Applications of Artificial Intelligence in Machine Learning: Review and Prospect (original) (raw)
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A Review on Applications of Artificial Intelligence in Machine Learning
International Journal of Mathematical Archive EISSN 2229-5046, 2019
The most exciting technology in artificial intelligence is Machine learning. Machine Learning provides systems for the machine and gives capability to automatically learn and improve from experience without being explicitly programmed. For Example the search engine Google is used for search something on internet, the main reasons its work well because of learning algorithm, Every time mailbox is used and it identify inbox, drafts box, sent box etc. That's also machines learning. The main aim of machine learning is the improvement of computer programs and to allow the computers learn automatically.
A Review on Applicability of Machine learning
Foundation of Computer Applications, 2020
Machine learning is an application of artificial intelligence in which the machines learn themselves and then work accordingly to the instructions. Basically, machine learning works on the data sets. Data is unprocessed raw facts and figures. The machine works on the data, tries to understand and correlate with different fields and then give output. In this paper, we will be discussing the basic knowledge required to build up the machine learning models, the hypes and reality related to machine learning and most importantly how machine learning and interrelated fields are used in various platforms. This is one of the fast-growing fields in the present world, as it is reducing the load of computation and helping companies to strategies accordingly. But as every coin has two sides, machine learning also has its own positive and negative views as day by day it is reducing human efforts. Almost every multinational company is using this technology for solving the problems of society and people. Machine learning is also linked to other branches like artificial intelligence, data science, computational statistics and probability. These all fields are linked with one another, machine learning is all about the mathematics mainly probability and statistics. Analyzing the data depending upon the various factors and then work according to them is a part of machine learning.
Modern Applications of Machine Learning
2006
Machine learning is one of the older areas of artificial intelligence and concerns the study of computational methods for the discovery of new knowledge and for the management of existing knowledge. Machine learning methods have been applied to various application domains. However, in the few last years due to various technological advances and research efforts (e.g. completion of the Human Genome Project, evolution of the Web), new data have been available and consequently new domains where machine learning can be applied have been arisen. Some of these modern applications are learning from biological sequences, learning from email data, and learning in complex environments such as Web. In this paper we present the above three application domains as well as some recent efforts, where machine learning techniques are applied in order to analyze the data provided by these domains.
An Overview of Artificial Intelligence and their Applications towards Machine Learning
The issue of learning and basic leadership is at the center level of contention in organic and in addition artificial angles. So researcher presented Machine Learning as broadly utilized idea in Artificial Intelligence. It is the idea which instructs machines to identify diverse examples and to adjust to new conditions. Machine Learning can be both experience and clarification based learning. In the field of mechanical technology machine learning assumes a fundamental part, it helps in taking an improved choice for the machine which in the long run builds the productivity of the machine and more sorted out method for preforming a specific errand. Presently a-days the idea of machine learning is utilized as a part of numerous applications and is a center idea for clever frameworks which prompts the presentation imaginative innovation and more propel ideas of artificial reasoning.
MACHINE LEARNING AND ITS USAGES
isara solutions, 2020
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. Based on the different criteria such as application domain, representation of knowledge and underlying learning strategy, the machine learning methods are categorized. ML techniques have been employed in various application domains since it improves the performance of any technology. ML is used in application domains such as chemical formula optimization, airline seating allocation, marketing analysis, credit card fraud detection, speech recognition, quality control in manufacturing, automatic classification of celestial objects, food and handwritten character recognition. At present, machine learning techniques are also deployed in agriculture sector for recognition of medicinal plants, classification of grains, recognition of objects, detection of weeds and grading of fruits etc. (Kanjalkar and Lokhande 2013). Machine Learning Techniques Used In Agriculture
Application of Machine Learning
2020
M)Machine (L)learning is seen as prominent energizing late advancements in Artificial(A) (I)Intelligence. One explanation that works admirably every time a web index such as Google or Bing is used to search at the web is that a learning calculation, one modified by Google or Microsoft, has found out how to rate sites. Each when Facebook is introduced, it perceives the photos of partners, which is also computer research. In text, spam networks spare the user from swimming through huge hundreds of spam electronic mail, which is also a study estimate. In this document, a fast survey and destiny opportunity of the broad utilizations of gadget studying has been made.Intelligence. This is what we use every day in various programmes to learn calculations. Assembling has undergone major modifications from industry 1.Zero to industry 4. For example, registering, photography coping, robotization, gadget vision, machine learning along enormous records, and the Internet of things, Zero with the...
MACHINE LEARNING ALGORITHMS IN AI
IEJRD, 2018
Machine learning is a branch of manmade brainpower science (artificial intelligence) i.e. structures that can read the details. For example, a typewriter can learn to receive email and determine the difference between spam and non-spam messages with each other. After preparation, the draft can place new messages in their envelopes using the setting. At the moment, we don’t know how to configure PCs keeping in mind the end goal to make people productive. Unless the strategies found are effective for certain purposes, they are not suitable for all reasons. For example, machine learning calculations are often used as part of information mines. Indeed, even in areas where data is relevant, these statistics are more efficient and effective than alternative strategies. For example, in news, for example, speech acceptance, counting by visual machine learning has come far more than other conversational strategies. Clearly, it seems that our understanding of PCs will improve step by step. Undoubtedly, one could say that the context of machine learning plays a major role in the field of software engineering and innovation. This paper shows machine learning statistics, including determination strategies, diminishing scales, and deleting nonsensical data
Machine learning towards intelligent systems: applications, challenges, and opportunities
Artificial Intelligence Review
The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.
Some uses of machine learning, 2018
Because of the widespread use of machine learning in several areas, these researches came to highlight some uses of machine learning, and mentioned some applications , strategies and methods. Related work This paper [1]. obviously indicates how a novel technique for combination of the current shading spaces delivers preferable outcomes practically speaking over individual shading spaces. The portioned items incorporate lips, faces, hands, fingers and tree clears out. Utilizing a few databases to speak to these issues, the ANN was prepared on the shade of the pixel and its encompassing 8 neighbors to be a question or non-protest; in the test mode the prepared set was utilized to fragment the 9 pixels in the test picture into question or non-question. The element vector was utilized for preparing and testing results from the combination of various kinds of shading data that originated from various shading models of the focused on pixel. A few trials were directed on various databases and items to assess the proposed strategy; huge outcomes were recorded, demonstrating the intensity of expressiveness of shading and some surface data to manage the question division issue. In this work [2]. a few analyses have been performed and assessed to evaluate different machine learning classifiers dependent on KDD interruption dataset. It prevailing to figure a few execution measurements with the end goal to assess the chose classifiers. The emphasis was on false negative and false positive execution measurements with the end goal to improve the location rate of the interruption identification framework. The actualized trials showed that the choice table classifier accomplished the most minimal estimation of false negative while the irregular woodland classifier has accomplished the most astounding normal precision rate. This paper [3]. presents an abnormality interruption location approach in the hypervisor layer to demoralize DDoS exercises between virtual machines. The proposed methodology is executed by the transformative neural system which incorporates the molecule swarm streamlining with neural system for recognition and order of the activity that is traded between virtual machines. The execution examination and aftereffects of our proposed methodology distinguish and arrange the DDoS assaults in the cloud condition with least false cautions and high recognition precision. This paper [4]. analyze the Intrusion Detection (ID) issue utilizing three machine learning calculations in particular, BayesNet calculation, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). The calculations are connected on a genuine, Management Information Based (MIB) dataset that is gathered from genuine condition. To upgrade the discovery procedure exactness, an arrangement of highlight