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Predicting property prices with machine learning algorithms

JOURNAL OF PROPERTY RESEARCH 2021年第1期38卷 48-70页

作者： Ho, Winky K. O. Tang, Bo-Sin Wong, Siu Wai Univ Hong Kong Dept Real Estate & Construct Hong Kong Peoples R China Univ Hong Kong Dept Urban Planning & Design Hong Kong Peoples R China Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China

This study uses three machine learning algorithms including, support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM) in the appraisal of property prices. It applies these methods to examine a data sample of about 40,000 housing transactions in a period of over 18 years in Hong Kong, and then compares the results of these algorithms. In terms of predictive power, RF and GBM have achieved better performance when compared to SVM. The three performance metrics including mean squared error (MSE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) associated with these two algorithms also unambiguously outperform those of SVM. However, our study has found that SVM is still a useful algorithm in data fitting because it can produce reasonably accurate predictions within a tight time constraint. Our conclusion is that machine learning offers a promising, alternative technique in property valuation and appraisal research especially in relation to property price prediction.

关键词： machine learning algorithms SVM RF GBM property valuation

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Prediction of the viscosity of iron-CuO/water-ethylene glycol non-Newtonian hybrid nanofluids using different machine learning algorithms

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Graish, Mohammed Shorbaz Ali, Ali B.M. Al-Zahiwat, Murtadha M. Alardhi, Saja Mohsen Baghoolizadeh, Mohammadreza Salahshour, Soheil Pirmoradian, Mostafa Department of Chemical Engineering University of Technology- Iraq Baghdad 10066 Iraq Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Chemical Engineering College of Engineering University of Misan Amarah Iraq Nanotechnology and Advanced Materials Research Center University of Technology – Iraq Baghdad Iraq Department of Mechanical Engineering Shahrekord University Shahrekord 88186-34141 Iran Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Department of Mechanical Engineering Khomeinishahr branch Islamic Azad University Khomeinishahr Iran

Viscosity is a crucial parameter for heat transfer systems, governing pumping power, Rayleigh number, and Reynolds number;thus, viscosity prediction for hybrid nanofluids is important. Although some studies have employed ML algorithms for predicting viscosity, limited ML algorithms or specific nanofluid types were examined in previous studies, disregarding the complexities involved in the rheological behavior of a complex nanofluid system such as non-Newtonian hybrid nanofluids. To overcome this limitation, this study offers a practical contribution by utilizing 20 different machine-learning models to predict the viscosity of iron-CuO/water-ethylene glycol non-Newtonian hybrid nanofluids. The influences of the input variables: solid volume fraction (SVF), temperature, and shear rate on viscosity prediction are systematically assessed. We evaluate the prediction accuracy and reliability of algorithms using ten performance metrics including RMSE, MAE, R² and NSE. Multivariate Polynomial Regression (MPR) outperforms the other algorithms, which is evident in the highest correlation coefficient (R² = 0.992) and lowest error metrics. At the other end, is the Extreme learning machine (ELM), which turns out to be the worst performer. A unique contribution of this paper is that we extract a mathematical equation from the MPR model that allows for straightforward calculation of viscosity, avoiding non-trivial ML computations. This simplicity aids in practical applications and increases usefulness for engineers and researchers alike. Using advanced data visualization techniques (heatmaps, box plots, KDE plots and Taylor diagrams), the relationships between input variables and viscosity as well as the model performance are explored. These results give a better understanding of the non-Newtonian hybrid nanofluid behavior and a solid predictor of design-efficient heat transfer systems. © 2025 The Authors

关键词： machine learning algorithms Non-Newtonian hybrid nano- antifreeze Viscosity

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Estimating the Chances of Getting Heart Disease using machine learning algorithms

Estimating the Chances of Getting Heart Disease using Machin...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： P.Yogendra Prasad Vamsi Kongara Pavan Kumar Ankireddy Santosh Jagga Srinivaas Guduru Shashank K Department of CSSE SVEC Tirupati

