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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

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Geocarto International 2023年第1期38卷

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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Findings of the sentiment analysis of Dravidian languages in code-mixed text

arXiv

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arXiv 2021年

作者： Chakravarthi, Bharathi Raja Priyadharshini, Ruba Thavareesan, Sajeetha Chinnappa, Dhivya Thenmozhi, Durairaj Sherly, Elizabeth McCrae, John P. Hande, Adeep Ponnusamy, Rahul Banerjee, Shubhanker Vasantharajan, Charangan Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway ULTRA Arts and Science College Tamil Nadu Madurai India Eastern University Sri Lanka Thomson Reuters United States Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu India Indian Institute of Information Technology and Management Kerala India Indian Institute of Information Technology Tiruchirappalli ADAPT Centre Data Science Institute National University Of Ireland Galway Department of Computer Science and Engineering University of Moratuwa Sri Lanka

We present the results of the Dravidian-CodeMix shared task1 held at FIRE 2021, a track on sentiment analysis for Dravidian Languages in Code-Mixed Text. We describe the task, its organization, and the submitted systems. This shared task is the continuation of last year’s Dravidian-CodeMix shared task2 held at FIRE 2020. This year’s tasks included code-mixing at the intra-token and inter-token levels. Additionally, apart from Tamil and Malayalam, Kannada was also introduced. We received 22 systems for Tamil-English, 15 systems for Malayalam-English, and 15 for Kannada-English. The top system for Tamil-English, Malayalam-English and Kannada-English scored weighted average F1-score of 0.711, 0.804, and 0.630, respectively. In summary, the quality and quantity of the submission show that there is great interest in Dravidian languages in code-mixed setting and state of the art in this domain still needs more improvement. © 2021, CC BY.

关键词： Sentiment analysis

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Epileptic Seizure Detection for Imbalanced datasets Using an Integrated Machine Learning Approach

Epileptic Seizure Detection for Imbalanced Datasets Using an...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Mohammad Masum Hossain Shahriar Hisham M. Haddad Analytics and Data Science Institute Kennesaw State University Kennesaw USA Department of Information Technology Kennesaw State University Marietta USA Department of Computer Science Kennesaw State University Marietta USA

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Epileptic Seizure (Epilepsy) is a neurological disorder that occurs due to abnormal brain activities. Epilepsy affects patients' health and lead to life-threatening situations. Early prediction of epilepsy is highly effective to avoid seizures. Machine Learning algorithms have been used to classify epilepsy from Electroencephalograms (EEG) data. These algorithms exhibited reduced performance when classes are imbalanced. This work presents an integrated machine learning approach for epilepsy detection, which can effectively learn from imbalanced data. This approach utilizes Principal Component Analysis (PCA) at the first stage to extract both high- and low- variant Principal Components (PCs), which are empirically customized for imbalanced data classification. Conventionally, PCA is used for dimension reduction of a dataset leveraging PCs with high variances. In this paper, we propose a model to show that PCs associated with low variances can capture the implicit pattern of minor class of a dataset. The selected PCs are then fed into different machine learning classifiers to predict seizures. We performed experiments on the Epileptic Seizure Recognition dataset to evaluate our model. The experimental results show the robustness and effectiveness of the proposed model.

关键词： Feature extraction Epilepsy Electroencephalography Support vector machines Principal component analysis Radio frequency Brain modeling

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Prevalence of associated factors on depression during COVID 19 in students in a minority serving institution: A cross sectional study

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Journal of Affective Disorders Reports 2023年 13卷 100605-100605页

作者： Rahman, Humairat H Akinjobi, Zainab Morales, Jonathan Barboza Munson-McGee, Stuart H Gard, Charlotte New Mexico State University Las Cruces 88003 NM United States Department of Economics Applied Statistics & International Business New Mexico State University. Las Cruces 88003 NM United States Department of Computer Science New Mexico State University. Las Cruces 88003 NM United States Data Forward Analytics LLC Principal. Las Cruces 88011 NM United States Department of Economics Applied Statistics and International Business New Mexico State University. Las Cruces 88003 NM United States

