Advancing Smart Sensor Networks and Carbon-Based Biosensors Through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation
This research proposes a novel artificial decision-marking framework suitable for modern smart sensor networks and carbon-based biosensor systems which deals with uncertainty and the peculiarity of the data. To achieve the goals, the approach relies on the optoelectronic properties of carbon nanomat...
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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/10988855/ |
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| author | Keliang Luo |
| author_facet | Keliang Luo |
| author_sort | Keliang Luo |
| collection | DOAJ |
| description | This research proposes a novel artificial decision-marking framework suitable for modern smart sensor networks and carbon-based biosensor systems which deals with uncertainty and the peculiarity of the data. To achieve the goals, the approach relies on the optoelectronic properties of carbon nanomaterials and already combines AI and deep learning. This raises the level of sensing, real-time data fusion, and adaptive decision-making within the environment to unprecedented levels. To enhance sensor evaluation in sensor manufacturing, we take a step further and implement Dombi Interval Valued Intuitionistic Fuzzy Sets (D-IVIFs) and work with Dombi Interval Valued Intuitionistic Fuzzy Dombi Bonferroni Mean (D-IVIFDBM) for hierarchical decision-making. Moreover, two Multi-Attribute Group Decision Making (MAGDM) methods IVIFWDBM and IVIFWDGBM are developed for expert evaluation aggregation in selection tasks of different criteria to enhance selection accuracy. These experiments did demonstrate improvements in decision accuracy as well as better overall performance than conventional models of comparison. The numerical experiments showed that these methods are more effective than traditional MAGDM models. Introducing such an advanced decision framework in deep learning systems enables improved adaptability, security, and resilience in next-generation sensor networks and biosensor devices. The new paradigm enables real-time signal interpretation and adaptive learning and provides effective solutions in harsh environments where severe fluctuations are common. This study assists in addressing the discrepancy between conceptual decision models and actual physical achievements in smart sensing, which will foster the development of more sophisticated and efficient optoelectronic devices. |
| format | Article |
| id | doaj-art-ba2806295ab043d3b8eaee70370f7944 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ba2806295ab043d3b8eaee70370f79442025-08-20T03:47:33ZengIEEEIEEE Access2169-35362025-01-0113860838610910.1109/ACCESS.2025.356756110988855Advancing Smart Sensor Networks and Carbon-Based Biosensors Through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device InnovationKeliang Luo0https://orcid.org/0009-0002-8446-6192Software Engineering Department, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Kuala Lumpur, MalaysiaThis research proposes a novel artificial decision-marking framework suitable for modern smart sensor networks and carbon-based biosensor systems which deals with uncertainty and the peculiarity of the data. To achieve the goals, the approach relies on the optoelectronic properties of carbon nanomaterials and already combines AI and deep learning. This raises the level of sensing, real-time data fusion, and adaptive decision-making within the environment to unprecedented levels. To enhance sensor evaluation in sensor manufacturing, we take a step further and implement Dombi Interval Valued Intuitionistic Fuzzy Sets (D-IVIFs) and work with Dombi Interval Valued Intuitionistic Fuzzy Dombi Bonferroni Mean (D-IVIFDBM) for hierarchical decision-making. Moreover, two Multi-Attribute Group Decision Making (MAGDM) methods IVIFWDBM and IVIFWDGBM are developed for expert evaluation aggregation in selection tasks of different criteria to enhance selection accuracy. These experiments did demonstrate improvements in decision accuracy as well as better overall performance than conventional models of comparison. The numerical experiments showed that these methods are more effective than traditional MAGDM models. Introducing such an advanced decision framework in deep learning systems enables improved adaptability, security, and resilience in next-generation sensor networks and biosensor devices. The new paradigm enables real-time signal interpretation and adaptive learning and provides effective solutions in harsh environments where severe fluctuations are common. This study assists in addressing the discrepancy between conceptual decision models and actual physical achievements in smart sensing, which will foster the development of more sophisticated and efficient optoelectronic devices.https://ieeexplore.ieee.org/document/10988855/Modern smart sensor networksbiosensor systems based on carbon materialshybrid neural-fuzzy systems with deep learningDombi operationsinnovations in optoelectronic devices and artificial intelligencesystems of uncertainty modeling |
| spellingShingle | Keliang Luo Advancing Smart Sensor Networks and Carbon-Based Biosensors Through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation IEEE Access Modern smart sensor networks biosensor systems based on carbon materials hybrid neural-fuzzy systems with deep learning Dombi operations innovations in optoelectronic devices and artificial intelligence systems of uncertainty modeling |
| title | Advancing Smart Sensor Networks and Carbon-Based Biosensors Through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation |
| title_full | Advancing Smart Sensor Networks and Carbon-Based Biosensors Through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation |
| title_fullStr | Advancing Smart Sensor Networks and Carbon-Based Biosensors Through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation |
| title_full_unstemmed | Advancing Smart Sensor Networks and Carbon-Based Biosensors Through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation |
| title_short | Advancing Smart Sensor Networks and Carbon-Based Biosensors Through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation |
| title_sort | advancing smart sensor networks and carbon based biosensors through artificial intelligence a deep learning approach to optoelectronic device innovation |
| topic | Modern smart sensor networks biosensor systems based on carbon materials hybrid neural-fuzzy systems with deep learning Dombi operations innovations in optoelectronic devices and artificial intelligence systems of uncertainty modeling |
| url | https://ieeexplore.ieee.org/document/10988855/ |
| work_keys_str_mv | AT keliangluo advancingsmartsensornetworksandcarbonbasedbiosensorsthroughartificialintelligenceadeeplearningapproachtooptoelectronicdeviceinnovation |