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Synthesizing realistic images for improved early detection of novel pandemics: A GAN enabled approach

Heliyon 2025年第6期11卷

作者： Rounaq, Sumera Shah, Shahid Munir Aljawarneh, Mahmoud Mohammad Khan, Sarah Muhammad, Ghulam Department of Computer Science DHA Suffa University Karachi Pakistan Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman Jordan System Architect Low Code Solutions London United Kingdom Department of Computer Science Bahria University Karachi Pakistan

Infectious diseases like the novel Coronavirus (COVID-19) affect millions of individuals if not managed well in time. Thus, to reduce the transmission rate, effective diagnostic techniques must be identified. Early detection and diagnosis may be aided by Artificial Intelligence (AI) based automated diagnostics;however, these algorithms need a lot of pertinent data to be trained. In the early stages of a pandemic, gathering a lot of data is never an easy task. This presents challenges for accurate automatic diagnosis of such infectious diseases. To address this issue, a customized Generative Adversarial Networks (GAN) based architecture i. e., CoroWGAN-GP is proposed to synthesize images and to expand datasets. A dataset of 588 images with COVID-19, Normal, and Viral Pneumonia categories (taken from Cohen's repository [1]) was expanded using the proposed CoroWGAN-GP approach and used to train a proposed customized deep CNN architecture i. e., DeepCNN-CovNet19. Experimental results demonstrate that the proposed model achieved a considerable detection accuracy. i. e., 98.0 % in detecting the disease. The effectiveness of the proposed method was further evaluated by comparing its results with the state-of-the-art methods in the literature. Comparative analyses indicate that the proposed approach offers a promising solution for the accurate diagnosis of COVID-19-like pandemics. © 2025 The Authors

关键词： Chest X-ray Convolutional neural networks COVID-19 CXR GAN Pandemic Wasserstein GAN

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Benchmarking Cryptographic Protocols for (IoT) Malware Defence

Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

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International Symposium on applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

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Adaptive folding and noise filtering for robust quantum error mitigation

arXiv

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arXiv 2025年

作者： Koenig, Kathrin F. Reinecke, Finn Wellens, Thomas Fraunhofer Institute for Applied Solid State Physics Tullastr. 72 Freiburg79108 Germany Department of Sustainable Systems Engineering University of Freiburg Emmy-Noether-Str. 2 Freiburg79110 Germany Department of Computer Science University of Freiburg Georges-Köhler-Allee 51 Freiburg79110 Germany

Coping with noise in quantum computation poses significant challenges due to its unpredictable nature and the complexities of accurate modeling. This paper presents noise-adaptive folding, a technique that enhances zero-noise extrapolation (ZNE) through the use of adaptive scaling factors based on circuit error measurements. Furthermore, we introduce two filtering methods: one relies on measuring error strength, while the other utilizes statistical filtering to improve the extrapolation process. Comparing our approach with standard ZNE reveals that adaptive scaling factors can be optimized using either a noise model or direct error strength measurements from inverted circuits. The integration of adaptive scaling with filtering techniques leads to notable improvements in expectation-value extrapolation over standard ZNE. Our findings demonstrate that these adaptive methods effectively strengthen error mitigation against noise fluctuations, thereby enhancing the precision and reliability of quantum computations. © 2025, CC BY.

关键词： Qubits

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Quantum-Inspired Resource Optimization for 6G Networks: A Survey

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IEEE Communications Surveys and Tutorials 2024年

作者： Butt, Muhammad Omair Waheed, Nazar Duong, Trung Q. Ejaz, Waleed Lakehead University Department of Electrical Engineering ON Canada Faculty of Computing Information Systems Higher Colleges of Technology United Arab Emirates Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

The Internet of things (IoT) drives an exponential surge in computing and communication devices. Consequently, it triggers capacity, coverage, interference, latency, and security issues in the existing communication networks. The forthcoming sixth-generation (6G) networks will address these issues by providing comprehensive solutions. In particular, quantum communication technology can potentially address the challenges of 6G networks. However, its implementation requires substantial infrastructure upgrades. Therefore, the quantum-inspired (QI) techniques offer an intermediate resort due to their ability to utilize the classical communication infrastructure for design and implementation. Hence, we review QI techniques in this survey that address radio resource optimization challenges across various communication aspects, including channel assignment, reconfigurable intelligent surfaces, spectrum sensing, unmanned aerial vehicle-assisted networks, and related areas. The analysis explores diverse aspects, including objectives, constraints, problem characterization, proposed solutions, and lessons learnt. Research indicates that QI techniques offer advantages such as faster convergence and reduced complexity, providing promising solutions to complex optimization problems in communication networks. Furthermore, we identify the future directions, research gaps, and ongoing challenges from the QI radio resource optimization dataset. © 1998-2012 IEEE.

