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28th international conference on Hydraulics, Water Resources, River and Coastal engineering, HYDRO 2023

作者： Tejaswi, Vangala Venkata Reddy, Keesara Loukika, K.N. National Institute of Technology Warangal Telangana Warangal India

ISBN: (纸本)9789819774661

Land Use and Land Cover (LULC) change refer to the loss of natural areas, particularly forests, agricultural areas, or water bodies, to urban or exurban development. Understanding how LULC will impact the district of Hanamkonda’s water resource availability is crucial. In order to conduct tasks like change detection analysis and theme mapping, baseline data on land cover must be determined. Expanding urban areas affects natural resources and makes them vulnerable. As it is observed that rapid changes are occurring in LULC around the water bodies, this will badly affect the quantity and quality of water resources, increasing the pressure on water availability in urban areas. It also creates flood hazards in the surrounding areas of the water bodies due to not protecting the boundaries of the water bodies. Any loss in the water surface area will also impact the groundwater resources in the region. The Hanamkonda district of Telangana state, India, has many water bodies. Over the period, the surroundings of some of the water bodies are highly urbanised, causing stress on water resource availability and flood-related problems during monsoon season. The land use and land cover changes for the four lake systems in the Hanamkonda district over a ten-year period, from 2013 to 2022, are presented in this paper using machine learning algorithms in the Google Earth Engine site. The accuracy assessment is used to compare the performance of the two machine learning algorithms such as Random Forest (RF) and Support Vector machine (SVM) in the classification of LULC. For the years 2013, 2016, 2019, and 2022, Landsat-8 data is used, and the major LULC classes are ‘water bodies’, ‘urban’, ‘vegetation’, and ‘barren’. The average overall accuracy of RF and SVM classifiers is 88.47% and 91.92%, respectively. The results suggest that the support vector machine classifier outperforms the random forest classifier in terms of accuracy. The findings revealed that from 2013 to 2022, water bo

关键词： Lakes

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Research on crime risk prediction model based on machine learning 24

Research on crime risk prediction model based on machine lea...

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13th international conference on Computing and Pattern Recognition, ICCPR 2024

作者： Hou, Xuehui Rexiti, Kudelaiti Aizezi, Yasen School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan China Faculty of Information Security and Engineering Xinjiang Police College Urumqi Xinjiang China

ISBN: (纸本)9798400717482

Crime prediction is an important method for public security departments to conduct crime early warning and investigation. According to the multidimensional characteristics of criminals, machine learning classification algorithm is used to establish a prediction model of whether a criminal commits a crime again, so as to quantitatively evaluate the possibility of the criminal commits a crime again, and provide a feasible scheme reference for preventing and reducing the recurrence of crime. Analysis of urban crime data in Boston, using Naive Bayes, Random Forest and XGboost algorithms to build a crime type prediction model, we can get the urban crime type tendency, help the public security department to predict the future crime, take effective measures to reduce the urban crime rate. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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Predicting the Total Export-import Volume of China's Economic Trade through machine learning

INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGE...

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international JOURNAL OF MATHEMATICAL engineering AND MANAGEMENT SCIENCES 2025年第1期10卷 63-75页

作者： Zhu, Qiuxia Shanxi Conservancy Tech Inst Yuncheng 044000 Shanxi Peoples R China

By forecasting the total export-import volume of China's economic trade, valuable guidance can be provided for the formulation of relevant policies, thus holding significant practical importance. This paper first analyzed the current situation of the import and export of China's economic trade. Subsequently, key indicators such as gross domestic product (GDP) and producer price index (PPI) were selected. After eliminating irrelevant indicators through correlation coefficient calculation, seven remaining indicators were employed for research. Building upon the machine learning algorithm-support vector machine (SVM), an improved sparrow search algorithm (ISSA) was developed to optimize SVM parameters, forming the ISSA-SVM prediction method. Experimental validation was conducted using data from 2003 to 2022. The results revealed that the average time consumed by ISSA-SVM for forecasting was 1.742401 s, displaying a minimal difference compared to the SVM method. In terms of total volume prediction, the ISSA-SVM approach achieved a mean absolute percentage error of 0.02% and a root-mean-square error of 1068.25, surpassing logistic regression, back-propagation neural network (BPNN), and other methods. These outcomes verify the reliability of the ISSA-SVM method in total volume prediction, showcasing its practical applicability.

