scholarly journals PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 721-726
Author(s):  
P. Singh ◽  
V. Maurya ◽  
R. Dwivedi
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Volcanic Eruptions ◽  
Google Earth ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Geological Survey ◽  
Support Vector ◽  
Landsat 8 ◽  
Multi Band

Abstract. Landslide is one of the most common natural disasters triggered mainly due to heavy rainfall, cloud burst, earthquake, volcanic eruptions, unorganized constructions of roads, and deforestation. In India, field surveying is the most common method used to identify potential landslide regions and update the landslide inventories maintained by the Geological Survey of India, but it is very time-consuming, costly, and inefficient. Alternatively, advanced remote sensing technologies in landslide analysis allow rapid and easy data acquisitions and help to improve the traditional method of landslide detection capabilities. Supervised Machine learning algorithms, for example, Support Vector Machine (SVM), are challenging to conventional techniques by predicting disasters with astounding accuracy. In this research work, we have utilized open-source datasets (Landsat 8 multi-band images and JAXA ALOS DSM) and Google Earth Engine (GEE) to identify landslides in Rudraprayag using machine learning techniques. Rudraprayag is a district of Uttarakhand state in India, which has always been the center of attention of geological studies due to its higher density of landslide-prone zones. For the training and validation purpose, labeled landslide locations obtained from landslide inventory (prepared by the Geological Survey of India) and layers such as NDVI, NDWI, and slope (generated from JAXA ALOS DSM and Landsat 8 satellite multi-band imagery) were used. The landslide identification has been performed using SVM, Classification and Regression Trees (CART), Minimum Distance, Random forest (RF), and Naïve Bayes techniques, in which SVM and RF outperformed all other techniques by achieving an 87.5% true positive rate (TPR).

scholarly journals Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms

2020 ◽  
Vol 12 (22) ◽  
pp. 3776
Author(s):  
Andrea Tassi ◽  
Marco Vizzari
Keyword(s):  
Machine Learning ◽  
Central Italy ◽  
Object Oriented ◽  
Google Earth ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Landsat 8 ◽  
Good Reliability ◽  
Google Earth Engine ◽  
Occurrence Matrix

Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be implemented, thanks to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms. OO approaches, including both object segmentation and object textural analysis, are still not common in the GEE environment, probably due to the difficulties existing in concatenating the proper functions, and in tuning the various parameters to overcome the GEE computational limits. In this context, this work is aimed at developing and testing an OO classification approach combining the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices, and two ML algorithms (Random Forest (RF) or Support Vector Machine (SVM)) to perform the final classification. A Principal Components Analysis (PCA) is applied to the main seven GLCM indices to synthesize in one band the textural information used for the OO classification. The proposed approach is implemented in a user-friendly, freely available GEE code useful to perform the OO classification, tuning various parameters (e.g., choose the input bands, select the classification algorithm, test various segmentation scales) and compare it with a PB approach. The accuracy of OO and PB classifications can be assessed both visually and through two confusion matrices that can be used to calculate the relevant statistics (producer’s, user’s, overall accuracy (OA)). The proposed methodology was broadly tested in a 154 km2 study area, located in the Lake Trasimeno area (central Italy), using Landsat 8 (L8), Sentinel 2 (S2), and PlanetScope (PS) data. The area was selected considering its complex LULC mosaic mainly composed of artificial surfaces, annual and permanent crops, small lakes, and wooded areas. In the study area, the various tests produced interesting results on the different datasets (OA: PB RF (L8 = 72.7%, S2 = 82%, PS = 74.2), PB SVM (L8 = 79.1%, S2 = 80.2%, PS = 74.8%), OO RF (L8 = 64%, S2 = 89.3%, PS = 77.9), OO SVM (L8 = 70.4, S2 = 86.9%, PS = 73.9)). The broad code application demonstrated very good reliability of the whole process, even though the OO classification process resulted, sometimes, too demanding on higher resolution data, considering the available computational GEE resources.

scholarly journals Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

2021 ◽  
Vol 17 (02) ◽  
pp. 90
Author(s):  
Inssaf El Guabassi ◽  
Zakaria Bousalem ◽  
Rim Marah ◽  
Aimad Qazdar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Predictive Model ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Future ◽  
Log Linear

