Architecture of LSTM-RBM.
<div><p>This research addresses the imperative need for efficient underwater exploration in the domain of deep-sea resource development, highlighting the importance of autonomous operations to mitigate the challenges posed by high-stress underwater environments. The proposed approach int...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2024
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| _version_ | 1852024974914617344 |
|---|---|
| author | Manimurugan S. (20314652) |
| author2 | Karthikeyan P. (20314655) Narmatha C. (20314658) Majed M. Aborokbah (20314661) Anand Paul (19646164) Subramaniam Ganesan (18173638) Rajendran T. (20314664) Mohammad Ammad-Uddin (20314667) |
| author2_role | author author author author author author author |
| author_facet | Manimurugan S. (20314652) Karthikeyan P. (20314655) Narmatha C. (20314658) Majed M. Aborokbah (20314661) Anand Paul (19646164) Subramaniam Ganesan (18173638) Rajendran T. (20314664) Mohammad Ammad-Uddin (20314667) |
| author_role | author |
| dc.creator.none.fl_str_mv | Manimurugan S. (20314652) Karthikeyan P. (20314655) Narmatha C. (20314658) Majed M. Aborokbah (20314661) Anand Paul (19646164) Subramaniam Ganesan (18173638) Rajendran T. (20314664) Mohammad Ammad-Uddin (20314667) |
| dc.date.none.fl_str_mv | 2024-11-22T18:25:17Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0313708.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Architecture_of_LSTM-RBM_/27891563 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Microbiology Neuroscience Evolutionary Biology Inorganic Chemistry Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified vanishing gradient problem underwater object detection stress underwater environments star fish object sea resource development restricted boltzmann machine efficient underwater exploration effective feature learning big fish object architecture handles variable achieves enhanced significance proposed approach introduces processing sequences bidirectionally directional long short capturing complex patterns big fish 98 capturing long rbm approach length sequences xlink "> urpc dataset term memory term dependencies robust solution research addresses rbm ). model benefits imperative need hybrid model future contexts findings underscore enhance comprehension comprehensive evaluations challenges posed autonomous operations abstract representations |
| dc.title.none.fl_str_mv | Architecture of LSTM-RBM. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>This research addresses the imperative need for efficient underwater exploration in the domain of deep-sea resource development, highlighting the importance of autonomous operations to mitigate the challenges posed by high-stress underwater environments. The proposed approach introduces a hybrid model for Underwater Object Detection (UOD), combining Bi-directional Long Short-Term Memory (Bi-LSTM) with a Restricted Boltzmann Machine (RBM). Bi-LSTM excels at capturing long-term dependencies and processing sequences bidirectionally to enhance comprehension of both past and future contexts. The model benefits from effective feature learning, aided by RBMs that enable the extraction of hierarchical and abstract representations. Additionally, this architecture handles variable-length sequences, mitigates the vanishing gradient problem, and achieves enhanced significance by capturing complex patterns in the data. Comprehensive evaluations on brackish, and URPC 2020 datasets demonstrate superior performance, with the BiLSTM-RBM model showcasing notable accuracies, such as big fish 98.5 for the big fish object in the brackish dataset and 98 for the star fish object in the URPC dataset. Overall, these findings underscore the BiLSTM-RBM model’s suitability for UOD, positioning it as a robust solution for effective underwater object detection in challenging underwater environments.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_e96f96cddc9c1982e18ae32b4eab20bb |
| identifier_str_mv | 10.1371/journal.pone.0313708.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27891563 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Architecture of LSTM-RBM.Manimurugan S. (20314652)Karthikeyan P. (20314655)Narmatha C. (20314658)Majed M. Aborokbah (20314661)Anand Paul (19646164)Subramaniam Ganesan (18173638)Rajendran T. (20314664)Mohammad Ammad-Uddin (20314667)MicrobiologyNeuroscienceEvolutionary BiologyInorganic ChemistryScience PolicyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedvanishing gradient problemunderwater object detectionstress underwater environmentsstar fish objectsea resource developmentrestricted boltzmann machineefficient underwater explorationeffective feature learningbig fish objectarchitecture handles variableachieves enhanced significanceproposed approach introducesprocessing sequences bidirectionallydirectional long shortcapturing complex patternsbig fish 98capturing longrbm approachlength sequencesxlink ">urpc datasetterm memoryterm dependenciesrobust solutionresearch addressesrbm ).model benefitsimperative needhybrid modelfuture contextsfindings underscoreenhance comprehensioncomprehensive evaluationschallenges posedautonomous operationsabstract representations<div><p>This research addresses the imperative need for efficient underwater exploration in the domain of deep-sea resource development, highlighting the importance of autonomous operations to mitigate the challenges posed by high-stress underwater environments. The proposed approach introduces a hybrid model for Underwater Object Detection (UOD), combining Bi-directional Long Short-Term Memory (Bi-LSTM) with a Restricted Boltzmann Machine (RBM). Bi-LSTM excels at capturing long-term dependencies and processing sequences bidirectionally to enhance comprehension of both past and future contexts. The model benefits from effective feature learning, aided by RBMs that enable the extraction of hierarchical and abstract representations. Additionally, this architecture handles variable-length sequences, mitigates the vanishing gradient problem, and achieves enhanced significance by capturing complex patterns in the data. Comprehensive evaluations on brackish, and URPC 2020 datasets demonstrate superior performance, with the BiLSTM-RBM model showcasing notable accuracies, such as big fish 98.5 for the big fish object in the brackish dataset and 98 for the star fish object in the URPC dataset. Overall, these findings underscore the BiLSTM-RBM model’s suitability for UOD, positioning it as a robust solution for effective underwater object detection in challenging underwater environments.</p></div>2024-11-22T18:25:17ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0313708.g002https://figshare.com/articles/figure/Architecture_of_LSTM-RBM_/27891563CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/278915632024-11-22T18:25:17Z |
| spellingShingle | Architecture of LSTM-RBM. Manimurugan S. (20314652) Microbiology Neuroscience Evolutionary Biology Inorganic Chemistry Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified vanishing gradient problem underwater object detection stress underwater environments star fish object sea resource development restricted boltzmann machine efficient underwater exploration effective feature learning big fish object architecture handles variable achieves enhanced significance proposed approach introduces processing sequences bidirectionally directional long short capturing complex patterns big fish 98 capturing long rbm approach length sequences xlink "> urpc dataset term memory term dependencies robust solution research addresses rbm ). model benefits imperative need hybrid model future contexts findings underscore enhance comprehension comprehensive evaluations challenges posed autonomous operations abstract representations |
| status_str | publishedVersion |
| title | Architecture of LSTM-RBM. |
| title_full | Architecture of LSTM-RBM. |
| title_fullStr | Architecture of LSTM-RBM. |
| title_full_unstemmed | Architecture of LSTM-RBM. |
| title_short | Architecture of LSTM-RBM. |
| title_sort | Architecture of LSTM-RBM. |
| topic | Microbiology Neuroscience Evolutionary Biology Inorganic Chemistry Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified vanishing gradient problem underwater object detection stress underwater environments star fish object sea resource development restricted boltzmann machine efficient underwater exploration effective feature learning big fish object architecture handles variable achieves enhanced significance proposed approach introduces processing sequences bidirectionally directional long short capturing complex patterns big fish 98 capturing long rbm approach length sequences xlink "> urpc dataset term memory term dependencies robust solution research addresses rbm ). model benefits imperative need hybrid model future contexts findings underscore enhance comprehension comprehensive evaluations challenges posed autonomous operations abstract representations |