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

R : Fuzzy logic with expert judgment to implement an adaptive risk-based access control model for IoT

The Internet of Things (IoT) is becoming the future of the Internet with a large number of connected devices that are predicted to reach about 50 billion by 2020. With proliferation of IoT devices and need to increase information sharing in IoT applications, risk-based access control model has become the best candidate for both academic and commercial organizations to address access control issues. This model carries out a security risk analysis on the access request by using IoT contextual information to provide access decisions dynamically. This model solves challenges related to flexibility and scalability of the IoT system. Therefore, we propose an adaptive risk-based access control model for the IoT. This model uses real-time contextual information associated with the requesting user to calculate the security risk regarding each access request. It uses user attributes while making the access request, action severity, resource sensitivity and user risk history as inputs to analyze and calculate the risk value to determine the access decision. To detect abnormal and malicious actions, smart contracts are used to track and monitor user activities during the access session to detect and prevent potential security violations. In addition, as the risk estimation process is the essential stage to build a risk-based model, this paper provides a discussion of common risk estimation methods and then proposes the fuzzy inference system with expert judgment as to be the optimal approach to handle risk estimation process of the proposed risk-based model in the IoT system

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

Automatic and early detection of Parkinson’s Disease by analyzing acoustic signals using classification algorithms based on recursive feature elimination method

Parkinson’s disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60–80% inability to produce dopamine, an organic chemical responsible for controlling a person’s movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient’s nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person’s voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson’s cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%

Effective early detection of epileptic seizures through EEG signals using classification algorithms based on t-distributed stochastic neighbor embedding and K-means

Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%

Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg–Marquardt (LM), Conjugate Gradient with Fletcher–Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children’s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features

Business Email Compromise Phishing Detection Based on Machine Learning: A Systematic Literature Review

The risk of cyberattacks against businesses has risen considerably, with Business Email Compromise (BEC) schemes taking the lead as one of the most common phishing attack methods. The daily evolution of this assault mechanism’s attack methods has shown a very high level of proficiency against organisations. Since the majority of BEC emails lack a payloader, they have become challenging for organisations to identify or detect using typical spam filtering and static feature extraction techniques. Hence, an efficient and effective BEC phishing detection approach is required to provide an effective solution to various organisations to protect against such attacks. This paper provides a systematic review and examination of the state of the art of BEC phishing detection techniques to provide a detailed understanding of the topic to allow researchers to identify the main principles of BEC phishing detection, the common Machine Learning (ML) algorithms used, the features used to detect BEC phishing, and the common datasets used. Based on the selected search strategy, 38 articles (of 950 articles) were chosen for closer examination. Out of these articles, the contributions of the selected articles were discussed and summarised to highlight their contributions as well as their limitations. In addition, the features of BEC phishing used for detection were provided, as well as the ML algorithms and datasets that were used in BEC phishing detection models were discussed. In the end, open issues and future research directions of BEC phishing detection based on ML were discussed

An efficient security risk estimation technique for Risk-based access control model for IoT

The need to increase information sharing in the Internet of Things (IoT) applications made the risk-based access control model to be the best candidate for both academic and com- mercial organizations. Risk-based access control model carries out a security risk analysis on the access request by using IoT contextual information to provide access decisions dy- namically. Unlike current static access control approaches that are based on predefined policies and give the same result in different situations, this model provides the required flexibility to access system resources and works well in unexpected conditions and situa- tions of the IoT system. One of the main issues to implement this model is to determine the appropriate risk estimation technique that is able to generate accurate and realistic risk values for each access request to determine the access decision. Therefore, this paper pro- poses a risk estimation technique which integrates the fuzzy inference system with expert judgment to assess security risks of access control operations in the IoT system. Twenty IoT security experts from inside and outside the UK were interviewed to validate the proposed risk estimation technique and build the fuzzy inference rules accurately. The proposed risk estimation approach was implemented and simulated using access control scenarios of the network router. In comparison with the existing fuzzy techniques, the proposed technique has demonstrated it produces precise and realistic values in evaluating security risks of access control operations in the IoT context.Non