scholarly journals Biogeographical Distribution Model of Flowering Plant Capparis micracantha Using Support Vector Machine (SVM) and Generalized Linear Model (GLM) and its Ex-situ Conservation Efforts

2021 ◽  
Vol 11 (6) ◽  
pp. 2328
Author(s):  
Inggit Puji Astuti ◽  
Angga Yudaputra ◽  
Dipta Sumeru Rinandio ◽  
Ade Yusuf Yuswandi
Keyword(s):  
Support Vector Machine ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Ex Situ Conservation ◽  
Flowering Plant ◽  
Ex Situ ◽  
Distribution Model ◽  
Support Vector ◽  
Biogeographical Distribution

scholarly journals Analisis Trend Topik Penelitian pada Web Of Science dan SINTA untuk Penentuan Tema Tugas Akhir Mahasiswa AMIK Indonesia Banda Aceh

2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Bahruni Bahruni ◽  
Fathurrahmad Fathurrahmad
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Decision Tree ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Core Collection ◽  
Web Of Science ◽  
Support Vector ◽  
Large Margin

Penelitian ini mencoba melakukan penambangan dengan menggunakan teknologi web untuk mengumpulkan data informasi yang berasal dari Web of Science dan SINTA yang dikumpulkan. Metodologi Cross Industry Standard Process for Data Mining (CRISP–DM) digunakan sebagai standard proses data mining sekaligus sebagai metode penelitian. Peneliti mengumpulkan data melalui daftar jurnal Web of Science dan SINTA. Untuk melacak trend topik penelitian, peneliti memilih rentang waktu dari tahun 2018 sampai dengan 2019 dan mengekspor data dari Web of Science Core Collection pada April 2019. Ada 38.162 publikasi yang berhasil diambil di Web-Science-defined kategori Ilmu Komputer dan Sistem Informasi dan 230 diambil dari website SINTA. Tetapi, penulis hanya mengambil 20 Jurnal dengan H-Index Tertinggi di Web of Science Core Collection. Sedangkan pada SINTA, penulis juga mengambil 20 Jurnal dengan rangking SINTA 1 dan 2. penelitian ini menyimpulkan topik penelitian dalam jurnal Web of Science dan dikaitkan dengan dengan tren topik penelitian dan yang muncul terbanyak adalah learning, network, analysis, system, control, data, image, optimization, systems, dan neural. Adapun untuk klasifikasi menggunakan model Naive Bayes, Generalized Linear Model, Logistic Regression, Fast Large Margin, Deep Learning, Decision Tree, Random Forest, Gradient Boosted Trees, dan Support Vector Machine. Berdasarkan hasil akurasi, model Generalized Linear Model dan Decision Tree memiliki akurasi sebesar 94.3%, sedangkan Gradient Boosted Trees memiliki  persentase akurasi sebesar 93.8%. Naive Bayes menunjukkan tingkat akurasi sebesar 91.4%, diikuti dengan model Fast Large Margin, Deep Learning, Random Forest, dan Support Vector Machine memiliki akurasi sebesar 91.4%. Nilai dengan akurasi terendah menggunakan model Logistic Regression sebesar 65.2%. Hal ini menunjukan bahwa tingkat akurasi tertinggi yaitu dengan menggunakan model Generalized Linear Model dan Decision Tree sehingga hasilnya dapat memprediksi cukup akurat.

scholarly journals Rescuing Magnolia sinica (Magnoliaceae), a Critically Endangered species endemic to Yunnan, China

Oryx ◽  
2015 ◽  
Vol 50 (3) ◽  
pp. 446-449 ◽  
Author(s):  
Bin Wang ◽  
Yongpeng Ma ◽  
Gao Chen ◽  
Congren Li ◽  
Zhiling Dao ◽  
...  
Keyword(s):  
Ex Situ Conservation ◽  
In Situ Conservation ◽  
Population Monitoring ◽  
Botanical Garden ◽  
Morphological Characters ◽  
Critically Endangered ◽  
Flowering Plant ◽  
Ex Situ ◽  
Herbarium Specimens ◽  
Endangered Tree

AbstractMagnolia sinica, a Critically Endangered tree endemic to Yunnan, China, is one of the 20 plant species with extremely small populations approved by the Yunnan government for urgent rescue action before 2015. Information on the geographical distribution and population size of this species had not previously been reported, hindering effective conservation. We therefore carried out a survey of the literature and of herbarium specimens, followed by a detailed field survey and morphological measurements and observations of surviving individuals. We located 52 individuals in the wild, in eight localities. Two distinguishing morphological characters (tepal colour and tepal number) were revised based on observations of all remaining wild individuals that produced flowers and on one 30-year-old flowering plant in Kunming Botanical Garden. The survival rate of individuals propagated from seed for ex situ conservation at the Garden was 100% over 5 years; of 100 individuals transplanted to each of two reinforcement sites, 20 and 18, respectively, were alive after 6 years. We propose two groups of measures to protect M. sinica: (1) in situ conservation, population monitoring, and public engagement, and (2) ex situ conservation with reinforcement or reintroduction.