关键词： Quantum communication

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An Adaptive Streaming Feature Selection Technique for Classifying Non-Stationary Data Streams

An Adaptive Streaming Feature Selection Technique for Classi...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Monika Arya Bhupesh Kumar Dewangan Tanupriya Choudhury Ketan Kotecha Sanjana Dewangan Akhilesh Gaidhane Department of Computer Science and Engineering Bhilai Institute of Technology Durg India Department of Computer Science and Engineering OP Jindal University Raigarh India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India School of Science OP Jindal University Raigarh India Department of Mathematics SAGES M.G. Ward Kondagaon India

In today's digital era, various real-world applications generate data in streams, and these data streams are of two types stationary data streams, which are static, and non-stationary data streams that are dynamic in nature. Non-stationary data streams are pro-to-concept drifts. To overcome this issue, an adaptable streaming feature selection technique for non-stationary data streams is proposed in this paper. The selection of relevant feature space from non-stationary data streams is crucial for enabling intelligence through various data mining tasks, specifically using data classification. Feature selection improves the overall performance of the classifier while increasing the computational overhead. The proposed approach aims to enable streaming feature selection whenever the classification accuracy is underperforming to enhance the classification accuracy, while the model remains stable if there is no change in the concepts. The proposed approach is adaptive and ameliorates the classifier performance by reducing the error rate during changes in the concept with time. This adaptability to concept drift improves the accuracy by approximately 16% and reduces the computation time by 50%. The proposed model avoids overfitting of the model.

关键词：

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Towards a Comprehensive Dataset for Next-Day Wildfire Prediction

Towards a Comprehensive Dataset for Next-Day Wildfire Predic...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Syed Haider Ali Anish Goel Aditya Singirikonda Ali Y Khan Ting Xiao Department of Computer Science and Engineering University of North Texas Denton TX USA Texas Academy of Math and Science Denton TX USA Department of Information Science University of North Texas Denton TX USA

Wildfires pose a significant threat with an increased risk of loss of life and property damage in recent years. Traditionally catastrophe modeling has relied on physical models to understand and forecast the behavior of such catastrophic events. In large part this has been due to the lack of a concise dataset that can bring together all the features required for properly modeling such phenomena, and also the required computational strength did not exist. In this paper, we produce a large-scale multivariate dataset to develop deep learning models to understand and forecast the spread of wildfires. We examine features such as topography, climate conditions, and population density which affect the severity of a natural disaster. We discuss challenges in deep learning approaches to next-day wildfires prediction. We expect that this approach can be utilized to produce state of the art deep learning models for other natural catastrophes as well.

关键词： Deep learning Computational modeling Sociology Fires Predictive models Surfaces Behavioral sciences

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HiKonv: Maximizing the Throughput of Quantized Convolution With Novel Bit-wise Management and Computation

arXiv

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arXiv 2022年

作者： Chen, Yao Pan, Junhao Liu, Xinheng Xiong, Jinjun Chen, Deming The Department of Computer Science National University of Singapore Singapore The Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL United States The School of Engineering and Applied Sciences University at Buffalo BuffaloNY United States

Quantization is proven to be effective for Convolutional Neural Networks (CNN) to reduce the cost of computation and storage with low-bitwidth data representations. However, the current execution of quantized data on the existing full-bitwidth processing units, such as ALU in CPUs and DSP in FPGAs, is through simply extending the lower bitwidth to the supported bitwidth, which leads to the underutilization of the computing unit and delivers low computational throughput. In this study, we propose HiKonv, a unified solution that maximizes the throughput of convolution on a given underlying processing unit with low-bitwidth quantized data as inputs through novel bit-wise management and parallel computation. We establish theoretical framework and performance models using a full-bitwidth multiplier for highly parallelized low-bitwidth convolution and demonstrate new breakthroughs for high-performance computing in this critical domain. For example, a single 32-bit processing unit in CPU can deliver 128 binarized convolution operations (multiplications and additions), thirteen 4-bit convolution operations or five 8-bit convolution operations with a single multiplication instruction, and a single 27×18 multiplier in the FPGA DSP core can deliver 60, 8 or 2 convolution operations with 1, 4 or 8-bit inputs in one clock cycle. We demonstrate the effectiveness of HiKonv on CPU and FPGA for both convolutional layers and a complete DNN model with our platform-oriented implementations. On CPU, HiKonv outperforms the baseline implementation with 1 to 8-bit inputs and provides up to 7.6× and 1.4× performance improvements for 1-D convolution. For the 2-D convolutional layer, HiKonv performs 2.74× and 3.19× over the baseline implementation for 4-bit signed and unsigned data inputs. HiKonv also provides over 2× latency improvement for a complete DNN model on both Intel and ARM CPUs. On FPGA, the HiKonv solution enables a single DSP to process the same convolution operations that requir

