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Multi-Step-Ahead Prediction of User Movement on 5G Networks Using the Long Short-Term Memory Method 18

Multi-Step-Ahead Prediction of User Movement on 5G Networks ...

引用

18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Timoteo, Adriel Benyamin, Fabian Rafinanda Anargyarahman, Naufal Bhanu Iskandar Yuliana, Hajiar School of Electrical Engineering and Informatics Telecommunications Engineering Program Bandung Institute of Technology Bandung Indonesia Jenderal Achmad Yani University Faculty of Engineering Department of Electrical Engineering Cimahi Indonesia

ISBN: (纸本)9798331527686

Since the initial launch of 5G networks in 2018, 5G technology has rapidly expanded to cover about 40% of the world's population, offering higher bandwidth, faster connectivity, and lower latency than 4G. However, due to its higher frequency operation, 5G necessitates more frequent handovers, making efficient handover performance crucial. This research focuses on enhancing handover efficiency by predicting user movement using Long Short-Term Memory (LSTM) models for multi-step-ahead prediction. The model created will utilize 60 seconds of historical data to predict 10 seconds ahead, with a target to achieve an average prediction accuracy of less than 20 meters with a latency under 100 ms. The study employs TensorFlow for model training, leveraging the Grab-Posisi dataset, and utilizes Plotly and TensorBoard for result visualization. Six model variations were trained, with the 16-unit model trained for 3106 epochs performing best, achieving 16.99 meters accuracy and 31 ms latency. The findings suggest that LSTM is effective for predicting user movement in 5G networks, with potential accuracy improvements through extended training epochs. © 2024 IEEE.

关键词： Prediction models

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Convolutional Neural Network for Cataract Clasification Using Primary Dataset

Convolutional Neural Network for Cataract Clasification Usin...

引用

International Conference on Business and Industrial Research (ICBIR)

作者： I Gede Totok Suryawan I Wayan Murmayasa Adi Putra Informatics Engineering Study Program Faculty of Technology and Informatics Institut Bisnis dan Teknologi Indonesia (INSTIKI) Denpasar Indonesia

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

Cataracts are the main factor causing blindness in Indonesia. Traditional diagnostic methods involving clinical examination by an ophthalmologist using a slit-lamp and visual tests have many challenges such as limited resources, time, and medical expertise may not be available in all locations, especially in less developed areas. Utilizing medical image technology is a wise choice because it allows early detection and monitoring of the development of this cataract disease Technology is needed to identify medical images that have valuable information so that early detection or monitoring of cataract development can be carried out. CNN as a part of deep learning has the ability to perform automatic feature extraction so that it can identify eye images. A CNN model for recognizing cataracts in eye images taken directly from Bhayangkara Hospital in Denpasar Bali has been developed in this research. The model developed is equipped with a dropout layer technique to prevent overfitting. The test results show that the CNN developed in this research produces very good performance with an average accuracy level of 92% on public dataset images, and 94% on primary dataset images. Tested using the primary dataset and public dataset, this CNN model shows very good performance because it can correctly identify an average of 94% of eye images in the primary dataset, and 92% of eye images in the public dataset..

关键词： Cataracts Training Visualization Image recognition Accuracy Convolutional neural networks Medical diagnostic imaging Monitoring Testing Overfitting

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Emotion Classification Through Speech Recognition Using LSTM Model and MFCC Feature Extraction

Emotion Classification Through Speech Recognition Using LSTM...

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International Conference on informatics and Computing (ICIC)

作者： Ivana Lucia Kharisma Somantri Dila Aura Futri Muhammad Ilham Nurdiansyah Alfajar Tirawati Ika Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

In an era where human-machine interaction is becoming increasingly important, the ability to understand emotions can enhance user experience across various applications, such as customer service and monitoring systems. This study aims to develop an emotion recognition system through voice using the Long Short-Term Memory (LSTM) model and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction. The dataset used in this research includes seven emotion classes: happy, sad, angry, surprised, neutral, fearful, and disgusted, with a total of 2.800 samples obtained from the open-source Kaggle platform. The LSTM model is implemented to capture temporal patterns in voice data, while MFCC features are employed to represent the characteristics of the voice. The evaluation results show a classification accuracy of 98.80%, with the LSTM model proving effective in addressing the challenges of time series data with long-term dependencies. This research not only contributes to the understanding of emotions through voice but also opens opportunities for further development of real-time emotion recognition systems. The implementation of this technology can enrich human-machine interaction, creating more responsive and emotionally aware systems, and contribute to innovations in artificial intelligence.

