Aligning vocational training to the electromobile transformation by establishing the “Training Factory Stator Production” – A methodical deficit analysis with derivation of measures

Hausmann, Ludwig; Wirth, Felix; Flammer, Max Oliveira; Hofmann, Jan; Fleischer, Jürgen

doi:10.1016/j.promfg.2020.04.051

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Medical data design in healthcare is one of the important and complicated tasks because as a result, it must show accuracy and efficiency in its performance. Computer-aided diagnosis (CAD) becomes a very useful tool that attempts to solve real-world health problems in the diagnosis and treatment of disease [1]. In present years, heuristic systems have found their significant hold in every field comprising health care.…”

Section: Introductionmentioning

confidence: 99%

Heart disease classification using various heuristic algorithms

Ullah

Khan²,

Alam³

et al. 2022

IJAAS

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<span>In the health sector, the computer-aided diagnosis (CAD) system is a rapidly growing technology because medical diagnostic systems make a huge change as compared to the traditional system. Now a day huge availability of medical data and it needs a proper system to extract them into useful knowledge. Heart disease accounts to be the leading cause of death worldwide. Heuristic algorithms have been exposed to be operative in supporting making decisions and classification from the large quantity of data produced by the healthcare sector. Classification is a prevailing heuristic approach which is commonly used for classification purpose some heuristic algorithm predicts accurate result according to the marks whereas some others exhibit limited accuracy. This paper is used to categorize the attendance of heart disease with a compact number of aspects. Original, 13 attributes are involved in classifying heart disease. A reasonable analysis of these techniques was done to conclude how the cooperative techniques can be applied for improving prediction accuracy in heart disease. Four main classifiers used to construct heart disease prediction based on the experimental results demonstrate that support vector machine, artificial bee colony (ABC), Bat algorithm, and memory-based learner (MBL) provide efficient results. The accuracy differs between 13 features and 8 features in the training dataset is 1.9% and in the validation, dataset is 0.92% of vector machine which is the most accurate heuristic algorithm. </span>

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Section: Introductionmentioning

confidence: 99%

Heart disease classification using various heuristic algorithms

Ullah

Khan²,

Alam³

et al. 2022

IJAAS

View full text Add to dashboard Cite

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Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Bongomin

Yemane

Kembabazi

et al. 2020

Journal of Engineering

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Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

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Aligning vocational training to the electromobile transformation by establishing the “Training Factory Stator Production” – A methodical deficit analysis with derivation of measures

Cited by 2 publications

References 3 publications

Heart disease classification using various heuristic algorithms

Heart disease classification using various heuristic algorithms

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

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