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2023 5th international conference on Communications, Information System and computer engineering, CISCE 2023

2023 5th International Conference on Communications, Informa...

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5th international conference on Communications, Information System and computer engineering, CISCE 2023

ISBN: (纸本)9798350326796

The proceedings contain 97 papers. The topics discussed include: a reconfigurable ARBW antenna with stable working frequency;a disk-shaped antenna with reconfigurable axial ratio beam;digital domain self-interference cancellation based on modified variable step-size LMS algorithm;joint receiver design and user scheduling for over-the-air aggregation in federated learning;research on mobile service satisfaction prediction model based on GBDT regression algorithm;design of intelligent push system for teaching resources based on user interest preference;research on antenna altitude setting of shore-base microwave over-the-horizon radar;a real-time gait phase detection method for lower limb exoskeleton based on simple recurrent units;design and implementation of container-based elastic high-availability file preview service cluster;design of electrical certificate examination system based on virtual simulation technology;research on positive emotion recognition based on EEG signals;and doppler principle based situational awareness method for autonomous driving.

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Transforming Healthcare with MedInsight: A Guide to Intelligent Decision Support

Transforming Healthcare with MedInsight: A Guide to Intellig...

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2023 international conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Uma Haimavathi, K. Kandavel, N. Vignesh, D. Hubert Chinnadurai, B. Chennai Institute of Technology Department of Biomedical Engineering Chennai600069 India Chennai Institute of Technology Department of Computer Science Engineering Chennai600069 India

ISBN: (纸本)9798350348910

A novel Prediction System has been developed to address the lack of a unified approach for multi-disease prediction in healthcare machine learning models. The To detect various ailments, the system uses machine learning neural network as well as artificial intelligence. simultaneously, specifically targeting heart, liver, and diabetes conditions. The system uses Support Vector Machine and logistic regression models to analyze heart disorders, diabetes, and breast cancer. The logistic regression model, trained on 8,000 patients, achieved an impressive overall accuracy of 85%. It was particularly effective in predicting hypertension and hyperlipidaemia, with accuracies of 90% and 87%, respectively. The system also showed proficiency in predicting diabetes and heart disease, with accuracies of 82% and 78%. This research represents a promising step towards a more comprehensive and accurate multi-disease prediction system. © 2023 IEEE.

关键词： Diseases

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Meta-learning Framework for Effective Few Shot Time Series Prediction 5

Meta-Learning Framework for Effective Few Shot Time Series P...

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5th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Wu, Gang Cong, Li Huang, Chengbin Ju, Ying Jiang, Jiange Chen, Chen State Grid Jilin Province Electric Power Company Changchun130000 China Engineering Technology Research Institute Xidian University Xi'an710071 China

ISBN: (纸本)9798331533694

Few shot time series forecasting is a significant challenge in fields such as healthcare, IoT, and finance, where data scarcity limits the effectiveness of traditional predictive models. Conventional methods often struggle to capture complex temporal patterns and generalize effectively with limited training data. To address this issue, this paper presents a novel meta-learning-based framework designed to enhance forecasting accuracy and adaptability in data-constrained scenarios. The framework leverages knowledge from related tasks through meta-training, enabling rapid adaptation to new tasks with minimal data. This work integrates attention mechanisms to identify salient temporal dependencies and task-specific adaptation layers to improve flexibility and handle task heterogeneity. By extracting and transferring shared patterns across tasks, the framework achieves robust generalization and efficient learning. Comprehensive experiments conducted on diverse real-world datasets demonstrate significant improvements in prediction accuracy and adaptability compared to traditional approaches and meta-learning baselines. The results highlight the proposed method's ability to generalize across domains, offering a practical solution for scenarios characterized by dynamic environments and limited data availability. This work underscores the potential of meta-learning to advance time series forecasting, supporting its broader application in resource-constrained and high-impact fields such as personalized healthcare, predictive maintenance, and financial analysis. © 2025 IEEE.

关键词： Zero-shot learning

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Indoor Positioning Using Wi-Fi and Machine learning for Industry 5.0

Indoor Positioning Using Wi-Fi and Machine Learning for Indu...

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2023 IEEE international conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Neupane, Inoj Alsinglawi, Belal Rabie, Khaled Western Sydney University Sydney Australia Manchester Metropolitan University Department of Engineering Manchester United Kingdom

ISBN: (纸本)9781665453813

Humans and robots working together in an environment to enhance human performance is the aim of Industry 5.0. Although significant progress in outdoor positioning has been seen, indoor positioning remains a challenge. In this paper, we introduce a new research concept by exploiting the potential of indoor positioning for Industry 5.0. We use Wi-Fi Received Signal Strength Indicator (RSSI) with trilateration using cheap and easily available ESP32 Arduino boards for positioning as well as sending effective route signals to a human and a robot working in a simulated-indoor factory environment in real-time. We utilized machine learning models to detect safe closeness between two co-workers (a human subject and a robot). Experimental data and analysis show an average deviation of less than 1m from the actual distance while the targets are mobile or stationary. © 2023 IEEE.