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

scholarly journals Financial Context News Sentiment Analysis for the Lithuanian Language

2021 ◽  
Vol 11 (10) ◽  
pp. 4443
Author(s):  
Rokas Štrimaitis ◽  
Pavel Stefanovič ◽  
Simona Ramanauskaitė ◽  
Asta Slotkienė
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Experimental Investigations ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Bayes Algorithm ◽  
Website Content

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

scholarly journals Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm

2020 ◽  
Author(s):  
Castro Mayleen Dorcas Bondoc ◽  
Tumibay Gilbert Malawit
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Video Tutorials ◽  
Learning Management ◽  
Face To Face Instruction

Today many schools, universities and institutions recognize the necessity and importance of using Learning Management Systems (LMS) as part of their educational services. This research work has applied LMS in the teaching and learning process of Bulacan State University (BulSU) Graduate School (GS) Program that enhances the face-to-face instruction with online components. The researchers uses an LMS that provides educators a platform that can motivate and engage students to new educational environment through manage online classes. The LMS allows educators to distribute information, manage learning materials, assignments, quizzes, and communications. Aside from the basic functions of the LMS, the researchers uses Machine Learning (ML) Algorithms applying Support Vector Machine (SVM) that will classify and identify the best related videos per topic. SVM is a supervised machine learning algorithm that analyzes data for classification and regression analysis by Maity [1]. The results of this study showed that integration of video tutorials in LMS can significantly contribute knowledge and skills in the learning process of the students.

scholarly journals PREDICTIVE MODELLING AND ANALYTICS FOR DIABETES USING A MACHINE LEARNING APPROACH

2021 ◽  
Vol 9 (1) ◽  
pp. 215-223
Author(s):  
Prateek Mishra, Dr.Anurag Sharma, Dr. Abhishek Badholia
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Machine Learning Algorithms ◽  
Computational Method ◽  
Supervised Machine Learning ◽  
Undiagnosed Diabetes ◽  
Support Vector ◽  
Entire Body ◽  
Data Manipulation ◽  
Kernel Support Vector Machine

Adverse effects can be seen in the entire body due to the major disorders known as Diabetes. The risk of dangers like diabetic nephropathy, cardiac stroke and other disorders can increase severally because of the undiagnosed diabetes. Around the globe the people are suffering from this disease. For a healthy life early detection of this disease is very curtail. As the causes of the diabetes is increasing rapidly this disease might turn up as a reason for worldwide concern. Increasing the chances for a more accurate predictions and form experiences automatic learning by computational method may be provided by Machine Learning (ML). With the help of R data manipulation tool for trends development and with risk factor patterns detection in Pima Indian diabetes technique of machine learning is been used in the current researches. With the use of R data manipulation tool analysis and development five different predictive models is done for the categorization of patients into diabetic and non- diabetic.  supervised machine learning algorithms namely multifactor dimensionality reduction (MDR), k-nearest neighbor (k-NN), artificial neural network (ANN) radial basis function (RBF) kernel support vector machine and linear kernel support vector machine (SVM-linear) are used for this purpose.

scholarly journals Encrypted DNP3 Traffic Classification Using Supervised Machine Learning Algorithms

2019 ◽  
Vol 1 (1) ◽  
pp. 384-399 ◽  
Author(s):  
Thais de Toledo ◽  
Nunzio Torrisi
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Smart Grids ◽  
Learning Algorithms ◽  
Electric Utility ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Communication Link

The Distributed Network Protocol (DNP3) is predominately used by the electric utility industry and, consequently, in smart grids. The Peekaboo attack was created to compromise DNP3 traffic, in which a man-in-the-middle on a communication link can capture and drop selected encrypted DNP3 messages by using support vector machine learning algorithms. The communication networks of smart grids are a important part of their infrastructure, so it is of critical importance to keep this communication secure and reliable. The main contribution of this paper is to compare the use of machine learning techniques to classify messages of the same protocol exchanged in encrypted tunnels. The study considers four simulated cases of encrypted DNP3 traffic scenarios and four different supervised machine learning algorithms: Decision tree, nearest-neighbor, support vector machine, and naive Bayes. The results obtained show that it is possible to extend a Peekaboo attack over multiple substations, using a decision tree learning algorithm, and to gather significant information from a system that communicates using encrypted DNP3 traffic.

