检索结果-内蒙古大学图书馆

International Conference on Distributed Computing and Optimization Tech-niques (ICDCOT)

作者： Gurram, Rajendra Babu, P. Dhanunjaya Tejaswi, Adusumalli Sai Ganesh, Chattu Sai Narendra, Karlaputi Lakireddy Bali Reddy Coll Engn Dept Informat Technol Mylavaram India Lakireddy Bali Reddy Coll Engn MCA Dept Mylavaram 521230 India

ISBN: (纸本)9789811922817;9789811922800

In the world of hacking, one of the simple and human-understandable web attacks is Cross-Site request forgery, but this is the most top priority attack that needed to handle with keen observation because of the very small difference in their appearance. A hacker develops a cross-site application that looks similar to the original site and embeds this URL in either back transactions or E-commerce applications, it misleads the user to deviate from the original website without his notice by disabling all the authentication mechanisms. The hackers develop this type of cross website by embedding the SQL injection queries either in HTML navigation pages or while executing the external Java Scripts. To develop a solution for this type of attack, the system needs a tool that balances both scalability and usability because of millions of users working with the internet to performs various tasks on social media platforms. This tool can also isolate web attacks by designing robust web applications which include advanced encryption algorithms, which require a lot of effort to decrypt the source code. The developers of the website have to work a lot to take care of the security functionalities, negligence of which may cost the website source code to be hacked. Accidentally, Cross-Site request forgery (CSRF) attacks can be left behind, which motivated recent research on asynchronous Cross-Site request forgery detection. The proposed paper uses the classification and regression Trees (CART) Algorithm to detect phishing websites based on the URL posted.

关键词： Cross-Site request forgery classification and regression Security attacks Website phishing

来源：评论

学校读者我要写书评

暂无评论

A Tool for classification and regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling

引用

ENVIRONMENTAL MODELING & ASSESSMENT 2017年第3期22卷 201-214页

作者： Lagomarsino, Daniela Tofani, V. Segoni, S. Catani, F. Casagli, N. Univ Firenze Earth Sci Dept Via La Pira 4 I-50121 Florence Italy

classification and regression problems are a central issue in geosciences. In this paper, we present classification and regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance;then, it selects the optimal configuration of explanatory variables;finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling.

关键词： classification and regression Random forest Feature selection Landslide susceptibility maps

来源：评论

学校读者我要写书评

暂无评论

Revealing the economic consequences of tornadoes using a two-stage SVM-based predictive approach

引用

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2025年 1-18页

作者： Ozgenc, Ramazan Ekmekcioglu, Omer Deniz, Ali Istanbul Tech Univ Fac Aeronaut & Astronaut Dept Meteorol Istanbul Turkiye Istanbul Tech Univ Disaster Management Inst Disaster & Emergency Management Dept Istanbul Turkiye

Limited studies have concentrated on estimating economic losses caused by tornadoes through combining socioeconomic and demographic vulnerability with geographic and terrain attributes. The current study built a two-stage support vector machine (SVM) based predictive tool for both determining the tornado-prone regions and the resultant economic losses. During the identification of tornado-prone regions, the SVM model achieved an accuracy of 80.57% and an area under the receiver operating characteristic (AUC) score of 86.79%, demonstrating its ability to classify the regions susceptible to experience tornadoes. Also, estimation of economic losses for the respective regions provided root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) values of 0.5352, 0.4814, and 0.9175, respectively. The analysis showed that 17.59% of Texas is under very high economic loss risk. The game theoretical Shapley Additive explanation (SHAP) analysis further highlighted the contribution of Fujita scale of tornadoes, longitude, number of houses, and tornado area into the likelihood of tornado incidents. Hence, the holistic predictive framework introduced is useful in creating instruments to help government disaster management officials, professionals, and insurance companies, given the anticipated rise in the severity, frequency, and duration of extreme weather events in the context of global warming.

