IMPACT OF MACHINE learning ON Management, healthcare AND AGRICULTURE

Pallathadka, Harikumar; Mustafa, Malik; Sanchez, Domenic T.; Sajja, Guna Sekhar; Gour, Sanjeev; Naved, Mohd

doi:10.1016/j.matpr.2021.07.042

Cited by 98 publications

(43 citation statements)

References 16 publications

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“…Furthermore, behavioral intention was significantly impacted by Personal Innovativeness (PInn) on actual acceptance Use behavior [34][35][36][37][38][39][40][41]. Hence, educational bodies in the Middle East should consider investing massively in virtual reality learning and in other innovations of information technology to increase their support towards efficient service delivery while also increasing the services of virtual reality learning [42][43][44][45][46][47][48].…”

Section: Research Studiesmentioning

confidence: 99%

Untitled

2022

GSC Adv. Eng. Technol.

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The goal of this study is to determine the importance of IS success and its relationships with the intent to use, utilization, user happiness, and net benefits of VR-learning at six Jordanian universities. This study focuses on the use of cultural and social elements that have been identified as important in improving students' learning processes, with the goal of persuading university administrators to pay attention to factors that influence the usage of VR-learning. At Jordan, however, there has been little success with VR-learning adoption in higher education institutions. In addition, only a few research have looked at the aspects that influence the success of VR-learning deployment.

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Section: Research Studiesmentioning

confidence: 99%

Untitled

2022

GSC Adv. Eng. Technol.

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“…In the field of healthcare, scientists have published a few scientific studies that use artificial intelligence based on machine learning algorithms and successfully linked them to real life [ 8 , 12 , 13 ]. Over the past few years, scientific publication in the field of medical artificial intelligence has increased nearly tenfold, as individual predictions of short-term diseases provide appropriate outcomes for multiple conditions including diabetes, cancer, heart, and mental illness [ 4 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…A large number of researchers around the world are using data from heart disease patients to anticipate or predict this disease and, as a result, prevent the untimely death of many people. Machine learning algorithms are an effective tool for classification and prediction tasks [ 8 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…In the coming decades, due to the acceleration of technological progress, much manual work will be automated [ 7 , 16 ]. Dentistry is expected to witness a remarkable shift in the automation of simple routine tasks and the support of dental medical staff; this shift will contribute to improving quality [ 8 ]. We concur that technology will not replace dentists but will rather complement and support dentists in providing the greatest possible patient experiences and treatments.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Dental Implants Using Machine Learning Algorithms

Alharbi

Almutiq

2022

Journal of Healthcare Engineering

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It has been claimed that artificial intelligence (AI) has transformative potential for the healthcare sector by enabling increased productivity and creative methods of delivering healthcare services. Recently, there has been a major shift to artificial intelligence by businesses, government, and private sectors in general and the health sector in particular. Many studies have proven that artificial intelligence is contributing greatly to the health sector by discovering diseases and determining the best treatments for patients. Dentistry requires new innovative methods that serve both the patient and the service provider in obtaining the best and appropriate medical services. Artificial intelligence has the ability to develop the field of dentistry through early diagnosis and prediction of dental implant cases. This research develops a set of four machine learning algorithms to predict when a patient might need dental implants. These models are the Bayesian network, random forest, AdaBoost algorithm, and improved AdaBoost algorithm. This work shows that the developed algorithms can predict when a patient needs dental implants. Also, we believe that this proposal will advise managers and decision-makers in targeting patients with particular diagnoses. Analysis of the obtained results indicates good performance of the developed machine learning. As a result of this research, we note that the proposed improved AdaBoost algorithm increases the level of prediction accuracy and gives significantly higher performance than the other studied methods with the accuracy for the improved AdaBoost algorithm reaching 91.7%.

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“…However, the model training is time-consuming and the process is tedious. These methods have achieved relatively good recognition accuracy and efficiency and provide necessary theoretical and technical support for orchard yield measurement and machine picking ( Pallathadka et al, 2021 ). However, most of these methods are studied for some specific scenarios and overly rely on the fruit color, shape, texture, and other features of the target fruit, which are greatly affected by light, resulting in poor robustness of the algorithms.…”

Section: Introductionmentioning

confidence: 99%

YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit

et al. 2022

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Accurate detection and segmentation of the object fruit is the key part of orchard production measurement and automated picking. Affected by light, weather, and operating angle, it brings new challenges to the efficient and accurate detection and segmentation of the green object fruit under complex orchard backgrounds. For the green fruit segmentation, an efficient YOLOF-snake segmentation model is proposed. First, the ResNet101 structure is adopted as the backbone network to achieve feature extraction of the green object fruit. Then, the C5 feature maps are expanded with receptive fields and the decoder is used for classification and regression. Besides, the center point in the regression box is employed to get a diamond-shaped structure and fed into an additional Deep-snake network, which is adjusted to the contours of the target fruit to achieve fast and accurate segmentation of green fruit. The experimental results show that YOLOF-snake is sensitive to the green fruit, and the segmentation accuracy and efficiency are significantly improved. The proposed model can effectively extend the application of agricultural equipment and provide theoretical references for other fruits and vegetable segmentation.

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IMPACT OF MACHINE learning ON Management, healthcare AND AGRICULTURE

Cited by 98 publications

References 16 publications

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Prediction of Dental Implants Using Machine Learning Algorithms

YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit

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