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Skillful Prediction of Indian Monsoon Intraseasonal Precipitation Using Central Indian Ocean Mode and machine learning

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GEOPHYSICAL RESEARCH LETTERS 2024年第24期51卷

作者： Zhou, Lei Yu, Yanwei Yan, Bingqi Zhao, Xingyu Qin, Jianhuang Tan, Wei Tang, Youmin Li, Xiaofeng Li, Xiaojing Dong, Junyu Chen, Dake Murtugudde, Raghu Shanghai Jiao Tong Univ Sch Oceanog Shanghai Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai Peoples R China Ocean Univ China Coll Comp Sci & Technol Qingdao Peoples R China Hohai Univ Coll Oceanog Nanjing Peoples R China Shandong Univ Sci & Technol Coll Ocean Sci & Engn Qingdao Peoples R China Univ Northern British Columbia Environm Sci & Engn Prince George BC Canada Chinese Acad Sci Big Data Ctr Inst Oceanol CAS Key Lab Ocean Circulat & Waves Qingdao Peoples R China Chinese Acad Sci Ctr Ocean Mega Sci Qingdao Peoples R China Minist Nat Resources Inst Oceanog 2 State Key Lab Satellite Ocean Environm Dynam Hangzhou Peoples R China Univ Maryland Dept Atmospher & Ocean Sci College Pk MD USA Indian Inst Technol Mumbai India

Monsoonal precipitation is dominated by intraseasonal variabilities, whose skillful prediction lead time is currently less than 5 days and remains a grand challenge. Here we show that an intrinsic variability in the Indian Ocean, the Central Indian Ocean (CIO) mode, when combined with a machine learning (ML) algorithm, can produce skillful predictions of intraseasonal precipitation over the monsoon region with a lead time of over 15 days, which is close to the theoretical predictability limit. This remarkable skill improvement stems from the fact that the CIO mode is dynamically related to the intraseasonal monsoon rainfall, while the data-driven ML algorithm suppresses unwanted high-frequency noise. Using the CIO mode and the ML algorithm, the forecast system hybridizes physical fundamentals and versatility of data-driven algorithms. The identification of CIO mode and the verification of its significant contribution to intraseasonal predictions advance our understanding of the coupled monsoon system and also underscores the great potential of ML techniques in weather forecasts and climate predictions.

关键词： intraseasonal rainfall forecast Indian summer monsoon Central Indian Ocean mode machine learning algorithm monsoon intraseasonal oscillation

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Conceptual machine learning Framework for Performance-Based Resource Allocation to the Institutions: A Hybrid Approach 3rd

Conceptual Machine Learning Framework for Performance-Based ...

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3rd International Conference on Smart Systems - Innovations in Computing (SSIC)

作者： Solanki, Hiteshkumar Virparia, Paresh Informat & Lib Network Ctr Infoc Gandhinagar 382007 India Sardar Patel Univ PG Dept Comp Sci & Technol Vallabh Vidyanagar 388120 Gujarat India

ISBN: (纸本)9789811628771;9789811628764

The paper elaborates the conceptual framework for resource allocation model based on performance of Higher Educational Institutions (HEIs). It provides logical workflow to develop input-oriented and output-oriented prediction model to assign academic, financial, and research enhancing resources to the institutions. The input- and output-oriented parameters such as faculty student ratio, number of publications, number of citations, number of patents, capital and operational expenditure, student admission ratio, number of PhD scholars enrolled, and number of PhD scholars graduated would be considered for the conceptual framework. All parameters would be identified by applying supervised machine learning algorithm like correlation and regression analysis. The data envelopment analysis (DEA) would be used to measure relative efficiency of each institution. The unsupervised machine learning algorithm would be implemented to find out clusters of institutions based on their relative efficiency. Finally, the input-oriented and output-oriented predication model would be developed to allocate resources to the institutions. The conceptual framework is being developed as a hybrid model with combination of optimization techniques of operation research and machine learning algorithms. The prediction model plays a vital role to allocate resources to the HEIs based on their performance. It would become benchmarking and decision-making model in perspective with higher education sector in India for administrators, funding agencies, and policymakers.

