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2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022

作者： Huang, Elijah Mui, Phil University of California Irvine Donald Bren School of Information and Computer Sciences Fremont United States Aspiring Scholars Directed Research Program Department of Computer Science and Engineering Fremont United States

ISBN: (纸本)9781665473453

Misinformation or so called 'fake news' has become a pressing issue around the world. This research proposes modeling the spread of misinformation through Q-learning, the game of Nim, and multi-Agent simulations. Through analyzing theoretical properties of the model and studying factors in how misinformation affects it, we show how the model reflects the real-life impact of fake news. The scalability of the framework and its emulation of real-human behavior make it versatile and believable. Through further study of this model, our hope is to shed light on effective tactics for combating fake news in the real world. © 2022 IEEE.

关键词： Game theory

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The Scientific Progress and Prospects of Artificial Intelligence for Cancer Detection: A Bibliometric Analysis 24

The Scientific Progress and Prospects of Artificial Intellig...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Ariyanto, Sandy Vikki Purnomo, Agung Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia National Research and Innovation Agency Telecommunication Research Center Jakarta11480 Indonesia Universitas Airlangga Faculty of Medicine Surabaya60132 Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia

ISBN: (纸本)9798350313956

Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosViewer and Rstudio software quantification method for literature analysis. The results of the flushed articles show data such as publication year, journal, country, keywords, and authors, to the highest number of citations. Common keywords used by researchers are artificial intelligence, medical, and human. Researchers limited the findings through keywords such as in the last ten years, documents are journals, and English only so that 1868 articles were obtained. The results found that Harvard Medical School affiliation had the highest number of articles, with 101 articles, by subject area, Medicine had a proportion of 41.9% (n=1320). This bibliometric study will be useful for other researchers to examine the development of research on Artificial Intelligence for Cancer Detection in the last ten years. © 2023 IEEE.

关键词： Diseases

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Unified Approach to Video-Based AI Inference Tasks in Augmented Reality Systems Assisted by Mobile Edge Computing 31

Unified Approach to Video-Based AI Inference Tasks in Augmen...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： Sousa, Alexandre Ladeira De Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Graduate Program in Information Engineering Sao Paulo Santo Andre Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

In the rapidly evolving field of Augmented Reality (AR), delivering real-time, immersive experiences places a significant demand on computational resources, particularly in the context of video-based Artificial Intelligence (AI) inference tasks. Some solutions explorate optimize the tasks using the potential of Mobile Edge Computing (MEC), in which MEC brings the advantages of reduced latency and network congestion, which are critical for AR systems. In this work, we designed a novel algorithm named AR-MEC Optimization for the distribution of AI inference tasks among edge servers while considering several factors including task complexity, server capacity, and network conditions. Our approach not only ensures low-latency responses but also improves overall system efficiency and the user's Quality of Experience (QoE). Experimental results demonstrate that our unified approach significantly outperforms traditional cloud-based methods in terms of response time and computational efficiency, thereby highlighting the potential of MEC-assisted AR systems for real-world applications. © 2023 University of Split, FESB.

关键词： Augmented reality

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A Novel Resource Allocation in Software-Defined Networks for IoT Application 31

A Novel Resource Allocation in Software-Defined Networks for...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： De Sousa, Alexandre Ladeira Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Santo Andre Graduate Program in Information Engineering Sao Paulo Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

The rapid proliferation of Internet of Things (IoT) devices has posed significant challenges for network resource allocation and management. In this paper, we propose a novel methodology for efficient resource allocation in Software-Defined Networks (SDNs) to meet the unique requirements of IoT applications. Our approach leverages the flexibility and programmability of SDN to dynamically allocate network resources based on the specific needs of IoT devices. Our proposal combines reinforcement learning with dynamic resource allocation, demonstrating its effectiveness in optimizing resource allocation and enhancing network performance. The results highlight the potential of our approach to addressing the resource challenges in SDN-based IoT environments. The proposed method obtains results superior to 30% of throughput in comparison to a traditional resource allocation in SDN to IoT applications. © 2023 University of Split, FESB.

关键词： Internet of things

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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FINE-TUNING TRANSFER LEARNING MODEL IN WOVEN FABRIC PATTERN CLASSIFICATION

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International Journal of Innovative Computing, Information and Control 2022年第6期18卷 1885-1894页

作者： Noprisson, Handrie Ermatita, Ermatita Abdiansah, Abdiansah Ayumi, Vina Purba, Mariana Setiawan, Hadiguna Program of Doctoral Program in Engineering Universitas Sriwijaya Jalan Raya Prabumulih-Inderalaya Palembang30128 Indonesia Faculty of Computer Science Universitas Sriwijaya Jalan Raya Prabumulih-Inderalaya Palembang30128 Indonesia Department of Research and Development Stikhafi Academy Jalan Timur Indah Raya Bengkulu38225 Indonesia

It is important to figure out the patterns of woven fabrics before producing woven fabric with a machine. Recognition of woven fabric pattern usually with the help of the human eye can understand the fabric pattern. However, this manual checking takes a lot of time, money, and work, which will raise the cost of making woven fabrics. This study uses the VGG16, VGG19, MobileNet, and Inception-V3 methods to classify woven fabric patterns. It also wants to see how fine-tuning method can help algorithms be more accurate at classifying images. The research was divided into four phases, including image acquisition, image preprocessing, image classification and evaluation. A total of 978 pictures of motifs included in the research dataset. There are 351 images for the cotton class, 76 images for linen, 195 images for silk, and 356 images for wool. As the result, the highest testing accuracy was in the Inception-V3 experiment (with fine-tuning) of 72.51%, and the lowest was in the VGG19 experiment (with fine-tuning) of 52.92%. © 2022 ICIC International.

