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6th International Conference on Blockchain computing and Applications, BCCA 2024

作者： Sunil, Sidharth Bhat, Anjum Zameer Abbas, Ali Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada Department of Computing and Electronics Engineering Middle East College Muscat Oman

ISBN: (纸本)9798350351538

Web 3.0 technologies such as blockchain, cryptocurrency, and Non-Fungible Tokens (NFTs) are progressively making waves. Despite their adoption in various sectors, their application in the education sector, particularly in Higher Educational Institutions (HEIs), is largely unexplored. Our research proposes the development of a secure NFT platform using blockchain technology, tailored for HEIs. The primary aim of this research is to authenticate rewards, and certificates, and digitalize badges using NFTs, minimizing potential fraud and enhancing data security. Reward NFTs can enhance students' and teachers' career recognition and credit. EduNFT is a ‘first of its kind’ in the Sultanate of Oman in creating a Web 3.0 technology-enabled campus where students are rewarded with digital assets (NFTs) for their achievements by the teachers or the institution over an NFT platform in a secured environment. It runs on the Polygon blockchain which is a "layer two" or "sidechain" scaling solution that runs alongside the Ethereum blockchain - allowing speedy transactions with low fees. The research methodology incorporates an authentication process that requires users to log in via their crypto wallets which enhances security and maintains user privacy. Instead of traditional physical rewards, this digital approach allows achievements to be stored perpetually in the blockchain, reducing risks of loss or damage. The proposed research empowers an impressive expansion of NFT utilization in higher education, contributing to the ongoing growth of the NFT space. The outcome, a pioneering NFT platform powered by Web 3.0 technology, stands to revolutionize the higher education sector, not only in Oman but potentially worldwide. In addition, the research serves as a crucial step toward integrating contemporary technologies into educational systems and furthering their evolution. © 2024 IEEE.

关键词： Smart contract

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Stacking Ensemble Model for Celestial Object Classification: Galaxies, Stars and Quasars 7

Stacking Ensemble Model for Celestial Object Classification:...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Sudharson, S. Annamalai, R. Reddy, Avuthu Avinash Varsha, P. Vellore Institute of Technology School of Computer Science And Engineering Department of Computer Science And Engineering Chennai600127 India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science And Engineering Chennai601103 India Chennai601103 India

ISBN: (纸本)9798350371406

In the field of astronomy, it is essential to classify celestial objects like stars, galaxies, and quasars based on their spectral characteristics. This spectral data provides valuable information about various properties, such as the elements present, temperature, density, and magnetic field. To tackle this classification task, we investigate the application of different classification and ensemble algorithms. The proposed approach uses a variety of machine learning classifiers, including logistic regression, support vector machines, k-nearest neighbors, decision trees, random forests, and XGBoost. These classifiers are combined to create a stacking classifier, which is then evaluated on its accuracy, precision, recall, F1 score, and support. The Stacking classifier demonstrates the highest accuracy, reaching an impressive 99.99% on the training data. Train Logloss is 0.011. The precision, recall, and f1 score values (all 1.00) indicate a robust classification capability a cross all classes of celestial objects. This outstanding accuracy means that it effectively identifies almost all celestial objects in the training data-set. Consequently, the Stacking model serves as a highly dependable and precise tool for recognizing galaxies, stars, and quasars based on their spectral characteristics. © 2023 IEEE.

关键词： Stars

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CDGNet: A Cross-Time Dynamic Graph-Based Deep Learning Model for Vehicle-Based Traffic Speed Forecasting

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Yuchen Luo, Haiyong Zhao, Fang Sun, Poly Z.H. Qin, Yanjun Zeng, Liang Hui, Bo Wang, Chenxing School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Industrial Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Tsinghua University Beijing China University of Tulsa Tulsa USA

Vehicle-based traffic speed forecasting aims to predict the average speed of vehicles on the road in the future, which is an essential side information in intelligent vehicles and beneficial to safe autonomous driving, yet is very challenging due to the complex and dynamic spatio-temporal dependencies in real-world vehicle-based traffic. To extract intricate correlations among multiple vehicle-based speed time series, previous methods have used graph convolution networks. However, the conventional static and dynamic graphs fail to reflect the traffic evolution and the hysteresis spatial influence caused by vehicle movement. To address this issue, we propose a novel cross-time dynamic graph-based deep learning model, named CDGNet, for vehicle-based traffic speed forecasting. The model is able to effectively capture hysteresis spatial dependencies between each time slice and its historical time slices through the cross-time dynamic graph-based GCN. Meanwhile, a gating mechanism is integrated into our cross-time dynamic graph, which conforms to the sparse correlation in the real world. Besides, GCNs are incorporated into a novel encoder-decoder architecture to forecast multi-step speed. Experimental results on three real-world vehicle-based traffic speed datasets demonstrate the superiority of our CDGNet over various state-of-the-art spatio-temporal forecasting methods and the effectiveness of each component. We additionally provide a visualization of our cross-time dynamic graph to show the capability of assisting intelligent vehicles to avoid congestion. IEEE

