Person identification is a pivotal challenge in the security domain, with important and impactful applications such as identifying crime suspects and locating missing persons. One convenient person identification method is gait identification, where individuals are identified by their unique walking style. However, traditional methods of gait identification are often affected by variations in appearance and occlusion. This work introduces a novel and robust spatiotemporal kinematics‐informed non‐invasive gait identification (STONI‐GID) method that uses human pose estimation, occlusion state estimation and deep machine learning. Furthermore, unlike some existing methods, we demonstrate that our method remains unaffected by everyday appearance changes, environment, or viewing angle. Our approach achieved identification accuracy of up to 98.66% when evaluated using our primary dataset of 65 diverse participants in real‐world environments. Moreover, the model outperformed existing methods during cross‐dataset validation on the large Southampton dataset and the Gait Recognition Image and Depth Dataset (GRIDDS), achieving identification accuracies of 97.68% and 99.12%, respectively. Our findings will particularly advance the research frontiers of real‐world gait identification and impact interdisciplinary areas of security and healthcare applications.

Knowledge about infant language can go a long way in supporting parents, nurses, and care providers in improving babies' health conditions. Crying is the most effective tool through which babies convey their requirements. In this work, several studies dealing with infant cry detection and classification are contrasted. Research demonstrates that machine learning techniques can effectively categorize and classify infant needs and certain disorders. Several datasets, including Baby Chillanto, Donate A Cry Corpus and Dunstan Baby Language, are presented. After reviewing existing Datasets, preprocessing methodologies and audio feature extraction such as MFCC, RMS energy, etc., are discussed. For infant cry detection and classification, several algorithms, such as support vector machines (SVM), convolutional neural networks (CNN), k-nearest neighbors (KNN), Random Forest, etc., have been analyzed and utilized for such processes in general. Finally, the study explores various applications of infant cry analysis, highlighting its potential to improve infant care and facilitate early diagnosis. As a result of the findings, it has been observed that infant cry analysis can effectively identify different needs and potential health concerns with high accuracy. These machine learning models' classification outputs have the potential to (1) improve childcare practices, (2) detect medical issues earlier, and (3) monitor infants continuously. These features give medical professionals and caregivers useful information for prompt intervention. The implementation of these findings can be applied in hospitals, neonatal intensive care units (NICUs), smart baby monitoring systems, and research studies focused on early childhood development.

Students' performance assessment is crucial in the context of education especially prior to levelling students or recommending personalised learning. In a multicultural environment, multiple factors play a role in students' performance that go beyond the students'-teacher interaction. These factors are related to students' characteristics and experience, learning environment and material as well as the material delivered. Considering the role of these factors in students' performance, this work proposed the use of two multivariate regression algorithms for predicting year 6 school students' performance in math over three semesters being autumn, spring and summer. Principal Component Regression (PCR) and Partial least Square Regression (PLSR) models were constructed and validated for predicting students' performance in end of block assessment over 15 blocks per year: six per autumn semester, six per spring semester and three in summer semester. The results showed that both PCR and PLSR models demonstrated accurate predictions for end of block assessments with correlation coefficient values up to 0.97 and low root mean square error of predictions that was below 5% in most cases. Best performing models were those that assessed fractions, perimeter and geometry and that indicated the strong relationship between the students' factors and end of block assessment. Out of 30 regression models, six models performed poorly and were related to four operations, decimals and ratios. Yet, the performance of students could be predicted accurately and precisely using multivariate regression algorithms. Future work involves evaluating these models to different cohorts of students to determine feasibility of the explored models.

This paper presents a sign language translation system developed to enhance accessibility for lesser-supported sign languages, with a specific focus on Indian Sign Language. Given that India has the world's largest deaf population, the communication gap between sign language users and non-users poses a significant barrier. The objective of this research is to examine existing support systems for Indian Sign Language and to build a system capable of detecting, recognizing, and translating signed words through the use of connected cameras. To achieve this goal, image classification using transfer learning techniques was applied to a small dataset of Indian Sign Language gestures, resulting in an average recognition accuracy rate of 83%. Over 10,000 images across 12 different gestures were collected, and the training/validation process reached peak testing accuracy levels of 95%. The final model was deployed into a web-based application specifically designed for use in areas with limited access to advanced technology, aiming to reduce communication barriers and improve inclusivity for the deaf and hard-of-hearing community.

This paper presents a semi-automated feedback system designed to support programming education by generating personalised feedback using rule-based analysis and Natural Language Generation (NLG). The centralised system, accessible across platforms via a web browser within institutions, evaluates Python code using Abstract Syntax Trees (AST), checking for syntax, indentation, comments, and required constructs. It also performs plagiarism checks and tracks student performance with data visualisation tools. Built with Django, the system ensures accuracy, consistency, and instructor oversight. Unlike existing tools, the system combines AST-based analysis, instructor-defined rules, and rule-based NLG in a modular and open-source platform designed for flexibility and scalability. Results show improved feedback efficiency and instructional value.