关键词： Data envelopment analysis Higher education machine learning algorithm Performance assessment Resource allocation system

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A machine learning Approach for the Classification of Wet and Dry Periods Using Commercial Microwave Link Data

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SN Computer Science 2022年第3期3卷 1-23页

作者： Kamtchoum, Evrad Venceslas Nzeukou Takougang, Armand Cyrille Tayou Djamegni, Clémentin Department of Mathematics and Computer Science University of Dschang Dschang 237 Cameroon Fotso Victor Institute of Technology University of Dschang Bandjoun 237 Cameroon

Rainfall estimation is essential for many reasons. The lack of rainfall monitoring and estimation infrastructure in some parts of the world, including sub-Saharan Africa, is a serious problem with severe consequences. For this reason, the possibility to use mobile network infrastructure, which are in continuous expansion all over the world, to monitor and estimate rainfall has been widely investigated during the last 2 decades. One of the critical tasks in the process of rainfall estimation using microwave links is the detection of rainy periods. Many methods have been proposed to realize this task in the literature, with satisfaction and underlying uncertainties resulting from mobile networks’ infrastructures and modeling assumptions. machine learning has been proven to be very powerful for classification tasks and patterns recognition in high-dimensional data, as evidenced by many applications in various fields of science and engineering. Deep learning in particular has been recently used to improve the wet–dry periods classification task in rainfall estimation using commercial microwave links. In this paper, we propose to use other machine learning techniques for the classification of dry and wet periods using commercial microwave link data. We evaluate and compare the ability of single machine learning classifiers, ensemble machine learning classifiers, and clustering algorithms to successfully realize this task. We consider a sample of four microwave links located in the Netherlands (Western Europe), for which minimum and maximum received signal levels are recorded for each 15 min period from 30/05/2012 to 01/09/2012. In general, the considered algorithms are performant for this classification task, particularly for ensemble machine learning classifiers, among which an ensemble voting classifier emerges as a good first choice with an overall accuracy, and precision of wet periods detection very close to 100%. Its precision of dry periods’ detection is also clos

关键词： Clustering machine learning algorithm Microwave links’ attenuation Rainfall observations Supervised classification

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Advanced machine learning algorithm Application for Rotating machine Health Monitoring

Advanced Machine learning Algorithm Application for Rotating...

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International Symposium on Future I&C for Nuclear Power Plants（ISOFIC2014）

作者： Shigeru KANEMOTO Masaya WATANABE Noritaka YUSA Graduate Department of Computer and Information System The University of Aizu Department of Quantum Science and Energy Engineering Tohoku University

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health *** techniques include support vector machine,deep leaning neural network,*** inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of *** we cannot find remarkable difference of anomaly discrimination performance,some methods give us the very interesting eigen patterns corresponding to normal and abnormal *** results will be useful for future more sensitive and robust anomaly monitoring technology.

关键词： Health Monitoring Rotating machine Anomaly Diagnosis machine learning algorithm

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An experimental study of objective pain measurement using pupillary response based on genetic algorithm and artificial neural network

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APPLIED INTELLIGENCE 2022年第2期52卷 1145-1156页

作者： Wang, Li Guo, Yikang Dalip, Biren Xiao, Yan Urman, Richard D. Lin, Yingzi Northeastern Univ Intelligent Human Machine Syst Lab Boston MA 02115 USA Univ Texas Arlington Coll Nursing & Hlth Innovat Arlington TX USA Harvard Med Sch Brigham & Womens Hosp Boston MA USA

