s aljameel

The novel coronavirus (COVID-19) outbreak produced devastating effects on the global economy and the health of entire communities Although the COVID-19 survival rate is high, the number of severe cases that result in death is increasing daily A timely prediction of at-risk patients of COVID-19 with precautionary measures is expected to increase the survival rate of patients and reduce the fatality rate This research provides a prediction method for the early identification of COVID-19 patient's outcome based on patients' characteristics monitored at home, while in quarantine The study was performed using 287 COVID-19 samples of patients from the King Fahad University Hospital, Saudi Arabia The data were analyzed using three classification algorithms, namely, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB) Initially, the data were preprocessed using several preprocessing techniques Furthermore, 10-k cross-validation was applied for data parti...

LANA CITS is a Conversational Intelligent Tutoring System that uses the Visual, Auditory, and Kinaesthetic learning style (VAK). It supports learning in autistic pupils, who are studying in mainstream primary schools. Facilitating the learning of these pupils using traditional teaching within mainstream schools is complex and poorly understood. This paper presents investigation into how LANA CITS using VAK learning style model can be adapted to autistic pupils learning style and improve their learning in mainstream schools. This paper provides a case study evaluation of three children with high-functioning autism examining the effectiveness of learning with LANA CITS. The case study took place in primary school in Saudi Arabia. The results were positive with the students engaged in the tutorial and the teacher noticed some improvement over classroom activities. This results support for the continuing development, evaluation, and use of CITS for pupils with autism in mainstream schools.

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL m...

Children with Autism Spectrum Disorder (ASD) share certain difficulties but being autistic will affect them in different ways in terms of their level of intellectual ability. Children with high functioning autism or Asperger syndrome are very intelligent academically but they still have difficulties in social and communication skills. Many of these children are taught within mainstream schools but there is a shortage of specialised teachers to deal with their specific needs. One solution is to use a virtual tutor to supplement the education of children with ASD in mainstream schools. This paper describes research to develop a novel Arabic Conversational Intelligent Tutoring System, called LANA-I, for children with ASD that adapts to the Visual, Auditory and Kinaesthetic learning styles model (VAK) to enhance learning. This paper also proposes an evaluation methodology and describes an experimental evaluation of LANA-I. The evaluation was conducted with neurotypical children and indi...

For individuals with autism spectrum disorder (ASD), the use of Computer technology to provide intervention in learning is promising. This review focuses on research that has used technology to improve the performance for school aged (10–16) children with ASD. This paper reviews technologies that enhanced intervention, which target three cognitive domains: (1) languages and literacy, (2) social skills, and (3) emotion recognition. A review of the literature from 2005 to the end of 2015 identified 19 studies that documented efficacy in order to determine whether empirical findings support technology as an evidence-based practice. The conclusion reports that it is important to support development, evaluation, and clinical usage of technology-based intervention for individuals with autism spectrum disorders. Future directions for research and practice with each technology are discussed.

Measuring the similarity between strings plays an increasingly important role in many applications such as information retrieval, short answer grading, and conversational agent software. There has been much recent research interest in applying string similarity within Arabic language applications; however, the use of string similarity in Arabic poses a substantial challenge such as the complexity of the morphological system, ambiguity, and lack of resources. This survey discusses the existing research into string similarity approaches and the difficulties posed by the Arabic language by dividing them into three approaches; lexical-based similarity, semantic-based similarity, and hybrid similarity. The aim of this paper is to review these approaches and to identify suitable approaches with Arabic language.

The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which e...

In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual’s awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency–Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the sout...

Precision agriculture is now essential in today’s world, especially for countries with limited water resources, fertile land, and enormous population. Smart irrigation systems can help countries efficiently utilize fresh water and use the excess water for barren lands. Smart water management platform (SWAMP) is an IoT-based smart irrigation project designed for efficient freshwater utilization in agriculture. The primary aim of SWAMP is to auto manage water reserves, distribution, and consumption of various levels, avoid over-irrigation and under-irrigation problems, and auto manage time to maximize production. This research proposed an energy-efficient water management platform (EEWMP), an improved version of SWAMP. EEWMP is an IoT-based smart irrigation system that uses field-deployed sensors, sinks, fusion centres, and open-source clouds. Both models’ performance is evaluated in energy consumption, network stability period, packet sent to destination, and packet delivery ratio. T...

Due to the successful application of machine learning techniques in several fields, automated diagnosis system in healthcare has been increasing at a high rate. The aim of the study is to propose an automated skin cancer diagnosis and triaging model and to explore the impact of integrating the clinical features in the diagnosis and enhance the outcomes achieved by the literature study. We used an ensemble-learning framework, consisting of the EfficientNetB3 deep learning model for skin lesion analysis and Extreme Gradient Boosting (XGB) for clinical data. The study used PAD-UFES-20 data set consisting of six unbalanced categories of skin cancer. To overcome the data imbalance, we used data augmentation. Experiments were conducted using skin lesion merely and the combination of skin lesion and clinical data. We found that integration of clinical data with skin lesions enhances automated diagnosis accuracy. Moreover, the proposed model outperformed the results achieved by the previous...