关键词： Machine learning

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Information technology basic course design based on Moodle platform

Information technology basic course design based on Moodle p...

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2022 international conference on computer Application and Information Security, ICCAIS 2022

作者： Hu, Suyao School of Information Engineering Xianning Vocational Technical College Hubei Xianning437100 China

ISBN: (纸本)9781510663459

Information technology plays a very important role in improving teaching effect and promoting education informatization. Cultivating talents with computer literacy through information technology education is gradually becoming a hot spot of educational development. Exploring a curriculum design platform with high efficiency, low technical threshold and simple operation has undoubtedly become the most urgent need for the development of education informatization. Therefore, this paper uses the Moodle platform to design the basic course of information technology, and meets the students' learning needs by integrating teaching resources and giving full play to the platform's teaching advantages. Through the platform teaching feedback experiment and teaching effect experiment, this paper analyzes whether the use of Moodle platform for information technology course teaching can improve learning interest and learning ability. The self-learning ability was improved, 76.39% of the students improved their collaborative learning ability, and 68.06% of the students improved their information processing ability. © 2023 SPIE.

关键词： Students

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Spread Analysis and Prediction of Covid-19 in India using Machine learning

Spread Analysis and Prediction of Covid-19 in India using Ma...

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2023 international conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Yenkikar, Anuradha Babu, C. Narendra Ramaiah University of Applied Sciences Department of Computer Science and Engineering Karnataka Bangalore India Pune Institute of Computer Technology Pune India

ISBN: (纸本)9798350396485

Since its breakout, the coronavirus (covid-19) has wreaked devastation all over the world. There hasn't been a nation that hasn't been affected by it, and India is no exception. Finding a treatment for this sickness and stopping its spread is one of the hardest problems humanities has ever faced. World Health organization (WHO) figures show that the mortality rate and the number of people with covid-19 are both increased exponentially in some countries during the peak waves. In this study, we use machine learning approaches to forecast the number of covid-19 confirmations, recoveries, and mortality cases over a period of time and assess the coronavirus outbreak in India. The polynomial regression (PR), support vector regression (SVR), and an autoregressive integrated moving average (ARIMA) model are three techniques that are used. The findings demonstrate that, in terms of prediction outcomes, the ARIMA model provides the least Root Mean Squared Error (RMSE), closely followed by polynomial regression. SVR doesn't perform well since predictions are either too low or too high. Overall, the proposed system can significantly aid in comprehending the pattern of spread in other nations and assist governmental bodies in taking action to lessen its effects in future. © 2023 IEEE.

关键词： Mean square error

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5th international conference on Higher Education learning Methodologies and Technologies Online, HELMeTO 2023

5th International Conference on Higher Education Learning Me...

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5th international conference on Higher Education learning Methodologies and Technologies Online, HELMeTO 2023

ISBN: (纸本)9783031673504

The proceedings contain 52 papers. The special focus in this conference is on Higher Education learning Methodologies and Technologies Online. The topics include: Get in (Multi)touch with the Tangent;internationalisation Experience on a Digital Platform and Its Impact on Self-efficacy: The Results on a Sample of Initial Teacher Education Students;digital Multisensory Storytelling as Educational-Didactic Methodology for Emotional Literacy;automated Online Assessment and Cloud-Based Programming: Advancing computer engineering Education;Leveraging Explainable AI Methods and Tools for Educational Data;botBid - From Botany to Big Data: Combining Citizen Science and Innovative Teaching Methodologies;detecting the Usage of Large Language Models Exploiting Generative Adversarial Networks;ontology for Constructively Aligned, Collaborative, and Evolving Engineer Knowledge-Management Platforms;augmented Didactic: The Potential of Gesture in Mobile learning to Enhance learning;digital Twins and E-learning: Navigating Challenges and Opportunities;emotions in Practice: Studying Lectures and Seminars in On-Line and Offline Education;performing Art-Based Methodology for Empathetic Transposition into Online learning Experiences;improving Student Online Interactions and Teacher’s Ability to Manage Them with the Quick Chat Moodle Plugin;promoting Meaningful learning in Topology Supported by Undergraduate Students’ Video Creations;the Feedback in a Formative Assessment Path: Development of Communicative Skills in a Workshop Online;online Resources for Training Pre-service Primary School Teachers in Mathematics;mathematics Interpretative Tasks and Formative Assessment: A Digital Device for Teachers Training;Cybersecurity for Teens (CS4T) – A Project by Ludoteca of Registro .it;learning CyberSecurity with Story-Driven CTF Challenges: CyberTrials 2023;superCyberKids: Enhancing Cybersecurity Education in K-12 Through Digital Game-Based learning;Empowering Higher Education with ChatGPT: Inn

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Relation Extraction Based on BERT and BGRU in the Chinese Music Scene 27

Relation Extraction Based on BERT and BGRU in the Chinese Mu...

