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Forecasting deforestation and carbon emissions in tropical developing countries facing demographic expansion: a case study in Madagascar

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ECOLOGY AND EVOLUTION 2013年第6期3卷 1702-1716页

作者： Vieilledent, Ghislain Grinand, Clovis Vaudry, Romuald Cirad UPR BSEF F-34398 Montpellier 5 France Cirad Madagascar DP Foret & & Biodivers Antananarivo 101 Madagascar GoodPlanet Fdn GoodPlanet F-75116 Paris France

Anthropogenic deforestation in tropical countries is responsible for a significant part of global carbon dioxide emissions in the atmosphere. To plan efficient climate change mitigation programs (such as REDD+, Reducing Emissions from Deforestation and forest Degradation), reliable forecasts of deforestation and carbon dioxide emissions are necessary. Although population density has been recognized as a key factor in tropical deforestation, current methods of prediction do not allow the population explosion that is occurring in many tropical developing countries to be taken into account. Here, we propose an innovative approach using novel computational and statistical tools, including R/GRASS scripts and the new phcfM R package, to model the intensity and location of deforestation including the effect of population density. We used the model to forecast anthropogenic deforestation and carbon dioxide emissions in five large study areas in the humid and spiny-dry forests of Madagascar. Using our approach, we were able to demonstrate that the current rapid population growth in Madagascar (+3.39% per year) will significantly increase the intensity of deforestation by 2030 (up to +1.17% per year in densely populated areas). We estimated the carbon dioxide emissions associated with the loss of aboveground biomass to be of 2.24 and 0.26 tons per hectare and per year in the humid and spiny-dry forest, respectively. Our models showed better predictive ability than previous deforestation models (the figure of merit ranged from 10 to 23). We recommend this approach to reduce the uncertainty associated with deforestation forecasts. We also underline the risk of an increase in the speed of deforestation in the short term in tropical developing countries undergoing rapid population expansion.

关键词： Anthropogenic deforestation biodiversity conservation climate change GRASS GIS greenhouse gas emission land use change logistic regression model phcfM R package population growth REDD

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Data mining for modeling students' performance: A tutoring action plan to prevent academic dropout

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 66卷 541-556页

作者： Burgos, Concepcion Campanario, Maria L. de la Pena, David Lara, Juan A. Lizcano, David Martinez, Maria A. MOU Fac Ciencias Econ & Empresariales Ctra CorunaKm 38-500Via Serv 15 Madrid 28400 Spain MOU Escuela Ciencias Tecn & Ingn Ctra CorunaKm 38-500Via Serv 15 Madrid 28400 Spain

E-learning systems generate huge amounts of data, whose analysis may become a daunting task which makes it necessary to use computational analytical techniques and tools. We propose the use of knowledge discovery techniques to analyse historical student course grade data in order to predict whether or not a student will drop out of a course. logistic regression models are used for the purpose of classification. Experiments conducted with data on over 100 students for several distance learning courses confirm the predictive power of our proposal. Using the resulting predictive models we have designed a tutoring action plan. Applying this plan, we managed to reduce the dropout rate by 14% with respect to previous academic years in which no dropout prevention mechanism was applied. Our main contribution is a tool and a tutoring plan that can be used by our educational institution (and others) to reduce dropout rate in e-learning courses. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： E-learning Student dropout prediction Educational data mining logistic regression model Temporal data Student dropout prevention Tutoring action plan

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Use of Chemometric Techniques to Design a Microbiological Method for Sulfonamide Detection in Milk

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CZECH JOURNAL OF FOOD SCIENCES 2013年第6期31卷 627-632页

作者： Nagel, Orlando G. Pilar Molina, Maria Althaus, Rafael L. Natl Univ Litoral Dept Basic Sci Fac Vet Sci Esperanza Argentina Univ Politecn Valencia Dept Anim Sci Valencia Spain

We proposed an experimental design of a microbial bioassay of dichotomous response (positive or negative) using Bacillus subtilis BGA for the detection of sulfonamide residues. In the first stage, the bioassay response time was reduced to 6 h by increasing the spore concentration of B. subtilis. Then, the effects of spore, indicator, trimethoprim (TMP) concentration, and volume of the culture medium were examined with a Plackett Burman design (2(4-1)). Finally, the effect of TMP concentration on the method detection capabilities and specificity was analysed using a logistic model with interaction. The detection capabilities of sulfonamides in milk are close to the MRLs when using 500 mu g/1 of TMP in the culture medium of the bioassay. It is concluded that the experimental design techniques and a logistic regression model can be used to design successfully a dichotomous response bioassay.

