Proceedings of Engineering and Technology Innovation https://ojs.imeti.org/index.php/PETI <p><strong><em>Proceedings of Engineering and Technology Innovation</em></strong> (PETI), ISSN 2518-833X (Online), ISSN 2413-7146 (Print), is an international, multidiscipline, open access, peer-reviewed scholarly journal, and dedicated to providing a fast publishing platform for researchers, developers, technical managers, and educators in the field of technology innovation. The officially abbreviated title is <em><strong>Proc. eng. technol. innov.</strong></em> It is published by Taiwan Association of Engineering and Technology Innovation. Currently, there is<em><strong> without any charge</strong></em> for the submission and publication of the papers submitted to PETI. You are invited to submit your works to the journal.</p> <p>PETI is indexed by:</p> <p><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;"><img style="width: 136px; height: 26px;" src="http://ojs.imeti.org/public/site/images/allen/image001.png" alt="" width="171" height="53"> </span><img src="/public/site/images/allen/DOAJ-small2.png">&nbsp;<img src="http://ojs.imeti.org/public/site/images/ijeti/google6.png" alt="">&nbsp;&nbsp; <img src="http://ojs.imeti.org/public/site/images/ijeti/CNKI1.png" alt="">&nbsp;&nbsp; <img src="http://ojs.imeti.org/public/site/images/allen/ProQuest-4.png" width="74" height="35">&nbsp;&nbsp; <img src="http://ojs.imeti.org/public/site/images/ijeti/Resarch_Bible6.png" alt="">&nbsp;&nbsp;<img src="http://ojs.imeti.org/public/site/images/ijeti/WorldCat6.png" alt="">&nbsp;&nbsp;<img src="http://ojs.imeti.org/public/site/images/allen/academia-13.png"> <img src="http://ojs.imeti.org/public/site/images/ijeti/TOCs6.jpg" alt=""> &nbsp;<img src="/public/site/images/allen/Publons-22.5_1.png"> &nbsp; <img src="/public/site/images/allen/crossref3.png" width="92" height="42"></p> <p>Under evaluation of SCI(E), Compendix(EI), INSPEC, etc.</p> <p>&nbsp;</p> Taiwan Association of Engineering and Technology Innovation en-US Proceedings of Engineering and Technology Innovation 2413-7146 <hr> <p style="line-height: 150%;"><span style="font-family: Times New Roman;">Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright of their article with no restrictions. Also, author can post the final, peer-reviewed manuscript version (postprint) to any repository or website.</span></p> <p>&nbsp;</p> <p style="line-height: 150%;"><img alt="" src="/public/site/images/ijeti/cc.png"><br><span style="font-family: Times New Roman, Times, serif;">Since Oct. 01, 2015, PETI will publish new articles with Creative Commons Attribution Non-Commercial License, under </span><span style="font-family: Times New Roman, Times, serif;"><a href="https://creativecommons.org/licenses/by-nc/4.0/">The Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License</a>.<br>The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes</span></p> Non-Facial Video Spatiotemporal Forensic Analysis Using Deep Learning Techniques https://ojs.imeti.org/index.php/PETI/article/view/10290 <p>Digital content manipulation software is working as a boon for people to edit recorded video or audio content. To prevent the unethical use of such readily available altering tools, digital multimedia forensics is becoming increasingly important. Hence, this study aims to identify whether the video and audio of the given digital content are fake or real. For temporal video forgery detection, the convolutional 3D layers are used to build a model which can identify temporal forgeries with an average accuracy of 85% on the validation dataset. Also, the identification of audio forgery, using a ResNet-34 pre-trained model and the transfer learning approach, has been achieved. The proposed model achieves an accuracy of 99% with 0.3% validation loss on the validation part of the logical access dataset, which is better than earlier models in the range of 90-95% accuracy on the validation set.</p> Premanand Ghadekar Vaibhavi Shetty Prapti Maheshwari Raj Shah Anish Shaha Vaishnav Sonawane Copyright (c) 2023 Premanand Ghadekar, Vaibhavi Shetty, Prapti Maheshwari, Raj Shah, Anish Shaha, Vaishnav Sonawane http://creativecommons.org/licenses/by-nc/4.0 2023-01-01 2023-01-01 23 01 14 10.46604/peti.2023.10290 Design and Performance Analysis of Band Pass Filter Using Frequency Selective Surface for 5G Communication https://ojs.imeti.org/index.php/PETI/article/view/9313 <p>In recent years, frequency selective surfaces (FSSs) have been extensively investigated in terms of their design and practical applications at microwave and optical frequencies. This study proposes a new design of a FSS layer, which is directly placed over the surface of an antenna to enhance its characteristics such as directivity, frequency selectivity, radiation efficiency, and gain. In the proposed design, two different substrates are used to analyze the improved performance of the FSS layer. For this purpose, FR-4 Epoxy and Duroid 5880 are used for cost effectiveness and to achieve the optimized performance of the antenna. The simulated and measured results are in good agreement, indicating the enhanced performance of antenna for WLAN and WiMAX applications. Finally, it is concluded that the proposed FSS layer ensures the best possible results of the filtering response as the first null gives divergence of more than 10 dB with its peak value layer.