Artificial-Intelligence-Based Time-Series Intervention Models to Assess the Impact of the COVID-19 Pandemic on Tomato Supply and Prices in Hyderabad, India

This study’s objective was to assess the impact of the COVID-19 pandemic on tomato supply and prices in Gudimalkapur market in Hyderabad, India. The lockdown imposed by the government of India from 25 March 2020 to 30 June 2020 particularly affected the supply chain of perishable agricultural products, including tomatoes as one of the major vegetable crops in the study area. The classical time series models such as autoregressive integrated moving average (ARIMA) intervention models and artificial intelligence (AI)-based time-series models namely support vector regression (SVR) intervention and artificial neural network (ANN) intervention models were used to predict tomato supplies and prices in the studied market. The modelling results show that the pandemic had a negative impact on supply and a positive impact on tomato prices. Moreover, the ANN intervention model outperformed the other models in both the training and test data sets. The superior performance of the ANN intervention model could be due to its ability to account for the nonlinear and complex nature of the data with exogenous intervention variable.

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Intervention Modeling

10.1093/oso/9780190661557.003.0005 ◽

2017 ◽

Author(s):

Richard McCleary ◽

David McDowall ◽

Bradley J. Bartos

Keyword(s):

Time Series ◽

Autocorrelation Function ◽

Moving Average ◽

Intervention Model ◽

Noise Component ◽

Autoregressive Integrated Moving Average ◽

Noise Function ◽

Modeling Strategy ◽

The Impact ◽

True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Journal of the Operations Research Society of China ◽

10.1007/s40305-021-00354-9 ◽

2021 ◽

Author(s):

Jia-Bin Zhou ◽

Yan-Qin Bai ◽

Yan-Ru Guo ◽

Hai-Xiang Lin

Keyword(s):

Support Vector Machine ◽

Negative Impact ◽

Twin Support Vector Machine ◽

Fuzzy Membership ◽

Support Vector ◽

Membership Functions ◽

Fuzzy Membership Functions ◽

Intuitionistic Fuzzy ◽

Benchmark Datasets ◽

The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

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Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

Mathematical Problems in Engineering ◽

10.1155/2019/3547416 ◽

2019 ◽

Vol 2019 ◽

pp. 1-21 ◽

Cited By ~ 15

Author(s):

Naeem Ratyal ◽

Imtiaz Ahmad Taj ◽

Muhammad Sajid ◽

Anzar Mahmood ◽

Sohail Razzaq ◽

...

Keyword(s):

Face Recognition ◽

Superior Performance ◽

Support Vector ◽

Complex Nature ◽

Alignment Algorithm ◽

3D Face Recognition ◽

3D Face ◽

Norm Minimization ◽

Wide Range ◽

The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

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Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

Remote Sensing ◽

10.3390/rs11212512 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2512 ◽

Cited By ~ 7

Author(s):

Nicolas Karasiak ◽

Jean-François Dejoux ◽

Mathieu Fauvel ◽

Jérôme Willm ◽

Claude Monteil ◽

...

Keyword(s):

Time Series ◽

Tree Species ◽

Cross Validation ◽

Satellite Image ◽

Support Vector ◽

Validation Data ◽

Spatial Instability ◽

Classification Errors ◽

Leave One Out ◽

The Impact

Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

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The Impact of the Change in Payment System for German Psychiatric Clinics on Treatment Success: An Interrupted Time Series Analysis

Das Gesundheitswesen ◽

10.1055/a-0828-7504 ◽

2019 ◽

Vol 82 (06) ◽

pp. 559-567

Author(s):

Christina Niedermeier ◽

Andrea Barrera ◽

Eva Esteban ◽

Ivana Ivandic ◽

Carla Sabariego

Keyword(s):

