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A Contemporary Framework for Detection of Phishing Website for Cyber Societal Safety

SN Computer Science 2025年第4期6卷 1-10页

作者： Lakshmi, B.N. Dhanalakshmi, B.K. Reddy, K. Tharun Department of Computer Science Engineering BMS Institute of Technology and Management Karnataka Yelahanka Bangalore 560064 India

In today’s digital age, phishing is a significant threat. It is a clever approach in which propagation of a website is done, and customers are enticed to a false website where customers are asked to enter critical personal information. Phishing website detection is clever and successful method that uses data mining algorithms to categories and associate websites. Detecting phishing websites is challenging since most of these methods are unable of making a dynamically right judgement about whether an encountered website is a phishing website or not. To determine efficiency, accuracy, the rules created, and speed, all rules and variables for grading phishing websites, also their relationships, were described and categorized. These algorithms are created. To identify phishing websites, we utilize a hybrid approach that includes developing description framework models for resource and categorizing websites using group learning algorithms. We utilize supervised learning methods to train our software. This strategy receives a high favorable rating, which is certainly noteworthy. We also used software to eliminate features that would allow us to estimate the frequency of each job in the dataset, a random forest classifier to cope with missing data sets. Our method will detect by utilizing the URL of website as input, helping us to have a good understanding of the factors that influence detection. The approach should be chosen as it increases accuracy in most situations. We get promising accuracy when our system investigates the strength of the random forest algorithm and ensemble learning techniques. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber safety KNN Learning techniques random forest algorithm Society SVM Web phishing

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Permeability determination of cores based on their apparent attributes in the Persian Gulf region using Navie Bayesian and random forest algorithms

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2017年 37卷 52-68页

作者： Far, Pouria Behnoud Hosseini, Pantea Azizi, Ali Amirkabir Univ Technol Dept Petr Engn Tehran Iran Islamic Azad Univ Dept Software Engn Esfahan Iran

To properly study a reservoir, one needs to consider permeability as one of the most important parameters. Regions of sufficiently high permeability are good production candidates. Several permeability determination methods have been suggested which mostly employ evaluations through well testing as well as laboratory core experiments. Nevertheless, these methods are costly and time consuming. Among them exists the air permeability determination using an Ultra-permeameter with regard to the Darcy's law which requires properly set up apparatus and takes time for each core. This paper presents a new method for estimating the air permeability in the Persian Gulf region with regard to the appearance of a core. Appearance attributes are divided into several groups enjoying the Archie's classification method while keeping in mind other parameters such as porosity, depth at which cores were taken and density. Air permeability values obtained for 718 cores are subdivided into four classes. Direct and indirect relations of the studied parameters on permeability are distinguished. For instance, porosity influences permeability. Having a crystalline matrix affects porosity, then has an indirect effect on permeability. The next step involves employing Navie Bayesian and a designed random forest algorithm to develop a model for permeability estimation. It is observed that the model based on Navie Bayesian gives acceptably precise results whereas the model obtained using random forest algorithm offers permeability estimations of higher accuracy especially in case of higher permeabilities. The models are applied on a second reservoir in the Persian Gulf. Two models predict the permeability with a decent confidence. This new approach has the privilege of being fast and convincingly accurate. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Persian Gulf Permeability Archie's classification Navie Bayesian algorithm random forest algorithm Apparent attributes

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Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

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TRANSBOUNDARY AND EMERGING DISEASES 2020年第2期67卷 935-946页

作者： Liang, Ruirui Lu, Yi Qu, Xiaosheng Su, Qiang Li, Chunxia Xia, Sijing Liu, Yongxin Zhang, Qiang Cao, Xin Chen, Qin Niu, Bing Shanghai Univ Sch Life Sci Shanghai 200444 Peoples R China Guangxi Bot Garden Med Plants Natl Engn Lab Southwest Endangered Med Resources Nanning Peoples R China Comp Ctr Guangxi Nanning Peoples R China Guangxi Inst Food & Drug Control Nanning Peoples R China Tech Ctr Anim Plant & Food Inspect Shanghai Peoples R China Quarantine Shanghai Customs Shanghai Peoples R China Fudan Univ Shanghai Med Coll Inst Clin Sci Zhongshan Hosp Shanghai 200032 Peoples R China

African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator-Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%-84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision-makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease.

