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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Jagadeeshwar, Kalyanpu Gottumukkala, V.S.S.P. Raju Srinivasarao, B. Kumar, Pala Mahesh Krishna, Namani Vamshi Kumar, P.R. Pavan VIT-AP University Department of Computer Science and Engineering Andhra Pradesh India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Sak Informatics Telangana Hyderabad India

ISBN: (纸本)9781665482714

Recently, COVID-19 is spreading rapidly and fast detection of COVID-19 can save millions of lives. Further, the COVID-19 can be detected easily from computed tomography (CT) images using artificial intelligence methods. However, the performance of these application and methods are reduced due to noises presented in the CT images, which degrading the performance of overall systems. Therefore, this article is focused on implementation of an innovative method for quickly processing CT images of low quality, which enhances the contrast using fuzzy logic. This method makes use of tuned fuzzy intensification operators and is intended to speed up the processing time. Extensive experiments were carried out to test the processing capacity of the method that was proposed, and the results obtained demonstrated that it was capable of filtering a variety of images that had become degraded. © 2022 IEEE.

关键词： COVID-19

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The Implementation of Hybrid Semantic Ontology-based Model on Movie Recommendation System

The Implementation of Hybrid Semantic Ontology-based Model o...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Ifada, Noor Cahyaningrum, Evinda Widia Rachman, Fika Hastarita Department of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

This paper adopts the Hybrid Semantic Ontology-based (HSO) model for a movie recommendation system. HSO consists of Collaborative Filtering (CF) and Content-based (CB) modules that respectively implement Matrix Factorization (MF) and ONTO Semantic Similarity algorithms. Since the feedback data type influences the MF algorithm choice, we individually implement the Non-Negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD) algorithms for handling the movie rating data. Accordingly, our proposed methods are called HSO-NMF and HSO-SVD. Meanwhile, since the domain determines the ontology, we build and use a new movie ontology on the CB module. The experiments show that HSO performs the best when implemented using the SVD algorithm. On average, the increased percentages of HSO-SVD to HSO-NMF are 1.18% and 1.62% in Precision and NDCG metrics, respectively. The experiments also show that implementing the Hybrid model yields more accurate results than the CB or CF model. © 2022 IEEE.

关键词： Collaborative filtering

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Empowering the adoption of blockchain-based cryptocurrency as a payment method: A user-friendly educational approach

IET Blockchain

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IET Blockchain 2025年第1期5卷

作者： Alrehaili, Ahmed White, Martin Beloff, Natalia University of Sussex School of Engineering and Informatics Informatics Brighton United Kingdom Islamic University of Madinah College of Computing and Information Systems Computer Science Department Medina Saudi Arabia

Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain-based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the technology acceptance model to explore cryptocurrency adoption through the blockchain-based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA) model. Factors within the model are assessed using an experimental vignette-task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that perceived ease of use, perceived usefulness, and perceived trust significantly impact participants’ intentions. Additionally, low transaction fees and age are the most influential factors on the technology acceptance model's core constructs. Statistical analysis indicates that decentralization and anonymity were insignificant and thus excluded from the revised BCAP-SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study's insights can guide strategies to promote wider cryptocurrency usage in the region. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： blockchain platforms blockchains cryptocurrencies models and analysis

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Enhancing Multimodal Rumor Detection with Statistical Image Features and Modal Alignment via Contrastive Learning 21st

Enhancing Multimodal Rumor Detection with Statistical Image...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhou, Chenyu Li, Xiuhong Li, Zhe Chen, Fan Sheng, Jiabao Chen, Bin Wang, Haoyu School of Computer Science and Technology Xinjiang University Xinjiang China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong Department of Electrical Engineering Stanford University CA United States Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9789819601219

The swift proliferation of multimodal rumors on social media, particularly those with manipulated images and complex intermodal interactions, significantly challenges current detection methods. In response, we utilize statistical image features, including mean and variance, to capture spatial attributes effectively and improve the detection of image-tampered tweets. To tackle complex intermodal correlations, we introduce a contrastive learning approach that aligns features across modalities efficiently. Additionally, we introduce a cross-attention fusion module (CAFM) that enhances the integration of image and text modalities, thereby improving multimodal rumor detection performance. In conclusion, we propose the cross-attention fusion network (ConCAFN), leveraging contrastive learning for robust multimodal rumor detection. Extensive experiments on two real-world datasets confirm the model’s enhanced capability to detect multimodal rumors accurately, demonstrating our methods’ effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Five-Stage Pipelined 32-Bit RISC-V Base Integer Instruction Set Architecture Soft Microprocessor Core in VHDL 23