The increasing prevalence of motor impairments due to stroke, spinal cord injuries, and neurodegenerative conditions has underscored the urgent need for accessible, noninvasive rehabilitation technologies. This study presents a computational framework for a brain-computer interface (BCI) system that analyses electroencephalography (EEG) signals with machine learning (ML) to simulate the control of a robotic hand for upper-limb rehabilitation. Leveraging a publicly available dataset for motor imagery (MI) and a simulated approach for steady-state visual evoked potential (SSVEP) data, the system extracts user intentions through ERD/ERS and frequency-domain features, which are then classified using models such as linear discriminant analysis (LDA), support vector machines (SVM), and convolutional neural networks (CNN). These outputs are translated into simulated commands for a servo-actuated artificial hand. Offline evaluation demonstrated high classification accuracy, with SVM achieving up to 92.3% on motor imagery (MI) tasks and 94.1% on SSVEP tasks. The simulated end-to-end latency per command was estimated at 1100-1250 ms for SSVEP and 2150 ms for MI. The results confirm the feasibility of the machine learning pipeline for a portable, low-cost, ML-powered BCI system and provide a robust, ethically straightforward foundation for future hardware integration. This research contributes to the development of adaptable, user-centred assistive technologies with the potential for home-based deployment and clinical integration.

We present a deployable dashboard for next-day stock prediction that combines a Long Short-Term Memory (LSTM) model with daily news-based sentiment. Using a 20-day sliding window of normalised close and aggregated VADER sentiment, the model forecasts the next-day close for large-cap equities. Across AAPL, MSFT, TSLA, and GOOGL, the system achieved MAE in the$2.5-\unicode{x0024} 5.0$range and directional accuracy up to 58%. In a two-month backtest, a simple rules-based trading bot driven by the model produced a$+2.5 \%$return with a 59% win rate, outperforming naïve and moving-average baselines. Gains were most evident during news-sensitive periods. Limitations include short horizons, lexicon-based sentiment, and simplified execution assumptions. The dashboard exposes forecasts, sentiment summaries, and simulated trades, offering practical utility for exploratory analysis and teaching.

Abstract The importance of image encryption has considerably increased, especially after the dramatic evolution of the internet and network communications, due to the simplicity of capturing and transferring digital images. Although there are several encryption approaches, chaos-based image encryption is considered the most appropriate approach for image applications because of its sensitivity to initial conditions and control parameters. Confusion and diffusion methods have been used in conventional image encryption methods, but the ideal encrypted image has not yet been achieved. This research aims to generate an encrypted image free of statistical information to make cryptanalysis infeasible. Additionally, the motivation behind this work lies in addressing the shortcomings of conventional image encryption methods, which have not yet achieved the ideal encrypted image. The proposed framework aims to overcome these challenges by introducing a new method, Equal Pixel Values Quantization (EPVQ), along with enhancing the confusion and diffusion processes using chaotic maps and additive white Gaussian noise. Key security, statistical properties of encrypted images, and withstanding differential attacks are the most important issues in the field of image encryption. Therefore, a new method, Equal Pixel Values Quantization (EPVQ), was introduced in this study in addition to the proposed confusion and diffusion methods to achieve an ideal image encryption framework. Generally, the confusion method uses Sensitive Logistic Map (SLM), Henon Map, and additive white Gaussian noise to generate random numbers for use in the pixel permutation method. However, the diffusion method uses the Extended Bernoulli Map (EBM), Tinkerbell, Burgers, and Ricker maps to generate the random matrix. Internal Interaction between Image Pixels (IIIP) was used to implement the XOR (Exclusive OR) operator between the random matrix and scrambled image. Basically, the EPVQ method was used to idealize the histogram and information entropy of the ciphered image. The correlation between adjacent pixels was minimized to have a very small value (×10 −3 ). Besides, the key space was extended to be very large (2 450 ) considering the key sensitivity to hinder brute force attacks. Finally, a histogram was idealized to be perfectly equal in all occurrences, and the resulting information entropy was equal to the ideal value (8), which means that the resulting encrypted image is free of statistical properties in terms of the histogram and information entropy. Based on the findings, the high randomness of the generated random sequences of the proposed confusion and diffusion methods is capable of producing a robust image encryption framework against all types of cryptanalysis attacks.

Abstract The field of forensic science is experiencing significant growth, largely driven by the increasing integration of holographic and immersive technologies, along with their associated head-mounted displays. These immersive systems have become increasingly vital in resolving critical crimes as they facilitate communication, interaction, and collaboration. Given the sensitive nature of their work, crime investigators require substantial technical support. There is a pressing need for accurate documentation and archiving of crime scenes, which can be addressed by leveraging 3D scanned scenes to accurately represent evidence and expected scenarios. This study aims to develop an enhanced AR. system that can be deployed on hologram facilities such as the Microsoft HoloLens. The proposed system encompasses two main approaches, namely image classification and image segmentation. Image classification utilizes various deep learning models, including lightweight convolutional neural networks (CNNs) and convolutional Long-Short Term Memory (ConvLSTM). Additionally, the image segmentation approach is based on the fuzzy active contour model (FACM). The effectiveness of the proposed system was evaluated for both classification and segmentation tasks, utilizing metrics such as accuracy, sensitivity, precision, and F1 score. The simulation results indicate that the proposed system achieved a 99% accuracy rate in classification and segmentation tasks, positioning it as an effective solution for detecting bloodstain patterns in AR applications.