关键词： Forecasting

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Enhancing Skin Cancer Detection Through Automated Classification Using Convolutional Neural Networks 1

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7th International Conference on Intelligent computing and Optimization, ICO 2023

作者： Hossain, Md Jakir Bhuiyan, M. D. Wahidul Islam Raisa, Sadia Afrin Tomal, Minhazul Amin Jennifer, Sanjeda Sara Reza, Ahmed Wasif Siddique, Nazmul Department of Computer Science and Engineering East West University Dhaka Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Skin cancer presents a significant public health concern, necessitating early detection and intervention to mitigate life-threatening consequences. Despite its prevalence, skin cancer is not always given the attention it deserves. In this paper, we propose an automated procedure for distinguishing skin cancers using several neural network algorithms. We used the MobileNet architecture whose accuracy is not very high and this is why we have also used other algorithms such as Sequential CNN (Convolution Neural Network), VGG16, and Xception to improve the accuracy of our model. By comparing these algorithms, we found that the Sequential CNN performed best with an accuracy of 94%. The proposed model utilizes the Sequential CNN algorithm to achieve a high level of accuracy, which is of utmost importance for the early detection of skin cancer. The high accuracy of the proposed model makes it a promising tool for detecting skin cancer early. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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FMCW Monopulse Radar with an Orthogonally Polarized Dual-Beam Dielectric Resonator Antenna

FMCW Monopulse Radar with an Orthogonally Polarized Dual-Bea...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Lee, Chanhee Shin, Kyeong-Han Lee, Ji-Hoon Lee, Hyo-Won Kim, Jeong-Wook Yu, Jong-Won School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Electrical and Computer Engineering Ajou University Suwon Korea Republic of

ISBN: (纸本)9798350364774

In this paper, frequency-modulated continuous-wave (FMCW) radar system using dielectric resonator antenna (DRA), which has two beams with orthogonal linear polarization to each other, is introduced to function as a monopulse radar. The DRA operates at 24.3 GHz with a 16.8% bandwidth and provides over 40 dB isolation between V-pol and H-pol ports. Simulations demonstrate effective target detection and advanced directional information extraction, similar to monopulse radar, using a signal processing technique to address overlapping beam regions. © 2024 IEEE.

关键词： Monopulse radar

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Highest Accuracy Based Automated Depression Prediction Using Natural Language Processing 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Tharun, S.V. Saranya, G. Tamilvizhi, T. Surendran, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

In today's fast-paced society, psychological health issues such as anxiety, depression, and stress have become prevalent among the general population. Researchers have explored the use of machine learning algorithms to predict the likelihood of depression in individuals. As datasets related to depression become more abundant and machine learning technology advances, there is an opportunity to develop intelligent systems capable of identifying symptoms of depression in written material. By applying natural language processing and machine learning algorithms to analyze written text, such as social media posts, emails, and chat messages, researchers can potentially identify patterns and linguistic cues associated with depression. These patterns may include changes in word usage, tone, and sentiment. The dataset consists of text-based questions on this information channel. At present, machine learning techniques are highly effective for analyzing data and identifying problems. Researchers have conducted comparisons of the accuracy achieved by different machine learning algorithms using the complete set of attributes as well as a subset of selected attributes. In summary, while the potential for AI to aid in mental health diagnosis and treatment is exciting, it's important to proceed with care and consideration for the complexities of the field and the needs of patients. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Harvesting Knowledge: An Innovative Ontology Framework for Agricultural Advancement using NPL methodologies 3

Harvesting Knowledge: An Innovative Ontology Framework for A...