Obtaining an objective measurement of the pain level of a patient has always been challenging for health care providers. The most common method of pain assessment in the hospital setting is asking the patients' verbal ratings, which is considered to be a subjective approach. In order to get an objective pain level of a patient, we propose measuring pain level objectively using the pupillary response and machine learning algorithms. Thirty-two healthy subjects were enrolled in this study at Northeastern University. A painful stimulus was applied to healthy subjects by asking them to place their hands inside a bucket filled with iced water. We extracted 11 features from the pupil diameter data. To get the optimal subset of the features, a genetic algorithm (GA) was used to select features for the artificial neural network (ANN) classifier. Before feature selection, the f1-score of ANN was 54.0 +/- 0.25% with all 11 features. After feature selection, ANN had the best performance with an accuracy of 81.0% using the selected feature subset, namely the Mean, the Root Mean Square (RMS), and the Pupillary Area Under Curve (PAUC). The experimental results suggested that pupillary response together with machine learning algorithms could be a promising method of objective pain level assessment. The outcomes of this study could improve patients' experience of pain measurement in telehealthcare, especially during a pandemic when most people had to stay at home.

关键词： Objective pain measurement Pupillary response Genetic algorithm Artificial neural network machine learning algorithm

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Impact of Enterprises Relationship on Ml's Ability to Forecast SME's Credit Risk in SCF: A Research Based on Complex Network

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EMERGING MARKETS FINANCE AND TRADE 2024年第11期60卷 2594-2612页

作者： Peng, Qiaosheng Xie, Chi Li, Jing Zhu, You Wang, Gang-Jin Hunan Univ Business Sch Changsha Hunan Peoples R China Hunan Univ Ctr Finance & Investment Management Changsha Hunan Peoples R China Hunan Univ Business Sch Changsha 410082 Hunan Peoples R China

In this study, we investigate the impact of introducing enterprise relationship to train machine learning (ML) algorithms on improving its ability of forecasting small and medium-sized enterprises' (SMEs) credit risk in supply chain finance (SCF). First, we incorporate attributes of SME node in the CEs-SMEs network, a bipartite network constructed with transaction data in the Chinese automobile industry, into financial indicators to train ML algorithms for improving forecasting performance. Second, we employ the weighted one-mode projection approach to extract a projected-SMEs network that reveals the relationship among SMEs from the CEs-SMEs network and examine whether the ML algorithms' forecasting performances is improved when considering attributes in the projected-SMEs network. Third, we analyze the correlation between SME's credit risk and individual network attribute to specifically understand the impact of enterprise relationship on credit risk. Overall, the empirical results indicate that the forecasting performance is obviously better when enterprise transaction relationship is considered, and it is further enhanced after we apply weighted one-mode projection approach. Meanwhile, the variable importance analysis and the binomial logistic regression demonstrate how significantly each network attribute is correlated with credit risk, and the discussion on partial dependence plot shows that SMEs with large degree are non-risky generally.

关键词： Supply chain finance small and-medium-sized enterprise's credit risk machine learning algorithm complex network weighted one-mode projection D85 G10 G21 G33 L14 M10

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Balancing Plug-In for Stream-Based Classification 1

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11st World Conference on Information Systems and Technologies (WorldCIST)

作者： de Arriba-Perez, Francisco Garcia-Mendez, Silvia Leal, Fatima Malheiro, Benedita Burguillo-Rial, Juan Carlos Univ Vigo atlanTTic Informat Technol Grp Vigo Spain Univ Portucalense REMIT Porto Portugal INESC TEC Porto Portugal Polytech Inst Porto ISEP Porto Portugal

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

The latest technological advances drive the emergence of countless real-time data streams fed by users, sensors, and devices. These data sources can be mined with the help of predictive and classification techniques to support decision-making in fields like e-commerce, industry or health. In particular, stream-based classification is widely used to categorise incoming samples on the fly. However, the distribution of samples per class is often imbalanced, affecting the performance and fairness of machine learning models. To overcome this drawback, this paper proposes Bplug, a balancing plug-in for stream-based classification, to minimise the bias introduced by data imbalance. First, the plugin determines the class imbalance degree and then synthesises data statistically through non-parametric kernel density estimation. The experiments, performed with real data from Wikivoyage and Metro of Porto, show that Bplug maintains inter-feature correlation and improves classification accuracy. Moreover, it works both online and offline.