In recent years, augmented reality has received a considerable lot of interest. Augmented reality application cases are expanding all the time. People may now readily experience augmented reality because to the widespread availability of smartphones. This enables scholars from all around the world to use it. The use of augmented reality-based interactive technologies in education is on the rise. In this study, we proposed a method that allows students to learn the human skeletal system and anatomy using their smartphones. The proposed method offers visualizing the human skeletal system, including bones, in 3D using augmented reality and also interacting with the 3D bones. The substantial finding of the study reflects on how students can benefit from this Augmented Reality-based interactive method. Qualitative assessment was accumulated from 159 medical students who have experience with the traditional human skeleton learning experience and also participated in the augmented reality-based human skeleton system.

This study explores the application of deep learning techniques, specifically Convolutional Neural Networks, for the classification of dog breeds from images. By employing varying input image resolutions, the research evaluates the impact of resolution on the accuracy and efficiency of the model. Five experiments were conducted using resolutions of 64, 128, 224, 256, and 512 pixels to assess model performance. The results indicate that an input resolution of 256x256 pixels yields the highest accuracy, achieving 94.74% with an optimal balance between detail and processing complexity. However, certain breeds, such as the American Foxhound and Anatolian Shepherd Dog, exhibited lower classification performance, highlighting the importance of considering breed-specific characteristics in model development. The findings emphasize the critical role of image resolution in training deep learning models and suggest that a 256x256 resolution offers the best trade-off between accuracy and computational efficiency for dog breed classification.

This paper presents the development and evaluation of a simulated inventory management system using NetLogo, with a focus on demonstrating the potential of TurtleBot 4 robotics to optimize stock control in warehouse environments. Faced with technical challenges in using a physical TurtleBot 4, the project shifted to a simulation approach, which allowed for a detailed exploration of how agent-based models can improve inventory management processes. Drawing on research into SLAM algorithms, real-world business practices, and inventory management systems, the simulation replicates key warehouse functions, including the receiving, storing, and dispatching of goods. The project’s design includes a graphical user interface (GUI) that simulates wireless data transfer, enabling real-time interaction with the system. The artefact was tested successfully in various scenarios, highlighting the potential of robotics to enhance efficiency and streamline operations. The artefact overall performed as intended, providing valuable insights into the future of robotic integration in inventory management.

Eye tracking and head pose estimation (HPE) have previously lacked reliability, interpretability, and comprehensibility. For instance, many works rely on traditional computer vision methods, which may not perform well in dynamic and realistic environments. Recently, a widespread trend has emerged, leveraging deep learning for HPE specifically framed as a regression task; however, considering the real-time applications, the problem could be better formulated as classification (e.g., left, centre, right head pose and gaze) using a hybrid approach. For the first time, we present a complete facial profiling approach to extract micro and macro facial movement, gaze, and eye state features, which can be used for various applications related to comprehension analysis. The multi-model approach provides discrete human-understandable head pose estimations utilising deep transfer learning, a newly introduced method of head roll calculation, gaze estimation via iris detection, and eye state estimation (i.e. , open or closed). Unlike existing works, this approach can automatically analyse the input image or video frame to produce human-understandable binary codes (e.g., eye open or close, looking left or right, etc.) for each facial component ( aka face channels). The proposed approach is validated on multiple standard datasets, indicating outperformance compared to existing methods in several aspects, including reliability, generalisation, completeness, and interpretability. This work will significantly impact several diverse domains, including psychological and cognitive tasks with a broad scope of applications, such as in police interrogations and investigations, animal behaviour, and smart applications, including driver behaviour analysis, student attention measurement, and automated camera flashes.

Abstract This research introduces a novel and robust watermarking scheme for medical Brain MRI DICOM images, addressing the challenge of maintaining high imperceptibility and robustness simultaneously. The scheme ensures privacy control, content authentication, and protection against the detachment of vital Electronic Patient Record information. To enhance imperceptibility, a Dynamic Visibility Threshold parameter leveraging the Human Visual System is introduced. Embeddable Zones and Non-Embeddable Zones are defined to enhance robustness, and an enhanced Knight Tour algorithm based on Slantlet Transform shuffles the embedding sequence for added security. The scheme achieves remarkable results with a Peak Signal-to-Noise Ratio (PSNR) evaluation surpassing contemporary techniques. Extensive experimentation demonstrates resilience to various attacks, with low Bit Error Rate (BER) and high Normalized Cross-Correlation (NCC) values. The proposed technique outperforms existing methods, emphasizing its superior performance and effectiveness in medical image watermarking.

