Progressive observation of Covid-19 vaccination effects on skin-cellular structures by use of Intelligent Laser Speckle Classification (ILSC)

We have made a progressive observation of Covid-19 Astra Zeneca Vaccination effect on Skin cellular network and properties by use of well established Intelligent Laser Speckle Classification (ILSC) image based technique and managed to distinguish between three different subjects groups via their laser speckle skin image samplings such as early-vaccinated, late-vaccinated and non-vaccinated individuals. The results have proven that the ILSC technique in association with the optimised Bayesian network is capable of classifying skin changes of vaccinated and non-vaccinated individuals and also of detecting progressive development made on skin cellular properties for a month period

Progressive observation of Covid-19 vaccination effects on skin-cellular structures by use of Intelligent Laser Speckle Classification (ILSC)

We have made a progressive observation of Covid-19 Astra Zeneca Vaccination effect on Skin cellular network and properties by use of well established Intelligent Laser Speckle Classification (ILSC) image based technique and managed to distinguish between three different subjects groups via their laser speckle skin image samplings such as early-vaccinated, late-vaccinated and non-vaccinated individuals. The results have proven that the ILSC technique in association with the optimised Bayesian network is capable of classifying skin changes of vaccinated and non-vaccinated individuals and also of detecting progressive development made on skin cellular properties for a month period

Explaining deep neural networks: A survey on the global interpretation methods

A substantial amount of research has been carried out in Explainable Artificial Intelligence (XAI) models, especially in those which explain the deep architectures of neural networks. A number of XAI approaches have been proposed to achieve trust in Artificial Intelligence (AI) models as well as provide explainability of specific decisions made within these models. Among these approaches, global interpretation methods have emerged as the prominent methods of explainability because they have the strength to explain every feature and the structure of the model. This survey attempts to provide a comprehensive review of global interpretation methods that completely explain the behaviour of the AI models. We present a taxonomy of the available global interpretations models and systematically highlight the critical features and algorithms that differentiate them from local as well as hybrid models of explainability. Through examples and case studies from the literature, we evaluate the strengths and weaknesses of the global interpretation models and assess challenges when these methods are put into practice. We conclude the paper by providing the future directions of research in how the existing challenges in global interpretation methods could be addressed and what values and opportunities could be realized by the resolution of these challenges

Emotions in mental healthcare and psychological interventions : towards an inventive emotions recognition framework using AI

One of the major impacts of COVID-19 in the nations is mental health issues. Constant mental health issues can cause disorders, as well as mortality. The growing demand for mental healthcare treatment and limited healthcare resources across the world has shown the need for an inventive framework solution. Artificial Intelligence (AI), Big Data Science, 5G, and Information Communication Technology (ICT) have proven to be able to bring many great improvements and could be the potential way forward to develop such a framework. AI could be a very effective tool to help the healthcare sector to provide more efficient services to patients with mental health issues through their emotions. This paper presents the initial overview and outcomes of the ongoing research programme to develop a proactive multimodal emotion AI recognition framework that detects emotion from various input data sources for early detection of mental health illnesses, as well as provides the required psychological interventions effectively and promptly when required. The data will be collected from various smart wearables and ad-hoc devices, facial expressions, and speech signals. Then, these data will be interpreted using AI into emotions. These emotions will be utilised using AI-based psychological system, which will provide immediate and customized interventions, as well as transmit critical data to the healthcare provider’s central database system for monitoring and supplying the required treatments

Anomaly detection system for Ethereum blockchain using machine learning

Over the past few years, Blockchain technology has been utilized in various applications to improve privacy and security. Although blockchain has proven its worth as a very powerful technology, research has shown that it is not entirely immune to security and privacy attacks. There was a successful 51% attack on Ethereum Classic back in January 2019 which shows that blockchain still facing security and privacy challenges. This paper aims to develop an anomaly detection solution for the Ethereum blockchain to overcome security challenges using Machine Learning (ML). The proposed solution focuses on using a dynamic approach where the normal operational behaviour of the Ethereum blockchain is used to train ML algorithms and any deviation will be tagged as an anomaly and will be detected by the system. Four ML algorithms including K-Nearest Neighbours (KNN), Gaussian Naive Bayes (GaussianNB), Random Forest, and Stochastic Gradient Descent (SDG) were utilized to train and verify the accuracy of the proposed solution. The experimental results demonstrated that the random forest algorithm provided the best accuracy of 99.84% over other ML algorithms

Weapon Violence Dataset 2.0: A synthetic dataset for violence detection

In the current era, satisfying the appetite of data hungry models is becoming an increasingly challenging task. This challenge is particularly magnified in research areas characterised by sensitivity, where the quest for genuine data proves to be elusive. The study of violence serves as a poignant example, entailing ethical considerations and compounded by the scarcity of authentic, real-world data that is predominantly accessible only to law enforcement agencies. Existing datasets in this field often resort to using content from movies or open-source video platforms like YouTube, further emphasising the scarcity of authentic data. To address this, our dataset aims to pioneer a new approach by creating the first synthetic virtual dataset for violence detection, named the Weapon Violence Dataset (WVD). The dataset is generated by creating virtual violence scenarios inside the photo-realistic video game namely: Grand Theft Auto-V (GTA-V). This dataset includes carefully selected video clips of person-to-person fights captured from a frontal view, featuring various weapons—both hot and cold across different times of the day. Specifically, WVD contains three categories: Hot violence and Cold violence (representing the violence category) as well as No violence (constituting the control class). The dataset is designed and created in a way that will enable the research community to train deep models on such synthetic data with the ability to increase the data corpus if the needs arise. The dataset is publicly available on Kaggle and comprises normal RGB and optic flow videos

Deep labeller: automatic bounding box generation for synthetic violence detection datasets

Manually labelling datasets for training violence detection systems is time-consuming, expensive, and labor-intensive. Mind wandering, boredom, and short attention span can also cause labelling errors. Moreover, collecting and distributing sensitive images containing violence has ethical implications. Automation is the future for labelling sensitive image datasets. Deep labeller is a two-stage Deep Learning (DL) method that uses pre-trained DL object detection methods on MS-COCO for automatic labelling. The Deep Labeller method labels violent and nonviolent images in WVD and USI. In stage 1, WVD generates weak labels using synthetic images. In stage 2, the Deep labeller method is retrained on weak labels. USI dataset is used to test our method on real-world violence. Deep labeller generated weak and strong labels with an IoU of 0.80036 in stage 1 and 0.95 in stage 2 on the WVD. Automatically generated labels. To test our method’s generalisation power, violent and nonviolent image labels on USI dataset had a mean IoU of 0.7450

Leaf segmentation in plant phenotyping: a collation study

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (>>90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://​www.​plant-phenotyping.​org/​datasets) to support future challenges beyond segmentation within this application domain