Editorial Board Members' Collection Series: Deep Learning for Sensor-Based Automatic Detection

Dear Colleagues,

Artificial intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of machine learning (ML) paradigms, it becomes convenient for researchers to predict the outcome, based on available information. Recently, deep learning (DL) has achieved outstanding performance in academic and industrial research, and is considered an important tool in a wide range of applications, such as computer vision, natural language processing, fraud detection, autonomous vehicles, healthcare-based systems and forecasting systems. However, it is still not clear how information is delivered through DL models, and how DL models work towards rapid, safe and robust prediction. Hence, experts/users wish also to know an interpretation of a DL model rather than a black-box nature performance, especially in critical humans applications.

This Special Issue aims to present high-quality, high-impact and original papers on the most recent and upcoming advancements in sensor development as well as the use of advanced deep learning systems for automatic-detection-based techniques in a variety of critical applications. These applications include medical image diagnosis and analysis, intrusion detection in IoT, smart sensors for environmental pollution management, smart sensors for precision agriculture and food science, biology data analysis, satellite-imagery-based vision systems for urban planning and defense applications, transportation-based applications (such as automatic detection of unexpected accidents or automotive perception), and DL-based systems for fault diagnosis in electromechanical systems.

Dr. Vassilis S. Kodogiannis
Prof. Dr. Stefania Perri
Guest Editors

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