Article
Computer Science, Information Systems
Simrandeep Singh, Nitin Mittal, Harbinder Singh, Diego Oliva
Summary: Image segmentation is a critical stage in image analysis and pre-processing, where pixels are divided into segments based on threshold values. Multi-level thresholding approaches are more effective than bi-level methods, and a new modified Otsu function is proposed that combines Otsu's between-class variance and Kapur's entropy. Experimental results demonstrate the high efficiency of the modified Otsu method in terms of performance metrics.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Mathematical & Computational Biology
Wenqi Ji, Xiaoguang He
Summary: The paper presents a moth-flame optimization (MFO) method based on Kapur's entropy to address the issues of low segmentation accuracy and high computational complexity in multilevel thresholding image segmentation. Experimental results demonstrate that MFO has better calculation accuracy, segmentation effect, and stability.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
J. Anitha, S. Immanuel Alex Pandian, S. Akila Agnes
Summary: This paper proposes a modified whale optimization algorithm for optimizing the selection of multilevel color image thresholds. The algorithm achieves a proper balance between exploration and exploitation phases, thus avoiding the issue of local optima.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Caijie Shang, Dong Zhang, Yan Yang
Summary: A gradient-based method different from bionic algorithms is proposed in this paper for optimal multilevel thresholds search in image segmentation. Experiments show that this method is efficient in computation and has equal or better performance of segmentation compared to other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Li Qiao, Kai Liu, Yanfeng Xue, Weidong Tang, Taybeh Salehnia
Summary: This paper presents a new hybrid optimization algorithm (AOA-HHO) for solving the multilevel thresholding image segmentation problem. The algorithm combines the features of arithmetic optimization algorithm and Harris hawks optimizer to obtain better thresholds in both local and global search, improving the accuracy and performance of image segmentation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Mohammad Hashem Ryalat, Osama Dorgham, Sara Tedmori, Zainab Al-Rahamneh, Nijad Al-Najdawi, Seyedali Mirjalili
Summary: Digital image processing techniques and algorithms are used to support medical experts in disease identification, studies, and diagnosis. Image segmentation methods are widely used in this area to simplify image representation and analysis. Among various approaches, multilevel thresholding methods have shown better results. However, traditional statistical approaches like the Otsu and Kapur methods suffer from high computational costs for multilevel thresholding. In this work, the Harris hawks optimization technique is combined with Otsu's method to reduce computational costs while maintaining optimal outcomes.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Aneesh Wunnava, Manoj Kumar Naik, Rutuparna Panda, Bibekananda Jena, Ajith Abraham
Summary: This study introduces an improved Harris hawks optimization algorithm, DEAHHO, which incorporates differential evolution and adaptive exploration to enhance the performance of search agents, successfully applied in image thresholding.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Manoj Kumar Naik, Rutuparna Panda, Aneesh Wunnava, Bibekananda Jena, Ajith Abraham
Summary: Multilevel image thresholding is essential in multimedia tools to understand objects in the real world, but the 1-D Masi entropy-based thresholding lacks consideration of contextual information. To address this, a 2-D Masi entropy-based thresholding method utilizing a 2-D histogram was proposed. The computational complexity was reduced by using a nature-inspired optimizer, the leader Harris hawks optimization (LHHO), which improved performance compared to the Harris hawks optimization (HHO).