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3rd International Conference on electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Deepa, R. Das, Manisha Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Andhrapradesh Rajam India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Kone Elevator India Technology & Engineering Centre Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

This paper introduces Agricultural Advancement using NLP (AA-NLP) techniques as a strategy to overcome the limitations of traditional agricultural management information systems. There is a lot of unstructured data available but this situation makes agricultural research inefficient because of its knowledge extraction and usage failure. This problem is addressed by combining an ontology framework developed for agricultural information management with Natural Language Processing (NLP) methods. The solution, AA-NLP, utilizes modern NLP algorithms which extract, classify and evaluate agricultural information from many textual sources that include research papers, reports and online repositories. AA-NLP achieves effective information retrieval and exploitation through named entity identification, sentiment analysis and semantic similarity as well as other techniques to convert unstructured input into structured ontological representations. Applying AA-NLP has several benefits such as increased discoverability of relevant agricultural information, automatic classification of scientific findings, and generation of actionable insights for decision making in farming practices, crop management and formulation of agricultural policies. The effectiveness of AA-NLP was analyzed in this paper by measuring the precision associated with knowledge extraction and ontology creation through various performance metrics including accuracy, sustainability farming practices, agricultural productivity and precision. © 2024 IEEE.

关键词： Semantics

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Temperature Sensor Circuit for a Wearable Perspiration Monitoring System

Temperature Sensor Circuit for a Wearable Perspiration Monit...

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2023 International Symposium on Devices, Circuits and Systems, ISDCS 2023

作者： Ichimura, Yuichiro Goto, Kenta Johguchi, Koh Graduate School of Science and Technology Shinshu University Electrical and Computer Engineering Division Department of Engineering Nagano Japan Shinshu University Faculty of Engineering Department of Electrical and Computer Engineering Nagano Japan

ISBN: (纸本)9798350315042

In this work, a prototype system has been designed with a 0.18-μm CMOS technology to capture perspiration rate in daily life. To calculate an amount of perspiration, a temperature sensor is necessary concurrently with a relative humidity sensing. The designed temperature sensor consists of 8-bit binary digital-to-analog converter and an analog front-end circuit for thermistors. The designed circuits for temperature and humidity sensors have successfully verified. © 2023 IEEE.

关键词： Humidity sensors

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SARP: Spatial Agnostic Radio Fingerprinting with Pseudo-Labeling 22

SARP: Spatial Agnostic Radio Fingerprinting with Pseudo-Labe...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Afrin, Fahmida Li, Haipeng Wang, Boyang Ghose, Nirnimesh School of Computing University of Nebraska-Lincoln United States University of Cincinnati Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331508050

Deep-learning radio fingerprinting is not robust against spatial variations, where a neural network trained on location A does not perform well over RF signals from location B. We promote the robustness of deep-learning radio fingerprinting against spatial variations by synergizing Complex-Valued Neural Networks (CVNNs) and pseudo-labeling. Compared to existing solutions, we leverage pseudo-labeling to fine-tune a CVNN without needing labeled RF signals from a new location. We collect large-scale real-world datasets across different locations. We conduct comprehensive evaluations of these datasets with multiple complex-valued activation functions. Our experimental results significantly improve the accuracy of radio fingerprinting when training data and test data are from two different locations (e.g., increasing accuracy from 40.0% to 62.8%). © 2025 IEEE.

关键词： Deep neural networks

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FSD-Net: a fuzzy semi-supervised distillation network for noise-resistant classification of medical images

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Multimedia Tools and Applications 2024年第36期83卷 83661-83676页

作者： Du, Xiangtong Shen, Ao Wang, Ximing Li, Zongsheng Deng, Hai School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Information Science and Engineering Hohai University Changzhou213022 China Department of Radiology First Affiliated Hospital of Soochou University Suzhou215006 China Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States

Deep Learning (DL)-based models have been successfully applied for medical image classifications. However, the performance of traditional medical image classifiers is limited by insufficient training samples and inaccurate annotations due to labeling noise. Numerous methods were proposed to address the issues, to no avail. To fill this gap, we propose a Fuzzy Semi-supervised Distillation Network (FSD-Net) to improve the noise-resistant classification performance. Firstly, with the aid of semi-supervised training based on the pseudo-labels, the problem of insufficient training samples can be alleviated by using unlabeled data to a certain extent. Then, a Confidence Filtering model (CF-model) is designed to improve the quality of the pseudo-labels and filter the noisy labeling data by combining the soft labels generated from the classification model and the improved Soft Fuzzy C-Means (SFCM) model. Compared with the setting thresholds, the data with low confidence are reclassified as unlabeled to participate in training, while the labeled and unlabeled data (generating pseudo-labels) with high confidences are added into the mixed dataset. Finally, the mixed data are fed to train a multi-model online distillation module consisting of the SFCM and two other student models. The weighted average value of the three student models is used as teacher knowledge to train the students and obtain the final classification results. Experimental results on multiple medical datasets show that the proposed FSD-Net can solve the problems of insufficient labeled samples and labeling noise simultaneously to some extent. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distillation

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