Artificial Intelligence in Pharmaceutical Field – A Critical Review

2021 ◽  
Vol 18 ◽  
Author(s):  
Maithri Shanbhogue H ◽  
Shailesh Thirumaleshwar ◽  
Pramod Kumar TM ◽  
Hemanth Kumar S
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Development ◽  
Successful Implementation ◽  
Thought Process ◽  
Time Consumption ◽  
Research Technology ◽  
Human Thought ◽  
Almost All ◽  
Analyze Data

: Artificial intelligence is an emerging sector in almost all fields. It is not confined only to a particular category and can be used in various fields like research, technology, and health. AI mainly concentrates on how computers analyze data and mimic the human thought process. As drug development involves high R & D costs and uncertainty in time consumption, artificial intelligence can serve as one of the promising solutions to overcome all these demerits. Due to the availability of enormous data, there are chances of missing out on some crucial details. For solving these issues, algorithms like machine learning, deep learning, and other expert systems are being used. On successful implementation of AI in the pharmaceutical field, the delays in drug development, and failure at the clinical and marketing level can be reduced. This review comprises information regarding the development of AI, its subfields, its overall implementation, and its application in the pharmaceutical sector and provides insights on challenges and limitations concerning AI.

scholarly journals Thematic Dynamics of Internet of Things (IoT): Impact on Digital Personalized Healthcare (PHC)

2021 ◽  
Author(s):  
Bongs Lainjo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Successful Implementation ◽  
Daily Lives ◽  
Personalized Healthcare ◽  
Iot Platform ◽  
Iot Platforms ◽  
Almost All

Abstract Background: Information technology has continued to shape contemporary thematic trends. Advances in communication have impacted almost all themes ranging from education, engineering, healthcare, and many other aspects of our daily lives. Method: This paper attempts to review the different dynamics of the thematic IoT platforms. A select number of themes are extensively analyzed with emphasis on data mining (DM), personalized healthcare (PHC), and thematic trends of a select number of subjectively identified IoT-related publications over three years. In this paper, the number of IoT-related-publications is used as a proxy representing the number of apps. DM remains the trailblazer, serving as a theme with crosscutting qualities that drive artificial intelligence (AI), machine learning (ML), and data transformation. A case study in PHC illustrates the importance, complexity, productivity optimization, and nuances contributing to a successful IoT platform. Among the initial 99 IoT themes, 18 are extensively analyzed using the number of IoT publications to demonstrate a combination of different thematic dynamics, including subtleties that influence escalating IoT publication themes. Results: Based on findings amongst the 99 themes, the annual median IoT-related publications for all the themes over the four years were increasingly 5510, 8930, 11700, and 14800 for 2016, 2017, 2018, and 2019 respectively; indicating an upbeat prognosis of IoT dynamics. Conclusion: The vulnerabilities that come with the successful implementation of IoT systems are highlighted including the successes currently achieved by institutions promoting the benefits of IoT-related systems like the case study. Security continues to be an issue of significant importance.

scholarly journals Thematic Dynamics of Internet of Things (IoT): Impact on Digital Personalized Healthcare (PHC)

2021 ◽  
Vol 6 (3) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Successful Implementation ◽  
Daily Lives ◽  
Personalized Healthcare ◽  
Iot Platform ◽  
Iot Platforms ◽  
Almost All

Information technology has continued to shape contemporary thematic trends. Advances in communication have impacted almost all themes ranging from education, engineering, healthcare, and many other aspects of our daily lives. This paper attempts to review the different dynamics of the thematic IoT platforms. A select number of themes are extensively analyzed with emphasis on data mining (DM), personalized healthcare (PHC), and thematic trends of a select number of subjectively identified IoT-related publications over three years. In this paper, the number of IoT-related-publications is used as a proxy representing the number of apps. DM remains the trailblazer, serving as a theme with crosscutting qualities that drive artificial intelligence (AI), machine learning (ML), and data transformation. A case study in PHC illustrates the importance, complexity, productivity optimization, and nuances contributing to a successful IoT platform. Among the initial 99 IoT themes, 18 are extensively analyzed using the number of IoT publications to demonstrate a combination of different thematic dynamics, including subtleties that influence escalating IoT publication themes. Based on findings amongst the 99 themes, the annual median IoT-related publications for all the themes over the four years were increasingly 5510, 8930, 11700, and 14800 for 2016, 2017, 2018, and 2019 respectively; indicating an upbeat prognosis of IoT dynamics. And finally, the vulnerabilities that come with the successful implementation of IoT systems are highlighted as part of the research. Security continues to be an issue of significant importance.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

scholarly journals AIoT Application for Food Recommendation based on Emotion Analysis

2021 ◽  
pp. 44-53
Author(s):  
Shivangi Ruhela ◽  
Pragati Chaudhary ◽  
Rishija Shrivas ◽  
Deepti Chopra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Exchange ◽  
The Internet ◽  
Human Intervention ◽  
User Interactions ◽  
Emotion Analysis ◽  
Efficiency Performance ◽  
Analyze Data

