scholarly journals A REVIEW OF MACHINE LEARNING AND ITS APPLICATIONS

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Vishal Bari ◽  
Dr.M.S Gaikwad ◽  
Dr. Rajendra Babar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Information Retrieval ◽  
Intrusion Detection ◽  
Malware Detection ◽  
Design Algorithms ◽  
Learning Machine ◽  
Image Decoding

Today, huge amounts of data are available everywhere. Therefore, analyzing this data is very important to derive useful information from it and develop an algorithm based on this analysis. This can be achieved through data mining and machine learning. Machine learning is an essential part of artificial intelligence used to design algorithms based on data trends and past relationships between data. Machine learning is used in a variety of areas such as bioinformatics, intrusion detection, information retrieval, games, marketing, malware detection, and image decoding. This paper shows the work of various authors in the field of machine learning in various application areas.

Rule-Based Bibliographic Computer Science Indexing Review with COVID-19 Disease

2021 ◽  
Author(s):  
Andrianingsih Andrianingsih ◽  
Tri Wahyu Widyaningsih ◽  
Meta Amalya Dewi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Google Scholar ◽  
Link Strength ◽  
Learning Machine ◽  
Scientific Fields

A researcher in conducting his research usually uses a search through the homepage of the publication, based on expertise, collaboration in research, and research interests. Today, the COVID-19 pandemic is becoming a trending topic for researchers from various scientific fields. The study classified the case based on publications located in the homepage sources such as Scopus, Crossref, IEEE Xplore, and Google Scholar, by analyzing the following topics, namely Artificial Intelligence, Data Mining, Deep Learning, Machine Learning and the Internet of Things by using Named Entity Recognition to detect and classify named entities in text and using occurence and link strength methods. Based on this study, the results were obtained that Scopus has the most equitable percentage, which has a good occurrence and link strength among the five scientific fields, namely Artificial Intelligence 33.33%, Machine Learning 15.38%, Deep Learning 23.08%, Data Mining 12.82% and IoT 15.38%. The second-best are Google Scholar, then IEEE Xplore, and Crossref.

scholarly journals Analisis Bibliometrik untuk Memetakan Tren Penelitian Covid-19 dalam Topik Ilmu Komputer

Techno Com ◽  
2021 ◽  
Vol 20 (3) ◽  
pp. 440-454
Author(s):  
Tri Wahyu Widyaningsih ◽  
Meta Amalya Dewi ◽  
- Andrianingsih
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Internet Of Things ◽  
Google Scholar ◽  
Link Strength ◽  
Learning Machine ◽  
Internet Of Thing

Covid-19 berdampak pada seluruh penduduk di dunia, pandemi ini tidak hanya mempengaruhi sektor kesehatan, namun juga ekonomi, pendidikan, transportasi, industri, dan pemerintahan. Covid-19 hadir sebagai topik menarik bagi para peneliti, hal tersebut nampak pada data yang diperoleh dari Scopus, Crossref, IEEEXplore, dan Google Scholar yang di dalamnya memuat penelitian di bidang ilmu komputer yang membahas covid-19, dengan berbagai tujuan penelitian untuk memperoleh inovasi maupun solusi dari permasalahan yang timbul akibat pandemi. Penelitian ini dilakukan untuk memperoleh topik apa saja yang paling diminati oleh para peneliti terkait dengan covid 19, dan menganalisis serta membandingkan relasi antara topik Artificial Intelligence, Data Mining, Deep Learning, Machine Learning, dan Internet of Thing dari sumber google scholar, scopus, IEEEXplore, dan crossref dengan menggunakan analisis bibliometrik. Metode occurrence dan link strength digunakan untuk memvisualisasikan jejaring berdasarkan kata kunci dari ke lima topik bidang ilmu komputer serta hubungan antara lima topik tersebut dengan topik riset lainnya. Hasil analisis bibliometric menunjukkan peringkat dari ke empat penyedia sumber data artikel di lihat dari persentase setiap topik penelitian adalah sebagai berikut : Scopus, Crossref, IEEEXplore, dan Google Scholar. Analisis link strength dan occurence  terhadap kelima topik penelitian menunjukkan peringkat yang dapat dilihat dari banyaknya link strength dan occurrence di setiap penyedia sumber artikel, dengan hasil peringkat sebagai berikut : Deep Learning, Artificial Intelligence, Internet of Things, Machine Learning, dan Data Mining. Kata kunci: Bibliometrik, Covid-19, Occurrence,  Link Strength, Ilmu Komputer

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

The Usage Analysis of Machine Learning Methods for Intrusion Detection in Software-Defined Networks

2021 ◽  
pp. 959-975
Author(s):  
Derya Yiltas-Kaplan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Network Architectures ◽  
Software Defined Networks ◽  
Learning Approaches ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Network Problems ◽  
Usage Analysis

This chapter focuses on the process of the machine learning with considering the architecture of software-defined networks (SDNs) and their security mechanisms. In general, machine learning has been studied widely in traditional network problems, but recently there have been a limited number of studies in the literature that connect SDN security and machine learning approaches. The main reason of this situation is that the structure of SDN has emerged newly and become different from the traditional networks. These structural variances are also summarized and compared in this chapter. After the main properties of the network architectures, several intrusion detection studies on SDN are introduced and analyzed according to their advantages and disadvantages. Upon this schedule, this chapter also aims to be the first organized guide that presents the referenced studies on the SDN security and artificial intelligence together.

Classification Techniques in Data Mining

2017 ◽  
pp. 153-188
Author(s):  
Ali Hosseinzadeh ◽  
S. A. Edalatpanah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Fuzzy Classification ◽  
Classification Techniques ◽  
Learning Machines ◽  
Advantages And Disadvantages ◽  
Behavior Based ◽  
Knowledge Learning

Learning is the ability to improve behavior based on former experiences and observations. Nowadays, mankind continuously attempts to train computers for his purpose, and make them smarter through trainings and experiments. Learning machines are a branch of artificial intelligence with the aim of reaching machines able to extract knowledge (learning) from the environment. Classical, fuzzy classification, as a subcategory of machine learning, has an important role in reaching these goals in this area. In the present chapter, we undertake to elaborate and explain some useful and efficient methods of classical versus fuzzy classification. Moreover, we compare them, investigating their advantages and disadvantages.

Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

First Monday ◽  
2019 ◽  
Author(s):  
Niel Chah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Historical Context ◽  
Historical Background ◽  
Deep Roots ◽  
Technical Guide ◽  
Learning Machine ◽  
High Level

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

The Machine is Doing the Work: Interview with Nicholas Mirzoeff

2018 ◽  
pp. 4-7
Author(s):  
Lenart Kučić ◽  
Nicholas Mirzoeff
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Machine Vision ◽  
Communication Technologies ◽  
The Body ◽  
X Rays ◽  
Optical Performance ◽  
The World ◽  
Learning Machine

Optical and mechanical tools were the first major “augmentation” of human senses. The microscope approached the worlds that were too small for the optical performance of the eye. The telescope touched the too far-off space; X-rays radiated the inaccessible interior of the body. Such augmentations were not innocent, as they demanded a different interpretation of the world, which would correspond to images of infinitely small, remote or hidden. Similar augmentation is now happening with cloud computing, machine vision and artificial intelligence. With these tools, it may be possible to compile and analyze billions of digital images created daily by people and machines. But who will analyze these images and for what purpose? Will they help us to better understand society and learn from past mistakes? Or have they already been hijacked by attention-merchants and political demagogues who are effectively spreading old ideologies with new communication technologies? Keywords: augmented photography, communication technologies, machine learning, machine vision, reality

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