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.

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 Evolution and Revolution in Artificial Intelligence

2020 ◽  
pp. 125-129
Author(s):  
Rahul Ner
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Deductive Reasoning ◽  
Long Time ◽  
Learning Machine ◽  
Time Away ◽  
Real World Problems

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques wont to solve various real-world problems. For a primer on machine learning, you'll want to read this seven-part series that I wrote. While human-like deductive reasoning, presumption, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Advanced Deep Learning Model

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Learning Model ◽  
Intelligence Analysis ◽  
Basic Parameters ◽  
Deep Learning Model ◽  
Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

First Monday ◽  
2019 ◽  
Author(s):  
Niel Chah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Historical Context ◽  
Historical Background ◽  
Deep Roots ◽  
Technical Guide ◽  
Learning Machine ◽  
High Level

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

scholarly journals Deep Learning to Detect Skin Cancer using Google Colab

2019 ◽  
Vol 8 (6) ◽  
pp. 2176-2183 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Skin Cancer ◽  
Domain Knowledge ◽  
Error Rates ◽  
Data Set ◽  
Training Models ◽  
Learning Machine ◽  
High Level

Different mathematical models, Artificial Intelligence approach and Past recorded data set is combined to formulate Machine Learning. Machine Learning uses different learning algorithms for different types of data and has been classified into three types. The advantage of this learning is that it uses Artificial Neural Network and based on the error rates, it adjusts the weights to improve itself in further epochs. But, Machine Learning works well only when the features are defined accurately. Deciding which feature to select needs good domain knowledge which makes Machine Learning developer dependable. The lack of domain knowledge affects the performance. This dependency inspired the invention of Deep Learning. Deep Learning can detect features through self-training models and is able to give better results compared to using Artificial Intelligence or Machine Learning. It uses different functions like ReLU, Gradient Descend and Optimizers, which makes it the best thing available so far. To efficiently apply such optimizers, one should have the knowledge of mathematical computations and convolutions running behind the layers. It also uses different pooling layers to get the features. But these Modern Approaches need high level of computation which requires CPU and GPUs. In case, if, such high computational power, if hardware is not available then one can use Google Colaboratory framework. The Deep Learning Approach is proven to improve the skin cancer detection as demonstrated in this paper. The paper also aims to provide the circumstantial knowledge to the reader of various practices mentioned above.

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.

scholarly journals Chatbot and its Practical Applications in the Materialistic World

2019 ◽  
Vol 8 (4) ◽  
pp. 4459-4463
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Gradual Increase ◽  
The Other ◽  
Way Of Life ◽  
Practical Applications ◽  
Other Hand ◽  
The World ◽  
Different Types

These days Chat has become the new way of conversation and changed the way of life and the view that the world used to see before and due to Industrial revolution 4.0 , the gradual increase in machine learning and artificial intelligence fields has gone to higher and many companies are reaching customers to get their products with more ease . This is where chatbots are used. It all started with one question! can machines think? The concept of chatbots came into existence to check whether the machines could fool users and make them think that they are actually talking to humans and not robots. On the Other hand, with the Successes Rate of Chat bots, Different companies Started using machines for having conversations with their customers about everything which made their work simpler and reduced the need of man power. There are many different types of building a chatbot but this paper will mainly concentrate on building a Chatbot using TensorFlow API in python

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.

Quantum-Computing-Inspired Algorithms in Machine Learning

2018 ◽  
pp. 1-26
Author(s):  
Deeksha Kaul ◽  
Harika Raju ◽  
B. K. Tripathy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Quantum Computing ◽  
Computer Science ◽  
Real Time ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.

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