关键词： Economic damage classification and regression Extreme weather phenomena Tornado, disaster, susceptibility

来源：评论

学校读者我要写书评

暂无评论

Deep learning-assisted fluorescence spectroscopy for food quality and safety analysis

引用

TRENDS IN FOOD SCIENCE & TECHNOLOGY 2025年 156卷

作者： Yuan, Yuan Ji, Zengtao Fan, Yanwei Xu, Qian Shi, Ce Lyu, Jian Ertbjerg, Per Tarim Univ Coll Food Sci & Engn Alar 843301 Peoples R China Beijing Acad Agr & Forestry Sci Informat Technol Res Ctr Beijing 100097 Peoples R China Minist Agr & Rural Affairs Key Lab Cold Chain Logist Technol Agroprod Beijing 100097 Peoples R China Beijing Acad Agr & Forestry Sci Natl Engn Res Ctr Informat Technol Agr Beijing 100097 Peoples R China Key Lab Special Agr Prod Further Proc Southern Xin Prod & Construction Grp Alar 843301 Peoples R China Univ Helsinki Dept Food & Nutr Helsinki 00014 Finland

Background: Fluorescence spectroscopy has been widely employed in the quality assessment of food and agricultural products due to its rapid and accurate measurement characteristics. The large amount of fluorescence data or images generated by fluorescence spectroscopy requires more efficient chemometric methods to process and analyze them. However, conventional machine learning models struggle to achieve high-precision predictions when analyzing high-dimensional fluorescence data samples. Deep learning algorithms exhibit powerful automatic learning capabilities in feature extraction and regression modeling of fluorescence spectra. Scope and approach: The complex, abstract and high-dimensional features of fluorescence spectroscopy are firstly demonstrated through the characterization of fluorescent substances in food products. Secondly, this paper highlights various challenges confronting the fluorescence spectrum analysis process and summarizes several deep learning algorithms that can address these solutions, including the convolutional neural network (CNN), long and short-term memory network (LSTM), and auto encoder (AE). Additionally, the application of deep learning models based on fluorescent data in food detection is reviewed in this article according to different testing objectives, including food safety inspections, food quality assessment, adulteration identification, and variety identification. The review also focuses on the future development trend of this technique in food quality and safety detection. Key findings and conclusions: Deep learning approaches combined with fluorescence spectroscopy exhibits immense potential in food quality detection and food discrimination classification. The selections of representative input parameters, suitable preprocessing methods and optimization methods can effectively tackle the problems of lack of samples and model over-fitting. Owing to the rapid advancement of artificial intelligence, the deep learning-based flu

关键词： Fluorescence spectroscopy Limitations of machine learning classification and regression Model optimization Model performance evaluation Application

来源：评论

学校读者我要写书评

暂无评论

Evaluation of traditional machine learning algorithms for featuring educational exercises

引用

APPLIED INTELLIGENCE 2025年第6期55卷 1-25页

作者： Jimenez-Macias, Alberto Munoz-Merino, Pedro J. Moreno-Marcos, Pedro Manuel Kloos, Carlos Delgado Univ Carlos III Madrid Telematic Engn Dept Ave Univ 30 Leganes 28911 Madrid Spain

Artificial intelligence (AI) algorithms are important in educational environments, and the use of machine learning algorithms to evaluate and improve the quality of education. Previous studies have individually analyzed algorithms to estimate item characteristics, such as grade, number of attempts, and time from student interactions. By contrast, this study integrated all three characteristics to discern the relationships between attempts, time, and performance in educational exercises. We analyzed 15 educational assessments using different machine learning algorithms, specifically 12 for regression and eight for classification, with different hyperparameters. This study used real student interaction data from ***, encompassing over 150 interactions per exercise, to predict grades and to improve our understanding of student performance. The results show that, in regression, the Bayesian ridge regression and random forest regression algorithms obtained the best results, and for the classification algorithms, Random Forest and Nearest Neighbors stood out. Most exercises in both scenarios involved more than 150 student interactions. Furthermore, the absence of a pattern in the variables contributes to suboptimal outcomes in some exercises. The information provided makes it more efficient to enhance the design of educational exercises.