This paper provides a comprehensive review of Brain-Computer Interface (BCI) technology, focusing on its application in developing an interactive hands-free game for individuals with disabilities. The introduction highlights BCI’s role in decoding brain signals for interaction and its potential to empower those with paralysis. The literature review traces the evolution of BCI from its inception in 1988, covering technologies like EEG and advancements in the field. It explores the development of BCI games, emphasizing their impact on motivation and neurofeedback training. The paper delves into the BCI process, classifying BCIs into dependent and independent categories, and discusses their applications in medicine, smart home control, and gaming. The concluding remarks underscore the positive influence of BCIs on various aspects of life, emphasizing the need for broader awareness and adoption of this transformative technology.

This research delves into the transformative potential of Augmented Reality (AR) and Virtual Reality (VR) in elevating the field of forensic science. Titled “Revelotinaising Forensic Science with the use of Augmented Reality and Virtual Reality,” the study primarily focuses on leveraging HoloLens technology to aid crime scene investigation, addressing the challenges posed by limited time and geographical variations among colleagues. By exploring contemporary techniques for storing, visualizing, and manipulating evidence, the research seeks to equip forensic investigation units with advanced technological tools. In a landscape where law enforcement increasingly adopts forensic techniques, the paper underscores the significant role played by forensic scientists in criminal investigation, civil litigation, and disaster response. It highlights the importance of teamwork and innovative investigative tools like GPS positioning, video imaging, and data mining in the success of crime scene investigations. The integration of tri-dimensional (3D) representations of objects and the recognition and preservation of physical evidence are emphasized as key aspects of efficient crime-solving. Furthermore, recent studies on the application of AR for collaboration among crime scene investigators are explored, showcasing how AR technology fosters consensus-building within investigative teams. The distinction between VR and AR, with the former immersing users in a wholly digital environment and the latter enhancing the real world with digital elements, is elucidated. This paper serves as a comprehensive exploration of the integration of AR and VR in crime scene investigations, promising to revolutionize the field of forensic science. It sets the stage for further research and development in leveraging these technologies to enhance crime-solving capabilities and facilitate more efficiency.

This paper introduces a proof-of-concept study focused on AI-powered pre-surgery planning for cheiloplasty, a vital aspect of plastic surgery dedicated to enhancing and reshaping the lips. The research tackles the intricate task of selecting the most suitable surgical approach by harnessing the capabilities of artificial intelligence (AI) and machine learning. The methodology entails a comprehensive analysis and the extraction of features from various facial components, including the lips, nose, eyes, and eyebrows alongside an assessment of overall facial aesthetics. Utilizing advanced computer vision algorithms, these facial components are extracted from preoperative images. Central to the system is a Multi-Modal Convolutional Neural Network (CNN) architecture, which leverages the extracted facial features to predict outcomes for specific cheiloplasty techniques. Rigorous training and validation of the CNN involve meticulous comparisons with ground truth data and expert assessments to ensure precision and dependability. Furthermore, the study underscores the system’s user-friendliness and practicality by gathering feedback from seasoned plastic surgeons. Adherence to strict ethical guidelines guarantees the protection of patient data and the mitigation of potential biases. The successful development of this proof of concept highlights the vast potential of AI integration in pre-surgery planning for cheiloplasty. By equipping surgeons with technique-specific insights derived from facial components and style analysis, the system enhances decision-making, ultimately benefiting patient care and treatment outcomes. Future endeavours will expand the dataset, consider additional variables, and encompass prospective clinical trials to validate the system’s real-world impact in the field of lip-repair surgery.

This comprehensive review explores the landscape of assistive technology designed to support physically disabled adults. The paper encompasses a thorough examination of technological interventions aimed at enhancing the independence and quality of life for individuals facing physical disabilities. Focusing on a diverse range of assistive devices and systems, including mobility aids, communication tools, and adaptive interfaces, the review assesses their effectiveness, usability, and impact on daily living. It synthesizes current research findings, technological advancements, and user experiences, offering insights into the evolving field of assistive technology. Additionally, the review addresses challenges, potential future developments, and the role of emerging technologies in furthering the integration of assistive solutions. This synthesis contributes to a holistic understanding of the state-of-the-art in assistive technology for physically disabled adults, providing a valuable resource for researchers, practitioners, and policymakers working towards enhancing inclusivity and empowerment.

Sign languages are languages that utilize the visual-manual modality to convey meaning. These languages are expressed through manual articulations combined with non-manual elements. Sign languages are complete natural languages, possessing their own grammar and lexicon. They are primarily used by individuals who are Deaf or have hearing impairments. Sign languages are not universal and are not mutually intelligible with one another, though they do exhibit striking similarities among them. In the context of Britain, the most prevalent form of Sign Language is known as British Sign Language (BSL). BSL possesses its own distinct grammatical structure and syntax; as a language, it is neither dependent on nor closely related to spoken English. This paper offers a comprehensive review of sign language systems, including an exploration of related studies on British Sign Language, as well as an examination of the legal, social, and ethical considerations associated with these languages.

