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Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Turkiye

ADVANCES IN SPACE RESEARCH 2024年第2期74卷 647-667页

作者： Akinci, Hazan Alkan Akinci, Halil Zeybek, Mustafa Artvin Coruh Univ Fac Forestry Dept Forestry Engn TR-08100 Artvin Turkiye Artvin Coruh Univ Fac Engn Dept Geomatics Engn TR-08100 Artvin Turkiye Selcuk Univ Guneysinir Vocat Sch Konya Turkiye

Antalya is one of the provinces with the highest number of forest fires in Turkiye. In 2021, 278 forest fires occurred within the administrative boundaries of Antalya Regional Directorate of Forestry. The main objective of this study is to produce forest fire susceptibility (FFS) maps of Antalya province using machine learning (ML) models. In addition to forest fire inventory data, 16 factors, including topographic, environmental, meteorological, and human-driven, were used in the study. Inventory data included 2166 fire ignition points from the General Directorate of Forestry. 70 % of the inventory dataset was used to train the ML models and 30 % to validate the models. Overall accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) approaches were considered as validation metrics. FFS maps of Antalya were produced using stand-alone ML algorithms, K-Nearest Neighbors, and Support Vector machines, as well as tree-based Conditional Inference Trees (CTREE), Random Forest (RF), Gradient Boosting machines (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. To the best of our knowledge, this is the first study using the CTREE algorithm for forest fire susceptibility mapping. Therefore, this study is important for the related literature. The validation results revealed that the XGBoost model outperformed other models. It is thought that the FFS map produced using the XGBoost model will guide forest engineers, wildland firefighting teams, and firefighters to minimize damage and control forest fires. (c) 2024 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Forest fire Susceptibility mapping machine learning algorithms GIS Antalya

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Multi-class classification of COVID-19 documents using machine learning algorithms

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2023年第2期60卷 571-591页

作者： Rabby, Gollam Berka, Petr Prague Univ Econom & Business Dept Informat & Knowledge Engn Prague Czech Republic

In most biomedical research paper corpus, document classification is a crucial task. Even due to the global epidemic, it is a crucial task for researchers across a variety of fields to figure out the relevant scientific research papers accurately and quickly from a flood of biomedical research papers. It can also assist learners or researchers in assigning a research paper to an appropriate category and also help to find the relevant research paper within a very short time. A biomedical document classifier needs to be designed differently to go beyond a "general" text classifier because it's not dependent only on the text itself (i.e. on titles and abstracts) but can also utilize other information like entities extracted using some medical taxonomies or bibliometric data. The main objective of this research was to find out the type of information or features and representation method creates influence the biomedical document classification task. For this reason, we run several experiments on conventional text classification methods with different kinds of features extracted from the titles, abstracts, and bibliometric data. These procedures include data cleaning, feature engineering, and multi-class classification. Eleven different variants of input data tables were created and analyzed using ten machine learning algorithms. We also evaluate the data efficiency and interpretability of these models as essential features of any biomedical research paper classification system for handling specifically the COVID-19 related health crisis. Our major findings are that TF-IDF representations outperform the entity extraction methods and the abstract itself provides sufficient information for correct classification. Out of the used machine learning algorithms, the best performance over various forms of document representation was achieved by Random Forest and Neural Network (BERT). Our results lead to a concrete guideline for practitioners on biomedical document classificatio

关键词： Multi-class classification machine learning algorithms Text mining COVID-19

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Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms

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JOURNAL OF MOLECULAR GRAPHICS & MODELLING 2019年 89卷 156-166页

作者： Sahu, Bhawana Shah, Sanchi Prabhudesai, Kaushiki Contini, Alessandro Idicula-Thomas, Susan ICMR Natl Inst Res Reprod Hlth Biomed Informat Ctr Mumbai 400012 Maharashtra India Univ Milan Dipartimento Sci Farmaceut Milan Italy

