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machine learning Classification of Endangered Tree Species in a Tropical Submontane Forest Using WorldView-2 Multispectral Satellite Imagery and Imbalanced Dataset

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REMOTE SENSING 2021年第24期13卷

作者： Jackson, Colbert M. Adam, Elhadi Univ Witwatersrand Sch Geog Archaeol & Environm Studies ZA-2050 Johannesburg South Africa

Accurate maps of the spatial distribution of tropical tree species provide valuable insights for ecologists and forest management. The discrimination of tree species for economic, ecological, and technical reasons is usually necessary for achieving promising results in tree species mapping. Most of the data used in tree species mapping normally have some degree of imbalance. This study aimed to assess the effects of imbalanced data in identifying and mapping trees species under threat in a selectively logged sub-montane heterogeneous tropical forest using random forest (RF) and support vector machine with radial basis function (RBF-SVM) kernel classifiers and WorldView-2 multispectral imagery. For comparison purposes, the original imbalanced dataset was standardized using three data sampling techniques: oversampling, undersampling, and combined oversampling and undersampling techniques in R. The combined oversampling and undersampling technique produced the best results: F1-scores of 68.56 +/- 2.6% for RF and 64.64 +/- 3.4% for SVM. The balanced dataset recorded improved classification accuracy compared to the original imbalanced dataset. This research observed that more separable classes recorded higher F1-scores. Among the species, Syzygium guineense and Zanthoxylum gilletii were the most accurately mapped whereas Newtonia buchananii was the least accurately mapped. The most important spectral bands with the ability to detect and distinguish between tree species as measured by random forest classifier, were the Red, Red Edge, Near Infrared 1, and Near Infrared 2.

关键词： tropical forests endangered tree species selective logging imbalanced data pixel-based classification machine learning algorithm

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Instrumentation for differentiation of exhaled air 15

Instrumentation for differentiation of exhaled air

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15th International Conference on Correlation Optics

作者： Santos, Paulo H. C. Vassilenko, Valentina B. Moura, Pedro C. Conduto, Carolina Fernandes, Jorge M. Bonifacio, Paulo NOVA Sch Sci & Technol Caparica Portugal NMT SA Caparica Portugal

ISBN: (纸本)9781510651296;9781510651289

Breath analysis is an emerging research field with tremendous potential for advance personalized, non-invasive health screening and diagnostics,while new sampling instrumentation tools andanalytical detection methods are developed. Notwithstanding of the quick development of commercially and researcher-built experimental samplers, no robust and repeatable VOCs' profile technologies have been clinically validated. Such is due to lack of an optimal standard procedure for selective breath sampling which ends in a wide range of contradictory reported results. Challenges of most breath samplers are also related to the substances' concentrations that are source (oral cavity, oesophageal and alveolar) dependent and their low values (in ppbv - pptv range). Here, we present a suitable and novel technology for selectively sampling exhaled air regarding the subject's: age, gender, metabolic production of CO2, smoking habits, nutrition and health conditions. The technology was aimed to perform real time flow measurements and collect a pre-determined portion of exhaled air by synchronizing a previously modelled respiratory cycle with the breathing cycle of the user. Through real-time synchronization of breathing cycles, the system can detect optimized sampling instants by machine learning-based algorithm. A first set of tests was conducted to evaluate the robustness and efficiency of the software's sampling algorithm with two cohorts of participants (n=15 and n=30) with different age groups (2-5 years old and 18-27 years old, respectively). The ability to selectively differentiate exhaled air was also tested through collection and posterior analysis of oesophageal and alveolar air samples obtained from an independent cohort of university students (n=31). Although it requires instrumentation improvements and optimization the breath sampling technology coupled with an efficient analyser device (GC-IMS), the results herein presented suggest a promise step forward in breath sampling

关键词： Exhaled Air Selective Air Acquisition Air Sampling/Monitoring Breath Rhythm Imposition Modelled Breath algorithm Average Time of Expiration machine learning algorithm Medical Instrumentation Prototyping

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Utilization of random forest classifier and artificial neural network for predicting the acceptance of reopening decommissioned nuclear power plant

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ANNALS OF NUCLEAR ENERGY 2022年 175卷

作者： Ong, Ardvin Kester S. Prasetyo, Yogi Tri Velasco, Kenzo Emmanuel C. Abad, Eman David R. Buencille, Adrian Louis B. Estorninos, Ezekiel M. Labso Cahigas, Maela Madel Chuenyindee, Thanatorn Persada, Satria Fadil Nadlifatin, Reny Sittiwatethanasiri, Thaninrat Mapua Univ Sch Ind Engn & Engn Management 658 Muralla St Manila 1002 Philippines Yuan Ze Univ Dept Ind Engn & Management 135 Yuan Tung Rd Chungli 32003 Taiwan Mapua Univ Young Innovators Res Ctr 658 Muralla St Manila 1002 Philippines Mapua Univ Sch Grad Studies 658 Muralla St Manila 1002 Philippines Navaminda Kasatriyadhiraj Royal Air Force Acad Dept Ind Engn & Aviat Management Bangkok 10220 Thailand Bina Nusantara Univ BINUS Business Sch Undergrad Program Entrepreneurship Dept Jakarta 11480 Indonesia Inst Teknol Sepuluh Nopember Dept Informat Syst Kampus ITS Sukolilo Surabaya 60111 Indonesia

