scholarly journals Adaptive Multi-state Pipe Framework Based on Set Pair Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 158-163
Author(s):  
Leixin Shi ◽  
Hongji Xu ◽  
Beibei Zhang ◽  
Xiaojie Sun ◽  
Juan Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Set Pair Analysis ◽  
New Approach ◽  
Test Error ◽  
Main Research ◽  
Main Pipe ◽  
Research Fields ◽  
Classification Tasks ◽  
Pair Analysis

Human Activity Recognition (HAR) is one of the main research fields in pattern recognition. In recent years, machine learning and deep learning have played important roles in Artificial Intelligence (AI) fields, and are proven to be very successful in classification tasks of HAR. However, there are two drawbacks of the mainstream frameworks: 1) all inputs are processed with the same parameters, which would cause the framework to incorrectly assign an unrealistic label to the object; 2) these frameworks lack generality in different application scenarios. In this paper, an adaptive multi-state pipe framework based on Set Pair Analysis (SPA) is presented, where pipes are mainly divided into three kinds of types: main pipe, sub-pipe and fusion pipe. In the main pipe, the input of classification tasks is preprocessed by SPA to obtain the Membership Belief Matrix (MBM). The sub-pipe shunt processing is performed according to the membership belief. The results are merged through the fusion pipe in the end. To test the performance of the proposed framework, we attempt to find the best configuration set that yields the optimal performance and evaluate the effectiveness of the new approach on the popular benchmark dataset WISDM. Experimental results demonstrate that the proposed framework can get the good performance by achieving a result of 1.4% test error.

scholarly journals Deformation analysis with Total Least Squares

2006 ◽  
Vol 6 (4) ◽  
pp. 663-669 ◽  
Author(s):  
M. Acar ◽  
M. T. Özlüdemir ◽  
O. Akyilmaz ◽  
R. N. Çelik ◽  
T. Ayan
Keyword(s):  
Least Squares ◽  
Total Least Squares ◽  
Transformation Model ◽  
Deformation Analysis ◽  
New Approach ◽  
Main Research ◽  
Research Fields ◽  
Analysis Process ◽  
Alternative Methodology ◽  
Coordinate Changes

Abstract. Deformation analysis is one of the main research fields in geodesy. Deformation analysis process comprises measurement and analysis phases. Measurements can be collected using several techniques. The output of the evaluation of the measurements is mainly point positions. In the deformation analysis phase, the coordinate changes in the point positions are investigated. Several models or approaches can be employed for the analysis. One approach is based on a Helmert or similarity coordinate transformation where the displacements and the respective covariance matrix are transformed into a unique datum. Traditionally a Least Squares (LS) technique is used for the transformation procedure. Another approach that could be introduced as an alternative methodology is the Total Least Squares (TLS) that is considerably a new approach in geodetic applications. In this study, in order to determine point displacements, 3-D coordinate transformations based on the Helmert transformation model were carried out individually by the Least Squares (LS) and the Total Least Squares (TLS), respectively. The data used in this study was collected by GPS technique in a landslide area located nearby Istanbul. The results obtained from these two approaches have been compared.

Risk Degree Assessment of Natural Disasters in China Using Set Pair Analysis Method

2012 ◽  
Vol 518-523 ◽  
pp. 1273-1277
Author(s):  
Yun Xia Xie ◽  
Wen Sheng Wang
Keyword(s):  
Natural Disasters ◽  
Critical Issue ◽  
Set Pair Analysis ◽  
Analysis Method ◽  
Full Account ◽  
New Approach ◽  
Risk Degree ◽  
Assessment Standard ◽  
Pair Analysis

It is a critical issue to assess risk degree of natural disasters in China. Firstly the assessment indexes and assessment standard grades of natural disasters risk degree are established, then comes up with a new approach to assess risk degree of natural disasters: Set Pair Analysis Method (SPAM), finally acquires the risk degree grades of Chinese natural disasters based on SPAM. The SPAM takes the fuzzy of grade standards into full account, and is simple in concept and easy in calculation and application. The results of evaluation about Chinese natural disasters risk degree are credible.