关键词： Human computer interaction Emotion recognition Technological innovation Time series analysis Speech recognition Feature extraction Real-time systems Mel frequency cepstral coefficient Artificial intelligence Long short term memory

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Cluster Dominance Analysis of Strength Training Motion Characteristics 12

Cluster Dominance Analysis of Strength Training Motion Chara...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Toba, Hapnes Wianto, Elizabeth Malinda, Maya Al Halim, Agung Wijaya Chen, Chien-Hsu Universitas Kristen Maranatha Program in Computer Science Bandung Indonesia Universitas Kristen Maranatha Program in Visual Communication Design Bandung Indonesia Universitas Kristen Maranatha Program in Management Bandung Indonesia Universitas Kristen Maranatha Program in Informatics Bandung Indonesia National Cheng Kung University Industrial Design Department Tainan Taiwan

ISBN: (纸本)9798350340181

This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each cluster. Data collection is performed using video-guided strength training exercises equipped with 1 kg dumbbells and recorded by a sensor embedded in smartwatches. The analysis method involves applying the concept of density affinity, which calculates the density ratio of clusters to the recognized motions. Subsequently, the dominance sequence is observed to identify which clusters exhibit distinct characteristics, ultimately determining the intended motions. The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches. © 2023 IEEE.

关键词： Cluster analysis

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Information Security Challenges in the Digitalisation of the Austrian Food Industry: Assessment of Food Suppliers and Implications 29

Information Security Challenges in the Digitalisation of the...

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29th International Conference on Engineering, Technology, and Innovation, ICE 2023

作者： Hetzenauer, Claudia Dobler, Martin Simma, Armin Vorarlberg University of Applied Sciences Master Program Business Administration: Bpm Dornbirn Austria Vorarlberg University of Applied Sciences Research Centre Business Informatics Dornbirn Austria Vorarlberg University of Applied Sciences Competence Area Informatics Dornbirn Austria

ISBN: (纸本)9798350315172

In 2021, a prominent Austria dairy producer suffered from an IT attack and was completely paralysed. Without clearly defined mitigation measures in place, major disruptions were caused alongside the whole supply chain, including logistics service providers, governmental food safety bodies, as well as retailers (i.e., supermarkets and convenience stores). In this paper, we ask the question how digitisation and digital transformation impact IT security, especially when considering the complex company ecosystems of food production and food supply chains in Austria. The problem statement stems from a gap in knowledge of key differences in approaches towards IT security, resilience, risk management and especially business interfaces between food suppliers, supermarkets, distributors, logistics and other service providers. In order to answer related research questions, firstly, the authors conduct literature research, and highlight common guidelines and standardisation as well as look at state-based recommendations for critical infrastructure. In a second step, the paper describes a quantitative and qualitative survey with Austrian food companies (producers and retailers) which is described in detail in the paper. A description of recommended measures for the industry, further steps, as well as an outlook conclude the paper. (Abstract) © 2023 IEEE.

关键词： Supply chain management

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Development of A Deep Learning Hybrid Model for Classification of Plastic Waste Objects in the Coastal Environment

Development of A Deep Learning Hybrid Model for Classificati...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Djafar, Imran Indrabayu Syarif, Syafruddin Hasanuddin University Dipa Makassar University Doctoral Program of Electrical Engineering Department Makassar Indonesia Hasanuddin University Makassar Department of Informatics Indonesia Hasanuddin University Department of Electrical Engineering Makassar Indonesia

ISBN: (纸本)9798331513320

Plastic, glass, metal, and wood waste has become a global scourge and nightmare to the marine environment and the development of the coastal areas. This paper aims at developing a hybrid model of deep learning technique using Convolutional Neural Networks (CNN) and Vision transformers (ViT) in the performance of detecting underwater waste materials. The model is designed with the use of transfer learning and high-resolution imaging sourced from underwater drones to help overcome the challenges of underwater work such as poor visibility and insufficient ambient light. The local features are extracted using CNNs and the global spatial relationships of the images are attended to by the ViTs. The results achieved a waste detection accuracy rate of about 90%, which is effective even in difficult environmental conditions. The concept is for a waste management system where drones or underwater robotics systems can assist automated waste management, waste treatment processes, and aid marine renovation activities. © 2025 IEEE.

关键词： Convolutional neural networks

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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...