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27th international conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Wang, Chaoguo Zhang, Liang Yan, Wei School of Information Science and Engineering Shandong Normal University Shandong Jinan250358 China Shandong University Shandong Qingdao266237 China

Due to the lack of music knowledge extraction tools and public datasets, there are relatively few studies on music entity relation extraction. The Bidirectional Encoder Representations from Transformer (BERT) model considers contextual information and provides more dynamic, richer vector representations of generated words. Bidirectional Gated Recurrent Units (BGRU) has long-distance learning capability, which can obtain rich contextual semantic feature. Therefore, this paper proposes a deep learning model based on BERT and BGRU combined with an attention mechanism for Chinese music relation extraction. Experimental results show that the proposed method obtains good results when extracting the relation between musical entities. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Bidirectional Encoder Representations from Transformer(BERT) Deep learning Gated Recurrent Units (GRU) Knowledge Graph Relation Extraction

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Maximising Efficiency of Network Resources in SDN through Optimal Packet Routing using Reinforcement learning

Maximising Efficiency of Network Resources in SDN through Op...

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2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Byakodi, Anush K Hungund, Mohammad Zakee Athani, Aman Khan N Afghan, Khadar Wali B Patil, Somashekar Narayan, D.G. KLE Technological University School of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350348217

Software-defined networking (SDN) has increased in popularity in recent years owing to its ability to centralize network device control. This is the cornerstone of data center networks, and as more IoT-enabled devices are added, it is expected to skyrocket. The employment of Machine Intelligence to handle and route the majority of traffic can enhance centralized SDN-based control, allowing for high and occasionally subpar network management. Owing to the development of Reinforcement learning (RL) combined with Deep learning, which allows autonomous flow management in SDN, intelligent routing has gained popularity. However, the existing SDN routing algorithm has poor link usage and is unable to update and change according to real-time network conditions, making it challenging to forecast and predict how communication networks will evolve in the future. Consequently, we present a routing optimization strategy based on Quality of Service (QoS) variables for SDN-based Data Center Networks using Deep Reinforcement learning. We provide a reinforcement learning-based routing method for SDN that outperforms Dijkstra's shortest path in terms of the throughput and average latency. In the proposed method, we integrated SDN with RL, which takes input from the state in which the current network is and routes traffic to achieve the maximum QoS parameters. The parameters considered were the throughput and delay. The presented solution is a network-oriented, self-learning routing system named DeepQoSR, based on Advantage Actor-Critic Networks. We compared our proposed technique with traditional algorithms, such as the shortest path, and present our analysis. © 2023 IEEE.

关键词： Reinforcement learning

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Bengali Music Genre Classification Using WaveNet 26

Bengali Music Genre Classification Using WaveNet

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26th international conference on computer and Information Technology, ICCIT 2023

作者： Khan, Md. Nabil Rahman Tuli, Khandaker Iffat Jahan Salsabil, Most. Sadia Reza, S.M. Raiyan Shah, Faisal Muhammad Joy, Sajib Kumar Saha Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Music genre classification plays a crucial role in music recommendation and information retrieval. While there have been numerous studies on classifying genres of English music using various machine learning approaches, there is a noticeable absence of similar work for Bangla music classification. Bangla music encompasses a wide range of types and styles that can be classified into different genres. In this research, we focus on six distinct Bangla music genres, including 'Bangla Adhunik,' 'Bangla Hip-Hop,' 'Bangla Band Music,' 'Nazrulgeeti,' 'Palligeeti,' and 'Rabindra Sangeet.' Our dataset consists of 250-300 songs (***3 format) for each genre. By comparing the performances of different models, we demonstrate that our proposed model, using a combination of Principal Component Analysis and K-Fold on the model WaveNet, achieves high accuracy in the multiclass classification of Bangla music genres by comparing with other machine learning and deep learning. This work contributes to the advancement of music genre classification specifically for Bangla music and paves the way for enhanced music recommendation systems and information retrieval in this rich musical tradition. © 2023 IEEE.

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