Article
Computer Science, Artificial Intelligence
Amin Hashemi, Mehdi Joodaki, Nazanin Zahra Joodaki, Mohammad Bagher Dowlatshahi
Summary: Ant Colony Optimization (ACO) is a probabilistic and approximation metaheuristic algorithm inspired by real ants' behavior. It uses pheromone trails to find optimal solutions to complex combinatorial optimization problems. This paper proposes an ACO algorithm based on the ensemble of heuristics using a Multi-Criteria Decision-Making (MCDM) procedure. The algorithm selects multiple heuristics to improve the performance and stability of ACO.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Amin Hashemi, Mohammad Bagher Dowlatshahi, Hossein Nezamabadi-pour
Summary: This paper presents a multi-target feature selection method based on the VIKOR algorithm, which uses cosine similarity to construct the decision matrix and ranks features with the VIKOR method. Experimental results demonstrate that this method outperforms other multi-output feature selection methods in terms of efficiency and optimization performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Ting-Yu Chang, Cooper Cheng-Yuan Ku
Summary: Ranking methods are widely used in social science and economics for various purposes. The novel fuzzy filtering ranking method combines Likert scale and discrete fuzzy scores to simplify the ranking process. Experimental results show high consistency between the proposed method and traditional methods in ranking outcomes.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
S. Kavitha, J. Satheeshkumar, K. Janani, T. Amudha, R. Rakkiyappan
Summary: This paper investigates ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) process. A novel algorithm called q-rung orthopair hesitant fuzzy MCDM extended to VIKOR (q-ROHFS VIKOR) is proposed for ensemble feature selection. The proposed method ranks every feature based on different rankers and obtains a preference matrix to assign scores to each feature. The efficiency and optimality of the proposed method is proven through comparisons with basic filter-based feature selections and ensemble feature selection using feature ranking strategy, with superior performance up to 0.96 in accuracy and F-score levels.
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
(2023)
Article
Mathematical & Computational Biology
Muhammad Akram, G. Muhiuddin, Gustavo Santos-Garcia
Summary: This paper proposes a new decision-making approach that combines the complex Fermatean fuzzy N-soft set with the VIKOR method to handle multi-attribute group decision-making. The proposed technique can handle uncertain and imprecise information and find the compromise solution by unifying the opinions of decision-makers and calculating group utility and individual regret.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Green & Sustainable Science & Technology
Benedictus Rahardjo, Fu-Kwun Wang, Shih-Che Lo, Jia-Hong Chou
Summary: Sustainability is gaining popularity in operations and supply chains, with Sustainable Supplier Selection (SSS) being employed. Alongside economic, social, and environmental considerations, the United Nations Sustainable Development Goals 2030 have influenced the choice of long-term suppliers, ensuring green operations and sustainable supply chains. The SSS criteria are multidimensional and interdependent, mimicking real-world scenarios instead of assuming independence through an analytic hierarchy process.
Article
Computer Science, Artificial Intelligence
Guopeng Liu, Jianbin Ma, Tongle Hu, Xiaoying Gao
Summary: This paper proposes a feature selection method using genetic programming and feature ranking, which can achieve better classification performance by further reducing the number of selected features using a multi-criteria fitness function.
CONNECTION SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Abdolreza Rashno, Milad Shafipour, Sadegh Fadaei
Summary: This paper introduces a novel multi-objective particle swarm optimization feature selection method. It decodes feature vectors as particles and ranks them in a two-dimensional optimization space. The proposed method incorporates feature ranks to update particle velocity and position during the optimization process. Experimental results demonstrate the effectiveness of the method in finding Pareto Fronts of the best particles in multi-objective optimization space.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Muhammad Jabir Khan, Poom Kumam, Wiyada Kumam
Summary: The VIKOR approach, proposed in 1979 by S. Opricovic, is widely used and successful in solving multi-criteria decision making problems.
