scholarly journals A Survey on Explainability: Why Should We Believe the Accuracy of A Model?

2020 ◽  
Author(s):  
Prarthana Dutta ◽  
Naresh Babu Muppalaneni ◽  
Ripon Patgiri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
New Technologies ◽  
Research Community ◽  
Learning Technologies ◽  
The World ◽  
Current Scenario ◽  
Day By Day ◽  
Modern Technologies

The world has been evolving with new technologies and advances day-by-day. With the advent of various learning technologies in every field, the research community is able to provide solution in every aspect of life with the applications of Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, etc. However, with such high achievements, it is found to lag behind the ability to provide explanation against its prediction. The current situation is such that these modern technologies are able to predict and decide upon various cases more accurately and speedily than a human, but failed to provide an answer when the question of why to trust its prediction is put forward. In order to attain a deeper understanding into this rising trend, we explore a very recent and talked-about novel contribution which provides rich insight on a prediction being made -- ``Explainability.'' The main premise of this survey is to provide an overview for researches explored in the domain and obtain an idea of the current scenario along with the advancements published to-date in this field. This survey is intended to provide a comprehensive background of the broad spectrum of Explainability.

A Survey on Explainability in Artificial Intelligence

2022 ◽  
pp. 55-75
Author(s):  
Prarthana Dutta ◽  
Naresh Babu Muppalaneni ◽  
Ripon Patgiri
Keyword(s):  
Artificial Intelligence ◽  
Broad Spectrum ◽  
New Technologies ◽  
Research Community ◽  
Current Situation ◽  
Learning Technologies ◽  
The World ◽  
Current Scenario ◽  
Modern Technologies

The world has been evolving with new technologies and advances everyday. With learning technologies, the research community can provide solutions in every aspect of life. However, it is found to lag behind the ability to explain its prediction. The current situation is such that these modern technologies can predict and decide upon various cases more accurately and speedily than a human, but has failed to provide an answer when the question of “how” it arrived at such a prediction or “why” one must trust its prediction, is put forward. To attain a deeper understanding of this rising trend, the authors surveyed a very recent and talked-about novel contribution, “explainability,” which would provide rich insight on a prediction being made by a model. The central premise of this chapter is to provide an overview of studies explored in the domain and obtain an idea of the current scenario along with the advancements achieved to date in this field. This survey aims to provide a comprehensive background of the broad spectrum of “explainability.”

Deep Learning in Computational Neuroscience

2020 ◽  
pp. 43-63
Author(s):  
Sanjay Saxena ◽  
Sudip Paul ◽  
Adhesh Garg ◽  
Angana Saikia ◽  
Amitava Datta
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Human Brain ◽  
Convolutional Neural Network ◽  
Computational Neuroscience ◽  
Deep Model ◽  
The World ◽  
Current Scenario

Computational neuroscience is inspired by the mechanism of the human brain. Neural networks have reformed machine learning and artificial intelligence. Deep learning is a type of machine learning that teaches computers to do what comes naturally to individuals: acquire by example. It is inspired by biological brains and became the essential class of models in the field of machine learning. Deep learning involves several layers of computation. In the current scenario, researchers and scientists around the world are focusing on the implementation of different deep models and architectures. This chapter consists the information about major architectures of deep network. That will give the information about convolutional neural network, recurrent neural network, multilayer perceptron, and many more. Further, it discusses CNN (convolutional neural network) and its different pretrained models due to its major requirements in visual imaginary. This chapter also deliberates about the similarity of deep model and architectures with the human brain.

FinTech Adoption in China

2021 ◽  
pp. 166-173
Author(s):  
Gagan Kukreja
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Financial Services ◽  
New Technologies ◽  
Large Population ◽  
Financial Inclusion ◽  
High Penetration ◽  
Digital Payments ◽  
Almost All

Almost all financial services (especially digital payments) in China are affected by new innovations and technologies. New technologies such as blockchain, artificial intelligence, machine learning, deep learning, and data analytics have immensely influenced all most all aspects of financial services such as deposits, transactions, billings, remittances, credits (B2B and P2P), underwriting, insurance, and so on. Fintech companies are enabling larger financial inclusion, changing in lifestyle and expenditure behavior, better and fast financial services, and lots more. This chapter covers the development, opportunities, and challenges of financial sectors because of new technologies in China. This chapter throws the light on opportunities that emerged because of the large population of 1.4 billion people, high penetration, and access to the latest and affordable technology, affordable cost of smartphones, and government policies and regulations. Lastly, this chapter portrays the untapped potentials of Fintech in China.