Artificial Intelligence(AI) and Internet of Things(IoT) are popular domains in Computer Science. AIoT converges AI and IoT, thereby applying AI into IoT. When ‘things’ are programmed and connected to the Internet, IoT comes into place. But when these IoT systems, can analyze data and have decision-making potential without human intervention, AIoT is achieved. AI powers IoT through Decision-Making and Machine Learning, IoT powers AI through data exchange and connectivity. With the AI’s brain and IoT’s body, the systems can have shot-up efficiency, performance and learning from user interactions. Some studies show that, by 2022, AIoT devices such as drones to save rainforests or fully automated cars, would be ruling the computer industries. The paper discusses AIoT at a greater depth, focuses on few case studies of AIoT for better understanding on practical levels, and lastly, proposes an idea for a model which suggests food through emotion analysis.

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 Artificial Intelligence & Machine Learning: a model for a new judicial system?

2020 ◽  
pp. 81-91
Author(s):  
Paula C. Arias
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
System Integration ◽  
Judicial System ◽  
Modern Life ◽  
Technological Advances ◽  
The Creation ◽  
Almost All

Artificial Intelligence and Machine Learning are a result not only of technological advances but also of the exploitation of information or data, which has led to its expansion into almost all aspects of modern life, including law and its practice. Due to the benefits of these technologies, such as efficiency, objectivity, and transparency, the trend is towards the integration of Artificial Intelligence and Machine Learning in the judicial system. Integration that is advocated at all levels and, today, has been achieved mostly under the implementation of tools to assist the exercise of the judiciary. The "success" of this integration has led to the creation of an automated court or an artificially intelligent judge as a futuristic proposal.

Artificial Intelligence, Machine Learning, and Autonomous Technologies in Mining Industry

2022 ◽  
pp. 478-492
Author(s):  
Zeshan Hyder ◽  
Keng Siau ◽  
Fiona Nah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Continuous Production ◽  
Mining Industry ◽  
Economic Benefits ◽  
Current Status ◽  
Future Application ◽  
Successful Implementation ◽  
Social Challenges ◽  
Advanced Technologies

The implementation of artificial intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with autonomous trucks. Artificial intelligence, machine learning, and autonomous technologies provide many economic benefits for the mining industry through cost reduction, efficiency, and improving productivity, reducing exposure of workers to hazardous conditions, continuous production, and improved safety. However, the implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This article discusses the current status of AI, machine learning, and autonomous technologies implementation in the mining industry and highlights potential areas of future application. The article presents the results of interviews with some of the stakeholders in the industry and what their perceptions are about the threats, challenges, benefits, and potential impacts of these advanced technologies. The article also presents their views on the future of these technologies and what are some of the steps needed for successful implementation of these technologies in this sector.

Artificial Intelligence, Machine Learning, and Autonomous Technologies in Mining Industry

2019 ◽  
Vol 30 (2) ◽  
pp. 67-79 ◽  
Author(s):  
Zeshan Hyder ◽  
Keng Siau ◽  
Fiona Nah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Continuous Production ◽  
Mining Industry ◽  
Economic Benefits ◽  
Current Status ◽  
Future Application ◽  
Successful Implementation ◽  
Social Challenges ◽  
Reduction Efficiency

The implementation of artificial intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with autonomous trucks. Artificial intelligence, machine learning, and autonomous technologies provide many economic benefits for the mining industry through cost reduction, efficiency, and improving productivity, reducing exposure of workers to hazardous conditions, continuous production, and improved safety. However, the implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This article discusses the current status of AI, machine learning, and autonomous technologies implementation in the mining industry and highlights potential areas of future application. The article presents the results of interviews with some of the stakeholders in the industry and what their perceptions are about the threats, challenges, benefits, and potential impacts of these advanced technologies. The article also presents their views on the future of these technologies and what are some of the steps needed for successful implementation of these technologies in this sector.

scholarly journals ARTificial intelligence raters: Neural networks for rating pictorial expression

2021 ◽  
Author(s):  
Thomas Gengenbach ◽  
Kerstin Schoch
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mean Squared Error ◽  
Quality Criterion ◽  
Learning Approaches ◽  
Human Expert ◽  
Machine Learning Approach ◽  
Professional Artists ◽  
Almost All

Previous studies on classification of fine art show that features of paintings can be captured and categorized using machine learning approaches. This progress can also benefit art psychology by facilitating data collection on artworks without the need to recruit experts as raters. In this study a machine learning approach is used to predict the ratings of RizbA, a Rating instrument for two-dimensional pictorial works. Based on a pre-trained model, the algorithm was fine-tuned via transfer learning on 886 pictorial works by contemporary professional artists and non-professionals. As quality criterion, artificial intelligence raters (ART) are compared with generic raters (GR) created from the real human expert raters, using error rate and mean squared error (MSE). ART ratings have been found to have the same error range as randomly chosen human ratings. Therefore, they can be seen as equivalent to real human expert raters for almost all items in RizbA. Further training with more data will close the gap to the human raters on all items.

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