Article
Computer Science, Information Systems
Tehseen Mazhar, Qandeel Nasir, Inayatul Haq, Mian Muhammad Kamal, Inam Ullah, Taejoon Kim, Heba G. Mohamed, Norah Alwadai
Summary: The early diagnosis of diseases is crucial for prevention and care. Expert systems can automatically diagnose diseases in their early stages and provide treatment advice. This research aims to find appropriate levels of heart disease risk using gathered data and a fuzzy inference engine.
Article
Engineering, Multidisciplinary
Wencong Liu, Ahmed Mostafa Khalil, Rehab Basheer, Yong Lin
Summary: In early December 2019, a new virus called 2019 novel coronavirus (2019-nCoV) emerged in Wuhan, China, leading to the global COVID-19 pandemic. This study proposes a novel fuzzy soft modal (i.e., fuzzy-soft expert system) for early detection of COVID-19. The system consists of five portions and is based on an exploratory study of sixty patients with symptoms similar to COVID-19. It utilizes an algorithm to detect potential COVID-19 patients and can assist physicians in making diagnoses.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Computer Science, Theory & Methods
Paulo Vitor de Campos Souza, Edwin Lughofer
Summary: This article proposes the integration of uncertainty in experts' feedback on class labels and expert rules into the classifier architecture for data stream classification with neuro-fuzzy approaches. The results show that explicitly considering the uncertainty of class labels improves the accuracy of the evolving neuro-fuzzy classifier.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Information Systems
Edwin Lughofer
Summary: This paper proposes three variants of evolving multi-user fuzzy classifier systems, which allow multiple users to provide label feedback. The classifiers are incrementally evolved using a single-pass learning approach and incorporate autonomous evolution and unsupervised clustering techniques. The method can handle varying labeling behaviors and uncertainty levels among different users, while ensuring economically practicable applicability.
INFORMATION SCIENCES
(2022)
Review
Engineering, Marine
Ioannis Hatzilygeroudis, Konstantinos Dimitropoulos, Konstantinos Kovas, John A. Theodorou
Summary: The expert system approach is still effective in scientific areas such as fish disease diagnosis where expert knowledge is required. In aquaculture, fish farmers lack the necessary expertise and equipment for accurate diagnosis, leading to the development of expert systems. This paper provides an overview of expert system approaches for fish disease diagnosis and proposes an improved system that can handle various types of fish diseases and provide explanations.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Energy & Fuels
S. Bhavani, V. Chithambaram, R. Muthucumaraswamy, S. Shanmugan, F. A. Essa, Ammar H. Elsheikh, P. Selvaraju, B. Janarthanan
Summary: An attempt has been made to design and study the performance of a solar cooker with a copper-coated absorber plate coated with mat black paint and NiO2 nanoparticles. Analytical solutions and the Laplacian approach are used to analyze the temperature components of the cooker and fuzzy rules influencing its thermal performance. The results show that the proposed cooker meets the Bureau of Indian Standard values and the method used is effective for simulation with minimal error.
Article
Computer Science, Theory & Methods
Nicolas Madrid
Summary: This paper introduces a novel probabilistic-fuzzy inference system that combines fuzzy theory and probability theory to represent uncertainty. The system uses quantile functions in its inference engine, distinguishing it from existing systems that use distribution or probability functions. The paper also presents methods for constructing probabilistic-fuzzy rules, defining significance measures for association rules, and validating the proposed system through experiments.
FUZZY SETS AND SYSTEMS
(2023)
Article
Mathematics, Interdisciplinary Applications
Nitesh Dhiman, Madan M. Gupta, Dhan Pal Singh, Vandana, Vishnu Narayan Mishra, Mukesh K. Sharma
Summary: In this research, an intuitionistic fuzzy fractional knowledge-based expert system is proposed for the diagnosis of diseases in the medical field. This system is able to handle discrete data and has effective analysis capabilities.
FRACTAL AND FRACTIONAL
(2022)
Article
Agriculture, Multidisciplinary
Eric A. Ibrahim, Daisy Salifu, Samuel Mwalili, Thomas Dubois, Richard Collins, Henri E. Z. Tonnang
Summary: Avocado production in Kenya faces challenges from insect pests, including the oriential fruit fly and fruit flies of the Ceratitis spp. This study used fuzzy neural network models to predict the population dynamics of these pests in avocado plantations, achieving satisfactory results.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Health Care Sciences & Services
Mouz Ramzan, Muhammad Hamid, Amel Ali Alhussan, Hussah Nasser AlEisa, Hanaa A. Abdallah
Summary: This paper introduces an expert system using patients' physical symptoms as input variables to predict anxiety levels through a fuzzy inference system (FIS). The system addresses anxiety's complexity and uncertainty by utilizing a comprehensive set of input variables and fuzzy logic techniques. The system achieved high accuracy in predicting anxiety levels based on real datasets.
