Systems based on wireless sensor networks convey a prevailing tool to monitor data in complex environments. In this work, we present an intelligent integrated system that monitors various crucial environmental parameters, and applies machine learning to classify multiple gases. The system is a complete, sophisticated, interactive multiple parameters-observing, with two-way communication via wireless mobile network. It consists of a number of buoys distributed throughout the sea and integrated with a suite of sensors capable of measuring a range of essential air and underwater parameters. Four well-known machine learning algorithms are implemented and compared, namely multilayer perceptron, Naïve Bayes, logistic regression, and support vector machines. We present a thorough investigation and deep analysis to evaluate the system performance. The experimental results show that the system achieves a correct classification rate of 92.66%, with a weighted average precision, recall, F-score, and MMC of 0.93, 0.93, 0.926, and 0.91, respectively.

This paper proposes a novel method for degraded thermal face recognition using Hu Li moments. The method deals with spatial variations of thermal images resulting from low resolution and changes in head pose. We describe thermal images with a set of scalar quantities that can capture its significant features at component level. Each thermal image is divided into components where the statistical features of these components are combined using a fusion method. The method finds a combination of multiple statistical patterns to produce an integrated result that is enhanced in terms of information content for correct classification. We show that local representations using Hu Li moments provide higher discriminability and offer robustness against variability due to spatial changes. To evaluate the performance of the proposed method, we conduct thorough experiments and detailed analysis on a database consisting of 7500 images of different poses and variable spatial resolutions. This database is generated from an initial one that originally consists of 1500 images, by down-sampling the original images to four different low resolution levels beside the original level. The results show that the proposed method achieves significantly high recognition rates over various conditions resembling practical scenarios.

The blue economy has gained considerable significance as a critical global catalyst for sustainable economic growth through the practical usage of marine resources. In this context, artificial intelligence researchers can substantially contribute to this growing field of knowledge. This work discusses new technology developments that facilitate the establishment of successful strategies for the digital transformation essential to the blue economy, where the role of artificial intelligence in securing sustainability within the maritime sector is explored. The work further discusses the need for advanced environmental protection and the related economic impacts. Moreover, the study will explain various facets and applications of digitization for the blue economy and sustainability. Furthermore, case studies on implementing new technologies within the context will be explained.

Researchers and scientists can transform interconnected data into valuable knowledge using computational-based models that can assist in disease diagnosis, inspection, and virus containment thanks to recent developments in the fields of artificial intelligence and machine learning. In this paper, we present a comprehensive analysis of how artificial intelligence and machine learning can contribute in the delivery of effective remedies and the fight against the COVID-19 pandemic, particularly in disease treatment and drug discovery. During the pandemic period, a large number of noteworthy studies were conducted in this direction by numerous academic and research communities from many fields. We explore the theoretical developments and practical applications of artificial intelligence algorithms and machine learning techniques that suggest potential solutions for accelerating the discovery of new drugs as well as repurposing existing ones, not only for COVID-19 but also for other related mutations and future pandemics, which unfortunately are highly predicted.

Imaging in the visible spectrum demonstrates difficulties in recognizing the faces in conditions of varying illumination, especially under total darkness conditions. Further, the pose variations in such images add extra burden and heavy challenge on successful performance. As such, thermal face recognition has laid itself as a successful alternative solution and eventually has become an area of growing interest. In this paper, we present a new technique for thermal face recognition based on affine moment invariants (AMI) technique. AMI technique has become one of the most important shape descriptors. The technique will be implemented at the component level by dividing the face image into non-overlapped components. We anticipate that this approach will offer robustness against variability due to changes in localized regions of the faces. The new method will be tested on a new database comprising of images of different expressions with various severe poses, and were taken within different time-lapse. The experimental results have shown that the proposed technique offers high discriminability and performs efficiently, with Rank-1 successful rate of ~95% over the different poses.

The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.