关键词： Weaving

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Enhancing Healthcare Monitoring Systems: A Case Analysis in Optimization

Enhancing Healthcare Monitoring Systems: A Case Analysis in ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Mohit Lalit Gaurav Bathla Surender Singh Computer Science and Engineering Department Chandigarh University Mohali India Head Career Program Code Quotient Mohali India

In the past few years, fog computing (FC) has emerged as a promising complement to cloud computing. It offers reduced latency, minimal bandwidth consumption, and real-time data transfer. In healthcare, particularly in IoT-enabled systems, FC integration has become pivotal for real-time monitoring and immediate data transfer to health experts. The researchers all around the world proposed and developed various communication frameworks and come up with certain outputs in terms of quality-of-service parameters (QoS). However, these QoS parameters are not always appropriate for effective execution of the application. Following that, this study explores the integration of FC with multi-objective Optimization Algorithms (such as FFLY and GWO) to optimize crucial quality of service (QoS) metrics using a proposed linear function to optimize multi objectives, essential for sustained communication. The research focuses on optimizing multi objectives in healthcare monitoring systems. Experimental results reveal the superiority of the Grey Wolf Optimizer (GWO) over the Firefly Algorithm (FFLY). This study emphasizes the crucial significance of multi-objective optimisation algorithms in optimizing critical parameters for successful healthcare communication frameworks, which can lead to improvements in healthcare monitoring efficiency.

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Human Activity Classification: A LSTM and MediaPipe Approach for Intelligent Systems Application 6

Human Activity Classification: A LSTM and MediaPipe Approach...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Chen, Yi-An Yang, Pei-Ching Chang Jung Christian University Master Degree Program in Information and Design Tainan Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan Taiwan Chang Jung Christian University Artificial Intelligence Research Center Tainan Taiwan

ISBN: (纸本)9798331518158

With advancements in artificial intelligence, computer vision technologies have seen significant progress, enabling applications like facial recognition, autonomous driving, and medical imaging. This study leverages the Multi-Camera Action Dataset (MCAD) and MediaPipe Pose to create a skeletal dataset for training a Long Short-Term Memory (LSTM) model to recognize 18 types of daily actions. Evaluation using confusion matrix and F1 score reveals that Action 16 (Object Carry) is often misclassified as Action 7 (Walk) due to similar leg movements, and Action 9 (Cell to Ear) as Action 1 (Wave) due to similar hand movements. Future work aims to enhance model accuracy and explore broader applications in physical activity and exercise analysis. © 2024 IEEE.

关键词： Intelligent systems

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Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using Random Forest, KNN, Decision Tree and Naïve Bayes 7

Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using ...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Pohan, Husni Iskandar Suparta, Wayan Heryadi, Yaya Wibowo, Antoni Lukas Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Dengue Hemorrhagic Fever is an acute viral infectious disease caused by the dengue virus. Transmitted through the bite of Aedes Mosquitoes and divided into 4 severity. Severity 1 and 2 are characterized by a decrease in platelet levels below normal and there is a change in hemoconcentration, while Severity 3 and 4 are included in Dengue Shock Syndrome (DSS). The purpose of the study was to classify the severity of dengue, based on age, gender, platelets, body temperature, bleeding, and also doctor's recommendation. The advancement of information technology is the reason for the development of medical science, especially with artificial intelligence. Abundant medical data becomes a source that can be processed in improving the quality of medical information. The method chosen is Random Forest, kNN, Decision Tree and Naïve Bayes. The dataset used is the medical records of all DHF patients for 30 months in the period Jan 2019 to June 2021, at the Nadhifa Al Ghiffari Clinic in Bojongsoang District, Bandung Regency. © 2022 IEEE.

关键词： Decision trees

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Information Rates Over DMCs with Many Independent Views

Information Rates Over DMCs with Many Independent Views

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IEEE International Symposium on Information Theory

作者： V. Arvind Rameshwar Nir Weinberger IUDX Program Unit Indian Institute of Science Bengaluru India Department of Electrical and Computer Engineering Technion Haifa Israel

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we investigate the fundamental limits of reliable communication over a discrete memoryless channel (DMC) when there are a large number of noisy views of a transmitted symbol, i.e., when several copies of a single symbol are sent independently through the DMC. We argue that the channel capacity and dispersion of such a multi-view DMC converge exponentially quickly in the number of views to to the entropy and varentropy of the input distribution, respectively, and identify the exact rate of convergence. This rate equals the smallest Chernoff information between two conditional distributions of the output given unequal inputs. Our results hence help us characterize the largest finite-blocklength rates achievable for any fixed error probability. We also present a new channel model that we call the Poisson approximation channel-of possible independent interest-whose capacity closely approximates the capacity of the multi-view binary symmetric channel (BSC).

关键词： Monte Carlo methods Error probability Symbols Entropy Memoryless systems Reliability Noise measurement

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