Article
Mathematics, Applied
M. B. Dowlatshahi, A. Hashemi
Summary: This article proposes a fuzzy multi-criteria decision-making method for unsupervised feature selection, utilizing an ensemble of unsupervised feature selection rankers to evaluate features. It is the first time a fuzzy multi-criteria decision-making approach has been used for this problem, and multiple comparisons are made to demonstrate its optimality and effectiveness.
IRANIAN JOURNAL OF FUZZY SYSTEMS
(2023)
Review
Environmental Studies
Farhad Samimi Namin, Aliakbar Ghadi, Farshad Saki
Summary: Mining Method Selection (MMS) is an important decision in mining design. Empirical models have limitations, thus Multi Criteria Decision Making (MCDM) is considered more effective. This research reviews literature on MCDM applications in MMS and presents decision methods and the importance of criteria in different ore bodies.
Article
Computer Science, Artificial Intelligence
Lucas Falch, Clarence W. de Silva
Summary: This paper introduces the VIKOR method, a new normalization method, and a minimum weight margin. By using the modified VIKOR method for design decision making, it can overcome the weaknesses of the original VIKOR method and improve the stability of the solutions.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2022)
Article
Thermodynamics
Akram Seifi, Mohammad Ehteram, Majid Dehghani
Summary: Accurate and stable prediction of global horizontal irradiation (GHI) is crucial for managing energy systems and decision making for future investment. This study aims to implement an ensemble strategy based on the Integrated Bayesian Multi-model Uncertainty Estimation Framework (IBMUEF) for quantification of uncertainty in artificial intelligence (AI). Evaluation results in two climate areas demonstrate the superiority of the ensemble IBMUEF over individual models in predicting GHI.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Guanghua Fu, Bencheng Li, Yongsheng Yang, Chaofeng Li
Summary: This paper proposes a four-stage ensemble feature selection method called RTEFS, which can reduce data dimensionality and improve the accuracy and computational cost of machine learning models. Experimental results show that RTEFS outperforms the base counterparts in terms of accuracy and F-measure scores.
PATTERN RECOGNITION LETTERS
(2023)
Article
Green & Sustainable Science & Technology
Mohamed Abdel-Basset, Abduallah Gamal, Osama M. ELkomy
Summary: Due to the increased demand for energy, renewable energy sources like solar energy have become more popular. The need to identify suitable locations for solar photovoltaic farms involves consideration of various factors such as technical, economic, environmental, sociopolitical dimensions, and risks. The hybrid multi-criteria decision-making approach used in this study facilitated the ranking of solar photovoltaic farm locations in Egypt, with the Benban solar photovoltaic farm identified as the most sustainable option.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Computer Science, Artificial Intelligence
Mohsen Paniri, Mohammad Bagher Dowlatshahi, Hossein Nezamabadi-pour
Summary: This paper proposes a new multi-label feature selection method based on Ant Colony Optimization, using a heuristic learning approach to enhance performance. Experimental results demonstrate that the proposed method significantly outperforms competing methods.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Mehdi Joodaki, Mohammad Bagher Dowlatshahi, Nazanin Zahra Joodaki
Summary: Feature selection is a crucial process in machine learning algorithms to improve performance by selecting the most relevant features. The proposed EFSPF method overcomes limitations of current methods by using weighted PageRank and fuzzy logic in feature selection. Evaluation against rival methods shows superior performance of EFSPF in terms of accuracy, precision, recall, and F1 score metrics.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Firoozeh Beiranvand, Vahid Mehrdad, Mohammad Bagher Dowlatshahi
Summary: This paper proposes a general unsupervised feature selection method called unsupervised feature selection using principal component analysis (UFSPCA). It utilizes PCA to create uncorrelated and orthogonal features, and uses a weighted bipartite graph and the Hungarian algorithm to obtain an optimal feature set. Evaluation results on five datasets demonstrate the superiority of the UFSPCA method over seven other algorithms.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Javad Mahmoodi, Hossein Nezamabadi-pour, Dariush Abbasi-Moghadam
Summary: This research proposes a new 3D ConvNet along with a technique for extracting interest frames to enhance the performance of violence detection. By selecting significant temporal information and noticing specific regions, the proposed method outperforms existing approaches in terms of accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Fatemeh Kargar Barzi, Hossein Nezamabadi-pour
Summary: This paper proposes a new approach for depth estimation based on integral imaging. Multiple viewpoint images are used to extract range information of a scene, and the scene is reconstructed computationally at different depths using an integral imaging reconstruction algorithm. The depth information of objects is then acquired by processing the reconstructed images and using a matching technique with speeded-up robust features (SURF). Experimental results show that the proposed method has high accuracy and overcomes limitations of standard image processing.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Water Resources
Amirhossein Najafabadipour, Gholamreza Kamali, Hossein Nezamabadi-pour
Summary: In this research, ARIMA and HWES models were used to forecast groundwater fluctuations in six piezometer wells near the Gohar Zamin Iron Ore Mine. An innovative combination method was applied to improve the accuracy of the forecasts.
