Abstract
Machine learning is known as the scientific study of various algorithms and statistics as well as models that can be used to create or perform certain tasks. These tasks are often based upon the dependability of the Interface as well as the patterns. As machine learning is also known as the subset of Artificial Intelligence, it enables the system to create or perform several tasks without the need for any manual changes. This mechanism relatively related to automatic performance and self- learning as it can be used to detect various faults within an inbuilt system or software and take necessary steps to debug and run diagnostics to reduce errors as much as it can. Due to the emergence of Artificial Intelligence, various industries and private firms such as Space X and Tesla have induced Machine Learning into their workspace, and especially when it comes to industrial usage, Corporations such as Tesla have developed AI-based vehicles which run under electricity and automatic debugging. As the emergence of Artificial Intelligence has given ways to Machine Learning, it is inevitable that we may or may even be quite close to being perceiving futuristic technology much earlier than intended.
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Author Keywords
- Artificial intelligence,
- machine learning,
- filtering,
- computer vision,
- clustering,
- concept learning
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IEEE Keywords
- Software,
- Machine learning,
- Task analysis,
- Biological cells,
- Genetic algorithms,
- Machine learning algorithms
Introduction
Machine learning is known to involve various concepts such as concept learning, clustering, training examples, etc. Concept Learning is known as one of the categories of Machine Learning which can be used to predict values give within a set of instructions and categorize the objects into classes in a process similar to partitioning. In the concept learning task, we obtain various attributes and each attribute is known to consist of certain instances. It is up to the system or software’s job to classify these instances into their respective attributes in order to reduce time complexity during the processing of large or several combinations of tasks. This component of Machine Learning can be used for boolean-valued functions as well as the training examples for the input and output.
Literature Survey
Candidate LIST-THEN-ELIMINATE Algorithm is known as one of the Machine Learning concepts which can be used to derive various components in order to obtain the version space. A Version space is known as one of the logical approaches to machine learning in which a set of logical values can be predefined within the hypotheses. In the candidate LIST-THEN-ELIMINATE Algorithm, certain examples from the Hypothesis can be obtain known as examples. These examples can then be used to obtain the version space. A representation of this mechanism is given below.
In the above representation, we can observe that the hypothesis space consists of a group of clustered data in which certain examples can be derived. Upon receiving the data, the mechanism undergoes certain algorithms that can be used to obtain a defined variable known as the version space.
Methodology
Over the years, machine learning has been implemented and used quite intensively such that almost every electronic equipment which can be connected to Wi-Fi is quite automatically associated with the Artificial Intelligence as the wireless network connection as well as the Bluetooth are known to be used as a medium between the software and the hardware, in which multiple hardware components can be operated remotely with the help or assistance of such medium [1]. Various corporations such as Tesla are known to make vehicles that contain self-driving where the automobile can detect any obstacles within the safety radius and take measures to avoid collision [3]. The future of the automobile industry is known to be closely associated with artificial intelligence and the implementation of machine learning is known to bring positive outputs as well. When it comes to the implementation of machine learning into various components such as automobiles, software, etc. The indulgence of clustering takes place [4]. Clustering is known as the combination of various instances sourced together in order to create learning types such as: 1.Supervised Learning 2.Unsupervised Learning Supervised Learning is known as the mechanism in which the learning task maps functions into input-output pairs. In supervised learning, the components undergo a detailed functioning of the clustering mechanism, whereas unsupervised learning on the other hand does not involve the indulgence of the input-output functions. Hierarchical clustering is also known to be associated with Machine learning in which the mechanism is used to create hierarchies of clusters ranging from small to big. In hierarchical clustering, the clusters are usually formed from the initial stage and gets carried out downwards or sideways into a more detailed form of clusters which enables the end-user to obtain several options and clusters and each cluster can be used to benefit the user in their respective ways [2]. There are several advantages of clustering in which the number of possibilities can be determined and can be used to find patterns as well.
Conclusion
There are several processes that can be obtained through machine learning in which the inclusion of clusters such as hierarchical clusters, non-hierarchical clusters, k- means algorithms, etc. take place and enable the involvement of machine learning alongside artificial intelligence. Due to the emergence of technology, artificial intelligence has seen a rise in technology involving several aspects such as genetic mutation, error detection, software development, artificial neural networks, and several such areas. Software Detection Systems are a well-known process when it comes to modern software development as the rise in bugs and faults enables the system to learn and avoid the repetition of faults. Although there are various ways to do so, the implementation of ANN and PSO not only allows the software to debug and repair but also enables it to be more refined in terms of performance. The indulgence of machine learning alongside artificial intelligence has enabled a noticeable growth spurt in worldwide in terms of economy as well as technically. The emergence of various multinational companies depends upon the upcoming technologies rather than the pre-existing ones. Thus allowing a great demand for technologies such as machine learning and artificial intelligence.
About KSRA
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
FULL Paper PDF file:
Machine Learning and its Emergence in the Modern World and its Contribution to Artificial IntelligenceBibliography
author
Year
2020
Title
Machine Learning and its Emergence in the Modern World and its Contribution to Artificial Intelligence
Publish in
2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020, pp. 1-4,
Doi
10.1109/INCET49848.2020.9154058.
PDF reference and original file: Click here
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
