Machine learning-based habitat mapping of the invasive Prosopis juliflora in Sharjah, UAE.

Prosopis juliflora, one of the most invasive trees, adversely affects the ecosystem and native plant communities in arid lands. This disrupts biodiversity and depletes water resources, posing significant ecological and economic challenges. Several attempts have been made to control this species in the United Arab Emirates (UAE) deserts but with little success.

Transfer Learning-Based Classifier to Automate the Extraction of False X-Ray Images From Hospital's Database.

Background: During preclinical training, dental students take radiographs of acrylic (plastic) blocks containing extracted patient teeth. With the digitisation of medical records, a central archiving system was created to store and retrieve all x-ray images, regardless of whether they were images of teeth on acrylic blocks, or those from patients. In the early stage of the digitisation process, and due to the immaturity of the data management system, numerous images were mixed up and stored in random locations within a unified archiving system, including patient record files.

A tree-based explainable AI model for early detection of Covid-19 using physiological data.

With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world.

Simulation of electricity consumption data using multiple artificial intelligence models and cross validation techniques.

Worldwide, electricity production exceeds its consumption which leads to wasted financial and energy resources. Machine learning models can be utilized to predict the future consumption to avoid these significant losses. This paper presents the data for the monthly electricity consumption on the community level during May 2017-December 2019 in Dubai, United Arab Emirates.

Software defect prediction using learning to rank approach.

Software defect prediction (SDP) plays a significant role in detecting the most likely defective software modules and optimizing the allocation of testing resources. In practice, though, project managers must not only identify defective modules, but also rank them in a specific order to optimize the resource allocation and minimize testing costs, especially for projects with limited budgets. This vital task can be accomplished using Learning to Rank (LTR) algorithm.

Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring.

Despite the declining COVID-19 cases, global healthcare systems still face significant challenges due to ongoing infections, especially among fully vaccinated individuals, including adolescents and young adults (AYA). To tackle this issue, cost-effective alternatives utilizing technologies like Artificial Intelligence (AI) and wearable devices have emerged for disease screening, diagnosis, and monitoring. However, many AI solutions in this context heavily rely on supervised learning techniques, which pose challenges such as human labeling reliability and time-consuming data annotation.

The Potential of Blockchain Technology in Dental Healthcare: A Literature Review.

Blockchain technology in the healthcare industry has potential to enable enhanced privacy, increased security, and an interoperable data record. Blockchain technology is being implemented in dental care systems to store and share medical information, improve insurance claims, and provide innovative dental data ledgers. Because the healthcare sector is a large and ever-growing industry, the use of blockchain technology would have many benefits.

Breast cancer detection using artificial intelligence techniques: A systematic literature review.

Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer Foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US.

Improved Optical Flow Estimation Method for Deepfake Videos.

Creating deepfake multimedia, and especially deepfake videos, has become much easier these days due to the availability of deepfake tools and the virtually unlimited numbers of face images found online. Research and industry communities have dedicated time and resources to develop detection methods to expose these fake videos. Although these detection methods have been developed over the past few years, synthesis methods have also made progress, allowing for the production of deepfake videos that are harder and harder to differentiate from real videos.

Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19.

Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19.

Blockchain-Based Authentication in Internet of Vehicles: A Survey.

Internet of Vehicles (IoV) has emerged as an advancement over the traditional Vehicular Ad-hoc Networks (VANETs) towards achieving a more efficient intelligent transportation system that is capable of providing various intelligent services and supporting different applications for the drivers and passengers on roads. In order for the IoV and VANETs environments to be able to offer such beneficial road services, huge amounts of data are generated and exchanged among the different communicated entities in these vehicular networks wirelessly via open channels, which could attract the adversaries and threaten the network with several possible types of security attacks. In this survey, we target the authentication part of the security system while highlighting the efficiency of blockchains in the IoV and VANETs environments.

Analytical study on the impact of technology in higher education during the age of COVID-19: Systematic literature review.

Unlabelled: With the advent of COVID-19 arose the need for social distancing measures, including the imposition of far-reaching lockdowns in many countries. The lockdown has wreaked havoc on many aspects of daily life, but education has been particularly hard hit by this unprecedented situation. The closure of educational institutions brought along many changes, including the transition to more technology-based education.

Artificial intelligence applications in solid waste management: A systematic research review.

The waste management processes typically involve numerous technical, climatic, environmental, demographic, socio-economic, and legislative parameters. Such complex nonlinear processes are challenging to model, predict and optimize using conventional methods. Recently, artificial intelligence (AI) techniques have gained momentum in offering alternative computational approaches to solve solid waste management (SWM) problems.

Enhancing computing studies in high schools: A systematic literature review & UAE case study.

Open source software (OSS) is increasingly being integrated into educational institutions, and many countries require the use of OSS in government departments. However, not much focus is placed on integrating it into the educational sector in a strategic and productive manner. This paper examines the existing literature on the use of OSS in terms of the potential enhancements it can provide for computer science studies in high schools in general, and those in the UAE more specifically.

Towards early software reliability prediction for computer forensic tools (case study).

Versatility, flexibility and robustness are essential requirements for software forensic tools. Researchers and practitioners need to put more effort into assessing this type of tool. A Markov model is a robust means for analyzing and anticipating the functioning of an advanced component based system.