关键词： Field programmable gate arrays (FPGA)

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Hyperbolic Convolution via Kernel Point Aggregation

arXiv

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arXiv 2023年

作者： Qu, Eric Zou, Dongmian The Department of Electrical Engineering and Computer Science University of California BerkeleyCA94720 United States CMCS DSRC Division of Natural and Applied Sciences Duke Kunshan University Jiangsu Kunshan215316 China

Learning representations according to the underlying geometry is of vital importance for non-Euclidean data. Studies have revealed that the hyperbolic space can effectively embed hierarchical or tree-like data. In particular, the few past years have witnessed a rapid development of hyperbolic neural networks. However, it is challenging to learn good hyperbolic representations since common Euclidean neural operations, such as convolution, do not extend to the hyperbolic space. Most hyperbolic neural networks do not embrace the convolution operation and ignore local patterns. Others either only use non-hyperbolic convolution, or miss essential properties such as equivariance to permutation. We propose HKConv, a novel trainable hyperbolic convolution which first correlates trainable local hyperbolic features with fixed kernel points placed in the hyperbolic space, then aggregates the output features within a local neighborhood. HKConv not only expressively learns local features according to the hyperbolic geometry, but also enjoys equivariance to permutation of hyperbolic points and invariance to parallel transport of a local neighborhood. We show that neural networks with HKConv layers advance state-of-the-art in various tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Low mechanical loss and high refractive index in amorphous Ta2O5 films grown by magnetron sputtering

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Physical Review Materials 2024年第3期8卷 035603-035603页

作者： M. Molina-Ruiz K. Shukla A. Ananyeva G. Vajente M. R. Abernathy T. H. Metcalf X. Liu A. Markosyan R. Bassiri M. M. Fejer M. Fazio L. Yang C. S. Menoni F. Hellman Department of Physics University of California Berkeley Berkeley California 94720 USA Department of Materials Science and Engineering University of California Berkeley Berkeley California 94720 USA LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Naval Research Laboratory Washington DC 20375 USA Edward L. Ginzton Laboratory Stanford University Stanford California 94305 USA Department of Applied Physics Stanford University Stanford California 94305 USA Department of Electrical and Computer Engineering Colorado State University Fort Collins Colorado 80523 USA Department of Chemistry Colorado State University Fort Collins Colorado 80523 USA

The ability to observe astronomical events through the detection of gravitational waves relies on the quality of multilayer coatings used on the optical mirrors of interferometers. Amorphous Ta2O5 (including TiO2:Ta2O5) currently limits detector sensitivity due to high mechanical loss. In this paper, mechanical loss measured at both cryogenic and room temperatures of amorphous Ta2O5 films grown by magnetron sputtering and annealed in air at 500 ∘C is shown to decrease for elevated growth temperature. Films grown at 310 ∘C and annealed yield a mechanical loss of 3.1×10−4 at room temperature, the lowest value reported for pure amorphous Ta2O5 grown by magnetron sputtering to date, and comparable to the lowest values obtained for films grown by ion beam sputtering. Additionally, the refractive index n increases 6% for elevated growth temperature, which could lead to improved sensitivity of gravitational-wave detectors by allowing a thickness reduction in the mirrors' coatings. Structural characterization suggests that the observed mechanical loss reduction in amorphous Ta2O5 films with increasing growth temperature correlates with a reduction in the coordination number between oxygen and tantalum atoms, consistent with TaOx polyhedra with increased corner-sharing and reduced edge- and face-sharing structures.

关键词： Structural properties Amorphous materials Film deposition Gravitational wave detectors Physical vapor deposition

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New vision of an e-health integrated system's model: Constructive factors and imperative components 3

New vision of an e-health integrated system's model: Constru...

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3rd Smart Cities Symposium, SCS 2020

作者： El Murabet, A. Abtoy, A. Tahiri, A. Younes, A. Computer Engineering Department National School of Applied Sciences of Tetuan Tetuan Morocco Computer Science Department Faculty of Sciences of Tetuan Morocco

ISBN: (纸本)9781839535635

A new generation of smart homes has emerged as a revolutionary solution for e-health. Smart health monitoring systems are becoming vital, and the integration of these systems will ease the progression and provide better chances for the users to adapt to the embedded systems in their environments. In this paper, we propose a novel model for integrated e-health systems. We believe that a number of imperative components should be adopted by the system's designers in order to ease the integration and facilitate the management from the user's perspective. We introduce our vision and we describe the different constructive factors to achieve the desirable results. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Automation

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