Dyslexia is a neurological problem that leads to obstacles and difficulties in the learning process, especially in reading. Generally, people with dyslexia suffer from weak reading, writing, spelling, and fluency abilities. However, these difficulties are not related to their intelligence. An early diagnosis of this disorder will help dyslexic children improve their abilities using appropriate tools and specialized software. Machine learning and deep learning methods have been implemented to recognize dyslexia with various datasets related to dyslexia acquired from medical and educational organizations. This review paper analyzed the prediction performance of deep learning models for dyslexia and summarizes the challenges researchers face when they use deep learning models for classification and diagnosis. Using the PRISMA protocol, 19 articles were reviewed and analyzed, with a focus on data acquisition, preprocessing, feature extraction, and the prediction model performance. The purpose of this review was to aid researchers in building a predictive model for dyslexia based on available dyslexia-related datasets. The paper demonstrated some challenges that researchers encounter in this field and must overcome.

Natural disasters greatly threaten our lives in addition to adversely affecting all activities. Unfortunately, most solutions currently used in flood management are suffering from many drawbacks related to latency and accuracy. Moreover, the previous solutions consider that the whole city has the same level of vulnerability to damage, while each area in the city may have different topologies and conditions. This study presents a new framework that collects data in real-time about bad weather, which may cause floods, where the framework has a proposed classification algorithm to process sensed data to determine the level of danger in each area of the city. In case of a threat, the framework will send early alerts to users and rescue teams. The framework depends on the Internet of Things (IoT) and fog computing coupled with multiple models of machine learning (Rain Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Deep Learning) to enhance performance and reliability. In addition, the research suggests some assistant services. To prove the efficiency of the framework, we applied the proposed algorithm to real data for the city of Jeddah, Saudi Arabia, for the years 2009 to 2013 and for the years 2018 to 2022. Then, we depended on standard metrics (accuracy, precision, recall, F1-score, and ROC curve). The Rain Forest and Decision Tree achieved the highest accuracy, exceeding 99 percent, followed by the K-Nearest Neighbor. The framework will provide flood detection systems that can predict floods early, send a multi-level warning, and reduce financial, human, and infrastructural damage.

Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many researchers have developed models to diagnose them in the early stages. This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis. We searched the Web of Science, ScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022, identified the pros and cons of the reviewed ML models, and discussed the possible recommendations to advance the studies in this field. We found that most of the articles used small datasets, and few of them used real-time data. Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal. Moreover, heterogeneous data could ensure the model’s generalization, while big data, many features, and a hybrid model will increase the resulting performance. Furthermore, using other techniques such as deep learning and NLP to extract vast features from unstructured data is a powerful approach to enhancing the performance of ML diagnostic models.

Internet of Things (IoT) is a massive network based on tiny devices connected internally and to the internet. Each connected device is uniquely identified in this network through a dedicated IP address and can share the information with other devices. In contrast to its alternatives, IoT consumes less power and resources; however, this makes its devices more vulnerable to different types of attacks as they cannot execute heavy security protocols. Moreover, traditionally used heavy protocols for web-based communication, such as the Hyper Text Transport Protocol (HTTP) are quite costly to be executed on IoT devices, and thus specially designed lightweight protocols, such as the Constrained Application Protocol (CoAP) are employed for this purpose. However, while the CoAP remains widely-used, it is also susceptible to attacks, such as the Distributed Denial-of-Service (DDoS) attack, which aims to overwhelm the resources of the target and make them unavailable to legitimate users. While protocols, such as the Datagram Transport Layer Security (DTLS) and Lightweight and the Secure Protocol for Wireless Sensor Network (LSPWSN) can help in securing CoAP against DDoS attacks, they also have their limitations. DTLS is not designed for constrained devices and is considered as a heavy protocol. LSPWSN, on the other hand, operates on the network layer, in contrast to CoAP which operates on the application layer. This paper presents a machine learning model, using the CIDAD dataset (created on 11 July 2022), that can detect the DDoS attacks against CoAP with an accuracy of 98%.

Detecting cyber intrusions in network traffic is a tough task for cybersecurity. Current methods struggle with the complexity of understanding patterns in network data. To solve this, we present the Hybrid Deep Learning Intrusion Detection Model (HD-IDM), a new way that combines GRU and LSTM classifiers. GRU is good at catching quick patterns, while LSTM handles long-term ones. HD-IDM blends these models using weighted averaging, boosting accuracy, especially with complex patterns. We tested HD-IDM on four datasets: CSE-CIC-IDS2017, CSE-CIC-IDS2018, NSL KDD, and CIC-DDoS2019. The HD-IDM classifier achieved remarkable performance metrics on all datasets. It attains an outstanding accuracy of 99.91%, showcasing its consistent precision across the dataset. With an impressive precision of 99.62%, it excels in accurately categorizing positive cases, crucial for minimizing false positives. Additionally, maintaining a high recall of 99.43%, it effectively identifies the majority of actual positive cases while minimizing false negatives. The F1-score of 99.52% emphasizes its robustness, making it the top choice for classification tasks requiring precision and reliability. It is particularly good at ROC and precision/recall curves, discriminating normal and harmful network activities. While HD-IDM is promising, it has limits. It needs labeled data and may struggle with new intrusion methods. Future work should find ways to handle unlabeled data and adapt to emerging threats. Also, making HD-IDM work faster for real-time use and dealing with scalability challenges is key for its broader use in changing network environments.

Most infectious diseases are caused by viruses, fungi, bacteria and parasites. Their ability to easily infect humans and trigger large-scale epidemics makes them a public health concern. Methods for early detection of these diseases have been developed; however, they are hindered by the absence of a unified, interoperable and reusable model. This study seeks to create a holistic and real-time model for swift, preliminary detection of infectious diseases using symptoms and additional clinical data. In this study, we present a medical knowledge graph (MKG) that leverages multiple data sources to analyse connections between different nodes. Medical ontologies were used to enhance the MKG. We applied various graph algorithms to extract key features. The performance of multiple machine-learning (ML) techniques for influenza and hepatitis detection was assessed, selecting multi-layer perceptron (MLP) and random forest (RF) models due to their superior outcomes. The hyperparameters of both graph-based ML models were automatically fine-tuned. Both the graph-based MLP and RF models showcased the least loss and error rates, along with the most specific, accurate recall, precision and F1 scores. Their Matthews correlation coefficients were also optimal. When compared with existing ML techniques and findings from the literature, these graph-based ML models manifested superior detection accuracy. The graph-based MLP and RF models effectively diagnosed influenza and hepatitis, respectively. This underlines the potential of graph data science in enhancing ML model performance and uncovering concealed relationships in the MKG.