The aim of the researcher was to determine the effectiveness of artificial intelligence techniques against cyber security risks particularly in case of Iraq, Researcher has opted for quantitative method of research design along with primary data. The researcher collected the data from employees working in this IT industry. The sample size for this study was 468 and confirmatory factor analysis, discriminant validity, basic analysis of model and lastly, hypothesis assessment was carried out. The P-values of all variables were obtained as significant apart from expert system which had no significant relation with artificial intelligence and cyber security. Geographical area, sample size, less variables and accessibility was the main issue.

Maintenance now plays a central role in the management of complex machines and systems. In modern plants, it is in fact increasingly important to guarantee pre-established levels of productivity and availability also in consideration of the fact that these are often parameters included in the contractual terms. In addition to these aspects, it is also essential to be able to control the overall management costs of the plants since, in an enlarged economic scenario, competition is fierce and globalization leads to a direct confrontation with Emerging Countries that have labour at lower costs. The issues related to effectiveness, measurable in terms of availability and productivity, and efficiency, which instead can be assessed on the basis of specific management costs, are increasingly interconnected and their control opens the scenario to multiple trade-offs that, especially in the case of complex systems in which the operating variables are many, it is not possible to fully govern with traditional methods. Industry 4.0 and Artificial Intelligence are making a truly significant contribution to the evolution of maintenance, which has gone from a purely reactive operation to a preventive, predictive and finally prescriptive operation.

Hurricane Ida was among the most intense and damaging hurricanes in Louisiana's history. The violent storm rose to a Category 1 hurricane on Friday, August 27. It then climbed another two categories in two days, jumping from Category 3 to 4 in only an hour. Thankfully, forecasting models help us predict when, where, and how strongly hurricanes may strike. Accurately predicting the brief windows in which these violent storms surge and strengthen is a lingering blind spot within the hurricane forecasting community.

RICHLAND, Wash.-Hurricane Ida was among the most intense and damaging hurricanes in Louisiana's history. The violent storm rose to a Category 1 hurricane on Friday, August 27. It then climbed another two categories in two days, jumping from Category 3 to 4 in only an hour. Thankfully, forecasting models help us predict when, where, and how strongly hurricanes may strike. Accurately predicting the brief windows in which these violent storms surge and strengthen is a lingering blind spot within the hurricane forecasting community.

Researchers are using artificial intelligence (AI) techniques to calibrate some of NASA's images of the Sun. Launched in 2010, NASA's Solar Dynamics Observatory (SDO) has provided high-definition images of the Sun for over a decade. The Atmospheric Imagery Assembly, or AIA, is one of two imaging instruments on SDO and looks constantly at the Sun, taking images across 10 wavelengths of ultraviolet light every 12 seconds. This creates a wealth of information of the Sun like no other, but like all Sun-staring instruments--AIA degrades over time, and the data needs to be frequently calibrated, NASA said in a statement. To overcome this challenge, scientists decided to look at other options to calibrate the instrument, with an eye towards constant calibration.

With advances in technology and science in the last century, artificial intelligence (AI) has evolved to a whole new level of effectiveness. As a result, AI is being increasingly applied in medicine and healthcare, geospatial technologies, business solutions, and other industries, especially with the advent of cloud computing and high-performance computing capacities. We are only beginning to understand, what AI can do. Today we will look into the history of its development and the most important areas of impact in our daily lives. AI is a term applied to the ability of intelligent systems to perform functions usually, typical of humans.

An advanced artificial intelligence technique known as deep learning can predict major adverse cardiac events more accurately than current standard imaging protocols, according to research presented at the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting. Utilizing data from a registry of more than 20,000 patients, researchers developed a novel deep learning network that has the potential to provide patients with an individualized prediction of their annualized risk for adverse events such as heart attack or death. Deep learning is a subset of artificial intelligence that mimics the workings of the human brain to process data. Deep learning algorithms use multiple layers of "neurons," or non-linear processing units, to learn representations and identify latent features relevant to a specific task, making sense of multiple types of data. It can be used for tasks such as predicting cardiovascular disease and segmenting lungs, among others.

Learn the basics of the most used Artificial Intelligence techniques in videogame development. Welcome to this Intermediate course designed for developers who want to learn the basics of the most Artificial Intelligence techniques. In this course we will learn how to implement some of the most popular AI techniques in Unity, we will learn how they work by creating a simple demo in each section. Unity already comes with some packages that will let you add AI Agents to your games without any effort. We will go beyond with cool AI such as FSM, Behavior Trees, Sensors, Flocking, and a GOAP System.

The novel technique introduced here aims to accomplish the first stage of transferring low-level cognitive skills between two individuals (e.g. from expert to learner) to ease the consecutive higher level declarative learning process for the target "learner" individual in a game environment. Such low-level cognitive skill is associated with the procedural knowledge and established at low-level of mind which can be unveiled and transferred by only a novel technique (rather than by a traditional educational environment ) like a highly interactive computer game domain in which a user exposes his/her unconscious mind behaviors via the game-hero non-deliberately during the game sessions. The cognitive data exposed by the game-hero would be recorded, and then be modelled by the artificial intelligence technique like Bayesian networks for an early stage of cognitive skill transfer and the cognitive stimuli are also generated to be used as game agents to train the learner.

There is no question deep learning and artificial intelligence techniques have transformed remote sensing, computer vision, and spatial analysis. Until now, most efforts would have had to code their efforts, segment or semantically segment data, and then also layer and parallelize their code to run on high performance or cloud-based systems. While this may not be a major issue for those with software engineering backgrounds, it was a restriction for those interested in conducting spatial and remote sensing analysis to have these additional skills. A new tool, called Picterra (https://picterra.ch/) which was discussed by Julien Rebetez in a recent Mapscaping podcast, enables a relatively easy to use interface that allows users to upload remote sensing images whereby users can identify and train an automated detector to find and detect objects of interest. This means that Web Map Service (WMS) and other raster data could be used directly for deep learning-based spatial analysis by those with minimal experience in artificial intelligence techniques.