Application process of machine learning in cyberspace security

2017 ◽

Vol 7 (2) ◽

Author(s):

Dicky R. M. Nainggolan

Keyword(s):

Machine Learning ◽

Big Data ◽

Cyber Security ◽

High Speed ◽

Data Science ◽

Predictive Analytics ◽

Cyber Attacks ◽

Artificial Intelligence Technology ◽

Science Methodology ◽

Cyberspace Security

Abstract – Data are the prominent elements in scientific researches and approaches. Data Science methodology is used to select and to prepare enormous numbers of data for further processing and analysing. Big Data technology collects vast amount of data from many sources in order to exploit the information and to visualise trend or to discover a certain phenomenon in the past, present, or in the future at high speed processing capability. Predictive analytics provides in-depth analytical insights and the emerging of machine learning brings the data analytics to a higher level by processing raw data with artificial intelligence technology. Predictive analytics and machine learning produce visual reports for decision makers and stake-holders. Regarding cyberspace security, big data promises the opportunities in order to prevent and to detect any advanced cyber-attacks by using internal and external security data. Keywords: Big Data, Cyber Security, Data Science, Intelligence, Predictive Analytics Abstrak – Data merupakan unsur terpenting dalam setiap penelitian dan pendekatan ilmiah. Metodologi sains data digunakan untuk memilah, memilih dan mempersiapkan sejumlah data untuk diproses dan dianalisis. Teknologi big data mampu mengumpulkan data dengan sangat banyak dari berbagai sumber dengan tujuan untuk mendapatkan informasi dengan visualisasi tren atau menyingkapkan pengetahuan dari suatu peristiwa yang terjadi baik dimasa lalu, sekarang, maupun akan datang dengan kecepatan pemrosesan data sangat tinggi. Analisis prediktif memberikan wawasan analisis lebih dalam dan kemunculan machine learning membawa analisis data ke tingkat yang lebih tinggi dengan bantuan teknologi kecerdasan buatan dalam tahap pemrosesan data mentah. Analisis prediktif dan machine learning menghasilkan laporan berbentuk visual untuk pengambil keputusan dan pemangku kepentingan. Berkenaan dengan keamanan siber, big data menjanjikan kesempatan dalam rangka untuk mencegah dan mendeteksi setiap serangan canggih siber dengan memanfaatkan data keamanan internal dan eksternal. Kata Kunci: Analisis Prediktif, Big Data, Intelijen, Keamanan Siber, Sains Data

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Implementation of Real-Time Medical and Health Data Mining System Based on Machine Learning

Journal of Healthcare Engineering ◽

10.1155/2021/7011205 ◽

2021 ◽

Vol 2021 ◽

pp. 1-5

Author(s):

Pengyuan Wang ◽

Jie Li

Keyword(s):

Machine Learning ◽

Data Mining ◽

Health Management ◽

Learning Algorithms ◽

Health Data ◽

Machine Learning Algorithms ◽

Application Process ◽

Mining System ◽

Sensing Technology ◽

Data Mining System

This article analyzes the application process of data mining technology in the medical and health management system and uses machine learning algorithms to design a medical and health data mining system. The system collects patient’s physical health data based on wireless sensing technology and uses machine learning algorithms to analyze the data. The system uploads the collected health data to the system for cluster analysis. Finally, the method is applied to the diagnosis data mining of patients, so as to prove the effectiveness of the classification method in the medical field through examples.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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The application process influences the applicant pool-we can send the applicants a message

Journal of Dental Education ◽

10.1002/j.0022-0337.1996.60.5.tb03044.x ◽

1996 ◽

Vol 60 (5) ◽

pp. 419-421 ◽

Cited By ~ 1

Author(s):

D Bloxham

Keyword(s):

Application Process ◽

Applicant Pool

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Student Experience Applying to Graduate School for Speech-Language Pathology

Perspectives of the ASHA Special Interest Groups ◽

10.1044/2019_persp-19-00102 ◽

2020 ◽

Vol 5 (1) ◽

pp. 192-205

Author(s):

Lesley Sylvan ◽

Andrea Perkins ◽

Carly Truglio

Keyword(s):

Graduate School ◽

Master's Degree ◽

Graduate Programs ◽

Student Experience ◽

Degree Programs ◽

Speech Language Pathology ◽

Application Process ◽

Master's Degree Programs ◽

Language Pathology ◽

Master’S Degree

Purpose The purpose of this study is to better understand the experiences faced by students during the application process for master's degree programs in speech-language pathology. Method Data were collected through administering an online survey to 365 volunteers who had applied to master's degree programs in speech-language pathology. Survey questions were designed to gain the student perspective of the application process through exploration of students' deciding factors for top choices of graduate programs, emotional involvement in the application process, biases/rumors heard, student challenges, advice to future applicants, and what students would change about the application process. Results Factors that influenced participants' reasoning for selecting their “top choice” programs were largely consistent with previous studies. Issues that shaped the student experience applying to graduate school for speech-language pathology included financial constraints, concern regarding the prominence of metrics such as Graduate Record Examinations scores in the admissions process, a perceived lack of guidance and advising from faculty, and confusion regarding variation among graduate program requirements. Conclusion Gaining insight into the student experience with the application process for graduate programs in speech-language pathology yields useful information from a perspective not frequently explored in prior literature. While the data presented in this study suggest the process is confusing and challenging to many applicants, the discussion highlights practical solutions and sheds light on key issues that should be considered carefully by individual graduate programs as well as the field as a whole.

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