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Selected Papers from Global Conference on Computer Applications, Sustainable Engineering, Manufacturing Practices, Mathematical Science and Environment

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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 computer applications, sustainable engineering, manufacturing practices, Mathematical Science and Environment (15th and 16th July’2023). 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. Altaf Yousuf Mir

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

Future fusion+: Breast cancer tissue identification and early detection of deep hybrid featured based healthcare system

Shruthishree Surendrarao Honnahalli;Harshvardhan Tiwari;Devaraj Verma Chitragar;
The exponential increase in cancer cases, particularly breast cancer (BC), has prompted academics and business to develop more effective and trustworthy methods for classifying and identifying BC tissues. In contrast to traditional machine learning (ML) techniques, this work presents the development of a fusion of features AlexResNet+: a deep hybrid feature-based system of early BC detection in healthcare tissue identification. For deep feature extraction, we employed three of the most popular and effective deep learning models, AlexNet, ResNet50 and ResNet101. We employed ResNet50 with modified layered architectures while using AlexNet with five convolutional layers in order to maintain high dimensional deep features while maintaining the best computational efficiency. As a result of combining the deep features from the AlexNet and ResNet DL models, we were able to perform two-class classification using the Support Vector Machine with Radial Basis Function (SVM-RBF). Performances for AlexNet, shorted ResNet50, and hybrid features were collected separately to evaluate the effectiveness of the various feature sets. The counseled hybrid deep capabilities (AlexResNet+)-primarily based version has a most class accuracy of 95.87%, precision of 0.9760, sensitivity of 1.0, specificity of 0.9621, F-Measure of 0.9878 and AUC of 0.960, according to simulation results using DDMS mammography breast cancer tissue pictures.

Novel machine learning based authentication technique in VANET system for secure data transmission

Anand N. Patil;Sujata V. Mallapur;
Adaptive transport technologies based on vehicular ad hoc networks (VANET) has proven considerable potential in light of the developing expansion of driver assistance and automobile telecommunication systems. However, confidentiality and safety are the vital challenges invehicular adhoc networks which could be seriously impaired by malicious attackers. While protecting vehicle privacy from threats, it is imperative to stop internal vehicles from putting out bogus messages. Considering these issues, a novel machine learning based message authentication combined with blockchain and inter planetary file system(IPFS) is proposed to achieve message dissemination in a secured way. Blockchain is the emerging technology whichattempts to solve these problems by producing tamper proof events of records in a distributed environment and inter planetary file system used in the framework is a protocol designed to store the event with content addressability. Along with this combinedtechnology, the source metadata information collected from the inter planetary file systemis stored via a smart contract and uploaded to the distributed ledger technology (DLT). For performing event authentication, K-means clustering and support vector machine (SVM) classifier is employed in this framework. K-means clustering performs clustering of vehicles and it is marked malicious or not malicious. After clustering, support vector machine classifier detects the malicious event messages. By this way, the malicious messages are identified and it is dropped. Only the secure messages are forwarded in the network. Finally, our approach is capable of creating a safe and decentralizedvehicular ad hoc network architecture with accountability and confidentiality through theoretical study and simulations.

Automated spinal MRI-based diagnostics of disc bulge and desiccating using LS-RBRP with RF

S. Shirly;R. Venkatesan;D. Jasmine David;T. Jemima Jebaseeli;
This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.

A Keyword Network Analysis of Research Trends on Metabolic Syndrome

Eun Sil Her;Yun Kyoung Oh;
Recently, the prevalence and mortality of metabolic syndrome has been increasing worldwide. Accordingly, interest in metabolic syndrome and scientific and clinical studies are increasing. Through keyword analysis of articles related to metabolic syndrome published in Korea Citation Index (KCI) journals for the past 10 years, this study identified key research issues, structural characteristics, and relationships between keywords. The research methodology included data collection, cleaning, visualization, and analysis. Keyword frequency analysis revealed obesity (129 times) was the highest, followed with Health> Exercise>Risk factor>Women>Elderly>Physical activity at more than 50 times. In the structural form of the network, the density was 0.415, average connection strength was 22.821, average connection distance was 1.586, diameter was 3, component was 1, and network centrality was 58.8%. In the structural characteristics of the network, the keyword “obesity” was the highest in both connection and mediation centrality. This study suggests a combination of specific research topics and directions for future metabolic syndrome-related research.

Establishment of data mining-based public education administrative work automation system and student activity analysis

Seung-Ryeol Joo;Jong-Chan Kim;Sung-Jun Kim;
There are several important factors in public education in Korea. Among them, it is very important to manage time to improve teachers' educational capabilities and students' grades. However, in Korea's public education, the existing school administrative work system has to deal with miscellaneous procedures, hindering teachers from guiding students. As a result, students also give low trust in public education. This study introduces procedures for an integrated public education data management system to automate administrative tasks of teachers and increase students' educational capabilities. By applying the Data Mining Problem Solving Methodology (ICAIS), we identified five stages in which data is processed. In addition, the activities required for students to go to college were processed with text mining techniques (from a simple word cloud to the construction of a neural network algorithm classification model), allowing students to check their grades themselves. Through this study, it reduces teachers' chores, concentrates student education, and provides students with the educational purpose of a self-directed method that determines their career path.

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