Gesture Recognition and Classification using Intelligent Systems

Gesture Recognition is defined as non-verbal human motions used as a method of communication in HCI interfaces. In a virtual reality system, gestures can be used to navigate, control, or interact with a computer. Having a person make gestures formed in specific ways to be detected by a device, like a camera, is the foundation of gesture recognition. Finger tracking is an interesting principle which deals with three primary parts of computer vision: segmentation of the finger, detection of finger parts, and tracking of the finger. Fingers are most commonly used in varying gesture recognition systems. Finger gestures can be detected using any type of camera; keeping in mind that different cameras will yield different resolution qualities. 2-dimensional cameras exhibit the ability to detect most finger motions in a constant surface called 2-D. While the image processes, the system prepares to receive the whole image so that it may be tracked using image processing tools. Artificial intelligence releases many classifiers, each one with the ability to classify data, that rely on its configuration and capabilities. In this work, the aim is to develop a system for finger motion acquisition in 2-D using feature extraction algorithms such as Wavelets transform (WL) and Empirical Mode Decomposition (EMD) plus Artificial Neural Network (ANN) classifier

Hand gesture recognition using deep learning neural networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHuman Computer Interaction (HCI) is a broad field involving different types of interactions including gestures. Gesture recognition concerns non-verbal motions used as a means of communication in HCI. A system may be utilised to identify human gestures to convey information for device control. This represents a significant field within HCI involving device interfaces and users. The aim of gesture recognition is to record gestures that are formed in a certain way and then detected by a device such as a camera. Hand gestures can be used as a form of communication for many different applications. It may be used by people who possess different disabilities, including those with hearing-impairments, speech impairments and stroke patients, to communicate and fulfil their basic needs. Various studies have previously been conducted relating to hand gestures. Some studies proposed different techniques to implement the hand gesture experiments. For image processing there are multiple tools to extract features of images, as well as Artificial Intelligence which has varied classifiers to classify different types of data. 2D and 3D hand gestures request an effective algorithm to extract images and classify various mini gestures and movements. This research discusses this issue using different algorithms. To detect 2D or 3D hand gestures, this research proposed image processing tools such as Wavelet Transforms and Empirical Mode Decomposition to extract image features. The Artificial Neural Network (ANN) classifier which used to train and classify data besides Convolutional Neural Networks (CNN). These methods were examined in terms of multiple parameters such as execution time, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood, negative likelihood, receiver operating characteristic, area under ROC curve and root mean square. This research discusses four original contributions in the field of hand gestures. The first contribution is an implementation of two experiments using 2D hand gesture video where ten different gestures are detected in short and long distances using an iPhone 6 Plus with 4K resolution. The experiments are performed using WT and EMD for feature extraction while ANN and CNN for classification. The second contribution comprises 3D hand gesture video experiments where twelve gestures are recorded using holoscopic imaging system camera. The third contribution pertains experimental work carried out to detect seven common hand gestures. Finally, disparity experiments were performed using the left and the right 3D hand gesture videos to discover disparities. The results of comparison show the accuracy results of CNN being 100% compared to other techniques. CNN is clearly the most appropriate method to be used in a hand gesture system.Imam Abdulrahman bin Faisal Universit

HEVC’s Intra Mode Selection Using Odds Algorithm

International audienceThe brute-force behaviour of High-Efficiency Video Coding (HEVC) is the biggest hurdle in the communication of the multimedia contents. Therefore, a novel method will be presented here to expedite the intra mode decision process of HEVC. In the first step, the feasibility of the odds-algorithm for the early intra mode decision is presented by using statistical evidence. Then, various elements of odds algorithm are analyzed and then mapped to the intra mode process (elements) of HEVC. Finally, the probability required by the odds algorithm is obtained by utilizing the correlation between the current and the neighboring blocks. The proposed algorithm accelerated the encoding process of the HEVC by 25% to 35%, while the Bjontegaard Delta Bit Rate (BD-BR) is 0.95% to 1.84%

Current Knowledge, Satisfaction, and Use of E-Health Mobile Application (Seha) Among the General Population of Saudi Arabia: A Cross-Sectional Study

BACKGROUND: General population knowledge, satisfaction, and barriers to using Seha app have not been evaluated from a large-scale perspective. Therefore, this study aimed to explore current knowledge, satisfaction, and barriers of using Seha app and identify the most common mobile health application used among the general population in Saudi Arabia. METHODS: A cross-sectional online survey, consisting of 25 questions, was distributed among the general population of Saudi Arabia. Descriptive statistics were used to describe the respondents’ characteristics. Categorical variables were reported as frequencies and percentages. A chi-square (χ 2) test was conducted to assess the statistical difference between respondents’ demographic characteristics and their knowledge and use of the app. RESULTS: Overall, 5008 respondents, both Saudi (3723: 74%) and non-Saudi (1285: 26%) as well as male 2142 (43%) and female 2866 (57%), across the Kingdom of Saudi Arabia completed the online survey. A total of 2921 (58%) had heard of the Seha app, although only 1286 (25%) had used the app. Higher percentages of users were from the western region, females and those within the age group of ≥ 51 years old, 388 users (29%: P< 0.001), 804 (28%; P< 0.001) and 67 (35%; P=0.013), respectively. Consulting a doctor was the most frequently utilized service, 576 users (58%). Respondents strongly agreed 402 (41%) that Seha was easy to use, and 538 (54%) strongly agreed that they would recommend Seha to others. The most common barrier of using Seha was a lack of knowledge about the app and its benefits, at 1556 (35%). Overall, the Tawakkalna app was the most utilized mobile health application provided by MOH used 2170 (48%). CONCLUSIONS: Utilization of the Seha app is quite low due to a lack of knowledge about the app and its benefits. Thus, the MOH should promote public awareness about the app and its benefits