The Bataan Nuclear Power Plant (BNPP) is one of the many decommissioned Nuclear Power Plant (NPP) globally and its reopening has led to different perceptions among Filipinos. It was established in 1984 but was not utilized due to political liability and safety reasons. This study aimed to predict factors affecting the acceptance of the BNPP by utilizing machine learning algorithms (MLA). The MLAs utilized in this study were Decision Tree, Random Forest Classifier (RFC), and Artificial Neural Network (ANN) as a high -light to predict human behavior. 1,252 Filipinos voluntarily answered an online questionnaire which con -sist of 37 questions, leading to 46,324 datasets. MLA showed that Filipinos are knowledgeable about the benefits of NPPs, leading to the acceptance of the reopening of the BNPP. In addition, MLA indicated that perceived benefits (PB) was found to be the highest factor that affect the Filipino's acceptance of the reopening of BNPP. Job opportunities, economic growth, lower and clean energy consumption, and sus-tainability were the indicators for the acceptance of the reopening of BNPP. Interestingly, the result showed that PB relatively outweighed the perceived risk of the BNPP. ANN and RFC proved to be effective with accuracy rates of 93.44% and 97.00%, respectively. Finally, the MLA approach in this study can be applied and extended in predicting the acceptance of NPPs worldwide.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Bataan nuclear power plant Artificial neural network Random forest classifier machine learning algorithm Human behavior Bataan nuclear power plant Artificial neural network Random forest classifier machine learning algorithm Human behavior

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A pilot study of machine learning of resting-state EEG and depression in Parkinson's disease

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CLINICAL PARKINSONISM & RELATED DISORDERS 2022年 7卷 100166页

作者： Espinoza, Arturo I. May, Patrick Anjum, Md Fahim Singh, Arun Cole, Rachel C. Trapp, Nicholas Dasgupta, Soura Narayanan, Nandakumar S. Univ Iowa Dept Neurol Iowa City IA USA Univ Iowa Dept Elect & Comp Engn Iowa City IA USA Univ South Dakota Sanford Sch Med Div Basic Biomed Sci Vermillion SD USA Univ Iowa Dept Psychiat Iowa City IA USA Univ Iowa 169 Newton RdPappajohn Biomed Discovery Bldg 5336 Iowa City IA 52242 USA

Introduction: Depression is a non-motor symptom of Parkinson's disease (PD). PD-related depression is difficult to diagnose, and the neurophysiological basis is poorly understood. Depression can markedly affect cortical func-tion, which suggests that scalp electroencephalography (EEG) may be able to distinguish depression in PD. We conducted a pilot study of depression and resting-state EEG in ***: We recruited 18 PD patients without depression, 18 PD patients with depression, and 12 demograph-ically similar non-PD patients with clinical depression. All patients were on their usual medications. We collected resting-state EEG in all patients and compared cortical brain signal features between patients with and without depression. We used a machine learning algorithm that harnesses the entire power spectrum (linear predictive coding of EEG algorithm for PD: LEAPD) to distinguish between ***: We found differences between PD patients with and without depression in the alpha band (8-13 Hz) globally and in the beta (13-30 Hz) and gamma (30-50 Hz) bands in the central electrodes. From two minutes of resting-state EEG, we found that LEAPD-based machine learning could robustly distinguish between PD patients with and without depression with 97 % accuracy and between PD patients with depression and non-PD patients with depression with 100 % accuracy. We verified the robustness of our finding by confirming that the classi-fication accuracy gracefully declines as data are randomly ***: Our results suggest that resting-state EEG power spectral analysis has the potential to distinguish depression in PD accurately. We demonstrated the efficacy of the LEAPD algorithm in identifying PD patients with depression from PD patients without depression and controls with depression. Our data provide insight into cortical mechanisms of depression and could lead to novel neurophysiological markers for non-motor symptoms of PD.

关键词： Parkinson's disease Depression Scalp electroencephalography (EEG) machine learning algorithm Classification

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Five Typical Stenches Detection Using an Electronic Nose

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SENSORS 2020年第9期20卷 2514页

作者： Jiang, Wei Gao, Daqi East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200030 Peoples R China

This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, gamma -undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.

关键词： stenches detection odor concentration electronic nose machine learning algorithm

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Analysis of Malware Prediction Based on Infection Rate Using machine learning Techniques

Analysis of Malware Prediction Based on Infection Rate Using...

