1
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Hodjat B, Shahrzad H, Miikkulainen R. Domain-Independent Lifelong Problem Solving Through Distributed ALife Actors. ARTIFICIAL LIFE 2024; 30:259-276. [PMID: 38048055 DOI: 10.1162/artl_a_00418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
A domain-independent problem-solving system based on principles of Artificial Life is introduced. In this system, DIAS, the input and output dimensions of the domain are laid out in a spatial medium. A population of actors, each seeing only part of this medium, solves problems collectively in it. The process is independent of the domain and can be implemented through different kinds of actors. Through a set of experiments on various problem domains, DIAS is shown able to solve problems with different dimensionality and complexity, to require no hyperparameter tuning for new problems, and to exhibit lifelong learning, that is, to adapt rapidly to run-time changes in the problem domain, and to do it better than a standard, noncollective approach. DIAS therefore demonstrates a role for ALife in building scalable, general, and adaptive problem-solving systems.
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Affiliation(s)
| | - Hormoz Shahrzad
- Cognizant AI Labs University of Texas at Austin Department of Computer Science
| | - Risto Miikkulainen
- Cognizant AI Labs University of Texas at Austin Department of Computer Science
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2
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Yuan Y, Du J, Luo J, Zhu Y, Huang Q, Zhang M. Discrimination of missing data types in metabolomics data based on particle swarm optimization algorithm and XGBoost model. Sci Rep 2024; 14:152. [PMID: 38168582 PMCID: PMC10762217 DOI: 10.1038/s41598-023-50646-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/22/2023] [Indexed: 01/05/2024] Open
Abstract
In the field of data analysis, it is often faced with a large number of missing values, especially in metabolomics data, this problem is more prominent. Data imputation is a common method to deal with missing metabolomics data, while traditional data imputation methods usually ignore the differences in missing types, and thus the results of data imputation are not satisfactory. In order to discriminate the missing types of metabolomics data, a missing data classification model (PX-MDC) based on particle swarm algorithm and XGBoost is proposed in this paper. First, the missing values in a given missing data set are obtained by panning the missing values to obtain the largest subset of complete data, and then the particle swarm algorithm is used to search for the concentration threshold of missing data and the proportion of low concentration deletions as a percentage of overall deletions. Next, the missing data are simulated based on the search results. Finally, the training data are trained using the XGBoost model using the feature set proposed in this paper in order to build a classifier for the missing data. The experimental results show that the particle swarm algorithm is able to match the traditional enumeration method in terms of accuracy and significantly reduce the search time in concentration threshold search. Compared with the current mainstream methods, the PX-MDC model designed in this paper exhibits higher accuracy and is able to distinguish different deletion types for the same metabolite. This study is expected to make an important breakthrough in metabolomics data imputation and provide strong support for research in related fields.
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Affiliation(s)
- Yang Yuan
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, 330004, China
| | - Jianqiang Du
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, 330004, China.
- Key Laboratory of Artificial Intelligence in Chinese Medicine, Jiangxi University of Chinese Medicine, Nanchang, 330004, China.
| | - Jigen Luo
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, 330004, China
- Key Laboratory of Artificial Intelligence in Chinese Medicine, Jiangxi University of Chinese Medicine, Nanchang, 330004, China
| | - Yanchen Zhu
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, 330004, China
- Key Laboratory of Artificial Intelligence in Chinese Medicine, Jiangxi University of Chinese Medicine, Nanchang, 330004, China
| | - Qiang Huang
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, 330004, China
| | - Mengting Zhang
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, 330004, China
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3
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Jiang X. A combined monthly precipitation prediction method based on CEEMD and improved LSTM. PLoS One 2023; 18:e0288211. [PMID: 37440489 DOI: 10.1371/journal.pone.0288211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
With the continuous decline of water resources due to population growth and rapid economic development, precipitation prediction plays an important role in the rational allocation of global water resources. To address the non-linearity and non-stationarity of monthly precipitation, a combined prediction method based on complementary ensemble empirical mode decomposition (CEEMD) and a modified long short-term memory (LSTM) neural network was proposed. Firstly, the CEEMD method was used to decompose the monthly precipitation series into a set of relatively stationary sub-sequence components, which can better reflect the local characteristics of the sequence and further understand the nonlinear dynamic characteristics of the sequence. Then, improved LSTM neural networks were employed to predict each sub-sequence. The proposed improvement method optimized the hyper-parameters of LSTM neural networks using particle swarm optimization algorithm, which avoided the randomness of artificial parameter selection. Finally, the predicted results of each component were superimposed to obtain the final prediction result. The proposed method was validated by taking the monthly precipitation data from 1961 to 2020 in Changde City, Hunan Province as an example. The results of the case study show that, compared with other traditional prediction methods, the proposed method can better reflect the trend of precipitation changes and has higher prediction accuracy.
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Affiliation(s)
- Xinyun Jiang
- College of Information and Intelligence, Hunan Agricultural University, Changsha Hunan, PR China
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4
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Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics 2023; 15:1916. [PMID: 37514102 PMCID: PMC10385763 DOI: 10.3390/pharmaceutics15071916] [Citation(s) in RCA: 61] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.
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Affiliation(s)
- Lalitkumar K Vora
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
| | - Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar 401404, Maharashtra, India
| | - Keshava Jetha
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
- Ph.D. Section, Gujarat Technological University, Ahmedabad 382424, Gujarat, India
| | | | - Hetvi K Solanki
- Pharmacy Section, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
| | - Vivek P Chavda
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
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5
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Tan C, Wang C, Tian J, Niu H, Wei Q, Zhang X. Optimization of Profile Control and Oil Displacement Scheme Parameters Based on Deep Deterministic Policy Gradient. ACS OMEGA 2023; 8:23739-23753. [PMID: 37426228 PMCID: PMC10324049 DOI: 10.1021/acsomega.3c02003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023]
Abstract
The parameter design of profile control and oil displacement (PCOD) scheme plays an important role in improving waterflooding efficiency and increasing the oil field production and recovery. In this paper, the parameter optimization model and solution method of the PCOD scheme based on deep deterministic policy gradient (DDPG) are constructed with the half-year increased oil production (Qi) of injection well group as the objective function and the parameter range of PCOD system type, concentration, injection volume, and injection rate as constraints. Using the historical data of PCOD and extreme gradient boosting (XGBoost) method to construct a proxy model of PCOD process as the environment, the change rate of Qi of well groups before and after optimization is taken as the reward function; the system type, concentration, injection volume, and injection rate are taken as the action; and the Gaussian strategy with noise is taken as the action exploration strategy. Taking XX block of offshore oil field as an example, the parameters of the compound slug PCOD process (pre-slug + main slug + protection slug) of the injection well group are analyzed, that is, parameters such as the system type, concentration, injection volume, and injection rate of each slug system are optimized. The research shows that the parameter optimization model of the PCOD scheme established based on DDPG can obtain higher oil production PCOD scheme for well groups with different PCOD, and has strong optimization and generalization ability compared with the particle swarm optimization (PSO) model.
