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Challenges on Cloud Computing Migration Strategy for Music Industry: A Systematic Literature Review

Challenges on Cloud Computing Migration Strategy for Music I...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Hijir Della Wirasti Henki Seta Deden Witarsyah Hamonagan K Prabu Ayu Azzahro Bayu Hananto Faculty of Engineering Industry Telkom University Bandung Indonesia Informatics Study Program Computer Science Faculty Universitas Pembangunan Nasional Veteran Jakarta

Cloud computing has emerged as a transformative technology that offers numerous benefits to various industries, including the music industry. Cloud computing has revolutionized the way businesses operate and has had a significant impact on various industries, including the music industry Cloud computing, with its scalability, flexibility, and cost-effectiveness, presents an opportunity for the music industry to leverage technology to enhance its operations and meet the evolving demands of the digital era. This paper presents a systematic literature review on cloud computing migration strategies specifically tailored for the music industry. The review aims to identify existing research, frameworks, and best practices related to cloud migration in the music industry, as well as highlight the challenges and opportunities associated with such migrations. Through an analysis of relevant literature, this study provides valuable insights to assist music industry stakeholders in developing effective cloud migration strategies.

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Vision Transformer Algorithm for Plant Disease Detection: A Systematic Literature Review

Vision Transformer Algorithm for Plant Disease Detection: A ...

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International Conference on informatics and Computing (ICIC)

作者： Reza Al Husna Lintang Yuniar Banowosari Tubagus Maulana Kusuma Tri Handhika Doctoral program Information Technology Universitas Gunadarma Jakarta Indonesia Department of Informatics Program Universitas Gunadarma Jakarta Indonesia Department of Technology and Engineering Universitas Gunadarma Jakarta Indonesia Department of Computational Mathematics Studies Universitas Gunadarma Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Advances in deep learning have significantly impacted various sectors, including agriculture, by improving efficiency and sustainability. Computer vision-based plant disease detection that utilizes deep learning models such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) has replaced manual methods that require specialized skills. Vision Transformers (ViTs) with self-attention mechanisms excel at recognizing complex patterns in images of diseased plants, enabling early detection and more effective management. This study conducted a systematic literature review to evaluate the use of feature fusion-based transformer algorithms in plant disease detection and to identify current research trends, challenges, and future opportunities. The results show that ViTs can improve accuracy and efficiency in plant disease diagnosis, particularly under diverse environmental conditions. However, the adoption of this technology faces obstacles such as the need for large annotated datasets, significant computational costs, and variability in field conditions that can affect model performance. This research highlights the need for further innovation to address these challenges and expand the accessibility and reliability of deep learning-based plant disease detection for the global agricultural sector.

关键词： Deep learning Plant diseases Computer vision Technological innovation Accuracy Computational modeling Transformers Feature extraction Computational efficiency Systematic literature review

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Car Detection over Network using Yolov8 in Forza Horizon 4

Car Detection over Network using Yolov8 in Forza Horizon 4

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Vitradisa Pratama Agus Sukoco Pebi Pebriadi Dyah Kusumastuti Vip Paramarta Ary Setijadi Prihatmanto Doctoral Program of School of Electrical Engineering and Infomatics Institut Teknologi Bandung Bandung Indonesia Departement of Informatics Universitas Bandar Lampung Lampung Indonesia Uniersitas Sangga Buana Bandung Indonesia School of Electical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

In the realm of autonomous vehicles, object detection holds a pivotal role in enabling accurate perception and safe navigation within complex environments. This study incorporates these domains by proposing a vehicle detection system employing YOLO v8 within the popular game Forza Horizon 4. YOLO v8’s real-time, accurate object detection prowess aligns seamlessly with the game’s dynamics, enriching the user experience through improved vehicle tracking. The research encompasses YOLO v8 model training on an expansive vehicle dataset, adaptation for Forza Horizon 4, and rigorous evaluation, comparing accuracy, speed, and efficiency against existing methods. The methodology entails dataset preparation, YOLO v8 configuration, object detection, result evaluation, optional fine-tuning, and iterative testing and optimization. By scrutinizing two data transmission protocols, RTMP and HTTPS, the research accentuates RTMP’s superiority in real-time image data transmission for object detection, surpassing HTTPS in processing speed. While RTMP excels in low-latency scenarios, HTTPS augments security through encrypted data transmission. The findings prove RTMP’s speed in image data transmission for real-time object detection, whereas HTTPS’s secure communication advantages.