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Biology
Sanjoy Chakraborty, Apu Kumar Saha, Sukanta Nama, Sudhan Debnath
Summary: The COVID-19 pandemic has had a significant impact on various aspects of human life, highlighting the importance of rapid diagnosis and treatment. This research focuses on developing a computational tool to improve diagnostic accuracy by enhancing the whale optimization method and evaluating its efficiency through population reduction.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Information Systems
Jinzhong Zhang, Gang Zhang, Min Kong, Tan Zhang
Summary: This paper proposes a hybrid golden jackal optimization with a sine cosine algorithm (SCGJO) based on Kapur's entropy to tackle the multilevel thresholding image segmentation. The experimental results demonstrate that the SCGJO is superior to the other algorithms in terms of convergence rate, computation accuracy, segmentation quality, and stability. Additionally, the SCGJO is a steady and trustworthy approach for tackling image segmentation.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Rifat Kurban, Ali Durmus, Ercan Karakose
Summary: This study uses multiple metaheuristic algorithms for multilevel color image thresholding, with MPA and TFWO outperforming other algorithms in terms of image quality evaluation and CPU time consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Mathematical & Computational Biology
Shikai Wang, Kangjian Sun, Wanying Zhang, Heming Jia
Summary: The paper proposes a modified ant lion optimizer algorithm based on opposition-based learning for optimizing multilevel thresholding in image segmentation, and experimental results show that the method outperforms others in terms of segmentation performance.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Jia Cai, Tianhua Luo, Guanglong Xu, Yi Tang
Summary: Biologically inspired computing is a method that uses elegantly modeled techniques motivated by the behaviors of creatures in nature to solve real-world problems. This paper investigates an improved Harris hawks optimizer (HHO) by introducing the grey wolf optimizer (GWO) and improving the balance between exploration and exploitation. The proposed approach combines different cognitive hunting behaviors of Harris' hawks and grey wolf packs and selects the best solutions through iterations. Experimental results demonstrate the effectiveness and efficiency of the proposed method.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Dong Zhao, Lei Liu, Fanhua Yu, Ali Asghar Heidari, Mingjing Wang, Guoxi Liang, Khan Muhammad, Huiling Chen
Summary: By enhancing the selection mechanism of the ACOR method and introducing random spare strategy and chaotic intensification strategy, the convergence speed and accuracy can be significantly improved, effectively avoiding local optima. Through a series of experiments, these improved methods demonstrate superior performance in problem-solving, and compared to other techniques, RCACO has a more reliable ability to step out of local optima.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Kashif Hussain, Laith Abualigah, Mohamed Abd Elaziz, Waleed Alomoush, Gaurav Dhiman, Youcef Djenouri, Erik Cuevas
Summary: The paper introduces an enhanced version of the Marine Predators Algorithm (MPA) called MPA-OBL, which incorporates Opposition-Based Learning (OBL) to improve search efficiency and convergence. Through comprehensive experiments, MPA-OBL is shown to outperform other algorithms in solving complex optimization problems, demonstrating superior quality of solutions and faster convergence speed.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Operations Research & Management Science
Angel A. Juan, Peter Keenan, Rafael Marti, Sean McGarraghy, Javier Panadero, Paula Carroll, Diego Oliva
Summary: In the context of simulation-based optimization, this paper reviews recent work related to metaheuristics, matheuristics, simheuristics, biased-randomised heuristics, and learnheuristics for solving complex and large-scale optimization problems in various domains. The paper provides an overview of the main concepts and updated references, and highlights the applications of these hybrid optimization-simulation-learning approaches in solving real-life challenges under dynamic and uncertainty scenarios. A numerical analysis is also included to illustrate the benefits across different application fields. The paper concludes by highlighting open research lines on extending the concept of simulation-based optimization.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Diego Oliva, Emre Celik, Marwa M. Emam, Rania M. Ghoniem
Summary: Feature selection is an optimization problem that aims to simplify and improve the quality of highly dimensional datasets by selecting prominent features and eliminating redundant and irrelevant data to enhance classification accuracy. The Sooty Tern Optimization Algorithm (STOA) and its improved version mSTOA are used to optimize the feature selection problem. However, mSTOA performs better than STOA in terms of convergence to optimal solutions, as validated through experiments and statistical analyses.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
S. Priyanka, Diego Oliva, Kethepalli Mallikarjuna, M. S. Sudhakar
Summary: This paper proposes a geometry-based characterization method using L-shape pattern for shape description, which is transformed into histograms for matching and retrieval. The results show that this method achieves superior performance on multiple datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Angel Casas-Ordaz, Diego Oliva, Mario A. Navarro, Alfonso Ramos-Michel, Marco Perez-Cisneros
Summary: This article introduces a method for determining the threshold values for image segmentation using the Runge Kutta (RUN) optimization algorithm. By combining it with opposition-based learning (OBL), a hybrid algorithm called RUN-OBL is created, which can effectively solve high-dimensional problems. Experimental results demonstrate that the proposed approach performs better in terms of image segmentation and optimization of complex problems.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Simrandeep Singh, Nitin Mittal, Harbinder Singh, Diego Oliva
Summary: Image segmentation is a critical stage in image analysis and pre-processing, where pixels are divided into segments based on threshold values. Multi-level thresholding approaches are more effective than bi-level methods, and a new modified Otsu function is proposed that combines Otsu's between-class variance and Kapur's entropy. Experimental results demonstrate the high efficiency of the modified Otsu method in terms of performance metrics.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Diego Oliva, Noe Ortega-Sanchez, Mario A. Navarro, Alfonso Ramos-Michel, Mohammed El-Abd, Seyed Jalaleddin Mousavirad, Mohammad H. Nadimi-Shahraki
Summary: This paper proposes a combination of the minimum cross-entropy method and the Global-best brain storm optimization algorithm for image segmentation. The method aims to find the best configuration of thresholds by optimizing the minimum cross entropy, and extract regions of interest.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Ehsan Bojnordi, Seyed Jalaleddin Mousavirad, Mahdi Pedram, Gerald Schaefer, Diego Oliva
Summary: This paper presents a novel MLP training algorithm based on Levy flight distribution, which uses random walks to explore the search space and optimize the performance of neural networks in pattern classification tasks. Experimental results show the superiority of the proposed algorithm compared to other methods.
