scholarly journals A Survey of Machine Learning models for the wide Spectrum of Computational Biology

2019 ◽  
pp. 604-611
Author(s):  
Divya Ebenezer Nathaniel ◽  
Sonia Panesar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Wide Spectrum ◽  
Machine Learning Techniques ◽  
Evolutionary Learning ◽  
Processing Data ◽  
Huge Data ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Rapid Pace

With the Advent of advancement in the field of Artificial Intelligence the computer is made more intelligent and can enable to think and make prediction accurately. The machine learning being a subfield of Artificial Intelligence is used in numerous research works. Different analysts feel that enormous data generated in field of biology have to be sorted in an intelligent way to yield best model. There are numerous kinds of Machine Learning Techniques like Unsupervised, Semi Supervised, Supervised, Reinforcement, and Evolutionary Learning and Deep Learning. These learning’s are used to classify huge data at a rapid pace. This paper discusses about the wide spectrum of Biology and the process of pre-processing data and the best suitable Machine learning model for each of them.

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

Role of Artificial Intelligence in Modeling Psychometrics to Detect Neurodevelopmental Disorders

2020 ◽  
pp. 213-227
Author(s):  
Navjot Singh ◽  
Amarjot Kaur
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neurodevelopmental Disorders ◽  
Machine Learning Techniques ◽  
Diagnosis And Prognosis ◽  
Processing Power ◽  
Learning Techniques ◽  
Numerous Data ◽  
Applications Of Machine Learning ◽  
Micro Patterns

The objective of the present chapter is to highlight applications of machine learning and artificial intelligence (AI) in clinical diagnosis of neurodevelopmental disorders. The proposed approach aims at recognizing behavioral traits and other cognitive aspects. The availability of numerous data and high processing power, such as graphic processing units (GPUs) or cloud computing, enabled the study of micro-patterns hundreds of times faster compared to manual analysis. AI, being a new technological breakthrough, enables study of human behavior patterns, which are hidden in millions of micro-patterns originating from human actions, reactions, and gestures. The chapter will also focus on the challenges in existing machine learning techniques and the best possible solution addressing those problems. In the future, more AI-based expert systems can enhance the accuracy of the diagnosis and prognosis process.

Leveraging Machine Learning in Financial Fraud Forensics in the Age of Cybersecurity

2022 ◽  
pp. 220-249
Author(s):  
Md Ariful Haque ◽  
Sachin Shetty
Keyword(s):  
Machine Learning ◽  
Financial Institutions ◽  
Machine Learning Techniques ◽  
Cyber Attack ◽  
Financial Industry ◽  
Security Breaches ◽  
Financial Gain ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Machine Learning Models

Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers' transactions. However, machine learning techniques are often challenging because of financial institutions' confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source e-commerce transaction dataset to illustrate the forensic processes by creating a machine learning model to classify fraudulent transactions. Overall, the chapter focuses on how the machine learning models can help detect and prevent fraudulent activities in the financial sector in the age of cybersecurity.

Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction

2019 ◽  
Vol 4 (2) ◽  
pp. 21-33
Author(s):  
Satya Kiranmai Tadepalli ◽  
P.V. Lakshmi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Future Generation ◽  
Machine Learning Techniques ◽  
Successful Pregnancy ◽  
Vitro Fertilization ◽  
Digital Revolution ◽  
Learning Techniques ◽  
Assistive Reproductive Technology

Infertility is the combination of factors that prevent pregnancy. It involves a lot of care and expertise while selecting the best embryo to lead to a successful pregnancy. Assistive reproductive technology (ART) helps to solve this issue. In vitro fertilization (IVF) is one of the methods of ART which is very popular. Artificial intelligence will have digital revolution and manifold advances in the field of reproductive medicine and will eventually provide immense benefits to infertile patients. The main aim of this article is to focus on the methods that can predict the accuracy of pregnancy without human intervention. It provides successful studies conducted by using machine learning techniques. This easily enables doctors to understand the behavior of the attributes which are suitable for the treatment. Blastocyst images can be deployed for the detection and prediction of the best embryo which has the maximum chance of a successful pregnancy. This pioneering work gives one a view into how this field could benefit the future generation.

Towards the collaborative development of machine learning techniques in planning support systems – a Sydney example

2020 ◽  
pp. 239980832093997 ◽  
Author(s):  
Oliver Lock ◽  
Michael Bain ◽  
Christopher Pettit
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support System ◽  
Support Systems ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Planning Support Systems ◽  
Planning Support ◽  
Learning Techniques ◽  
Planning Support System

The rise of the term ‘big data’ has contributed to recent advances in computational analysis techniques, such as machine learning and more broadly, artificial intelligence, which can extract patterns from large, multi-dimensional datasets. In the field of urban planning, it is pertinent to understand both how such techniques can advance our understanding of cities, and how they can be embedded within transparent and effective digital planning tools, known as planning support systems. This research specifically focuses on two related contributions. First, it investigates the role of planning support systems in supporting a participatory data analytics approach through an iterative process of developing and evaluating a planning support system environment. Second, it investigates how specifically machine learning planning support systems can be co-designed by built environment practitioners and stakeholders in this environment to solve a real planning issue in Sydney, Australia. This paper presents the results of applied research undertaken through the design and implementation of four workshops, involving 57 participants who were involved in a co-design process. The research follows a mixed-methods approach, studying a wide array of measures related to participatory analytics, task load, perceived added value, recordings and observations. The results highlight recommendations regarding the design and evaluation of planning support system environments for co-design and their coupling with machine learning techniques. It was found that consistency and transparency are highly valued and central to the design of a planning support system in this context. General attitudes towards machine learning and artificial intelligence as techniques for planners and developers were positive, as they were seen as both potentially transformative but also as simply another technique to assist with workflows. Some conceptual challenges were encountered driven by practitioners' simultaneous need for concrete scenarios for accurate predictions, paired with a desire for predictions to drive the development of these scenarios. Insights from this work can inform future planning support system evaluation and co-design studies, in particular those aiming to support democracy enhancement, greater inclusion and more efficient resource allocation through a participatory analytics approach.

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