关键词： B. subtilis microbial inhibition method bioassay experimental design logistic regression model

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Tool condition monitoring and degradation estimation in rotor slot machining process

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第1-4期91卷 39-48页

作者： Liu, Yingchao Hu, Xiaofeng Yan, Shan Sun, Shixu Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

Tool wear degradation and working status of slotting cutter have a great effect on the surface quality of rotor slot;therefore, tool condition monitoring and its degradation estimation are needed for guaranteeing slot machining quality. This paper proposes a two-phase method based on acoustic emission (AE) signal classification and logistic regression model for slotting cutter condition monitoring and its degradation estimation. In the first phase, the failure reliability estimation models corresponding to different machining processes are established considering the variability of process system like tool regrinding times and material randomness of workpiece. In the second phase, the most appropriate estimation model corresponding to the optimum cluster is selected and used for failure reliability estimation and status determination of slotting cutter. This approach has been validated on a CNC rotor slot machine in a factory. Experimental results show that the proposed method can be effectively used for cutting tool degradation estimation and status determination of slotting cutter with high accuracy.

关键词： Tool condition monitoring Degradation estimation Slotting cutter Acoustic emission logistic regression model

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Detection of Ovarian Tumors in Obstetric Ultrasound Imaging Using logistic regression Classifier With an Advanced Machine Learning Approach

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IEEE ACCESS 2020年 8卷 44999-45008页

作者： Zhang, Zheng Han, Yibo Nanyang Inst Technol Sch Software Nanyang 473000 Peoples R China

This paper addresses the use in different stages of pregnancy of ultrasound imaging and to examine the tumors diagnosed during lactation or pregnancy. There are recent advancements in the application of obstetric ultrasound and imaging techniques helpful for improving the outcome of the pregnancy using various Learning techniques. This paper addresses the need to implement sustainable ultrasound standards with an acceptably high maternal and perinatal mortality rates to provide better and more affordable, quality Ultrasonic Flaw (UT) equipment which can improve Obstetric health care. The state-of-the-art learning approach for obstetric ultrasound is a category of methods in machine learning that are gaining popularity and attracting interest in various fields, including image processing and computer vision. In this paper advanced Machine learning processes map a raw input image to the desired output image using logistic regression classifier(LRC) and Convolution neural networks (CNNs) are of particular interest among all Machine learning methods. Furthermore, we have utilized the Internet of Medical Things (IoMT) for obstetric tumor image segmentation and identification of tumors for the medical experts. The experimental results show the LRC based on CNN can be utilized to predict the output of the ultrasound of obstetric with increased maternal and perinatal mobility rates.

关键词： Ultrasonic imaging Tumors Machine learning Pregnancy logistics Biomedical imaging Machine learning convolution neural network logistic regression model obstetric ultrasound IoMT

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Predicting Student-Teachers Dropout Risk and Early Identification: A Four-Step logistic regression Approach

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IEEE ACCESS 2022年 10卷 6470-6482页

作者： Singh, Harman Preet Alhulail, Hilal Nafil Univ Hail Coll Business Adm Dept Management & Informat Syst Hail 81451 Saudi Arabia

