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Data mining algorithms for the prediction of inorganic nitrogen in water:Mechanisms,techniques,and applications 认领 引用
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作者 Jiaran Zhang Chuang Tang +1 位作者 Liang Wang Chuyuan Wei 《Information Processing in Agriculture》 SCIE EI CSCD 2026年第2期318-342,共25页
Accurate prediction of inorganic nitrogen concentrations is critical for assessing water quality and supporting sustainable aquatic ecosystem management.Data mining algorithms have demonstrated significant potential i... Accurate prediction of inorganic nitrogen concentrations is critical for assessing water quality and supporting sustainable aquatic ecosystem management.Data mining algorithms have demonstrated significant potential in inorganic nitrogen prediction.This review provides a systematic examination of data mining algorithms applied to inorganic nitrogen prediction for aquatic environments,focusing on the complete modeling pipeline from data preparation to predictive algorithm deployment.Essential data preparation techniques involving data acquisition,data cleaning,dimensionality reduction,and dataset partitioning are described in detail.Then,the mechanisms,techniques,and applications are critically evaluated according to the following five categories of data mining algorithms:machine learning models,deep learning models,tree-based models,fuzzy neural networks,and hybrid models.FNN and deep learning models yield top-tier accuracy(mean R2>0.90).In addition,hybrid models integrating LSTM have emerged as the most promising and prevalent framework,driving advancements in prediction precision.Despite significant progress,the field continues to face challenges such as insufficient data quality,limited model generalizability,and a lack of interpretability.Integrating multimodal data,developing lightweight real-time prediction systems,combining explainable artificial intelligence with physics-informed constraints,and strengthening uncertainty quantification will strongly advance inorganic nitrogen prediction models toward more accurate,reliable,and practical decision-support tools.This review provides a systematic reference for researchers and practitioners. 展开更多
关键词 Inorganic nitrogen Data mining algorithm Data preprocessing Machine learning Prediction
Enhancing Network Intrusion Detection Model Using Machine Learning Algorithms 认领 引用 被引量：1
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作者 Nancy Awadallah Awad 《Computers, Materials & Continua》 SCIE EI 2021年第4期979-990,共12页
After the digital revolution,large quantities of data have been generated with time through various networks.The networks have made the process of data analysis very difficult by detecting attacks using suitable techn... After the digital revolution,large quantities of data have been generated with time through various networks.The networks have made the process of data analysis very difficult by detecting attacks using suitable techniques.While Intrusion Detection Systems(IDSs)secure resources against threats,they still face challenges in improving detection accuracy,reducing false alarm rates,and detecting the unknown ones.This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection.Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion dataset.Our study focuses on several data mining algorithms such as;naïve Bayes,decision trees,support vector machines,decision tables,k-nearest neighbor algorithms,and artificial neural networks.Moreover,this paper is concerned with the association process in creating attack rules to identify those in the network audit data,by utilizing a KDD99 dataset anomaly detection.The focus is on false negative and false positive performance metrics to enhance the detection rate of the intrusion detection system.The implemented experiments compare the results of each algorithm and demonstrate that the decision tree is the most powerful algorithm as it has the highest accuracy(0.992)and the lowest false positive rate(0.009). 展开更多
关键词 Intrusion detection association rule data mining algorithms KDD99
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MICkNN:Multi-Instance Covering kNN Algorithm 认领 引用 被引量：7
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作者 Shu Zhao Chen Rui Yanping Zhang 《Tsinghua Science and Technology》 SCIE EI CAS 2013年第4期360-368,共9页
Mining from ambiguous data is very important in data mining. This paper discusses one of the tasks for mining from ambiguous data known as multi-instance problem. In multi-instance problem, each pattern is a labeled b... Mining from ambiguous data is very important in data mining. This paper discusses one of the tasks for mining from ambiguous data known as multi-instance problem. In multi-instance problem, each pattern is a labeled bag that consists of a number of unlabeled instances. A bag is negative if all instances in it are negative. A bag is positive if it has at least one positive instance. Because the instances in the positive bag are not labeled, each positive bag is an ambiguous. The mining aim is to classify unseen bags. The main idea of existing multi-instance algorithms is to find true positive instances in positive bags and convert the multi-instance problem to the supervised problem, and get the labels of test bags according to predict the labels of unknown instances. In this paper, we aim at mining the multi-instance data from another point of view, i.e., excluding the false positive instances in positive bags and predicting the label of an entire unknown bag. We propose an algorithm called Multi-Instance Covering kNN （MICkNN） for mining from multi-instance data. Briefly, constructive covering algorithm is utilized to restructure the structure of the original multi-instance data at first. Then, the kNN algorithm is applied to discriminate the false positive instances. In the test stage, we label the tested bag directly according to the similarity between the unseen bag and sphere neighbors obtained from last two steps. Experimental results demonstrate the proposed algorithm is competitive with most of the state-of-the-art multi-instance methods both in classification accuracy and running time. 展开更多
关键词 mining ambiguous data multi-instance classification constructive covering algorithm kNN algorithm
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