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IEEE-Region-10 Symposium (TENSYMP) - Technology for Impactful Sustainable Development

作者： Bin Asad, Ashub Mansur, Raiyan Zawad, Safir Evan, Nahian Hossain, Muhammad Iqbal BRAC Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781728173665

In this modern, technological age, the internet has been adopted by the masses. And with it, the danger of malicious attacks by cybercriminals have increased. These attacks are done via Malware, and have resulted in billions of dollars of financial damage. This makes the prevention of malicious attacks an essential part of the battle against cybercrime. In this paper, we are applying machine learning algorithms to predict the malware infection rates of computers based on its features. We are using supervised machine learning algorithms and gradient boosting algorithms. We have collected a publicly available dataset, which was divided into two parts, one being the training set, and the other will be the testing set. After conducting four different experiments using the aforementioned algorithms, it has been discovered that LightGBM is the best model with an AUC Score of 0.73926.

关键词： malware prediction machine learning algorithm lgbm neural network k-fold decision tree microsoft malware dataset

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Analysis of EEG Signal to Classify Sleep Stages Using machine learning

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Sleep and Vigilance 2020年第2期4卷 145-152页

作者： Santaji, Sagar Desai, Veena Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belagavi Karnataka India

Automated classification of sleep stages is in demand to overcome the limitations of manual sleep stage classification. Analyzing sleep stages manually using neurophysiological signals and inspecting visually is very difficult, time-consuming process. Many techniques have been proposed already in the past two decades. Sleep experts, physicians do not have assurance with such techniques concerned with accuracy, specificity and sensitivity. The aim of the paper is to propose an efficient technique for sleep stage classification based on Electroencephalogram (EEG) signals analysis using machine learning algorithms by considering 10 s of epochs. EEG signals have played significant role in automatic sleep stage classification. EEG signals are filtered and decomposed into frequency sub-bands using band-pass filter. Statistical features are extracted and trained with Decision Tree, Support Vector machine and Random Forest algorithms with different testing dataset percentage. Results show Random Forest algorithm achieves 97.8% of accuracy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： 10-s EEG signal segments EEG analysis Frequency sub-bands machine learning algorithm Sleep stage classification

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Novel color space representation extracted by NMF to segment a color image

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Journal of Computational Mathematics and Data Science 2024年 13卷

作者： Castiello, Ciro Del Buono, Nicoletta Esposito, Flavia Department of Computer Science University of Bari Aldo Moro Bari Italy Department of Mathematics University of Bari Aldo Moro Bari Italy

This paper considers the task of separating pixels in color image into background and foreground classes. Using the machine learning technique known as Nonnegative Matrix Factorization, data pertaining to different color channels – selected by color spaces – are combined, and a novel space representation is extracted. The novel representation of the image includes additional information, namely “metacolor”, which could be related to foreground and background and adopted to improve binary segmentation of the investigated image. In both qualitative and quantitative experiments, the use of novel color space representation produces some improvements in the binary segmentation results when it compared to those obtained applying common simpler thresholding algorithms directly to the original image. © 2024 The Author(s)

关键词： Color spaces Feature extraction Image segmentation Low rank data representation machine learning algorithm Nonnegative matrix factorization

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Reflections on gaze data in statistics education

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TEACHING STATISTICS 2023年第S1期45卷 S40-S51页

作者： Boels, Lonneke Univ Appl Sci Utrecht Utrecht Netherlands Univ Utrecht Utrecht Netherlands

Gaze data are still uncommon in statistics education despite their promise. Gaze data provide teachers and researchers with a new window into complex cognitive processes. This article discusses how gaze data can inform and be used by teachers both for their own teaching practice and with students. With our own eye-tracking research as an example, background information on eye-tracking and possible applications of eye-tracking in statistics education is provided. Teachers indicated that our eye-tracking research created awareness of the difficulties students have when interpreting histograms. Gaze data showed details of students' strategies that neither teachers nor students were aware of. With this discussion paper, we hope to contribute to the future usage and implementation of gaze data in statistics education by teachers, researchers, educational and textbook designers, and students.

关键词： eye-tracking histogram machine learning algorithm teaching statistics

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Analysis of machine learning for Securing Software-Defined Networking

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Procedia Computer Science 2021年 194卷 229-236页

作者： Hassan A. Alamri Vijey Thayananthan Faculty of Computing and IT King Abdulaziz University Jeddah 21589 Saudi Arabia College of Computing in Al-Qunfudhah Umm Al-Qura University Makkah 21955 Saudi Arabia

Advancements in Software-Defined Networking (SDN) are growing with the emerging network architectures which face the security issues and challenges when adversary launches severe attacks such as Distributed Denial of Service (DDoS) attacks. Therefore, a considerable number of solutions have been devised to alleviate DDoS attacks in SDN using a machine learning approach. Thus, the aim of this study is to review and analyze the machine learning-based schemes for securing the SDN environment targeted by DDoS attacks. The schemes’ method, performance metrics, datasets, and other remarks such as benchmarks, strengths, and weaknesses are discussed. The CIC-DDoS 2019 dataset was utilized to evaluate the performance of a set of classification algorithms that are widely used in machine learning-based DDoS attack detection in the SDN environment. Finally, challenges and future directions in the development of machine learning-based detection schemes in SDN are discussed.

关键词： Distributed Denial of Service (DDoS) Attack machine learning algorithm Security Software-Defined Networking (SDN)

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