One of the deadliest illnesses that cause death is heart disease. Worldwide, almost 17 million people died each year because of various heart diseases. To aid in the early diagnosis of heart illness, improved diagnosis, high-risk individuals, and enhanced decision-making for extra treatment and prevention, a prediction model can be proposed. Many academics have looked at the heart disease risk variables and suggested certain machine learning algorithms. However, these models need to be enhanced in order to produce findings that are extremely precise due to the large dimensionality of the data. This study intends to develop a novel framework for accurate heart disease diagnosis. The proposed model can generate precise data for the training model by applying effective approaches for data collection, pre-processing, and transformation. The proposed model employs a combined dataset from the universities of Switzerland, Hungarian, Cleveland, Long Beach VA. This model employs Relief methods for feature selection. Ensemble learning is used to generate novel hybrid classifiers. The outcomes demonstrated that hybrid classifiers performed better than current models that displayed an accuracy of above 95%. These results suggests that the model with relief feature selection and hybrid classifiers may be a more effective approach for predicting heart diseases.

关键词： Heart Training Surveys machine learning algorithms Predictive models Data collection Feature extraction

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Detecting Text-Bullying on Twitter Using machine learning algorithms 6th

Detecting Text-Bullying on Twitter Using Machine Learning Al...

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6th International Conference on Information, Communication and Computing Technology (ICICCT)

作者： Amer, Abdullah Yahya Abdullah Siddiqui, Tamanna Aligarh Muslim Univ Dept Comp Sci Aligarh Uttar Pradesh India

ISBN: (纸本)9783030883782;9783030883775

The increasing use of social media leads to a large number of users-created unstructured text data. Due to the prevalence of social media, cyberbullying has become the main problem. Cyberbullying may cause many serious consequences on a person's life and community. That is because of various cultures, intellectual and educational backgrounds. The distinction between offensive language and hate speech is an essential Challenge in Detecting noxious textual content. In our work, we proposed a method in order to automatically analyze that tweet on Twitter within binary labels: Offensive and non-offensive. Utilizing the tweets data set, then we implement analyses granting N-grams as a feature and comparative between Term Frequency-Inverse Document Frequencies (TFIDF), and we achieve in, Using Decision Tree Classifier, Multinomial NB (Naive B ayes), Linear SVC (Support Vector Classifier), and AdaBoost Classifier, K-neighbors Classifier, and Logistic Regression machine learning models. Then tuning a model fitting the best results we get in (TFIDF). We achieve the best accuracy, 0.924 in the Linear SVC classifier, best Fl Score 0.942% in the same classifier, the best Precision 0.975 in the AdaBoost classifier, and Best Recall 0.977 in Multinomial NB. In the other model (Count-Vectorizer), we achieve Best accuracy 0.925 in Logistic Regression classifier, Best F1 Score 0.942% in the same classifier, best Precision 0.976 in AdaBoost classifier, and Best Recall 0.941 in Multinomial NB.

关键词： Cyberbullying detection Feature extraction machine learning algorithms Twitter Hate speech

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Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities

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EXPERT SYSTEMS 2022年第5期39卷 e12781-e12781页

作者： Vashisht, Manisha Kumar, Brijesh Manav Rachna Int Inst Res & Studies Faridabad India

In recent past, emerging technology, such as AI has revolutionized progress and advancement of traffic management solutions in context of smart cities. This paper describes a novel approach of using 3D colour-texture based feature for image detection on public datasets of Chinese traffic sign research database (TSRD) and Mapillary image database. The implementation of 3D colour texture feature for traffic sign detection is evaluated using artificial neural network and multiple other machine learning algorithms as classifier for image detection purposes. Both datasets used in our experiments are considered among most diverse traffic signage datasets globally with annotations of almost all classes. Image datasets have been publicly made available to researchers for academic purposes. For classification of traffic sign images on Chinese TSRD and Mapillary datasets, the result outcome from back-propagation neural network (NN) classifier outperforms all other and produced best results and under multiple ML algorithms, support vector machines (SVM) cubic has best results. For classification of traffic sign images on Mapillary dataset, the result outcome from rational quadratic Gaussian process regression has best results. Also, the comparisons of results with other similar novel works have also been discussed. The proposed approach outperformed the previous experiments as observed through comparative analysis performed on both datasets. The conclusion of the research work highlights that the image detection performance is noticeably improved by using the combined 3D colour-texture feature based proposed approach.

