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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： QiSong Wang JiaRou Wu YingZhuo Wang HuiLi Qu Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Australia

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet’s strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images.

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Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

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computers, Materials & Continua 2022年第1期70卷 963-979页

作者： Awais Mahmood Muhammad Imran Aun Irtaza Qammar Abbas Habib Dhahri Esam Mohammed Asem Othman Arif Jamal Malik Aaqif Afzaal Abbasi College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Shaheed Zulfikar Ali Bhutto Institute of Science and Technology(Islamabad Campus) 44000Pakistan University of Engineering and Technology Taxila Pakistan International Islamic University Islamabad44000Pakistan Faculty of Sciences and Technology University of KairouanSidi Bouzid4352Tunisia Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan

Searching images fromthe large image databases is one of the potential research areas of multimedia *** most challenging task for nay CBIR system is to capture the high level semantic of *** researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback(RF).However existing RF based approaches needs a number of iteration to fulfill user’s *** paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the *** previous research work it is reported that SVM based RF approach generating better results for ***,this paper focused on SVM based RF *** enhance the performance of SVM based RF approach this research work applied Particle Swarm Optimization(PSO)and Genetic Algorithm(GA)before applying SVM on user *** main objective of using thesemeta-heuristic was to increase the positive image sample size from *** steps PSO is applied by incorporating the user feedback and secondly GA is applied on the result generated through PSO,finally SVM is applied using the positive sample generated through *** proposed technique is named as Particle Swarm Optimization Genetic Algorithm-Support Vector Machine Relevance Feedback(PSO-G A-SVMRF).Precisions,recall and F-score are used as performance metrics for the assessment and validation of PSO-GA-SVM-RF approach and experiments are conducted on coral image dataset having 10908 *** experimental results it is proved that PSO-GA-SVM-RF approach outperformed then various well known CBIR approaches.

关键词： Feature selection image retrieval particle swarm optimization

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Generating synthetic data on agricultural crops with DCGAN

Generating synthetic data on agricultural crops with DCGAN

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Fieter Brain Pasaribu Luh Joni Erawati Dewi Kadek Yota Ernanda Aryanto Ketut Agus Seputra Pariwate Varnakovida Ni Ketut Kertiasih Computer Science Universitas Pendidikan Ganesha Singaraja Indonesia Software Engineering Universitas Pendidikan Ganesha Singaraja Indonesia Faculty of Science King Mongkut's University of Technology Thonburi Thailand

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Convolutional Neural Network (CNN) is one of the deep learning architectures that is very effective for handling images. CNN is able to automatically extract important features from images, making it very suitable for various image processing tasks such as classification, object detection, and segmentation. However, even though CNN has great capabilities, one important thing to note is the amount of data. A considerable amount of data is needed for the CNN model to work optimally and avoid overfitting. To handle this problem, a synthetic data augmentation process is used using the Deep Convolutional Generative Adversarial Network (DCGAN) method. The generator network contained in the DCGAN model has a latent space dimension input whose value can vary. The size of the latent space dimension is very important in enabling data or image reconstruction during the training process. This study tested latent space dimension values on a corn plant dataset totaling 9159. The latent space values used in this experiment were 64, 100, and 128. In addition, this study also tested different batch sizes, namely 64 and 128. The model was evaluated using Fre'chet Inception Distance (FID) and Inception Score (IS). From the evaluation results, the best score on FID was 0.018001 and IS 1.239421. The greater the latent space value, the more realistic and clear the image results will be. Likewise, the smaller the batch size value, the more realistic and clear the image results.

关键词： Training Plants (biology) Generative adversarial networks Feature extraction Data augmentation Size measurement Data models Stability analysis Convolutional neural networks Synthetic data

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Path Selection Optimization Algorithms for Mobile Agent Based on Push-All-Data Strategy

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第10期19卷 112-128页

作者： Alzyoud, Faisal Tarawneh, Monther Al-Shrouf, Faiz Almaghthawi, Ahmed Alrehaili, Meaad Kanaker, Hasan Department of Computer Science Isra University Amman Jordan Department of Information Technology College of Information Technology and Communications Tafila Technical University Tafila Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Computer Science College of Science & Art at Mahayil King Khalid University Abha62529 Saudi Arabia Department of Computer Sciences and Artificial Intelligence Collège of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Information Security Faculty of Information Technology Petra University Amman Jordan

With the advent of 5G and 6G technologies and the growing ubiquity of the Internet, the Mobile Agent (MA) paradigm is increasingly seen as a promising alternative to the conventional client-server model. MAs, which are software entities capable of moving and processing data across different systems, offer potential efficiencies in data management. However, their operation in dynamic and mobile environments can lead to challenges, such as incomplete or delayed tasks. This study addresses these issues by focusing on reducing the relocation time of MAs. A numerical procedure and a streamlining strategy were developed to expedite the transfer of an agent from the source to the target hub. Utilizing the itinerary design pattern and the Ant Colony Optimization (ACO) algorithm, implemented via the Java Agent Development Framework (JADE), this study sought the most efficient path for the MA. The proposed algorithm demonstrated a significant improvement, selecting the optimal path in just 271.511 seconds. This performance represents a substantial enhancement over previous approaches using the master-slave design pattern with either the Genetic Algorithm (GA) or the Node Compression Algorithm (NCA). The implications of this improvement are far-reaching, potentially enhancing the efficiency and reliability of data management systems in a variety of applications. © 2025 by the authors of this article. Published under CC-BY.

