Aritifical intelligence

Fly ash, as a supplemental pozzolanic material, reduces concrete’s adverse environmental footprint by decreasing the emission of carbon dioxide (CO2) during the cement manufacturing process. Fly ash, which is a waste material, can enhance both the mechanical characteristics and durability of concrete, and has the capability to play an important role in sustainable design. Considering the widespread interest in applying Fly ash, and despite research studies, the level of replacement is still unclear. In this paper, a novel method using artificial neural networks (ANN) is presented to predict concrete’s mechanical characteristics by adding Fly ash. In this regard, a host of available experimental data, such as the properties of Fly ash, along with concrete additives, was fed into an ANN model. Concrete samples’ tensile and compressive strengths, in addition to their modulus of elasticity, were defined as outputs. It was observed that the predicted outcomes agreed well with the experim...

Rapid growth is seen in health care services over the past few years. Heart disease causes millions of death worldwide. Many wireless communication technologies have been developed for heart disease prediction. Data mining algorithms are very useful in the detection and diagnosis of heart disease. In this paper, survey is carried out on several single and hybrid data mining algorithms in order to identify the algorithm that best suits the heart disease prediction with high level of accuracy.

This dissertation is looking at effective leadership strategies for implementation of artificial intelligence and automation by looking at employee engagement in organizations with automation implemented and contrasting it with findings from interviews with leaders in organizations who have extensive automation as part of their services or already implemented projects with artificial intelligence

I dette kapitel giver vi et bud på, hvordan implementering af kunstig intelligens i våbensystemer både kan og formentlig også vil transformere situationsforståelsen på den militære kampplads, herunder hvilke muligheder, men også hvilke nye problemer, denne transformation potentielt vil medføre. Selvom AI og automatisk måludpegning allerede er under udvikling og anvendelse i en række våbensystemer – såvel i land-, vand- og luftbaserede systemer – vil vi for begrænsningens skyld fokusere på særligt én type kampplads og ét militært domæne; nemlig luftrummet. Luftrummet er det mest enkle domæne at operere i, da det er fri for forhindringer, når man ser bort fra andre fly eller missiler. Her er den teknologiske udvikling med AI-baseret luftforsvar og ubemandede fly (UAVs) også længst fremme. Luftrummet er desuden særligt interessant set med danske øjne, eftersom Danmark fra 2023 modtager de første F-35 kampfly, som er verdens nyeste og foreløbigt mest avancerede kampfly. Flyene, der er produceret af amerikanske Lockheed Martin, har indbygget en række avancerede sensorer og anvender AI og maskinlæring til sensorintegration, selvforsvar, måludvælgelse og databehandling (Masuhr 2019). F-35 er ligeledes omdrejningspunkt for et antal projekter – hvor det mest omtalte bærer det spektakulære navn “Skyborg” – der har til formål at udvikle ubemandede luftfartøjer, som kan operere integreret med og som støtte til F-35 kampflyet.