Student-teachers' dropout is a complicated and serious issue in the learning process, with its attendant negative implications on students, academic institutions, economic resources, and society. This study investigated the composite and relative impact of personal (student), academic and socioeconomic predictive variables on student-teacher dropout. The study improves the early identification of at-risk student-teachers by developing a model that optimizes predictability. We used questionnaires and adopted a four-step logistic regression procedure on a sample of 1723 student-teachers in public teachers training colleges (TTCs) of a least-developed country (LDC). The study confirmed twin academic performance and aspirations factors as the highest predictors of student-teacher attrition. Academic reasons for choosing TTC were significant, as vocational motivation and goals established by student-teachers early in their education help prevent dropout. Contrary to expectations, student-teachers' cultural values, parents' level of education, and cost of financing education had no significant impact on dropout decisions. This is most likely due to the Government's financial support for student-teachers in LDCs and the widespread belief that higher education can improve one's social and economic status. The findings indicate that early identification and dropout prevention efforts should integrate various support services to foster a healthy learning and retention environment.

关键词： Education Predictive models logistics Training Developing countries Costs Analytical models Dropout risk early identification least-developed economy logistic regression model prediction student-teachers teacher training colleges

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Krylov subspace solvers for l1 regularized logistic regression method

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2023年第6期52卷 2738-2751页

作者： El Guide, M. Jbilou, K. Koukouvinos, C. Lappa, A. Mohammed VI Polytech Univ Ctr Behav Econ & Decis Making CBED FGSES Green City Morocco Univ Littoral Cote dOpale Dept Math Calais France Natl Tech Univ Athens Dept Math Athens 15773 Greece

In this paper, we propose an approach based on Krylov subspace methods for the solution of l(1) regularized logistic regression problem. The main idea is to transform the constrained l(2)-l(1) minimization problem obtained by applying the IRLS method to a l(2)-l(2) one that allow regularization matrices in the usual 2-norm regularization term. The regularization parameter that controls the equilibrium between the minimization of the two terms of the l(2)-l(2) minimization problem can be then chosen inexpensively by solving some reduced minimization problems related to generalized cross-validation (GCV) methods. These reduced problems can be obtained after a few iterations of Krylov subspace based methods. The goal of our simulation study is directed toward the variable selection and the prediction accuracy performance of the proposed method in solving a l(1) regularized logistic regression problem in large dimensional data with different correlation structures among predictors. Finally, real data are used to confirm the efficiency of the proposed method in terms of the computational cost.

关键词： logistic regression model Krylov subspace methods regularization l(1)-norm variable selection

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Evaluation of monofractal and multifractal properties of inter-beat (R-R) intervals in cardiac signals for differentiation between the normal and pathology classes

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IET SIGNAL PROCESSING 2019年第9期13卷 798-805页

作者： Gorshkov, Oleg Ombao, Hernando KAUST Stat Program Biostat Res Grp Thuwal 239556900 Saudi Arabia

In this study, the monofractal and multifractal properties of inter-beat (R-R) intervals in cardiac signals for normal and pathology classes were studied and applied to a dataset in PhysioNet which consists of 24 h R-R intervals from 54 healthy subjects (hs) and 92 patients with various diagnoses [44 with congestive heart failure (chf), 25 with atrial fibrillation (af) and 23 diagnosed with sudden death syndrome (sd)]. The results in this study indicate that the most suitable method for estimating the monofractal properties of R-R intervals is detrending moving average (DMA). The Hurst exponents (H) of the healthy and pathological groups, calculated using the DMA method, are shown to be statistically different by the Kruskal-Wallis test [p-value < 0.01;healthy $H_{{\rm hs}}$Hhs = 0.23(0.18-0.27);pathological group $H_{{\rm chf}}$Hchf = 0.06(0.03-0.10), $H_{{\rm af}}$Haf = 0.05(0.03-0.07), $H_{{\rm sd}}$Hsd = 0.05(0.03-0.08)]. To study the multifractal properties of R-R intervals, the multifractal detrended fluctuation analysis was used. Formal statistical tests indicate statistically significant differences (p-value < 0.05, ANOVA and pairwise testing) between the width of the spectrum of the chf group $W_{{\rm chf}}$Wchf = 1.11 +/- 0.12 and the width of the spectra of the af group $W_{{\rm af}}$Waf = 0.58 +/- 0.15, the sd group $W_{{\rm sd}}$Wsd = 0.60 +/- 0.20, and the hs group $W_{{\rm hs}}$Whs = 0.61 +/- 0.08. Using these results, a logistic regression model was developed for differentiating chf from other pathologies (af and sd).