Looking to the future is always something people have been interested in from the old days. What people can do using their current ability is ambiguous, but this can be achieved psychologically. In this study, a systematic Augmented Reality (AR) system named iCan has been proposed to reveal the ability of what we can do and which way we should choose when facing any junction of choices. iCan has been developed in four steps: basic AR development implementation, building 3D-Models, making the best interaction UX(User-Experience) and test-debug-publish. In the first step, booklet pages have been chosen for AR development instruction. Next, 3D models have made and have been presented. Next, interaction methods are under development. Some individuals have tested the system, and the results show the robustness of the presented system, which there is a hope to change people's future soon.

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. In this paper, we describe some of Gesture recognition techniques such as Vision based gesture recognition and Graph based gesture recognition. Also, we explore these techniques with previous studies.

Gesture recognition is a computing process that attempts to recognize and interpret human gestures through the use of mathematical algorithms. In this paper, we describe Point Based Gesture Recognition and Point Clouds nearest neighbors and sampling. Also, we explore these techniques with previous studies.

The transition from IPv4 to IPv6 marks a significant evolution in internet protocol technology, bringing forth both opportunities and challenges. This paper conducts a thorough survey of various detection techniques and solutions specifically tailored for IPv6, addressing the unique security vulnerabilities inherent to this new protocol. We delve into the nuances of IPv6’s architecture that make traditional IPv4 detection methods inadequate, highlighting the necessity for specialized IPv6-focused strategies. The survey covers a broad range of techniques, including advanced intrusion detection systems, machine learning algorithms, and hybrid approaches. We critically analyze the effectiveness of these methods in detecting and mitigating common IPv6 threats like evasion attacks, distributed denial of service (DDoS) attacks, and fragmentation exploits. Additionally, the paper explores innovative solutions developed to address IPv6-specific vulnerabilities, such as improved packet inspection methods and enhanced firewall capabilities. Through this comprehensive review, we aim to provide insights into the current state of IPv6 detection techniques and offer guidance for future research and development in this crucial area of network security.

Security authentication involves the process of verifying a person's identity. Authentication technology has played a crucial role in data security for many years. However, existing typical biometric authentication technologies exhibit limitations related to usability, time efficiency, and notably, the long-term viability of the method. Recent technological advancements have led to the development of specific devices capable of reproducing human biometrics due to their visibility and tactile nature. Consequently, there is a demand for a new biometric method to address the limitations of current authentication systems. Human brain signals have been utilized in various Brain-Computer Interface (BCI) applications. Nevertheless, this approach also faces challenges related to usability, time efficiency, and most importantly, the stability of the method over time. Studies reveal that the stability of brain patterns poses a significant challenge in EEG-based authentication techniques. Stability refers to the capacity to withstand changes or disruptions, while permanency implies a lasting and unchanging state. Notably, stability can be temporary and subject to fluctuations, whereas permanency suggests a more enduring condition. Research demonstrates that utilizing alpha brainwaves is a superior option for authentication compared to other brainwave types. Many brain states lack stability in different situations. Interestingly, deep breathing can enhance alpha waves irrespective of the brain's current state. To explore the potential of utilizing deep breathing as a security pattern for authentication purposes, an experiment was conducted to investigate its effects on brain activity and its role in enhancing alpha brainwaves. By focusing on bolstering the permanency of brain patterns, our aim is to address the challenges associated with stability in EEG-based authentication techniques. The experimental results exhibited a high success rate of 91% and 90% for Support Vector Machine and Neural Network classifiers, respectively. These results suggest that deep breathing not only enhances permanency but could also serve as a suitable option for a brainwave-based authentication method.

Abstract Finger vein patterns contain highly discriminative characteristics, which are difficult to be forged due to residing underneath the skin. Several pieces of research have been carried out in this field but there is still an unresolved issue when data capturing and processing is of low quality. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. The objective of this paper is to address this issue by presenting two methods, a new image enhancement, and a feature extraction method. The image enhancement, Composite Median-Wiener (CMW) filter, improves image quality and preserves the edges. Moreover, the feature extraction method, Hierarchical Centroid Feature Method (HCM), is fused with the statistical pixel-based distribution feature method at the feature-level fusion to improve the performance of finger vein identification. These methods were evaluated on public SDUMLA-HMT and FV-USM finger vein databases. Each database was divided into training and testing sets. The average result of the experiments conducted was taken to ensure the accuracy of the measurements. The k-Nearest Neighbor classifier with city block distance to match the features was implemented. Both these methods produced accuracy as high as 97.64% for identification rate and 1.11% of equal error rate (EER) for measures verification rate. These showed that the accuracy of the proposed finger vein identification method is higher than the existing methods. The results have proven that the CMW filter and HCM have significantly improved the accuracy of finger vein identification.

Covid-19 vaccine hesitancy and acceptance delay is an unprecedented challenge for concerned authorities. Existing studies lack the investigation about public vaccination acceptance, specifically for children. In this study, we surveyed the adult population in the UK to determine the diversity in public perception and acceptance of Covid-19 vaccination specifically for the children, among different sociodemographic groups. Statistical results and intelligent clustering outcomes indicate significant relationships between sociodemographic diversity and vaccination acceptance for children and their families. Acceptability for children is significantly dependent on ethnicity$(\mathrm{p}=3.7\mathrm{e}-05)$, age group, and gender, where only 47% of participants show willingness towards children's vaccination. Primary dataset in this study, along with the experimental outcomes, might be useful for public awareness and policy makers towards better preparation for future epidemics as well as working globally to combat the ongoing Covid-19 variations while running effective vaccination campaigns in the identified sociodemographic groups.