Article
Chemistry, Multidisciplinary
Amirhossein Najafabadipour, Gholamreza Kamali, Hossein Nezamabadi-pour
Summary: This study develops multiple machine learning models and optimization algorithms to predict groundwater levels. The committee machine intelligence system (CMIS) model demonstrates high accuracy in predicting groundwater levels. Sensitivity analysis identifies the electrical resistivity of sediments as the most influential factor on groundwater levels.
Article
Computer Science, Artificial Intelligence
Amin Hashemi, Mehdi Joodaki, Nazanin Zahra Joodaki, Mohammad Bagher Dowlatshahi
Summary: Ant Colony Optimization (ACO) is a probabilistic and approximation metaheuristic algorithm inspired by real ants' behavior. It uses pheromone trails to find optimal solutions to complex combinatorial optimization problems. This paper proposes an ACO algorithm based on the ensemble of heuristics using a Multi-Criteria Decision-Making (MCDM) procedure. The algorithm selects multiple heuristics to improve the performance and stability of ACO.
APPLIED SOFT COMPUTING
(2022)
Article
Neurosciences
Hoda Jalalkamali, Amirhossein Tajik, Rashid Hatami, Hossein Nezamabadipour
Summary: This study used machine learning methods to detect an individual's time perception from EEG signals, finding that the P3 component could be a potential candidate for detecting sub-second periods in future research on brain-computer interface (BCI) applications.
INTERNATIONAL JOURNAL OF NEUROSCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Behzad Mirzaei, Farshad Rahmati, Hossein Nezamabadi-pour
Summary: This paper proposes a score-based preprocessing technique based on under-sampling and over-sampling to overcome the weakness of classifiers in class imbalance problems. The technique selects suitable samples based on their importance in the feature space and balances the classes' distribution. Experiments show the superiority and effectiveness of the proposed method compared to other methods.
PATTERN ANALYSIS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Hamid Bayati, Mohammad Bagher Dowlatshahi, Amin Hashemi
Summary: Multi-label learning is important in real-world applications, but existing methods have limited performance when dealing with datasets of different dimensions. This paper proposes a new feature selection algorithm based on subspace learning and memetic algorithm, which outperforms other methods according to multiple evaluation criteria.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Robotics
Mohadese Soleimanpour-moghadam, Hossein Nezamabadi-pour
Summary: A new multi-robot task allocation algorithm inspired by the Newtonian law of gravity is proposed in this paper, where robots and targets are treated as movable and fixed objects, respectively. The algorithm uses the law of gravity to determine the best allocation of robots to targets. Self-adaptive mechanisms and control parameters are employed to balance exploration and exploitation ability, with a selection memory designed to accelerate computation.
INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Zahra Pakdaman, Hossein Nezamabadi-pour, Saeid Saryazdi
Summary: This paper proposes a new reversible watermarking scheme, utilizing Reversible Walsh-Hadamard Transform for image processing and performing Singular Value Decomposition on the transformed image for watermark embedding. Quick Response (QR) code is embedded using a prediction-based method for full recovery.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)