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Informatica (Slovenia) 2024年第5期48卷 55-62页

作者： Oraibi, Zakariya A. Albasri, Safaa Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Electrical Engineering Department College of Engineering Mustansiriyah University Baghdad Iraq

In late 2019, COVID-19 virus emerged as a dangerous disease that led to millions of fatalities and changed how human beings interact with each other and forced people to wear masks with mandatory lockdown. The ability to diagnose and detect this novel disease can help in isolating the infected patients and curb the spread of the virus. Artificial intelligence techniques including machine learning and deep learning showed huge potential in accurately classifying COVID-19 chest X-ray images. In this paper, we propose to combine the feature maps of multiple powerful CNN models (Xception, VGG-16, VGG-19) using the rule of sum. Each of these models is trained from scratch and tested on the given test images. The dataset was collected from a large public repository of COVID images with three classes: COVID, Normal, and Pneumonia. During experiments, data augmentation is also applied to provide more training samples. Experimental results show that combining multiple models improve the classification accuracy and achieve better performance than standalone models. An accuracy of 97.91% was achieved using a combination of three models which outperforms state-of-the-art techniques. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Coronavirus

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Robust subspace clustering via two-way manifold representation

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Multimedia Tools and Applications 2025年第16期84卷 16339-16356页

作者： Ezeora, Nnamdi Johnson Anichebe, Gregory Emeka Nzeh, Royransom Chiemela Uzo, Izuchukwu Uchenna Department of Computer Science University of Nigeria Nsukka Nigeria School of Computer Science and Communication Engineering JiangSu University JiangSu 212013 China

Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overlapping subspaces. To tackle this problem and ensure that each sample is only assigned to a single subspace, a new method is proposed in this paper. Specifically, a two-way learning technique is introduced by inducing data manifold via two representative structures. The first is a low-rank structure learned directly from original data. The second structure is an affinity matrix obtained via a k symmetric nearest neighbor graph. By further introducing a dual regularization term, both structures are allowed to guild themselves adaptively to find robust clustering directly without spectral post-processing. In order to evaluate the effectiveness of the proposed method, several experiments are conducted on multiple benchmark datasets. The experimental results, evaluated using six standard metrics, clearly demonstrate that the proposed method outperforms state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clustering algorithms

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A New Profiling-Based Side-Channel Attack on Graphics Processing Units

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Journal of Computing science and engineering 2023年第1期17卷 30-40页

作者： Wang, Xin Zhang, Wei Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA United States Department of Computer Science and Engineering University of Louisville LouisvilleKY United States

Graphics processing units (GPUs) have been increasingly used to solve a range of compute-intensive and data-parallel scientific computing problems that can be perfectly parallelized for performance speedups. Particularly, GPUs have recently become popular to host the encryption/decryption algorithms due to its high-throughput computing capability. However, the security issues of moving the cryptographic algorithms onto GPUs have not been studied adequately. Consequently, with absence of any protection strategy, the potential vulnerabilities of GPUs to side-channel attacks (SCAs) may expose the confidential information with high risk. In this paper, we proposed a new profiling-assisted correlationbased side-channel attack (pacSCA) to demonstrate that ignoring security issues and naively moving security services onto GPUs can offer adversaries fatal vulnerabilities to thieve critical information. The results showed that the proposed SCA can rebuild the secure key of the AES-128 algorithm in less than 6 seconds, revealing the urgency of protecting GPUs against side-channel threats. © Copyright 2023. The Korean Institute of Information Scientists and Engineers

关键词： Graphics processing unit

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Semantic deep learning and adaptive clustering for handling multimodal multimedia information retrieval

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Multimedia Tools and Applications 2025年第13期84卷 11795-11831页

作者： Sattari, Saeid Yazici, Adnan Computer Engineering Department Middle East Technical University Ankara06531 Turkey Computer Science Department Nazarbayev University Astana010000 Kazakhstan

