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Effective Automated Prediction of Vertebral Column Pathologies Based on logistic model tree with SMOTE Preprocessing

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JOURNAL OF MEDICAL SYSTEMS 2014年第5期38卷 50-50页

作者： Karabulut, Esra Mahsereci Ibrikci, Turgay Gaziantep Univ Vocat High Sch Higher Educ TR-27310 Gaziantep Turkey Cukurova Univ Dept Elect Elect Engn TR-01330 Adana Turkey

This study develops a logistic model tree based automation system based on for accurate recognition of types of vertebral column pathologies. Six biomechanical measures are used for this purpose: pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis. A two-phase classification model is employed in which the first step is preprocessing the data by use of Synthetic Minority Over-sampling Technique (SMOTE), and the second one is feeding the classifier logistic model tree (LMT) with the preprocessed data. We have achieved an accuracy of 89.73 %, and 0.964 Area Under Curve (AUC) in computer based automatic detection of the pathology. This was validated via a 10-fold-cross-validation experiment conducted on clinical records of 310 patients. The study also presents a comparative analysis of the vertebral column data with the use of several machine learning algorithms.

关键词： Vertebral column pathologies logistic model tree SMOTE Biomechanical measures

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Industrial Part Faults Prediction for Nonlinearity and Implied Temporal Sequences

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PROCESSES 2025年第2期13卷 436-436页

作者： Zhang, Shuyu Zhang, Mengyi Bo, Cuimei Wang, Cunsong Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211800 Peoples R China Nanjing Tech Univ Inst Intelligent Mfg Nanjing 210009 Peoples R China

The ability to preemptively identify potential failures in industrial parts is crucial for minimizing downtime, reducing maintenance costs and ensuring system reliability and safety. However, challenges such as data nonlinearity, temporal dependencies, and imbalanced datasets complicate accurate fault prediction. In this study, we propose a novel combined approach that integrates the logistic model tree Forest (LMT) with Stacked Long Short-Term Memory (LSTM) networks, addressing these challenges effectively. This hybrid method leverages the decision-making capability of the LMT and the temporal sequence learning ability of Stacked LSTM to improve fault prediction accuracy. Additionally, to tackle the issues posed by imbalanced datasets and noise, we employ the ENN-SMOTE (Edited Nearest Neighbors-Synthetic Minority Over-sampling Technique), a technique for data preprocessing, which enhances data balance and quality. Experimental results show that our approach significantly outperforms traditional methods, achieving a fault prediction accuracy of up to 98.2%. This improvement not only demonstrates the effectiveness of the combined model but also highlights its potential for real-world industrial applications, where high accuracy and reliability are paramount.

关键词： fault prediction logistic model tree clarification LSTM

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Evaluation of an Image-based Classification model to Identify Glioma Subtypes Using Arterial Spin Labeling Perfusion MRI On the Publicly Available UCSF Glioma Dataset

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CLINICAL NEURORADIOLOGY 2025年第1期35卷 151-158页

作者： Amador, K. Kniep, H. Fiehler, J. Forkert, N. D. Lindner, T. Univ Calgary Dept Radiol & Clin Neurosci Calgary AB Canada Univ Hosp Hamburg Eppendorf Dept Diagnost & Intervent Neuroradiol Martinistr 52 D-20251 Hamburg Germany

PurposeGlioma is a complex cancer comprising various subtypes and mutations, which may have different metabolic characteristics that can potentially be investigated and identified using perfusion imaging. Therefore, the aim of this work was to use radiomics and machine learning analysis of arterial spin labeling MRI data to automatically differentiate glioma subtypes and *** total of 495 Arterial Spin Labeling (ASL) perfusion imaging datasets from the UCSF Glioma database were used in this study. These datasets were segmented to delineate the tumor volume and classified according to tumor grade, pathological diagnosis, and IDH status. Perfusion image data was obtained from a 3T MRI scanner using pseudo-continuous ASL. High level texture features were extracted for each ASL dataset using PyRadiomics after tumor volume segmentation and then analyzed using a machine learning framework consisting of ReliefF feature ranking and logistic model tree classification *** results of the evaluation revealed balanced accuracies for the three endpoints ranging from 55.76% (SD = 4.28, 95% CI: 53.90-57.65) for the tumor grade using 25.4 +/- 37.21 features, 62.53% (SD = 2.86, 95% CI: 61.27-63.78) for the mutation status with 23.3 +/- 29.17 picked features, and 80.97% (SD = 1.83, 95% CI: 80.17-81.78) for the pathological diagnosis which used 47.3 +/- 32.72 selected *** and machine learning analysis of ASL perfusion data in glioma patients hold potential for aiding in the diagnosis and treatment of glioma, mainly for discerning glioblastoma from astrocytoma, while performance for tumor grading and mutation status appears limited.