Time Series ◽

Negative Impact ◽

Treatment Success ◽

Interrupted Time Series ◽

Sensitivity Analyses ◽

Interrupted Time Series Analysis ◽

Post Intervention ◽

Intervention Point ◽

The Impact ◽

New System

Abstract Background In Germany a new reimbursement system for psychiatric clinics was proposed in 2009 based on the § 17d KHG Psych-Entgeltsystem. The system can be voluntary implemented by clinics since 2013 but therapists are frequently afraid it might affect treatment negatively. Objectives To evaluate whether the new system has a negative impact on treatment success by analysing routinely collected data in a Bavarian clinic. Material and methods Aggregated data of 1760 patients treated in the years 2007–2016 was analysed with segmented regression analysis of interrupted time series to assess the effects of the system on treatment success, operationalized with three outcome variables. A negative change in level after a lag period was hypothesized. The robustness of results was tested by sensitivity analyses. Results The percentage of patients with treatment success tends to increase after the new system but no significant change in level was observed. The sensitivity analyses corroborate results for 2 outcomes but when the intervention point was shifted, the positive change in level for the third outcome became significant. Conclusions Our initial hypothesis is not supported. However, the sensitivity analyses disclosed uncertainties and our study has limitations, such as a short observation time post intervention. Results are not generalizable as data of a single clinic was analysed. Nevertheless, we show the importance of collecting and analysing routine data to assess the impact of policy changes on patient outcomes.

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Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods

Water ◽

10.3390/w11061226 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1226 ◽

Cited By ~ 2

Author(s):

Mohammed Falah Allawi ◽

Faridah Binti Othman ◽

Haitham Abdulmohsin Afan ◽

Ali Najah Ahmed ◽

Md. Shabbir Hossain ◽

...

Keyword(s):

Artificial Intelligence ◽

Prediction Accuracy ◽

Evaporation Rate ◽

Climatic Conditions ◽

Support Vector ◽

Time Increment ◽

Accuracy Level ◽

Increment Rate ◽

Input Variables ◽

The Impact

The current study explored the impact of climatic conditions on predicting evaporation from a reservoir. Several models have been developed for evaporation prediction under different scenarios, with artificial intelligence (AI) methods being the most popular. However, the existing models rely on several climatic parameters as inputs to achieve an acceptable accuracy level, some of which have been unavailable in certain case studies. In addition, the existing AI-based models for evaporation prediction have paid less attention to the influence of the time increment rate on the prediction accuracy level. This study investigated the ability of the radial basis function neural network (RBF-NN) and support vector regression (SVR) methods to develop an evaporation rate prediction model for a tropical area at the Layang Reservoir, Johor River, Malaysia. Two scenarios for input architecture were explored in order to examine the effectiveness of different input variable patterns on the model prediction accuracy. For the first scenario, the input architecture considered only the historical evaporation rate time series, while the mean temperature and evaporation rate were used as input variables for the second scenario. For both scenarios, three time-increment series (daily, weekly, and monthly) were considered.

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Does Foreign Direct Investment More Important Than Domestic Direct Investment for Export Structure? Evidence from Indonesia

10.21203/rs.3.rs-713874/v1 ◽

2021 ◽

Author(s):

Ariyo DP Irhamna ◽

Ely Nurhayati ◽

Adinda Putri Safira ◽

Galuh Indra Wijaya

Keyword(s):

Time Series ◽

Foreign Investment ◽

Direct Investment ◽

Negative Impact ◽

Positive Impact ◽

Domestic Investment ◽

Fdi Inflows ◽

Export Structure ◽

The Impact ◽

Export Product

Abstract Scholars have long studied the spillover of FDI on trade. However, there has been limited study which spesifically investigate the impact of FDI on the export structure in a developing country. Does FDI more important than domestic investment for export structure? To examine the question, we test the impact of FDI and DDI on the export structure in time series framework, utilizing data on FDI inflows to Indonesia and export data based on product stage over 1992–2017. The export structure is analyzed based on three categories, namely primary product, intermediate product, and final product. Our results show that domestic investment has a negative impact on the primary export product, while foreign investment has a positive impact on the final export product. The result highlights the importance of domestic and foreign investment in export upgrading.