Multimedia data encompasses various modalities, including audio, visual, and text, necessitating the development of robust retrieval methods capable of harnessing these modalities to extract and retrieve semantic information from multimedia sources. This paper presents a highly scalable and versatile end-to-end framework for multimodal multimedia information retrieval. The core strength of this system lies in its capacity to learn semantic contexts within individual modalities and across different modalities, achieved through the utilization of deep neural models. These models are trained using combinations of queries and relevant shots obtained from query logs. One of the distinguishing features of this framework is its ability to create shot templates, representing videos that have not been encountered previously. To enhance retrieval performance, the system employs clustering techniques to retrieve shots similar to these templates. To address the inherent uncertainty in multimodal concepts, an improved variant of fuzzy clustering is applied. Additionally, a fusion method incorporating an OWA operator is introduced. This method employs various measures to aggregate ranked lists produced by multiple retrieval systems. The proposed approach leverages parallel processing and transfer learning to extract features from three distinct modalities, ensuring the adaptability and scalability of the framework. To assess its effectiveness and efficiency, the system is rigorously evaluated through experiments conducted on six widely recognized multimodal datasets. Remarkably, our approach outperforms previous studies in the literature on four of these datasets, achieving performance improvements ranging from 1.5% to 10.1% over the best reported results in those studies. The experimental findings, substantiated by statistical tests, conclusively establish the effectiveness of the proposed approach in the field of multimodal multimedia information retrieval. © The Author(s), und

关键词： Fuzzy clustering

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IoT enabled plant leaf disease segmentation and multi-classification using mayfly bald eagle optimization-enabled machine learning

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Multimedia Tools and Applications 2024年第21期83卷 59747-59781页

作者： Mishra, Monalisa Choudhury, Prasenjit Pati, Bibudhendu Department of Computer Science and Engineering National Institute of Technology Durgapur India Department of Computer Science Rama Devi Women’s University Bhubaneswar India

Agricultural production is critical to the economy. This is one of the reasons why disease detection in plants is so important in agricultural settings, as plant disease is rather common. Farmers are not engaged in increasing their agricultural productivity daily since there are no technologies in the previous system to detect diseases in various crops in an agricultural environment. With the exponential population growth, food scarcity is a huge concern globally. In addition to this, the productivity of agricultural products has been highly impacted by the rapid increase in phytopathological adversities. The main challenges in leaf segmentation and plant disease identification are prior knowledge is required for segmentation, the implementation still lacks the accuracy of results, and more tweaking is required. To reduce the devastating impacts of illnesses on the economy, early detection of illnesses in plants is therefore essential. This paper describes an approach for segmenting and detecting plant leaf diseases based on images acquired via the Internet of Things (IoT) network. Here, a plant leaf area is segmented with a UNet, whose trainable parameters are optimized using the Mayfly Bald Eagle Optimization (MBEO) algorithm. Further, plant type classification is carried out by the Deep batch normalized AlexNet (DbneAlexNet), optimized by the Sine Cosine Algorithm-based Rider Neural Network (SCA-based RideNN). Finally, the DbneAlexNet, with weights adapted by the MBEO algorithm, is used to identify plant disease. The Plant Village dataset is used to evaluate the proposed DbneAlexNet-MBEO for plant-type classification and disease detection. The efficiency of the UNet-MBEO for segmentation is examined based on the Dice coefficient and Intersectin over Union (IOU) and has achieved superior values of 0.927 and 0.907. Moreover, the DbneAlexNet-MBEO is examined considering accuracy, Test Negative Rate (TNR), and Test Positive Rate (TPR) and offered superior values of 0

关键词： Classification (of information)

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Data hiding with thumbnail-preserving encryption for cloud medical images

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Multimedia Tools and Applications 2024年第36期83卷 84161-84177页

作者： Xu, Shuying Chang, Chin-Chen Horng, Ji-Hwei Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan Department of Electrical Engineering National Quemoy University Kinmen892 Taiwan

To serve a convenient healthcare network, storing medical images and diagnosis records in the cloud is a straightforward solution. Encrypting the medical images before uploading them to the cloud is a trivial strategy to prevent data breaches. However, the availability of images is also reduced. Since all images look noisy, one cannot obtain the intended image by a quick viewing of the file folder. In this paper, we propose a total solution to these considerations. A reversible data hiding scheme based on thumbnail-preserving image encryption is presented. By leveraging a multi-scale Haar wavelet transform and extended run-length coding, we can produce thumbnail-preserving encrypted medical images that provide a sufficient vacating room for embedding electronic patient records. The experimental results demonstrate that our scheme effectively balances the confidentiality and availability of encrypted medical images. It provides over 1 bit per pixel of capacity for embedding electronic patient records, making it suitable for cloud-based medical data management. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Fortifying Multi-Agent Architectures in Smart Manufacturing: Leveraging Federated Learning and Blockchain for Security and Resilience 22

