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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Xiao, Sanxiang Deng, Yihui Li, Ming School of Computer Science Guangzhou College of Applied Science and Technology Guangdong Guangzhou511300 China Guangzhou College of Applied Science and Technology Experimental Training Center Guangdong Guangzhou511300 China School of Computer Science Guangzhou City Construction College Guangdong Guangzhou511300 China

ISBN: (纸本)9798350324433

The development of the Internet has brought convenience to people, but also caused security risks. How to establish a protection system that can not only effectively prevent computer network(CN)s from being attacked by hackers and viruses, but also effectively protect user privacy, real-time and stability has become an urgent problem to be solved. In this paper, the system performance test and analysis, including system test environment, system test content and so on. This paper mainly analyzes the system performance and improvement maethods of the network information security protection system(SPS) under relevant algorithms by means of experimental simulation and data comparison. Experimental data show that the recall rate after incremental training can reach more than 90%. More than 97% of website operation files are recovered in a short time. In order to further improve the performance of the system, a wider range of web pages should be selected for testing. © 2023 IEEE.

关键词： Genetic algorithms

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High Resolution Solar Image Generation Using Generative Adversarial Networks

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Annals of Data Science 2024年第5期11卷 1545-1561页

作者： Dash, Ankan Ye, Junyi Wang, Guiling Jin, Huiran Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States School of Applied Engineering and Technology New Jersey Institute of Technology NewarkNJ07102 United States

We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line of sight magnetogram images to SDO/Atmospheric Imaging Assembly (AIA) 0304-Å images. The Ultraviolet (UV)/Extreme Ultraviolet observations like the SDO/AIA 0304-Å images were only made available to scientists in the late 1990s even though the magnetic field observations like the SDO/HMI have been available since the 1970s. Therefore, by leveraging Deep Learning algorithms like GANs we can give scientists access to complete datasets for analysis. For generating high resolution solar images, we use the Pix2PixHD and Pix2Pix algorithms. The Pix2PixHD algorithm was specifically designed for high resolution image generation tasks, and the Pix2Pix algorithm is by far the most widely used image to image translation algorithm. For training and testing we used the data for the year 2012, 2013 and 2014. After model training, we evaluated the model on the test data. The results show that our deep learning models are capable of generating high resolution (1024 × 1024 pixels) SDO/AIA0304 images from SDO/HMI line of sight magnetograms. Specifically, the pixel-to-pixel Pearson Correlation Coefficient of the images generated by Pix2PixHD and original images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate images. The results we get for our Pix2PixHD model is better than the results obtained by previous works done by others to generate SDO/AIA 0304 images. Thus, we can use these models to generate AIA0304 images when the AIA0304 data is not available which can be used for understanding space weather and giving researchers the capability to predict solar events such as Solar Flares and Coronal Mass Ejections. As far as we know, our work is the first attempt to leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image translation. © Th

关键词： Generative adversarial networks

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SimV: A Self-extension Similarity Viterbi Algorithm for Service Function Chain Deployment for Multi-access Edge Computing 22

SimV: A Self-extension Similarity Viterbi Algorithm for Serv...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Deng, Wenqiang Guangzhou College of Applied Science and Technology School of Computer Science Zhaoqing China

ISBN: (纸本)9798331509712

Advancements in Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) offer a lot of potential for providing flexible and low-latency network services at the edge. In NFV, traditional middleboxes are transformed into Virtual Network Functions (VNFs), which are then combined into Service Function Chains (SFCs) to deliver complex network services. However, the features of MEC hosts and VNFs, along with the heterogeneity of their resources, make it challenging to develop low-latency deployment solutions. In this paper, we formulate the problem of Service Function Chain Deployments in Edge networks to minimize Latency under Heterogeneous conditions (SDELH). To address this problem, we designed a self-extension Similarity Viterbi algorithm (SimV), which contains three sub-algorithms. Firstly, a basic Viterbi algorithm is employed to find the delay-optimized SFC deployment path. Secondly, a similarity algorithm is designed to assess suitable hosts for each VNF to streamline the matching process and reduce the time complexity of the Viterbi algorithm. Lastly, a self-extension method is devised to broaden the solution space for enhancing the SFC acceptance rate. Extensive simulations are conducted. Based on the comparison simulation results, SimV significantly reduces decision time for deployment, and outperforms the compared algorithms in terms of latency, number of accepted requests, and scalability, with average improvements of 20.37%, 6.04%, and 14.49%, respectively. © 2024 IEEE.