关键词： Glioma Radiomics Arterial Spin Labeling ReliefF logistic model tree

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Enhancing PDF Malware Detection through logistic model trees

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Computers, Materials & Continua 2024年第3期78卷 3645-3663页

作者： Muhammad Binsawad Department of Information Systems King Abdulaziz UniversityP.O.Box 80217Jeddah21589Saudi Arabia

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection *** study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial *** article presents a new method that improves PDF virus detection by using document analysis and a logistic model *** a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient *** traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware *** use of logistic model tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus *** paper emphasizes logistic model tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the *** results reveal that the logistic model tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

关键词： Malware detection PDF files logistic model tree feature selection cybersecurity

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HIV-1 CRF01_AE coreceptor usage prediction using kernel methods based logistic model trees

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COMPUTERS IN BIOLOGY AND MEDICINE 2012年第9期42卷 885-889页

作者： Shoombuatong, Watshara Hongjaisee, Sayamon Barin, Francis Chaijaruwanich, Jeerayut Samleerat, Tanawan Chiang Mai Univ Dept Comp Sci Chiang Mai 50200 Thailand Chiang Mai Univ Bioinformat Res Lab Chiang Mai 50200 Thailand Chiang Mai Univ Fac Associated Med Sci Dept Med Technol Chiang Mai 50200 Thailand Univ Tours INSERM U966 F-37032 Tours France

The determination of HIV-1 coreceptor usage plays a major role in HIV treatment. Since Maraviroc has been used in a treatment for patients those exclusively harbor R5-tropic strains, the efficient performance of classifying HIV-1 coreceptor usage can help choose the most advantaged HIV treatment. In general, HIV-1 variants are classified as R5-tropic and X4-tropic or dual/mixed tropic based on their coreceptor usages. The classification of the coreceptor usage has been developed by using the various computational methods or genotypic algorithms based on V3 amino acid sequences. Most genotypic tools have been designed based on a data set of the HIV-1 subtype B that seemed to be reliable only for this subtype. However, the performance of these tools decreases in non-B subtypes. In this study, the support vector machine (SVM) method has been used to classify the HIV-1 coreceptor. To develop an efficient SVM classifier, we present a feature selector using the logistic model tree (LMT) method to select the most relevant positions from the V3 amino acid sequences. Our approach achieves as high as 97.8% accuracy, 97.7% specificity, and 97.9% sensitivity measured by ten-fold cross-validation on 273 sequences. (C) 2012 Published by Elsevier Ltd.

关键词： HIV-1 coreceptor CRF01_AE V3 amino acid sequences Support vector machine logistic model tree Feature selection Machine learning

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A new approach for fault classification and section detection in compensated transmission line with TCSC

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EUROPEAN TRANSACTIONS ON ELECTRICAL POWER 2011年第1期21卷 997-1014页

作者： Moravej, Zahra Pazoki, Mohammad Abdoos, Ali Akbar Semnan Univ Fac Elect & Comp Engn Semnan Iran

This paper presents a new combined S-Transform and logistic model tree (ST-LMT) techniques for fault classification and fault section identification in advanced series compensated transmission system. The Thyristor Controlled Series Capacitor (TCSC) is employed at the midpoint of a line. Initially, ST is utilized to extract some useful features of post fault three line currents for one cycle duration to accomplish fault diagnosis task. A key attribute of ST is that it uniquely combines a frequency dependent resolution of the time-frequency space. The features extracted are used to train a single LMT model for fault type classification and determining fault section (whether the fault occurs before or after TCSC) simultaneously. The feasibility of the proposed algorithm has been tested on transmission line with TCSC for 11 types of fault using PSCAD/EMTDC. The result indicates that the ST-LMT method reliably classifies all types of fault and detects fault section with high accuracy on a 300 km, 400 kV TCSC-based transmission line. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： distance protection fault classification advanced series compensation logistic model tree S-transform

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Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches

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JOURNAL OF HYDROLOGY 2018年 565卷 248-261页

