AI and ML Techniques for Computer Vision and Image Processing

• Deependra Rastogi: IILM University, India.
  Email: deependra.libra@gmail.com

• Prashant Johri: Galgotias University, India.
  Email: johri.prashant@gmail.com

• Karan Singh: Jawaharlal Nehru University, India.
  Email: karan@mail.jnu.ac.in

• Arfat Ahmad Khan: Khon Kaen University, Thailand.
  Email: arfatkhan@kku.ac.th

• Yogesh Kumar: Pandit Deendayal Energy University, India.
  Email: yogesh.kumar@sot.pdpu.ac.in

• VDS Baghela, Galgotias University, India
• Sunil K Khatri, Amity University, India
• Vimal Baghel, Bennett University, India
• Alok Katiyar, Galgotias University, India
• Masoud Mohammiadian, Canberra University, Australia
• P. Ojha, G. L. Bajaj, A.K.T.U. University, India
• Vipin Rai, Galgotias University, India
• V. Koushik, Arden University, United Kingdom
• Shailendra Pratap Singh, Amity University, India
• Jaideep Kumar, Monad University, India
• Christo Ananth, Samarkand State University, Uzbekistan
• Md. Ahmar Khan, Dhofar University, Sultanate of Oman
• Syed Hamid Hassan, King Abdulaziz University, Jeddah, Saudi Arabia
• Ahmed A. Elngar, Beni-Suef University, Beni Suef City, Egypt
• Fasee Ullah, Universiti Teknologi PETRONAS, Malaysia
• Adisorn Kaewpukdee, Nakhon Pathom Rajabhat University, Thailand

In the era of artificial intelligence, vast amounts of high-quality data fuel the advancement of numerous emerging fields. AI significantly enhances big data management and data mining capabilities. Despite its potential to amplify the value of big data, AI also poses serious threats to personal privacy and corporate secrets. Traditional privacy protection technologies struggle with efficiency, functionality, and applicability in safeguarding big data. Therefore, leveraging advanced cryptography, privacy protection, and AI technologies is crucial for promoting secure data sharing and fostering the high-quality development of AI.

The rapid advancement and widespread adoption of emerging technologies, such as advanced sensing, mobile computing, the Internet of Things, pervasive computing, data mining, and deep learning, have led to a new wave of user-centered applications. These include personalized care and medicine, just-in-time independent living, self-care and self-management, and early risk detection and intervention. Applications based on concepts like smart homes, smart cities, smart healthcare, intelligent transport, and service robots hold significant societal and economic potential. User-centered systems emphasize data intelligence, encompassing computational, interactive, and cognitive intelligence, as well as co-design, co-development, user experience, and accessibility, particularly in future smart environments.

This special session provides an excellent international platform for sharing information, theories, approaches, and emerging predictive models for diverse applications. Machine learning, especially deep learning, excels in handling complex prediction models due to its outstanding performance with large-scale datasets of uniform characteristics. The session aims to bring together researchers, practitioners, academics, and industry professionals from various disciplines related to machine learning to share ideas and algorithms for current and future applications of machine learning, image processing, and computer vision in real life. The primary goal is to explore the potential of current technology, fostering interaction and professional relationships for future collaboration among participants.

Topics to be discussed in this special session include (but are not limited to) the following:

• AI-based techniques and applications
• Applications of fuzzy systems, neural networks, and convolutional neural networks
• Machine learning methods for security and privacy preservation
• Deep learning and statistical methods for data mining and their applications
• Decision support and recommendation techniques
• Computational, interactive, and cognitive intelligence
• Image processing, classification, segmentation, enhancement, restoration, and retrieval
• Medical image analysis
• Big data in computer vision and image analysis
• Deep learning and transfer learning for smart healthcare
• Computational intelligence in smart healthcare

Researchers and practitioners are invited to submit papers for this special theme session on AI and ML Techniques for Computer Vision and Image Processing on or before September 10^th, 2024 (Extended Deadline).

All submissions must be original and may not be under review by another publication.

All papers should be submitted

electronically through the easychair submission system as mentioned below:

The paper prior to submission should be checked for plagiarism from licensed plagiarism softwares like Turnitin/iAuthenticate etc. The similarity content should not exceed 20%.

INTERESTED AUTHORS SHOULD CONSULT THE CONFERENCE’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at https://icactce-conf.com/call-for-papers-2/.

All submitted papers will be reviewed on a double-blind, peer review basis.

All the accepted papers (after double blinded peer review) will be published within the ICACTCE’24 Proceedings (SCOPUS Indexed).

NOTE: While submitting paper in this special session, please specify AI and ML Techniques for Computer Vision and Image Processing at the top (above paper title) of the first page of your paper.