Background: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has also been well established. Currently, steroid preparations of estrogen and progesterone are being used for managing fertility, in spite of the harmful side effects, as there has not been much success in identification of effective FSHR modulators. Structure based drug design initiatives for identification of potent and specific FSHR modulators have been impeded due to the non-availability of the complete crystal structure of hFSHR complexed with FSH. Methods: In this study, we have modeled the 3D structure of transmembrane domain (TMD) of hFSHR and identified molecules that demonstrate good binding affinity by virtual screening of drug-like library of compounds. The 3D structural and pharmacophoric features of the binders and non-binders obtained from virtual screening were further used to develop Support Vector machine based classifier for TMD binding. Based on the observations from docking and SVM classification, a small molecule was identified for extensive MD simulations and in vitro assays for FSHR modulatory activity. Results: The molecule selected based on docking score and SVM prediction was found to inhibit FSH-induced CAMP activity by 80% at 300 mu M concentration. Conclusion: The study proposes 1,3-diphenyl-1H-pyrazole-5-carboxylate as a promising scaffold for the design of new and potent FSHR allosteric inhibitors. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Follicle Stimulating Hormone Receptor Side Effect FUEL SLEEVE HOUSING small molecule FSHR gene structural bioinformatics Tumor Progression Drug Design Binders Follicle Stimulating Hormone Modulators machine learning algorithms

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Driver identification in intelligent vehicle systems using machine learning algorithms

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第1期13卷 40-47页

作者： Li, Zhengping Zhang, Kai Chen, Bokui Dong, Yuhan Zhang, Lin Tsinghua Univ Div Logist & Transportat Grad Sch Shenzhen Shenzhen Peoples R China Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Tsinghua Univ Div Informat Sci & Technol Grad Sch Shenzhen Shenzhen Peoples R China Tsinghua Berkeley Shenzhen Inst Data Sci & Informat Technol Res Ctr Shenzhen Peoples R China

This study proposes an applicable driver identification method using machine learning algorithms with driving information. The driving data are collected by a 3-axis accelerometer, which records the lateral, longitudinal and vertical accelerations. In this research, a data transformation way is developed to extract interpretable statistics features from raw 3-axis sensor data and utilise machine learning algorithms to identify drivers. To eliminate the bias caused by the sensor installation and ensure the applicability of their approach, they present a data calibration method which proves to be necessary for a comparative test. Four basic supervised classification algorithms are used to perform on the data set for comparison. To improve classification performance, they propose a multiple classifier system, which combines the outputs of several classifiers. Experimental results based on real-world data show that the proposed algorithm is effective on solving driver identification problem. Among the four basic algorithms, random forests (RFs) algorithm has the greatest performance on accuracy, recall and precision. With the proposed multiple classifier system, a greater performance can be achieved in small number of drivers' groups. RFs algorithm takes the lead in running speed. In their experiment, ten drivers are involved and over 5,500,000 driving records per driver are collected.

关键词： learning (artificial intelligence) intelligent transportation systems feature extraction traffic engineering computing image classification calibration statistical analysis RFs algorithm random forests algorithm multiple classifier system supervised classification algorithms data calibration method sensor installation raw 3-axis sensor data interpretable statistics feature extraction data transformation lateral accelerations longitudinal accelerations vertical accelerations 3-axis accelerometer driving data collection machine learning algorithms intelligent vehicle systems driver identification method

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A novel approach for Parkinson's disease detection using Vold-Kalman order filtering and machine learning algorithms

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NEURAL COMPUTING & APPLICATIONS 2024年第16期36卷 9297-9311页

作者： Latifoglu, Fatma Penekli, Sultan Orhanbulucu, Firat Chowdhury, Muhammad E. H. Erciyes Univ Dept Biomed Engn Kayseri Turkiye Inonu Univ Dept Biomed Engn Malatya Turkiye Qatar Univ Dept Elect Engn Doha Qatar

Parkinson's disease (PD) is the second most common neurological disorder caused by damage to dopaminergic neurons. Therefore, it is important to develop systems for early and automatic diagnosis of PD. For this purpose, a study that will contribute to the development of systems for the automatic diagnosis of PD is presented. The Electroencephalography (EEG) signals were decomposed into sub-bands using adaptive decomposition methods, such as empirical mode decomposition, variational mode decomposition, and Vold-Kalman order filtering (VKF). Various features were extracted from the sub-band decomposed signals, and the significant ones were determined by Chi-squared test. These important features were applied as input to support vector machine (SVM), fitch neural network (FNN), k-nearest neighbours (KNN), and decision trees (DT), machine learning (ML) models and classification was performed. We analysed the performance of ML models by obtaining accuracy, sensitivity, specificity, positive predictive value, negative predictive values, F1-score, false-positive rate, kappa statistics, and area under the curve. The classification process was performed for two cases: PD ON-HC and PD OFF-HC groups. The most successful method in this study was the VKF method, which was applied for the first time in this field with the approach specified for both cases. In both instances, the SVM algorithm was employed as the ML model, with classifier performance criterion values close to 100%. The results obtained in this study seem to be successful compared to the results of recent research on the diagnosis of PD.