关键词： Viterbi algorithm

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Comparison of Convolutional Neural Networks in SARS-CoV-2 Identification 7

Comparison of Convolutional Neural Networks in SARS-CoV-2 Id...

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7th International Conference on Information and computer Technologies, ICICT 2024

作者： Ramaphosa, Khokhoni Innocentia Mpho Zuva, Tranos Otunniyi, Temidayo Vaal University Of Technology Faculty Of Applied And Computer Sciences Vanderbijlpark South Africa

ISBN: (纸本)9798350385625

Severe Acute Respiratory Syndrome SARS-CoV-2 is a global pandemic that has resulted in numerous fatalities and affected millions of people worldwide. The global community has been experiencing conditions resembling lockdowns due to the COVID-19 pandemic. This public health crisis has posed a significant challenge for scientists, researchers, and healthcare professionals worldwide, extending from the virus's detection to its treatment. Healthcare professionals would greatly benefit from a technological tool that enables swift and precise screening for COVID-19 infections. Prompt recognition of this specific virus can contribute to easing the burden on healthcare systems. X-rays have demonstrated their significance in pinpointing ailments like Pneumonia. The notable advancements achieved in the realm of Machine Learning (ML) have paved the way for the development of artificial intelligent systems proficient in differentiating between COVID-19 cases and those considered normal. The latter is contributed by Deep Learning (DL) advancements. This research utilizes advanced deep learning methods, particularly the training of CNN models using the Python programming language. Its primary aim is to differentiate between chest X-ray images of COVID-19 patients and those considered normal. The CNN models which were used are VGG19, Xception and VGG16. The dataset incorporated was 400 normal X-ray chest images and 399 COVID-19 X-ray chest images. The primary performance metric employed is classification accuracy. Remarkably, the VGG19 model outperformed the others with the highest accuracy of 99%. VGG16 model achieved the accuracy 97%, while the Xception model demonstrated the lowest accuracy at 96%. The above results prove that deep learning holds a promising detection of COVID-19 from the chest X-ray images. © 2024 IEEE.

关键词： Coronavirus

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A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm

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Journal of Experimental and Theoretical Artificial Intelligence 2025年第2期37卷 307-342页

作者： Tabagchi Milan, Sara Jafari Navimipour, Nima Lohi Bavil, Hamed Yalcin, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Engineering University of Applied Sciences & Technology Tabriz Iran Department of Computer Engineering Nisantasi University Istanbul Turkey

Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cloud computing

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inliSAMP, a Modularly Designed Fluid Downhole Sampler for Deep Geothermal Applications - Proof of Concept

inliSAMP, a Modularly Designed Fluid Downhole Sampler for De...

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2024 Geothermal Rising Conference: Using the Earth to Save the Earth

作者： Berentelg, Gawan Dietze, Stefan Hagenmeyer, Veit Institute for Applied Computer Science IAI Karlsruhe Institute of Technology KIT Germany

ISBN: (纸本)0934412308

The fluid sampler inliSAMP is being developed for remote controlled downhole sampling in deep geothermal boreholes. inliSAMP is designed based on the system platform for downhole tools hotToolKIT. It is a follow-up development to the inliCAM. Thus, both probes are built up from modular components of the construction kit. A new sampling and storage concept is developed and realized in a functional demonstrator (sampling volume 480 cm3). The sampling process was tested and proven to work under laboratory conditions up to 90 bars. The demonstrator sampling system is remotely controlled via wireline using real time data. Temperature, pressure in different locations of the sample chamber, and electronic components are permanently monitored and recorded. In the design, most electronic components such as a highly efficient power supply, motor drivers and control units are placed inside a supply probe Dewar flask. The in-situ storage of the monophasic sample is being realized by an adaptive hydraulic and heating control system. The hydraulic components are developed for high dynamic loads and small installation spaces. Furthermore, the functional demonstrator fulfils the requirements of the design standard downhole logging tools (T = 200 °C, p = 60 MPa, harsh and corrosive fluids). Currently, the mechanic units such as the sample chamber, sensors, and hydraulic power pack are mounted on a socket in a demonstrator housing and are tested under realistic conditions on a test rig at the Karlsruhe Institute of technology (KIT) in preparation for a borehole deployment. The functional tests of the sample procedure include descent of the probe, the sample intake, maintenance of sample temperature, and dispensing at changing ambient temperatures on a test rig. The focus of the research is on temperature monitoring, control, and operation management of in situ downhole sampler. This paper gives an overview over commercially available fluid sampler, the development process, a test rig

关键词： Geothermal logging

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Prediction of COVID-19 Epidemiology with Chest computerized Tomography Images Using Machine Learning Techniques 4

Prediction of COVID-19 Epidemiology with Chest Computerized ...