DFFMD: A Deepfake Face Mask Dataset for Infectious Disease Era With Deepfake Detection Algorithms

Deepfake is a technology that creates fake images and videos with replaced or synthesized faces. Deepfakes are becoming a concerning social phenomenon, as they can be maliciously used to generate false political news, disseminate dangerous information, falsify electronic evidence, and commit digital harassment and fraud. The ease and accuracy of creating Deepfakes have been bolstered by the popularity of wearing face masks since the beginning of the infectious disease outbreak (2020). Because these masks obstruct defining facial features, fake videos are now even more challenging to identify, increasing the necessity for advanced Deepfake detection technology. The research also creates a real/fake video dataset with face masks because the field lacks the dataset required for detection-model training. The proposed research proposes a Deepfake Face Mask Dataset (DFFMD) based on a novel Inception-ResNet-v2 with preprocessing stages, feature-based, residual connection, and batch normalization. The combination of preprocessing stages, feature-based, residual connection, and batch normalization increases the detection accuracy of deepfake videos in the presence of facemasks, unlike the traditional methods. The study&#x2019;s results compared with existing state-of-the-art methods detect face-mask-Deepfakes with 99.81&#x0025; accuracy compared to the traditional InceptionResNetV2 and VGG19, whose accuracy is 77.48&#x0025;, and 99.25&#x0025;, respectively. Future work should evaluate the accuracy of developing a subsequent experimental work for increased detection of deepfake with facemasks

Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization

This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of the optimization by a considerable amount. The exhibition of the mathematical test shows the viability of the proposed calculation over regular ACO and PSO-ACO based strategies

Public awareness and use of health tools provided by the portal of the Ministry of Health of Saudi Arabia

Purpose: The objective of this study was to assess the awareness and use of the health tools provided by the portal of the Ministry of Health (MOH) of Saudi Arabia to the Saudi public. Method: ology: A cross-sectional study was conducted to assess the awareness and use of the health tools provided by the portal of the MOH to the Saudi public. The questionnaire was distributed to the family, friends, and co-workers through WhatsApp. Snow-ball sampling was used to encourage participants to forward the survey link to their friends and colleagues. A total of 317 people received the questionnaire and 110 respondents completed it. Results: The respondents had used the following health tools; the ideal body weight calculator (10.5%), body mass index calculator (9.9%), calorie calculator (8.0%), calculator to assess depression level (6.1%), pregnancy date calculator (4.3%), visual acuity test (4.3%), calculator to assess the best time to get pregnant (3.7%), asthma control test (2.5%), calculator to assess anxiety level (2.5%), eating disorder test (1.9%), pre-diabetic risk test (1.2%), and the sleep disorder test (1.2%). More than half of the participants (58.2%) were not aware of the health tools that the MOH provides through its website, and 64.5% of the respondents had never used these tools. 55.5% of the participants found that the services provided by the MOH through its portal meet their needs, and 91.8% of them preferred to use mobile applications to access e-services provided by the MOH. 59.1% of the respondents were satisfied and very satisfied with the e-services that the MOH provides through its website. The results indicated that there was no statistically significant association between the awareness and use of health tools and the gender, age, area of residence, educational degree, and marital status of the participants. Conclusion: The results showed that the majority of the respondents are not aware and never had used the health tools provided by the portal of the MOH of Saudi Arabia. Therefore, the findings of this study could provide insight for the government to analyze the different factors that could affect the promotion, awareness, and use of these tools by the Saudi Arabian population

Evaluation of the Patient Experience with the Mawid App during the COVID-19 Pandemic in Al Hassa, Saudi Arabia

(1) Introduction: The objective of this study was to evaluate the patient experience with the Mawid application during the COVID-19 pandemic in Al Hassa, Saudi Arabia. (2) Methodology: A quantitative cross-sectional survey was designed to evaluate the patient experience with the Mawid app during the COVID-19 pandemic in Al Hassa, Saudi Arabia. A total of 146 respondents completed the questionnaire. (3) Results: More than half of the participants (65.8%) opined that application was easy to use. Furthermore, 65.1% of the participants considered it to be very easy and easy to search for the required information; and 63.7% of the respondents reflected that it was easy to book an appointment. There was a statistically significant difference between the ease of searching for the required information (p-value = 0.006); the ease of undoing an unwanted move and gender (p-value = 0.049); the ease of searching for the required information and educational level (p-value = 0.048); the ease of booking an appointment and educational level (p = 0.049); and the ease of searching for the required information and the labor sector of the respondents (p value= 0.049) among the genders. No significant differences were identified among the age groups. (4) Conclusions: Overall, most participants suggested that the Mawid app was easy to use and had a potentially useful set of features to help mitigate and manage the COVID-19 pandemic in Al Hassa, Saudi Arabia