关键词： Parkinson's disease classification Electroencephalography signals Vold-Kalman order filtering machine learning algorithms

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Tomato volume and mass estimation using computer vision and machine learning algorithms: Cherry tomato model

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JOURNAL OF FOOD ENGINEERING 2019年 263卷 288-298页

作者： Nyalala, Innocent Okinda, Cedric Nyalala, Luke Makange, Nelson Chao, Qi Chao, Liu Yousaf, Khurram Chen, Kunjie Nanjing Agr Univ Coll Engn Nanjing 210031 Jiangsu Peoples R China Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

A prediction method of mass and volume of cherry tomato based on a computer vision system and machine learning algorithms were introduced in this study. The relation between tomato mass and volume was established as M = 1.312V(0.9551), and was used to estimate mass on a test dataset at an R-2 of 0.9824 and RMSE of 15.84g. Depth images of tomatoes at different orientations were acquired and features extracted by image processing techniques. Five regression prediction models based on 2D and 3D image features were developed. The RBF-SVM outperformed all explored models with an accuracy of 0.9706 (only 2D features) and 0.9694 (all features) in mass and volume estimation respectively. The model predicted mass or volume can then be applied to the established mass-volume power function. This introduced system can be applied as a non-destructive, accurate and consistent technique to in-line sorting and grading of cherry tomatoes based on mass, volume or density.

关键词： Computer vision Cherry tomato Mass estimation machine learning algorithms Volume estimation Fruit sorting and grading

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Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 669卷 844-855页

作者： Chen, Di Chang, Naijie Xiao, Jingfeng Zhou, Qingbo Wu, Wenbin Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Beijing 100081 Peoples R China Minist Agr Key Lab Agr Remote Sensing Beijing 100081 Peoples R China Univ New Hampshire Earth Syst Res Ctr Inst Study Earth Oceans & Space Durham NH 03824 USA

As an important indicator of soil quality, soil organicmatter (SOM) significantly contributes to land productivity and ecosystemhealth. Accuratelymapping SOMat regional scales is of critical importance for sustainable agriculture and soil utilization management and remains a grand challenge. Many studies used soil sampling data and machine learning algorithms to predict SOM at regional scales for a given year, while few studies mapped SOM formultiple years and examined its temporal dynamics. We compared the performance of fourmachine learning algorithms: decision tree (DT), bagging decision tree (BDT), randomforest (RF), and gradient boosting regression trees (GBRT) in mapping SOM in Hubei province, China over the 18-year period from 2000 to 2017. Our results showed that RF and DT had the highest coefficient of determination (R-2) (0.61) and the lowest potential bias (9.48 g/kg), respectively, while GBRT had the lowest mean error (ME) (1.26 g/kg), root mean squared error (RMSE) (5.41 g/kg) and Lin's concordance correlation coefficient (LCCC) (0.72). The SOM map based on GBRT better captured the distribution of the soil sample data than that based on RF. The trained GBRT model and the spatially explicitly data on explanatory variables (e.g., climate, terrain, remote sensing) were used to predict SOM for each 500 m x 500 m grid cell in Hubei for the period from 2000 to 2017. Our results showed that the SOM content of cropland was relatively high in the southeast and relatively low in the north. The SOM content in the topsoil varied from 0.89 to 58.86 g/kg and was averaged at 20.52 g/kg. The mean cropland SOM content of the province exhibited an increasing trend from 2000 to 2017 with an increase of 0.26 g/kg and a growth rate of 1.28%. Spatially, the SOMcontent increased in southernHubei and decreased in central and northern parts of the province. A large portion of the areas with decreasing SOM content in northern Hubei was reclaimed cropland, while a large part of t

