scholarly journals The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

2021 ◽  
Vol 8 ◽  
Author(s):  
Raffaele Nuzzi ◽  
Giacomo Boscia ◽  
Paola Marolo ◽  
Federico Ricardi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Computer Science ◽  
New Technology ◽  
Eye Diseases ◽  
Human Intelligence ◽  
Basic Principles ◽  
The Impact ◽  
And Training ◽  
Clinical Overview

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology.

scholarly journals Deep Learning does not Replace Bayesian Modeling : Comparing research use via citation counting

2021 ◽  
Author(s):  
Breck Baldwin
Keyword(s):  
Artificial Intelligence ◽  
Popular Culture ◽  
Deep Learning ◽  
Computer Science ◽  
Bayesian Modeling ◽  
Citation Count ◽  
Funding Agency ◽  
Research Use ◽  
Learning Approaches ◽  
The Impact

One could be excused for assuming that deep learning had or will soon usurp all credible work in reasoning, artificial intelligence and statistics, but like most ‘meme’ class broad generalizations the concept does not hold up to scrutiny. Memes don’t generally matter since the experts will always know better but in the case of Bayesian software like Stan and PyMC3 even its developers and advocates bemoan the apparent dominance of deep learning as manifested in popular culture, breathtaking performance and most problematically from funding agency peer review that impacts our ability to further advance the field. The facts however do not support the assumed dominance of deep learning in science upon closer examination. This letter simply makes the argument by the crudest of possible metrics, citation count, that once Computer Science is subtracted, Bayesian software accounts for nearly a third of research citations. Stan and PyMC3 dominate some fields, PyTorch, Keras and TensorFlow dominate others with lots of variation in between. Bayesian and deep learning approaches are related but very different technologies in goals, implementation and applicability with little actual overlap so this is not a surprise. While deep learning is backed by industry behemoths (Google, Facebook) the Bayesian efforts are not and it would behoove funders to recognize the impact of Bayesian software given its centrality to science.

Artificial Intelligence in Medicine

2021 ◽  
pp. 49-68
Author(s):  
Gia Merlo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
New Technologies ◽  
New Technology ◽  
Technological System ◽  
Medical Professionals ◽  
Human Intelligence ◽  
Connected Health ◽  
The Future

Disruptive forces are challenging the future of medicine. One of the key forces bringing change is the development of artificial intelligence (AI). AI is a technological system designed to perform tasks that are commonly associated with human intelligence and ability. Machine learning is a subset of AI, and deep learning is an aspect of machine learning. AI can be categorized as either applied or generalized. Machine learning is key to applied AI; it is dynamic and can become more accurate through processing different results. Other new technologies include blockchain, which allows for the storage of all of patients’ records to create a connected health ecosystem. Medical professionals ought to be willing to accept new technology, while also developing the skills that technology will not be able to replicate.

scholarly journals Artificial Intelligence-Based Method to Improve Tourism with the Help of Android Studio

2021 ◽  
Vol 10 (6) ◽  
pp. 50-53
Author(s):  
Anusha . ◽  
Poorva Agrawal ◽  
Gagandeep Kaur
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Computer Science ◽  
Mobile Application ◽  
New Technology ◽  
Future Research ◽  
Human Intelligence ◽  
Research Directions ◽  
Research Areas ◽  
Future Research Directions

Artificial intelligence is a new technology in the field of computer science and information technology. Artificial intelligence techniques are very useful and also applied in many businesses. There are many research areas and opportunities in the field of Artificial intelligence. Artificial intelligence refers to the simulation of human intelligence in machines. Artificial intelligence allows app developers to achieve better mobile application experiences and also helps to improve personalized selections for users. This paper explores the different areas of the artificial intelligence-based system in tourism. This paper presents important future research directions.

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

scholarly journals ARTIFICIAL INTELLIGENCE: IS IT A THREAT OR AN OPPORTUNITY BASED ON ITS LEGAL PERSONALITY AND CRIMINAL LIABILITY?

2021 ◽  
Vol 6 (20) ◽  
pp. 01-09
Author(s):  
Mark Louis ◽  
Angelina Anne Fernandez ◽  
Nazura Abdul Manap ◽  
Shamini Kandasamy ◽  
Sin Yee Lee
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Criminal Liability ◽  
Legal Person ◽  
Human Intelligence ◽  
Legal Research ◽  
Legal Personality ◽  
The World ◽  
Technological World ◽  
The Impact

