Artificial Intelligence and its Impact on the Fourth Industrial Revolution: A Review

Velarde, Gissel

doi:10.5121/ijaia.2019.10604

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…The accuracy of the model can be assessed by several statistical parameters, such as absolute mean absolute error, root mean square error, regression coefficient, and average absolute deviation percentage. These parameters are defined by equations (3)(4)(5)(6).…”

Section: Performance Analysis Of Machine Learning Modelsmentioning

confidence: 99%

“…The 1950s marked the beginning of machine learning's history, which advanced until the 1980s and 1990s when it came to a halt. But in the middle of the 2000s, a massive leap forward was made possible by the enormous amount of big data that was gathered online and the hardware that was being developed to manage it [5]. Recent advances in machine learning have yielded impressive results in the fields of translation, speech recognition, and image recognition.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of mechanical properties of dental composite materials using machine learning algorithms

Suryawanshi,

Behera

2023

Materialwissenschaft Werkst

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The durability of dental materials, when used in the mouth, is determined by their mechanical qualities. Composite resins are frequently used in dental restorations. Flexural tests and Vickers micro‐hardness tests on selected dental composite materials were performed in a universal testing machine (ASTM D790‐10 standard) and Vickers micro‐hardness tester (ASTM E384‐11e1standard). In this study, four different dental composite material samples are employed. The samples are dipped in a chewing tobacco solution for a few days before being removed and put through the tests. Also in this work, four different machine learning models were tested to see how well they could analyze the mechanical characteristics of dental composite materials when submerged in a chewing tobacco solution. For predicting the mechanical properties of dental composite specimens, four distinct machine‐learning models (extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest, and k‐nearest neighbors (KNN) have been selected. AdaBoost machine learning model yields a coefficient of regression value of 0.9903 in predicting the flexural strength, whereas the XGBoost model gives a coefficient of regression value of 0.9890 in predicting the Vickers hardness distinctly better than the other models.

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Section: Performance Analysis Of Machine Learning Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of mechanical properties of dental composite materials using machine learning algorithms

Suryawanshi,

Behera

2023

Materialwissenschaft Werkst

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“…Exacerbation of Wealth Inequality AI's impact on wealth distribution is a growing concern. Rodrigues (2020) and Velarde (2019) highlight how the benefits of AI implementation can be unevenly distributed, potentially exacerbating wealth inequality. If certain sectors benefit more from AI while others suffer job losses, the gap between high-skilled and low-skilled workers could widen.…”

Section: Erosion Of Privacy and Autonomymentioning

confidence: 99%

The Impact of Artificial Intelligence on Human Workers

Farhan

2023

Joce-Ip

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The impact of artificial intelligence on human workers is a complex and ongoing topic of research and debate, and the extent to which AI will replace human jobs is still uncertain. Therefore, this research aims to answer following research questions. How does artificial intelligence affect human workers? What jobs will be replaced by artificial intelligence? What jobs can endure without being replaced by artificial intelligence?. This research was conducted in qualitative approach based on secondary data from previous studies. Data analysis techniques through four stages, namely data collection, data reduction, data presentation and drawing conclusions. After reviewing the remaining articles for eligibility in terms of the inclusion criteria, 45 papers were included in the data analysis. This study conclude that artificial intelligence has the ability to augment human talents, generate innovation, offer new career possibilities, and improve decision-making. Adopting AI technologies not only improves employee skills and productivity, but also enables organizations to function more efficiently. This study found that the negative effects of AI, such as job displacement, lower labour demand, employee deskilling, erosion of privacy and autonomy, exacerbation of wealth inequality, and shifting employee requirements. AI has the potential to disrupt traditional work functions in routine and repetitive tasks, manual labour, high-paying occupations, decision-making roles, HR operations, and certain service industry jobs. AI struggles to imitate creative endeavours, emotional intelligence, physical dexterity, human interaction, and complicated decision-making. These professions will continue to thrive as technology advances, reminding us that the essence of human experience is irreplaceable.

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“…The immense development of AI, machine learning and deep learning is showing us a new future where these three technologies will be used in almost every sector. [8] A greater part of medical science has already adopted machine learning and artificial intelligence for analyzing the patient's data, detecting and predicting disease and that too with a great precision and reliability. Adoption of machine learning is saving the valuable time of the technicians as well as other medical workers.…”

Section: Related Workmentioning

confidence: 99%

Insight about Detection, Prediction and Weather Impact of Coronavirus (Covid-19) using Neural Network

Haque¹,

Pranto²,

Noman³

et al. 2020

IJAIA

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The world is facing a tough situation due to the catastrophic pandemic caused by novel coronavirus (COVID-19). The number people affected by this virus are increasing exponentially day by day and the number has already crossed 6.4 million. As no vaccine has been discovered yet, the early detection of patients and isolation is the only and most effective way to reduce the spread of the virus. Detecting infected persons from chest X-Ray by using Deep Neural Networks, can be applied as a time and laborsaving solution. In this study, we tried to detect Covid-19 by classification of Covid-19, pneumonia and normal chest X-Rays. We used five different Convolutional Pre-Trained Neural Network models (VGG16, VGG19, Xception, InceptionV3 and Resnet50) and compared their performance. VGG16 and VGG19 shows precise performance in classification. Both models can classify between three kinds of X-Rays with an accuracy over 92%. Another part of our study was to find the impact of weather factors (temperature, humidity, sun hour and wind speed) on this pandemic using Decision Tree Regressor. We found that temperature, humidity and sun-hour jointly hold 85.88% impact on escalation of Covid-19 and 91.89% impact on death due to Covid-19 where humidity has 8.09% impact on death. We also tried to predict the death of an individual based on age, gender, country, and location due to COVID-19 using the LogisticRegression, which can predict death of an individual with a model accuracy of 94.40%.

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Artificial Intelligence and its Impact on the Fourth Industrial Revolution: A Review

Cited by 10 publications

References 19 publications

Prediction of mechanical properties of dental composite materials using machine learning algorithms

Prediction of mechanical properties of dental composite materials using machine learning algorithms

The Impact of Artificial Intelligence on Human Workers

Insight about Detection, Prediction and Weather Impact of Coronavirus (Covid-19) using Neural Network

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