关键词： Data bias fairness imbalanced data sets machine learning algorithm stream classification

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Research on prediction and analysis of supercritical water heat transfer coefficient based on support vector machine

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第11期55卷 4102-4111页

作者： Ma, Dongliang Li, Yi Zhou, Tao Huang, Yanping Hebei Finance Univ Sch Informat Engn & Comp Sci Baoding 071051 Peoples R China Hebei Finance Univ Sch Financial Technol Baoding 071051 Hebei Peoples R China Southeast Univ Sch Energy & Environm Nanjing 211189 Peoples R China Nucl Power Inst China Sci & Technol Reactor Syst Design Technol Lab Chengdu 610041 Peoples R China

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experi-mental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

关键词： Supercritical water Support vector machine Radial basis kernel function Data normalization Heat transfer coefficient machine learning algorithm

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LiDAR Image-Based Climate Change Detection with Their Economic Ecosystem Modelling: Artificial Intelligence Application

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Remote Sensing in Earth Systems Sciences 2025年第1期8卷 173-181页

作者： Sravani, Chekka Ananth, Prithiviraj Sasikala, C. Kumar, N. Sathish Kumari, Ch D. V. P. Thrimurthulu, V. Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad 500043 India Department of CSE Sona College of Technology Salem-5 Salem India Department of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of ECE Sri Ramakrishna Engineering College Coimbatore 22 India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad India

Over the past few decades, change detection has been the subject of more research in both close-quarters and distant sensing due to its significance in environment monitoring and database updating. The advancement of sensing technology has made data collecting more accessible and economical, which has increased the amount of data available from different sensing platforms. Studying these atmospheric components has become much easier because of LiDAR technology, which can measure backscattered signals and explore the atmosphere at very high spatial and temporal resolution. This research proposes a novel technique in satellite data security analysis with climate change detection utilizing ML algorithm. Input has been collected as input as satellite data based on security and processed for noise removal as well as normalisation. Processed data has been segmented and classified using SegNet histogram graph cut contour model and transfer AlexNet adversarial Markov model. The experimental analysis has been carried out in terms of data integrity, precision, normalised coefficient, recall and F-1 score. The proposed technique attained data integrity of 98%, F-1 score 95%, PRECISION of 97%, normalised coefficient of 93% and RECALL of 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial Markov Climate change detection Graph cut contour model LiDAR technology machine learning algorithm

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A Fine-Grained Flash-Memory Fragment Recognition Approach for Low-Level Data Recovery

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Chinese Journal of Electronics 2022年第4期31卷 732-740页

作者： ZHANG Li HAO Shengang ZHANG Quanxin Department of Media Engineering Zhejiang University of Media and Communication School of Computer Science Beijing Institute of Technology

Data recovery from flash memory in the mobile device can effectively reduce the loss caused by data corruption. Type recognition of data fragment is an essential prerequisite to the low-level data recovery. Previous works in this field classify data fragment based on its file type. Still, the classification efficiency is low, especially when the data fragment is a part of a composite *** propose a fine-grained approach to classifying data fragment from the low-level flash memory to improve the classification accuracy and efficiency. The proposed method redefines flash-memory-page data recognition problem based on the encoding format of the data segment, and applies a hybrid machine learning algorithm to detect the data type of the flash page. The hybrid algorithm can significantly decompose the given data space and reduce the cost of training. The experimental results show that our method achieves better classification accuracy and higher time performance than the existing methods.

关键词： Flash fragmentation recognition Fine-grained machine learning algorithm Support vector machine Decision tree Digital forensics

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