most prevalent issue among students. Students were found to be stressed regardless of their college of study or age. In comparison to others, younger students and students from financially disadvantaged families faced more difficulties. In terms of remote practical class satisfaction, health/medical stream students were the most dissatisfied group. They also faced more difficulties than students from other colleges. The analysis results show that problems such as stress, poor internet connectivity, the need for technical support, a lack of proper interaction with faculty, a lack of proper academic advising, a lack of proper study space at home etc. must be addressed in order to improve the effectiveness of online classes. This paper also includes recommendations for resolving the various issues that students face