scholarly journals Does the Human Brain Resort to AI's Deep Learning in Order to Solve Problems

2020 ◽  
pp. 73-86
Author(s):  
Prof. M S S El Namaki ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Problem Solving ◽  
Human Brain ◽  
Learning Technologies ◽  
Human Brains

Problem solving is a daily occurrence in business and, also, in human brains. Businesses resort to a variety of modes in order to find an answer to these problems.Human brains adopt, also, a variety of measures to solve their own brand of problems. Artificial Intelligence technologies seem to have been extending a helping hand to business in the search for problem solving mechanisms. Machine learning and deep learning are currently recognized as prime modes for business insight and problem solving. Does the human brain possess competencies and instruments that could compare to the deep learning technologies adopted by AI?

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.

Machine Learning and Deep Learning Technologies

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Model Building ◽  
Learning Technologies ◽  
Decision Makers ◽  
Human Intervention ◽  
Learning Techniques ◽  
Tools And Techniques

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using machine learning, which is the application of advanced deep learning techniques on big data. This paper aims to analyse some of the different machine learning and deep learning algorithms and methods, aswell as the opportunities provided by the AI applications in various decision making domains.

scholarly journals Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

2021 ◽  
Vol 18 (3) ◽  
pp. 1117
Author(s):  
Tarik Alafif ◽  
Abdul Muneeim Tehame ◽  
Saleh Bajaba ◽  
Ahmed Barnawi ◽  
Saad Zia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Diagnosis And Treatment ◽  
Future Directions ◽  
Health Community ◽  
The World ◽  
The Status ◽  
And Performance

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

scholarly journals Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haseeb Tariq ◽  
Muhammad Kashif Hanif ◽  
Muhammad Umer Sarwar ◽  
Sabeen Bari ◽  
Muhammad Shahzad Sarfraz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Crime Prevention ◽  
Future Trend ◽  
Violent Crimes ◽  
Statistical Tool ◽  
Forecasting Models ◽  
The World ◽  
Day By Day

Crime is a bone of contention that can create a societal disturbance. Crime forecasting using time series is an efficient statistical tool for predicting rates of crime in many countries around the world. Crime data can be useful to determine the efficacy of crime prevention steps and the safety of cities and societies. However, it is a difficult task to predict the crime accurately because the number of crimes is increasing day by day. The objective of this study is to apply time series to predict the crime rate to facilitate practical crime prevention solutions. Machine learning can play an important role to better understand and analyze the future trend of violations. Different time-series forecasting models have been used to predict the crime. These forecasting models are trained to predict future violent crimes. The proposed approach outperforms other forecasting techniques for daily and monthly forecast.

A Systematic Review on Artificial Intelligence/Deep Learning Applications and Challenges to battle against COVID-19 Pandemic

2021 ◽  
pp. 90-99
Author(s):  
Manoj Agrawal ◽  
Shweta Agrawal
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Vaccine Development ◽  
State Of The Art ◽  
Design Strategies ◽  
Look Ahead ◽  
The World ◽  
Fast Spreading ◽  
Day By Day

The eruption of COVID-19 Corona Virus, namely SARS-CoV-2, has created a disastrous condition throughout the world. The cumulative incidence of COVID-19 is increasing rapidly day by day all over the world. Technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big Data and Deep Learning can support healthcare system to fight and look ahead against fast spreading of new disease COVID-19. These technologies can significantly improve treatment consistency and decision making by developing useful algorithms. These technologies can be deployed very effectively to track the disease, to predict growth of the epidemic, design strategies and policy to manage its spread and drug and vaccine development. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this study aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. This study first presents an overview of AI and big data along with their applications in fighting against COVID-19 and then an attempt is made to standardize ongoing AI and deep learning activities in this area. Finally, this study highlighted challenges and issues associated with State-of-the-Art solutions to effectively control the COVID-19 situation.

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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