Journal of Autonomous Intelligence

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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

Prof. Anuj Kumar

Apeejay School of Management, Dwarka, India

Interests: Technological advancements, SMEs, Emerging technology

Dr. Nishu Ayedee

Rishihood University, Haryana, India

Interests: Technology, SMEs, and Entrepreneurship

Dr. Surya Kant Pal

Sharda University, Greater Noida, U.P., India

Interests: Artificial Intelligence, Machine Learning, Cloud Computing

Dr. Jayanta Banerjee

School of Business, Manipal Academy of Higher Education, Dubai

Interests: Management

Dr. Prabha Kiran

Westminster International University, Uzbekistan

Interests: Technology, A.I., Digital Marketing, Analytics

Dr. Altaf Yousuf Mir

International Institute of Health Management and Research, India

Interests: Health Management, Service Marketing

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.

Emotion-Sensitive Analysis of Learners' Cognitive State: Integrating Deep Learning and Attention Assessment

S. Aruna;Swarna Kuchibhotla;

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.

Enabling Edge Computing-Based Coverage Hole Detection Framework for Lossless Data Tracking

Anitha Christy Angelin;Salaja Silas;

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 networksâ€™ 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.

Deep ResNet 18 and Enhanced Firefly Optimization Algorithm for on-road Vehicle Driver Drowsiness Detection

Suvarna Nandyal;Sharanabasappa Sharanabasappa;

The Driver Drowsiness Detection (DDD) technology is based on vehicle safety, and this system prevents many accidents and deaths that occur due to driver drowsiness. As a result, it is monitored and detected when vehicle drivers become drowsy. The DDD method, which is aided by Alex Net and deep learning models, has limitations such as vanishing gradients and overfitting issues as the depth of the model increases. The Enhanced firefly optimisation algorithm has solved the problem of lower optimisation exploration. The NTHU-DDD dataset's input image contains individual groups of female and male drivers of various vehicles. The Min-Max normalisation method is a general method for normalising data. The Convolutional Neural Network (CNN) is used to extract features from input images and images classified by the neural network. ResNet 18 refers to the deepest of the convolutional neural network's 18 layers. A network of pre-trained models can be used to classify the model classified by the 1000 image objects. The state-of-the-art HDDD model with SVM assistance has an effective high dimensional space. The CNN-EFF-ResNet 18 models have a high accuracy of 91.3%, while the HDDD method has a higher accuracy of 87.19% than the Ensemble and PMLDB methods in DDD.

Verification of the Effectiveness of Digital Therapeutics Principle Textbooks for Elementary and Secondary School Teachers

Eunsun Choi;Namje Park;

As the digital transformation of medical systems accelerates, digital therapeutics based on digital technology is attracting attention. Research on digital therapeutics has just begun, and the digital therapeutic market is growing internationally. For students to prepare for an intelligent information society, teachers must be prepared to lead and teach the principles of promising convergence technologies in the future. In this paper, we developed textbooks that can teach the principle of Digital Therapeutics(DTx) to elementary and secondary school teachers. Textbooks for elementary school teachers were designed with the principle of DTx for ADHD, and textbooks for middle school teachers were developed with DTx corresponding to digital dramas as the central theme. The textbooks were developed based on the ASSURE model and included teaching and learning plans, worksheets, and reading materials for immediate use in the school field. In addition, textbooks can be used in non-face-to-face classes by using customized online teaching tools. In order to confirm the effectiveness of the developed textbooks, we applied the textbooks to 33 Korean teachers. As a result, teachers' teaching and learning competency and information teaching efficacy were improved in preparation for the intelligent information society of elementary and secondary school teachers. It was a statistically significant result.

The 3D reconstruction technology which is one of various restoration technologies, implements and shapes 2D pixels of an object that actually exists in a 3D form. As accurate 3D model for artificial intelligence learning information pre-processing is required. The pre-processing need to accurate size information and coordinate information of an 3D object for artificial intelligence learning. Also, the 3d model information created through the preprocessing process can be expressed as a point cloud (point-based coordinate data). In this study, the pixel data was analyzed by voxelizing the 3d model for artificial intelligence learning, and the 3D model data was digitized to form a mesh file. In this study, 3D modeling was done with a 3D modeling tool and the object was exported to STL. In addition, it was converted into mesh file data including 3D coordinate data in Python language on Google's colab platform. The mesh data created in this way is used in DataSet for artificial intelligence learning (CNN, RNN, GAN). Currently, there are many datasets for 2D artificial intelligence learning, but this study provided an opportunity to collect 3D artificial intelligence learning datasets. The research results can be used in the fields of robots, autonomous driving, games, and product design.

Study on Prediction and Diagnosis AI Model of Frequent Chronic Diseases Based on Health Checkup Big Data

Jae Young Park;Jai-Woo Oh;

Editor-in-Chief

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