NEW GENERATION COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Chunbo Lang, Gong Cheng, Binfei Tu, Junwei Han
Summary: Few-Shot segmentation (FSS) is a challenging task that aims to identify unseen classes with only a few annotated samples. Current approaches based on prototype learning fail to fully utilize support image-mask pairs, leading to segmentation failures. To address this, we propose a flexible framework that divides the segmentation mask, generates support-induced proxies, and incorporates parallel decoding and semantic consistency regularization.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2023)
Correction
Engineering, Multidisciplinary
Mohammad Sh. Daoud, Mohammad Shehab, Laith Abualigah, Mohammad Alshinwan, Mohamed Abd Elaziz, Mohd Khaled Yousef Shambour, Diego Oliva, Mohammad A. Alia, Raed Abu Zitar
JOURNAL OF BIONIC ENGINEERING
(2023)
Review
Engineering, Multidisciplinary
Mohammad Sh. Daoud, Mohammad Shehab, Laith Abualigah, Mohammad Alshinwan, Mohamed Abd Elaziz, Mohd Khaled Yousef Shambour, Diego Oliva, Mohammad A. A. Alia, Raed Abu Zitar
Summary: Chimp Optimization Algorithm (ChOA) is a recent metaheuristic swarm intelligence method that has been widely used for various optimization problems. It has impressive characteristics such as few parameters, no need for derivation information, simplicity, flexibility, scalability, and a capability to balance exploration and exploitation. Due to these advantages, ChOA has gained significant research interest and has been applied in several domains. This review paper provides an overview of research publications using ChOA, including introductory information, discussions on its operations and theoretical foundation, and detailed descriptions of its recent versions and applications. The evaluation of ChOA is also provided. Overall, this review paper is helpful for researchers and practitioners in various fields and provides potential future research directions.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Taymaz Akan, Diego Oliva, Ali-Reza Feizi-Derakhshi, Amir-Reza Feizi-Derakhshi, Marco Perez-Cisneros, Mohammad Alfrad Nobel Bhuiyan
Summary: Image segmentation is a fundamental step in image processing with crucial applications in computer vision, medical imaging, and object recognition. Histogram-based thresholding is a prevalent method for image segmentation. The Battle Royal Optimizer (BRO) is a recent optimization algorithm that shows promise in multilevel image thresholding.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mosa E. Hosney, Diego Oliva, Eman M. G. Younis, Abdelmgeid A. Ali, Waleed M. Mohamed
Summary: This paper proposes a wrapper feature selection approach that combines the rat swarm optimization algorithm with genetic operators to improve classification accuracy and reduce the number of features. The approach converts the continuous search space into a discrete space using transfer functions, achieving a balance between local and global search. Experimental results demonstrate the efficiency and effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Somnath Chatterjee, Debyarati Saha, Shibaprasad Sen, Diego Oliva, Ram Sarkar
Summary: This paper presents a two-stage facial expression recognition system using thermal images. The first stage utilizes the MobileNet model to extract features from input images. The second stage employs the Moth-flame Optimization algorithm to select the optimal feature subset. The proposed model achieves an accuracy of 97.47% on the thermal image-based facial expressions dataset while using only 29% features generated from the MobileNet model.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Mohamed Abd Elaziz, Abdelghani Dahou, Mohammed Azmi Al-Betar, Shaker El-Sappagh, Diego Oliva, Ahmad O. Aseeri
Summary: Social IoT systems improve the user experience in various applications and the developed QAHA algorithm enhances feature selection using Quantum optimization. Experiments demonstrate the efficiency of QAHA in both UCI and SIoT datasets, showing increased accuracy and decreased feature count.
Article
Computer Science, Information Systems
Laith Abualigah, Diego Oliva, Heming Jia, Faiza Gul, Nima Khodadadi, Abdelazim G. Hussien, Mohammad Al Shinwan, Absalom E. Ezugwu, Belal Abuhaija, Raed Abu Zitar
Summary: A novel hybrid optimization algorithm called IPDOA is proposed in this paper to solve various benchmark functions. By enhancing the search process of PDOA using the primary updating mechanism of DMOA, the proposed method aims to address the main weaknesses of the original methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