作者： Rahmati, Omid Naghibi, Seyed Amir Shahabi, Himan Dieu Tien Bui Pradhan, Biswajeet Azareh, Ali Rafiei-Sardooi, Elham Samani, Aliakbar Nazari Melesse, Assefa M. Islamic Azad Univ Khorramabad Branch Young Researchers & Elites Club Khorramabad Iran Tarbiat Modares Univ Dept Watershed Management Engn Mazandaran Iran Univ Kurdistan Fac Nat Resources Dept Geomorphol Sanandaj Iran Univ Coll Southeastern Norway Dept Business & IT Geog Informat Syst Grp Gullbringvegen 36 N-3800 Bo I Telemark Norway Univ Technol Sydney Fac Engn & IT Ctr Adv Modeling & Geospatial Syst CAMGIS Sydney NSW 2007 Australia Sejong Univ Dept Energy & Mineral Resources Engn 209 Neungdong Ro Seoul 05006 South Korea Univ Jiroft Dept Geog Kerman Iran Univ Jiroft Fac Nat Resources Kerman Iran Univ Tehran Fac Nat Resources Dept Reclamat Arid & Mountainous Reg Karaj Iran Florida Int Univ Dept Earth & Environm AHC 5-390 Miami FL 33199 USA

Sustainable water resources management in arid and semi-arid areas needs robust models, which allow accurate and reliable predictive modeling. This issue has motivated the researchers to develop hybrid models that offer solutions on modelling problems and accurate predictions of groundwater potential zonation. For this purpose, this research aims to investigate the capability and robustness of a novel hybrid model, namely the logistic model tree (LMT) and compares it with state-of-the-art models such as the support vector machine and C4.5 models that locate potential zones for groundwater springs. A spring location dataset consisting of 359 springs was provided by field surveys and national reports and from which three different sample data sets (S1-S3) were randomly prepared (70% for training and 30% for validation). Additionally, 16 spring-related factors were analyzed using regression logistic analysis to find which factors play a significant role in spring occurrence. Twelve significant geo-environmental and morphometric factors were identified and applied in all models. The accuracy of models was evaluated by three different threshold-dependent and -Independent methods including efficiency (E), true skill statistic (TSS), and area under the receiver operating characteristics curve (AUC-ROC) methods. Results showed that the LMT model had the highest accuracy performance for all three validation datasets (E-mean = 0.860, TSSmean = 0.718, AUC-ROCmean = 0.904);although a slight sensitivity to change in input data was sometimes observed for this model. Furthermore, the findings showed that relative slope position (RSP) was the most important factor followed by distance from faults and lithology.

关键词： Hybrid model Groundwater spring Robustness GIS logistic model tree

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An improved potential landslide hazard points evaluating method considering the heterogeneity of environmental features

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2023年第7期20卷 7951-7968页

作者： Zhu, S. Kong, R. Luo, X. Xu, Z. Zhu, F. China Univ Geosci Sch Geog & Informat Engn Wuhan 430074 Peoples R China Natl Engn Res Ctr Geog Informat Syst Wuhan 430074 Peoples R China Wuhan Zhongdi Yunshen Technol Co Ltd Wuhan 430000 Peoples R China

Landslide causes great damage to the environment and threatens the safety of people's life and property. The environmental features show heterogeneous changes in an area, but it is rarely used in the evaluation of landslide susceptibility. In this study, cluster analysis and ensemble learning were combined to verify the importance of environmental heterogeneity, and an improved landslide susceptibility mapping method (FCM-RF) was proposed considering the heterogeneity of environmental features. Taking Qichun County, China, as a study area, this paper analyzed the correlation and importance of thirteen landslide causal factors. The landslide and non-landslide samples were used for cluster analysis using k-means and fuzzy C-means, respectively. The study area was divided into several areas, and the type represented by each area was used as one of the input factors of the landslide model. Three methods, random forest, logistic model tree and Bagging, were selected to establish landslide susceptibility models. Finally, receiver operation curve and five statistic indicators (accuracy, precision, recall, specificity and F-measure) were employed to compare and validate the performance of these methods. The results showed that the accuracy of the three models was improved by about 2% after considering the heterogeneity factor and the FCM-RF (AUC = 0.8872) model with better performance was used for landslide susceptibility mapping. Therefore, considering the heterogeneity of environmental features can effectively improve the performance of landslide model, which will be helpful for landslide prediction.