Introduction: The COVID-19 pandemic changed the learning style of university students in the US, affecting their mental health of students. This study aims to understand the factors that influenced depression during the COVID-19 pandemic in the New Mexico State University (NMSU) student population. Methods: A questionnaire assessing mental health and lifestyle factors was delivered to NMSU students by using Qualtrics^XM software. Depression was assessed using the Patient Health Questionnaire- 9 (PHQ-9);depression was defined as a score ≥10. Single and multifactor logistic regression was performed using R software. Results: This study determined that the prevalence of depression among female students was 72% and 56.30% among male students. Several covariates were significant for increased odds of depression in students, including decreased diet quality (OR: 5.126, 95% CI: 3.186–8.338), annual household income $10,000 - $20,000 (OR: 3.161, 95% CI: 1.444–7.423), increased alcohol consumption (OR: 2.362, 95% CI: 1.504–3.787), increased smoking (OR: 3.581, 95% CI:1.671–8.911), quarantining due to COVID (OR: 2.001, 95% CI: 1.348–2.976), and family member dying of COVID (OR: 1.916, 95% CI: 1.072–3.623). Covariates of being male (OR: 0.501, 95% CI: 0.324–0.776), married (OR: 0.499, 95% CI: 0.318–0.786), eating a balanced diet (OR: 0.472, 95% CI: 0.316–0.705), and sleeping 7–8 h per night (OR: 0.271, 95% CI: 0.175–0.417) were all protective factors for depression in NMSU students. Limitation: This is a cross-sectional study, and therefore, causation cannot be determined. Conclusion: Several factors regarding demographics, lifestyle, living arrangements, alcohol and tobacco use, sleeping behavior, family vaccination, and COVID status were significantly associated with depression in students during the COVID-19 pandemic. © 2023

关键词： Alcohol COVID-19 Depression Smoking Student Vaccine

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Fast emulation of two-point angular statistics for photometric galaxy surveys

arXiv

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arXiv 2022年

作者： Bonici, Marco Biggio, Luca Carbone, Carmelita Guzzo, Luigi INAF-IASF Milano Via Alfonso Corti 12 MilanoI-20133 Italy Data Analytics Lab Institute of Machine Learning Department of Computer Science ETH Zürich Switzerland Dipartimento di Fisica Aldo Pontremoli Università Degli Studi di Milano INFN Sez. di Milano Via Celoria 16 MilanoI-20133 Italy INAF Osservatorio Astronomico di Brera Milano Italy

We develop a set of machine-learning based cosmological emulators, to obtain fast model predictions for the C() an-gular power spectrum coefficients characterising tomographic observations of galaxy clustering and weak gravitational lensing from multi-band photometric surveys (and their cross-correlation). A set of neural networks are trained to map cosmological parameters into the coefficients, achieving a speed-up O(103) in computing the required statistics for a given set of cosmological parameters, with respect to standard Boltzmann solvers, with an accuracy better than 0.175% ( Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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A precision diagnostic framework of renal cell carcinoma on whole-slide images using deep learning

arXiv

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arXiv 2021年

作者： Wu, Jialun Zhang, Haichuan Gao, Zeyu Bao, Xinrui Gong, Tieliang Wang, Chunbao Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University Park PA United States School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Department of Pathology The First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. Tumor region detection, subtype and grade classification are the fundamental diagnostic indicators for renal cell carcinoma (RCC) in whole-slide images (WSIs). However, pathological diagnosis is subjective, differences in observation and diagnosis between pathologists is common in hospitals with inadequate diagnostic capacity. The main challenge for developing deep learning based RCC diagnostic system is the lack of large-scale datasets with precise annotations. In this work, we proposed a deep learning-based framework for analyzing histopathological images of patients with renal cell carcinoma, which has the potential to achieve pathologist-level accuracy in diagnosis. A deep convolutional neural network (InceptionV3) was trained on the high-quality annotated dataset of The Cancer Genome Atlas (TCGA) whole-slide histopathological image for accurate tumor area detection, classification of RCC subtypes, and ISUP grades classification of clear cell carcinoma subtypes. These results suggest that our framework can help pathologists in the detection of cancer region and classification of subtypes and grades, which could be applied to any cancer type, providing auxiliary diagnosis and promoting clinical consensus. © 2021, CC BY.

