Article
Environmental Studies
Cankun Wei, Meichen Fu, Li Wang, Hanbing Yang, Feng Tang, Yuqing Xiong
Summary: The field of real estate appraisal is increasingly impacted by big data technology, with the trend towards integrated application in the future. Current studies confirm the variety of big data types used in real estate appraisal, with web crawler technology identified as a key data acquisition method. Furthermore, the issue of uneven data quality is highlighted as a new challenge that requires further attention.
Article
Environmental Studies
Fatma Bunyan Unel, Sukran Yalpir
Summary: This article discusses the importance of sustainable land management and proposes the development of a tax system that is easy to understand and use while accommodating innovations. A sustainable mass plot appraisal system was designed and successfully tested in the Konya/Lalebahce Neighborhood, demonstrating its feasibility and sustainability.
Article
Business
Regina Fang-Ying Lin, Chiye Ou, Kuo-Kun Tseng, Bowen Deng, K. L. Yung, W. H. Ip
Summary: This study introduces the Property Appraisal 4.0, utilizing the SNN model and deep learning technologies to accurately predict property values and uncover hidden neighborhood features. Experimental results demonstrate that this approach outperforms traditional models such as Hedonic Pricing Model and Support Vector Machines.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Computer Science, Artificial Intelligence
Tansu Alkan, Yesim Dokuz, Alper Ecemis, Asli Bozdas, S. Savas Durduran
Summary: This study utilized machine learning techniques to examine the real estate market in the Alanya district of Antalya province in Turkey, which is influenced by the tourism sector. By predicting changes in value, the study aimed to balance the market in an objective and scientific manner. Unlike previous research, algorithm-based valuation was performed using machine learning algorithms. The results demonstrated that the support vector machines algorithm achieved the best performance.
Article
Geography, Physical
Alex Okiemute Onojeghuo, Ajoke Ruth Onojeghuo, Michelle Cotton, Johnathan Potter, Brennan Jones
Summary: This study utilized SAR and optical imagery combined with topographic wetness index (TWI) to capture the temporal variations of wetlands in the Grassland Natural Region (GNR) of southern Alberta. The pixel-based random forest (RF) classified dataset showed the highest overall accuracy, outperforming other classification methods such as CART and SVM. The methodology adopted in this study is promising for accurately mapping the spatial distribution of wetland habitats across the seasonally dynamic GNR of Alberta.
GISCIENCE & REMOTE SENSING
(2021)
Article
Green & Sustainable Science & Technology
Martha Katafygiotou, Pavlos Protopapas, Thomas Dimopoulos
Summary: In recent years, there has been an increased desire and requirement for green buildings. This research aims to determine the extent of this increased demand and investigate whether it is driven by a new need or simply a desire of buyers. The study also explores people's knowledge and awareness of greenness and sustainability and their willingness to live and work in sustainable buildings. The research methodology involves the use of questionnaires to understand residents' awareness, needs, and desires related to sustainability. The findings suggest that increased knowledge and awareness of sustainable design can impact the real estate market, with people willing to pay more for green properties. The study also highlights the importance of people's desire and awareness in influencing their investments in green real estate.
Article
Construction & Building Technology
Manuel Rama, Elena Andrade, Maria Teresa Moreira, Gumersindo Feijoo, Sara Gonzalez-Garcia
Summary: In this study, the sustainability of 31 Spanish cities was evaluated using Classification And Regression Trees (CART) and Random Forest, identifying key indicators and corresponding thresholds for defining a sustainable city. The results showed that urban sustainability can be accurately assessed using just three indicators, providing valuable information for policymakers without the need for extensive data compilation.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Agriculture, Dairy & Animal Science
Cem Tirink, Dariusz Piwczynski, Magdalena Kolenda, Hasan Onder
Summary: This study aimed to estimate body weight from various biometric measurements and features using data mining and machine learning algorithms. The results showed that the random forest algorithm can help improve important characteristics and breed an elite population in Poland.
Article
Environmental Studies
Gualter Couto, Dulce Martins, Pedro Pimentel, Rui Alexandre Castanho
Summary: This study evaluates unexploited urban land in Portugal for new apartment construction using real options analysis, demonstrating the sensitivity of deferral options to changes in the time horizon. The study concludes that deferring construction can add significant value to undeveloped land in the Portuguese market.
