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Predicting Rock Unconfined Compressive Strength Based on Tunnel Face Boreholes Measurement-While-Drilling Data

KSCE JOURNAL OF CIVIL ENGINEERING 2024年第12期28卷 5946-5962页

作者： Ling, Xuepeng Wang, Mingnian Yi, Wenhao Xia, Qinyong Sun, Hongqiang Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ State Key Lab Intelligent Geotech & Tunnelling Chengdu 610031 Sichuan Peoples R China

Quick and accurate acquisition of the uniaxial compressive strength (UCS) of the surrounding rock at the tunnel face effectively ensures the safety of tunnel construction. This paper proposes a model for estimating the USC of the tunnel surrounding rock based on boreholes measurement-while-drilling data and stacking ensemble algorithm. Firstly, four original drilling parameters of hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (Vp) as well as the rock UCS are collected from 1489 rock UCS test cases. Then, data cleaning and feature extension are carried out, and a UCS estimation database containing 66 features of the drilling parameters is established. Furthermore, traditional machine learning algorithms (SVM, KNN, RF, ET, GB, Bag), Bayesian optimization, cross-validation, and staking ensemble algorithms are employed to build a rock UCS estimation model. The performance of six traditional and integrated machine learning models is comparatively analyzed. The R2, RMSE and MAE of the prediction set are used as model performance evaluation metrics. The results show that the ensemble model performs best with an R2 of 87.9%. Finally, the reliability of the model is verified by field tests. Compared with the traditional field UCS testing method, this method can accurately and quickly predict the UCS of rocks without additional manpower and material resources, which possesses a greater application prospect.

关键词： Unconfined compressive strength Drilling parameters ensemble algorithm Bayesian optimization

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A weighted intrusion detection model of dynamic selection

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APPLIED INTELLIGENCE 2021年第7期51卷 4860-4873页

作者： Feng, Tao Dou, Manfang Lanzhou Univ Technol Sch Comp & Commun 287 Lan Gong Ping Rd Lanzhou 730050 Gansu Peoples R China

In view of the difficulty of existing intrusion detection methods in dealing with new forms, large scale, and high concealment of network intrusion behaviors, this paper presents a weighted intrusion detection model of the dynamic selection (WIDMoDS) based on data features. The aim is to customize intrusion detection models for network intrusion data sets of different types, sizes and structures. First, according to data features, single classifiers are clustered using a hierarchical clustering algorithm based on the classifiers evaluation indicators, and then, the classifiers selection is by means of accuracy of the single classifiers, in addition, the data-classifier applicable indicators (DCAI) and of the classifiers performances are used for calculating the weights of subjective and objective, and then calculating combined weight ranks. Finally, a custom intrusion detection model is generated by the Weight-voting (W-voting) algorithm. Our experiments show that this model can optimize the number of classifiers based on the data sets features, reduce the problem of redundant or insufficient classifiers in the ensemble process. A new network intrusion detection model of combining the classifier characteristics with the dataset attributes can improve the accuracy of intrusion detection.

关键词： Dynamic selection Combined weighting ensemble algorithm Intrusion detection

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Integration of Landsat time-series vegetation indices improves consistency of change detection

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International Journal of Digital Earth 2023年第1期16卷 1276-1299页

作者： Mingxing Zhou Dengqiu Li Kuo Liao Dengsheng Lu Institute of Geography Fujian Normal UniversityFuzhouPeople’s Republic of China Fujian Provincial Key Laboratory for Subtropical Resources and Environment Fujian Normal UniversityFuzhouPeople’s Republic of China School of Environmental&Resource Sciences Zhejiang A&F UniversityHangzhouPeople’s Republic of China Wuyi Mountain National Meteorological Observation Station WuyishanPeople’s Republic of China