关键词： medical signal processing statistical testing fluctuations electrocardiography regression analysis medical disorders fractals multifractal properties multifractal detrended fluctuation analysis formal statistical tests cardiac signals congestive heart failure atrial fibrillation monofractal properties healthy groups pathological groups DMA method Kruskal-Wallis test interbeat intervals sudden death syndrome electrocardiogram logistic regression model

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Suitable habitat model for walleye (Sander vitreus) in Lake Erie: Implications for inter-jurisdictional harvest quota allocations

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JOURNAL OF GREAT LAKES RESEARCH 2013年第4期39卷 591-601页

作者： Pandit, Shubha N. Zhao, Yingming Ciborowski, Jan J. H. Gorman, Ann Marie Knight, Carey T. Univ Windsor Dept Biol Sci Windsor ON N9B 3P4 Canada Ontario Minist Nat Resources Aquat Res & Dev Sect Wheatley ON NOP 2P0 Canada Ohio Dept Nat Resources Fairport Fish Res Stn Fairport Harbor OH 44077 USA

models predicting habitat distributions can give insight into species-habitat requirements and anticipate how populations respond to environmental change. Despite the economic and ecological importance of walleye (Sander vitreus) in Lake Erie, no preferred-habitat model exists and the spatial extent of suitable habitat is poorly understood. Empirical species-habitat models for three groups of walleye (juveniles, adults, and all walleye) was developed using records from a long term gill net data base (21 years). We examined the degree to which habitat suitability varies with vertical stratum for each group and whether the new model yields different estimates of available walleye habitat when compared to the current depth-based approach. Walleye occurrence in gill nets was positively related to water temperature, negatively related to water depth and water clarity, and unrelated to dissolved oxygen concentration. A model that incorporated interaction terms among the independent variables performed better than the linear, quadratic, and cubic generalized linear models (GLMs) for all three groups. Our results indicate that the extent of suitable habitat varies spatially in Lake Erie and is greatest in the West basin. Weighted Habitat Suitability Areas (WHSA), a combination of habitat quality and quantity, differed significantly among basins and vertical strata in Lake Erie. The current quota allocation strategy for Lake Erie walleye is based on the proportional amount of preferred habitat by jurisdiction. However, the current depth-based definition of preferred habitat may not be an adequate representation of walleye suitable habitat shared by each jurisdiction. (C) 2013 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

关键词： Walleye Sander vitreus logistic regression model Habitat suitability model Lake Erie

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A Data Driven Cycle Time Prediction With Feature Selection in a Semiconductor Wafer Fabrication System

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2018年第1期31卷 173-182页

作者： Wang, Junliang Zhang, Jie Wang, Xiaoxi Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Donghua Univ Coll Mech Engn Shanghai 201620 Peoples R China

Accurate prediction of cycle time (CT) plays an important role in the promises of a good delivery-time for semiconductor manufacturers. However, the CTs of wafer lots are difficult to forecast since they are influent by a lot factors (e.g., workload for every machine). The identification of key factors (called CT-related) can not only improve the prediction performance but also facilitate the control of CT, which is of particular significance. This paper focuses on gathering subtle variables into the candidate variables set along with the further analysis which is required in the correlation analysis between various candidate factors and CTs of wafer lots. The candidates set is first constructed to collect all factors that may affect wafer lots' CT. Then, a regression-based model is proposed to select CT-related variables, which consists of three parts: 1) discretization;2) adaptive logistic regression-based correlation analysis;and 3) CT-related factor selection. Subsequently, a parallel computation model is implemented to forecast the CTs of wafer lots. In the numerical experiments, 108 CT-related factors stood out from 774 candidates and replaced six global factors (used as reference) to predict CTs of wafer lots. The results indicated that the proposed approach had higher accuracy than linear regression and back propagation network in CT forecasting in large scale data analysis.

关键词： Cycle time forecasting data driven semiconductor manufacturing logistic regression model

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