关键词： Digital soil mapping Multi-year Soil organic carbon MODIS machine learning algorithms Cropland

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The use of machine learning algorithms in recommender systems: A systematic review

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 97卷 205-227页

作者： Portugal, Ivens Alencar, Paulo Cowan, Donald Univ Waterloo David R Cheriton Sch Comp Sci 200 Univ Ave West Waterloo Waterloo ON N2L 3G1 Canada

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems;(ii) identify open questions in the use or research of machine learning algorithms;and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Systematic review of the literature Recommender systems machine learning machine learning algorithms Application domains Performance metrics

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Time-Series Data Classification and Analysis Associated With machine learning algorithms for Cognitive Perception and Phenomenon

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IEEE ACCESS 2020年 8卷 222417-222428页

作者： Jeong, Taikyeong Sehan Univ Dept Artificial Intelligence Software Engn & Sci Pochon 31746 South Korea

Analysis and collection of time-series data as a major role of machine learning has been emphasized with an important key in cognitive science. Because the cognitive mechanisms such as human sensation and perception from cognitive science are fast responses ranging from a few milliseconds to hundreds of milliseconds, the method of pattern recognition and analysis of these brain signals must be done and it is necessary to derive some information. In this paper, we investigated time-series data of cognitive function of the brain obtained using a non-invasive technique on multiple channels via signal classification and analysis, using a cognitive science approach and experiments. The test dataset was collected in 19 channels using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) techniques with multiple rests and working conditions on eight subjects. From this perspective, the main contributions of this paper are that it completes the collection and analysis of cognitive-scientific time-series data and has scientific implications that extend to other integrated domains, energy, manufacturing, bioinformatics, and finance area. The use of Shapelet and DTW (Dynamic Time Warping) classification techniques on brain signal time-series shows the potential to identify neuro-biological phenomena that can proactively signal a disease or disorder. EEG bandwidth and frequency-specific data have also been categorized as machine learning algorithms and have shown accurate patterns and trends in measuring cognitive functions of scientific, biological and academic importance.

关键词： Electroencephalography Feature extraction Diseases machine learning Task analysis Time series analysis machine learning algorithms Time-series machine learning data classification cognitive science shapelets DTW neurophysiological brain signal

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A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and machine learning algorithms

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IEEE ACCESS 2020年 8卷 64514-64533页

作者： Al-Dhief, Fahad Taha Latiff, Nurul Mu'azzah Abdul Abd Malik, Nik Noordini Nik Salim, Naseer Sabri Baki, Marina Mat Albadr, Musatafa Abbas Abbood Mohammed, Mazin Abed Univ Teknol Malaysia UTM Sch Elect Engn Fac Engn Johor Baharu 81310 Malaysia Sohar Univ Comp & Informat Technol Sohar 311 Oman Univ Kebangsaan Malaysia Fac Med Dept Otorhinolaryngol Med Ctr Kuala Lumpur 56000 Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol CAIT Bangi 43600 Malaysia Univ Anbar Coll Comp Sci & Informat Technol Ramadi 45654 Iraq

The incorporation of the cloud technology with the Internet of Things (IoT) is significant in order to obtain better performance for a seamless, continuous, and ubiquitous framework. IoT has many applications in the healthcare sector, one of these applications is voice pathology monitoring. Unfortunately, voice pathology has not gained much attention, where there is an urgent need in this area due to the shortage of research and diagnosis of lethal diseases. Most of the researchers are focusing on the voice pathology and their finding is only to differentiating either the voice is normal (healthy) or pathological voice, where there is a lack of the current studies for detecting a certain disease such as laryngeal cancer. In this paper, we present an extensive review of the state-of-the-art techniques and studies of IoT frameworks and machine learning algorithms used in the healthcare in general and in the voice pathology surveillance systems in particular. Furthermore, this paper also presents applications, challenges and key issues of both IoT and machine learning algorithms in the healthcare. Finally, this paper highlights some open issues of IoT in healthcare that warrant further research and investigation in order to present an easy, comfortable and effective diagnosis and treatment of disease for both patients and doctors.

关键词： Internet of Things machine learning algorithms the healthcare sector voice pathology surveillance systems

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