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Multi-graph Networks with Graph Pooling for COVID-19 Diagnosis

Journal of Bionic Engineering 2024年第6期21卷 3179-3200页

作者： Chaosheng Tang Wenle Xu Junding Sun Shuihua Wang Yudong Zhang Juan Manuel Górriz School of Computer Science and Technology Henan Polytechnic UniversityHenanJiaozuo454003People’s Republic of China Department of Biological Sciences School of ScienceXi’an Jiaotong Liverpool UniversitySuzhou215123JiangsuChina School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University21589JeddahSaudi Arabia Department of Signal Theory Networking and CommunicationsUniversity of Granada52005GranadaSpain

Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between *** address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global ***,these approaches typically neglect the topological structure information of the graph during the global feature extraction *** paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image *** approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual *** that different nodes contribute differently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global ***,we introduce a G-LFF module to fuse the local and global features effectively.

关键词： Convolutional neural networks Graph convolutional networks Graph pooling COVID-19

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Design of Chest Visual Based Image Reclamation Method Using Dual Tree Complex Wavelet Transform and Edge Preservation Smoothing Algorithm

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SN computer science 2024年第4期5卷 1-19页

作者： Kumar, B. P. Pradeep Darshan, S. L. Shiva Naresh, E. Srinidhi, N.N. Shreyas, J. Department of ECE HKBKCE Karnataka Bangalore 560045 India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal 506004 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The proposed article put forward a new scheme for image reclamation using second phase discrete symlet transform for medical images. The current medical image reclamation approaches have limitations in providing accurate reclamation fallouts with high visual insight and low computational complexity. To address these issues, this model presents a methodology for creating a medical image database using Image Reclamation using DT-CWT and EPS filter suited for the Resolution Enhancement of query chest image sample well utilized to get better retrieval rate where DWT algorithm is utilized for feature extraction of query input images. Flat and perpendicular prognoses of summation of pixels are analyzed to extract BC quantities, which are then used to compute the matching score of similarity for the images present in the database. The system selects the samples that are most pertinent to the given query sample image based on the matching score. The system’s untrained database is used to obtain the photographs with the highest BC value. The projected method aims to improve the enhancement of sampled image by DT-CWT EPS algorithm to leads to increase the accuracy and efficiency of medical image reclamation for various research applications. © The Author(s) 2024.

关键词： B quantities DWT Healthcare systems Medical image Visual BIR

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Integrating Machine Learning and OBIA for Vegetation Classification in Archived Grayscale Aerial Imagery 8

Integrating Machine Learning and OBIA for Vegetation Classif...

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8th International Conference on information technology, InCIT 2024

作者： Teeravech, Kumpee Ounban, Phummipat Srimala, Wira Samma, Taweesak Sawangsri, Kraisri Viriyasatr, Kittakorn Luangluewut, Warakorn Kumsap, Chamnan Rambhai Barni Rajabhat University Faculty of Computer Science and Information Technology Chanthaburi Thailand Ministry of Justice Department of Special Investigation Nonthaburi Thailand Defence Technology Institute Nonthaburi Thailand

ISBN: (纸本)9798350366303

This study explores the use of machine learning models to classify water, vegetation, and non-vegetation land cover types in archived grayscale aerial imagery. The input images are segmented using a superpixel algorithm, and the resulting segments are mapped to expert-provided reference data. The region-based and patch-based approaches are evaluated using artificial neural networks and convolutional neural networks, respectively. The region-based method achieves an average accuracy of 0.83, while the patch-based method reaches 0.79. Although the patch-based method shows slightly lower overall accuracy, it significantly improves recall rates, particularly for the water and non-vegetation classes. © 2024 IEEE.