The Covid-19 pandemic has forced a change in the way people work, and the location that they work from. The impact has caused significant disruption to education, the work environment and how social interactions take place. Online user habits have also changed due to lockdown restrictions and virtual conferencing software has become a vital cog in team communication. In result, a spate in software solutions have emerged in order to support the challenges of remote learning and working. The conferencing software landscape is now a core communication solution for company-wide interaction, team discussions, screen sharing and face-to-face contact. Yet the number of existing platforms is diverse. In this article, a systematic literature review investigation on virtual conferencing is presented. As output from the analysis, 67 key features and 74 obstacles users experience when interacting with virtual conferencing technologies are identified from 60 related open-source journal articles from 5 digital library repositories.

Abstract Fingerprint has been widely used in biometric applications. Numerous established researches on image enhancement techniques have been done to improve the quality of fingerprint images. However, the production of low-quality images due to the presence of scars remains a challenge in biometrics. The scars damage the fingerprint minutiae information due to broken ridges and they reduce the accuracy of identification. This research developed an image enhancement approach to improve the quality of scarred fingerprint images to generate accurate minutiae extraction. To achieve the aim, the scarred image was improved by removing noise using a new filter, Median Sigmoid (MS), and the corrected ridges were reconstructed using ridges structure enhancement algorithm. This was done to enhance the broken ridges structure. MS filter is a combination of median filter and modified sigmoid function that improves the image contrast and simultaneously removes noise in the fingerprint image. Following that, the filtered image was used in the ridges structure enhancement process. To identify true minutiae, the broken ridges structure in the filtered image needed to be accurately verified. In the ridges structure reconstruction process, an algorithm was enhanced to identify the best value of Sigma parameter (σ) used in the Gaussian Low-pass filter to generate a better orientation image. The image is important to reconstruct the corrupted fingerprint ridges structure. The evaluation for the proposed approach used the National Institute of Standards and Technology Special Database 14, and the results showed a 37% improvement of the quality index in comparison to approaches found in related research. The findings of the evaluation showed that the proposed enhancement approach produced a better minutiae extraction result and this is very significant in the field of fingerprint image enhancement.

In modern life, many people are sedentary and do not move enough which can lead to a series of physical and mental health conditions including obesity, diabetes, and poor mental health. The aim of this study was to reduce or eliminate this entirely by offering a solution which will work for everyone, even those with a very full schedule and who may find it difficult to stay active. This was carried out by gathering data from potential users, using both questionnaires and interviews to do this. After gathering data on what potential users would like the solution to do, designs were made and then implementation of the app was carried out, based on these designs. Following on from this, sufficient testing was carried out to ensure it worked as intended and satisfied the user requirements. The main findings of this study were that every person will benefit from increasing physical activity, while many people struggled sticking to a workout schedule due to reasons such as the gym being intimidating, expensive and a hassle to commute to. Our solution was made to address these issues and allow people an alternative way to increase physical activity.

Augmented Reality (AR) and Virtual Reality (VR) based treatment has proven that it is easy to use, also motivating and more attractive, and enjoyable as most of the studies proved that. Furthermore, the cost of human resources and the equipment with standard rehabilitation therapy is high to some extent. Whereas, the AR and VR systems are low cost and easy to popularize, beside the unbelievable number of the saved time. This paper presents the comprehensive review on Augmented Reality Technology.

Mild Cognitive Impairment (MCI) is a definition of the diagnosis of early memory loss and disorientation. This study aims to identify people's symptoms through technology. However, machine learning (ML) can classify Cognitive Normal (CN) and Mild Cognitive Impairment (MCI) and Early Mild Cognitive Impairment (EMCI) using standard assessments from the Alzheimer's Disease Neuroimaging Initiative (ADNI); Montreal Cognitive (MoCA), Mini-Mental State Examination (MMSE), Functional Activities Questionnaire (FAQ). Consequently, a Multilayer Perceptron (MLP) model was assembled into tables; MCI vs CN, MCI vs EMCI, and CN vs MCI. Additionally, an MLP model was developed for CN vs MCI vs EMCI. As a result, of advanced model performance, a cascade 3-path categorisation approach was created. Similarly, the exploitation of meta-analysis indicated a combination of MLP models (MCI vs CN, MCI vs EMCI, and CN vs MCI) with an overall accuracy within an acceptable limit. In addition, better results were found when assessments were combined rather than individually. Furthermore, applying class weights and probability thresholds could improve the MLP framework by performance achieving a balanced specificity and sensitivity ratio. Altering class weights and probability thresholds when training the MLP neuro network model, the sensitivity and Accuracy could be progressed further. In conclusion, ML, VR and electrodermal activity are constrained. Introducing the possibility of activity-based applications to enhance innovative solutions for cognitive impairment diagnosis and treatment.

