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8th Conference on Information Communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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Beyond Charts: A Mixed-Reality System for Visualizing and Experiencing data 23

Beyond Charts: A Mixed-Reality System for Visualizing and Ex...

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6th International Conference on Mathematics and Statistics, ICoMS 2023

作者： Wen, Like Zhuang, Mansheng Kwok, Alex Pak Ki Yan, Mian Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong999077 Hong Kong School of Management Jinan University Guangzhou510000 China Data Science and Policy Studies Programme Faculty of Social Science The Chinese University of Hong Kong Hong Kong999077 Hong Kong School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

ISBN: (纸本)9798400700187

data visualization plays a crucial role in understanding complex datasets. However, traditional data visualization methods often fall short of engaging non-professionals and providing immersive experiences. This drawback can make it challenging and less attractive for students to understand the information and story hidden in the data. This research developed a mixed-reality-based data experience system to enhance student engagement during lectures. The system takes inspiration from car driving games and leverages mixed reality (MR) technology to create an interactive and visually captivating experience for data exploration. The system seamlessly integrates entertainment and data visualization to bring historical data to life by processing and to visualize the relevant data using a mountain driving track. Additionally, it allows multiple students to participate simultaneously, fostering collaborative learning. This novel approach to data experience holds significant potential to revolutionize student engagement and historical data presentations within educational settings. © 2023 ACM.

关键词： data visualization driving simulator education and training mixed reality student engagement

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Evaluating Pretrained Deep Learning Models for COVID-19 Detection in Medical Imaging 1

Evaluating Pretrained Deep Learning Models for COVID-19 Dete...

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1st Renewable Energy Technologies and Modern Communications systems: Future and Challenges Conference, RETMCS 2024

作者： AL-Shehri, Bushra A. Sabbeh, Sahar F. Dessouky, Mohamed M. Department of Data Science College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Faculty of Computers and Artificial Intelligence Information Systems Department Benha University Benha13518 Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9798331519445

COVID-19 is a respiratory disease for which reverse transcription-polymerase chain reaction (RT-PCR) is the standard detection method. This study introduces a hybrid deep learning approach to support the diagnosis of respiratory abnormalities, using COVID-19 as a case study. Multiple datasets were utilized, including the COVID-19 Radiography dataset (Kaggle), the SARS-CoV-2 CT-Scan dataset, and proprietary data from the King Faisal Medical Complex. Seven pre-trained deep learning models—VGG16, VGG19, MobileNet, DenseNet201, ResNet50, InceptionResNetV2, and InceptionV3—were evaluated for their performance. InceptionResNetV2 and InceptionV3 achieved perfect accuracy (100%) on the COVID-19 Radiography dataset and 99% accuracy on the SARS-CoV-2 CT-Scan dataset. Additionally, augmentation techniques were explored to improve model robustness. This research contributes significantly to medical imaging, offering practical insights for advancing diagnostic and monitoring practices for COVID-19 and other respiratory conditions. ©2024 IEEE.

关键词： COVID-19

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Zero-padding on Connected Directed Acyclic Graphs for Spectral Processing

Zero-padding on Connected Directed Acyclic Graphs for Spectr...

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Telecommunications Forum (TELFOR)

作者： Ljubiša Stanković Miloš Daković Miloš Brajović Isidora Stanković Ali Bagheri Bardi Faculty of Electrical Engineering University of Montenegro Podgorica Montenegro Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Busher Iran

Directed acyclic graphs (DAGs) are often utilized for modeling causal relationships, dependencies, and flows in various systems. However, spectral analysis becomes impractical in this setting because the eigendecomposition of the adjacency matrix yields all eigenvalues equal to zero. This inherent property of DAGs results in an inability to differentiate between frequency components of signals on such graphs. To address this limitation, we propose, in this paper, a zero-padding approach for connected DAGs. This approach involves augmenting the original connected DAG with additional nodes that are connected to the existing structure. These added nodes are characterized by signal values set to zero and does not interfere with the original DAG except at the source and sink. The proposed technique enables the spectral evaluation of system outputs on DAGs, that is the computation of vertex-domain convolution (output of a graph filter) in without the adverse effects of aliasing due to the added nodes (added nodes do not change the original output of a filter on a DAG).