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Exploring Approaches to Answer Biomedical Questions: From Pre-processing to GPT-4 Notebook for the BioASQ Lab at CLEF 2023 24

Exploring Approaches to Answer Biomedical Questions: From Pr...

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24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023

作者： Kim, Hyunjae Hwang, Hyeon Lee, Chaeeun Seo, Minju Yoon, Wonjin Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Computational Health Informatics Program Boston Children’s Hospital BostonMA02115 United States Harvard Medical School BostonMA02115 United States AIGEN Sciences Seoul04778 Korea Republic of

Biomedical question answering (QA) plays a crucial role in assisting researchers, healthcare professionals, and even patients in accessing and retrieving accurate and up-to-date information from the vast amount of biomedical knowledge available in literature. To enhance the efficiency of knowledge discovery and information retrieval, we investigate the efficacy of various pre-processing, model training, data augmentation, and ensemble methods and evaluate a range of advanced pre-trained models such as BioLinkBERT and GPT-4. Additionally, we explore data augmentation and ensemble methods to further improve system performance. In our participation in BioASQ Task 11b-Phase B, our systems achieved a top ranking in all four batches for the yes/no type of questions, in one out of four batches for factoid questions, and in two out of four batches for list-type questions. © 2023 Copyright for this paper by its authors.

关键词： BioASQ 11b BioLinkBERT Data Augmentation Ensemble GPT-4

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Methodology for the design of a nanosatellite telecommunication system in S-Band based on Software for Digital Mission engineering

Methodology for the design of a nanosatellite telecommunicat...

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International Conference on AeroSpace Science and Technology 2023, ICASST 2023

作者： García-Guillén, L.A. Sanchéz-Sanchéz, J.L. Ramírez-Aguilar, J.A. Ferrer-Pérez, J.A. Romo-Fuentes, J.C. Farah-Simón, A. Chávez-Moreno, R.G. Sánchez Hernández, D.C. Sánchez-Guzmán, J.A. Valdés-Galicia, J.F. Master's and Doctorate Programm in Engineering Engineering Faculty Universidad Nacional Autónoma de México CDMX Mexico Bachelor's in Aero Systems Electronics Engineering Universidad Aeronáutica en Querétaro Queretaro Mexico Advanced Technology Unit School of Engineering UNAM Queretaro Juriquilla Mexico Astronomy Institute Universidad Nacional Autónoma de México CDMX Mexico Faculty of Informatics Autonomous University of Queretaro Queretaro Juriquilla Mexico Space Program University Universidad Nacional Autónoma de México CDMX Mexico

A trend in the development of nanosatellite platforms for technology demonstration has increased in both private industry and academic projects. Communication link design plays a crucial role in the success of space missions, enabling efficient and reliable data transmission between satellites and ground stations. In this context, the use of advanced tools such as System Tool Kit (STK) has gained significant importance by enabling a comprehensive analysis of the technical and operational aspects of the communication link. In an effort to make the future design of the communications subsystem payload on a nanosatellite more efficient and accessible, this paper shows a methodology for the efficient use of STK to perform link analysis. © Published under licence by IOP Publishing Ltd.

关键词： Nanosatellites

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Maylia Fatmawati Mega Novita Nugroho Dwi Saputro Alok Singh Chauhan Noora Qotrun Nada Amit Sinha Bambang Agus Herlambang Shruti Gupta Department of Informatics Faculty of Engineering and Informatics Universitas PGRI Semarang Semarang Indonesia Postgraduate Program of Science Education Universitas PGRI Semarang Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering ABES Engineering College Ghaziabad Uttar Pradesh India

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks.

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tsung-Wei Lin Sofia Ormazabal Arriagada Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan International Graduate Program AIoT NTU Research Center for Information Technology Innovation Academia Sinica Department of Electrical Engineering NTU Center for Advanced Computing and Imaging in Biomedicine NTU Smart Medicine and Health Informatics Program NTU

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples.