关键词： image processing machine learning algorithms smart cities traffic sign management

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Performance Evaluation of Container-Level Anomaly-Based Intrusion Detection Systems for Multi-Tenant Applications Using machine learning algorithms 21

Performance Evaluation of Container-Level Anomaly-Based Intr...

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16th International Conference on Availability, Reliability and Security (ARES)

作者： Cavalcanti, Marcos A. O. Inacio, Pedro R. M. Freire, Mario M. Inst Telecomunicacoes Covilha Portugal Univ Beira Interior Covilha Portugal

ISBN: (纸本)9781450390514

The virtualization of computing resources provided by containers has gained increasing attention and has been widely used in cloud computing. This new demand for container technology has been growing and the use of Docker and Kubernetes is considerable. According to recent technology surveys, containers are now mainstream. However, currently, one of the major challenges rises from the fact that multiple containers, with different owners, may cohabit on the same host. In container-based multi-tenant environments, security issues are of major concern. In this paper we investigate the performance of container-level anomaly-based intrusion detection systems for multi-tenant applications. We investigate the use of Bag of System Calls (BoSC) technique and the sliding window with the classifier and we consider eight machine learning algorithms for classification purposes. We show that among the eight machine learning algorithms, the best classification results are obtained with Decision Tree and Random Forest which lead to an F-Measure of 99.8%, using a sliding window with a size of 30 and the BoSC algorithm in both cases. We also show that, although both Decision Tree and Random Forest algorithms leads to the best classification results, the Decision Tree algorithm has a shorter execution time and consumes less CPU and memory than the Random Forest.

关键词： Containers Intrusion Detection Systems System Calls machine learning algorithms

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CREDIT CARD FRAUD DETECTION USING machine learning algorithms 5

CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING ALGORITHM...

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5th International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Tyagi, Rishabh Ranjan, Ravi Priya, S. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9781665426428

Wrenching out of information from imbalanced information has been accepting expanding intrigued all over the world. Credit card based online installments has developed heightening, compelling the money related organizations to execute and ceaselessly progress their extortion location framework. In any case, credit card extortion dataset is intensely imbalanced and diverse sorts of misclassification mistakes may have distinctive costs and it is important to control them, to a certain degree, to compromise those mistakes. Classification procedures are the promising arrangements to identify the fraudulent and non-fraud exchanges. Classification procedures don't perform well when it comes to highly imbalanced dataset as the dataset is biased with the majority classes the accuracy score will come 99% but it will be of no use. So, with the use of different classifiers based on Random Forest, SVM, Decision Tree, logistics regression and oversampling by using SMOTE the performance metrics can be compared between balance and imbalance dataset.

关键词： Support vector machines Measurement machine learning algorithms Costs Organizations Credit cards Classification algorithms

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Assessing land cover change detection in a developing country with machine and deep learning algorithms: a case study

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2025年 1-21页

作者： Hmamou, Mohamed Chafiq, Tarik Ouhammou, Imrane Lmoudden, Othmane Ait Raji, Mohammed Ouaskit, Said Univ Hassan II Casablanca Fac Sci Ben MSik LTIM BP 7955 Casablanca Morocco Univ Hassan II Casablanca Fac Sci Ben MSik LCMP BP 7955 Casablanca Morocco Univ Hassan II Casablanca Fac Sci Ben MSik LGAGE BP 7955 Casablanca Morocco