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p-norms of histogram of oriented gradients for X-ray images

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i5.pp4423-4430 ◽

2021 ◽

Vol 11 (5) ◽

pp. 4423

Author(s):

Nuha H. Hamada ◽

Faten F. Kharbat

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Euclidean Distance ◽

Support Vector ◽

Histogram Of Oriented Gradients ◽

Lebesgue Spaces ◽

X Ray ◽

Original Algorithm ◽

Norm Values ◽

The Impact

Lebesgue spaces (Lp over Rn) play a significant role in mathematical analysis. They are widely used in machine learning and artificial intelligence to maximize performance or minimize error. The well-known histogram of oriented gradients (HOG) algorithm applies the 2-norm (Euclidean distance) to detect features in images. In this paper, we apply different p-norm values to identify the impact that changing these norms has on the original algorithm. The aim of this modification is to achieve better performance in classifying X-ray medical images related to of COVID-19 patients. The efficiency of the p-HOG algorithm is compared with the original HOG descriptor using a support vector machine implemented in Python. The results of the comparisons are promising, and the p-HOG algorithm shows greater efficiency in most cases.

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Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran

Journal of Irrigation and Drainage Engineering ◽

10.1061/(asce)ir.1943-4774.0001471 ◽

2020 ◽

Vol 146 (6) ◽

pp. 04020010 ◽

Cited By ~ 12

Author(s):

Afshin Ashrafzadeh ◽

Ozgur Kişi ◽

Pouya Aghelpour ◽

Seyed Mostafa Biazar ◽

Mohammadreza Askarizad Masouleh

Keyword(s):

Time Series ◽

Support Vector Machines ◽

Comparative Study ◽

Support Vector ◽

Time Series Models ◽

Northern Iran ◽

Vector Machines

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Indication of Electromagnetic Field Exposure via RBF-SVM Using Time-Series Features of Zebrafish Locomotion

Sensors ◽

10.3390/s20174818 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4818

Author(s):

Yaqing He ◽

Kim Fung Tsang ◽

Richard Yuen-Chong Kong ◽

Yuk-Tak Chow

Keyword(s):

Time Series ◽

Environmental Parameters ◽

Empirical Method ◽

Support Vector ◽

The Novel ◽

Adult Zebrafish ◽

Field Exposure ◽

Magnetic Exposure ◽

Tank Test ◽

The Impact

This paper introduces a novel model based on support vector machine with radial basis function kernel (RBF-SVM) using time-series features of zebrafish (Danio rerio) locomotion exposed to different electromagnetic fields (EMFs) to indicate the corresponding EMF exposure. A group of 14 adult zebrafish was randomly divided into two groups, 7 in each group; the fish of each group have the novel tank test under a sham or real magnetic exposure of 6.78 MHz and about 1 A/m. Their locomotion in the tests was videotaped to convert into the x, y coordinate time-series of the trajectories for reforming time-series matrices according to different time-series lengths. The time-series features of zebrafish locomotion were calculated by the comparative time-series analyzing framework highly comparative time-series analysis (HCTSA), and a limited number of the time-series features that were most relevant to the EMF exposure conditions were selected using the minimum redundancy maximum relevance (mRMR) algorithm for RBF-SVM classification training. Before this, ambient environmental parameters (AEPs) had little effect on the locomotion performance of zebrafish processed by the empirical method, which had been quantitatively verified by regression using another group of 14 adult zebrafish. The results have demonstrated that the purposed model is capable of accurately indicating different EMF exposures. All classification accuracies can be 100%, and the classification precision of several classifiers based on specific parameters and feature sets with specific dimensions can reach higher than 95%. The speculative reason for this result is that the specified EMF has affected the zebrafish neural aspect, which is then reflected in their behaviors. The outcomes of this study have provided a new indication model for EMF exposures and provided a reference for the investigation of the impact of EMF exposure.

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