MALADY: A Machine Learning-Based Autonomous Decision-Making System for Sensor Networks

2009 ◽  
Author(s):  
Sudha Krishnamurthy ◽  
Geethapriya Thamilarasu ◽  
Christian Bauckhage
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Sensor Networks ◽  
Autonomous Decision ◽  
Decision Making System

scholarly journals Intelligent Predicting Learning Disabilities in School Going Children using Fuzzy Logic K Mean Clustering in Machine Learning.

2019 ◽  
Vol 8 (4) ◽  
pp. 1694-1698
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Decision Making ◽  
Learning Disabilities ◽  
Fuzzy Logic ◽  
Side Effects ◽  
Early Analysis ◽  
Comprehensive Investigation ◽  
Engineering Statistics ◽  
Decision Making System

Learning disabilities (LD) is turning into a major issue in various nations around the globe which can even contrarily influence human common advancement. The undertaking of this work is to help the specialized programme network in their task to be with the standard. The underlying section of the paper gives a comprehensive investigation of the distinctive components of diagnosing learning disabilities. Despite the fact that LD can be analysed early - before 5 years of age, most youngsters were not determined to have LD until the age of nine on account of its unpredictable side effects and unclear indication in children disorder issue. Fuzzy logic K-means clustering has inspired a tremendous transformation in Machine learning and can take and able to resolve a variation of problems. This paper is the elaboration on the strategy for utilizing this mix to encourage the early analysis of LD. Since Fuzzy Logic clustering in Machine Learning is generally considered and connected in different areas of science, we invite all the related analysts from the fields of computer science, engineering, statistics, social sciences, healthcare, and so on, etc. The result of the paper demonstrates that the previously mentioned methodology can possibly be the potential of the supporting decision-making system in LD investigating and diagnosing.

scholarly journals MACHINE LEARNING FOR DECISION-MAKING IN THE REMANUFACTURING OF WORN-OUT GEARS AND BEARINGS

2021 ◽  
Vol 32 (2) ◽  
pp. 135-150
Author(s):  
Wayne Tsimba ◽  
Gibson Chirinda ◽  
Stephen Matope
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Mechanical Equipment ◽  
Spur Gears ◽  
Element Analysis ◽  
Wear And Tear ◽  
Decision Making System ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Day To Day Operations

Mechanical industries use rotating mechanical equipment in their day to day operations. The equipment suffers from wear and tear, and is usually discarded as scrap. But is there a way to recover some of this equipment and reuse it? This paper uses machine learning to capture and analyse the wearing damage of bearings and gears to determine whether they can be redeemed. Finite element analysis is conducted on worn-out spur gears and pillow bearings in order to facilitate feature extraction in image processing algorithms. This converts the actual gears, bearings, and seals into CAD files. The decision-making system is designed, and it uses these CAD files to decide on the optimum manufacturing process to restore redeemable components. The mechanical components of the system are designed using SOLIDWORKS. MATLAB, Proteus software, and the Arduino micro-controller are used for the system application design and simulation. The results from tests conducted on a worn-out gear and bearing show that the gear is 4% non-redeemable, while the bearing is 60.2% non-redeemable. The decision taken by the system is to redeem the gear and to discard the bearing.

scholarly journals A decision support system for primary headache developed through machine learning

PeerJ ◽  
2022 ◽  
Vol 10 ◽  
pp. e12743
Author(s):  
Fangfang Liu ◽  
Guanshui Bao ◽  
Mengxia Yan ◽  
Guiming Lin
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Statistical Analysis ◽  
Diagnostic Accuracy ◽  
Confusion Matrix ◽  
Primary Headache ◽  
Primary Headaches ◽  
Decision Making System ◽  
Tension Type Headaches

Background Primary headache is a disorder with a high incidence and low diagnostic accuracy; the incidence of migraine and tension-type headache ranks first among primary headaches. Artificial intelligence (AI) decision support systems have shown great potential in the medical field. Therefore, we attempt to use machine learning to build a clinical decision-making system for primary headaches. Methods The demographic data and headache characteristics of 173 patients were collected by questionnaires. Decision tree, random forest, gradient boosting algorithm and support vector machine (SVM) models were used to construct a discriminant model and a confusion matrix was used to calculate the evaluation indicators of the models. Furthermore, we have carried out feature selection through univariate statistical analysis and machine learning. Results In the models, the accuracy, F1 score were calculated through the confusion matrix. The logistic regression model has the best discrimination effect, with the accuracy reaching 0.84 and the area under the ROC curve also being the largest at 0.90. Furthermore, we identified the most important factors for distinguishing the two disorders through statistical analysis and machine learning: nausea/vomiting and photophobia/phonophobia. These two factors represent potential independent factors for the identification of migraines and tension-type headaches, with the accuracy reaching 0.74 and the area under the ROC curve being at 0.74. Conclusions Applying machine learning to the decision-making system for primary headaches can achieve a high diagnostic accuracy. Among them, the discrimination effect obtained by the integrated algorithm is significantly better than that of a single learner. Second, nausea/vomiting, photophobia/phonophobia may be the most important factors for distinguishing migraine from tension-type headaches.

scholarly journals Research prospect of autonomous driving decision technology under complex traffic scenarios

2022 ◽  
Vol 355 ◽  
pp. 03031
Author(s):  
Yaoguang Cao ◽  
Yuyi Chen ◽  
Lu Liu
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Urban Traffic ◽  
Knowledge Map ◽  
Autonomous Decision ◽  
Decision Making System ◽  
Research Prospect ◽  
Safety And Stability

Decision-making system is the essential part of the autonomous vehicle “brain”, which determines the safety and stability of vehicles, and is also the key to reflect the intelligent level of autonomous vehicles. Compared with simple scenarios such as expressway, urban traffic scenarios have the characteristics of complex and frequent interaction between traffic participants. Carrying out in-depth research on complex traffic scenarios and optimizing autonomous decision-making algorithms are the key methods for the purpose of promoting the application of autonomous driving technologies. In the future, we can further combine the artificial intelligence methods such as cognitive or knowledge map, behaviour prediction of traffic participants, and humanoid intelligence, so as to enhance the intelligent level of autonomous driving.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
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