Gas Leakage Detection Using Tiny Machine Learning DOI Open Access

Majda El Barkani,

Nabil Benamar, Hanaa Talei

и другие.

Electronics, Год журнала: 2024, Номер 13(23), С. 4768 - 4768

Опубликована: Дек. 2, 2024

Gas leakage detection is a critical concern in both industrial and residential settings, where real-time systems are essential for quickly identifying potential hazards preventing dangerous incidents. Traditional often rely on centralized data processing, which can lead to delays scalability issues. To overcome these limitations, this study, we present solution based tiny machine learning (TinyML) process directly devices. TinyML has the execute algorithms locally, real time, using devices, such as microcontrollers, ensuring faster more efficient responses dangers. Our approach combines an MLX90640 thermal camera with two optimized convolutional neural networks (CNNs), MobileNetV1 EfficientNet-B0, deployed Arduino Nano 33 BLE Sense. The results show that our system not only provides analytics but does so high accuracy—88.92% 91.73% EfficientNet-B0—while achieving inference times of 1414 milliseconds just 124.8 KB memory. Compared existing solutions, edge-based overcomes common challenges related latency scalability, making it reliable, fast, option. This work demonstrates low-cost, scalable gas be widely enhance safety various environments. By integrating cutting-edge models affordable IoT aim make accessible, regardless financial pave way further innovation environmental monitoring solutions.

Язык: Английский

Integrating IoB in Sustainable Supply Chain Management of Composites for Enhancing Efficiency and Reducing Waste DOI
Deepak Gupta,

Deepanshu Arora,

Arun Kumar Chaudhary

и другие.

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Год журнала: 2025, Номер unknown, С. 301 - 336

Опубликована: Янв. 22, 2025

The incorporation of Internet Behavior (IoB) with sustainable supply chain management can provide a game-changing solution to drive operational efficiency and reduce waste across the composite materials industry. During this chapter, transformative potentials IoB technologies including Things (IoT) sensors predictive analytics but also blockchain material sourcing, utilization traceability were examined in systematic manner. empirical case studies Siemens Gamesa Boeing show how decrease waste, improve transparency as well enhance overall real-world situations. It then defines KPI applied measure integration aimed at fulfilling sustainability goals. This chapter outlines challenges suggests opportunities that offer crucial insights for researchers practitioners who intend digitally innovate their operations towards behaviours management.

Язык: Английский

Процитировано

0
Managing Ethical and Regulatory Challenges in Insights From IoB Applications DOI
Deepak Gupta, Deepak Agarwal,

Arun Kumar Chaudhary

и другие.

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Год журнала: 2025, Номер unknown, С. 497 - 524

Опубликована: Янв. 22, 2025

Internet of Behavior (IoB) is a powerful technology that uses human related insight as an data to monitor, evaluate and even alter behavior through connected devices & appended applications. But the rising IoB wave comes with heavy ethical regulatory hurdles, especially in relation privacy, security, informed consent algorithmic bias. This chapter looks at challenges navigating these questions, arguing for rigorous structure place helps maintain guidelines behaviorial usage. Facebook-Cambridge Analytica scandal Fitbit health privacy are real-world case studies offer indications just how bad things could get almost zero protections will ensure particular safe. Global legislations AI frameworks like GDPR, HIPAA, CCPA necessary any organization abide by legions regulations. Chapter concludes exploration stakeholder theory risk management, well measures designed compliance companies part which it includes consequences

Язык: Английский

Процитировано

0
Effective Gas Classification Using Singular Spectrum Analysis and Random Forest in Electronic Nose Applications DOI
Yuntao Wu, Jinchang Ren, Rongjun Chen

и другие.

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 283 - 293

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Gas Leakage Detection Using Tiny Machine Learning DOI Open Access

Majda El Barkani,

Nabil Benamar, Hanaa Talei

и другие.

Electronics, Год журнала: 2024, Номер 13(23), С. 4768 - 4768

Опубликована: Дек. 2, 2024

Gas leakage detection is a critical concern in both industrial and residential settings, where real-time systems are essential for quickly identifying potential hazards preventing dangerous incidents. Traditional often rely on centralized data processing, which can lead to delays scalability issues. To overcome these limitations, this study, we present solution based tiny machine learning (TinyML) process directly devices. TinyML has the execute algorithms locally, real time, using devices, such as microcontrollers, ensuring faster more efficient responses dangers. Our approach combines an MLX90640 thermal camera with two optimized convolutional neural networks (CNNs), MobileNetV1 EfficientNet-B0, deployed Arduino Nano 33 BLE Sense. The results show that our system not only provides analytics but does so high accuracy—88.92% 91.73% EfficientNet-B0—while achieving inference times of 1414 milliseconds just 124.8 KB memory. Compared existing solutions, edge-based overcomes common challenges related latency scalability, making it reliable, fast, option. This work demonstrates low-cost, scalable gas be widely enhance safety various environments. By integrating cutting-edge models affordable IoT aim make accessible, regardless financial pave way further innovation environmental monitoring solutions.

Язык: Английский

Процитировано

1