Introduction

A vision on the robotics system is a camera fitted as sensors to provide images and videos to the controller to make an accurate decision. It is the process of combining the hardware of the camera and deep learning algorithms to act on robots by gathering real-world data. Recognizing real-world data and processing images, and videos require deep adaptive technologies in robots. Robotics withstands intelligence in recent applications like space computing, Health care, object recognition, automatic vehicles, human interactions, disastrous management, etc. Detecting the obstacles in the environment like plastics, pollution, animals, and humans requires special intelligence and training. Machine intelligence becomes prominent in providing excellent solutions for robotic visions. Digital transformation in every field like government services, transport, medical surgery, and health diagnosis, transforms humans into robotic vision. Deep learning-based adaptive techniques for processing applications with 2D and 3D images are necessary for military security systems.

Industrial revolution 4.0 is fully blown by IoT, artificial intelligence, Robotics, and Evolutionary techniques. Several industrial applications require adaptive learning techniques for handling all types of environments. Artificial intelligence boosts robotic visions through machine learning, deep learning, and pattern recognition techniques. Robotic experts expect deep learning to support faster communication and decision-making. AI has the potential to make faster intelligent decisions using image processing algorithms. Unmanned aerial vehicles and drones act as active robots in object detection and security applications. Sometimes captured data required remote storage for storing data. Cloud computing, the edge computing model for handling robotic vision is a big challenge. Robotic vision is similar to machine vision together grows artificial vision technologies.

The special issue focused on image and video data processing techniques for adaptive robots. Various solution using deep adaptive algorithms for image and video analysis is required. Computation resources like storage, energy consumption, and communication cost for robotic applications will be achieved using tremendous technologies. Society development with smart cities, smart parking, smart food serving robots, and smart security systems require advanced machine vision techniques for training robots. Also, technology must be made robots to adapt to various new unexpected situations.

Recommended topics (but not limited to)

The primary objective of this special issue is to provide a platform for researchers and scientists to share and discuss the most recent innovations, trends, and concerns, as well as practical challenges and solutions adopted in the field of scalable computing algorithms for material sciences.

• Deep learning-based adaptive image and video processing
• Robotics data storage and processing
• Cloud Robots for handling big data applications
• Adaptive Deep learning for object detection
• AI-based image segmentation, classification, and clustering techniques
• Adaptive deep learning Robotic vision security system
• Robots lighting techniques and integral image formations
• Robotics and machine vision for smart cities and smart vehicles
• Deep adaptive situation Handling Robotic vision for healthcare and surgeon
• Deep visualization for machine and computer visions
• Internet of Medical Things (IoMT), Internet of things (IoT) in Robotic vision
• Adaptive image processing for industrial applications
• Drones communication vision technologies
• Nano-Robots in computer Visions
• Robotics in Wireless sensor networks
• Smart robots for environmental sustainability

Important dates

Submission deadline: 15 November, 2024 (stopped in May 2024 due to submission problems)

Authors notification:  15 February, 2025

Revision submission: 15 April, 2025

Submission guidelines

Original and unpublished works on any of the topics aforementioned or related are welcome. The SCPE journal has a rigorous peer-reviewing process and papers will be reviewed by at least two referees. All submitted papers must be formatted according to the journal's instructions, which can be found here.

During submission please select a Special Issue that you want to submit to and provide this information in the Comments for the Editor field.

Guest Editors

Lead: Dr. Sathishkumar V E, Lecurer Department of Computing and Information Systems, Sunway University, 47500, Malaysia, email: sathishv@sunway.edu.my

Sathishkumar V E is a Lecturer in the Department of Computing and Information Systems at Sunway University, Malaysia. He has held significant roles, including Postdoctoral researcher positions at the Department of Software Engineering, Jeonbuk National University, and the Department of Industrial Engineering, Hanyang University in the Republic of Korea. In 2021, he served as an Assistant Professor in the Department of Computer Science and Engineering at Kongu Engineering College, India. He earned his Bachelor's degree in Information Technology from Madras Institute of Technology, Anna University in 2013, and a Master's degree in Biometrics and Cyber Security from PSG College of Technology in 2015. Through the Korean Government Scholarship Program, he completed a one-year Korean Language program at Inha University and earned a doctoral degree from the Department of Computer and Communication Systems at Sunchon National University in 2021. Sathishkumar is an accomplished academic, having reviewed over 2000 research articles for more than 200 journals, and he currently serves as an academic editor for the journal PLOS ONE and BMC Research Notes. His research interests encompass Data Mining, Big Data Analytics, Cryptography, Digital Forensics, and Computational Chemistry, and he has authored over 100 research articles in reputable journals and conferences.

Dr. Durga Prasad Bavirisetti, Norwegian University of Science and Technology, Norway, email: durga.bavirisetti@ntnu.no

Dr. Durga Prasad Bavirisetti is a researcher at the Norwegian University of Science and Technology, Norway. He received a Ph.D. in Computer Vision from VIT, Vellore, India. He pursued his Postdoctoral research at Shanghai Jiao Tong University, China, and was a Visiting Researcher at the University of British Columbia, Okanagan, Canada. He worked as an Algorithm Expert at the Department of AI of Alibaba Research Lab, Alibaba Group of Companies, Shanghai. He also worked as a contract researcher at the Norwegian University of Science and Technology, Norway.

Mohamed Abouhawwash, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, email: abouhawwash@utexas.edu

Mohamed Abouhawwash received the B.Sc. and M.Sc. degrees in statistics and computer science from Mansoura University, Mansoura, Egypt, in 2005 and 2011, respectively, the Ph.D. degree in statistics and computer science in a channel program from Michigan State University, East Lansing, MI, USA, in 2015, and the Ph.D. degree from Mansoura University in 2015. In 2018, he was a Visiting Scholar with the Department of Mathematics and Statistics, Faculty of Science, Thompson Rivers University, Kamloops, BC, Canada. He is currently with the Computational Mathematics, Science, and Engineering (CMSE), Biomedical Engineering (BME), Radiology, Institute for Quantitative Health Science and Engineering (IQ), Michigan State University. He is an Associate Professor with the Department of Mathematics, Faculty of Science, Mansoura University. His current research interests include evolutionary algorithms, machine learning, image reconstruction, and mathematical optimization. He was a recipient of the Best Master’s and Ph.D. Thesis Awards from Mansoura University, in 2012 and 2018, respectively.