关键词： machine learning China's economy Total export-import volume Trade Forecasting

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EncoderMU: machine Unlearning in Contrastive learning 6th

EncoderMU: Machine Unlearning in Contrastive Learning

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Wang, Zixin Mi, Bing Chen, Kongyang Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Guangdong University of Finance and Economics Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9783031736988

machine unlearning is a complex process that necessitates the model to diminish the influence of the training data while keeping the loss of accuracy to a minimum. Despite the numerous studies on machine unlearning in recent years, the majority of them have primarily focused on supervised learning models, leaving research on contrastive learning models relatively underexplored. With the conviction that self-supervised learning harbors a promising potential, surpassing or rivaling that of supervised learning, we set out to investigate methods for machine unlearning centered around contrastive learning models. In this study, we introduce a novel gradient constraint-based approach for training the model to effectively achieve machine unlearning. Our method only necessitates a minimal number of training epochs and the identification of the data slated for unlearning. Remarkably, our approach demonstrates proficient performance not only on contrastive learning models but also on supervised learning models, showcasing its versatility and adaptability in various learning paradigms. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Contrastive learning

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Quantitative analysis of oxide content in ChemCam spectral data using sparrow search algorithm-kernel extreme learning machine 2

Quantitative analysis of oxide content in ChemCam spectral d...

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2nd international Academic conference on Optics and Photonics, IACOP 2024

作者： Wang, Manping Lu, Yang Yu, Liandong No. 66 West Changjiang Road Huangdao District Qingdao266580 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

Laser-induced breakdown spectroscopy (LIBS), as a pivotal technique in the field of planetary exploration, is rapidly advancing its application in precise analysis of target chemical composition. To further enhance the accuracy of LIBS in quantitative analysis, this study employs the least absolute shrinkage and selection operator (LASSO) to extract spectral features as inputs for subsequent quantitative models. Subsequently, the sparrow search algorithm (SSA) is utilized to optimize the regularization coefficient and kernel function parameters of kernel extreme learning machine (KELM), establishing an SSA-KELM quantitative analysis model. This model is applied to predict the ChemCam dataset and compared with other methods to validate its effectiveness. The research findings demonstrate that integrating LIBS technology with LASSO-SSA-KELM method holds great potential for the quantitative analysis of surface elements on Mars, thus providing a reference for rapid and accurate analysis of surface elements on Mars. © 2025 SPIE.

关键词： Martian surface analysis

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Integration of data science with the intelligent IoT(IIoT): Current challenges and future perspectives

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Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon University University of Electronic Science and Technology of China College of Information Science and Engineering Hohai University Department of Computer Science University of Salerno Graduate School of Informatics and Engineering The University of Electro-Communications

The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence(AI) and machine learning, to make accurate decisions. data science is the science of dealing with data and its relationships through intelligent approaches. Most state-of-the-art research focuses independently on either data science or IIoT, rather than exploring their integration. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions.

关键词： data science Internet of things (IoT) Big data Communication systems Networks Security data science analytics

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Using machine learning to Analyze the Impact of Lifestyle and Socioeconomic Factors on the Incidence of Depression Among Young Brazilians

Using Machine Learning to Analyze the Impact of Lifestyle an...

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18th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Rodrigues, Thayris G. F. Da Silva, Ariane C. B. Nobre, Cristiane N. Institute of Exact Sciences and Informatics Pontifical Catholic University of Minas Gerais Dom José Gaspar Belo Horizonte Brazil

Depression is a growing mental health problem among young people in Brazil, with factors such as socioe-conomic and lifestyle conditions influencing its prevalence. This study investigates how variables such as education, family situation, and access to services impact the incidence of depression, using data from the National Health Survey (PNS) of the Brazilian Institute of Geography and Statistics (IBGE). Using machine learning algorithms such as Random Forest, XGBoost, SVM, and MLP, the analysis identified patterns among the factors, highlighting sleep problems and depressive feelings as the main determinants, with Recall above 70%. These results support the creation of more inclusive mental health policies. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Propagation tree says: dynamic evolution characteristics learning approach for rumor detection

