scholarly journals Survei Penerapan Model Machine Learning Dalam Bidang Keamanan Informasi

2019 ◽  
Vol 2 (1) ◽  
pp. 47-60
Author(s):  
Arif Rachmat
Keyword(s):  
Machine Learning ◽  
Information Security ◽  
Learning Models ◽  
Machine Learning Applications ◽  
Machine Learning Models

This paper provides a survey that discusses the spread used of machine learning models and algorithm for problems in information security. The breadth of the various types of techniques and methods by machine learning on this survey also figured by given examples of each model in the application for problems related to information security. The results of the study can be concluded that the use of machine learning in information security has spread widely in its use. Some methods are published in standard ways, with expectations this paper will give the insight to develop better models of machine learning applications in information security.

scholarly journals An Introduction on Interpretable Machine Learning

2020 ◽  
Vol 9 (7S) ◽  
pp. 107-111
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Life ◽  
Accuracy Score ◽  
Learning Models ◽  
Ethical Practices ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Machine Learning Applications ◽  
Machine Learning Models

As Artificial Intelligence penetrates all aspects of human life, more and more questions about ethical practices and fair uses arise, which has motivated the research community to look inside and develop methods to interpret these Artificial Intelligence/Machine Learning models. This concept of interpretability can not only help with the ethical questions but also can provide various insights into the working of these machine learning models, which will become crucial in trust-building and understanding how a model makes decisions. Furthermore, in many machine learning applications, the feature of interpretability is the primary value that they offer. However, in practice, many developers select models based on the accuracy score and disregarding the level of interpretability of that model, which can be chaotic as predictions by many high accuracy models are not easily explainable. In this paper, we introduce the concept of Machine Learning Model Interpretability, Interpretable Machine learning, and the methods used for interpretation and explanations.

Application of Machine Learning in Pipeline Integrity Reliability

2019 ◽  
Author(s):  
Eunice Yin ◽  
Phil Fernandes ◽  
Janine Woo ◽  
Doug Langer ◽  
Sherif Hassanien
Keyword(s):  
Machine Learning ◽  
Management Practices ◽  
Large Population ◽  
Classification Model ◽  
Learning Technology ◽  
Learning Models ◽  
Simulation Techniques ◽  
Depth Analysis ◽  
Machine Learning Applications ◽  
Machine Learning Models

Abstract Probabilistic analysis is becoming increasingly adopted by pipeline integrity management practices in recent years. The practice employs reliability engineering methods to address pipeline integrity and safety concerns. At present, the industry is beginning to pair reliability methods with numerical methods to estimate probabilities of failure (PoF) for individual defects, or features, in a pipeline. The effort required for this can be intensive, since it must be repeated on hundreds of thousands of features, which need to be analyzed on a regular basis. This poses a challenge for pipeline reliability engineers, given limited human and computational resources. In the meantime, machine learning applications in many industries have grown significantly due to advancements in algorithms and raw computing power. With massive amounts of raw data available from inline inspection (ILI) tools, and artificial data available through simulation techniques, pipeline integrity reliability becomes a promising field in which to apply machine learning technology to fast-track PoF estimation. Since a large population of reported features have low PoFs and pose low risk to integrity and safety, they can be safely screened out using fast machine learning models to free up engineers who can be dedicated to in-depth analysis of more critical features, which could have a much larger impact on pipeline operational safety. In this paper, two machine learning models are proposed to address the pipeline integrity reliability challenges. The regression model was able to predict features with low PoFs with 99.99% confidence. The classification model was able to conservatively predict PoFs so that no high PoF feature was misclassified as being low PoF, while correctly filtering out 99.6% of the low PoF features. The proposed approach is presented and validated through pipeline integrity simulated case studies.

scholarly journals Impact of Machine Learning Applications For Price Prediction Of Onion In India

2021 ◽  
Vol 14 (1) ◽  
pp. 30
Author(s):  
Sayed Mannan Ahmad ◽  
Ankush Kumar Gaur ◽  
Anil Kumar
Keyword(s):  
Machine Learning ◽  
Rural Economy ◽  
Climate Changes ◽  
Learning Models ◽  
Bone Thickness ◽  
Malignant Growth ◽  
Price Prediction ◽  
Machine Learning Applications ◽  
The Cost ◽  
Machine Learning Models

Conceptual India is one of the second-biggest maker of onion on the planet. Onion is the most extravagant wellspring of nutrients and cell reinforcements. Furthermore, it contains malignant growth battling mixes, assists with keeping up heart more advantageous and lifts the bone thickness, additionally controls the glucose. Natural just as Climate changes sway on the rural economy of any nation. The creation of onion typically relies upon factors like science, atmosphere, economy, and topography, these elements impact on agriculture. It is been recorded that significant expense chance in the horticulture part constantly bound ranchers to endeavor suicide. The value figures are helpful for homesteads, policymakers, and agribusiness ventures. Utilizing time arrangement information in horticulture, nonstop endeavors are made by the analysts to foresee the costs utilizing different straight and nonlinear anticipating models. These days, Artificial knowledge/Machine learning models are utilized as traditional measurable models in the gauging exercise. Utilizing AI strategies in this way, utilizing authentic cost and information factors. Henceforth this exploration will assume a significant job to anticipate the cost of onion. Just as help to agriculturists to turn out to be financially well and has a more advantageous existence.

scholarly journals Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology

2021 ◽  
pp. annrheumdis-2021-220454
Author(s):  
Stephanie J W Shoop-Worrall ◽  
Katherine Cresswell ◽  
Imogen Bolger ◽  
Beth Dillon ◽  
Kimme L Hyrich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care Professionals ◽  
Machine Learning Techniques ◽  
Advisory Group ◽  
Learning Models ◽  
Clinical Images ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Machine Learning Models

Novel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support for therapy selection, and automated interpretation of clinical images. Nevertheless, such data-driven approaches could potentially model noise, or miss true clinical phenomena. One proposed solution to ensure clinically meaningful machine learning models is to involve primary stakeholders in their development and interpretation. Including patient and health care professionals’ input and priorities, in combination with statistical fit measures, allows for any resulting models to be well fit, meaningful, and fit for practice in the wider rheumatological community. Here we describe outputs from workshops that involved healthcare professionals, and young people from the Your Rheum Young Person’s Advisory Group, in the development of complex machine learning models. These were developed to better describe trajectory of early juvenile idiopathic arthritis disease, as part of the CLUSTER consortium. We further provide key instructions for reproducibility of this process.Involving people living with, and managing, a disease investigated using machine learning techniques, is feasible, impactful and empowering for all those involved.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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