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Affiliation(s)
- Chaodong Tan
- Department
of Automation, China University of Petroleum,
Changping, Beijing 102249, China
- College
of Petroleum Engineering, China University
of Petroleum, Beijing 102249, China
| | - Chunqiu Wang
- College
of Petroleum Engineering, China University
of Petroleum, Beijing 102249, China
| | - Jinjie Tian
- CNOOC
Energy Development Co., Ltd. Engineering Technology Branch, Tianjin 300452, China
| | - HuiZhao Niu
- Beijing
Yadan Petroleum Technology Development Co., Ltd., Beijing 102200, China
| | - Qi Wei
- College
of Petroleum Engineering, China University
of Petroleum, Beijing 102249, China
| | - Xiongying Zhang
- Beijing
Yadan Petroleum Technology Development Co., Ltd., Beijing 102200, China
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6
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Jakšić Z, Devi S, Jakšić O, Guha K. A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics. Biomimetics (Basel) 2023; 8:278. [PMID: 37504166 PMCID: PMC10807478 DOI: 10.3390/biomimetics8030278] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/29/2023] Open
Abstract
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.
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Affiliation(s)
- Zoran Jakšić
- Center of Microelectronic Technologies, Institute of Chemistry, Technology and Metallurgy, National Institute of the Republic of Serbia University of Belgrade, 11000 Belgrade, Serbia;
| | - Swagata Devi
- Department of Electronics and Communication Engineering, B V Raju Institute of Technology Narasapur, Narasapur 502313, India;
| | - Olga Jakšić
- Center of Microelectronic Technologies, Institute of Chemistry, Technology and Metallurgy, National Institute of the Republic of Serbia University of Belgrade, 11000 Belgrade, Serbia;
| | - Koushik Guha
- Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar 788010, India;
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7
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Empereur-mot C, Pedersen KB, Capelli R, Crippa M, Caruso C, Perrone M, Souza PCT, Marrink SJ, Pavan GM. Automatic Optimization of Lipid Models in the Martini Force Field Using SwarmCG. J Chem Inf Model 2023; 63:3827-3838. [PMID: 37279107 PMCID: PMC10302490 DOI: 10.1021/acs.jcim.3c00530] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Indexed: 06/08/2023]
Abstract
After two decades of continued development of the Martini coarse-grained force field (CG FF), further refinment of the already rather accurate Martini lipid models has become a demanding task that could benefit from integrative data-driven methods. Automatic approaches are increasingly used in the development of accurate molecular models, but they typically make use of specifically designed interaction potentials that transfer poorly to molecular systems or conditions different than those used for model calibration. As a proof of concept, here, we employ SwarmCG, an automatic multiobjective optimization approach facilitating the development of lipid force fields, to refine specifically the bonded interaction parameters in building blocks of lipid models within the framework of the general Martini CG FF. As targets of the optimization procedure, we employ both experimental observables (top-down references: area per lipid and bilayer thickness) and all-atom molecular dynamics simulations (bottom-up reference), which respectively inform on the supra-molecular structure of the lipid bilayer systems and on their submolecular dynamics. In our training sets, we simulate at different temperatures in the liquid and gel phases up to 11 homogeneous lamellar bilayers composed of phosphatidylcholine lipids spanning various tail lengths and degrees of (un)saturation. We explore different CG representations of the molecules and evaluate improvements a posteriori using additional simulation temperatures and a portion of the phase diagram of a DOPC/DPPC mixture. Successfully optimizing up to ∼80 model parameters within still limited computational budgets, we show that this protocol allows the obtainment of improved transferable Martini lipid models. In particular, the results of this study demonstrate how a fine-tuning of the representation and parameters of the models may improve their accuracy and how automatic approaches, such as SwarmCG, may be very useful to this end.
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Affiliation(s)
- Charly Empereur-mot
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario
Lugano, Campus Est, Via
la Santa 1, 6962 Lugano-Viganello, Switzerland
| | - Kasper B. Pedersen
- Department
of Chemistry, Aarhus University, Langelandsgade 140, 8000 Aarhus C, Denmark
| | - Riccardo Capelli
- Department
of Biosciences, Università degli
Studi di Milano, Via Celoria 26, 20133 Milano, Italy
| | - Martina Crippa
- Politecnico
di Torino, Department of Applied
Science and Technology, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Cristina Caruso
- Politecnico
di Torino, Department of Applied
Science and Technology, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Mattia Perrone
- Politecnico
di Torino, Department of Applied
Science and Technology, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Paulo C. T. Souza
- Molecular
Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS & University of Lyon, 7 Passage du Vercors, 69007 Lyon, France
| | - Siewert J. Marrink
- Molecular
Dynamics, Groningen Biomolecular Sciences and Biotechnology Institute
(GBB), University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Giovanni M. Pavan
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario
Lugano, Campus Est, Via
la Santa 1, 6962 Lugano-Viganello, Switzerland
- Politecnico
di Torino, Department of Applied
Science and Technology, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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8
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Xu M, Cao L, Lu D, Hu Z, Yue Y. Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization. Biomimetics (Basel) 2023; 8:235. [PMID: 37366829 DOI: 10.3390/biomimetics8020235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 05/27/2023] [Accepted: 06/01/2023] [Indexed: 06/28/2023] Open
Abstract
Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.
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Affiliation(s)
- Minghai Xu
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Li Cao
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Dongwan Lu
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
| | - Zhongyi Hu
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
| | - Yinggao Yue
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
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9
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Islam RB, Akhter S, Iqbal F, Saif Ur Rahman M, Khan R. Deep learning based object detection and surrounding environment description for visually impaired people. Heliyon 2023; 9:e16924. [PMID: 37484219 PMCID: PMC10360957 DOI: 10.1016/j.heliyon.2023.e16924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 05/31/2023] [Accepted: 06/01/2023] [Indexed: 07/25/2023] Open
Abstract
Object detection, one of the most significant contributions of computer vision and machine learning, plays an immense role in identifying and locating objects in an image or a video. We recognize distinct objects and precisely get their information through object detection, such as their size, shape, and location. This paper developed a low-cost assistive system of obstacle detection and the surrounding environment depiction to help blind people using deep learning techniques. TensorFlow object detection API and SSDLite MobileNetV2 have been used to create the proposed object detection model. The pre-trained SSDLite MobileNetV2 model is trained on the COCO dataset, with almost 328,000 images of 90 different objects. The gradient particle swarm optimization (PSO) technique has been used in this work to optimize the final layers and their corresponding hyperparameters of the MobileNetV2 model. Next, we used the Google text-to-speech module, PyAudio, playsound, and speech recognition to generate the audio feedback of the detected objects. A Raspberry Pi camera captures real-time video where real-time object detection is done frame by frame with Raspberry Pi 4B microcontroller. The proposed device is integrated into a head cap, which will help visually impaired people to detect obstacles in their path, as it is more efficient than a traditional white cane. Apart from this detection model, we trained a secondary computer vision model and named it the "ambiance mode." In this mode, the last three convolutional layers of SSDLite MobileNetV2 are trained through transfer learning on a weather dataset. The dataset comprises around 500 images from four classes: cloudy, rainy, foggy, and sunrise. In this mode, the proposed system will narrate the surrounding scene elaborately, almost like a human describing a landscape or a beautiful sunset to a visually impaired person. The performance of the object detection and ambiance description modes are tested and evaluated in a desktop computer and Raspberry Pi embedded system. Detection accuracy and mean average precision, frame rate, confusion matrix, and ROC curve measure the model's accuracy on both setups. This low-cost proposed system is believed to help visually impaired people in their day-to-day life.
