scholarly journals A New Three-Dimensional Indoor Positioning Mechanism Based on Wireless LAN

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jiujun Cheng ◽  
Yueqiao Cai ◽  
Qingyang Zhang ◽  
Junlu Cheng ◽  
Chendan Yan
Keyword(s):  
Machine Learning ◽  
Wireless Lan ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Indoor Positioning ◽  
Divide And Conquer ◽  
Machine Learning Algorithm ◽  
Two Dimensional ◽  
Service Areas ◽  
Error Distance

The researches on two-dimensional indoor positioning based on wireless LAN and the location fingerprint methods have become mature, but in the actual indoor positioning situation, users are also concerned about the height where they stand. Due to the expansion of the range of three-dimensional indoor positioning, more features must be needed to describe the location fingerprint. Directly using a machine learning algorithm will result in the reduced ability of classification. To solve this problem, in this paper, a “divide and conquer” strategy is adopted; that is, first through k-medoids algorithm the three-dimensional location space is clustered into a number of service areas, and then a multicategory SVM with less features is created for each service area for further positioning. Our experiment shows that the error distance resolution of the approach with k-medoids algorithm and multicategory SVM is higher than that of the approach only with SVM, and the former can effectively decrease the “crazy prediction.”

scholarly journals Recreation of the periodic table with an unsupervised machine learning algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Minoru Kusaba ◽  
Chang Liu ◽  
Yukinori Koyama ◽  
Kiyoyuki Terakura ◽  
Ryo Yoshida
Keyword(s):  
Machine Learning ◽  
Periodic Table ◽  
Data Science ◽  
Learning Algorithm ◽  
Chemical Properties ◽  
Human Cognition ◽  
Machine Learning Algorithm ◽  
Two Dimensional ◽  
Generative Topographic Mapping ◽  
Unsupervised Machine Learning

AbstractIn 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev. In terms of data science, his achievement can be viewed as a successful example of feature embedding based on human cognition: chemical properties of all known elements at that time were compressed onto the two-dimensional grid system for a tabular display. In this study, we seek to answer the question of whether machine learning can reproduce or recreate the periodic table by using observed physicochemical properties of the elements. To achieve this goal, we developed a periodic table generator (PTG). The PTG is an unsupervised machine learning algorithm based on the generative topographic mapping, which can automate the translation of high-dimensional data into a tabular form with varying layouts on-demand. The PTG autonomously produced various arrangements of chemical symbols, which organized a two-dimensional array such as Mendeleev’s periodic table or three-dimensional spiral table according to the underlying periodicity in the given data. We further showed what the PTG learned from the element data and how the element features, such as melting point and electronegativity, are compressed to the lower-dimensional latent spaces.

scholarly journals RULES-5: A rule induction algorithm for classification problems involving continuous attributes

2003 ◽  
Vol 217 (12) ◽  
pp. 1273-1286 ◽  
Author(s):  
D T Pham ◽  
S Bigot ◽  
S S Dimov
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Rule Extraction ◽  
Rule Induction ◽  
Divide And Conquer ◽  
Benchmark Problems ◽  
Machine Learning Algorithm ◽  
Classification Problems ◽  
Rule Sets

This paper presents RULES-5, a new induction algorithm for effectively handling problems involving continuous attributes. RULES-5 is a ‘covering’ algorithm that extracts IF-THEN rules from examples presented to it. The paper first reviews existing methods of rule extraction and dealing with continuous attributes. It then describes the techniques adopted for RULES-5 and gives a step-by-step example to illustrate their operation. The paper finally gives the results of applying RULES-5 and other algorithms to benchmark problems. These clearly show that RULES-5 generates rule sets that are more accurate than those produced by its immediate predecessor RULES-3 Plus and by a well-known commercially available divide-and-conquer machine learning algorithm.

scholarly journals Zebrafish behavior feature recognition using three-dimensional tracking and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peng Yang ◽  
Hiro Takahashi ◽  
Masataka Murase ◽  
Motoyuki Itoh
Keyword(s):  
Machine Learning ◽  
Electric Shock ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Machine Learning Algorithm ◽  
Resonance Theory ◽  
3D Tracking ◽  
Adaptive Resonance ◽  
Ability Tests

AbstractIn this work, we aim to construct a new behavior analysis method by using machine learning. We used two cameras to capture three-dimensional (3D) tracking data of zebrafish, which were analyzed using fuzzy adaptive resonance theory (FuzzyART), a type of machine learning algorithm, to identify specific behavioral features. The method was tested based on an experiment in which electric shocks were delivered to zebrafish and zebrafish swimming was tracked in 3D simultaneously to find electric shock-associated behaviors. By processing the obtained data with FuzzyART, we discovered that distinguishing behaviors were statistically linked to the electric shock based on the machine learning algorithm. Moreover, our system could accept user-supplied data for detection and quantitative analysis of the behavior features, such as the behavior features defined by the 3D tracking analysis above. This system could be applied to discover new distinct behavior features in mutant zebrafish and used for drug administration screening and cognitive ability tests of zebrafish in the future.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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