</p> Muhammad Haroon Tariq Muhammad Noaman Zahid Copyright (c) 2023 Muhammad Haroon Tariq, Muhammad Noaman Zahid http://creativecommons.org/licenses/by-nc/4.0 2023-01-01 2023-01-01 23 15 22 10.46604/peti.2023.9313 Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data https://ojs.imeti.org/index.php/PETI/article/view/9617 <p>Forecasting urban sprawl is important for land-use and transport planning. The aim of this study is to model and predict the future urban sprawl in Sylhet Sadar using remote sensing data. The ordinary least square (OLS) regression model and the geographic information system (GIS) are used for modeling urban expansion. The model is calibrated for the years 2014 to 2017 using eight explanatory variables extracted from the regression model. The regression coefficients of the variables are found statistically significant at a 99% confidence level. The cellular automata (CA) model is then used to analyze, model, and simulate the land-use and land-cover (LULC) changes by incorporating the algorithm of logistic regression (LR). The calibrated model is used to predict the 2020 map, and the result shows that the predicted map and the actual map of 2020 are well agreed. By using the calibrated model, the simulated prediction map of 2035 shows an urban cell expansion of 220% between 2020 and 2035.</p> Md Aminul Islam Tanzina Ahmed Rickty Pramit Kumar Das Md Bashirul Haque Copyright (c) 2023 Md Aminul Islam, Tanzina Ahmed Rickty, Pramit Kumar Das, Md Bashirul Haque http://creativecommons.org/licenses/by-nc/4.0 2023-01-01 2023-01-01 23 23 35 10.46604/peti.2023.9617 Development of a New Ground Motion Model for a Peninsular Indian Rock Site https://ojs.imeti.org/index.php/PETI/article/view/10526 <p>The ground motion model (GMM) plays a vital role in the generation of seismic design basis ground motion parameters. Even though many intra-plate GMMs are available, very few of them are based on Peninsular India (PI) region-specific seismological parameters. Hence, it is imperative to develop a GMM using seismological parameters derived from earthquakes in the Peninsular Indian region. In this study, a new GMM is developed for a PI rock site. Due to the scarcity of real earthquakes, artificial earthquake records are simulated to generate a new GMM for PI. The accelerograms of these artificial earthquakes are obtained from the stochastic finite fault simulation technique. Region-specific seismological parameters are obtained from the available PI earthquakes. The generated GMM is compared with other intra-plate GMMs for different earthquake magnitudes. Also, the generated GMM is validated with the Koyna earthquake record and it is observed that the GMM’s predictions are closer to the record.</p> Ravi Kiran Akella Mohan Kumar Agrawal Jayanta Chattopadhyay Copyright (c) 2023 Ravi Kiran Akella, Mohan Kumar Agrawal, Jayanta Chattopadhyay http://creativecommons.org/licenses/by-nc/4.0 2023-01-01 2023-01-01 23 36 47 10.46604/peti.2023.10526 Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection https://ojs.imeti.org/index.php/PETI/article/view/10101 <p>This study aims to investigate the effectiveness of local interpretable model-agnostic explanation (LIME) and Shapley additive explanation (SHAP) approaches for chronic heart disease detection. The efficiency of LIME and SHAP are evaluated by analyzing the diagnostic results of the XGBoost model and the stability and quality of counterfactual explanations. Firstly, 1025 heart disease samples are collected from the University of California Irvine. Then, the performance of LIME and SHAP is compared by using the XGBoost model with various measures, such as consistency and proximity. Finally, Python 3.7 programming language with Jupyter Notebook integrated development environment is used for simulation. The simulation result shows that the XGBoost model achieves 99.79% accuracy, indicating that the counterfactual explanation of the XGBoost model describes the smallest changes in the feature values for changing the diagnosis outcome to the predefined output.</p> Tsehay Admassu Copyright (c) 2023 Tsehay Admassu http://creativecommons.org/licenses/by-nc/4.0 2023-01-01 2023-01-01 23 48 59 10.46604/peti.2023.10101 Deep Learning-Based Iris Segmentation Algorithm for Effective Iris Recognition System https://ojs.imeti.org/index.php/PETI/article/view/10002 <p>In this study, a 19-layer convolutional neural network model is developed for accurate iris segmentation and is trained and validated using five publicly available iris image datasets. An integrodifferential operator is used to create labeled images for CASIA v1.0, CASIA v2.0, and PolyU Iris image datasets. The performance of the proposed model is evaluated based on accuracy, sensitivity, selectivity, precision, and F-score. The accuracy obtained for CASIA v1.0, CASIA v2.0, CASIA Iris Interval, IITD, and PolyU Iris are 0.82, 0.97, 0.9923, 0.9942, and 0.98, respectively. The result shows that the proposed model can accurately predict iris and non-iris regions and thus can be an effective tool for iris segmentation.</p> Sruthi Kunkuma Balasubramanian Vijayakumar Jeganathan Thavamani Subramani Copyright (c) 2023 Sruthi Kunkuma Balasubramanian, Vijayakumar Jeganathan, Thavamani Subramani http://creativecommons.org/licenses/by-nc/4.0 2023-01-01 2023-01-01 23 60 70 10.46604/peti.2023.10002