关键词： Exercise modeling Machine learning classification and regression Content modeling Learning analytics

来源：评论

学校读者我要写书评

暂无评论

Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants

引用

CHEMOSENSORS 2024年第12期12卷

作者： Agiomavriti, Aikaterini-Artemis Nikolopoulou, Maria P. Bartzanas, Thomas Chorianopoulos, Nikos Demestichas, Konstantinos Gelasakis, Athanasios I. Agr Univ Athens Sch Anim Biosci Dept Anim Sci Lab Anat & Physiol Farm Anim Iera Odos 45 Str Athens 11855 Greece TCB Avgidis Automations SA R&D Dept Athens 11744 Greece Agr Univ Athens Dept Nat Resources Management & Agr Engn Lab Farm Struct Iera Odos 45 Str Athens 11855 Greece Telefarm SA R&D Dept Athens 11744 Greece Agr Univ Athens Dept Food Sci & Human Nutr Lab Microbiol & Biotechnol Food Iera Odos 45 Str Athens 11855 Greece Agr Univ Athens Dept Agr Econ Lab Comp Sci Iera Odos 45 Str Athens 11855 Greece

Milk analysis is critical to determine its intrinsic quality, as well as its nutritional and economic value. Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made the development of analytical tools and real-time monitoring and prediction systems in the dairy ruminant sector feasible. The objectives of the current review were (i) to describe the most widely applied spectroscopy-based and supervised machine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drug residues, (ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and (iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain.

关键词： milk analysis dairy ruminants spectroscopy supervised machine learning precision livestock farming classification and regression near-infrared mid-infrared laser-induced breakdown spectroscopy neural networks

来源：评论

学校读者我要写书评

暂无评论

A novel bounded loss framework for support vector machines

引用

NEURAL NETWORKS 2024年 178卷 106476页

作者： Li, Feihong Yang, Hu Chongqing Univ Coll Math & Stat Chongqing 401331 Peoples R China

This paper introduces a novel bounded loss framework for SVM and SVR. Specifically, using the Pinball loss as an illustration, we devise a novel bounded exponential quantile loss ( L eq -loss) for both support vector machine classification and regression tasks. For L eq -loss, it not only enhances the robustness of SVM and SVR against outliers but also improves the robustness of SVM to resampling from a different perspective. Furthermore, EQSVM and EQSVR were constructed based on L eq -loss, and the influence functions and breakdown point lower bounds of their estimators are derived. It is proved that the influence functions are bounded, and the breakdown point lower bounds can reach the highest asymptotic breakdown point of 1/2. Additionally, we demonstrated the robustness of EQSVM to resampling and derived its generalization error bound based on Rademacher complexity. Due to the L eq -loss being non -convex, we can use the concave-convex procedure (CCCP) technique to transform the problem into a series of convex optimization problems and use the ClipDCD algorithm to solve these convex optimization problems. Numerous experiments have been conducted to confirm the effectiveness of the proposed EQSVM and EQSVR.

关键词： Robustness Leq-loss Influence function Breakdown point classification and regression

来源：评论

学校读者我要写书评

暂无评论

Fully Convolutional Network-Based Fast UAV Detection in Pulse Doppler Radar

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1-12页

作者： Tian, Jiangmin Wang, Chenxing Cao, Jiuwen Wang, Xiaohong Hangzhou Dianzi Univ Machine Learning & I Hlth Int Cooperat Base Zhejia Hangzhou 310018 Zhejiang Peoples R China Hangzhou Dianzi Univ Artificial Intelligence Inst Hangzhou 310018 Zhejiang Peoples R China China Satellite Network Explorat Co Ltd Chengdu Peoples R China

With the popularity of drones, how to conduct effective and fast detection of unmanned aerial vehicle (UAV) to prevent unauthorized flying becomes a hot topic. Based on statistical theory, traditional constant false alarm rate (CFAR) works well on data with uniform background. But for low-slow-small UAV, it is prone to miss detection. In recent years, data-driven deep learning (DL) method is proved to have better performance than CFAR. However, the use of sliding window to convert complex detection task into simple classification task leads to low efficiency. In this article, we propose a fast detection method that applies a fully convolutional network (FCN) on the whole range-Doppler (RD) map. To achieve comparable accuracy to our previous work, the network is first designed on the principle that the effective receptive field (ERF) of unit in the feature map for prediction is close to the size of the sliding window. And the best bifurcation position of classification and regression is searched. Then, considering the imbalance of positive and negative samples, a new scheme to create ground-truth (GT) data is designed to expand the positive samples, and random sampling (RS) of negative samples is adopted further. Lastly, a post-processing mechanism combining probability thresholding and minimum deviation positioning (MDP) is developed for accurate location of target. Comparison with existing methods on the experimental data shows that the proposed method can increase the detection speed by up to 47 times while maintain a promising accuracy.