Five-Stage Pipelined 32-Bit RISC-V Base Integer Instruction ...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： Phangestu, Aaron Elson Mujiono, Ir. Totok Kom, M.I. Ahmad Zaini, St Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Computer Engineering Department Surabaya60111 Indonesia

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

Proprietary technologies with complicated licensing currently dominate the microprocessor industry. As a result, we must seek out a freely available, open-source alternative. In this paper, we discussed the implementation of a five-stage pipelined soft processor core. The core uses the RISC-V RV32I Base Integer Instruction Set Architecture. RISC-V is an open standard ISA that is freely available to use and modify. To implement our processor core, we followed the FPGA design methodology. First, we implemented the design specification with the VHDL Hardware Description Language. Then, siwe mulated the design in the ModelSim simulation environment. Following the verification, we analyzed the resource usage, critical path, and maximum frequency of the processor, then uploaded the processor core to an actual Cyclone IV EP4CE6E22C FPGA. Our CPU core successfully executed all the RV32I instructions, except FENCE, ECALL, and CSR instructions. The proposed processor core runs on 2115 LUTs, 558 flip-flops, and 67,608 memory bits, with a maximum frequency of 62.95 MHz. © 2022 IEEE.

关键词： computer hardware description languages

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Fistula Condition Monitoring System by the Noise Changes Dynamics

Fistula Condition Monitoring System by the Noise Changes Dyn...

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2023 International Conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Yumagulov, N.I. Fedorov, D.A. Topolsky, D.V. Nizhnevartovsk State University Dep. for Education in Physics and Mathematics Nizhnevartovsk Russia Surgut State University Department of Informatics and Computer Engineering Surgut Russia Department of Electronic Computing Machines Chelyabinsk Russia

ISBN: (纸本)9781665475952

The paper presents an automated system for collecting and processing data for diagnosing the condition of an arteriovenous fistula. In the fistula, due to increased blood flow, characteristic noises and vibrations of the vessel walls occur. Using the data acquisition device, the acoustic noise of the arteriovenous fistula is recorded. The system processes and analyzes the measured data, evaluates the main indicators of the fistula condition. Changing the main indicators allows you to evaluate the dynamics of changes in the fistula condition. The developed system can be used for remote, long-term and continuous diagnostics of blood vessels in patients on hemodialysis. © 2023 IEEE.

关键词： Dynamics

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Explainable multi-layer COSFIRE filters robust to corruptions and boundary attack with application to retina and palmprint biometrics

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Neural Computing and Applications 2024年第30期36卷 19231-19245页

作者： Apap, Adrian Bhole, Amey Fernández-Robles, Laura Castejón-Limas, Manuel Azzopardi, George Department of Artificial Intelligence University of Malta Msida Malta Information Systems Group Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh 9 Groningen9747AG Netherlands Department of Mechanical Informatics and Aerospace Engineering Universidad de León Spain Campus de Vegazana s/n León24071 Spain

We propose a novel and versatile computational approach, based on hierarchical COSFIRE filters, that addresses the challenge of explainable retina and palmprint recognition for automatic person identification. Unlike traditional systems that treat these biometrics separately, our method offers a unified solution, leveraging COSFIRE filters’ trainable nature for enhanced selectivity and robustness, while exhibiting explainability and resilience to decision-based black-box adversarial attack and partial matching. COSFIRE filters are trainable, in that their selectivity can be determined with a one-shot learning step. In practice, we configure a COSFIRE filter that is selective for the mutual spatial arrangement of a set of automatically selected keypoints of each retina or palmprint reference image. A query image is then processed by all COSFIRE filters and it is classified with the reference image that was used to configure the COSFIRE filter that gives the strongest similarity score. Our approach, tested on the VARIA and RIDB retina datasets and the IITD palmprint dataset, achieved state-of-the-art results, including perfect classification for retina datasets and a 97.54% accuracy for the palmprint dataset. It proved robust in partial matching tests, achieving over 94% accuracy with 80% image visibility and over 97% with 90% visibility, demonstrating effectiveness with incomplete biometric data. Furthermore, while effectively resisting a decision-based black-box adversarial attack and impervious to imperceptible adversarial images, it is only susceptible to highly perceptible adversarial images with severe noise, which pose minimal concern as they can be easily detected through histogram analysis in preprocessing. In principle, the proposed learning-free hierarchical COSFIRE filters are applicable to any application that requires the identification of certain spatial arrangements of moderately complex features, such as bifurcations and crossovers. Moreover, the sele

关键词： Visibility

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Leveraging Social Media Posts for Natural Disaster Response Using CNN-BiLSTM Model

Leveraging Social Media Posts for Natural Disaster Response ...

