Multi-Agent-Based Cognitive Intelligence in Non-Linear Mental Healthcare-Based Situations
This study introduces a novel framework for the early detection of anxiety and depression symptoms through the integration of Ambient Intelligence (AmI) and Multi-Agent Systems (MAS). Leveraging a Belief-Desire-Intention (BDI) reasoning mechanism, our system enables real-time monitoring and interven...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10896654/ |
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| author | Kiran Saleem Misbah Saleem Ahmad Almogren Alanod Almogren Upinder Kaur Salil Bharany Ateeq Ur Rehman |
| author_facet | Kiran Saleem Misbah Saleem Ahmad Almogren Alanod Almogren Upinder Kaur Salil Bharany Ateeq Ur Rehman |
| author_sort | Kiran Saleem |
| collection | DOAJ |
| description | This study introduces a novel framework for the early detection of anxiety and depression symptoms through the integration of Ambient Intelligence (AmI) and Multi-Agent Systems (MAS). Leveraging a Belief-Desire-Intention (BDI) reasoning mechanism, our system enables real-time monitoring and intervention with high precision. Compared to existing methods such as PMMHA, DWDM, MHL, and SMAD, the proposed methodology demonstrates significant improvements in multiple performance metrics. The system achieves an accuracy of 95%, surpassing competing approaches, and reduces latency to under 6 milliseconds for emergent decision-making. It maintains a success rate above 95% while effectively managing energy consumption, which increases non-linearly from 1.0 Joules at 100 KB to 6.1 Joules at 1000 KB of data. This scalable and adaptive approach addresses critical limitations in mental health detection, offering a reliable solution for improving mental healthcare. Future work will focus on testing the framework with publicly available mental health datasets and conducting clinical trials to further validate its efficacy. |
| format | Article |
| id | doaj-art-8d1b3082b4a641399f6ba68ae31babf2 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8d1b3082b4a641399f6ba68ae31babf22025-08-20T03:00:02ZengIEEEIEEE Access2169-35362025-01-0113361623617410.1109/ACCESS.2025.354409610896654Multi-Agent-Based Cognitive Intelligence in Non-Linear Mental Healthcare-Based SituationsKiran Saleem0https://orcid.org/0000-0003-0278-6492Misbah Saleem1Ahmad Almogren2https://orcid.org/0000-0002-8253-9709Alanod Almogren3Upinder Kaur4Salil Bharany5https://orcid.org/0000-0002-2282-0419Ateeq Ur Rehman6https://orcid.org/0000-0001-5203-0621School of Software, Dalian University of Technology, Dalian, ChinaDepartment of Food Science and Technology, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, ThailandDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Biochemistry, College of Science, King Saud University, Riyadh, Saudi ArabiaSchool of Computer Science and Engineering, Lovely Professional University, Phagwara, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, IndiaSchool of Computing, Gachon University, Seongnam-si, Republic of KoreaThis study introduces a novel framework for the early detection of anxiety and depression symptoms through the integration of Ambient Intelligence (AmI) and Multi-Agent Systems (MAS). Leveraging a Belief-Desire-Intention (BDI) reasoning mechanism, our system enables real-time monitoring and intervention with high precision. Compared to existing methods such as PMMHA, DWDM, MHL, and SMAD, the proposed methodology demonstrates significant improvements in multiple performance metrics. The system achieves an accuracy of 95%, surpassing competing approaches, and reduces latency to under 6 milliseconds for emergent decision-making. It maintains a success rate above 95% while effectively managing energy consumption, which increases non-linearly from 1.0 Joules at 100 KB to 6.1 Joules at 1000 KB of data. This scalable and adaptive approach addresses critical limitations in mental health detection, offering a reliable solution for improving mental healthcare. Future work will focus on testing the framework with publicly available mental health datasets and conducting clinical trials to further validate its efficacy.https://ieeexplore.ieee.org/document/10896654/Situation-awarenessenergy consumptionadaptive systemintelligent decision support systemmulti-agent systemBDI reasoning mechanism |
| spellingShingle | Kiran Saleem Misbah Saleem Ahmad Almogren Alanod Almogren Upinder Kaur Salil Bharany Ateeq Ur Rehman Multi-Agent-Based Cognitive Intelligence in Non-Linear Mental Healthcare-Based Situations IEEE Access Situation-awareness energy consumption adaptive system intelligent decision support system multi-agent system BDI reasoning mechanism |
| title | Multi-Agent-Based Cognitive Intelligence in Non-Linear Mental Healthcare-Based Situations |
| title_full | Multi-Agent-Based Cognitive Intelligence in Non-Linear Mental Healthcare-Based Situations |
| title_fullStr | Multi-Agent-Based Cognitive Intelligence in Non-Linear Mental Healthcare-Based Situations |
| title_full_unstemmed | Multi-Agent-Based Cognitive Intelligence in Non-Linear Mental Healthcare-Based Situations |
| title_short | Multi-Agent-Based Cognitive Intelligence in Non-Linear Mental Healthcare-Based Situations |
| title_sort | multi agent based cognitive intelligence in non linear mental healthcare based situations |
| topic | Situation-awareness energy consumption adaptive system intelligent decision support system multi-agent system BDI reasoning mechanism |
| url | https://ieeexplore.ieee.org/document/10896654/ |
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