关键词： classification and regression fast unmanned aerial vehicle (UAV) detection fully convolutional network (FCN) radar target detection range-Doppler (RD) map receptive field (RF)

来源：评论

学校读者我要写书评

暂无评论

Chemiresistive sensor array for quantitative prediction of CO and NO2 gas concentrations in their mixture using machine learning algorithms

引用

MICROCHIMICA ACTA 2024年第12期191卷 1-14页

作者： Naganaboina, Venkata Ramesh Jana, Soumya Singh, Shiv Govind Indian Inst Technol Hyderabad Dept Elect Engn Sangareddy 502284 Telangana India Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Amaravati Campus Amaravati 522503 Andhra Pradesh India

Single sensors have been developed for specific gas detection in real-time environments, but their selectivity is limited by interference from other gases when considering mixtures of gases. Consequently, accurate detection of target gases in mixed gas environments is essential. Therefore, this study develops a sensor array approach to quantitatively estimate the concentration of carbon monoxide (CO) and nitrogen dioxide (NO2) gases in their binary mixture (CO and NO2). The sensor array consists of two different sensors, developed with zinc oxide and graphene-cobalt sulfide. The sensor array was tested in the presence of 29 different proportions of the binary mixture at room temperature. Subsequently, machine learning (ML) algorithms are applied on sensor signals to estimate the concentration of gases. The ML models unfortunately exhibited inaccurate prediction when all sensor signals were considered, therefore, to improve the prediction accuracy, the sensor signals were divided into three levels based on the mixed gas concentration regime. Interestingly, the classification and regression algorithms provided good classification accuracy (85.13 +/- 3.2%) and reasonable predictions of target gas concentrations at three levels. The proposed computational framework can be extended to include additional gases in mixed gases and used in various applications, including automotive, industrial, and environmental monitoring.

关键词： Air pollutants Chemiresistive sensor array classification and regression Machine learning Low-cost gas sensors Selectivity and cross-sensitivity

来源：评论

学校读者我要写书评

暂无评论

Bowerbird courtship-inspired feature selection for efficient high-dimensional data analysis using a novel meta-heuristic

引用

DISCOVER COMPUTING 2025年第1期28卷 1-24页

作者： Mallidi, S. Kumar Reddy Ramisetty, Rajeswara Rao Jawaharlal Nehru Technol Univ Comp Sci & Engn Kakinada 533003 AP India Sri Vasavi Engn Coll Comp Sci & Engn Tadepalligudem 534101 AP India Jawaharlal Nehru Technol Univ Gurajada Comp Sci & Engn Vizianagaram 535003 AP India

The challenge of feature selection in high-dimensional datasets is critical in enhancing machine learning model performance and interpretability while reducing computational costs. This paper introduces a novel feature selection method inspired by the unique courtship behaviors of bowerbirds, termed Bowerbird Courtship-Inspired Feature Selection (BBFS). Mimicking the decorative strategies and selective adaptation of bowerbirds, BBFS optimizes the selection of feature subsets through a meta-heuristic approach that efficiently balances exploration and exploitation. We compare the effectiveness of BBFS with established algorithms like Binary Cuckoo Search Optimization (BCSO) and Binary Grey Wolf Optimization (BGWO) across seven diverse datasets. Our results demonstrate that BBFS consistently selects fewer features and maintains or enhances classification accuracy using Decision Trees and Naive Bayes classifiers. This study underscores the potential of BBFS as a scalable and robust tool for high-dimensional data analysis, offering significant improvements over traditional and contemporary feature selection methods.

关键词： Feature selection Nature-inspired algorithms Bowerbird courtship Swarm optimization classification and regression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：