scholarly journals Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1557 ◽  
Author(s):  
Ilaria Conforti ◽  
Ilaria Mileti ◽  
Zaccaria Del Prete ◽  
Eduardo Palermo
Keyword(s):  
Machine Learning ◽  
Material Handling ◽  
Learning Algorithm ◽  
Occupational Injuries ◽  
Wearable Sensors ◽  
High Stress ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Kinematic Parameters

Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

Latest Advances in Fractional Snow Cover Mapping on MODIS Data by Machine Learning Algorithms

2020 ◽  
Author(s):  
Semih Kuter ◽  
Zuhal Akyurek
Keyword(s):  
Machine Learning ◽  
Snow Cover ◽  
General Circulation ◽  
Snow Water Equivalent ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Landsat 8 ◽  
European Alps ◽  
Fractional Snow Cover

&lt;p&gt;Spatial extent of snow has been declared as an essential climate variable. Accurate modeling of snow cover is crucial for the better prediction of snow water equivalent and, consequently, for the success of general circulation and weather forecasting models as well as climate change and hydrological studies. This presentation mainly focuses on the representation of the latest findings of our efforts in fractional snow cover mapping on MODIS images by data-driven machine learning methodologies. For this purpose, a dataset composed of 20 MODIS - Landsat 8 image pairs acquired between Apr 2013 and Dec 2016 over European Alps were employed. Artificial neural networks (ANN), multivariate adaptive regression splines (MARS), support vector regression (SVR) and random forest (RF) models were trained and tested by using reference FSC maps generated from higher spatial resolution Landsat 8 binary snow maps. ANN, MARS, SVR and RF models exhibited quite good performance with average R &amp;#8776; 0.93, whereas the agreement between the reference FSC maps and the MODIS&amp;#8217; own product MOD10A1 (C5) was slightly poorer with R &amp;#8776; 0.88.&lt;/p&gt;

scholarly journals Automatic recognition of self-acknowledged limitations in clinical research literature

2018 ◽  
Vol 25 (7) ◽  
pp. 855-861 ◽  
Author(s):  
Halil Kilicoglu ◽  
Graciela Rosemblat ◽  
Mario Malički ◽  
Gerben ter Riet
Keyword(s):  
Machine Learning ◽  
Clinical Research ◽  
Binary Classification ◽  
Classification Performance ◽  
Research Literature ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Rule Based ◽  
Research Transparency

Abstract Objective To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency. Methods To develop our recognition methods, we used a set of 8431 sentences from 1197 PubMed Central articles. A subset of these sentences was manually annotated for training/testing, and inter-annotator agreement was calculated. We cast the recognition problem as a binary classification task, in which we determine whether a given sentence from a publication discusses self-acknowledged limitations or not. We experimented with three methods: a rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines (SVM). Results Annotators had good agreement in labeling limitation sentences (Krippendorff’s α = 0.781). Of the three methods used, the rule-based method yielded the best performance with 91.5% accuracy (95% CI [90.1-92.9]), while self-training with SVM led to a small improvement over fully supervised learning (89.9%, 95% CI [88.4-91.4] vs 89.6%, 95% CI [88.1-91.1]). Conclusions The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.

scholarly journals Machine Learning for Predictions in Academics

2020 ◽  
Vol 8 (5) ◽  
pp. 4624-4627
Keyword(s):  
Machine Learning ◽  
Career Path ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Bayes Classifier ◽  
Logistics Regression ◽  
Learning Techniques

In recent years, a lot of data has been generated about students, which can be utilized for deciding the career path of the student. This paper discusses some of the machine learning techniques which can be used to predict the performance of a student and help to decide his/her career path. Some of the key Machine Learning (ML) algorithms applied in our research work are Linear Regression, Logistics Regression, Support Vector machine, Naïve Bayes Classifier and K- means Clustering. The aim of this paper is to predict the student career path using Machine Learning algorithms. We compare the efficiencies of different ML classification algorithms on a real dataset obtained from University students.

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