Article
Automation & Control Systems
Jagdish Kumar Pahade, Manoj Jha
Summary: This paper addresses the uncertainty in the financial domain of modern portfolio selection problems by using fuzzy set theory. By formulating risk return as fuzzy numbers, and considering V-shaped transaction costs, the portfolio is modified using possibility theory. The possibilistic semi-absolute deviation portfolio selection technique is employed, and the computational complexity is reduced using a hybrid algorithm called SCOOT which integrates the salp swarm algorithm with the coot algorithm.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ruchika Sehgal, Pattem Jagadesh
Summary: The portfolio optimization model with SMAD risk measure is commonly used due to its ease of solving linear programming model. We propose a robust PO model with SMAD that considers the uncertainty associated with asset expected returns. The effectiveness of the model is demonstrated by constructing optimal portfolios with global market indices.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Business, Finance
Xianhe Wang, Yuliang Ouyang, You Li, Shu Liu, Long Teng, Bo Wang
Summary: This article proposes a portfolio selection model that integrates prospect theory and disappointment theory, considering emotional factors and providing a comprehensive depiction of individuals' decision-making processes in uncertain situations.
FINANCE RESEARCH LETTERS
(2023)
Article
Business, Finance
Zohreh Hosseini-Nodeh, Rashed Khanjani-Shiraz, Panos M. Pardalos
Summary: This study investigates tractable formulations of portfolio selection problems using the Wasserstein metric to define an ambiguity set. It proposes a robust mean absolute deviation model and extends it to a weighted mean absolute deviation model in the presence of uncertain information. A decomposition algorithm based on the Benders decomposition approach is then constructed to solve these problems. Real data is used for efficient comparison of the optimization programs acquired.
FINANCE RESEARCH LETTERS
(2023)
Article
Computer Science, Information Systems
Pankaj Gupta
Summary: This paper investigates the elliptic entropy and semi-entropy of coherent fuzzy numbers and discusses their properties. It proposes a methodology that combines the adaptive index of coherent fuzzy numbers with the elliptic entropy and semi-entropy to measure risk incorporating investor attitude. The proposed methodology is applied to a portfolio optimization problem and demonstrated to be effective through a large-scale case study and out-of-sample analysis.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Hemant Jalota, Pawan Kumar Mandal, Manoj Thakur, Garima Mittal
Summary: Investment decision making is a multi-objective optimization problem in an uncertain environment. This study proposes a novel method to integrate investor's preferences into the portfolio selection model, effectively modeling uncertain portfolio attributes. Practical applications demonstrate the efficiency of the proposed models.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Sanjay Yadav, Arun Kumar, Mukesh Kumar Mehlawat, Pankaj Gupta, Vincent Charles
Summary: This paper proposes a sustainable financial portfolio selection approach using an intuitionistic fuzzy framework, consisting of three stages. The assets are ethically screened in the first stage, sustainability scores are calculated based on social, environmental, and economic criteria in the second stage, and a multi-objective financial portfolio selection model is developed in the third stage. Investors can choose efficient and sustainable financial portfolios based on their preferences.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xiaomin Gong, Changrui Yu, Liangyu Min, Zhipeng Ge
Summary: This paper discusses fuzzy portfolio selection problems under bounded rationality, proposing a regret cross-efficiency (RCE) evaluation model and a multi-objective portfolio selection model based on regret theory, incorporating the uncertainty in data.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoliang Ma, Jian Chen, Yiwen Sun, Zexuan Zhu
Summary: Portfolio selection aims to achieve an optimal trade-off between profit and risk by considering multiple optimization objectives. The use of expert acknowledge-based fuzzy number variables for modeling the return of risky assets improves the accuracy of return estimation. An assistant reference point guided evolutionary algorithm is proposed to solve this multi-objective portfolio problem efficiently and effectively.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Mathematics, Applied
M. Junaid Basha, S. Nandhini
Summary: This study proposes a method called Fuzzy Multiple Objective Linear Programming Problem (FMOLPP) to solve the Linear Programming Problem (LPP). It solves the Multiple Objective Linear Programming Problem (MOLPP) using Chandra Sen's approach and different types of mean approaches. Furthermore, it solves the FMOLPP using Chandra Sen's approach and various categories of fuzzy mean techniques. The study compares the optimum values of MOLPP with FMOLPP to highlight the significance of the proposed method.
Article
Computer Science, Interdisciplinary Applications
Xue Deng, Weimin Li, Yuying Liu
Summary: This paper extends the concept of downside risk from a stochastic environment to a hesitant fuzzy environment and introduces the concept of hesitant semi-variance (HSV). Theoretical analysis of the new definitions is provided through mathematical deduction. The proposed two new score-HSV portfolio selection models effectively prevent neglecting portfolios with high membership degrees.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Gao-Feng Yu, Deng-Feng Li, De-Cui Liang, Guang-Xu Li
Summary: This paper proposes a novel and unified Intuitionistic Fuzzy Multi-Objective Linear Programming (IFMOLP) model to solve multi-objective decision problems in portfolio selection. By constructing nonmembership functions and utilizing IF inequalities to represent decision maker's hesitation degrees towards multiple objectives, the model avoids tedious computational burden of traditional methods and enhances solution efficiency while reducing complexity.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2021)
Article
Economics
Gumsong Jo, Hyokil Kim, Hoyong Kim, Gyongho Ri
Summary: We propose a fuzzy portfolio selection model using stochastic correlation (FPSMSC) that overcomes limitations in both the fuzzy and stochastic world. The model considers future stock price movements based on fuzzy expertise knowledge and optimizes investment weights using monthly return data. Compared to other available portfolio selection models, the FPSMSC provides higher returns within a risk range and exhibits better smoothness of return variations with respect to risk aversion parameter.