Vegetation indices(VIs)were used to detect when and where vegetation changes ***,different VIs have different or even diametrically opposite results,which obstructed the in-depth understanding of vegetation ***,this study examined the spatial and temporal consistency offive VIs(EVI;NBR;NDMI;NDVI;and NIRv)in detecting abrupt and gradual vegetation changes,and provided an ensemble algorithm which integrated the change detection results of thefive indices to reduce the uncertainty of change detection using a single *** spatial consistency of thefive indices in abrupt change detection accounted for 50.6%of the study area,but the temporal consistency was low(21.6%).Wetness indices(NBR,NDMI)were more sensitive to negative abrupt changes,greenness indices(EVI,NDVI,NIRv)were more sensitive to positive abrupt changes;and both types of indices were similar in detecting gradual and total *** overall accuracy of the ensemble method was 81.60%and higher than that of any single index in abrupt change *** study provides a comprehensive evaluation of the spatial and temporal inconsistencies of change detection in model-fitting errors and various types of vegetation *** proposed ensemble method can support robust change-detection.

关键词： Breaks for Additive Season and Trend ensemble algorithm consistence of vegetation change vegetation index

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Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions

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NEUROCOMPUTING 2018年 276卷 2-22页

作者： Kalantari, Ali Kamsin, Amirrudin Shamshirband, Shahaboddin Gani, Abdullah Alinejad-Rokny, Hamid Chronopoulos, Anthony T. Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City Vietnam UNSW Australia Sydney NSW Australia Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA Univ Patras Dept Comp Sci Patras Greece

The explosive growth of data in volume, velocity and diversity that are produced by medical applications has contributed to abundance of big data. Current solutions for efficient data storage and management cannot fulfill the needs of heterogeneous data. Therefore, by applying computational intelligence (CI) approaches in medical data helps get better management, faster performance and higher level of accuracy in detection. This paper aims to investigate the state-of-the-art of computational intelligence approaches in medical data and to categorize the existing CI techniques, used in medical fields, as single and hybrid. In addition, the techniques and methodologies, their limitations and performances are presented in this study. The limitations are addressed as challenges to obtain a set of requirements for Computational Intelligence Medical Data (CIMD) in establishing an efficient CIMD architectural design. The results show that on the one hand Support Vector Machine (SVM) and Artificial Immune Recognition System (AIRS) as a single based computational intelligence approach were the best methods in medical applications. On the other hand, the hybridization of SVM with other methods such as SVM-Genetic algorithm (SVM-GA), SVM-Artificial Immune System (SVM-AIS), SVM-AIRS and fuzzy support vector machine (FSVM) had great performances achieving better results in terms of accuracy, sensitivity and specificity. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Computational intelligence Medical application Big data Detection ensemble algorithm

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An improved random forest algorithm and its application to wind pressure prediction

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第8期36卷 4016-4032页

作者： Li Lang Liang Tiancai Ai Shan Tang Xiangyan Hainan Univ Sch Comp Sci & Cyberspace Secur Haikou Hainan Peoples R China GRG Banking Equipment Co Ltd Guangzhou 510000 Peoples R China

When making regression predictions, the traditional random forest (RF) algorithm can only make predictions within the training set, which can easily lead to overfitting when modeling data have some specific noise. To solve the problem of over-fitting, an improved RF method is proposed in this paper for wind pressure prediction. With the aim to verify the prediction performance of the improved RF algorithm, this paper predicts the wind pressure coefficients of a high-rise building model without wind pressure measurement points. The results show that the improved RF can achieve good results in predicting the mean and fluctuating wind pressure coefficients of high-rise buildings, and its relative error for each measurement point is basically controlled at 5%, which is acceptable in engineering terms. Further applications show that this improved RF can be used for wind pressure distribution prediction in other large-span building type wind tunnel tests.

关键词： decision tree ensemble algorithm machine learning random forest wind engineering

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Machine-Learning-Based Diabetes Prediction Using Multisensor Data

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IEEE SENSORS JOURNAL 2023年第22期23卷 28370-28377页

作者： Site, Aditi Nurmi, Jari Lohan, Elena Simona Tampere Univ Elect Engn Unit Tampere 33720 Finland

