Artificial Intelligence and Machine Learning based Legal Application: The State-of-the-Art and Future Research Trends

2019 ◽  
Author(s):  
Riya Sil ◽  
Abhishek Roy ◽  
Bharat Bhushan ◽  
A.K. Mazumdar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
Research Trends ◽  
The State ◽  
Future Research

scholarly journals Artificial intelligence and machine learning in design of mechanical materials

2021 ◽  
Author(s):  
Kai Guo ◽  
Zhenze Yang ◽  
Chi-Hua Yu ◽  
Markus J. Buehler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
The State

This review revisits the state of the art of research efforts on the design of mechanical materials using machine learning.

scholarly journals Artificial Intelligence in Dermatopathology: New Insights and Perspectives

2021 ◽  
Vol 8 (3) ◽  
pp. 418-425
Author(s):  
Gerardo Cazzato ◽  
Anna Colagrande ◽  
Antonietta Cimmino ◽  
Francesca Arezzo ◽  
Vera Loizzi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Differential Diagnosis ◽  
State Of The Art ◽  
The State ◽  
Embryonic Stage ◽  
Future Perspectives ◽  
Review Of The Literature ◽  
Melanocytic Lesions ◽  
Modern Era

In recent years, an increasing enthusiasm has been observed towards artificial intelligence and machine learning, involving different areas of medicine. Among these, although still in the embryonic stage, the dermatopathological field has also been partially involved, with the attempt to develop and train algorithms that could assist the pathologist in the differential diagnosis of complex melanocytic lesions. In this article, we face this new challenge of the modern era, carry out a review of the literature regarding the state of the art and try to determine promising future perspectives.

Artificial Intelligence Approaches to Detect Neurodegenerative Disease From Medical Records

2021 ◽  
pp. 254-267
Author(s):  
Abhranil Gupta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neurodegenerative Disease ◽  
Medical Records ◽  
State Of The Art ◽  
The State ◽  
Learning Approaches ◽  
Brain Scans ◽  
The Future

This chapter gives a brief overview of the state of the art of machine learning approaches in detection of the neurodegenerative disease from medical records (brain scans, etc.). It starts with an understanding of the sub-field of artificial intelligence, machine learning, then goes to understand neurodegenerative disease, with a focus on four major diseases and then goes on to giving an overview of how such diseases are detected using machine learning. In the end, it discusses the future areas of research that needs to be done in order to improve the field of research.

scholarly journals New Business Models on Artificial Intelligence—The Case of the Optimization of a Blast Furnace in the Steel Industry by a Machine Learning Solution

2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Andrés Redchuk ◽  
Federico Walas Mateo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Business Model ◽  
Steel Industry ◽  
Business Models ◽  
State Of The Art ◽  
The State ◽  
Steel Manufacturing ◽  
New Business ◽  
New Business Models

This article took the case of the adoption of a Machine Learning (ML) solution in a steel manufacturing process through a platform provided by a Canadian startup, Canvass Analytics. The content of the paper includes a study around the state of the art of AI/ML adoption in steel manufacturing industries to optimize processes. The work aimed to highlight the opportunities that bring new business models based on AI/ML to improve processes in traditional industries. Methodologically, bibliographic research in the Scopus database was performed to establish the conceptual framework and the state of the art in the steel industry, then the case was presented and analyzed, to finally evaluate the impact of the new business model on the operation of the steel mill. The results of the case highlighted the way the innovative business model, based on a No-Code/Low-Code solution, achieved results in less time than conventional approaches of analytics solutions, and the way it is possible to democratize artificial intelligence and machine learning in traditional industrial environments. This work was focused on opportunities that arise around new business models linked to AI. In addition, the study looked into the framework of the adoption of AI/ML in a traditional industrial environment toward a smart manufacturing approach. The contribution of this article was the proposal of an innovative methodology to put AI/ML in the hands of process operators. It aimed to show how it was possible to achieve better results in a less complex and time-consuming adoption process. The work also highlighted the need for an important quantity of data from the process to approach this kind of solution.

Towards Interconnected Blockchains

2021 ◽  
Vol 54 (7) ◽  
pp. 1-39
Author(s):  
Ankur Lohachab ◽  
Saurabh Garg ◽  
Byeong Kang ◽  
Muhammad Bilal Amin ◽  
Junmin Lee ◽  
...  
Keyword(s):  
State Of The Art ◽  
The State ◽  
Future Research ◽  
Missing Link ◽  
Layered Architecture ◽  
Blockchain Technology ◽  
Wide Scale ◽  
Effective Development ◽  
Insight Into ◽  
Game Changer

Unprecedented attention towards blockchain technology is serving as a game-changer in fostering the development of blockchain-enabled distinctive frameworks. However, fragmentation unleashed by its underlying concepts hinders different stakeholders from effectively utilizing blockchain-supported services, resulting in the obstruction of its wide-scale adoption. To explore synergies among the isolated frameworks requires comprehensively studying inter-blockchain communication approaches. These approaches broadly come under the umbrella of Blockchain Interoperability (BI) notion, as it can facilitate a novel paradigm of an integrated blockchain ecosystem that connects state-of-the-art disparate blockchains. Currently, there is a lack of studies that comprehensively review BI, which works as a stumbling block in its development. Therefore, this article aims to articulate potential of BI by reviewing it from diverse perspectives. Beginning with a glance of blockchain architecture fundamentals, this article discusses its associated platforms, taxonomy, and consensus mechanisms. Subsequently, it argues about BI’s requirement by exemplifying its potential opportunities and application areas. Concerning BI, an architecture seems to be a missing link. Hence, this article introduces a layered architecture for the effective development of protocols and methods for interoperable blockchains. Furthermore, this article proposes an in-depth BI research taxonomy and provides an insight into the state-of-the-art projects. Finally, it determines possible open challenges and future research in the domain.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Designing of Nanomaterials-Based Enzymatic Biosensors: Synthesis, Properties, and Applications

Electrochem ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 149-184
Author(s):  
Francisco T. T. Cavalcante ◽  
Italo R. R. de A. Falcão ◽  
José E. da S. Souza ◽  
Thales G. Rocha ◽  
Isamayra G. de Sousa ◽  
...  
Keyword(s):  
Enzyme Immobilization ◽  
State Of The Art ◽  
Volume Ratio ◽  
The State ◽  
Future Research ◽  
Synthesis Properties ◽  
Signal Response ◽  
Enzymatic Biosensors ◽  
The Many ◽  
Biological Entities

Among the many biological entities employed in the development of biosensors, enzymes have attracted the most attention. Nanotechnology has been fostering excellent prospects in the development of enzymatic biosensors, since enzyme immobilization onto conductive nanostructures can improve characteristics that are crucial in biosensor transduction, such as surface-to-volume ratio, signal response, selectivity, sensitivity, conductivity, and biocatalytic activity, among others. These and other advantages of nanomaterial-based enzymatic biosensors are discussed in this work via the compilation of several reports on their applications in different industrial segments. To provide detailed insights into the state of the art of this technology, all the relevant concepts around the topic are discussed, including the properties of enzymes, the mechanisms involved in their immobilization, and the application of different enzyme-derived biosensors and nanomaterials. Finally, there is a discussion around the pressing challenges in this technology, which will be useful for guiding the development of future research in the area.

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