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10
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Biswal AK, Panda L, Chakraborty S, Pradhan SK, Dash MR, Misra PK. Production of a nascent cellulosic material from vegetable waste: Synthesis, characterization, functional properties, and its potency for a cationic dye removal. Int J Biol Macromol 2023:124959. [PMID: 37247704 DOI: 10.1016/j.ijbiomac.2023.124959] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 04/26/2023] [Accepted: 05/16/2023] [Indexed: 05/31/2023]
Abstract
The present work reports the production of cellulose nanocrystals, CNC30 and CNC60, developed using vegetable waste, i.e., bottle gourd peel through sulfuric acid hydrolysis with a 30 and 60 min hydrolysis process coupled with ultrasonication. The FTIR confirmed the absence of hemicellulose and lignin, and XRD confirmed the crystallinity of the cellulose nanocrystals. DLS studies indicated the hydrodynamic diameter of CNC30 and CNC60 to be 195.5 nm and 192.2 nm, respectively. The TEM image and SAED pattern established the shape of CNC60 to be spherical, with an average particle size of 38.32 nm. CNC60 possessed lesser negative potential and higher thermal stability than CNC30, possibly due to the demolition of the crystalline regions containing sulfate groups. The functional properties, such as swelling power, water, and oil holding capacities of CNC60, were superior to that of CNC30. The adsorption batch parameters yielded 95.68 % methylene dye removal by CNC60 against the predicted value of 96.16 % by the RSM-PSO hybrid approach. The analyses of adsorption isotherms, kinetics, and thermodynamic parameters revealed the nature of the adsorbed layer and adsorption mechanism. Overall observations recommend that CNC60 could be a good and potent functional agent in paper technology, food technology, water treatment, and biomedical applications.
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Affiliation(s)
- Achyuta Kumar Biswal
- Centre of Studies in Surface Science and Technology, School of Chemistry, Sambalpur University, Jyoti Vihar 768 019, Odisha, India
| | - Laxmipriya Panda
- Centre of Studies in Surface Science and Technology, School of Chemistry, Sambalpur University, Jyoti Vihar 768 019, Odisha, India
| | - Sourav Chakraborty
- Department of Food Processing Technology, Ghani Khan Choudhury Institute of Engineering and Technology, Malda 732141, West Bengal, India
| | - Subrat Kumar Pradhan
- Organic Chemistry Laboratory, School of Chemistry, Sambalpur University, Jyoti Vihar 768 019, Odisha, India
| | - Manas Ranjan Dash
- Department of Chemistry, DIT University, Dehradun 248009, Uttarakhand, India
| | - Pramila Kumari Misra
- Centre of Studies in Surface Science and Technology, School of Chemistry, Sambalpur University, Jyoti Vihar 768 019, Odisha, India.
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11
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Tong R, Feng Y, Wang J, Wu Z, Tan M, Yu J. A Survey on Reinforcement Learning Methods in Bionic Underwater Robots. Biomimetics (Basel) 2023; 8:168. [PMID: 37092420 PMCID: PMC10123646 DOI: 10.3390/biomimetics8020168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 04/25/2023] Open
Abstract
Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope. In recent years, the application of reinforcement learning algorithms in the field of bionic underwater robots has gained considerable attention, and continues to grow. In this paper, we present a comprehensive survey of the accomplishments of reinforcement learning algorithms in the field of bionic underwater robots. Firstly, we classify existing reinforcement learning methods and introduce control tasks and decision making tasks based on the composition of bionic underwater robots. We further discuss the advantages and challenges of reinforcement learning for bionic robots in underwater environments. Secondly, we review the establishment of existing reinforcement learning algorithms for bionic underwater robots from different task perspectives. Thirdly, we explore the existing training and deployment solutions of reinforcement learning algorithms for bionic underwater robots, focusing on the challenges posed by complex underwater environments and underactuated bionic robots. Finally, the limitations and future development directions of reinforcement learning in the field of bionic underwater robots are discussed. This survey provides a foundation for exploring reinforcement learning control and decision making methods for bionic underwater robots, and provides insights for future research.
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Affiliation(s)
- Ru Tong
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yukai Feng
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jian Wang
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhengxing Wu
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Min Tan
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junzhi Yu
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China
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12
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An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT). Comput Biol Med 2023; 158:106734. [PMID: 36989745 DOI: 10.1016/j.compbiomed.2023.106734] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 01/31/2023] [Accepted: 02/28/2023] [Indexed: 03/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Valvular heart diseases (VHDs) are one of the dominant causes of cardiovascular abnormalities that have been associated with high mortality rates globally. Rapid and accurate diagnosis of the early stage of VHD based on cardiac phonocardiogram (PCG) signal is critical that allows for optimum medication and reduction of mortality rate. METHODS To this end, the current study proposes novel deep learning (DL)-based high-performance VHD detection frameworks that are relatively simpler in terms of network structures, yet effective for accurately detecting multiple VHDs. We present three different frameworks considering both 1D and 2D PCG raw signals. For 1D PCG, Mel frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC) features, whereas, for 2D PCG, various deep convolutional neural networks (D-CNNs) features are extracted. Additionally, nature/bio-inspired algorithms (NIA/BIA) including particle swarm optimization (PSO) and genetic algorithm (GA) have been utilized for automatic and efficient feature selection directly from the raw PCG signal. To further improve the performance of the classifier, vision transformer (ViT) has been implemented levering the self-attention mechanism on the time frequency representation (TFR) of 2D PCG signal. Our extensive study presents a comparative performance analysis and the scope of enhancement for the combination of different descriptors, classifiers, and feature selection algorithms. MAIN RESULTS Among all classifiers, ViT provides the best performance by achieving mean average accuracy Acc of 99.90 % and F1-score of 99.95 % outperforming current state-of-the-art VHD classification models. CONCLUSIONS The present research provides a robust and efficient DL-based end-to-end PCG signal classification framework for designing a automated high-performance VHD diagnosis system.