Article
Engineering, Industrial
Adel Mottahedi, Farhang Sereshki, Mohammad Ataei, Ali Nouri Qarahasanlou, Abbas Barabadi
Summary: Resilience is a growing concept in managing engineering systems, but estimating system resilience is challenged by lack of historical data and limited information. Current studies use various indices to quantify resilience, but lack detailed examination of influencing factors. This paper aims to develop a practical methodology using expert judgment and fuzzy set theory to effectively model factors influencing resilience, demonstrated with an underground coal mine fan system.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Mathematics
Manuel Casal-Guisande, Alberto Comesana-Campos, Alejandro Pereira, Jose-Benito Bouza-Rodriguez, Jorge Cerqueiro-Pequeno
Summary: This study proposes a new method for monitoring the work conditions of machining tools by incorporating expert systems and sound-processing techniques. The method can identify undesirable behaviors of the tools and improve the workplace ergonomics.
Article
Biochemistry & Molecular Biology
Zhangwei Chen, Danbo Lu, Baoling Qi, Yuan Wu, Yan Xia, Ao Chen, Su Li, Huiru Tang, Juying Qian, Junbo Ge
Summary: This study measured serum carnitine levels in heart failure patients and found that all 25 carnitines were increased in these patients, with 20 being independently associated with heart failure diagnosis. In non-ischemic dilated cardiomyopathy patients, seven carnitines were identified to independently increase the diagnostic probability. Adding isobutyryl-L-carnitine and stearoyl-L-carnitine to clinical factors improved the accuracy of DCM-HF diagnosis. Certain carnitine levels were found to independently predict the risk of death and rehospitalization in heart failure patients. Therefore, serum carnitines can serve as diagnostic, prognostic, and etiological biomarkers in heart failure.
Article
Computer Science, Artificial Intelligence
Ehsan Javanmardi, Ahmadreza Nadaffard, Negar Karimi, Mohammad Reza Feylizadeh, Sadaf Javanmardi
Summary: This research provides a timely diagnosis and prediction mechanism for drill failure, improving the maintenance process through fuzzy inference systems. By utilizing if-then rules and the Z-number approach, the method reduces uncertainty in predicting failures and covers expert ideas during drill operation. It also helps experts handle ambiguous situations and uncertainties.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Athiraja Atheeswaran, K. V. Raghavender, B. N. Lakshmi Chaganti, Ashok Maram, Norbert Herencsar
Summary: Agriculture is a crucial occupation that supports the world's population, and the loss in sugarcane production in India can be attributed to various factors. Farmers rely on local knowledge to identify plant diseases, but a more accurate and efficient solution is needed. This article proposes a smart farming system that utilizes machine learning and image processing to accurately diagnose sugarcane diseases and provide timely treatment.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Operations Research & Management Science
Mostafa Khatami, Amir Salehipour
Summary: This paper investigates the coupled task scheduling problem, obtaining competitive lower bounds through procedures like solving 0-1 knapsack problems, proposing a binary search heuristic algorithm, and conducting computational experiments to demonstrate the effectiveness of the method. Results also show the proposed solution method outperforms the standard exact solver Gurobi.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2021)
Article
Engineering, Manufacturing
Mostafa Khatami, Amir Salehipour
Summary: The study focuses on the single machine coupled task scheduling problem, aiming to minimize the makespan with identical processing time and delay period for the first task, and time-dependent processing time for the second task. This approach can be applied to model and solve certain healthcare appointment scheduling problems.
JOURNAL OF SCHEDULING
(2021)
Article
Management
Mohammad Mahdi Ahmadian, Amir Salehipour
Summary: We develop an efficient matheuristic algorithm for the aircraft landing problem that minimizes the deviation from target arrival times. Our algorithm performs relax and solve iterations to reconstruct a complete sequence and schedule the aircraft landings, and it outperforms the state-of-the-art algorithm and solver CPLEX in terms of speed and accuracy.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2022)
Article
Mathematics, Applied
Gur Mosheiov, Daniel Oron, Amir Salehipour
Summary: In this study, we investigated a single machine scheduling problem with coupled tasks and limited resources. Each job consists of two tasks with a specific delay between them. While the processing times for initial tasks and delay periods are identical for all jobs, the completion task processing time is dependent on the job and modeled as a convex function of the allocated resources. The objective of the scheduling is to minimize the makespan, with a constraint on the resource availability. We provided properties of an optimal solution and an O(n^2) time algorithm for this problem.