关键词： Classification (of information)

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PIMIP: An open source platform for pathology information management and integration

arXiv

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arXiv 2021年

作者： Wu, Jialun Mao, Anyu Bao, Xinrui Zhang, Haichuan Gao, Zeyu Wang, Chunbao Gong, Tieliang Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA United States Department of Pathology First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Digital pathology plays a crucial role in the development of artificial intelligence in the medical field. The digital pathology platform can make the pathological resources digital and networked, and realize the permanent storage of visual data and the synchronous browsing processing without the limitation of time and space. It has been widely used in various fields of pathology. However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information. Most platforms cannot integrate image viewing, annotation and analysis, and text information management. To solve the above problems, we propose a comprehensive and extensible platform, PIMIP (Pathology Information Management & Integration Platform). Our PIMIP has developed the image annotation functions based on the visualization of digital pathological sections. Our annotation functions support multi-user collaborative annotation and multi-device annotation, and realize the automation of some annotation tasks. In the annotation task, we invited a professional pathologist for guidance. We introduce a machine learning module for image analysis. The data we collected included public data from local hospitals and clinical examples. Our platform is more clinical and suitable for clinical use. In addition to image data, we also structured the management and display of text information. So our platform is comprehensive. The platform framework is built in a modular way to support users to add machine learning modules independently, which makes our platform extensible. © 2021, CC BY.

关键词： Information management

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A personalized diagnostic generation framework based on multi-source Heterogeneous data

arXiv

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arXiv 2021年

作者： Wu, Jialun Gao, Zeyu Zhang, Haichuan Zhang, Ruonan Gong, Tieliang Wang, Chunbao Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA United States Library of Xi’an Jiaotong University Xi’an China Department of Pathology First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Personalized diagnoses have not been possible due to sear amount of data pathologists have to bear during the day-today routine. This lead to the current generalized standards that are being continuously updated as new findings are reported. It is noticeable that these effective standards are developed based on a multi-source heterogeneous data, including whole-slide images and pathology and clinical reports. In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient. We use nuclei-level image feature similarity and content-based deep learning method to search for a personalized group of population with similar pathological characteristics, extract structured prognostic information from descriptive pathology reports of the similar patient population, and assign importance of different prognostic factors to generate a personalized pathological diagnosis result. We use multi-source heterogeneous data from TCGA (The Cancer Genome Atlas) database. The result demonstrate that our framework matches the performance of pathologists in the diagnosis of renal cell carcinoma. This framework is designed to be generic, thus could be applied for other types of cancer. The weights could provide insights to the known prognostic factors and further guide more precise clinical treatment protocols. © 2021, CC BY.

关键词： Diagnosis

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CAN-D: A modular four-step pipeline for comprehensively decoding controller area network data

arXiv

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arXiv 2020年

作者： Verma, Miki E. Bridges, Robert A. Sosnowski, Jordan J. Hollifield, Samuel C. Iannacone, Michael D. Cyber & Applied Data Analytics Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Computer Science & Software Engineering Auburn University

Controller area networks (CANs) are a broadcast protocol for real-time communication of critical vehicle subsystems. Original equipment manufacturers of passenger vehicles hold secret their mappings of CAN data to vehicle signals, and these definitions vary according to make, model, and year. Without these mappings, the wealth of real-time vehicle information hidden in the CAN packets is uninterpretable, severely impeding vehicle-related research, including CAN cybersecurity and privacy studies, aftermarket tuning, efficiency and performance monitoring, and fault diagnosis to name a few. Guided by the four-part CAN signal definition, we present CAN-D (CAN-Decoder), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with meaningful, physical interpretation. En route to CAN-D, we provide a comprehensive review of the CAN signal reverse engineering research. All previous methods ignore endianness and signedness, rendering them incapable of decoding many standard CAN signal definitions. Incorporating endianness grows the search space from 128 to 4.72E21 signal tokenizations and introduces a web of changing dependencies. In response, we formulate, formally analyze, and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings. In addition, we provide two novel, state-of-the-art signal boundary classifiers-both of which are superior to previous approaches in precision and recall in three different test scenarios-and the first signedness classification algorithm, which exhibits a > 97% F-score. Overall, CAN-D is the only solution with the potential to extract any CAN signal that is also the state of the art. In evaluation on 10 vehicles of different makes, CAN-D's average l1 error is five times bette

关键词： Control system synthesis

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical Engineering 2023年 1027 LNEE卷 xi-xii页

作者： Dubey, Ashwani Kumar Chong, Peter Han Joo Sugumaran, Vijayan Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Decision and Information Sciences Center for Data Science and Big Data Analytics Oakland University RochesterMI United States

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