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Nahes AL, Bagajewicz MJ, Costa AL. A novel method for the globally optimal design of fixed bed catalytic reactors. Chem Eng Sci 2023. [DOI: 10.1016/j.ces.2023.118524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Mirabnejad M, Mohammadi H, Mirzabaghi M, Aghsami A, Jolai F, Yazdani M. Home Health Care Problem with Synchronization Visits and Considering Samples Transferring Time: A Case Study in Tehran, Iran. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15036. [PMID: 36429755 PMCID: PMC9690415 DOI: 10.3390/ijerph192215036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 10/31/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Health care facilities have not increased in response to the growing population. Therefore, government and health agencies are constantly seeking cost-effective alternatives so they can provide effective health care to their constituents. Around the world, health care organizations provide home health care (HHC) services to patients, especially the elderly, as an efficient alternative to hospital care. In addition, recent pandemics have demonstrated the importance of home health care as a means of preventing infection. This study is the first to simultaneously take into account nurses' working preferences and skill levels. Since transferring samples from the patient's home to the laboratory may affect the test results, this study takes into account the time it takes to transfer samples. In order to solve large instances, two metaheuristic algorithms are proposed: Genetic Algorithms and Particle Swarm Optimization. Nurses are assigned tasks according to their time windows and the tasks' time windows in a three-stage scheduling procedure. Using a case study set in Tehran, Iran, the proposed model is demonstrated. Even in emergencies, models can generate effective strategies. There are significant implications for health service management and health policymakers in countries where home health care services are receiving more attention. Furthermore, they contribute to the growing body of knowledge regarding health system strategies by providing new theoretical and practical insights.
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Affiliation(s)
- Mahyar Mirabnejad
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1439955961, Iran
| | - Hadi Mohammadi
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1439955961, Iran
| | - Mehrdad Mirzabaghi
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1439955961, Iran
| | - Amir Aghsami
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1439955961, Iran
- School of Industrial Engineering, K.N. Toosi University of Technology (KNTU), Tehran 1999143344, Iran
| | - Fariborz Jolai
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1439955961, Iran
| | - Maziar Yazdani
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney 2052, Australia
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15
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An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics. ROBOTICA 2022. [DOI: 10.1017/s026357472200145x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Abstract
With the rapid development of the national economy, the demand for electricity is also growing. Thermal power generation accounts for the highest proportion of power generation, and coal is the most commonly used combustion material. The massive combustion of coal has led to serious environmental pollution. It is significant to improve energy conversion efficiency and reduce pollutant emissions effectively. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed to establish the boiler combustion model. The proposed modeling method is applied to the combustion modeling process of a 300 MWe pulverized coal boiler. The simulation results show that compared with the same type of modeling method, ELM-IKPSO can better predict the boiler thermal efficiency and NOx emission concentration and also show better generalization performance. Finally, multi-objective optimization is carried out on the established model, and a set of mutually non-dominated boiler combustion solutions is obtained.
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Senoro DB, Monjardin CEF, Fetalvero EG, Benjamin ZEC, Gorospe AFB, de Jesus KLM, Ical MLG, Wong JP. Quantitative Assessment and Spatial Analysis of Metals and Metalloids in Soil Using the Geo-Accumulation Index in the Capital Town of Romblon Province, Philippines. TOXICS 2022; 10:toxics10110633. [PMID: 36355926 PMCID: PMC9699329 DOI: 10.3390/toxics10110633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 06/01/2023]
Abstract
The municipality of Romblon in the Philippines is an island known for its marble industry. The subsurface of the Philippines is known for its limestone. The production of marble into slab, tiles, and novelty items requires heavy equipment to cut rocks and boulders. The finishing of marble requires polishing to smoothen the surface. During the manufacturing process, massive amounts of particulates and slurry are produced, and with a lack of technology and human expertise, the environment can be adversely affected. Hence, this study assessed and monitored the environmental conditions in the municipality of Romblon, particularly the soils and sediments, which were affected due to uncontrolled discharges and particulates deposition. A total of fifty-six soil and twenty-three sediment samples were collected and used to estimate the metal and metalloid (MM) concentrations in the whole area using a neural network-particle swarm optimization inverse distance weighting model (NN-PSO). There were nine MMs; e.g., As, Cr, Ni, Pb, Cu, Ba, Mn, Zn and Fe, with significant concentrations detected in the area in both soils and sediments. The geo-accumulation index was computed to assess the level of contamination in the area, and only the soil exhibited contamination with zinc, while others were still on a safe level. Nemerow's pollution index (NPI) was calculated for the samples collected, and soil was evaluated and seen to have a light pollution level, while sediment was considered as "clean". Furthermore, the single ecological risk (Er) index for both soil and sediment samples was considered to be a low pollution risk because all values of Er were less than 40.
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Affiliation(s)
- Delia B. Senoro
- Resiliency and Sustainable Development Center, Yuchengco Innovation Center, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
- School of Civil, Environmental and Geological Engineering, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
- School of Graduate Studies, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
- Mapua-RSU Joint Research Laboratory, Romblon State University, Sawang, Romblon 5500, Philippines
| | - Cris Edward F. Monjardin
- Resiliency and Sustainable Development Center, Yuchengco Innovation Center, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
- School of Civil, Environmental and Geological Engineering, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
- School of Graduate Studies, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
| | - Eddie G. Fetalvero
- Mapua-RSU Joint Research Laboratory, Romblon State University, Sawang, Romblon 5500, Philippines
- Research and Development Office, Romblon State University, Odiongan, Romblon 5505, Philippines
| | - Zidrick Ed C. Benjamin
- Resiliency and Sustainable Development Center, Yuchengco Innovation Center, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
- Mapua-RSU Joint Research Laboratory, Romblon State University, Sawang, Romblon 5500, Philippines
| | - Alejandro Felipe B. Gorospe
- Resiliency and Sustainable Development Center, Yuchengco Innovation Center, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
- Mapua-RSU Joint Research Laboratory, Romblon State University, Sawang, Romblon 5500, Philippines
| | - Kevin Lawrence M. de Jesus
- Resiliency and Sustainable Development Center, Yuchengco Innovation Center, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
- School of Graduate Studies, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
| | - Mark Lawrence G. Ical
- Electrical Engineering Department, Romblon State University, Odiongan, Romblon 5505, Philippines
| | - Jonathan P. Wong
- Mapua-RSU Joint Research Laboratory, Romblon State University, Sawang, Romblon 5500, Philippines
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Gao Y, Yang L, Song Y, Tian J, Yang M. Designing water-saving-ecological check dam sites by a system optimization model in a region of the loess plateau, Northwest China. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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18
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Souidi MEH, Haouassi H, Ledmi M, Maarouk TM, Ledmi A. A discrete particle swarm optimization coalition formation algorithm for multi-pursuer multi-evader game. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Multi-Pursuers Multi-Evader Game (MPMEG) is considered as a multi-agent complex problem in which the pursuers must perform the capture of the detected evaders according to the temporal constraints. In this paper, we propose a metaheuristic approach based on a Discrete Particle Swarm Optimization in order to allow a dynamic coalition formation of the pursuers during the pursuit game. A pursuit coalition can be considered as the role definition of each pursuer during the game. In this work, each possible coalition is represented by a feasible particle’s position, which changes the concerned coalition according to its velocity during the pursuit game. With the aim of showcasing the performance of the new approach, we propose a comparison study in relation to recent approaches processing the MPMEG in term of capturing time and payoff acquisition. Moreover, we have studied the pursuit capturing time according to the number of used particles as well as the dynamism of the pursuit coalitions formed during the game. The obtained results note that the proposed approach outperforms the compared approaches in relation to the capturing time by only using eight particles. Moreover, this approach improves the pursuers’ payoff acquisition, which represents the pursuers’ learning rate during the task execution.
