Internet of Things (IoT) deployments face significant security challenges due to the limited energy and computational power of IoT devices. These challenges are more serious in the quantum communications era, where certain attackers might have quantum computing capabilities, which renders IoT devices more vulnerable. This paper addresses the problem of IoT security by investigating quantum key distribution (QKD) in beyond 5G networks. An algorithm for detecting an attacker between a transmitter and receiver is proposed, with the side effect of interrupting the QKD process while detecting the attacker. Afterwards, Artificial neural network (ANN) and deep learning (DL) techniques are proposed in order to detect the presence of an attacker during QKD without the need to disrupt the key distribution process. An architecture for implementing QKD in beyond 5G IoT networks is proposed, offloading the heavy computational tasks to IoT controllers. In addition, an implementation scenario for securing IoT communications for sensors deployed in railroad networks is described. The results show that the proposed ML techniques can reach 99% accuracy in detecting attackers.

Dysarthria, a speech disorder stemming from neurological conditions, affects communication and life quality. Precise classification and severity assessment are pivotal for therapy but are often subjective in traditional speech-language pathologist evaluations. Machine learning models offer objective assessment potential, enhancing diagnostic precision. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels, highlighting effective features for automatic classification and optimal AI techniques. We systematically reviewed the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.

In recent years, the field of digital imaging has made significant progress, so that today every smartphone has a built-in video camera that allows you to record high-quality video for free and without restrictions. On the other hand, rapidly growing internet technology has contributed significantly to the widespread use of digital video via web-based multimedia systems and mobile smartphone applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital videos have become affordable nowadays, security issues have become threatening and spread worldwide. One of the security issues is identifying source cameras on videos. There are some new challenges that should be addressed in this area. One of the new challenges is individual source camera identification (ISCI), which focuses on identifying each device regardless of its model. The first step towards solving the problems is a popular video database recorded by modern smartphone devices, which can also be used for deep learning methods that are growing rapidly in the field of source camera identification. In this paper, a smartphone video database named Qatar University Forensic Video Database (QUFVD) is introduced. The QUFVD includes 6000 videos from 20 modern smartphone representing five brands, each brand has two models, and each model has two identical smartphone devices. This database is suitable for evaluating different techniques such as deep learning methods for video source smartphone identification and verification. To evaluate the QUFVD, a series of experiments to identify source cameras using a deep learning technique are conducted. The results show that improvements are essential for the ISCI scenario on video.

Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.

Dysarthria, a motor speech disorder, poses challenges in accurate severity assessment. Recent research has excelled in classifying dysarthria based on severity levels, primarily utilizing annotated datasets and achieving high accuracies. However, these classification-based approaches may not readily translate to real-world scenarios without predefined labels. This study follows a different path by proposing a two-stage approach leveraging binary classification and clustering to comprehensively analyze and classify dysarthria severity levels. We begin by employing binary classification to differentiate control from dysarthric cases by experiencing eight different feature extraction techniques and two classifiers in order to support the largest amount of dysarthric cases to the second stage, where k-means clustering uncovers hidden patterns and boundaries within dysarthria severity levels, enabling a more nuanced understanding of the disorder. We applied our methodology to the TORGO dataset, a benchmark in dysarthria research, and evaluated it on the UA Speech dataset. After optimizing the number of clusters, our approach achieved an accuracy of 91% with sentence-based features and 85% with word-based features in clustering. This research extends previous studies by exploring unsupervised clustering to differentiate severity levels in unannotated cases, bridging the gap between controlled datasets and practical applications. Our findings highlight the effectiveness of clustering-driven two-stage analysis in improving dysarthria severity-level classification, with implications for real-world clinical settings.

Ischemic stroke, a severe medical condition triggered by a blockage of blood flow to the brain, leads to cell death and serious health complications. One key challenge in this field is accurately predicting infarction growth - the progressive expansion of damaged brain tissue post-stroke. Recent advancements in artificial intelligence (AI) have improved this prediction, offering crucial insights into the progression dynamics of ischemic stroke. One such promising technique, the Adaptive Neuro-Fuzzy Inference System (ANFIS), has shown potential, but it faces the 'curse of dimensionality' and long training times as the number of features increased. This paper introduces an innovative, automatic method that combines Binary Particle Swarm Optimization (BPSO) with ANFIS architecture, achieves reduction in dimensionality by reducing the number of rules and training time. By analyzing the Pearson correlation coefficients and P-values, we selected clinically relevant features strongly correlated with the Infarction Growth Rate (IGR II), extracted after one CT scan. We compared our model's performance with conventional ANFIS and other machine learning techniques, including Support Vector Regressor (SVR), shallow Neural Networks, and Linear Regression.