关键词： African swine fever global online prediction system random forest algorithm WorldClim

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Water Quality Prediction and Suggesting Filter using Machine Learning 3

Water Quality Prediction and Suggesting Filter using Machine...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chaudhari, Archana Mane, Rutuja Khot, Anant Kadam, Anup Rajam, Nagraj Department of Instrumentation Engineering Vishwakarma Institute of technology Bibwewadi Maharashtra Pune India

ISBN: (纸本)9798331527549

Survival of all living beings and efficient working of industries and agriculture are largely dependent on the availability of clean water. However, contamination is a major issue that requires dependable methods for assessing the quality of water before its use in drinking, chemical applications, or animal hydration. This research utilizes machine learning to predict water quality based on parameters such as pH, turbidity, hardness, conductivity, and dissolved particles. Based on a broad dataset, models such as time-series analysis and random forest classifier are applied to categorize water as safe or unsafe for domestic use. A hardware system equipped with sensors captures real-time data, which enables dynamic assessment of water quality. The proposed solution shows high accuracy in its evaluation using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Regression Analysis, offering a practical approach to ensure water safety in diverse applications. © 2025 IEEE.

关键词： artificial intelligence data visualization dataset filter hardware machine learning preprocessed data random forest algorithm TDS water quality

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A study on financial early warning for technology companies incorporating big data and random forest algorithms

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2024年第3-4期15卷 343-351页

作者： Wang, Xuemei Shandong Foreign Trade Vocat Coll Dept Accounting & Finance Qingdao Shandong Peoples R China

This study establishes a model to predict the financial risk and the possibility of fraud of technology enterprises by using the random forest algorithm and SMOTE algorithm. By testing and identifying data sets, the model shows good accuracy and efficiency, with an ACC index of 97.2%, an accuracy of 96.8%, a recall rate of 1, and an F1 index of 98.4%. Compared with the traditional model, the ratio of cash income to main business income is the highest, which shows that the prediction accuracy of this model is obviously better than that of the traditional model. The results show that this model will have some practical value in evaluating the financial status of science and technology enterprises.

关键词： financial fraud financial malpractice machine learning random forest algorithm SMOTE algorithm

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Solar Power Plant Capacity Monitoring Using random forest Machine Learning algorithm

Solar Power Plant Capacity Monitoring Using Random Forest Ma...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Ogar, Emmanuel Ede Chaitusaney, Surachai Benjapolakul, Watit Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Chulalongkorn University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350352290

The natural abundance of solar energy and its favorable environmental impacts have led to growing acceptance and utilization as the world transitions from a fossil fuel-based economy to a cleaner, more sustainable, and ecologically friendly alternative. An increasing number of solar photovoltaic projects have emerged to capitalize on these benefits. However, factors such as weather conditions, exposure to natural elements, device deterioration, improper installations, and maintenance concerns can eventually compromise these systems' power-generating performance and dependability over time. This study demonstrates how the random forest machine learning classifier can be used to monitor and categorize the performance of solar PV plants to ensure their reliability and efficiency. The study collected and preprocessed a field dataset containing various environmental and electrical parameters. These parameters include plant capacity, active power, generating capacity, ambient temperature, global radiation, and module temperature. This data was used to train a random forest machine learning algorithm to categorize the operational condition of the solar power plant. The results demonstrate that the model efficiently classified the operational condition of the solar power plant by effectively identifying conditions such as optimal mode, moderate sub-optimal mode, severe sub-optimal mode, critical mode, emergency mode, and offline mode, achieving 99.92% accuracy on the test set. The proposed method provides an effective technique for estimating and monitoring the generating capacity and operational condition of solar power plants in near real time, thereby serving as a distinctive instrument for boosting the efficiency and reliability of solar power generation. © 2024 IEEE.

关键词： Artificial Intelligence Capacity monitoring Machine learning random forest algorithm Solar cell

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Hydroponic using Deep Water Culture for Lettuce Farming using random forest Compared with Decision Tree algorithm 8

Hydroponic using Deep Water Culture for Lettuce Farming usin...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Jegan, D. Surendran, R. Madhusundar, N. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

This research aims to optimize lettuce cultivation in a Deep Water Culture (DWC) hydroponic system by leveraging machine learning techniques. By analyzing various parameters such as temperature, pH, and nutrient concentration, the study seeks to identify optimal conditions for maximizing lettuce growth and yield. A random forest algorithm is employed to predict optimal parameter settings and compare its performance with a Decision Tree algorithm. The random forest's ensemble approach is expected to enhance prediction accuracy and robustness. By optimizing hydroponic parameters through machine learning, this research contributes to sustainable and efficient agriculture, reducing resource consumption and maximizing crop yield. © 2024 IEEE.