COMPUTATIONAL ECONOMICS
(2023)
Article
Management
N. Meade, J. E. Beasley, C. J. Adcock
Summary: In this study, a new methodology is proposed to identify the consistency region in the risk-expected return space, where ex-post performance matches ex-ante estimates. By improving the accuracy of ex-ante estimates using the developed Berkowitz statistic, we demonstrate the superior performance of investment strategies based on consistency rather than efficiency.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Derya Deliktas, Ozden Ustun
Summary: This study proposes an integrated approach incorporating fuzzy multi-objective decision-making method and classical mean-variance models for stock evaluation and portfolio optimization. A multi-objective genetic algorithm is employed with three different scalarization techniques for solving the problem. Computational results indicate that the multi-objective genetic algorithm with weighted-sum performs better in most cases.
APPLIED INTELLIGENCE
(2023)
Article
Environmental Sciences
Maria Grau-Perez, Ana Navas-Acien, Inmaculada Galan-Chilet, Laisa S. Briongos-Figuero, David Morchon-Simon, Jose D. Bermudez, Ciprian M. Crainiceanu, Griselda de Marco, Pilar Rentero-Garrido, Tamara Garcia-Barrera, Jose L. Gomez-Ariza, Jose A. Casasnovas, Juan C. Martin-Escudero, Josep Redon, F. Javier Chaves, Maria Tellez-Plaza
ENVIRONMENTAL POLLUTION
(2018)
Article
Nutrition & Dietetics
Maria Cuevas-Tena, Jose D. Bermudez, Ramona de los Angeles Silvestre, Amparo Alegria, Maria Jesus Lagarda
CLINICAL NUTRITION
(2019)
Article
Biochemistry & Molecular Biology
Samara F. Kiihl, Maria Jose Martinez-Garrido, Arce Domingo-Relloso, Jose Bermudez, Maria Tellez-Plaza
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
(2019)
Article
Obstetrics & Gynecology
Javier Estan-Capell, Beatriz Alarcon-Torres, Jose Domingo Bermudez, Laura Martinez-Rodriguez, Cecilia Martinez-Costa
EARLY HUMAN DEVELOPMENT
(2019)
Article
Operations Research & Management Science
Ana B. Ruiz, Ruben Saborido, Jose D. Bermudez, Mariano Luque, Enriqueta Vercher
JOURNAL OF GLOBAL OPTIMIZATION
(2020)
Article
Multidisciplinary Sciences
K. C. Florez, A. Corberan-Vallet, A. Iftimi, J. D. Bermudez
Article
Mathematics
Jose V. Segura-Heras, Jose D. Bermudez, Ana Corberan-Vallet, Enriqueta Vercher
Summary: This paper discusses the weighted combination of forecasting methods using intelligent strategies to achieve accurate forecasts. By developing an algorithm that optimizes both the methods used in the combination and the weights assigned to the individual forecasts, the study aims to improve forecasting accuracy. The performance can be further enhanced by analyzing seasonal and non-seasonal time series separately. Additionally, the study reveals the importance of setting the size of the validation set correctly for prediction accuracy.
Article
Oncology
Vassilios Papadakis, Vanessa Segura, Massimo Conte, Dominique Plantaz, Andrea Di Cataldo, Gudrun Schleiermacher, Kate Wheeler, Jose D. Bermudez, Shifra Ash, Benedicte Brichard, Ruth Ladenstein, Valerie Combaret, Sabine Sarnacki, Anna Maria Fagnani, Claudio Granata, Adela Canete
Summary: This study aimed to evaluate whether expectant observation could avoid unnecessary surgery for small suprarenal masses in infants and not impact the outcome. The results showed that clinical follow-up and timely imaging allowed surgery to be avoided in the majority of patients, demonstrating the safety and effectiveness of expectant observation.
Article
Computer Science, Artificial Intelligence
Ana Corberan-Vallet, Enriqueta Vercher, Jose Segura, Jose D. Bermudez
Summary: In this paper, the portfolio selection problem is analyzed and predicted based on the time series of the portfolio's value. The value of the portfolio is defined at the time of its acquisition, and historical prices of different financial assets are used to calculate the portfolio's past values. A damped trend model is used to analyze and predict the future values, providing estimates of mean and variance for different forecasting horizons. A multi-objective genetic algorithm is then used to solve the portfolio selection problem. The performance of this procedure is demonstrated using a data set of asset prices from the New York Stock Market.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Public, Environmental & Occupational Health
Maria Grau-Perez, Golareh Agha, Yuanjie Pang, Jose D. Bermudez, Maria Tellez-Plaza
CURRENT ENVIRONMENTAL HEALTH REPORTS
(2019)
Article
Biochemistry & Molecular Biology
Islam J. A. Hamdan, Luis Manuel Sanchez-Siles, Esther Matencio, Jose D. Bermudez, Guadalupe Garcia-Llatas, Maria Jesus Lagarda
Meeting Abstract
Oncology
V. Papadakis, M. Conte, D. Plantaz, V. Segura, S. Ash, B. Brichard, R. Ladenstein, V. Combaret, A. Di Cataldo, G. Schleiermacher, K. Wheeler, J. Bermudez, A. Canete
PEDIATRIC BLOOD & CANCER
(2018)
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)