This study examines the evolution of land cover in the Fez province of Morocco through a detailed analysis of Land Cover Change Detection (LCCD). Using satellite imagery from Landsat-8 and Landsat-9, we generated high-resolution land cover maps by applying a range of machine learning and deep learning techniques. The analysis spans the period from 2014 to 2022, aiming to identify the most suitable algorithm for accurate LCCD. The methods tested in this research include Support Vector machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Upon thorough comparison, SVM emerged as the top-performing model, yielding the most reliable classification results, while MLP demonstrated comparatively limited accuracy in this context. Our findings also underscore significant land use transitions, particularly an expansion of urban and industrial zones accompanied by a decline in agricultural lands trends largely attributed to anthropogenic pressures. These patterns are intricately linked to broader environmental shifts, including those related to climate. By analyzing spatial and temporal variations in land cover, this research contributes to a better grasp of the environmental transformations occurring in Fez, offering valuable knowledge to support evidence-based planning and the promotion of sustainable land use strategies.

关键词： Land cover classification GIS-based analysis Satellite image processing machine learning algorithms Deep learning models

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Classification of Swallowing Foods Using machine learning algorithms

Classification of Swallowing Foods Using Machine Learning Al...

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IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET)

作者： Lim, Ji Hyun Djuric, Petar M. Stanacevic, Milutin SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

ISBN: (纸本)9781665442312

There are limits in assessing healthy and abnormal swallowing by Videofluoroscopic swallowing study. Classification of accelerometric swallowing signals is much more efficient method to judge healthy swallowing. However, these methods have developed mostly with dual axis accelerometric signals and classifying two-class problems. This study is to examine classification methods with multi-class three-axis accelerometric signals. Swallowing signals of five foods are classified with both supervised learning algorithm and unsupervised learning algorithm. Three-axis signals denoised by 10-level discrete wavelet transform with soft thresholding before feature calculation. The result confirmed that classification with support vector machine and K-nearest neighbor can predict with 90% accuracy. However, Classification with fuzzy c-mean clustering produce low purity and normalized mutual information.

关键词： classification swallowing machine learning algorithms supervised learning unsupervised learning

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Hannes Prosthesis Control Based on Regression machine learning algorithms

Hannes Prosthesis Control Based on Regression Machine Learni...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Di Domenico, D. Marinelli, A. Boccardo, N. Semprini, M. Lombardi, L. Canepa, M. Stedman, S. Bellingegni, A. Dellacasa Chiappalone, M. Gruppioni, E. Laffranchi, M. De Michieli, L. Ist Italiano Tecnol Rehab Technol Lab I-16163 Genoa Italy Univ Genoa Dept Informat Bioengn Robot & Syst Engn I-16145 Genoa Italy INAIL Prosthet Ctr I-40054 Bologna Italy

ISBN: (纸本)9781665417143

The quality of life for upper limb amputees can be greatly improved by the adoption of poly-articulated myoelectric prostheses. Typically, in these applications, a pattern recognition algorithm is used to control the system by converting the recorded electromyographic activity (EMG) into complex multi-degrees of freedom (DoFs) movements. However, there is currently a trade-off between the intuitiveness of the control and the number of active DoFs. We here address this challenge by performing simultaneous multi-joint control of the Hannes system and testing several state-of-the-art classifiers to decode hand and wrist movements. The algorithms discriminated multi-DoF movements from forearm EMG signals of 10 healthy subjects reproducing hand opening-closing, wrist flexion-extension and wrist pronation-supination. We first explored the effect of the number of employed EMG electrodes on device performance through the classifiers optimization in terms of F1Score. We further improved classifiers by tuning their respective hyperparameters in terms of the Embedding Optimization Factor. Finally, three mono-lateral amputees tested the optimized algorithms to intuitively and simultaneously control the Hannes system. We found that the algorithms performances were similar to that of healthy subjects, particularly identifying the Non-Linear Regression classifier as the ideal candidate for prosthetic applications.

关键词： Wrist Performance evaluation machine learning algorithms Control systems Electromyography Classification algorithms Optimization

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