scholarly journals A.I. in Islam: The Crossroad of A.I. and Islamic Teachings

2019 ◽  
pp. 9
Author(s):  
Helmi Zakariah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Homo Sapiens ◽  
Health Sciences ◽  
Historical Narrative ◽  
Collective Knowledge ◽  
The Earth ◽  
Supplementary Issue

Mankind in its historical narrative – almost always immodestly regards itself as the most intelligent among all God’s creation. Either through a self – label of “Homo Sapiens” (the wise men) or the dogma of being the Khalifah (leader) of the earth. But what does it mean by intelligence? What is the epistemology (origin) of our collective knowledge? And does it bring us closer to wisdom? These points that we commonly take for granted, must be examined continuously in our trending pursuit of translating (or, imposing) our thinking architecture to machine learning and Artificial Intelligence. From the origin of the commonly-used term “algorithm” in A.I. (spoiler: it was originally coined by a Muslim mathematician of the 9th century, of a similar-sounding name) to the interjunction of A.I. and the concept of Ihsan, this plenary intends to demystify A.I. and an attempt to harmonize this leap-of-faith tool, into a tool for the faithfulInternational Journal of Human and Health Sciences Supplementary Issue: 2019 Page: 9

Machine-driven earth exploration: Artificial intelligence in oil and gas

2021 ◽  
Vol 40 (4) ◽  
pp. 298-301
Author(s):  
Tariq Alkhalifah ◽  
Ali Almomin ◽  
Ali Naamani
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fault Detection ◽  
Oil And Gas ◽  
Image Resolution ◽  
The Earth ◽  
Recent Advances ◽  
Potential Applications ◽  
Boundary Mapping

Artificial intelligence (AI), specifically machine learning (ML), has emerged as a powerful tool to address many of the challenges we face as we try to illuminate the earth and make the proper prediction of its content. From fault detection, to salt boundary mapping, to image resolution enhancements, the quest to teach our computing devices how to perform these tasks accurately, as well as quantify the accuracy, has become a feasible and sought-after objective. Recent advances in ML algorithms and availability of the modules to apply such algorithms enabled geoscientists to focus on potential applications of such tools. As a result, we held the virtual workshop, Artificially Intelligent Earth Exploration Workshop: Teaching the Machine How to Characterize the Subsurface, 23–26 November 2020.

scholarly journals An empirical assessment of different kernel functions on the performance of support vector machines

2021 ◽  
Vol 10 (6) ◽  
pp. 3403-3411
Author(s):  
Isaac Kofi Nti ◽  
Owusu Nyarko-Boateng ◽  
Felix Adebayo Adekoya ◽  
Benjamin Asubam Weyori
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High Accuracy ◽  
Kernel Functions ◽  
Support Vector ◽  
Technological Advancement ◽  
Specific Domain ◽  
The Earth ◽  
Vector Machines ◽  
Rbf Kernel

Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day activities in this era of technological advancement, making a living more comfortable on the earth. Among the several AI and ML algorithms, the support vector machine (SVM) has become one of the most generally used algorithms for data mining, prediction and other (AI and ML) activities in several domains. The SVM’s performance is significantly centred on the kernel function (KF); nonetheless, there is no universal accepted ground for selecting an optimal KF for a specific domain. In this paper, we investigate empirically different KFs on the SVM performance in various fields. We illustrated the performance of the SVM based on different KF through extensive experimental results. Our empirical results show that no single KF is always suitable for achieving high accuracy and generalisation in all domains. However, the gaussian radial basis function (RBF) kernel is often the default choice. Also, if the KF parameters of the RBF and exponential RBF are optimised, they outperform the linear and sigmoid KF based SVM method in terms of accuracy. Besides, the linear KF is more suitable for the linearly separable dataset.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Sign in / Sign up

Export Citation Format

Share Document