Emotional Facial Expression Based On Action Units and Facial Muscle

The virtual human play vital roles in virtual reality and game. The process of Enriching the virtual human through their expression is one of the aspect that most researcher studied and improved. This study aims to demonstrate the combination of facial action units (FACS) and facial muscle to produce a realistic facial expression. The result of experiment succeed on producing particular expression such as anger, happy, sad which are able to convey the emotional state of the virtual human. This achievement is believed to bring full mental immersion towards virtual human and audience. The future works will able to generate a complex virtual human expression that combine physical factos such as wrinkle, fluid dynamics for tears or sweating

TOU-AR:Touchable Interface for Interactive Interaction in Augmented Reality Environment

Touchable interface is one of the future interfaces that can be implemented at any medium such as water, table or even sand. The word multi touch refers to the ability to distinguish between two or more fingers touching a touch-sensing surface, such as a touch screen or a touch pad. This interface is provided tracking the area by using depth camera and projected the interface into the medium. This interface is widely used in augmented reality environment. User will project the particular interface into real world medium and user hand will be tracked simultaneously when touching the area. User can interact in more freely ways and as natural as human did in their daily lif

Telerobotic 3D Articulated Arm-Assisted Surgery Tools with Augmented Reality for Surgery Training

In this research, human body will be marked and tracked using depth camera. The arm motion from the trainer will be sent through network and then mapped into 3D robotic arm in the destination server. The robotic arm will move according to the trainer. In the meantime, trainee will follow the movement and they can learn how to do particular tasks according to the trainer. The telerobotic-assisted surgery tools will give guidance how to slice or do simple surgery in several steps through the 3D medical images which are displayed in the human body. User will do training and selects some of the body parts and then analyzes it. The system provide specific task to be completed during training and measure how many tasks the user can accomplish during the surgical time. The telerobotic-assisted virtual surgery tools using augmented reality (AR) is expected to be used widely in medical education as an alternative system with low-cost solution

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques

An Efficient and Lightweight Deep Learning Model for Human Activity Recognition Using Smartphones

Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified by specialized smartphones into well known movements that can be done with human activity recognition. With the high success and wide adaptation of deep learning approaches for the recognition of human activities, these techniques are widely used in wearable devices and smartphones to recognize the human activities. In this paper, convolutional layers are combined with long short-term memory (LSTM), along with the deep learning neural network for human activities recognition (HAR). The proposed model extracts the features in an automated way and categorizes them with some model attributes. In general, LSTM is alternative form of recurrent neural network (RNN) which is famous for temporal sequences’ processing. In the proposed architecture, a dataset of UCI-HAR for Samsung Galaxy S2 is used for various human activities. The CNN classifier, which should be taken single, and LSTM models should be taken in series and take the feed data. For each input, the CNN model is applied, and each input image’s output is transferred to the LSTM classifier as a time step. The number of filter maps for mapping of the various portions of image is the most important hyperparameter used. Transformation on the basis of observations takes place by using Gaussian standardization. CNN-LSTM, a proposed model, is an efficient and lightweight model that has shown high robustness and better activity detection capability than traditional algorithms by providing the accuracy of 97.89%

Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Background Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings In 2021, there were 529 million (95% uncertainty interval [UI] 500–564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9]) and, at the regional level, in Oceania (12·3% [11·5–13·0]). Nationally, Qatar had the world’s highest age-specific prevalence of diabetes, at 76·1% (73·1–79·5) in individuals aged 75–79 years. Total diabetes prevalence—especially among older adults—primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases and 95·4% (94·9–95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5–71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5–30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%.Peer ReviewedPostprint (published version

Using cultural familiarity for usable and secure recognition-based graphical passwords

Recognition-based graphical passwords (RBGPs) are a promising alternative to alphanumeric passwords for user authentication. The literature presented several schemes in order to find the best types of pictures in terms of usability and security. This thesis contributes the positive use of cultural familiarity with pictures for usable and secure recognition-based graphical passwords in two different countries: Scotland and Saudi Arabia. This thesis presents an evaluation of a culturally-familiar graphical password scheme (CFGPS). This scheme is based on pictures that represent the daily life in different cultures. Those pictures were selected from a database containing 797 pictures representing the cultures of 30 countries. This database was created as the first step in this thesis from the responses of 263 questionnaires. The evaluation of the scheme goes through five phases: registration phase, usability phase, security phase, interviews phase, and guidelines phase. In the registration phase, a web-based study was conducted to determine the cultural familiarity impact on choosing the pictures for the GPs. A large number of participants (Saudi and Scottish) registered their GPs. The results showed that users were highly affected by their culture when they chose pictures for their GPs; however, the Saudis were significantly more affected by their culture than the Scottish. This study showed the developers the importance of having a selection of pictures that are as familiar as possible to users in order to create suitable GPs. In the usability phase, the participants were asked to log in with their GPs three months after the registration phase. The main results showed that the memorability rate for GPs consisting only of pictures belonging to participants’ culture was higher than the memorability rate for GPs consisting of pictures that did not belong to participants’ culture. However, there was no evidence regarding a cultural familiarity effect on login time. In the security phase, a within-subject user study was conducted to examine the security of culturally-familiar GPs against educated guessing attacks. This study was also the first attempt to investigate the risk of using personal information shared by users on social networks to guess their GPs. The results showed high guessability for CFGPs. The interviews phase evaluated the qualitative aspects of the CFGP password in order to improve its performance. In-depth interviews with the users of the scheme suggested guidelines for both developers and users to increase the usability and security of the scheme. Those guidelines are not exclusive to the culturally-familiar scheme, as they can be used for all RBGP schemes. Finally, as one of the instructions stated in the developers’ guidelines, different challenge sets’ designs were evaluated based on their cultural familiarity to users. The results showed a high usability of the culturally-familiar challenge set while the security target was met in the culturally-unfamiliar challenge set. To balance between these two factors, following the user guidelines covered the weaknesses of both designs

Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text

Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets