Current Trends in Deep Learning Frameworks With Opportunities and Future Prospectus

2020 ◽  
pp. 63-77
Author(s):  
Chitra A. Dhawale ◽  
Krtika Dhawale
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
End Users ◽  
The Other ◽  
Qualitative Review ◽  
Current Trends ◽  
Real Time Applications ◽  
Learning Frameworks

Artificial Intelligence (AI) is going through its golden era by playing an important role in various real-time applications. Most AI applications are using Machine learning and it represents the most promising path to strong AI. On the other hand, Deep Learning (DL), which is itself a kind of Machine Learning (ML), is becoming more and more popular and successful at different use cases, and is at the peak of developments. Hence, DL is becoming a leader in this domain. To foster the growth of the DL community to a greater extent, many open source frameworks are available which implemented DL algorithms. Each framework is based on an algorithm with specific applications. This chapter provides a brief qualitative review of the most popular and comprehensive DL frameworks, and informs end users of trends in DL Frameworks. This helps them make an informed decision to choose the best DL framework that suits their needs, resources, and applications so they choose a proper career.

scholarly journals Assessment on Recurrent Applications of Machine Learning and its Behaviours

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1174-1180
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Science Fiction ◽  
Real Time ◽  
Industrial Revolution ◽  
The Other ◽  
Intelligent Decision Making ◽  
Learning Machine

Artificial intelligence (AI) and machine learning are at the moment measured to be the unique widespread inventions. Artificial Intelligence rummage-sale to stand an unbelievable conception from science fiction, but nowadays it’s flattering a day-to-day authenticity. On the other hand, a neural network emulates the procedure of actual neurons in the brain that are parquet the track near innovations in machine learning, baptised deep learning. Machine learning can cosiness us living cheerier, improved, and additional dynamic conscious, if the power of the Deep learning concepts and its proper utilization as an industrial revolution that harness mental and cognitive ability. Currently lots of research papers deal with the Artificial Intelligence of deep learning in various real time applications that includes intelligent gaming, smart driving, and environmental protection and so on. Irrespective of all applications an intelligent decision making must be done timely to improve the accuracy in one end and simultaneously on the other end to consume energy and system efficiency. This paper presents the various applications using deep learning efficiently by better decision making and also how to visualize the problems in order to take a conclusion for better solution. The analysis of such real time problems is done by logically in the form of using artificial neurons through supervised and unsupervised data.

Advanced-Level Security in Network and Real-Time Applications Using Machine Learning Approaches

2019 ◽  
pp. 84-104 ◽  
Author(s):  
Mamata Rath ◽  
Sushruta Mishra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Learning Approaches ◽  
Advanced Level ◽  
Real Time Applications

Machine learning is a field that is developed out of artificial intelligence (AI). Applying AI, we needed to manufacture better and keen machines. Be that as it may, aside from a couple of simple errands, for example, finding the briefest way between two points, it isn't to program more mind boggling and continually developing difficulties. There was an acknowledgment that the best way to have the capacity to accomplish this undertaking was to give machines a chance to gain from itself. This sounds like a youngster learning from itself. So, machine learning was produced as another capacity for computers. Also, machine learning is available in such huge numbers of sections of technology that we don't understand it while utilizing it. This chapter explores advanced-level security in network and real-time applications using machine learning.

scholarly journals Deep learning, past present and future: An odyssey

2021 ◽  
Author(s):  
Anwaar Ulhaq
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Review Paper ◽  
Complex Problems ◽  
Current Trends

Machine learning has grown in popularity and effectiveness over the last decade. It has become possible to solve complex problems, especially in artificial intelligence, due to the effectiveness of deep neural networks. While numerous books and countless papers have been written on deep learning, new researchers want to understand the field's history, current trends and envision future possibilities. This review paper will summarise the recorded work that resulted in such success and address patterns and prospects.

A Review on Cyber Security Mechanisms Using Machine and Deep Learning Algorithms

2020 ◽  
pp. 23-41 ◽  
Author(s):  
Thiyagarajan P.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Information Security ◽  
Cyber Security ◽  
Learning Algorithms ◽  
The Other ◽  
The Internet ◽  
Learning Techniques

Digitalization is the buzz word today by which every walk of our life has been computerized, and it has made our life more sophisticated. On one side, we are enjoying the privilege of digitalization. On the other side, security of our information in the internet is the most concerning element. A variety of security mechanisms, namely cryptography, algorithms which provide access to protected information, and authentication including biometric and steganography, provide security to our information in the Internet. In spite of the above mechanisms, recently artificial intelligence (AI) also contributes towards strengthening information security by providing machine learning and deep learning-based security mechanisms. The artificial intelligence (AI) contribution to cyber security is important as it serves as a provoked reaction and a response to hackers' malicious actions. The purpose of this chapter is to survey recent papers which are contributing to information security by using machine learning and deep learning techniques.

A Review on Deep Learning Applications

2020 ◽  
pp. 21-31
Author(s):  
Chitra A. Dhawale ◽  
Kritika Dhawale ◽  
Rajesh Dubey
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Civil Society ◽  
Use Cases ◽  
The Other ◽  
Strong Ai ◽  
Other Hand

Artificial intelligence (AI) is going through its golden era. Most AI applications are indeed using machine learning, and it currently represents the most promising path to strong AI. On the other hand, deep learning, which is itself a kind of machine learning, is becoming more and more popular and successful at different use cases and is at the peak of developments by enabling more accurate forecasting and better planning for civil society, policymakers, and businesses. As a result, deep learning is becoming a leader in this domain. This chapter presents a brief review of ground-breaking advances in deep learning applications.

scholarly journals Managing Social Distancing using Smart Detector

2021 ◽  
Vol 1 (2) ◽  
pp. 1-10
Author(s):  
Saurabh Yadav ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Social Distance ◽  
Learning Models ◽  
Social Distancing ◽  
Detection Model

This paper presents a methodology for social distance detection using deep learning models and algorithms such as YOLO and CNN. Deep learning is one of those technologies which have greatly enhanced the overall experience of the technology that humans use. Deep learning has brought a lot of changes from self-driven cars made by Tesla to the smallest object detection model. Deep learning, artificial intelligence, and machine learning provide a way to be able to put things to use. The purpose of this paper is to be able to implement real-time object detection to detect social distancing.

scholarly journals Use of Artificial Intelligence & Machine Learning with Deep Learning for Glaucoma Detection in Human Eyes & its Real Time Hardware Implementation

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Tian Jipeng ◽  
Suma P. ◽  
T. C. Manjunath
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Hardware Implementation ◽  
Project Work ◽  
Post Graduate ◽  
Glaucoma Detection ◽  
Second Year ◽  
Human Eyes

In this paper, a brief introduction to AI, ML and the Eye w.r.t. Deep Learning for Glaucoma Detection and Hardware Implementation is being presented.  The result is the outcome of the Post-Graduate project work of the student that is going to be carried out in the second year of the course & this work is just the synopsis that is being framed for the carrying out of the detection of glaucoma disease.

Challenges of Implementing Artificial Intelligence in Interventional Radiology

2021 ◽  
Vol 38 (05) ◽  
pp. 554-559
Author(s):  
Sina Mazaheri ◽  
Mohammed F. Loya ◽  
Janice Newsome ◽  
Mathew Lungren ◽  
Judy Wawira Gichoya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Interventional Radiology ◽  
Deep Learning ◽  
Clinical Practice ◽  
Intellectual Property ◽  
Use Cases ◽  
The Other ◽  
Limited Resources ◽  
Learning Patterns

AbstractArtificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and “learning” patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.

scholarly journals Artificial Intelligence in POCUS: The Vanguard of Technology in COVID-19 Pandemic

2020 ◽  
Vol 4 (02) ◽  
pp. 116-120
Author(s):  
Srinath Damodaran ◽  
Arjun Alva ◽  
Srinath Kumar ◽  
Muralidhar Kanchi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Point Of Care ◽  
Learning Technologies ◽  
Point Of Care Ultrasound ◽  
Intelligent Software ◽  
Physiologic Responses ◽  
Insight Into

AbstractThe creation of intelligent software or system, machine learning, and deep learning technologies are the integral components of artificial intelligence. Point-of-care ultrasound involves the bedside use of ultrasound to answer specific diagnostic questions and to assess real-time physiologic responses to treatment. This article provides insight into the pearls and pitfalls of artificial intelligence in point-of-care ultrasound for the coronavirus disease 2019 pandemic.

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