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Hossain, Ikram
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : Control Systems and Optimization Letters

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The Role of IoT and Artificial Intelligence in Advancing Nanotechnology: A Brief Review Islam, Md Monirul; Hossain, Ikram; Martin, Md. Hasnat Hanjala
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.124

Abstract

The main objective of this research is to review the importance of IoT and Artificial Intelligence for Nanotechnology. Several industries are seeing notable breakthroughs due to the convergence of nanotechnology, artificial intelligence, and the Internet of Things. This succinct overview examines how IoT and AI are essential for improving the capabilities and uses of nanotechnology. Real-time monitoring, data gathering, and control at the nanoscale are made possible by IoT, improving the accuracy and efficiency of operations including industrial manufacturing, healthcare monitoring, and environmental sensing. The design, optimization, and predictive modeling of nanomaterials and systems are made easier by artificial intelligence (AI), which provides strong tools for evaluating the complicated data produced by nanoscale devices. The convergence of IoT, AI, and nanotechnology facilitates the creation of intelligent systems that possess the ability to monitor themselves and make decisions on their own. IoT and AI amplify the potential of nanotechnology by enabling real-time data collection, advanced data analytics, and autonomous decision-making, with vast applications across industries from healthcare to energy. Even while this integration seems promising, there are still issues to be resolved, such as privacy issues, data security, and technical difficulties in creating dependable nanoscale Internet of Things devices. It is anticipated that as research advances, the confluence of these technologies will transform industries including smart manufacturing, environmental monitoring, and medicine, making this a critical area for future innovation.
Potential Applications and Limitations of Artificial Intelligence in Remote Sensing Data Interpretation: A Case Study Hossain, Ikram; Islam, Md Monirul; Martin, Md. Hasnat Hanjala
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.128

Abstract

This research aims to comprehensively review the applications and limitations of artificial intelligence (AI) in interpreting remote sensing data, highlighting its potential through a detailed case study. AI technologies, particularly machine learning and deep learning, have shown remarkable promise in enhancing the accuracy and efficiency of data interpretation tasks in remote sensing, such as anomaly detection, change detection, and land cover classification. AI-driven analysis has a lot of options because to remote sensing, which can gather massive amounts of environmental data via drones, satellites, and other aerial platforms. AI approaches, in particular machine learning and deep learning, have demonstrated potential to improve the precision and effectiveness of data interpretation tasks, including anomaly identification, change detection, and land cover classification. Nevertheless, the research also points to a number of drawbacks, including challenges related to data quality, the need for large labeled datasets, and the risk of model overfitting. Furthermore, the intricacy of AI models can occasionally result in a lack of transparency, which makes it challenging to understand and accept the outcomes. The case study emphasizes the necessity for a balanced strategy that makes use of the advantages of both AI and conventional techniques by highlighting both effective applications of AI in remote sensing and areas where traditional methods still perform better than AI. This research concludes that while AI holds significant potential for advancing remote sensing data interpretation, careful consideration of its limitations is crucial for its effective application in real-world scenarios.
Co-Authors ISLAM, MD MONIRUL Martin, Md. Hasnat Hanjala