Spanish Flu (Great Influenza) 1918: The Tale of The Most deadly Pandemic in History

Al-Mistarehi

2022

AJPNMS

Nowadays, artificial intelligence, machine learning, and deep learning are among the most popular and applied topics in many scientific and life fields that serve humanity. This science seeks to impose itself strongly on the various activities and academic circles and information science. Artificial intelligence techniques have proven their worth to be important in many fields, especially in the medical fields, business administration, military applications, communications, and many others. In short, artificial intelligence is from another world that will be of great importance in the future. In this article, the importance of digitisation and artificial intelligence in the healthcare sector will be addressed, what services they provide to this sector, and how they contribute to the service of healthcare workers and patient satisfaction. This article concluded that artificial intelligence and digital technologies are of great importance in the healthcare sector and can never be dispensed with.

Section: Digitalisation and Ai In Healthcarementioning

confidence: 99%

The Significance of Digitalisation and Artificial Intelligence in The Healthcare Sector: A Review

Al-Mistarehi

2022

AJPNMS

“…In addition, the data brought from all contact points with clients and customers can be used, where if it is managed in a very acceptable manner, it will support organisations in creating customised marketing responses, making very cool and new ideas, and designing professional products and services. In other words, it will provide high value to customers and gain a competitive advantage that puts the organisation in the forefront [30][31][32][33][34][35][36][37][38][39].…”

Section: Customer Relationship Managementmentioning

confidence: 99%

Deep Learning Applications and Their Worth: A Short Review

Mutar

Mahmood

et al. 2022

AJAS

Deep learning has become a favoured trend in many applications serving humanity in the past few years. Since deep learning seeks useful investigation and can learn and train huge amounts of unlabelled data, deep learning has been applied in many fields including the medical field. In this article, the most noteworthy applications of deep learning are presented shortly and positively, they are image recognition, automatic speech recognition, natural language processing, drug discovery and toxicology, customer relationship management, recommendation systems and bioinformatics. The report concluded that these applications have a significant and vital role in all areas of life.

“…Fashion images are among the most difficult images because fashions are a pack of colours, additions, different characteristics and depth of classification. In addition, deep learning techniques have been employed to analyse images of COVID-19 patients to a large extent, as these techniques have contributed to diagnosing Asian Journal of Applied Sciences (ISSN: 2321 -0893) Volume 10 -Issue 5, October 2022 the condition of patients and determining the infection rate with the virus [43][44][45][46][47][48]. In recent years, many algorithms have been designed that contribute to analysing a wide range of complex medical images with satisfactory time periods [49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

The Distinction between R-CNN and Fast R-CNN in Image Analysis: A Performance Comparison

Aggarwal

Doshi

et al. 2022

AJAS

Deep learning techniques have become vital in many fields in the modern era because they are excellent at analysing and predicting real big data to act in different situations. Although it is marvellous in many aspects, it is prone to misinterpretation of data, so teams of experienced specialists cannot be dispensed with in following up on the execution stages of data analysis. Convolutional Neural Network is one of the most significant deep learning techniques. It is widely employed in visual image analysis. In this article, R-CNN and Fast R-CNN are summarised and compared and are the best in image analysis. This article concluded that the most suitable performance is for Fast R-CNN in testing and training.