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Affiliation(s)
| | - Hichem Haouassi
- Department of Computer Science, ICOSI Lab, University of Khenchela, Khenchela, Algeria
| | - Makhlouf Ledmi
- Department of Computer Science, ICOSI Lab, University of Khenchela, Khenchela, Algeria
| | | | - Abdeldjalil Ledmi
- Department of Computer Science, ICOSI Lab, University of Khenchela, Khenchela, Algeria
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Abdelraouf OAM, Wang Z, Liu H, Dong Z, Wang Q, Ye M, Wang XR, Wang QJ, Liu H. Recent Advances in Tunable Metasurfaces: Materials, Design, and Applications. ACS NANO 2022; 16:13339-13369. [PMID: 35976219 DOI: 10.1021/acsnano.2c04628] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Metasurfaces, a two-dimensional (2D) form of metamaterials constituted by planar meta-atoms, exhibit exotic abilities to tailor electromagnetic (EM) waves freely. Over the past decade, tremendous efforts have been made to develop various active materials and incorporate them into functional devices for practical applications, pushing the research of tunable metasurfaces to the forefront of nanophotonics. Those active materials include phase change materials (PCMs), semiconductors, transparent conducting oxides (TCOs), ferroelectrics, liquid crystals (LCs), atomically thin material, etc., and enable intriguing performances such as fast switching speed, large modulation depth, ultracompactness, and significant contrast of optical properties under external stimuli. Integration of such materials offers substantial tunability to the conventional passive nanophotonic platforms. Tunable metasurfaces with multifunctionalities triggered by various external stimuli bring in rich degrees of freedom in terms of material choices and device designs to dynamically manipulate and control EM waves on demand. This field has recently flourished with the burgeoning development of physics and design methodologies, particularly those assisted by the emerging machine learning (ML) algorithms. This review outlines recent advances in tunable metasurfaces in terms of the active materials and tuning mechanisms, design methodologies, and practical applications. We conclude this review paper by providing future perspectives in this vibrant and fast-growing research field.
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Affiliation(s)
- Omar A M Abdelraouf
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634, Singapore
| | - Ziyu Wang
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634, Singapore
| | - Hailong Liu
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634, Singapore
| | - Zhaogang Dong
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634, Singapore
| | - Qian Wang
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634, Singapore
| | - Ming Ye
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Xiao Renshaw Wang
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Qi Jie Wang
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Hong Liu
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634, Singapore
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20
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A back-diffusion median integrated evolutionary optimization algorithm. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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21
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Raut V, Gunjan R, Shete VV, Eknath UD. Gastrointestinal tract disease segmentation and classification in wireless capsule endoscopy using intelligent deep learning model. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2099298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
- Vrushali Raut
- Electronics & Communication Engineering, MIT School of Engineering, MIT Art, Design and Technology University, Pune, India
| | - Reena Gunjan
- Electronics & Communication Engineering, MIT School of Engineering, MIT Art, Design and Technology University, Pune, India
| | - Virendra V. Shete
- Electronics & Communication Engineering, MIT School of Engineering, MIT Art, Design and Technology University, Pune, India
| | - Upasani Dhananjay Eknath
- Electronics & Communication Engineering, MIT School of Engineering, MIT Art, Design and Technology University, Pune, India
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22
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Shao L, Yang S, Fu T, Lin Y, Geng H, Ai D, Fan J, Song H, Zhang T, Yang J. Augmented reality calibration using feature triangulation iteration-based registration for surgical navigation. Comput Biol Med 2022; 148:105826. [PMID: 35810696 DOI: 10.1016/j.compbiomed.2022.105826] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/24/2022] [Accepted: 07/03/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Marker-based augmented reality (AR) calibration methods for surgical navigation often require a second computed tomography scan of the patient, and their clinical application is limited due to high manufacturing costs and low accuracy. METHODS This work introduces a novel type of AR calibration framework that combines a Microsoft HoloLens device with a single camera registration module for surgical navigation. A camera is used to gather multi-view images of a patient for reconstruction in this framework. A shape feature matching-based search method is proposed to adjust the size of the reconstructed model. The double clustering-based 3D point cloud segmentation method and 3D line segment detection method are also proposed to extract the corner points of the image marker. The corner points are the registration data of the image marker. A feature triangulation iteration-based registration method is proposed to quickly and accurately calibrate the pose relationship between the image marker and the patient in the virtual and real space. The patient model after registration is wirelessly transmitted to the HoloLens device to display the AR scene. RESULTS The proposed approach was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with six advanced AR calibration methods. The proposed method obtained average fusion errors of 0.70 ± 0.16 and 0.91 ± 0.13 mm in phantom and volunteer experiments, respectively. The fusion accuracy of the proposed method is the highest among all comparison methods. A volunteer liver puncture clinical simulation experiment was also conducted to show the clinical feasibility. CONCLUSIONS Our experiments proved the effectiveness of the proposed AR calibration method, and revealed a considerable potential for improving surgical performance.
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Affiliation(s)
- Long Shao
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Shuo Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Tianyu Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Yucong Lin
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Haixiao Geng
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Danni Ai
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Jingfan Fan
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Hong Song
- School of Computer Science & Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Tao Zhang
- Peking Union Medical College Hospital, Department of Oral and Maxillofacial Surgery, Beijing, 100730, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
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23
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Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems. ELECTRONICS 2022. [DOI: 10.3390/electronics11132104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks which, in spite of the great success of deep architectures, are still in use, for example, in robotic systems. The paper analyses the influence of different mechanisms incorporated into HCAE on the effectiveness of evolved neural networks and compares it with a number of rival algorithms. In order to verify the ability of HCAE to evolve effective small/mid-scale neural networks, both feed forward and recurrent, it was tested on fourteen identification problems including the two-spiral problem, which is a well-known binary classification benchmark, and on two control problems, i.e., the inverted-pendulum problem, which is a classical control benchmark, and the trajectory-following problem, which is a real problem in underwater robotics. Four other neuro-evolutionary algorithms, four particle swarm optimization methods, differential evolution, and a well-known back-propagation algorithm, were applied as a point of reference for HCAE. The experiments reported in the paper revealed that the evolutionary approach applied in the proposed algorithm makes it a more effective tool for solving the test problems than all the rivals.