DISCRETE APPLIED MATHEMATICS
(2021)
Review
Management
Mohammad Mahdi Ahmadian, Mostafa Khatami, Amir Salehipour, T. C. E. Cheng
Summary: The open-shop scheduling problem involves scheduling jobs on different machines to optimize performance metrics. Recent research interest has been focused on the computational complexity and solution methodologies for variants of the problem.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Mathematics, Interdisciplinary Applications
Mohsen Imeni, Mohammad Fallah, Seyyed Ahmad Edalatpanah
Summary: This study found a positive relationship between managerial ability and earnings classification shifting, with managerial ability reducing the impact of earnings management. Additionally, managerial ability positively affects agency costs.
DISCRETE DYNAMICS IN NATURE AND SOCIETY
(2021)
Article
Computer Science, Artificial Intelligence
Samaneh Daroudi, Hamed Kazemipoor, Esmaeel Najafi, Mohammad Fallah
Summary: The paper analyzes an integrated fuzzy model with minimal latency in the location routing inventory of perishable multi-product materials, considering environmental constraints. A multi-period model was designed with three main objective functions to minimize total supply chain costs, network time, and pollution. Results show better performance of NSGA II in small sizes and higher efficiency of PESA II in medium and large dimensions. Sensitivity analysis indicates that increasing corruption duration increases goods arrival time, while reducing transport costs significantly reduces total costs.
APPLIED SOFT COMPUTING
(2021)
Article
Management
Mostafa Khatami, Amir Salehipour
Summary: The gradual minimum covering location problem with distance constraints (GMCLPDC) deals with locating undesirable facilities on a geographical map, considering a minimum distance between them. We propose a mixed-integer program and a threshold accepting heuristic to solve the problem. Computational experiments show the effectiveness of the heuristic in delivering quality solutions, outperforming the solver Gurobi.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Management
Hiwa Esmaeilzadeh, Alireza Rashidi Komijan, Hamed Kazemipoor, Mohammad Fallah, Reza Tavakkoli-Moghaddam
Summary: This paper presents a bi-objective mixed-integer programming model for the aircraft maintenance routing problem (AMRP) in the aviation industry. The model considers factors such as aircraft efficiency and flight tasks. The results show that the proposed algorithm has high efficiency and accuracy in solving the problem.
JOURNAL OF FACILITIES MANAGEMENT
(2022)
Article
Green & Sustainable Science & Technology
Azra Ghobadi, Mohammad Fallah, Reza Tavakkoli-Moghaddam, Hamed Kazemipoor
Summary: With the increase in pollutants, the need for electric vehicles for urban logistics activities is becoming more important. This research proposes a fuzzy two-echelon vehicle routing problem involving a mixed fleet of EVs and ICVs, considering factors such as load, vehicle speed, and recyclable waste. The models are solved using CPLEX, GWO, and TS.
Article
Computer Science, Artificial Intelligence
Soroush Esmikhani, Hamed Kazemipoor, Farzad Movahedi Sobhani, Seyyed Mohammad Hadji Molana
Summary: Facility layout problems refer to the placement of facilities in a plant region. This paper proposed a facility layout problem based on fuzzy random variables, aiming to minimize materials handling cost and maximize cranes usability. Two algorithms were proposed and six case studies were conducted, showing that each algorithm has its own advantages in different aspects.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Management
Mostafa Khatami, Amir Salehipour
Summary: This article proposes a new method for the general single machine coupled task scheduling problem and demonstrates its superiority in improving solution quality and reducing the average gap to the best known solution.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2022)
Article
Operations Research & Management Science
Mostafa Khatami, Daniel Oron, Amir Salehipour
Summary: This paper introduces the problem of scheduling a set of coupled-task jobs on parallel identical machines with the objective of minimizing makespan in the context of patient appointment scheduling. The majority of these problems are proven to be (strongly) NP-hard, but optimal scheduling policies are provided for two settings consisting of identical jobs. An important result is that the existence of a (2-ε)-approximation algorithm for the problem implies P=NP, improving a recently proposed bound for the open-shop counterpart.
OPTIMIZATION LETTERS
(2023)
Article
Medicine, General & Internal
Zain Al-Abedin Rouhani, Hamed Kazemipoor, Alireza Mir Mohammad Sadeghi, Mohammad Fallah
Summary: This study presents a combined model based on value engineering and the house of quality to improve the chemotherapy processes of adult patients. By implementing different scenarios, the study successfully reduces the time and cost of chemotherapy and increases the value index of patient's length of stay.
IRANIAN RED CRESCENT MEDICAL JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad Mahdi Ahmadian, Amir Salehipour
Summary: The JIT-JSS problem involves operations with distinct due-dates and penalties for early or late completion. The proposed matheuristic algorithm outperforms existing methods by decomposing and optimizing sub-problems, delivering optimal schedules for a majority of instances.
JOURNAL OF HEURISTICS
(2021)
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)