关键词： Adversarial machine learning

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Assessing a BERT-based model for analyzing subjectivity and classifying academic articles

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Multimedia Tools and Applications 2024年第42期83卷 90511-90532页

作者： Mehmood, Atif Shahid, Farah Khan, Rizwan Ahmed, Shahzad Ibrahim, Mostafa M. Zheng, Zhonglong School of Computer Science and Technology Zhejiang Normal University Jinhua321002 China Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt

The metaverse concept extends beyond virtual worlds and can be applied to collaborative analysis environments. Data analysts worldwide may read academic article extracts in real-time in a shared digital workplace to mine and analyze data using the metaverse. Furthermore, a semantic metaverse in natural language processing might also involve creating a digital environment with linguistic and semantic connections. Many dedicated researchers navigating the complexities of natural language processing in the metaverse era have spent considerable time searching for relevant papers. However, online reviews and evaluations of articles are helpful for their assistance and may save the researcher time. In this work, human specialists manually produced a dataset from four conferences and evaluated subjectively using rule-based techniques. Subsequently, we aim to evaluate the effectiveness of pre-trained word embeddings and pre-trained BERT models seamlessly integrated with convolutional neural networks. This endeavor focuses on the subjective analysis and classification of contributions and previous work sentences extracted from academic literature. For comparison, various deep learning architectures were systematically employed, including long short-term memory-GloVe and bi-directional long short-term memory-GloVe, alongside classical machine learning methods. Our findings show that the proposed BERT model achieved state-of-the-art performance in classification and subjective analysis tasks with an accuracy of 91.50% and F1 score of 91.00%. Finally, we plan to utilize sentence similarity to identify contributions within abstracts, thus outlining potential avenues for future research. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Natural language processing systems

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Landslide susceptibility assessment along the major transport corridor using decision tree model: a case study of Kullu-Rohtang Pass

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International Journal of Business Intelligence and Data Mining 2023年第1期24卷 1-24页

作者： Nirbhav Malik, Anand Maheshwar Prasad, Mukesh Department of Geography Delhi School of Economics University of Delhi Delhi India Swami Shraddhanand College University of Delhi Delhi India TGT Computer Science Directorate of Education Delhi Government Delhi India Faculty of Engineering and Information Technology School of Computer Science Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

To lessen damages from landslides, the key challenge is to predict the events precisely and accurately. The objective of this study is to assess landslide susceptibility in the study area. To achieve this objective, a detailed landslide inventory has been prepared based on imagery data and frequent field visits of 153 rock slides and 44 debris slides. Nine landslide factors were prepared initially and their relationships with each other and with the type of landslide was analysed. information gain ratio measure is used to eliminate triggering factors with least score. Train_test_split method was used to classify the dataset into training and testing groups. Decision tree classification model of machine learning was applied for landslide susceptibility model (LSM). The performance was evaluated using classification report and receiver operating characteristic (ROC) curve. Results obtained have proven that the decision tree classification model performed well with good accuracy in forecasting landslide susceptibility. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Landslides

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification system (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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Design and Implementation of Embedded Qinghai Tourism Customer Service Question-and-Answer Robot Based on ChatGPT 24

Design and Implementation of Embedded Qinghai Tourism Custom...

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9th International Conference on Cyber Security and information Engineering, ICCSIE 2024

作者： Wu, Jing Guo, Zhenxin Wang, Zhicheng Zhang, Haotian Kang, Xaolin Ma, Xiaoguang Department of Computer and Information Science Qinghai Institute of Technology China Foshan Graduate School Northeastern University China School of Computer Science and Application Qinghai University China

ISBN: (纸本)9798400718137

The application of big data technology has brought new impetus and possibilities to the development of tourism. The design of an embedded Q&A robot system for Qinghai tourism customer service based on ChatGPT system aims to provide an intelligent customer service solution to help users obtain information about Qinghai tourism and answer questions. The system uses ChatGPT as the core dialogue model, combines the knowledge and data in the field of Qinghai tourism, and realizes the intelligent answer to user questions. This study application in the field of natural language processing, and analyzes the design requirements and implementation scheme of embedded travel question answering system in detail. The function module includes user login and registration, travel tickets, hotel information query, scenic spot ticket price query and other essential basic function modules of the tourism website. Whether the design is reasonable or not will also directly affect the user experience. The results show that ChatGPT embedded in Qinghai tourism Q&A system can not only provide more intelligent and accurate answers, but also enhance the interactivity and practicability of the system, and provide users with more convenient and personalized tourism information services. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