Abstract Hand pose tracking is essential in sign languages. An automatic recognition of performed hand signs facilitates a number of applications, especially for people with speech impairment to communication with normal people. This framework which is called ASLNN proposes a new hand posture recognition technique for the American sign language alphabet based on the neural network which works on the geometrical feature extraction of hands. A user’s hand is captured by a three-dimensional depth-based sensor camera; consequently, the hand is segmented according to the depth analysis features. The proposed system is called depth-based geometrical sign language recognition as named DGSLR. The DGSLR adopted in easier hand segmentation approach, which is further used in segmentation applications. The proposed geometrical feature extraction framework improves the accuracy of recognition due to unchangeable features against hand orientation compared to discrete cosine transform and moment invariant. The findings of the iterations demonstrate the combination of the extracted features resulted to improved accuracy rates. Then, an artificial neural network is used to drive desired outcomes. ASLNN is proficient to hand posture recognition and provides accuracy up to 96.78% which will be discussed on the additional paper of this authors in this journal.

Abstract Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population.

Abstract Security authentication is one of the most important levels of information security. Nowadays, human biometric techniques are the most secure methods for authentication purposes that cover the problems of older types of authentication like passwords and pins. There are many advantages of recent biometrics in terms of security; however, they still have some disadvantages. Progresses in technology made some specific devices, which make it possible to copy and make a fake human biometric because they are all visible and touchable. According to this matter, there is a need for a new biometric to cover the issues of other types. Brainwave is human data, which uses them as a new type of security authentication that has engaged many researchers. There are some research and experiments, which are investigating and testing EEG signals to find the uniqueness of human brainwave. Some researchers achieved high accuracy rates in this area by applying different signal acquisition techniques, feature extraction and classifications using Brain–Computer Interface (BCI). One of the important parts of any BCI processes is the way that brainwaves could be acquired and recorded. A new Signal Acquisition Strategy is presented in this paper for the process of authorization and authentication of brain signals specifically. This is to predict image memorability from the user’s brain to use mental imagery as a visualization pattern for security authentication. Therefore, users can authenticate themselves with visualizing a specific picture in their minds. In conclusion, we can see that brainwaves can be different according to the mental tasks, which it would make it harder using them for authentication process. There are many signal acquisition strategies and signal processing for brain-based authentication that by using the right methods, a higher level of accuracy rate could be achieved which is suitable for using brain signal as another biometric security authentication.

Data security plays a significant role in data transfer in cloud-based smart cities. Chaotic maps are commonly used in designing modern cryptographic applications, in which one-dimensional (1D) chaotic systems are widely used due to their simple design and low computational complexity. However, 1D chaotic maps suffer from different kinds of attacks because of their chaotic discontinuous ranges and small key-space. To own the benefits of 1D chaotic maps and avoid their drawbacks, the cascading of two integrated 1D chaotic systems has been utilized. In this paper, we report an image cryptosystem for data transfer in cloud-based smart cities using the cascading of Logistic-Chebyshev and Logistic-Sine maps. Logistic-Sine map has been utilized to permute the plain image, and Logistic-Chebyshev map has been used to substitute the permuted image, while the cascading of both integrated maps has been utilized in performing XOR procedure on the substituted image. The security analyses of the suggested approach prove that the encryption mechanism has good efficiency as well as lower encryption time compared with other related algorithms.

In this paper, we have provided an interactive email phishing training website to complete the presented security strategy for a Qatar organisation based on targeted email phishing which is Email Phishing Training. The problem that leads users to be hacked using phishing emails is a lack of awareness on the domain senders. Over half of the email users do not have knowledge on phishing domain senders. The Email Phishing Training project aims to provide email phishing and targeted phishing awareness to organisations and government institutions through interactive training in the State of Qatar. In this study, we have presented a new idea on how to familiarize people to user domain senders using an interactive website. Training will be presented for each user based on a pre-quiz on the knowledge of the users then a quiz will be conducted based on the learning outcomes. The results show that the presented idea is an effective solution to tackle phishing emails.

Formal languages are used in symbolic computations, because the formal languages are accurate in semantic and syntax. Formal languages are used to describe vital systems such as airspace control. This paper describes a verification of the airspace available in Software Engineering Somerville, a formal model of critical operations and workflows, in the Lisp programming language. In functional language of the Lisp, the stages of the program are written in the form of a combination of functions and calls. Safety issues are discussed in airspace control in detail. The performance of each function represents the observance of all safety issues by using its algebraic specifications.

Nowadays advanced technologies with a variety of enormous capabilities and superior quality characteristics enables software product developers to produce the finest developments in software regardless of the underlying challenge. The invention of augmented reality (AR) is often used in almost every field of this era. AR is a brazen phenomenon with smart systems and mobile devices as part of everyday life. In this sense the accessibility navigation intelligence AR system for the University of Petra in Amman-Jordan has been created, allowing students, guests and staff to find a specific position within the campus and to navigate its environment. In addition, the UOP-WF-AR application of Augmented Reality will gather knowledge about campus lecturers and officials and give guidance in the open air area. The UOP-WF-AR application is expected to help and direct students, guests and staff in the university, by providing useful details regarding outdoor mobility positions and by providing information on lecturers and officials who are not in their offices at all times.

