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

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.

scholarly journals Deep Learning to Detect Skin Cancer using Google Colab

2019 ◽  
Vol 8 (6) ◽  
pp. 2176-2183 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Skin Cancer ◽  
Domain Knowledge ◽  
Error Rates ◽  
Data Set ◽  
Training Models ◽  
Learning Machine ◽  
High Level

Different mathematical models, Artificial Intelligence approach and Past recorded data set is combined to formulate Machine Learning. Machine Learning uses different learning algorithms for different types of data and has been classified into three types. The advantage of this learning is that it uses Artificial Neural Network and based on the error rates, it adjusts the weights to improve itself in further epochs. But, Machine Learning works well only when the features are defined accurately. Deciding which feature to select needs good domain knowledge which makes Machine Learning developer dependable. The lack of domain knowledge affects the performance. This dependency inspired the invention of Deep Learning. Deep Learning can detect features through self-training models and is able to give better results compared to using Artificial Intelligence or Machine Learning. It uses different functions like ReLU, Gradient Descend and Optimizers, which makes it the best thing available so far. To efficiently apply such optimizers, one should have the knowledge of mathematical computations and convolutions running behind the layers. It also uses different pooling layers to get the features. But these Modern Approaches need high level of computation which requires CPU and GPUs. In case, if, such high computational power, if hardware is not available then one can use Google Colaboratory framework. The Deep Learning Approach is proven to improve the skin cancer detection as demonstrated in this paper. The paper also aims to provide the circumstantial knowledge to the reader of various practices mentioned above.

scholarly journals Data Mining, Machine Learning, Deep Learning, Chemometrics. Definitions, common points and Trends (Spoiler Alert: VALIDATE your models!)

2021 ◽  
Vol 8 (32) ◽  
pp. 22-38
Author(s):  
José Manuel Amigo
Keyword(s):  
Analytical Chemistry ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Daily Life ◽  
Big Data Analysis ◽  
Mining Machine ◽  
Learning Data ◽  
Made In

Concepts like Machine Learning, Data Mining or Artificial Intelligence have become part of our daily life. This is mostly due to the incredible advances made in computation (hardware and software), the increasing capabilities of generating and storing all types of data and, especially, the benefits (societal and economical) that generate the analysis of such data. Simultaneously, Chemometrics has played an important role since the late 1970s, analyzing data within natural science (and especially in Analytical Chemistry). Even with the strong parallelisms between all of the abovementioned terms and being popular with most of us, it is still difficult to clearly define or differentiate the meaning of Machine Learning, Data Mining, Artificial Intelligence, Deep Learning and Chemometrics. This manuscript brings some light to the definitions of Machine Learning, Data Mining, Artificial Intelligence and Big Data Analysis, defines their application ranges and seeks an application space within the field of analytical chemistry (a.k.a. Chemometrics). The manuscript is full of personal, sometimes probably subjective, opinions and statements. Therefore, all opinions here are open for constructive discussion with the only purpose of Learning (like the Machines do nowadays).

Técnicas de inteligência artificial para reconhecimento de sepse em ambientes hospitalares: revisão integrativa

2020 ◽  
Vol 9 (1) ◽  
pp. 15-31
Author(s):  
Everton Osnei Cesario ◽  
Cristiane Yumi Nakamura ◽  
Yohan Bonescki Gumiel ◽  
Deborah Ribeiro Carvalho
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning

A sepse é uma inflamação generalizada com elevada morbidade e mortalidade, cujo reconhecimento e tratamento precoce são fatores essenciais para uma melhor qualidade de vida para o paciente; caso não seja identificada e tratada rapidamente, poderá levar a óbito. Este artigo de revisão integrativa objetiva identificar as técnicas baseadas em inteligência artificial adotadas, sua respectiva acurácia, sensibilidade e especificidade para a identificação precoce nos casos de sepse em ambiente hospitalar. A pesquisa, adaptada do método PRISMA, foi realizada em cinco bases de dados indexadas a partir dos seguintes descritores: sepse, septic, sepsis, forecasting, predict, prediction, detection, predicting, diagnosis, assessment, machine learning, artificial intelligence, data mining e deep learning. Foram identificados 333 artigos, sendo 21 com referência ao reconhecimento precoce da sepse por meio de 16 técnicas. Os resultados demonstram que as redes neurais tiveram melhor desempenho, variando a acurácia entre 76% e 93%, as árvores de decisão entre 69,0% e 91,5% e os métodos estatísticos entre 56% e 89%. Conclui-se que o fator mais influente na identificação precoce do diagnóstico são a variedade e a qualidade dos dados. Também se evidencia o desafio em relação ao pré-processamento, visto que os dados em geral são oriundos de fontes heterogêneas, coletados com critérios, métodos e objetivos distintos.

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.

scholarly journals Crossing Point of Artificial Intelligence in Cybersecurity

2015 ◽  
Vol 2 (3) ◽  
pp. 121-128
Author(s):  
Praveen Kumar Donepudi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Cyber Threats ◽  
Crossing Point ◽  
Insight Into ◽  
Distinct Approach ◽  
Learning Data ◽  
Applications Of Ai

There is a wide scope of interdisciplinary crossing points between Artificial Intelligence (AI) and Cybersecurity. On one hand, AI advancements, for example, deep learning, can be introduced into cybersecurity to develop smart models for executing malware classification and intrusion detection and threatening intelligent detecting. Then again, AI models will confront different cyber threats, which will affect their sample, learning, and decision making. Along these lines, AI models need specific cybersecurity defense and assurance advances to battle ill-disposed machine learning, preserve protection in AI, secure united learning, and so forth. Because of the above two angles, we audit the crossing point of AI and Cybersecurity. To begin with, we sum up existing research methodologies regarding fighting cyber threats utilizing artificial intelligence, including receiving customary AI techniques and existing deep learning solutions. At that point, we analyze the counterattacks from which AI itself may endure, divide their qualities, and characterize the relating protection techniques. And finally, from the aspects of developing encrypted neural networks and understanding safe deep learning, we expand the current analysis on the most proficient method to develop a secure AI framework. This paper centers mainly around a central question: "By what means can artificial intelligence applications be utilized to upgrade cybersecurity?" From this question rises the accompanying set of sub-questions: What is the idea of artificial intelligence and what are its fields? What are the main areas of artificial intelligence that can uphold cybersecurity? What is the idea of data mining and how might it be utilized to upgrade cybersecurity? Hence, this paper is planned to reveal insight into the idea of artificial intelligence and its fields, and how it can profit by applications of AI brainpower to upgrade and improve cybersecurity. Using an analytical distinct approach of past writing on the matter, the significance of the need to utilize AI strategies to improve cybersecurity was featured and the main fields of application of artificial intelligence that upgrade cybersecurity, for example, machine learning, data mining, deep learning, and expert systems.  

scholarly journals Machine learning and deep learning

2021 ◽  
Author(s):  
Christian Janiesch ◽  
Patrick Zschech ◽  
Kai Heinrich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Analytical Model ◽  
Intelligent Systems ◽  
Model Building ◽  
Training Data ◽  
Learning Models ◽  
Human Machine Interaction ◽  
Learning Machine

AbstractToday, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.

scholarly journals Evolution and Revolution in Artificial Intelligence

2020 ◽  
pp. 125-129
Author(s):  
Rahul Ner
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Deductive Reasoning ◽  
Long Time ◽  
Learning Machine ◽  
Time Away ◽  
Real World Problems

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques wont to solve various real-world problems. For a primer on machine learning, you'll want to read this seven-part series that I wrote. While human-like deductive reasoning, presumption, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms

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