ARTIFICIAL Intelligence (AI) is the wave of the future. This area of computer science emphasising the creation of intelligent machines that work and react like humans is heavily influencing and taking over the way we get on with daily life. Artificial Intelligence is revolutionising industries and improving the way business is conducted. More importantly, it is revolutionising industries and improving the way business is done, being already widely used in applications including automation, data analytics and natural language processing. On a bigger spectrum, from self-driving cars to voice-initiated mobile phones and computer-controlled robots, the presence of AI is seen and felt almost everywhere.

There's still no completely safe and surefire method for locating unexploded ordinance after a war is over, but researchers at Ohio State University have found a way to harness image processing algorithms, powered by machine learning, to study satellite imagery and locate hot spots where UXO are likely to be located. The researchers focused their efforts on a 100-square-kilometre area near Kampong Trabaek, Cambodia, which was the target of carpet-bombing missions carried out by the United States Air Force during the Vietnam War. The team was given access to declassified military data that revealed that 3,205 bombs had been dropped in the area between 1970 and 1973. Determining exactly how many of those bombs didn't explode has gotten harder and harder as, six decades later, nature has slowly reclaimed the country's heaviest hit areas, hiding and obscuring the craters that are counted and used to make accurate estimates. The OSU study used a two-step process to come up with a more accurate estimate of how many bombs were still left in the area.

Effective triage is critical to mitigating the effect of increased volume by accurately determining patient acuity, need for resources, and establishing effective acuity-based patient prioritization. The purpose of this retrospective study was to determine whether historical EHR data can be extracted and synthesized with clinical natural language processing (C-NLP) and the latest ML algorithms (KATE) to produce highly accurate ESI predictive models. An ML model (KATE) for the triage process was developed using 166,175 patient encounters from two participating hospitals. The model was then tested against a gold set that was derived from a random sample of triage encounters at the study sites and correct acuity assignments were recorded by study clinicians using the Emergency Severity Index (ESI) standard as a guide. At the two study sites, KATE predicted accurate ESI acuity assignments 75.9% of the time, compared to nurses (59.8%) and average individual study clinicians (75.3%). KATE accuracy was 26.9% higher than the average nurse accuracy (p-value < 0.0001). On the boundary between ESI 2 and ESI 3 acuity assignments, which relates to the risk of decompensation, KATE was 93.2% higher with 80% accuracy, compared to triage nurses with 41.4% accuracy (p-value < 0.0001). KATE provides a triage acuity assignment substantially more accurate than the triage nurses in this study sample. KATE operates independently of contextual factors, unaffected by the external pressures that can cause under triage and may mitigate the racial and social biases that can negatively affect the accuracy of triage assignment. Future research should focus on the impact of KATE providing feedback to triage nurses in real time, KATEs impact on mortality and morbidity, ED throughput, resource optimization, and nursing outcomes.

This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting. The model combines exponential smoothing (ETS), advanced Long Short-Term Memory (LSTM) and ensembling. ETS extracts dynamically the main components of each individual time series and enables the model to learn their representation. Multi-layer LSTM is equipped with dilated recurrent skip connections and a spatial shortcut path from lower layers to allow the model to better capture long-term seasonal relationships and ensure more efficient training. A common learning procedure for LSTM and ETS, with a penalized pinball loss, leads to simultaneous optimization of data representation and forecasting performance. In addition, ensembling at three levels ensures a powerful regularization. A simulation study performed on the monthly electricity demand time series for 35 European countries confirmed the high performance of the proposed model and its competitiveness with classical models such as ARIMA and ETS as well as state-of-the-art models based on machine learning.