关键词： Bagging Environmental heterogeneity Landslide susceptibility mapping logistic model tree Random forest

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A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment

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SCIENCE OF THE TOTAL ENVIRONMENT 2018年 642卷 1032-1049页

作者： Khosravi, Khabat Sartaj, Majid Tsai, Frank T-C Singh, Vijay P. Kazakis, Nerantzis Melesse, Assefa M. Prakash, Indra Bui, Dieu Tien Binh Thai Pham Sari Agr Sci & Nat Resources Univ Fac Nat Resources Sari Iran Univ Ottawa Civil Engn Dept Ottawa ON K1N 6N5 Canada Louisiana State Univ Dept Civil & Environm Engn Baton Rouge LA 70803 USA Texas A&M Univ Dept Biol & Agr Engn College Stn TX 77843 USA Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77843 USA Aristotle Univ Thessaloniki Sch Geol Thessaloniki Greece Florida Int Univ Dept Earth & Environm AHC 5-390 Miami FL USA Bhaskarchalya Inst Space Applicat & Geoinformat B Dept Sci & Technol Gandhinagar India Univ South Eastern Norway Dept Business & IT Geog Informat Syst Grp Gullbringvegen 36 N-3800 Bo I Telemark Norway Ton Duc Thang Univ Geog Informat Sci Res Grp Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Environm & Labour Safety Ho Chi Minh City Vietnam

Groundwater vulnerability assessment is a measure of potential groundwater contamination for areas of interest. The main objective of this study is to modify original DRASTIC model using four objective methods, Weights-of-Evidence (WOE), Shannon Entropy (SE), logistic model tree (LMT), and Bootstrap Aggregating (BA) to create a map of groundwater vulnerability for the Sari-Behshahr plain, Iran. The study also investigated impact of addition of eight additional factors (distance to fault, fault density, distance to river, river density, land-use, soil order, geological time scale, and altitude) to improve groundwater vulnerability assessment. A total of 109 nitrate concentration data points were used for modeling and validation purposes. The efficacy of the four methods was evaluated quantitatively using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). AUC value for original DRASTIC model without any modification of weights and rates was 0.50. Modification of weights and rates resulted in better performance with AUC values of 0.64, 0.65, 0.75, and 0.81 for BA, SE, LMT, and WOE methods, respectively. This indicates that performance of WOE is the best in assessing groundwater vulnerability for DRASTIC model with 7 factors. The results also show more improvement in predictability of the WOE model by introducing 8 additional factors to the DRASTIC as ADC value increased to 0.91. The most effective contributing factor for ground water vulnerability in the study area is the net recharge. The least effective factors are the impact of vadose zone and hydraulic conductivity. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Groundwater vulnerability DRASTIC Weights-of-Evidence Shannon Entropy logistic model tree Bootstrap Aggregating

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Polychronicity tendency-based online behavioral signature

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第8期10卷 2103-2118页

作者： Ikuesan, Adeyemi Richard Abd Razak, Shukor Venter, Hein S. Salleh, Mazleena Univ Pretoria Pretoria Gauteng South Africa Univ Teknol Malaysia Dept Comp Sci Johor Baharu Malaysia

The proliferation of ubiquitous and pervasive computing devices has led to the emergence of research areas like Internet of things, and the Big-Data, which has seen a rise in obfuscation of online identity thus fueling an increase in online anonymity. Online anonymity constitutes a major platform for the exploitation of the potentials of cyber-crime;at the same time, it also inhibits the potential economic power that can be harnessed from the surging Internet population. Methods of online identification, such as usage profiling, demographic profiling, cookie-based identification process, media fingerprinting as well as token-based identification processes, are limited to either system identification or one-to-one identification. Current one-to-one identification mechanisms require huge volume of templates of known users, and cannot be applied to novel users. This study proposed a psychosocial approach that integrates the composition of human Polyphasia tendency into online identification processes for a one-to-many identification process. To achieve this, the study administered a Polychronic-Monochronic tendency scale measurement instrument to staff members of a research unit in a university, and the server-side network traffic of each respondent was monitored and collected in eight-months duration. A logistic model tree-after an initial classifier exploration process-was adapted for the one-to-many classification model based on human intrinsic features extracted from the network traffic and Polyphasia dichotomy. High degree of reliable accuracy of > 80% was achieved which suggests a reliable model that supports the underlying hypothesis of the proposed model. Based on this accuracy, the approach finds practical relevance in online profiling process for online identification as well as online demographic profiling for e-commerce and e-learning. Furthermore, this approach can be applied to improve recommender systems in areas such as prediction and profile delivery t

关键词： Polyphasia tendency logistic model tree Digital time preference signature One-to-many identification Intrinsic network feature Online psychosocial fingerprint

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