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International Conference on informatics and Computing (ICIC)

作者： Muhammad Rafi Mohammad Reza Faisal Irwan Budiman Muliadi Dodon Turianto Nugrahadi Mera Kartika Delimayanti Department of Computer Science Lambung Mangkurat University Banjarbaru Indonesia Department Politeknik Negeri Jakarta Computer and Informatics Engineering Depok Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

In the contemporary digital era, social media, particularly Twitter, has become a vital channel for sharing realtime information on natural disasters like floods, forest fires, and earthquakes. This rapid dissemination enables swift responses to disasters. However, classification models relying solely on social media data face challenges, notably the lack of richness and diversity in word embedding vectors, limiting precise analysis. To address this, the research integrates data from Wikipedia to improve word embeddings. The classification model employs Word Embedding, Convolutional Neural Network (CNN) 1D, and Bidirectional Long Short-Term Memory (BiLSTM) techniques. By combining embedding methods such as Word2Vec, GloVe, and FastText, derived from both social media and Wikipedia Indonesia, the model enhances word representation and improves classification accuracy. Results demonstrate accuracies of 85.61% for floods, $92.56 \%$ for forest fires, and $84.11 \%$ for earthquakes. This research contributes to advancing natural disaster communication classification by utilizing more diverse data and proposing methodologies for unstructured social media content analysis.

关键词： Accuracy Social networking (online) Disasters Bidirectional long short term memory Encyclopedias Data models Vectors Internet Online services Convolutional neural networks

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Implementation of MTCNN Facial Feature Extraction on Sleepiness Scale Classification Using CNN

Implementation of MTCNN Facial Feature Extraction on Sleepin...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Putra, Adima Mahardika Zaini, Ahmad Pramunanto, Eko Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Sleepiness is a condition when the level of human consciousness decreases. Sleepiness is not easy to measure externally. If this is allowed just like that, it would be very dangerous if we were doing activities that requires full control of consciousness such as activities driving. This set of tools and methods for detecting drowsiness has been developed. However, in its implementation the intrusive method uses this tool less practical. In addition, the sleep detection method uses video images as well experiencing problems due to the potential for data loss in the form of facial features. To get this data, you must use a near infrared camera which has a low resolution and lacks detail. Therefore it is necessary an algorithm that is able to detect facial features to the maximum for classifying a person's sleepiness scale. To achieve the goal, a program will be created that functions to perform the extraction facial features and eye features. The program will function to detect number of frames containing facial features and performing condition classification eyes closed or open which will then be saved in a 'csv' file to be processed. Furthermore, the data will be carried out in the training process using 1D CNN architecture. The results of the training process that has been carried out The previous model is the model that will be used in performing the scale classification drowsiness. There are 6 experimental scenarios to get the best results. From all the results that have been obtained, it can be concluded that the best model results are the result of the training process with epoch values by 30 and the addition of synthetic data. Accuracy value obtained of 89% and the loss value of 34%. © 2022 IEEE.

关键词： Classification (of information)

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A Mini-Review of Machine Learning in Big Data Analytics:Applications,Challenges,and Prospects

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Big Data Mining and Analytics 2022年第2期5卷 81-97页

作者： Isaac Kofi Nti Juanita Ahia Quarcoo Justice Aning Godfred Kusi Fosu the Department of Computer Science and Informatics University of Energy and Natural ResourcesSunyani BS2103Ghana the Department of Electrical&Electronic Engineering Sunyani Technical UniversitySunyani BS2103Ghana the Department of Computer Science Sunyani Technical UniversitySunyani BS2103Ghana

The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of *** capability to process these gigantic amounts of data in real-time with Big Data Analytics(BDA)tools and Machine Learning(ML)algorithms carries many ***,the high number of free BDA tools,platforms,and data mining tools makes it challenging to select the appropriate one for the right *** paper presents a comprehensive mini-literature review of ML in BDA,using a keyword search;a total of 1512 published articles was *** articles were screened to 140 based on the study proposed novel *** study outcome shows that deep neural networks(15%),support vector machines(15%),artificial neural networks(14%),decision trees(12%),and ensemble learning techniques(11%)are widely applied in *** related applications fields,challenges,and most importantly the openings for future research,are detailed.

关键词： Big Data Analytics(BDA) Machine Learning(ML) Big Data(BD) Hadoop MapReduce

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