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

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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. (https://www.confab360degree.com/past-events.php )

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

Keywords

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

Published Paper

Need of Li-Fi (light fidelity) technology for the world to track COVID-19 patients

Supriya Dinesh;Bharti Chourasia;
In this modern world, a single day without light or the internet is unimaginable. Nowadays, wireless fidelity, often known as Wi-Fi, is the most well-known and commonly utilized conventional wireless technology. Wi-Fi employs radio waves or electromagnetic waves to carry data across networks. Imagine if a basic LED light in and around the hospital could link us to high-speed wireless internet with just a simple flickering of light at a very high speed where eyes cannot detect it. This technology is known as Li-Fi, or light fidelity, and it is 10,000 times faster than Wi-Fi. Hospitals are among the locations where Wi-Fi is absolutely forbidden. As doctors are the frontline soldiers against COVID-19, the objective of this project is to develop smart healthcare systems that use green communications to monitor COVID-19 patients using temperature, pressure, and heart rate sensors from Li-Fi transmitter to Li-Fi receiver by using simple LED light as a medium to transmit the data or information of COVID-19 to the cloud by using Li-Fi Dongle.

Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem

Shabnam Sayyad;Farook Sayyad;
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.

Conditioning and monitoring of grinding wheels: A state-of-the-art review

Shrinath M. Patil-Mangore;Niranjan L. Shegokar;Nand Jee Kanu;
Grinding wheel condition monitoring is an important step towards the prediction of grinding wheel faulty conditions. It is beneficial to define techniques to minimize the wear of the grinding wheels and finally enhance the life of the grinding wheels. Grinding wheel condition monitoring is done by two techniques such as (i) direct and (ii) indirect. Direct monitoring employs optical sensors and computer vision techniques, and indirect monitoring is done by signal analysis such as acoustic emission (AE), vibration, cutting force, etc. Methods implemented for grinding wheel monitoring in the published research papers are reviewed. The review is compiled in five sections: (a) process parameters measurement, (b) data acquisition systems, (c) signal analysis techniques, (d) feature extraction, and (e) classification methods. In today’s era of Industry 4.0, a large amount of manufacturing data is generated in the industry. So, conventional machine learning techniques are insufficient to analyze real-time conditioning monitoring of the grinding wheels. However, deep learning techniques such as artificial neural network (ANN), convolutional neural network (CNN) have shown prediction accuracy above 99%.

Moving towards a sustainable world with the circular economy practices concerning the SMEs in Visakhapatnam’s ice-cream industry

Mukesh Kondala;Sai Sudhakar Nudurupati;Eko Riwayadi;Abhijeet Chavan;Shaik Rajah Asif;Nimit Gupta;
The Circular Economy (CE) is getting its attention these days, which has a massive impact on the industries, par-ticularly in the manufacturing segment. The countries worldwide started believing in CE, and its practices got the bene-fits after thoroughly implementing it to their current practices. The concept is not new, but it came up with a new ideol-ogy and new techniques already proven by countries like China and the UK. Different industries show their innovative-ness by adapting to the change for the future. We found that the Ice Cream Industry is one of them that adopt change quickly. The paper discusses the introduction of the CE, the current trends, the comparison of the olden style with new style after implementing CE practices, the challenges and barriers in implementing, and the benefits of implementing CE Practices in Visakhapatnam’s dairy industry. We followed a personal interview method for getting first-hand and rich information from the CEOs and operational managers of the company. Also, we followed the case study method to extract how they shifted from traditional manufacturing practices to the current and latest trends in manufacturing. In their manufacturing practices, we aimed to get factual information on the changeover from linear to circular.

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