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Evaluation of Cloud Recovery System for Financial Applications Based on Hadoop 7

Evaluation of Cloud Recovery System for Financial Applicatio...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Lu, Li Lu, Tong Sali, Aduwati School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Department of Computer and Communication Systems Engineering Faculty of Engineering Selangor Serdang43400 Malaysia Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

The rise of cloud-based disaster recovery systems has reshaped disaster recovery practices. Integrated into the Hadoop framework with core components like Hadoop Distributed File System (HDFS), MapReduce, and Yet Another Resource Negotiator (YARN), these systems ensure data integrity and business continuity during crises while enhancing data reliability and operational efficiency, surpassing traditional recovery systems. This study presents a comprehensive comparative analysis of traditional and Hadoop-integrated cloud recovery systems, focusing on hardware architecture and performance. Architecturally, it highlights the stark differences between conventional models and Hadoop-based innovations. The results show that the Hadoop-backed recovery system exhibits remarkable improvement, reducing Recovery Time Objective (RTO) from over 18 seconds to under 7 seconds while preserving data integrity as per Recovery Point Objective (RPO) standards. This finding underscores the academic importance of evaluating cloud-based disaster recovery within the Hadoop context, offering insights into its transformative potential for disaster recovery strategies. © 2024 IEEE.

关键词： data integrity

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Channel Estimation for 29GHz mm Wave MIMO systems in Tropical Region 7

Channel Estimation for 29GHz mm Wave MIMO Systems in Tropica...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Lu, Li Sali, Aduwati School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Faculty of Engineering Department of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

This paper focuses on the 29GHz mmWave MIMO communication system in tropical environments, considering the impact of rain attenuation and providing a detailed analysis of channel estimation and system performance. Experimental results indicate that as the signal-to-noise ratio (SNR) increases, the mean square error (MSE) of channel estimation decreases. Under the same SNR conditions, MIMO systems with different antenna configurations exhibit distinct channel estimation error characteristics. Notably, in the 128x128 antenna configuration, the channel estimation error is most pronounced. Furthermore, increasing the number of training signals significantly reduces channel estimation errors, especially in the 128x128 antenna configuration. This study offers a performance evaluation of the 29GHz mmWave MIMO communication system in tropical regions, providing valuable insights for further research and optimization in this field. © 2024 IEEE.

关键词： Signal to noise ratio

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Enhanced Monarch Butterfly Optimization Algorithm with Rank-Based Selection and Random Jump Strategy for Constrained engineering Problems

Enhanced Monarch Butterfly Optimization Algorithm with Rank-...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Kathiroli, Panimalar Kanmani, S. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India Puducherry Technological University Department of Information Technology Puducherry India

ISBN: (纸本)9798350333244

Nature-inspired algorithms have become increasingly prevalent in engineering and mathematical optimization due to their efficacy and adaptability. Among these algorithms, the Monarch Butterfly Optimization Algorithm (MBOA) is a population-based metaheuristic, characterized by its simple implementation and the utilization of the Migration Operator (MO) and Butterfly Adjusting Rate (BAR) to guide the search path of butterflies. However, the algorithm can suffer from susceptibility to local optima traps, early convergence, and inferior performance due to a lack of random factors. To address these challenges, we propose an Enhanced Monarch Butterfly Optimization Algorithm (E-MBOA) that incorporates Ranking-based Selection (RBS) and a Random Jumping strategy. The RBS prioritizes the search direction based on individuals' ranks in the population, enhancing the search process by encouraging the exploration of feasible areas with lower objective function values. By employing the Random Jumping strategy, the process can effectively investigate the search space and avoid local optima, thus mitigating early convergence issues. The proposed model is assessed on a variety of mechanical and engineering design problems. Results are contrasted to other optimization approaches published in the literature, demonstrating that the proposed model offers faster convergence to global optima and effectively avoids local optima traps. E-MBOA's performance validates its feasibility and effectiveness as a powerful tool for resolving complex engineering optimization problems. © 2023 IEEE.