Diabetes is one such chronic disease that, if undetected, can result in several adverse symptoms or diseases. It requires continuous and active monitoring, for example, by using various smartphone sensors, wearable/smart watches, etc. These devices are minimally invasive in nature and can also track various physiological signals, which are important for the prediction of diabetes. Machine-learning algorithms and artificial intelligence are some of the most important tools used for the prediction/detection of diabetes using different types of physiological signals. In this study, we have focused on using multiple sensors such as glucose, electrocardiogram (ECG), accelerometer (ACC), and breathing sensors for classifying patients with diabetes disease. We analyzed whether a single sensor or multiple sensors can predict diabetes well. We identified various time-domain and interval-based features that are used for predicting diabetes and also the optimal window size for the feature calculation. We found that a multisensor combination using glucose, ECG, and ACC sensors gives the highest prediction accuracy of 98.2% with the extreme gradient boosting (XGBoost) algorithm. Moreover, multisensor prediction shows nearly 4%-5% increase in the diabetes prediction rates as compared to single sensors. We observed that breathing-sensor-related data have very little influence on the prediction of diabetes. We also used the score-fit-times curve as one of the metrics for the evaluation of models. From the performance curves, we observed that three-sensor combinations using glucose, ECG, and ACC converge faster as compared to a four-sensor combination while achieving with same accuracy.

关键词： Accelerometer (ACC) sensor breathing sensor complexity evaluation diabetes electrocardiogram (ECG) sensor ensemble algorithm extreme gradient boosting (XGBoost) feature fusion glucose sensors

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Combined Prediction of Wind Power in Extreme Weather Based on Time Series Adversarial Generation Networks

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IEEE ACCESS 2024年 12卷 102660-102669页

作者： Ye, Wenjie Yang, Dongmei Tang, Chenghong Wang, Wei Liu, Gang NARI Grp Corp State Grid Elect Power Res Inst Nanjing 211100 Peoples R China

The current global climate is complex with an increasing frequency of extreme weather events. The randomness, variability, and intermittency of new energy sources pose significant challenges for the balance between power generation and consumption in electricity grids. This challenge is particularly pronounced during extreme weather events such as cold waves, which aggravate the challenges of power supply stability. Accurate prediction of the wind power output can reduce the need for system backup capacity, ensuring the stable operation and reliability of the power system. However, current prediction models do not effectively consider the impact of extreme weather, leading to low prediction accuracy and large deviations. Additionally, the mechanisms by which extreme weather affects wind power differ from those under normal weather conditions. Extreme weather events such as cold waves are rare, and sample data are scarce, making it difficult to establish precise prediction models. To address these issues, this study proposes a wind power output prediction correction method based on time-series generative adversarial networks. First, a time-series generative adversarial network algorithm was used to generate samples from meteorological and power data, constructing an extreme weather meteorological and power database to effectively address the issue of sample scarcity. Second, the improved particle swarm algorithm (IPSO) and Bayesian optimization (BO) algorithm were used to optimize the parameters of single-prediction algorithms such as eXtreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO). Subsequently, by combining the algorithm performance and principle of maximum diversity, the optimal algorithm combination was determined using the evaluation indicators of the prediction error and Pearson correlation coefficients to construct an extreme weather power prediction model under the stacking ensemble learning framework, overcomin

关键词： Extreme weather power prediction ensemble algorithm small sample learning Extreme weather power prediction ensemble algorithm small sample learning

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Review Rating Prediction on Location-Based Social Networks Using Text, Social Links, and Geolocations

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2018年第9期E101D卷 2298-2306页

作者： Wang, Yuehua Zhong, Zhinong Yang, Anran Jing, Ning Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China

Review rating prediction is an important problem in machine learning and data mining areas and has attracted much attention in recent years. Most existing methods for review rating prediction on Location-Based Social Networks only capture the semantics of texts, but ignore user information (social links, geolocations, etc.), which makes them less personalized and brings down the prediction accuracy. For example, a user's visit to a venue may be influenced by their friends' suggestions or the travel distance to the venue. To address this problem, we develop a review rating prediction framework named TSG by utilizing users' review Text, Social links and the Geolocation information with machine learning techniques. Experimental results demonstrate the effectiveness of the framework.

