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Koide-Majima N, Nishimoto S, Majima K. Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation. Neural Netw 2024; 170:349-363. [PMID: 38016230 DOI: 10.1016/j.neunet.2023.11.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 09/22/2023] [Accepted: 11/08/2023] [Indexed: 11/30/2023]
Abstract
Visual images observed by humans can be reconstructed from their brain activity. However, the visualization (externalization) of mental imagery is challenging. Only a few studies have reported successful visualization of mental imagery, and their visualizable images have been limited to specific domains such as human faces or alphabetical letters. Therefore, visualizing mental imagery for arbitrary natural images stands as a significant milestone. In this study, we achieved this by enhancing a previous method. Specifically, we demonstrated that the visual image reconstruction method proposed in the seminal study by Shen et al. (2019) heavily relied on low-level visual information decoded from the brain and could not efficiently utilize the semantic information that would be recruited during mental imagery. To address this limitation, we extended the previous method to a Bayesian estimation framework and introduced the assistance of semantic information into it. Our proposed framework successfully reconstructed both seen images (i.e., those observed by the human eye) and imagined images from brain activity. Quantitative evaluation showed that our framework could identify seen and imagined images highly accurately compared to the chance accuracy (seen: 90.7%, imagery: 75.6%, chance accuracy: 50.0%). In contrast, the previous method could only identify seen images (seen: 64.3%, imagery: 50.4%). These results suggest that our framework would provide a unique tool for directly investigating the subjective contents of the brain such as illusions, hallucinations, and dreams.
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Affiliation(s)
- Naoko Koide-Majima
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka 565-0871, Japan; Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan
| | - Shinji Nishimoto
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka 565-0871, Japan; Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan; Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan
| | - Kei Majima
- Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan; JST PRESTO, Saitama 332-0012, Japan.
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Xie X, Xia F, Wu Y, Liu S, Yan K, Xu H, Ji Z. A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0039. [PMID: 37228513 PMCID: PMC10204742 DOI: 10.34133/plantphenomics.0039] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/28/2023] [Indexed: 05/27/2023]
Abstract
Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition. However, it has limited interpretability for deep features. With the transfer of expert knowledge, handcrafted features provide a new way for personalized diagnosis of plant diseases. However, irrelevant and redundant features lead to high dimensionality. In this study, we proposed a swarm intelligence algorithm for feature selection [salp swarm algorithm for feature selection (SSAFS)] in image-based plant disease detection. SSAFS is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of features. To verify the effectiveness of the developed SSAFS algorithm, we conducted experimental studies using SSAFS and 5 metaheuristic algorithms. Several evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from PlantVillage. Experimental results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms, confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image classification. This computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.
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Affiliation(s)
- Xiaojun Xie
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
- Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Fei Xia
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Yufeng Wu
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Bioinformatics Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Shouyang Liu
- Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Ke Yan
- Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
| | - Huanliang Xu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Zhiwei Ji
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
- Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
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Parallel evolutionary algorithm for Water Distribution Network Design, using the Masters-Students model in distributed environment. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.109986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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4
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Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method. MATHEMATICS 2022. [DOI: 10.3390/math10020274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms.
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Torre-Bastida AI, Díaz-de-Arcaya J, Osaba E, Muhammad K, Camacho D, Del Ser J. Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions. Neural Comput Appl 2021:1-31. [PMID: 34366573 PMCID: PMC8329000 DOI: 10.1007/s00521-021-06332-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.