Classification of schizophrenia using general linear model and support vector machine via fNIRS

2020 ◽  
Vol 43 (4) ◽  
pp. 1151-1160
Author(s):  
Lei Chen ◽  
Qiang Li ◽  
Hong Song ◽  
Ruiqi Gao ◽  
Jian Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Linear Model ◽  
General Linear Model ◽  
General Linear ◽  
Support Vector

scholarly journals Modeling dengue vector population with earth observation data and a generalized linear model

2021 ◽  
Author(s):  
Oladimeji Mudele ◽  
Alejandro Frery ◽  
Lucas FR Zanandrez ◽  
Alvaro E Eiras ◽  
Paolo Gamba
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Earth Observation ◽  
Environmental Data ◽  
Environmental Drivers ◽  
Support Vector ◽  
Mosquito Vector ◽  
Response Variable ◽  
Vector Population ◽  
Count Response

Mosquitoes propagate many human diseases, some widespread and with no vaccines. The Ae. aegypti mosquito vector transmits Zika, Chikungunya, and Dengue viruses. Effective public health interventions to control the spread of these diseases and protect the population require models that explain the core environmental drivers of the vector population. Field campaigns are expensive, and data from meteorological sites that feed models with the required environmental data often lack detail. As a consequence, we explore temporal modeling of the population of Ae. aegypti mosquito vector species and environmental conditions- temperature, moisture, precipitation, and vegetation- have been shown to have significant effects. We use earth observation (EO) data as our source for estimating these biotic and abiotic environmental variables based on proxy features, namely: Normalized difference vegetation index, Normalized difference water index, Precipitation, and Land surface temperature. We obtained our response variable from field-collected mosquito population measured weekly using 791 mosquito traps in Vila Velha city, Brazil, for 36 weeks in 2017, and 40 weeks in 2018. Recent similar studies have used machine learning (ML) techniques for this task. However, these techniques are neither intuitive nor explainable from an operational point of view. As a result, we use a Generalized Linear Model (GLM) to model this relationship due to its fitness for count response variable modeling, its interpretability, and the ability to visualize the confidence intervals for all inferences. Also, to improve our model, we use the Akaike Information Criterion to select the most informative environmental features. Finally, we show how to improve the quality of the model by weighting our GLM. Our resulting weighted GLM compares well in quality with ML techniques: Random Forest and Support Vector Machines. These results provide an advancement with regards to qualitative and explainable epidemiological risk modeling in urban environments.

scholarly journals Mapping wind erosion hazard with regression-based machine learning algorithms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hamid Gholami ◽  
Aliakbar Mohammadifar ◽  
Dieu Tien Bui ◽  
Adrian L. Collins
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Wind Erosion ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Support Vector ◽  
Erosion Hazard ◽  
Stochastic Gradient Boosting

AbstractLand susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monotone multi-layer perception neural network (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR), Stochastic gradient boosting (SGB), support vector machine (SVM), Relevance vector machine (RVM) and the Cubist and Adaptive network-based fuzzy inference system (ANFIS). Thirteen factors controlling wind erosion were mapped, and multicollinearity among these factors was quantified using the tolerance coefficient (TC) and variance inflation factor (VIF). Model performance was assessed by RMSE, MAE, MBE, and a Taylor diagram using both training and validation datasets. The result showed that five models (MMLPNN, SGAM, Cforest, BGAM and SGB) are capable of delivering a high prediction accuracy for land susceptibility to wind erosion hazard. DEM, precipitation, and vegetation (NDVI) are the most critical factors controlling wind erosion in the study area. Overall, regression-based machine learning models are efficient techniques for mapping land susceptibility to wind erosion hazards.

scholarly journals Modeling dengue vector population with earth observation data and a generalized linear model

2021 ◽  
Author(s):  
Oladimeji Mudele ◽  
Alejandro Frery ◽  
Lucas FR Zanandrez ◽  
Alvaro E Eiras ◽  
Paolo Gamba
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Earth Observation ◽  
Environmental Data ◽  
Environmental Drivers ◽  
Support Vector ◽  
Mosquito Vector ◽  
Response Variable ◽  
Vector Population ◽  
Count Response