Fortifying Multi-Agent Architectures in Smart Manufacturing:...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Tshakwanda, Petro M. Tsegaye, Henok B. Almaayn, Raddad Devetsikiotis, Michael University of New Mexico Department of Electrical and Computer Engineering United States University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798331508050

This work presents a blockchain-based communication architecture for multi-agent smart manufacturing systems, enhanced with federated learning to improve security, data privacy, and scalability. Comparative evaluation with traditional centralized systems across various configurations reveals that our approach achieves up to 12 % higher model accuracy and 15 % greater security, particularly in large-scale deployments, with scalability scores surpassing those of centralized systems by 18 % as agent numbers increase. Although initial latency and energy consumption are higher, these factors improve significantly as the system scales, indicating suitability for complex manufacturing networks. This research underscores the potential of combining federated learning and blockchain to enhance resilience, security, and efficiency in Industry 4.0 smart manufacturing systems. © 2025 IEEE.

关键词： Sensitive data

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A new speaker-diarization technology with denoising spectral-LSTM for online automatic multi-dialogue recording

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Multimedia Tools and Applications 2024年第15期83卷 45407-45422页

作者： Chan, Din Yuen Wang, Jhing-Fa Chin, Hsu-Ting Department of Computer Science and Information Engineering National Chiayi University Chiayi City Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

In AI pandemic applications, the online automatic AI recording apparatus for official councils such as court trials, business conferences and commercial meetings will become imperative because it could let the opinion identification and consensus of participants be synchronically available to implicitly diminish social costs such as follow-up disputes and controversies. Hence, in this study, an automatic on-line multi-dialogue recording system is completely constructed, where the unbounded interleaved-state recurrent neural networks (UIS-RNN) with proposed crux improvements is exploited to achieve confident speaker-diarization. For keeping the systematic robustness, a denoising spectral-LSTM, which is precisely modified from the dual-signal transformation LSTM (DTLN), can strengthen its subsequent crux-improved UIS-RNN and automatic speech recognition (ASR). Finally, the MacBERT model is set to rectify the possible wrong words in conversed sentences according to the learned rational context. For making our system being a practical software apparatus in the use of unmarked multi-person councils, we have also completed the convenient interfaces for the operations of ASR and speaker-diarization, which can exhibit on-line denoising efficacy and speaker-diarization results as well as offer real-time hand-crafted rectifications to common users. In extensive experiments, the proposed recording system can promise high accuracy rates of online speaker diarization and speech-separated ASR. Our proposed system had been examined by the cooperated law court staffs, who offered the noise-embedded speeches of practical court field to test our system. Since the tight recording burden had been indeed noticeably alleviated in their legal-action councils, the court staffs had endorsed that the proposed entire system could be a friendly labor-saving AI apparatus for on-line automatic multi-dialogue recording. © The Author(s), under exclusive licence to Springer science+Business Media,

关键词： Audio recordings

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Safeguarding Industrial Automation: A Fuzzing Framework for PLC Control Logic

Safeguarding Industrial Automation: A Fuzzing Framework for ...

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2025 International Conference on Computing, Networking and Communications, ICNC 2025

作者： Uwibambe, Marie Louise Li, Qinghua University of Arkansas Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798331520960

As Programmable Logic Controllers (PLCs) become more integrated into complex industrial systems and networks, their software components face increased security vulnerabilities. Fuzz testing, which uses unexpected inputs to find vulnerabilities, has been used to enhance PLC security but is primarily focused on communication protocols, often overlooking control logic. This paper presents a fuzzing framework for PLC control logic programs. The framework is designed to handle control logic's unique execution process, which may involve translating PLC languages into general-purpose languages. It generates structurally valid test cases and is platform-independent. We implement our prototype around the OpenPLC environment and expand our experiments to a Wago PLC image to evaluate the feasibility of our method on real-world devices. Our approach exhibits strong performance in terms of code coverage, test case pass rate, and crash detection, while ensuring high time efficiency. © 2025 IEEE.

关键词： Logic programming

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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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Multimedia Tools and Applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;LGXP and GLCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes LSTM and AML-CNN models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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