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computers, Materials & Continua 2022年第7期72卷 561-576页

作者： Aymen Saad Israa SKamil Ahmed Alsayat Ahmed Elaraby Department of Information Technology Technical College of ManagementKufaAl Furat Alawsat UniversityIraq Department of Medical Laboratory Techniques Al-Mustaqbal University CollegeHillaIraq Department of Computer Science College of Computer and Information SciencesJouf UniversitySaudi Arabia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world *** spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment.X-ray images are one of the most classifiable images that are used widely in diagnosing patients’data depending on radiographs due to their structures and tissues that could be *** Neural Networks(CNN)is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification *** using CNNs techniques requires a large number of images to learn and obtain satisfactory *** this paper,we used SqueezNet with a modified output layer to classify X-ray images into three groups:COVID-19,normal,and *** this study,we propose a deep learning method with enhance the features of X-ray images collected from Kaggle,Figshare to distinguish between COVID-19,Normal,and Pneumonia *** this regard,several techniques were used on the selected image samples which are Unsharp filter,Histogram equal,and Complement image to produce another view of the *** Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type(COVID-19,Normal and Pneumonia).In the first scenario,the model has been tested without any enhancement on the *** achieved an accuracy of 91%.But,in the second scenario,the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%.The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images.A comparison of the outcomes demonstrated the effectiveness of ourDLmethod for classifying COVID-19 based on enhanced X-ray images.

关键词： COVID-19 X-ray image enhancement classification CNN SqueezNet model

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Secure key exchange protocol and storage of logic locking key 28

Secure key exchange protocol and storage of logic locking ke...

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28th International Symposium on VLSI Design and Test, VDAT 2024

作者： Sarojam Chellam, Manjith Baby Natarajan, Ramasubramanian Naganathan, Nagi Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering-Cybersecurity Kottayam India National Institute of Technology Tiruchirappalli Department of Computer Science and Engineering Tiruchirappalli India Northrop Grumman Corporation MD United States

ISBN: (纸本)9798350380101

Logic locking has been proposed as a hardware security solution for both integrated circuits and FPGA-based design. The paper presents a secure key exchange protocol and secure LUT memory storage for the key used for unlocking the circuit. The work focuses on protecting the logic locking key for FPGA-based designs. Key exchange protocol will be accessed each time an Intellectual Property has been loaded to an FPGA. It can happen by full reconfiguration or partial reconfiguration of the bitstream. The key storage will be protected by preventing/identifying illegal access to the memory. This has been done through a firewall and access control/monitoring system. The proposed method provides security for the hardware IPs on FPGA which are locked by the logic locking method. © 2024 IEEE.

关键词： Keys (for locks)

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Clean Sustainable Society Using CNN 4th

Clean Sustainable Society Using CNN

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Sharma, Shivani Goel, Taniya Piplani, Radhika Rai, Bipin Kumar Department of Information Technology ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India

ISBN: (纸本)9789819994410

Waste disposal is a significant challenge in India. The rapid increase in the global human population is leading to a surge in mass production. The prevailing model of mass production and disposal follows a linear approach, wherein waste is primarily lost and not recycled or reused. This not only exacerbates environmental problems by increasing pollution but also results in economic losses due to the wasted potential of these materials. This paper basically focuses on an intelligent waste management system for redirecting the various kinds of trash materials to their correct positions by uploading the image of waste in the web application and it will classify images into following categories: reuse, recyclable, and organic waste. To identify the category of waste items, a machine learning model is used, followed by the implementation of a Convolutional Neural Network model to direct the item to the appropriate trash bin. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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