Mosquitoes propagate many human diseases, some widespread and with no vaccines. The Ae. aegypti mosquito vector transmits Zika, Chikungunya, and Dengue viruses. Effective public health interventions to control the spread of these diseases and protect the population require models that explain the core environmental drivers of the vector population. Field campaigns are expensive, and data from meteorological sites that feed models with the required environmental data often lack detail. As a consequence, we explore temporal modeling of the population of Ae. aegypti mosquito vector species and environmental conditions- temperature, moisture, precipitation, and vegetation- have been shown to have significant effects. We use earth observation (EO) data as our source for estimating these biotic and abiotic environmental variables based on proxy features, namely: Normalized difference vegetation index, Normalized difference water index, Precipitation, and Land surface temperature. We obtained our response variable from field-collected mosquito population measured weekly using 791 mosquito traps in Vila Velha city, Brazil, for 36 weeks in 2017, and 40 weeks in 2018. Recent similar studies have used machine learning (ML) techniques for this task. However, these techniques are neither intuitive nor explainable from an operational point of view. As a result, we use a Generalized Linear Model (GLM) to model this relationship due to its fitness for count response variable modeling, its interpretability, and the ability to visualize the confidence intervals for all inferences. Also, to improve our model, we use the Akaike Information Criterion to select the most informative environmental features. Finally, we show how to improve the quality of the model by weighting our GLM. Our resulting weighted GLM compares well in quality with ML techniques: Random Forest and Support Vector Machines. These results provide an advancement with regards to qualitative and explainable epidemiological risk modeling in urban environments.

scholarly journals Avaliando aprendizado de máquina na previsão de curto prazo de séries temporais de energia solar

2021 ◽  
Vol 13 (2) ◽  
pp. 105-112
Author(s):  
Naylene Fraccanabbia ◽  
Viviana Cocco Mariani
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Principal Components Analysis ◽  
Linear Model ◽  
Principal Components ◽  
Generalized Linear Model ◽  
Support Vector ◽  
Components Analysis

Fontes alternativas de energia estão se tornando cada vez mais frequentes, tendo como objetivo reduzir a poluição ambiental, além de serem ideais para superar a crise energética, logo, neste contexto, a energia solar se destaca por ser abundante. Devido ao alto nível de incerteza dos fatores que interferem diretamente na geração de energia solar, como temperatura e radiação solar, realizar previsões de energia solar com alta precisão é um desafio. Assim, o objetivo deste artigo é desenvolver um modelo de previsão por meio de séries temporais que possibilite prever a produção de energia solar, para 1, 3 e 6 passos à frente, enfatizando a potencialidade da rede neural, utilizando um banco de dados de uma usina fotovoltaica localizada no Uruguai. Para o desenvolvimento da proposta, técnicas de pré-processamento e os métodos de previsão regressão de vetores de suporte (Support Vector Regression, SVR), rede neural perceptron multicamadas com regularização bayesiana (Bayesian Regularized Neural Network, BRNN) e modelo linear generalizado (Generalized Linear Model, GLM) foram combinados. Por fim, tais combinações foram comparadas usando medidas de desempenho. Notou-se que a combinação da análise de componentes principais (Principal Components Analysis - PCA) e a Rede Neural Perceptron Multicamadas com Regularização Bayesiana obteve os melhores resultados, utilizando as três medidas de desempenho.

scholarly journals Establishing Ex Situ Conservation Methods for Dactylorhiza ebudensis and D. traunsteinerioides, a Combination of In Situ Turf Removal and In Vitro Germinations

2012 ◽  
pp. 71-84
Author(s):  
Berta Millas Xanco ◽  
Jaime V. Aguilar ◽  
Gregory J. Kenicer ◽  
Heather McHaffie
Keyword(s):  
Ex Situ Conservation ◽  
Flowering Plant ◽  
Ex Situ ◽  
Diverse Range ◽  
Ecological Requirements ◽  
Orchid Seed ◽  
In The Wild ◽  
Plant Families ◽  
Set Up

Orchidaceae is one of the most diverse flowering plant families in the world, occupying a diverse range of habitats from epiphytes to terrestrial forms. It is also one of the most vulnerable to changes in land use because of its complex ecological requirements. In nature, orchid seed will only grow if infected with a compatible fungus which provides all the carbohydrates and nutrients needed for its development. This mycotrophic mode of nourishment can persist underground for years in some orchids, which makes them difficult to observe in the wild. Understanding their behaviour is essential for their successful propagation and conservation. In an investigation looking into conservation and propagation, turves were lifted from wild populations of two rare Scottish orchid species in order to ensure the best possible association between these species and their growing environment. A combined in vitro experiment was set up for the wild harvested seeds under different media to compare their effects. Two different successful ex situ conservation methods for Dactylorhiza ebudensis and D. traunsteinerioides are presented.

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