Article
Computer Science, Artificial Intelligence
Zhigang Sun, Guotao Wang, Pengfei Li, Hui Wang, Min Zhang, Xiaowen Liang
Summary: In this paper, an improved random forest algorithm based on the classification accuracy and correlation measurement of decision trees is proposed. The algorithm retains decision trees with better classification effects and reduces the correlations between decision trees to improve the performance of the random forest. Experimental results demonstrate that the proposed improved random forest achieves higher average classification accuracy and outperforms traditional random forests in terms of G-means and other metrics.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Information Systems
Sutong Wang, Jiacheng Zhu, Yunqiang Yin, Dujuan Wang, T. C. Edwin Cheng, Yanzhang Wang
Summary: With the development of online real estate trading platforms, multi-modal housing trading data is being accumulated. In this study, an interpretable multi-modal stacking-based ensemble learning (IMSEL) method is proposed for accurate real estate appraisals. The predictive results show that IMSEL outperformed previous state-of-art methods in terms of various evaluation metrics. The research findings also have important managerial implications.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Environmental Sciences
Jaionto Karmokar, Mohammad Aminul Islam, Machbah Uddin, Md Rakib Hassan, Md Sayeed Iftekhar Yousuf
Summary: This study examined the impact of meteorological parameters on COVID-19 transmission in Bangladesh and used a combination of Random Forest, CART, and Lasso feature selection techniques to analyze their actual effects. The results revealed that minimum temperature and cloud cover are significant factors influencing COVID-19, while wind speed and air quality have a negative impact.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Engineering, Environmental
Luhua You, Xuneng Tong, Shu Harn Te, Ngoc Han Tran, Nur Hanisah Bte Sukarji, Yiliang He, Karina Yew-Hoong Gin
Summary: This study investigated the co-occurrence and spatiotemporal trends of cyanobacterial metabolites in a tropical freshwater lake. The researchers developed predictive models for these metabolites and successfully identified key environmental drivers. The findings provide valuable insights into the relationships between cyanotoxins and water quality indicators, offering useful information for policy decisions.
Article
Green & Sustainable Science & Technology
Eduard Hromada, Tomas Krulicky
Summary: This study investigates the dependence between technical and socioeconomic factors in the real estate market that affect the return on investment, using data from various districts in the Czech Republic and identifying significant dependencies through regression analysis.
Article
Environmental Studies
Sabine Horvath, Matthias Soot, Sebastian Zaddach, Hans Neuner, Alexandra Weitkamp
Summary: Property valuation in areas with few transactions based on linear regression fails due to insufficient data, requiring non-linear models such as artificial neural networks for accurate estimation. Aggregating data from different submarkets improves model accuracy, with extended Kalman filter estimation showing better performance compared to standard optimization procedures like Levenberg Marquardt. The cross-submarket ANN estimation aims to achieve accuracies comparable to local property valuation procedures in areas with few transactions.
Article
Business
Evgeny A. Antipov, Elena B. Pokryshevskaya
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2016)
Article
Computer Science, Information Systems
Evgeny A. Antipov, Elena B. Pokryshevskaya
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2019)
Article
Psychology, Multidisciplinary
Evgeny A. Antipov, Elena B. Pokryshevskaya
JUDGMENT AND DECISION MAKING
(2020)
Article
Business, Finance
Evgeny A. Antipov, Elena B. Pokryshevskaya
JOURNAL OF REVENUE AND PRICING MANAGEMENT
(2020)
Article
Hospitality, Leisure, Sport & Tourism
Elena B. Pokryshevskaya, Evgeny A. Antipov
INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT
(2017)
Article
Business, Finance
Evgeny A. Antipov, Elena B. Pokryshevskaya
JOURNAL OF REVENUE AND PRICING MANAGEMENT
(2017)
Article
Psychology, Multidisciplinary
Evgeny A. Antipov, Elena B. Pokryshevskaya
JUDGMENT AND DECISION MAKING
(2017)
Article
Humanities, Multidisciplinary
Evgeny A. Antipov, Elena B. Pokryshevskaya
EMPIRICAL STUDIES OF THE ARTS
(2016)
Article
Psychology, Multidisciplinary
Evgeny A. Antipov, Elena B. Pokryshevskaya
JUDGMENT AND DECISION MAKING
(2015)
Article
Economics
Elena B. Pokryshevskaya, Evgeny A. Antipov
ECONOMICS BULLETIN
(2015)
Article
Economics
Elena B. Pokryshevskaya, Evgeny A. Antipov
ECONOMICS BULLETIN
(2015)
Article
Economics
Evgeny A. Antipov
ECONOMICS BULLETIN
(2014)
Article
Economics
Evgeny A. Antipov, Elena B. Pokryshevskaya
ECONOMICS BULLETIN
(2014)
Article
Economics
Elena B. Pokryshevskaya, Evgeny A. Antipov
ECONOMICS BULLETIN
(2011)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