Information technology is taking the world by storm. The technological world is changing rapidly and drastically. Human activities are taken over by robots and computers. The usage of computers and robots has increased productivity in various sectors. The emergence of artificial intelligence has stirred up many debates on both its importance and limitations. Artificial intelligence is directed to the usage of Information Technology in conducting tasks that normally require human intelligence. The expectation of artificial intelligence is high, nevertheless, artificial intelligence has its shortcomings namely the impact of artificial intelligence on the concept of a legal personality. The problem with artificial Intelligence is the debate on whether does it have a legal personality? And another problem is under what situation does the law treat artificial intelligence as an entity with its own rights and obligations. The objective of this article is to examine the various definitions of legal personality and whether artificial intelligence can become a legal person. The article will also examine the criminal liability of artificial intelligence when a crime has been committed. The methodology adopted is qualitative namely Doctrinal Legal Research by analyzing the relevant legal views from various journals on artificial intelligence. The study found out that artificial intelligence has its limitations in defining its legal personality and also in examining the criminal liability when a crime has been committed by robots.

scholarly journals Artificial Intelligence-Enabled Traffic Monitoring System

2020 ◽  
Vol 12 (21) ◽  
pp. 9177
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Stage Of Development ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

scholarly journals Use of artificial intelligence in imaging in rheumatology – current status and future perspectives

RMD Open ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. e001063 ◽  
Author(s):  
Berend Stoel
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Basic Research ◽  
Current Status ◽  
Future Perspectives ◽  
Clinical Imaging ◽  
The World ◽  
Human Interpretation ◽  
The Impact ◽  
Insight Into

After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock wave that probably exceeds the impact of the first AI victory of defeating the world chess champion in 1997, some reflection may be appropriate on the consequences for clinical imaging in rheumatology. In this narrative review, a short explanation is given about the various AI techniques, including ‘deep learning’, and how these have been applied to rheumatological imaging, focussing on rheumatoid arthritis and systemic sclerosis as examples. By discussing the principle limitations of AI and deep learning, this review aims to give insight into possible future perspectives of AI applications in rheumatology.

scholarly journals Service Robots Usage in Marketing Hospitality in China

2021 ◽  
Vol 10 (2) ◽  
pp. 13
Author(s):  
Ayse Begum Ersoy ◽  
Ziqi Cui
Keyword(s):  
Artificial Intelligence ◽  
Hospitality Industry ◽  
Essential Role ◽  
Service Robots ◽  
Human Intelligence ◽  
The Public ◽  
Advanced Technologies ◽  
The World ◽  
Interpersonal Distancing ◽  
The Impact

Since the coronavirus disease 2019(COVID-19) has had brought severe impact on all aspects of the world. A series of interpersonal distancing methods such as ensuring effective and safe social distancing among people, wearing masks, and traffic lockdown measures are also continuing to take effect to curb the continuing outbreak of the COVID-19 (“Advice for the public on COVID-19”, 2020). In response to the globally spread of COVID-19, many advanced technologies in the field of Artificial Intelligence (AI) were applied rapidly and played an essential role in the operation for several months. There are many different leading technology categories in the field of artificial intelligence and many different sub-categories within each main technology categories. Moreover, since the AGI technology does not yet reach the basic human intelligence level, this study will focus on the impact of service robots, which are already widely used in the NAI application category, on hospitality marketing in the current situation in China. In this paper the aim is to assess the effectiveness of use of service robots in Marketing Hospitality Industry during the pandemic through a quantitative study.

scholarly journals Using CycleGAN to Achieve the Sketch Recognition Process of Sketch-Based Modeling

2021 ◽  
pp. 26-34
Author(s):  
Yuqian Li ◽  
Weiguo Xu
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Learning Technology ◽  
Modeling Technology ◽  
Image Translation ◽  
Mapping Process ◽  
And Training ◽  
Difficult Part ◽  
2D Sketches

AbstractArchitects usually design ideation and conception by hand-sketching. Sketching is a direct expression of the architect’s creativity. But 2D sketches are often vague, intentional and even ambiguous. In the research of sketch-based modeling, it is the most difficult part to make the computer to recognize the sketches. Because of the development of artificial intelligence, especially deep learning technology, Convolutional Neural Networks (CNNs) have shown obvious advantages in the field of extracting features and matching, and Generative Adversarial Neural Networks (GANs) have made great breakthroughs in the field of architectural generation which make the image-to-image translation become more and more popular. As the building images are gradually developed from the original sketches, in this research, we try to develop a system from the sketches to the images of buildings using CycleGAN algorithm. The experiment demonstrates that this method could achieve the mapping process from the sketches to images, and the results show that the sketches’ features could be recognised in the process. By the learning and training process of the sketches’ reconstruction, the features of the images are also mapped to the sketches, which strengthen the architectural relationship in the sketch, so that the original sketch can gradually approach the building images, and then it is possible to achieve the sketch-based modeling technology.

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