A new approach to water resources system assessment — set pair analysis method

2009 ◽  
Vol 52 (10) ◽  
pp. 3017-3023 ◽  
Author(s):  
WenSheng Wang ◽  
JuLiang Jin ◽  
Jing Ding ◽  
YueQing Li
Keyword(s):  
Water Resources ◽  
Set Pair Analysis ◽  
Analysis Method ◽  
New Approach ◽  
Water Resources System ◽  
System Assessment ◽  
Pair Analysis

scholarly journals Machine learning as the new approach in understanding biomarkers of suicidal behavior

2020 ◽  
Author(s):  
Alja Videtič Paska ◽  
Katarina Kouter
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
High Throughput ◽  
Suicidal Behavior ◽  
Technological Development ◽  
Machine Learning Algorithms ◽  
Classical Approach ◽  
New Approach

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore ‘omic’ studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

scholarly journals XMAP: eXplainable mapping analytical process

2021 ◽  
Author(s):  
Su Nguyen ◽  
Binh Tran
Keyword(s):  
Artificial Intelligence ◽  
Binary Classification ◽  
Predictive Performance ◽  
Black Box ◽  
Large Datasets ◽  
Machine Learning Algorithms ◽  
New Approach ◽  
Analytical Process ◽  
Explainable Ai ◽  
Classification Tasks

AbstractAs the number of artificial intelligence (AI) applications increases rapidly and more people will be affected by AI’s decisions, there are real needs for novel AI systems that can deliver both accuracy and explanations. To address these needs, this paper proposes a new approach called eXplainable Mapping Analytical Process (XMAP). Different from existing works in explainable AI, XMAP is highly modularised and the interpretability for each step can be easily obtained and visualised. A number of core algorithms are developed in XMAP to capture the distributions and topological structures of data, define contexts that emerged from data, and build effective representations for classification tasks. The experiments show that XMAP can provide useful and interpretable insights across analytical steps. For the binary classification task, its predictive performance is very competitive as compared to advanced machine learning algorithms in the literature. In some large datasets, XMAP can even outperform black-box algorithms without losing its interpretability.

scholarly journals A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models

2021 ◽  
Author(s):  
Mohammadtaghi Avand ◽  
Maziar Mohammadi ◽  
Fahimeh Mirchooli ◽  
Ataollah Kavian ◽  
John P Tiefenbacher
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Soil Erosion ◽  
Classification Tree ◽  
Slope Angle ◽  
Rainfall Erosivity ◽  
Learning Models ◽  
New Approach ◽  
Erosion Modelling ◽  
Machine Learning Models

Abstract Despite advances in artificial intelligence modelling, the lack of soil erosion data and other watershed information is still one of the important factors limiting soil-erosion modelling. Additionally, the limited number of parameters and the lack of evaluation criteria are major disadvantages of empirical soil-erosion models. To overcome these limitations, we introduce a new approach that integrates empirical and artificial intelligence models. Erosion-prone locations (erosion ≥16 tons/ha/year) are identified using RUSLE model and a soil-erosion map is prepared using random forest (RF), artificial neural network (ANN), classification tree analysis (CTA), and generalized linear model (GLM). This study uses 13 factors affecting soil erosion in the Talar watershed, Iran, to increase prediction accuracy. The results reveal that the RF model has the highest prediction performance (AUC=0.95, Kappa=0.87, Accuracy=0.93, and Bias=0.88), outperforming the three machine-learning models. The results show that slope angle, land use/land cover, elevation, and rainfall erosivity are the factors that contribute the most to soil erosion propensity in the watershed. Curvature and topography position index (TPI) were removed from the analysis due to multicollinearity with other factors. The results can be used to improve the identification of hot spots of soil erosion, especially in watersheds for which soil-erosion data are limited.

Impact of Digitalization on Category Management

2019 ◽  
pp. 169-191
Author(s):  
Arif Mohammed ◽  
Ricardo Cesar Panserini
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Recognition ◽  
Traditional Approach ◽  
Massive Transformation ◽  
Management Process ◽  
Category Management ◽  
New Approach ◽  
Iot Devices ◽  
Transactional Data

The discipline of category management has always played an important role within retailers as well as their CPG manufacturer suppliers. While the eight steps within the category management process (category definition, category role, category assessment, category scorecard, category strategies, category tactics, category implementation, and category review) have remained the same, with digitalization the discipline is undergoing a massive transformation, and the approach to the process is getting disrupted through the availability of huge volumes of transactional data, customer loyalty data; advancement in hardware technology through better scanners, image recognition devices, sensors and IoT devices and machine learning, and artificial intelligence. In this chapter, the authors take a closer look at the eight-step category management process, the traditional approach, the enabler for disruption, the new approach, and its benefits and what the future may hold.

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