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Wadhwa A, Thakur MK. Rapid surveillance of COVID-19 by timely detection of geographically robust, alive and emerging hotspots using Particle Swarm Optimizer. APPLIED GEOGRAPHY (SEVENOAKS, ENGLAND) 2022; 144:102719. [PMID: 35645430 PMCID: PMC9127146 DOI: 10.1016/j.apgeog.2022.102719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 05/10/2022] [Accepted: 05/12/2022] [Indexed: 06/15/2023]
Abstract
A novel virus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a pandemic called Coronavirus disease 2019 (COVID-19). According to the World Health Organization, COVID-19 was first detected in Wuhan city in December 2019 and has affected 216 countries with 9473214 confirmed cases and 484249 deaths globally as on June 26th, 2020. Also, this outbreak continues to grow in many countries like the United States of America (U.S.), Brazil, India, and Russia. To ensure rapid surveillance and better decision-making by government authorities in different countries, it is vital to identify alive and emerging hotspots within a country promptly. State-of-the-art methods based on space-time scan statistics (like SaTScan) are not geographically robust. Also, due to the enumeration of many Spatio-temporal cylinders, the computation cost of Spatio-temporal SaTScan (ST-SaTScan) is very high. In the applications like COVID-19 where we need to detect the emerging hotspots daily as soon as the new count of cases gets updated, ST-SaTScan seems inefficient. Therefore, this paper proposes a Particle Swarm Optimizer-based scheme to timely detect geographically robust, alive, and emerging COVID-19 hotspots in a country. Timely detection can help government officials design better control strategies like increasing testing in hotspots, imposing stricter containment rules, or setting up temporary hospital beds. Performance of ST-SaTScan and proposed scheme have been analyzed for four worst-hit U.S. states for the incubation period of 14 days between June 11th, 2020, and June 24th, 2020. Results indicate that the proposed scheme detects hotspots of a higher likelihood ratio (a measure to indicate the significance of hotspot) than ST-SaTScan in significantly less time. We also applied the proposed scheme to detect the emerging COVID-19 hotspots in all states of the U.S. During the study period, the proposed scheme has detected 104 emerging COVID-19 hotspots.
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Affiliation(s)
- Ankita Wadhwa
- Department of Computer Science Engineering and IT, Jaypee Institute of Information Technology, A-10 Sector 62, Noida, UP, 201309, India
| | - Manish Kumar Thakur
- Department of Computer Science Engineering and IT, Jaypee Institute of Information Technology, A-10 Sector 62, Noida, UP, 201309, India
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Adaptive Multistrategy Ensemble Particle Swarm Optimization with Signal-to-Noise Ratio Distance Metric. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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26
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Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2020.09.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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27
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An Optimized BP Neural Network Model and Its Application in the Credit Evaluation of Venture Loans. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8791968. [PMID: 35548097 PMCID: PMC9085346 DOI: 10.1155/2022/8791968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/26/2022] [Accepted: 04/15/2022] [Indexed: 11/29/2022]
Abstract
With the rapid development of entrepreneurship loans in China, the construction of a credit evaluation system of risk loans has become an important financial safeguard measure. This paper mainly studies the following three aspects. Firstly, in view of the subjective factors in the approval process of venture loans, based on the credit evaluation system of commercial banks and the data characteristics of venture loans, a credit evaluation system based on venture loans is constructed. Secondly, the randomized uniform design method is used to improve the population initialization method to realize the uniform distribution of the individual population. Finally, aiming at the problem of low efficiency of venture loan audit, this paper proposes an optimized BP neural network to evaluate the venture loan. Especially, through data processing, a credit index system is constructed, and then the optimized BP neural network model is determined in parameters. The model contains 15 input nodes, 1 hidden layer, and 2 output layers. Finally, the simulation shows that the optimized BP neural network model has obvious advantages in the loan evaluation. This paper includes the development status of credit evaluation of venture loans is empirically studied by using an optimized BP neural network model of nonexpected output.
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Ajibade SSM, Chaudhury S, Oyebode OJ, Ngo Hoang DL, Rabbi F, Ajibade SSM. Feature Selection for Metaheuristics Optimization Technique with Chaos. 2022 IEEE 18TH INTERNATIONAL COLLOQUIUM ON SIGNAL PROCESSING & APPLICATIONS (CSPA) 2022. [DOI: 10.1109/cspa55076.2022.9781989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Affiliation(s)
| | | | - Oluwadare Joshua Oyebode
- Afe Babalola University,Department of Civil and Environmental Engineering,Ado Ekiti,Ekiti State,Nigeria
| | - Dai-Long Ngo Hoang
- Vietnam National University Ho Chi Minh City – Campus in Ben Tre, Ben Tre Province, Vietnam
| | - Fazle Rabbi
- Australian Computer Society,Victoria,Australia
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29
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Modified normative fish swarm algorithm for optimizing power extraction in photovoltaic systems. EVOLUTIONARY INTELLIGENCE 2022. [DOI: 10.1007/s12065-022-00724-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Baseri Saadi S, Tataei Sarshar N, Sadeghi S, Ranjbarzadeh R, Kooshki Forooshani M, Bendechache M. Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4703682. [PMID: 35368933 PMCID: PMC8967525 DOI: 10.1155/2022/4703682] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/04/2022] [Accepted: 03/07/2022] [Indexed: 02/08/2023]
Abstract
One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in the machine learning and computer vision fields. CNN is composed of several filters accompanied by nonlinear functions and pooling layers. It enforces limitations on the weights and interconnections of the neural network to create a good structure for processing spatial and temporal distributed data. A CNN can restrain the numbering of free parameters of the network through its weight-sharing property. However, the training of CNNs is a challenging approach. Some optimization techniques have been recently employed to optimize CNN's weight and biases such as Ant Colony Optimization, Genetic, Harmony Search, and Simulated Annealing. This paper employs the well-known nature-inspired algorithm called Shuffled Frog-Leaping Algorithm (SFLA) for training a classical CNN structure (LeNet-5), which has not been experienced before. The training method is investigated by employing four different datasets. To verify the study, the results are compared with some of the most famous evolutionary trainers: Whale Optimization Algorithm (WO), Bacteria Swarm Foraging Optimization (BFSO), and Ant Colony Optimization (ACO). The outcomes demonstrate that the SFL technique considerably improves the performance of the original LeNet-5 although using this algorithm slightly increases the training computation time. The results also demonstrate that the suggested algorithm presents high accuracy in classification and approximation in its mechanism.
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Affiliation(s)
| | | | - Soroush Sadeghi
- School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | - Ramin Ranjbarzadeh
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Dublin, Ireland
| | | | - Malika Bendechache
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Dublin, Ireland
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31
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Dr.PathFinder: hybrid fuzzing with deep reinforcement concolic execution toward deeper path-first search. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07008-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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32
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Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12052285] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Nowadays, optimization problems are solved through meta-heuristic algorithms based on stochastic search approaches borrowed from mimicking natural phenomena. Notwithstanding their successful capability to handle complex problems, the No-Free Lunch Theorem by Wolpert and Macready (1997) states that there is no ideal algorithm to deal with any kind of problem. This issue arises because of the nature of these algorithms that are not properly mathematics-based, and the convergence is not ensured. In the present study, a variant of the well-known swarm-based algorithm, the Particle Swarm Optimization (PSO), is developed to solve constrained problems with a different approach to the classical penalty function technique. State-of-art improvements and suggestions are also adopted in the current implementation (inertia weight, neighbourhood). Furthermore, a new local search operator has been implemented to help localize the feasible region in challenging optimization problems. This operator is based on hybridization with another milestone meta-heuristic algorithm, the Evolutionary Strategy (ES). The self-adaptive variant has been adopted because of its advantage of not requiring any other arbitrary parameter to be tuned. This approach automatically determines the parameters’ values that govern the Evolutionary Strategy simultaneously during the optimization process. This enhanced multi-strategy PSO is eventually tested on some benchmark constrained numerical problems from the literature. The obtained results are compared in terms of the optimal solutions with two other PSO implementations, which rely on a classic penalty function approach as a constraint-handling method.