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international JOURNAL OF machine learning AND CYBERNETICS 2025年第3期16卷 1589-1605页

作者： Zhao, Shouhao Ji, Shujuan Lv, Jiandong Fang, Xianwen Shandong Univ Sci & Technol Sch Comp Sci & Engn Qianwangang Rd Qingdao Shandong Peoples R China Anhui Univ Sci & Technol Anhui Prov Engn Lab Big Data Anal & Early Warning Huainan Anhui Peoples R China

Due to the rapid spread of rumors on social media, which has a detrimental effect on our lives, it is becoming increasingly important to detect rumors. It has been proved that the study of dynamic graphs is helpful to capture the temporal change of information transmission and understand the evolution trend and pattern change of events. However, the dynamic learning methods currently studied do not fully consider the interaction characteristics of the evolutionary process. Therefore, it is difficult to fully capture the structural and semantic differences between them. In order to fully exploit the potential correlations of such temporal information, we propose a novel model named dynamic evolution characteristics learning (DECL) method for rumor detection. First, we partition the temporal snapshot sequences based on the propagation structure of rumors. Secondly, a multi-task graph contrastive learning method is adopted to enable the graph encoder to capture the essential features of rumors, and to fully explore the temporal structural differences and semantic similarities between true rumor and false rumor events. Experimental results on three real-world social media datasets confirm the effectiveness of our model for rumor detection tasks.

关键词： Rumor detection Propagation structure Graph representation Contrastive learning

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Literature Review of Personalizing learning Recommendation Systems Using machine learning in Chinese Higher Education 24

Literature Review of Personalizing Learning Recommendation S...

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7th international conference on Educational Technology Management, ICETM 2024

作者： Huang, Mingjing Cheong, Ngai Liu, Jiaqi Zhang, Zhuofan Faculty of Applied Sciences Macao Polytechnic University China Intelligent Transportation Engineering Institute Guangdong Communication Polytechnic Guangdong Guangzhou China

ISBN: (纸本)9798400717468

With the rapid development of information technology, personalized education recommendation systems have gained widespread attention and rapid development in China’s education sector. These systems provide customized learning resources and course recommendations to meet the individual needs of different students by analyzing their learning behaviors, preferences, and performance. This study comprehensively searched 45, 623 literatures from 2002 to 2022, focusing on the current status, key technologies, and application practices of educational recommendation systems in China. It first investigates the basic deep learning recommendation methods used in current personalized learning recommendation systems, and then summarizes the current status of machine learning techniques applied in personalized learning recommendation systems for higher education in China. Finally, through a comprehensive analysis of the existing literature, we discuss the current challenges and future development direction of China’s educational recommendation systems, and put forward improvement suggestions for domestic educational recommendation systems, aiming to fill the research gaps and promote technological progress and innovation. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Challenges of Generalizing machine learning Models in Healthcare

Challenges of Generalizing Machine Learning Models in Health...

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18th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Kessler, Steven Dewitz, Bastian Sundarara, Santhoshkumar Salinas, Favio Lichtenberg, Artur Schmid, Falko Aubin, Hug Digital Health Lab Düsseldorf University Clinic Düsseldorf Moorenstrasse 5 Düsseldorf Germany

Generalization problems are common in machine learning models, particularly in healthcare applications. This study addresses the issue of real-world generalization and its challenges by analyzing a specific use case: predicting patient readmissions using a Recurrent Neural Network (RNN). Although a previously developed RNN model achieved robust results on the Medical Information Mart for Intensive Care (MIMIC-III) dataset, it showed near-random predictive accuracy when applied to the local hospital’s data (Moazemi et al., 2022). We hypothesize that this discrepancy is due to patient demographics, clinical practices, data collection methods, and healthcare differences in infrastructure. By employing statistical methods and distance metrics for time series, we identified critical disparities in demographic and vital data between the MIMIC and hospital data. These findings highlight possible challenges in developing generalizable machine learning models in healthcare environments and the need to improve not just algorithmic solutions but also the process of measuring and collecting medical data. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Contrastive learning

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