This paper outlines the literature on phishing attacks in Qatar and techniques for countering them. It begins with the background of phishing, specifically spearphishing, in Qatar and the urgency of the issue. Then, it presents some of the literature and researches on anti-phishing techniques. There are several technical solutions proposed in the literature to detect and defend email phishing attacks, however, these techniques cannot detect and stop genuinely looking phishing emails. Email phishing does not attack devices but it attacks human perception which is hard to defend using technical solutions alone. However, awareness training programs are essential to effectively reduce the success of email phishing attacks. In this systematic review paper, we have tried to classify the existing applications and limitations to figure out the open issues and propose potential solutions.

Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for both researchers and industry alike. The need to manage or even reduce healthcare costs while improving the quality of service is high government agendas. Although, technology has a major role to play in achieving these aspirations, any solution must be designed, implemented and validated using appropriate domain knowledge. In order to overcome these challenges, the remote real-time monitoring of a person’s health can be used to identify relapses in conditions, therefore, enabling early intervention. Thus, the development of a smart healthcare monitoring system, which is capable of observing elderly people remotely, is the focus of the research presented in this paper. The technology outlined in this paper focuses on the ability to track a person’s physiological data to detect specific disorders which can aid in Early Intervention Practices. This is achieved by accurately processing and analysing the acquired sensory data while transmitting the detection of a disorder to an appropriate career. The finding reveals that the proposed system can improve clinical decision supports while facilitating Early Intervention Practices. Our extensive simulation results indicate a superior performance of the proposed system: low latency (96% of the packets are received with less than 1 millisecond) and low packets-lost (only 2.2% of total packets are dropped). Thus, the system runs efficiently and is cost-effective in terms of data acquisition and manipulation.

In recent years, the UK’s emergency call and response has shown elements of great strain as of today. The strain on emergency call systems estimated by a 9 million calls (including both landline and mobile) made in 2014 alone. Coupled with an increasing population and cuts in government funding, this has resulted in lower percentages of emergency response vehicles at hand and longer response times. In this paper, we highlight the main challenges of emergency services and overview of previous solutions. In addition, we propose a new system call Smart Hospital Emergency System (SHES). The main aim of SHES is to save lives through improving communications between patient and emergency services. Utilising the latest of technologies and algorithms within SHES is aiming to increase emergency communication throughput, while reducing emergency call systems issues and making the process of emergency response more efficient. Utilising health data held within a personal smartphone, and internal tracked data (GPU, Accelerometer, Gyroscope etc.), SHES aims to process the mentioned data efficiently, and securely, through automatic communications with emergency services, ultimately reducing communication bottlenecks. Live video-streaming through real-time video communication protocols is also a focus of SHES to improve initial communications between emergency services and patients. A prototype of this system has been developed. The system has been evaluated by a preliminary usability, reliability, and communication performance study.

Rendering atmospheric phenomena is known to have its basis in the fields of atmospheric optics and meteorology and is increasingly used in games and movies. Although many researchers have focused on generating and enhancing realistic light shafts, there is still room for improvement in terms of both qualification and quantification. In this paper, a new technique, called ReLiShaft, is presented to generate realistic light shafts for outdoor rendering. In the first step, a realistic light shaft with respect to the sun position and sky colour in any specific location, date and time is constructed in real-time. Then, Hemicube visibility-test radiosity is employed to reveal the effect of a generated sky colour on environments. Two different methods are considered for indoor and outdoor rendering, ray marching based on epipolar sampling for indoor environments, and filtering on regular epipolar of z-partitioning for outdoor environments. Shadow maps and shadow volumes are integrated to consider the computational costs. Through this technique, the light shaft colour is adjusted according to the sky colour in any specific location, date and time. The results show different light shaft colours in different times of day in real-time.

In marker based augmented reality applications, the markers share nearly the same standard design that can interfere the natural vision. By using the image inpainting, the marker can be hidden from the final view. In image inpainting, a texture is generated to fill in lost or corrupted regions using its surrounding background. This paper presents an enhanced exemplar based inpainting algorithm with reduced computational load and optimized performance to be applied to the marker area. The neighboring area of the marker is used in generating the texture that hides the marker area along with preserving the structure lines that can assist in the realistic completion of the generated result. The results of the enhanced exemplar based algorithm are convenient for hiding the large target area of the marker and also for real time usage in the augmented reality environment.

Calculating and measuring shadows with respect to the sun position in any outdoor games is a challenging and heavy task which must be taken into account. In this paper an intelligent real-time platform for outdoor environment is presented to control the amount and direction of shadows with respect to the sun position. The sun position reveals an attractive part of outdoor games. To create shadow, shadow volume using stencil buffer and depth buffer are used. By calculating the sun position in any specific date, time and location, shadow is casted accordingly. Length and angle of shadow are two parameters measured for building designers and both of them are calculated in this real-time platform. Therefore, the application can be used for investigation on behavior of the sun position and direction of shadows for any outdoor games and outdoor rendering.