关键词： Constrained optimization

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Effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images)

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Multimedia Tools and Applications 2024年 1-25页

作者： Ramadass, Sudhir Narayanan, Sreekumar Kumar, Rajiv K, Thilagavathi Data Analytics Sterck Systems Private Limited Chennai Nungambakkam India Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

For clinical analysis or medical intrusion, imaging technology is a potent tool for producing visual images of a body's inside. However, several existing methods include restricted access to high-quality and diverse datasets, overfitting, a lack of interpretability, high computational costs, varying medical imaging data, and ethical issues concerning patient privacy, consent, and bias. To overcome these issues, the effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images) is proposed. The input CT images are first gathered and preprocessed to reduce undesired noise using Altered Phase Preserving Dynamic Range Compression (APPDRC). The General Adversarial Network (GAN) is then given these preprocessed images to cope with the noisy areas of CT/MRI while also complying with maintaining image structures such as boundary details while preventing over-smoothing, and the weight parameters for the GAN are optimized using the Golden Eagle Optimization (GEO) technique. General Adversarial Network (GAN) has piqued the interest of academics studying medical image-denoising mechanisms and developing sophisticated medical image-denoising algorithms based on deep learning. In light of reducing the difficulty in feature learning and improving final diagnosis accuracy, data preprocessing is necessary and crucial in gan-based medical image denoising methods. This work focuses on GAN-based effective denoising approaches in CT/MRI. The potential challenges and research objectives are in the context of data preprocessing in medical image denoising. Because of how CT/MRI images are acquired, it was observed that noise in CT/MRI data follows the distribution. Generative Adversarial Networks (GANs) for denoising medical imaging, mainly CT and MRI images, have yielded promising results of 99% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Optimal Structure Exploration of Bayesian Network Based on Dealing with the Route Choice Model 6

Optimal Structure Exploration of Bayesian Network Based on D...

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6th International Conference on Computer Information science and Application Technology, CISAT 2023

作者： Wang, Ziyi Zeng, Yufei Wang, Rui College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Computer Science and Technology Harbin Institute of Technology Yichun China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781510668546

Route selection in residents' travel is a key link in travel decision-making in transportation systems. In existing studies, discrete choice models are often used to describe residents' travel path choices. However， with today's highly developed big data, the traditional discrete choice model is too simple to represent residents' travel path choices, ignoring the connection between various factors. Therefore, a big data-driven approach is proposed to solve this problem in this paper. Driven by research data and with the introduction of the Bayesian network model, the paper adopts a hill-climbing search strategy to improve the search efficiency, uses a variety of scoring functions to train data, which optimizes the Bayesian network structure. The experimental results show that the model fully considers the causation between the independent variables, which is more logical and makes the construction of the resident travel route selection model more accurate and efficient. © 2023 SPIE.

关键词： Bayesian networks

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GENSIMO - A Generic Framework for Modelling Social Insurance systems 25th

GENSIMO - A Generic Framework for Modelling Social Insuranc...

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25th International Workshop on Multi-Agent-Based Simulation, MABS 2024

作者： de Haan, Fjalar J. Thompson, Jason School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Parkville Australia Melbourne Centre for Data Science The University of Melbourne Parkville Australia Department of Psychiatry Faculty of Medicine Dentistry and Health Sciences The University of Melbourne Parkville Australia

ISBN: (纸本)9783031880162

We report on the design, development and prototype testing of gensimo (GENeric Social Insurance MOdeling), a free and open-source software framework for modelling and simulation of social insurance systems. We discuss the conceptual and software design considerations of the framework in general before demonstrating how it works in detail by setting it up to model a particular social insurance scheme. We then present how we used this prototype set up to model the impact of a policy intervention on the workload of the insurer. While gensimo is under active development and we are a while away from serious verification and validation, this prototype testing shows realistic results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Software testing

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