关键词： Lungs Genomics Medical services Colorectal cancer Breast Multitasking Oncology Graph neural networks Prognostics and health management Cancer

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Asynchronous Data Communication Architecture on Real-Time Face Recognition for Hybrid Laboratory System

Asynchronous Data Communication Architecture on Real-Time Fa...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Nina Lestari Trisna Purnama Slamet Risnanto Budi Fitriadi Ach Maulana Habibi Yusuf Ary Setijadi Prihatmanto Doctoral Program of School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Electrical Engineering Department Faculty of Engineering Universitas Sangga Buana Bandung Indonesia Faculty of Engineering Universitas Sangga Buana Bandung Indonesia Universitas Komputer Indonesia Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

The Internet of Things (IoT) and Artificial Intelligence (AI) have reshaped educational technology, increasing the chances of developing more effective and engaging learning environments. This study proposes an architecture for asynchronous data communication with real-time face recognition, a virtual and remote labs confluence. The architecture uses IoT hardware and software to comprise critical components such as IoT cameras, message brokers, FTP servers, and AI processing servers. The aim is to optimize resource usage and maximize response time efficiency. The architecture employs message queuing to manage high volumes of face image data, facilitating immediate verification and rapid access. Our findings demonstrate that the system can achieve face recognition with 91.7% accuracy and an average system communication latency of 2.631 seconds, effectively leveraging asynchronous communication to optimize resource usage and computational efficiency. The research comprehensively measures the data communication metrics and face recognition metrics. It also explains how asynchronous data communication can be strategically deployed in a hybrid lab environment to enhance educational and operational efficacy.

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Constructing a Segmentation Model for Patients with Leukoaraiosis Using Deep Learning Algorithms 24

Constructing a Segmentation Model for Patients with Leukoara...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Lo, Li-Ting Tsai, Yi-Chia Chang, Yi-Jou Ting, Hsien-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Many patients with leukoaraiosis (LA) exhibit mild and difficult-to-detect symptoms in the early stages, and due to the lack of effective detection methods, the optimal timing for treatment is often missed. This not only makes later treatment more difficult but, in severe cases, can even lead to mild to severe dementia. Therefore, in addition to relying on radiologists to manually interpret magnetic resonance imaging (MRI) scans, finding more effective detection methods has become extremely urgent. This study aimed to combine image processing and deep learning technologies to develop a model capable of accurately identifying and segmenting white matter hyperintensities (WMHs) to assist doctors in the early diagnosis of LA. We first performed efficient preprocessing on MR image slices from two different series and used the ResNet18 model to identify key slices containing WMHs, ultimately achieving precise segmentation of WMH areas with the U-Net model. This research focused on developing innovative preprocessing techniques, improving the accuracy by 12.3% with a limited dataset through preprocessing. Ultimately, the ResNet18 model achieved an F1 score of 0.829, while the U-Net model’s intersection over union (IoU) and Dice coefficient reached 0.750 and 0.857, respectively, showing the potential of this architecture. We propose improvements in the final chapter to construct a more comprehensive model. © 2024 Copyright ACM.

关键词： Deep learning

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Base on GAN Combined with CNN Architecture to Generate Brain Stroke CT Images 24

Base on GAN Combined with CNN Architecture to Generate Brain...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Shih, Guan-Yu Ting, Hsien-Wei Chen, Yi-Xiang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program inBiomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

In recent years, many fields have expanded their research methods through the integration of artificial intelligence. In the current medical field, it is widely used in image recognition to diagnose patient symptoms, train clinical prediction models, and improve clinical workflow processes to avoid missing treatment opportunities. These methods have all achieved good results so far. Now, generative AI is also impacting the development of the medical field, not only generating prescriptions that match patient conditions but also being applied in generating medical images for data augmentation and simulating various pathological conditions, thereby aiding in training deep learning models. Stroke is a leading cause of death or disability, ranking as the second leading cause of death worldwide, next to cancer. If cancers were categorized individually, stroke would surpass all single diseases, making it a primary focus of research in brain disease treatment. There are already studies where generative AI has been used to create medical images, which are then manually evaluated by doctors to determine if they were generated by AI. In this project, we plan to generate CT images of strokes using generative AI and use a trained model (to diagnose stroke) to improve accuracy. Through this project, we hope to solve the problem of scarce stroke CT images while also providing support for training stroke-related models. © 2024 Copyright ACM.

关键词： Deep learning

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