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115 results on '"Webber, Julian L."'

5. Deep Convolutional Neural Networks-Based Market Strategy for Early-Stage Product Development

Author

James, Gladson Maria Britto, Mehbodniya, Abolfazl, Maria, Anto Bennet, Webber, Julian L., Stalin David, D., Rangasamy, Rajasekar, Sengan, Sudhakar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2023
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6. Equipment Planning for an Automated Production Line Using a Cloud System

Author

Bhavana Raj, K., Webber, Julian L., Marimuthu, Divyapushpalakshmi, Mehbodniya, Abolfazl, Stalin David, D., Rangasamy, Rajasekar, Sengan, Sudhakar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2023
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7. Glioma Segmentation in MR Images Using 2D Double U-Net: An Empirical Investigation

Author

Webber, Julian L., Nancy Noella, R. S., Mehbodniya, Abolfazl, Ramachandran, V., Stalin David, D., Rangasamy, Rajasekar, Sengan, Sudhakar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2023
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8. Classification of Cervical Cells Using Deep Learning Feature Extraction

Author

Mehbodniya, Abolfazl, Webber, Julian L., Mani, Devi, Stalin David, D., Kandasamy, Amudha, Rangasamy, Rajasekar, Sengan, Sudhakar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2023
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9. Design of Concurrent Engineering Systems for Global Product Development Using Artificial Intelligence

Author

Karn, Arodh Lal, Mehbodniya, Abolfazl, Webber, Julian L., Jayagopal, Vellingiri, Stalin David, D., Rangasamy, Rajasekar, Sengan, Sudhakar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2023
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10. Medical Images Analysis for Segmentation and Classification Using DNN

Author

Mehbodniya, Abolfazl, Narayanasami, Satheesh, Webber, Julian L., Kothalanka, Amarendra, Sengan, Sudhakar, Rangasamy, Rajasekar, Stalin David, D., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2023
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11. Certain Investigations of MEMS for Optimised Sensor Coverage

Author

Mehbodniya, Abolfazl, Rajamanickam, Muruganantham, Webber, Julian L., Stalin David, D., Mani, Devi, Rangasamy, Rajasekar, Sengan, Sudhakar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2023
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12. Evaluation and Language Training of Multinational Enterprises Employees by Deep Learning in Cloud Manufacturing Resources

Author

Karn, Arodh Lal, Webber, Julian L., Mehbodniya, Abolfazl, Stalin David, D., Subramaniam, Balu, Rangasamy, Rajasekar, Sengan, Sudhakar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2023
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13. Hybrid Power Generation Forecasting Using an Intellectual Evolutionary Energy-Preserve Rate Clustering Technique

Author

Webber, Julian L., Jayagopal, Vellingiri, Mehbodniya, Abolfazl, Sengan, Sudhakar, Velayutham, Priya, Rangasamy, Rajasekar, Stalin David, D., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2023
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42. RETRACTED ARTICLE: Machine Learning Technique to Detect Sybil Attack on IoT Based Sensor Network

Author

Mehbodniya, Abolfazl, Webber, Julian L., Shabaz, Mohammad, Mohafez, Hamidreza, and Yadav, Kusum

Abstract

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:Abolfazl Mehbodniya, Julian L. Webber, Mohammad Shabaz, Hamidreza Mohafez & Kusum Yadav (2021) Machine Learning Technique to Detect Sybil Attack on IoT Based Sensor Network, IETE Journal of Research, DOI: 10.1080/03772063.2021.2000509Following publication, concerns were raised about the peer review and decision-making processes for this special issue.After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and policy.As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue.The journal has not confirmed if the authors were aware of this compromised peer review process.The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision.We have been informed in our decision-making by our editorial policies and the COPE guidelines.The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

Published
2023
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43. Predicting Lumbar Spondylolisthesis: A Hybrid Deep Learning Approach.

Author

Saravagi, Deepika, Agrawal, Shweta, Saravagi, Manisha, Jain, Sanjiv K., Sharma, Bhisham, Mehbodniya, Abolfazl, Chowdhury, Subrata, and Webber, Julian L.

Subjects
SPONDYLOLISTHESIS, DEEP learning, CAPSULE neural networks, FEATURE extraction, WEB-based user interfaces, IMAGE registration, LUMBAR vertebrae
Abstract

Spondylolisthesis is a chronic disease, and a timely diagnosis of it may help in avoiding surgery. Disease identification in x-ray radiographs is very challenging. Strengthening the feature extraction tool in VGG16 has improved the classification rate. But the fully connected layers of VGG16 are not efficient at capturing the positional structure of an object in images. Capsule network (CapsNet) works with capsules (neuron clusters) rather than a single neuron to grasp the properties of the provided image to match the pattern. In this study, an integrated model that is a combination of VGG16 andCapsNet (S-VCNet) is proposed. In the model, VGG16 is used as a feature extractor. After feature extraction, the output is fed to CapsNet for disease identification. A private dataset is used that contains 466 X-ray radiographs, including 186 images displaying a spine with spondylolisthesis and 280 images depicting a normal spine. The suggested model is the first step towards developing a web-based radiological diagnosis tool that can be utilized in outpatient clinics where there are not enough qualified medical professionals. Experimental results demonstrate that the developed model outperformed the other models that are used for lumbar spondylolisthesis diagnosis with 98% accuracy. After the performance check, the model has been successfully deployed on theGradio web app platformto produce the outcome in less than 20 s. [ABSTRACT FROM AUTHOR]

Published
2023
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49. Wireless Sensor Network Technology for Vibration Condition Monitoring of Mechanical Equipment.

Author

Shan Wang, Yadav, Ranjeet, Raffik, R., Bhola, Jyoti, Rakhra, Manik, Webber, Julian L., and Mehbodniya, Abolfazl

Subjects
WIRELESS sensor networks, TOPOLOGY, ONLINE monitoring systems, ELECTRIC lines, ELECTRIC fields
Abstract

The application of wireless sensor networks in mechanical equipment condition monitoring is proposed to make up for the limitations of wired mechanical equipment condition monitoring systems. To record vibration signals from mechanical equipment, it is essential to design a dual-processor wireless sensor network node. Researching the self-organizing network, wireless transmission protocol, and on-chip processing function is also important to develop a full wireless sensor network mechanical vibration monitoring system. The clustering network topology, which is advantageous for applying distributed algorithms, has good scalability, can quickly adapt to system changes, and is appropriate for large-scale networks. Through the design of high-performance vibration measurement nodes, the problem of limited acquisition and storage capacity when a wireless sensor network is applied to mechanical vibration signal acquisition is preliminarily solved. The experimental results show that the time domain signal collected by the node is small, and the corresponding amplitude will be small, but the on-chip processing results of the node correspond to the processing results of the host computer, and the amplitude difference is small, and the maximum error is 8.18%, indicating that the node performance can meet the requirements of on-chip processing. The feasibility of the design scheme is verified by experiments. [ABSTRACT FROM AUTHOR]

Published
2023
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