引用

2023 International Conference on informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

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

ISBN: (纸本)9798350307306

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. © 2023 IEEE.

关键词： Cloud computing architecture

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YoBagi's User Experience Evaluation using User Experience Questionnaire 7

YoBagi's User Experience Evaluation using User Experience Qu...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Juniawan, Fransiskus Panca Sylfania, Dwi Yuny Putra, Rendy Rian Chrisna Henderi Program Study of Technology Information University of Bangka Belitung Pangkalpinang Indonesia Program Study of Informatics Engineering Institut Sains Dan Bisnis Atma Luhur Pangkalpinang Indonesia University of Raharja Informatics Engineering Faculty of Science and Technology Tangerang Indonesia

ISBN: (纸本)9798350345711

The pandemic has brought many negative impacts on human life. One of the negative impacts is economic downturn of Micro, Small, and Medium Enterprise (MSME) actors in Bangka Belitung Province. To solve this problem, a website-based marketplace platform called Yobagi was created. As we know, a new software product must have a good user experience. By having a good user experience means that the product has met the needs of its users. For this reason, an evaluation of the user experience is necessary. This study aims to measure the user experience of using Yobagi. The evaluation uses the User Experience Questionnaire (UEQ) tool, which consists of six scales: Attractiveness, Efficiency, Perspicuity, Dependability, Stimulation, and Novelty. There were 40 respondents who were taken from the top of MSME actors. As the UX evaluation results, it is known that the final score of the six scales is above 0.8 and at the excellent level. This means that Yobagi users have a very good user experience in using Yobagi. In addition, Yobagi has met the criteria for good software by having an excellent user experience value. © 2022 IEEE.

关键词： User experience

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The Improvement of Personal Service in the MSMEs Marketplace Using a Rank-Based Collaborative Filtering Approach

The Improvement of Personal Service in the MSMEs Marketplace...

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2022 International Conference on Electrical and Information Technology, IEIT 2022

作者： Lestari, Sri Yulmaini Aswin Sulyono Nul Fikri, Ruki Rizal Pratama, Yan Aditiya Informatics Engineering Study Program Faculty of Computer Science Lampung Bandar Indonesia Management Study Program Faculty of Business Economics Lampung Bandar Indonesia

ISBN: (数字)9781665453035

ISBN: (纸本)9781665453035

Indonesian MSMEs in 2022 will begin to enter the transformational recovery phase. Various preparations must be made in order to adapt. One that has been done is to increase the number of MSMEs entering the digital ecosystem, such as the MSME marketplace. The MSME Marketplace is used for marketing various MSME products online. The MSME Marketplace must be supported by methods that are able to provide good and fast personal services, considering the types and variety of products offered are increasing in number and varying from time to time. Therefore, this study proposes the Porat_Rank method to produce product recommendations that match user interests quickly so that users are served well. The experimental results show that the Porat_Rank method is superior to the Copelan Score method both in producing recommendations and in processing speed. © 2022 IEEE.

关键词： Collaborative filtering

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ANALYSIS AND DETECTION OF SINGING TECHNIQUES IN REPERTOIRES OF J-POP SOLO SINGERS 23

ANALYSIS AND DETECTION OF SINGING TECHNIQUES IN REPERTOIRES ...

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23rd International Society for Music Information Retrieval Conference, ISMIR 2022

作者： Yamamoto, Yuya Nam, Juhan Terasawa, Hiroko Doctoral Program in Informatics University of Tsukuba Japan Graduate School of Culture Technology KAIST Korea Republic of

ISBN: (纸本)9781732729926

In this paper, we focus on singing techniques within the scope of music information retrieval research. We investigate how singers use singing techniques using real-world recordings of famous solo singers in Japanese popular music songs (J-POP). First, we built a new dataset of singing techniques. The dataset consists of 168 commercial J-POP songs, and each song is annotated using various singing techniques with timestamps and vocal pitch contours. We also present descriptive statistics of singing techniques on the dataset to clarify what and how often singing techniques appear. We further explored the difficulty of the automatic detection of singing techniques using previously proposed machine learning techniques. In the detection, we also investigate the effectiveness of auxiliary information (i.e., pitch and distribution of label duration), not only providing the baseline. The best result achieves 40.4% at macro-average F-measure on nine-way multi-class detection. We provide the annotation of the dataset and its detail on the appendix website0,. © L. Liu, Q. Kong, V. Morfi, and E. Benetos.

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