关键词： decision tree algorithm deep-water culture hydroponic lettuce farming random forest algorithm

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The Response of Small Watershed Storm Floods to Climate Change

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WATER 2025年第1期17卷 33-33页

作者： Qian, Jing-Lin Wu, Yun-Xin Zhang, Qi-Ting Zhejiang Univ Water Resources & Elect Power Sch Hydraul Engn Hangzhou 310018 Peoples R China

This study utilizes historical monitoring data from the Xu Fan small watershed spanning 1962 to 2021 and employs the K-means clustering algorithm to classify extreme rainfall events into three distinct categories: short-duration high-intensity rainfall, sustained moderate-intensity rainfall, and long-duration heavy rainfall. Through the application of the random forest model, key factors influencing flood characteristics are identified, including total rainfall, maximum rainfall intensity, the timing of maximum intensity, and rainfall duration. The comparative analysis of data before and after 1990 highlights that climate change has led to increased maximum rainfall intensity, reduced rainfall duration, and shifts in the temporal distribution of rainfall, thereby exerting a significant influence on the flood generation process.

关键词： climate change small watershed random forest algorithm extreme rainfall flood

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Remote Sensing Detection of forest Changes in the South Ridge Corridor and an Attribution Analysis

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forestS 2025年第2期16卷 205-205页

作者： Wu, Nan Huang, Linghui Zhang, Meng Dou, Yaqing Mo, Kehan Liu, Junang Cent South Univ Forestry & Technol Key Lab Natl Forestry & Grassland Adm Control Arti Changsha 410004 Peoples R China Hunan Linkeda Agroforestry Tech Serv Co Ltd Changsha 410004 Peoples R China Zhejiang A&F Univ Coll Environm & Resource Sci Hangzhou 311300 Peoples R China

As the largest mountain range in Southern China, the natural vegetation of Nanling plays an irreplaceable role in maintaining the stability of the ecosystem and exerting its functions. The forested area of the Nanling Corridor encompasses 168,633 km2, with a forest coverage rate exceeding 60% of all cities together. Long-term analysis of the temporal and spatial evolution of this forest and the disturbance factors in this region is of great importance for realizing the "dual carbon" goals, sustainable forest management, and protecting biodiversity. In this study, remote sensing images from a Landsat time series with a resolution of 30 m were obtained from the GEE (Google Earth Engine) cloud processing platform, and forest disturbance data were obtained using the LandTrendr algorithm. Using a machine learning random forest algorithm, the forest disturbance status and disturbance factors were explored from 2001 to 2020. The results show that the estimated disturbed forest area from 2001 to 2020 was 11,904.3 km2, accounting for 7.06% of the total area of the 11 cities in the Nanling Corridor, and the average annual disturbed area was 595.22 km2. From 2001 to 2016, the overall disturbed area increased, reaching a peak value of 1553.36 km2 in 2008, with a low value of 37.71 km2 in 2002. After 2016, the disturbed area showed a downward trend. In this study, an attribution analysis of forest disturbance factors was carried out. The results showed that the overall accuracy of forest disturbance factor attribution was as high as 82.48%, and the Kappa coefficient was 0.70. Among the disturbance factors, deforestation factors accounted for 58.45% of the total area of forest disturbance, followed by fire factors (28.69%) and building or road factors (12.85%). The regional distribution of each factor also had significant characteristics, and the Cutdown factors were mostly distributed in the lower elevations of the mountain margin, with most of them distributed in sheets. The fi

关键词： forest disturbance Landsat time-series image LandTrendr algorithm attribution of disturbances random forest algorithm

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Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning

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SENSORS 2025年第2期25卷 420-420页

作者： Liu, Ning Shen, Yu Zhang, Shuangcheng Zhu, Xuejian Changan Univ Coll Geol Engn & Geomat Xian 710054 Peoples R China

Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average. At the same time, the PWV was inverted by radiosonde data in the study area as a reference to verify the water vapor inversion results of the new model, and it was found that the BIAS of the new model is only 0.8 mm, which has high accuracy. Further, compared with the LSTM model, the new model is more universal when the accuracy is comparable. In addition, in order to evaluate the spatial and temporal variation characteristics of the atmospheric water vapor retrieved by the new model, based on the rainstorm event caused by typhoon in Hong Kong of September 2023, the ERA5 GSMaP rainfall products and inverted PWV information were comprehensively used for analysis. The results show that the PWV increased sharply with the arrival of the typhoon and the occurrence of a rainstorm event. After the rain stopped, the PWV gradually decreased and tended to be stable. The spatial and temporal variation in the PWV have a strong correlation with the occurrence of extreme rainstorm events. This shows that the PWV inverted by the new model can respond well to extreme rainstorm events, which proves the feasibility and reliabilit

关键词： ground-based GNSS random forest algorithm atmospheric precipitable water accuracy assessment spatial and temporal characteristics of water vapor

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