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Affiliation(s)
| | - Josu Díaz-de-Arcaya
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Eneko Osaba
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul, 143-747 Republic of Korea
| | - David Camacho
- Universidad Politécnica de Madrid, 28031 Madrid, Spain
| | - Javier Del Ser
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
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Abstract
Extensile fluidic artificial muscles (EFAMs) are soft actuators known for their large ranges of extension, low weight, and blocked forces comparable to those of pneumatic cylinders. EFAMs have yet to be studied in a way that thoroughly focuses on their manufacturing, experimental characterization, and modeling. A fabrication method was developed for production of two EFAMs. The quasi-static axial force response of EFAMs to varying displacement was measured by testing two specimens under isobaric conditions over a pressure range of 103.4–517.1 kPa (15–75 psi) with 103.4 kPa (15 psi) increments. The muscles were characterized by a blocked force of 280 N and a maximum stroke of 98% at 517.1 kPa (75 psi). A force-balance model was used to analyze EFAM response. Prior work employing the force-balance approach used hyper-elastic constitutive models based on polynomial expressions. In this study, these models are validated for EFAMs, and new constitutive models are proposed that better represent the measured stress values of rubber as a function of strain. These constitutive models are compared in terms of accuracy when estimating pressure-dependent stress–strain relationships of the bladder material. The analysis demonstrates that the new hyper-elastic stress models have an error 5% smaller than models previously employed for EFAMs for the same number of coefficients. Finally, the analysis suggests that the new stress functions have smaller errors than the polynomial stress model with the same number of coefficients, guarantee material stability, and are more conservative about the stress values for strains outside of the testing range.
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Cheng S, Ma L, Lu H, Lei X, Shi Y. Evolutionary computation for solving search-based data analytics problems. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09882-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Tan Y, Shi Y, Tuba M. Swarm Intelligence in Data Science: Applications, Opportunities and Challenges. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7354777 DOI: 10.1007/978-3-030-53956-6_1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Swarm Intelligence (SI) algorithms have been proved to be a comprehensive method to solve complex optimization problems by simulating the emergence behaviors of biological swarms. Nowadays, data science is getting more and more attention, which needs quick management and analysis of massive data. Most traditional methods can only be applied to continuous and differentiable functions. As a set of population-based approaches, it is proven by some recent research works that the SI algorithms have great potential for relevant tasks in this field. In order to gather better insight into the utilization of these methods in data science and to provide a further reference for future researches, this paper focuses on the relationship between data science and swarm intelligence. After introducing the mainstream swarm intelligence algorithms and their common characteristics, both the theoretical and real-world applications in the literature which utilize the swarm intelligence to the related domains of data analytics are reviewed. Based on the summary of the existing works, this paper also analyzes the opportunities and challenges in this field, which attempts to shed some light on designing more effective algorithms to solve the problems in data science for real-world applications.
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Affiliation(s)
- Ying Tan
- Peking University, Beijing, China
| | - Yuhui Shi
- Southern University of Science and Technology, Shenzhen, China
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Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data. CYBERNETICS AND INFORMATION TECHNOLOGIES 2018. [DOI: 10.2478/cait-2018-0031] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Abstract
In modern times there is an increasing trend of applications for handling Big data. However, negotiating with the concepts of the Big data is an extremely difficult issue today. The MapReduce framework has been in focus recently for serious consideration. The aim of this study is to get the task-scheduling over Big data using Hadoop. Initially, we prioritize the tasks with the help of k-means clustering algorithm. Then, the MapReduce framework is employed. The available resource is optimally selected using optimization technique in map-phase. The proposed method uses the FireFly Algorithm and BAT algorithms (FFABAT) for choosing the optimal resource with minimum cost value. The bat-inspired algorithm is a meta-heuristic optimization method developed by Xin-She Yang (2010). This bat algorithm is established on the echo-location behaviour of micro-bats with variable pulse rates of emission and loudness. Finally, the tasks are scheduled with the optimal resource in reducer-phase and stored in the cloud. The performance of the algorithm is analysed, based on the total cost, time and memory utilization.
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He M, Wang Z, Leach M, Jiang Z, Lim EG. Bio-inspired optimization algorithms applied to rectenna design. BIG DATA ANALYTICS 2018. [DOI: 10.1186/s41044-017-0026-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-3278-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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