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Application of hybrid metaheuristic with Levenberg-Marquardt algorithm for 6-dimensional magnetic localization. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09418-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Empereur-Mot C, Capelli R, Perrone M, Caruso C, Doni G, Pavan GM. Automatic multi-objective optimization of coarse-grained lipid force fields using SwarmCG. J Chem Phys 2022; 156:024801. [PMID: 35032979 DOI: 10.1063/5.0079044] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The development of coarse-grained (CG) molecular models typically requires a time-consuming iterative tuning of parameters in order to have the approximated CG models behave correctly and consistently with, e.g., available higher-resolution simulation data and/or experimental observables. Automatic data-driven approaches are increasingly used to develop accurate models for molecular dynamics simulations. However, the parameters obtained via such automatic methods often make use of specifically designed interaction potentials and are typically poorly transferable to molecular systems or conditions other than those used for training them. Using a multi-objective approach in combination with an automatic optimization engine (SwarmCG), here, we show that it is possible to optimize CG models that are also transferable, obtaining optimized CG force fields (FFs). As a proof of concept, here, we use lipids for which we can avail reference experimental data (area per lipid and bilayer thickness) and reliable atomistic simulations to guide the optimization. Once the resolution of the CG models (mapping) is set as an input, SwarmCG optimizes the parameters of the CG lipid models iteratively and simultaneously against higher-resolution simulations (bottom-up) and experimental data (top-down references). Including different types of lipid bilayers in the training set in a parallel optimization guarantees the transferability of the optimized lipid FF parameters. We demonstrate that SwarmCG can reach satisfactory agreement with experimental data for different resolution CG FFs. We also obtain stimulating insights into the precision-resolution balance of the FFs. The approach is general and can be effectively used to develop new FFs and to improve the existing ones.
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Affiliation(s)
- Charly Empereur-Mot
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
| | - Riccardo Capelli
- Politecnico di Torino, Department of Applied Science and Technology, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Mattia Perrone
- Politecnico di Torino, Department of Applied Science and Technology, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Cristina Caruso
- Politecnico di Torino, Department of Applied Science and Technology, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Giovanni Doni
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
| | - Giovanni M Pavan
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
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35
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A systematic overview of developments in differential evolution and particle swarm optimization with their advanced suggestion. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02803-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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36
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A Novel Path Planning Optimization Algorithm Based on Particle Swarm Optimization for UAVs for Bird Monitoring and Repelling. Processes (Basel) 2021. [DOI: 10.3390/pr10010062] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting become inefficient in the long run, requiring high maintenance and reducing mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A novel path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this paper. This path planning optimization algorithm aims to manage the drone’s distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm’s performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on battery management and height influence. All cases were tested in the three possible situations: same incidence rate, different rates, and different rates with no bird damage to fruit crops. The field tests were also essential to understand the algorithm’s behavior of the path planning algorithm in the UAV, showing that there is less efficiency with fewer points of interest, but this does not correlate with the flight time. In addition, there is no association between the maximum horizontal speed and the flight time, which means that the function to calculate the total distance for path planning needs to be adjusted. Thus, the proposed algorithm presents promising results with an outstanding reduced average error in the total distance for the path planning obtained and low execution time, being suited for this and other applications.
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Tang X, Liu J, Zhu J, Zhou L, Zhang Y. Multi-swarm UPSO algorithm based on seed strategy for atomic clusters structure optimization. Comput Biol Chem 2021; 95:107598. [PMID: 34781248 DOI: 10.1016/j.compbiolchem.2021.107598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/13/2021] [Accepted: 10/29/2021] [Indexed: 10/19/2022]
Abstract
Particle Swarm Optimization (PSO) algorithm is prone to get trapped in local optima and insufficient information exchange among particles. To solve this problem, this paper proposes a Multi-swarm Unified Particle Swarm Optimization algorithm based on Seed Strategy (SS-DMS-UPSO) to optimize the atomic clusters structure. In this algorithm, the population is divided into some sub-populations evolving randomly and evenly, and each sub-population uses UPSO algorithm with different unification factors to evolve independently in parallel. After a certain number of independent evolution, the particles of all sub-populations are merged into a new population, and the population is again randomly divided into average sub-populations. Iterate the algorithm repeatedly in this way. And finally the global best particle can be obtained. The experimental results show that the SS-DMS-UPSO algorithm can search for the optimal structure or extremely similar optimal structure for atomic clusters with atomic numbers between 2 and 31. For atomic clusters with atomic numbers between 32 and 35, the algorithm can find its approximate optimal structure. Compared with other algorithms, the difference between the lowest energy value and the ideal energy value obtained by the SS-DMS-UPSO algorithm is much smaller. It means that its optimal structure of the atomic clusters is closer to the stable structure, and the algorithm is more stable, which proves the effectiveness of the SS-DMS-UPSO algorithm.
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Affiliation(s)
- Xinghua Tang
- Information Engineering College, Shanghai Maritime University, Shanghai, China.
| | - Jing Liu
- Information Engineering College, Shanghai Maritime University, Shanghai, China.
| | - Jingjing Zhu
- Information Engineering College, Shanghai Maritime University, Shanghai, China
| | - Lihai Zhou
- Information Engineering College, Shanghai Maritime University, Shanghai, China
| | - Yining Zhang
- Information Engineering College, Shanghai Maritime University, Shanghai, China
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39
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Hill Climb Modular Assembler Encoding: Evolving Modular Neural Networks of fixed modular architecture. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107493] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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40
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A Cascade Flexible Neural Forest Model for Cancer Subtypes Classification on Gene Expression Data. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:6480456. [PMID: 34650605 PMCID: PMC8510824 DOI: 10.1155/2021/6480456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/22/2021] [Accepted: 09/20/2021] [Indexed: 12/15/2022]
Abstract
The correct classification of cancer subtypes is of great significance for the in-depth study of cancer pathogenesis and the realization of accurate treatment for cancer patients. In recent years, the classification of cancer subtypes using deep neural networks and gene expression data has become a hot topic. However, most classifiers may face the challenges of overfitting and low classification accuracy when dealing with small sample size and high-dimensional biological data. In this paper, the Cascade Flexible Neural Forest (CFNForest) Model was proposed to accomplish cancer subtype classification. CFNForest extended the traditional flexible neural tree structure to FNT Group Forest exploiting a bagging ensemble strategy and could automatically generate the model's structure and parameters. In order to deepen the FNT Group Forest without introducing new hyperparameters, the multilayer cascade framework was exploited to design the FNT Group Forest model, which transformed features between levels and improved the performance of the model. The proposed CFNForest model also improved the operational efficiency and the robustness of the model by sample selection mechanism between layers and setting different weights for the output of each layer. To accomplish cancer subtype classification, FNT Group Forest with different feature sets was used to enrich the structural diversity of the model, which make it more suitable for processing small sample size datasets. The experiments on RNA-seq gene expression data showed that CFNForest effectively improves the accuracy of cancer subtype classification. The classification results have good robustness.