关键词： Information management

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VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reality Exergaming 4

VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reali...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Mambu, Joe Y. Rismayani Sihotang, Jay Idoan Rantung, Vivi Peggy Computer Science Faculty Universitas Klabat Airmadidi Indonesia Dipa Makassar University Dept. of Software Engineering Makassar Indonesia Faculty of Information Technology Universitas Advent Indonesia Bandung Indonesia Faculty of Information System Universitas Negeri Manado Tondano Indonesia

ISBN: (纸本)9781665453950

There are many different types of exergaming, but one of the most engaging one is the one with virtual reality. This type of gaming allows you to immerse yourself in a completely different world, and it can be a lot of fun. Due to Covid-19 pandemic many people resort to their usual exercise activities and opt to do it at home yet only to find a monotonous and boring fitness routine. VR Real Run is aimed to give a solution on how to have fun and to get fit at the same time. By using the latest and front runner in the VR headsets products, the Oculus Quest 2, we developed an exergame that require user to run in place and jump to play. It also features high score to motivate users in perform better in the next session. A black box test has been well performed and a usability test was came out with a 79 SUS score which translate the application as 'good' therefore its usability is acceptable. © 2022 IEEE.

关键词： Virtual reality

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Playing the video games during COVID-19 pandemic and its effects on player's well-being 2

Playing the video games during COVID-19 pandemic and its eff...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Noaman, Samar Billi Ibrahim, Amer Ali, Liaqat Iqbal, M.W. Ashraf, Asma Haseeb, Usama Muneer, Salman Almajed, Rasah Hamid, K. Canadian University Dubai Faculty of Management Dubai United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Superior University Department of Software Engineering Lahore54000 Pakistan Superior University Department of Computer Science Lahore54000 Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan American University in the Dubai College of Computer and Information Technology United Arab Emirates

ISBN: (纸本)9798350335644

COVID-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COVID-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COVID-19. The carried out survey is based on the social video games results on players' well-being and additionally on the effects of gamers' health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COVID-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COVID-19 practical life1. © 2023 IEEE.

关键词： Coronavirus

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Road Traffic Accident Pattern Analysis with Data Mining

Road Traffic Accident Pattern Analysis with Data Mining

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International Conference on Awareness science and Technology (iCAST)

作者： Albe Bing Zhe Chai Bee Theng Lau Mark Kit Tsun Tee Chris McCarthy Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

The discovery of Road Traffic Accident (RTA) patterns is vital to formulate mitigation strategies based on the characteristics of RTA. Various studies have applied association rule mining for RTA pattern discovery. However, limited research has conducted a comparative analysis of RTA patterns in different regions. This work is necessary to identify the similarities and differences of RTA characteristics between different regions, especially the developed and developing countries with different RTA trends in fatality rates. This work explored the application of association rule mining (FP-Growth algorithm) to discover and compare the RTA patterns for developed and developing countries. The Orange Data Mining software is used to discover RTA patterns for Ethiopia (12,317 samples) and the UK (United Kingdom) (1,048,575 samples), representing developing and developed countries. Eventually, findings showed similarities and differences in RTA patterns between Ethiopia and the UK. Road characteristics and conditions are the common factors contributing to RTA severity levels, while other crucial factors include the light condition and speed limit. In conclusion, the discovery of RTA patterns in different regions is beneficial to assist in formulating strategies to reduce the risk of RTA. Future work is necessary to discover the RTA patterns from different perspectives, such as seasonal or periodical variations of RTA patterns.

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Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Bashir, Ali Kashif Mondal, Tapas Hu, Xiping Deen, M. Jamal Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India McMaster University Faculty of Health Science and the Department of Pediatrics Hamilton Canada McMaster University McMaster School of Biomedical Engineering Hamilton Canada McMaster University Electrical and Computer Engineering Department Hamilton Canada

With the rise of the Internet of Things (IoT), wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns inter-lead relationships to improve multi-label ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a Temporal Graph Construction (ETGC) module to convert ECG signals into temporal graphs and uses a Residual Pyramid Graph Convolution (RPG) module for multi-level graph embeddings, utilizing a Graph Convolutional Network (GCN) with independently learnable adjacency matrices. Combined with a Temporal Context Convolution (TCC) module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on 7 tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

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IoST: Internet of Softwarized Things Networks, Security Challenges and Future Research Directions

IoST: Internet of Softwarized Things Networks, Security Chal...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Adil, Muhammad Attique, Muhammad Wang, Jian Alrefaei, Faisal Song, Houbing Farouk, Ahmed University at Buffalo Computer Science & Engineering New York United States Sejong University Department of Software Seoul05006 Korea Republic of University of Tennessee Department of Computer Science MartinTN39238 United States Embry Riddle Aeronautical Universit Electrical Engineering & Computer Science Florida United States South Valley University Faculty of Computers and Artificial Intelligence Hurghada Egypt

ISBN: (纸本)9781665459754

Internet of Softwarized Things (IoST) is a promising and dynamic programmable technology that has the capability to interconnect sensor devices with an objective to share the accumulated data in the network without the intervention of human beings. Despite its advantages, this technology suffers from numerous security vulnerability threats, which can damage the trust of clients. Therefore, it is very important to familiarize the readers, students, experts, and industry stakeholders working in this domain with the existing counteraction schemes followed by potential security threats. For redressal of identified security threats, the literature presents various schemes, but somehow they are unable to provide fool-proof security infrastructure for these networks, because the attackers work restlessly to compromise the new security countermeasure schemes. To explore this discussion and evaluate the open security challenges with future research directions, in this paper, we present a survey regarding the most recent published literature from 2020 to 2022. Following this, we have covered the architectural and interoperability literature in the context of security threats and their countermeasure techniques to identify the weak aspects of them. Based on underscored problems, we have highlighted the unresolved security challenges to set a footstep for the future research work that could be useful for all stakeholders associated with IoST technology. © 2022 IEEE.

关键词： Interoperability

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