To accommodate diverse Quality-of-Service (QoS) requirements in 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying network conditions. This brings high computing overheads and long processing delays. In this work, we develop a deep learning framework to approximate the optimal resource allocation policy that minimizes the total power consumption of a base station by optimizing bandwidth and transmit power allocation. We find that a fully-connected neural network (NN) cannot fully guarantee the QoS requirements due to the approximation errors and quantization errors of the numbers of subcarriers. To tackle this problem, we propose a cascaded structure of NNs, where the first NN approximates the optimal bandwidth allocation, and the second NN outputs the transmit power required to satisfy the QoS requirement with given bandwidth allocation. Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks. Simulation results validate that the cascaded NNs outperform the fully connected NN in terms of QoS guarantee. In addition, deep transfer learning can reduce the number of training samples required to train the NNs remarkably. I. INTRODUCTION A. Background The 5th Generation (5G) cellular networks are expected to support various emerging applications with diverse Quality-of-Service (QoS) requirements, such as enhanced mobile broadband services, massive This paper has been presented in part at the IEEE Global Communications Conference 2019 [1]. The authors are with the School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia (email: {rui.dong, To guarantee the QoS requirements of different types of services, existing optimization algorithms for radio resource allocation are designed to maximize spectrum efficiency or energy efficiency by optimizing scarce radio resources, such as time-frequency resource blocks and transmit power, subject to QoS constraints [3-9]. There are two major challenges for implementing existing optimization algorithms in practical 5G networks. First, QoS constraints of some services, such as delay-sensitive and URLLC services, may not have closed-form expressions. To execute an optimization algorithm, the system needs to evaluate the QoS achieved by a certain policy via extensive simulations or experiments, and thus suffers from long processing delay [9, 10]. Second, even if the closed-form expressions of QoS constraints can be obtained in some scenarios, the optimization problems are non-convex in general [8,10,11].

This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs). PSFMs applied in this study include $k$-nearest neighbor model, fuzzy neighborhood model, kernel regression model, and general regression neural network. An integral part of PSFMs is a time series representation using patterns of time series sequences. Pattern representation ensures the input and output data unification through filtering a trend and equalizing variance. Two types of ensembles are created: heterogeneous and homogeneous. The former consists of different type base models, while the latter consists of a single-type base model. Five strategies are used for controlling a diversity of members in a homogeneous approach. The diversity is generated using different subsets of training data, different subsets of features, randomly disrupted input and output variables, and randomly disrupted model parameters. An empirical illustration applies the ensemble models as well as individual PSFMs for comparison to the monthly electricity demand forecasting for 35 European countries.

Human faces pop up on a screen, hundreds of them, one after another. Some have their eyes stretched wide, others show lips clenched. Some have eyes squeezed shut, cheeks lifted and mouths agape. For each one, you must answer this simple question: is this the face of someone having an orgasm or experiencing sudden pain? Psychologist Rachael Jack and her colleagues recruited 80 people to take this test as part of a study1 in 2018.

Artificial intelligence is improving the ability of healthcare providers to effectively respond to the coronavirus pandemic – allowing for faster diagnoses and speedy dissemination of trusted information as well as detecting fraudulent insurance claims and accurately evaluating patient data in real time. SoftBank-backed AI company Automation Anywhere is offering free healthcare bots to help the industry manage increased workloads due to the outbreak. "Bots are software that will be configured within the company's system in 24 to 48 hours. They can keep a track of infected people, analyse data, find new trends and perform clerical tasks," Milan Sheth, the company's executive vice president for India, the Middle East and Africa, told The National. Collaborating with one of its technology partners in Macau, Automation Anywhere has developed a global positioning system-enabled dashboard that shows local statistics, sites of infection, hospital wait times, local availability of masks and other useful information which is updated every few minutes.

Writers argue advancements in specialized robots for hospitals, medical practices could hit the market and save lives.

Canadian startup DarwinAI and researchers from the University of Waterloo are open-sourcing COVID-Net, a convolutional neural network that aims to detect COVID-19 in X-ray imagery. In response to the pandemic, a global community of health care and AI researchers have produced a number of AI systems for identifying COVID-19 in CT scans. Companies like Alibaba and AI startups RadLogics and Lunit claim they've created systems capable of recognizing COVID-19 in X-ray or CT scans with more than 90% accuracy. Early work from Chinese medical researchers and a system published in the journal Radiology last week demonstrated similar results. Like other companies making AI to detect COVID-19 from chest X-rays, DarwinAI said it's creating COVID-Net and the accompanying COVIDx data set to give doctors a way to quickly triage and screen potential cases.