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4th International Multidisciplinary Information technology and Engineering Conference, IMITEC 2024

作者： Ramaphosa, Khokhoni Innocentia Mpho Zuva, Tranos Otunniyi, Temidayo Vaal University of Technology Faculty of Applied and Computer Sciences Vanderbijlpark South Africa

ISBN: (纸本)9798350387988

The COVID-19 pandemic placed immense pressure healthcare systems, emphasizing the need for efficient prediction methods. This research proposes a deep learning-based solution using Convolutional Neural Networks (CNNs) to classify chest CT scan images as either COVID-19 positive or negative. A dataset of 400 normal and 399 COVID-19 CT scan images was used to evaluate CNN models, including VGG19, DenseNet201, Xception, InceptionV3, and VGG16, over 25 and 50 epochs. At 25 epochs, DenseNet201 achieved the highest accuracy of 97%, followed by VGG16 at 96%, InceptionV3 at 93%, and VGG19 and Xception at 91%. At 50 epochs, accuracy improved, with VGG16 and DenseNet201 both reaching 98%, and VGG19 at 97%. Xception and Inception V3also saw improvements, reaching 96%. VGG16 consistently achieved 100% for both Precision and Recall across all epochs, while DenseNet201 maintained 100% Precision at 25 epochs but dropped to 95% at 50 epochs. Xception showed significant improvement in Recall, rising from 85% to 97%. Enhanced preprocessing, including noise reduction and contrast adjustment, further boosted model performance. A COVID-19 Prediction Web Application (CPWA) was developed using the fine-tuned VGG19 model to offer a practical tool for healthcare professionals. This study demonstrates the potential of AI-driven diagnostics to streamline COVID-19 screening and improve early diagnosis, providing scalable solutions for clinical settings. © 2024 IEEE.

关键词： COVID-19

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Investigating the effects of embodiment on presence and perception in remote physician video consultations: a between-participants study comparing a tablet and a telepresence robot

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i-com 2025年第1期24卷 213-226页

作者： Schulz, Dominik Unbehaun, David Doernbach, Tobias Clausthal University of Technology Computer Science Institute Clausthal-Zellerfeld Germany Ostfalia University of Applied Sciences Human-Centered Robotics Lab Wolfenbuettel Germany

Increasing life expectancy and low birth rates have led to a larger aging population requiring more care, especially in rural areas. Information and communications technology may enhance older adults' quality of life and digital health services. Assistive robotic systems, especially telepresence robots, are increasingly used in healthcare. These robots represent a significantly higher embodiment level than tablets, as they can provide the remote person with an additional body. This work explores the effects of different embodiment levels in video consultation, focusing on Presence and Perception from a Human-computer Interaction (HCI) perspective. For this purpose, between-participants experiments were conducted in which 18 participants assumed the role of the patient and engaged in a video consultation with a remote, medically experienced person acting as the physician. A shortened medical check-up simulated the real assessment. Qualitative and quantitative data were collected during and after the experiment focusing on presence and interaction. Results show no significant influence of embodiment level in a video consultation from a HCI perspective. However, a trend towards more natural communication with stronger embodiment was observed, suggesting it may positively influence interaction. © 2025 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Electronic health record

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Throughput Prediction Using LS-SVM For 4g-LTE Network and Beyond

Throughput Prediction Using LS-SVM For 4g-LTE Network and Be...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Hamdani, Mostefa Sahnoune, Ahmed Merzougui, Mokhtar Douara, Abdelmalek Khatemi, Fouad University El Wancharissi Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology Department of Mathematics and Computer Science Algeria University El Wancharissi Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology Department of Science and Technology Algeria

ISBN: (纸本)9798331527396

The integration of Artificial Intelligence(AI) with Long-Term Evolution (LTE) networks offers substantial potential for improving communication infrastructure. By harnessing AI algorithms, it is possible to dynamically optimize network parameters, proactively predict and mitigate equipment failures, and enhance Quality of Service (QoS) by prioritizing critical services through real-time analysis of key performance indicators. This study focuses on predicting network throughput using Least Square Support Vector Machine (LS-SVM). The predictive models developed can trigger adaptive network reconfigurations, ensuring that the network responds promptly to changing conditions. Simulation results reveal that the integration of LS-SVM significantly improves prediction accuracy, enabling more effective implementation of self-organizing network strategies in real-time. © 2024 IEEE.

关键词： Long Term Evolution (LTE)

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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