Journal of Autonomous Intelligence

Selected Papers from Global Conference on Emerging Technologies, Business, Sustainable Innovative Business Practices and Social Well-Being

Submission deadline: 2023-12-10
Special Issue Editors

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to the Global conference on emerging technologies, business, sustainable innovative business practices and social well-being in December 2022. The authors are invited to submit the revised and extended papers. ( )

The topics relevant to this Special Issue include, but are not limited to:

· Machine learning algorithms for big data analytics

· Artificial neural network, neural systems and its applications

· Hybrid intelligent systems for cloud security and privacy

· Big data analytics in the cloud for decision-making and predictive modeling

· Novel computational intelligence paradigms for cloud forensics and incident response

· Cloud-based anomaly detection and intrusion detection systems using computational intelligence techniques

· Fuzzy systems and other soft computing techniques for cloud resource management and optimization

· Cloud-based intelligent systems for real-time security threat detection and mitigation

· Novel applications of big data analytics and computational intelligence in the cloud

· Privacy-preserving big data analytics in the cloud using cryptographic techniques

· Performance optimization of big data analytics using cloud-based computing and distributed processing

· Computational intelligence solutions to security and privacy issues in mobile cloud computing

· Privacy concepts and applications in cloud platforms

· Cloud computing security data analysis tools and services

Prof. Anuj Kumar

Dr. Nishu Ayedee

Dr. Surya Kant Pal

Dr. Jayanta Banerjee

Dr. Prabha Kiran

Dr. Shirmila T

Guest Editors


Artificial Intelligence; Machine Learning; Cloud Computing; Physics Chemistry; Cloud Computing; Big Data Analytics; Computational Intelligence; Engineering Applications; Electronics; Mathematical Science

Published Paper

In this study, artificial neural networks (ANNs) are being used to diagnose hair loss in patients. An autoimmune condition known as Alopecia Areata (AA) results in hair loss in the affected area. The most recent figures from throughout the world show that AA affects 1 in 1000 persons and has a 2% incidence rate. Based on the look of photographs with healthy hair in the dataset, machine learning techniques were employed to classify the conditions. Before making predictions, each of these ANNs algorithms creates a prediction model using pictures of healthy hair. The aim of this study is to evaluate the accuracy of neural networks for alopecia detection in human subjects. The study presents a classification framework for distinguishing between healthy hairs (HHs) and Alopecia Areata (AA). The framework incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement and segmentation techniques to enhance the quality of the images. Additionally, Data Augmentation (DA) is employed to generate additional data and improve the precision of the proposed framework. To extract features from the images, two powerful techniques are utilized. The Visual Geometry Group (VGG), which consists of very deep convolutional networks designed for large-scale image recognition, is employed. VGG networks have proven to be effective in learning complex features directly from data, eliminating the need for manual feature extraction. Additionally, a Convolutional Neural Network (CNN), a deep learning network architecture specifically designed for image processing tasks, is employed. To create a machine learning model for classification, the Support Vector Machine (SVM) approach is utilized. SVM is a widely used algorithm in supervised learning, capable of solving both classification and regression problems. Its versatility and effectiveness make it a suitable choice for the classification task in this study. By combining the CLAHE enhancement, segmentation, data augmentation, feature extraction using VGG and CNN, and classification using SVM, the proposed framework aims to accurately classify HHs and AA cases.