Struktur sastra lisan Mori

Buku Struktur Sastra Lisan Mori ini merupakan salah satu hasil Proyek Penelitian Bahasa dan Sastra Indonesia dan Daerah Sulawesi Tengah tahun 1992 yang pelaksanaannya dipercayakan kepada tim peneliti dari Kecamatan Lembo. Untuk itu, kami ingin menyatakan penghargaan dan ucapan terima kasih kepada Pemimpin Proyek Penelitian Bahasa dan Sastra Indonesia dan Daerah Sulawesi Tengah beserta stafnya, dan para peneliti, yaitu Tim Peneliti Drs. Ahmad Saro, Drs. Amir Kadir, Drs. llyas Abd. Hamid

Struktur bahasa Besoa

Buku Struktur Bahasa Besoa ini merupakan salah satu hasil Proyek Penelitian Bahasa dan Sastra Indonesia dan Daerah Sulawesi Tengah tahun 1986 yang pelaksanaannya dipercayakan kepada tim penelitidari Universitas Tadulako. Penelitian struktur bahasa Besoa ini dilaksanakan oleh satu tim peneliti untuk memperoleh data di desa Rarnpo, salah satu desa yang dihuni oleh suku Besoa. Pemilihan desa ini sebagai daerahsampel ditetapkan berdasarkan beberapa pertimbangan guna kepentingan penelitian. Data yang diperoleh berdasarkan langkah-langkah yang telah ditetapkan dalam instrumen penelitian memungkinkan adanya variasi setelah melihat kepentingan data yang ada. Pengolahan data itu dimuat dalam bab-bab yang ada

Struktur bahasa Besoa

Buku Struktur Bahasa Besoa ini merupakan salah satu hasil Proyek Penelitian Bahasa dan Sastra Indonesia dan Daerah Sulawesi Tengah tahun 1986 yang pelaksanaannya dipercayakan kepada tim peneliti dari Universitas Tadulako. Untuk itu, kami ingin menyatakan penghargaan dan ucapan terima kasih kepada Pemimpin Proyek Penelitian Bahasa dan Sastra Indonesia dan Darah Sulawesi Tengah tahun 1986 beserta stafnya, dan para peneliti, yaitu Ahmad Saro, Hanafi Sulaiman, Abdillah A. Rahim, Sudarmin Kuruda

Land subsidence susceptibility mapping in South Korea using machine learning algorithms

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results

A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas

Struktur bahasa Besoa

xiv, 85 p. : il.; 21 cm