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41
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Learnability and robustness of shallow neural networks learned by a performance-driven BP and a variant of PSO for edge decision-making. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06019-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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42
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A Novel Three-parameter Weibull Distribution Parameter Estimation Using Chaos Simulated Annealing Particle Swarm Optimization in Civil Aircraft Risk Assessment. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05467-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2021. [DOI: 10.3390/make3030035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics, and sentiment analysis, to name a few. It is therefore important to verify the effectiveness of the clustering algorithm in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of two popular clustering mechanisms by comparing their performance over publicly available benchmarking data sets that capture a variety of data point distributions as well as the number of attributes, especially from a computational point of view by incorporating techniques that alleviate some of the issues that plague these algorithms. Sensitivity to initialization conditions and stagnation to local minima are explored. Further, an implementation of a feedforward neural network utilizing a fully connected topology in particle swarm optimization is introduced. This serves to be a guided random search technique for the neural network weight optimization. The algorithms utilized here are studied and compared, from which their applications are explored. The study aims to provide a handy reference for practitioners to both learn about and verify benchmarking results on commonly used real-world data sets from both a supervised and unsupervised point of view before application in more tailored, complex problems.
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Optimized Control of Virtual Coupling at Junctions: A Cooperative Game-Based Approach. ACTUATORS 2021. [DOI: 10.3390/act10090207] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Recently, virtual coupling has aroused increasing interest in regard to achieving flexible and on-demand train operations. However, one of the main challenges in increasing the throughput of a train network is to couple trains quickly at junctions. Pre-programmed train operation strategies cause trains to decelerate or stop at junctions. Such strategies can reduce the coupling efficiency or even cause trains to fail to reach coupled status. To fill this critical gap, this paper proposes a cooperative game model to represent train coupling at junctions and adopts the Shapley theorem to solve the formulated game. Due to the discrete and high-dimensional characteristics of the model, the optimal solution method is non-convex and is difficult to solve in a reasonable amount of time. To find optimal operation strategies for large-scale models in a reasonable amount of time, we propose an improved particle swarm optimization algorithm by introducing self-adaptive parameters and a mutation method. This paper compares the strategy for train coupling at junctions generated by the proposed method with two naive strategies and unimproved particle swarm optimization. The results show that the operation time was reduced by using the proposed cooperative game-based optimization approach.
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45
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Qawqzeh Y, Alharbi MT, Jaradat A, Abdul Sattar KN. A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments. PeerJ Comput Sci 2021; 7:e696. [PMID: 34541313 PMCID: PMC8409329 DOI: 10.7717/peerj-cs.696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/05/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. METHODOLOGY SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015-2021) that belongs to SI algorithms are reviewed and summarized. RESULTS It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. CONCLUSIONS The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.
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Affiliation(s)
- Yousef Qawqzeh
- Department of Computer Science and Engineering, Hafr Al Batin University, Hafr AL Batin, Saudi Arabia
| | - Mafawez T. Alharbi
- Department of Natural and Applied Sciences, Buraydah Community College, Qassim University, Buraydeh, Qassim, Saudi Arabia
| | - Ayman Jaradat
- Computer Science and Information Department, Majmaah University, AlZulfi, Riyadh, Saudi Arabia
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Weiel M, Götz M, Klein A, Coquelin D, Floca R, Schug A. Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00366-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
AbstractMolecular simulations are a powerful tool to complement and interpret ambiguous experimental data on biomolecules to obtain structural models. Such data-assisted simulations often rely on parameters, the choice of which is highly non-trivial and crucial to performance. The key challenge is weighting experimental information with respect to the underlying physical model. We introduce FLAPS, a self-adapting variant of dynamic particle swarm optimization, to overcome this parameter selection problem. FLAPS is suited for the optimization of composite objective functions that depend on both the optimization parameters and additional, a priori unknown weighting parameters, which substantially influence the search-space topology. These weighting parameters are learned at runtime, yielding a dynamically evolving and iteratively refined search-space topology. As a practical example, we show how FLAPS can be used to find functional parameters for small-angle X-ray scattering-guided protein simulations.
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Abstract
The problem of real-time optimal guidance is extremely important for successful autonomous missions. In this paper, the last phases of autonomous lunar landing trajectories are addressed. The proposed guidance is based on the Particle Swarm Optimization, and the differential flatness approach, which is a subclass of the inverse dynamics technique. The trajectory is approximated by polynomials and the control policy is obtained in an analytical closed form solution, where boundary and dynamical constraints are a priori satisfied. Although this procedure leads to sub-optimal solutions, it results in beng fast and thus potentially suitable to be used for real-time purposes. Moreover, the presence of craters on the lunar terrain is considered; therefore, hazard detection and avoidance are also carried out. The proposed guidance is tested by Monte Carlo simulations to evaluate its performances and a robust procedure, made up of safe additional maneuvers, is introduced to counteract optimization failures and achieve soft landing. Finally, the whole procedure is tested through an experimental facility, consisting of a robotic manipulator, equipped with a camera, and a simulated lunar terrain. The results show the efficiency and reliability of the proposed guidance and its possible use for real-time sub-optimal trajectory generation within laboratory applications.
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48
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Hybrid Artificial Intelligence HFS-RF-PSO Model for Construction Labor Productivity Prediction and Optimization. ALGORITHMS 2021. [DOI: 10.3390/a14070214] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper presents a novel approach, using hybrid feature selection (HFS), machine learning (ML), and particle swarm optimization (PSO) to predict and optimize construction labor productivity (CLP). HFS selects factors that are most predictive of CLP to reduce the complexity of CLP data. Selected factors are used as inputs for four ML models for CLP prediction. The study results showed that random forest (RF) obtains better performance in mapping the relationship between CLP and selected factors affecting CLP, compared with the other three models. Finally, the integration of RF and PSO is developed to identify the maximum CLP value and the optimum value of each selected factor. This paper introduces a new hybrid model named HFS-RF-PSO that addresses the main limitation of existing CLP prediction studies, which is the lack of capacity to optimize CLP and its most predictive factors with respect to a construction company’s preferences, such as a targeted CLP. The major contribution of this paper is the development of the hybrid HFS-RF-PSO model as a novel approach for optimizing factors that influence CLP and identifying the maximum CLP value.
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Bastos G, Sales L, Di Cesare N, Tayeb A, Le Cam JB. Inverse-Pagerank-particle swarm optimisation for inverse identification of hyperelastic models: a feasibility study. J RUBBER RES 2021. [DOI: 10.1007/s42464-021-00113-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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50
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Alnajjar IA, Mahmuddin M. Feature indexing and search optimization for enhancing the forensic analysis of mobile cloud environment. INFORMATION SECURITY JOURNAL: A GLOBAL PERSPECTIVE 2021. [DOI: 10.1080/19393555.2020.1839605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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