关键词： review rating prediction location-based social networks multi-class classification ensemble algorithm

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Improving Electricity Theft Detection Using Electricity Information Collection System and Customers' Consumption Patterns

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ENERGY EXPLORATION & EXPLOITATION 2024年第5期42卷 1684-1714页

作者： Kawoosa, Asif Iqbal Prashar, Deepak Anantha Raman, G. R. Bijalwan, Anchit Haq, Mohd Anul Aleisa, Mohammed Alenizi, Abdullah Lovely Profess Univ Punjab Sch Comp Applicat Phagwara Punjab India Lovely Profess Univ Punjab Sch Comp Sci & Engn Phagwara Punjab India Koneru Lakshmaiah Educ Fdn Dept CSE Guntur Andhra Prades India British Univ Vietnam Sch Comp & Innovat Technol Hung Yen Vietnam Majmaah Univ Coll Comp & Informat Sci Dept Comp Sci Al Majmaah 11952 Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Informat Technol Al Majmaah Saudi Arabia

Electricity theft detection (ETD) techniques employed to identify fraudulent consumers often fail to accurately pinpoint electricity thieves in real time. The patterns associated with electricity use are leveraged to identify anomalies indicative of electricity theft. However, challenges in the benchmark ETD include overfitting and a high incidence of false positives (FPs) resulting from incorrect usage patterns formed by considering only electricity consumption patterns without accounting for external factors that contribute to variations in normal consumption patterns. Further investigation is required to precisely detect instances of electricity theft, thereby minimizing nontechnical losses and forecasting future electricity demand to maintain a stable supply. This study employs a master energy meter located on the transformer side to monitor the amount of energy distributed to the region. Zones with a high likelihood of energy theft are detected by comparing the sum of readings from all the smart meters with the readings from the master energy meter installed on the HV of the substation transformer within the same electric feeder. ensemble XGBoost machine-learning algorithm and K-Means algorithm are used for the classification of malicious and nonmalicious samples and grouping similar types of consumers together, respectively. This makes the proposed model resistant to false-positive rates caused by changes in usage patterns that aren't done on purpose. Furthermore, energy thieves are identified by detecting anomalies in consumption behavior while maintaining constant internal and external environmental variables. This novel model proposed here mitigates the FP rate found in the present research of electricity usage data. Our approach outperforms support vector machines, convolution neural network, and logistic regression in simulations. Precision, F1-score, recall, Matthews Correlation Coefficient, Receiver Operating Characteristics (ROC)-Area Under The Curve (

关键词： Electricity theft detection false positives nontechnical losses nonmalicious ensemble algorithm

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Performance up-gradation of Symbiotic Organisms Search by Backtracking Search algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第12期13卷 5505-5546页

作者： Nama, Sukanta Saha, Apu Kumar Sharma, Sushmita Maharaja Bir Bikram Univ Dept Appl Math Agartala Tripura India Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India

Symbiotic Organisms Search (SOS) algorithm is characterized based on the framework of relationships among the ecosystem species. Nevertheless, it is suffering from wasteful discovery, little productivity, and slack convergence rate. These deficiencies cause stagnation at the local optimum, which is hazardous in deciding the genuine optima of the optimization problem. Backtracking Search algorithm (BSA) is likewise another streamlining method for comprehending the non-direct complex optimization problem. Consequently, in the current paper, an endeavor has been made toward the expulsion of the downsides from the traditional SOS by proposing a novel ensemble technique called e-SOSBSA to overhaul the degree of intensification and diversification. In e-SOSBSA, firstly, the mutation operator of BSA with the self-adaptive mutation rate is incorporated to produce a mutant of population and leap out from the local optima. Secondly, the crossover operator of BSA with the adaptive component of mixrate is incorporated to leverage the entire active search regions visited previously. The suggested e-SOSBSA has been tested with 20 classical benchmark functions, IEEE CEC2014, CEC2015, CEC2017, and the latest CEC 2020 test functions. Statistical analyses, convergence analysis, and diversity analysis are performed to show the stronger search capabilities of the proposed e-SOSBSA in contrast with the component algorithms and several state-of-the-art algorithms. Moreover, the proposed e-SOSBSA is applied to find the optimum value of the seven problems of engineering optimization. The numerical investigations and examinations show that the proposed e-SOSBSA can be profoundly viable in tackling real-world engineering optimization problems.

关键词： Symbiotic Organisms Search Backtracking Search algorithm ensemble algorithm Function optimization CEC2014 CEC2015 CEC2017 CEC2020 Engineering Problem

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