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Design and Simulation of Intelligent Matching Algorithm for Online Primary School Mathematics Curriculum Based on Deep Learning 1

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Mo, Yi Tang, Jishen Chang, Xunyun Wu, Dong Hechi University Guangxi Yizhou546300 China Foreign Languages Department Sichuan Vocational and Technical College Sichuan Suining629000 China

ISBN: (数字)9783031631399

ISBN: (纸本)9783031631382

With the continuous development of new information technologies such as big data, cloud computing, and Internet of Things, people's access to knowledge and information has undergone profound changes. New concepts such as machine learning, wisdom education, and educational big data are changing the traditional educational ecology, teaching form, and learning methods, and at the same time, promoting the deep development of educational modernization to informationization. People's needs in the fields of artificial intelligence such as information retrieval, automatic question and answer, dialogue system, etc. begin to appear, and intelligent matching algorithms are needed to meet the high demand of users. Text matching algorithm is the core problem in natural language processing technology. In the traditional text matching field, the dimension disaster of text representation and data sparseness have affected the development of natural language processing field. It is necessary for the times and students’ development to carry out subject teaching under the guidance of deep learning, which is of great significance. Unit review class is an important class type of primary school mathematics, which is of great value to students’ development, but there are many problems in actual teaching. This paper presents a multi-view collaborative teaching network based on sparse interactive data. The matching network is mainly composed of two parts, a text-based matching model and a relationship-based matching model. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Big data

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Third international conference on Computer Vision and Information Technology, CVIT 2022

Third International Conference on Computer Vision and Inform...

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3rd international conference on Computer Vision and Information Technology, CVIT 2022

ISBN: (纸本)9781510663039

The proceedings contain 15 papers. The topics discussed include: relative vectors clustering and temporal constraint based generalized Hough transform for high frame rate and ultra-low delay arbitrary shape detection;multi-line buffer based pipeline architecture with junction connectivity analysis for high frame rate and ultra-low delay contour-based corner detection;vision-based fall detection system for the elderly using image processing and deep learning;convolutions vs. sequences: understanding performances of neural-based methods for automatic Baybayin script recognition;research of pavement crack detection system based on image processing;research on target detection based table tennis drop point recognition system;a survey on potential reactive fault tolerance approach for distributed systems in big data;position reset and hybrid feature based particle filter tracking for large-size and long-term full occlusion;and comparative study of informal learning spaces in colleges based on the occurrence probability of informal learning.

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Smart Industrial Scanner for Implementation of Relevant data Parsing from Prescriptions Using SSWF Algorithm 3rd

Smart Industrial Scanner for Implementation of Relevant Data...

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

作者： Jose, Jephin V. Sherin Eliyas, M. Kumar, Sathish Benitta, Angeline Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789811979811

Scanners have a wide range of functions. When it comes to an embedded system that converts scanned documents into meaningful data, there is seemingly a void in the industry. data that can be mined from prescriptions are invaluable and any approach to make meaningful sense of such data is always a beneficial. Such approaches not only push the bounds of how much creative programming can achieve, but also how much it means to the people who can benefit from such innovations. We implement one such innovative approach to convert medicine data within a prescription to make meaningful sense of the data that resides within the prescriptions. We propose a device for converting a medical prescription into a standardized format. To achieve this, we implemented a HP Ink Tank 410 scanner connected to a Raspberry Pi 4 running Ubuntu, we propose a revised algorithm for detecting and parsing related medicinal information from a prescription. This algorithm guarantees a reduction in processing time and improve improves performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： data handling

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Ground extraction and road obstacle segmentation method based on point cloud data 3

Ground extraction and road obstacle segmentation method base...

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3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Li, XiaoWei Yang, HuiCheng Feng, HaiHong Xu, ShuQi School of Electrical Engineering Anhui Polytechnic University Wuhu China Wuhu Angong Cognitive Technology Co LTD China

ISBN: (纸本)9781510661301

With the rise of artificial intelligence and other technologies, unmanned driving has become an important direction of future automobile development. The ground extraction of roads is a key task. The traditional method of ground extraction is limited to flat roads and the speed is slow. In view of the under-segmentation problem between the ground point cloud and the multi-objective object point cloud, we propose a ground segmentation algorithm for point cloud data based on multi-region segmentation. In this paper, the ground data can be accurately found by using the method of concentric region division for point cloud data. At the same time, it also has good robustness for uneven road surface, so as to provide a good foundation for the subsequent segmentation of road obstacles. For the extraction of road obstacles, we adopt the DBSCAN clustering method based on sub-region. According to the characteristics of near-density and distant point cloud, we adopt adaptive parameter selection for different regions to improve the accuracy of obstacle extraction. In order to verify the results, the use of KITTI data set on ubuntu18.04 using ROS system to complete the test, can maintain about 28fps processing speed. © 2023 SPIE.

关键词： Extraction

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EEG-Based Stress Detection: EEMD-Enhanced Features with SVM and Twin-SVM Models 3

EEG-Based Stress Detection: EEMD-Enhanced Features with SVM ...

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3rd IEEE international conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Kudachi, Anusha Torse, Dattaprasad A. Kle Technological University Department of Computer Science Engineering Dr. M. S. Sheshgiri Campus Belagavi India Kle Technological University Department of Electronics and Communication Dr. M. S. Sheshgiri Campus Belagavi India

ISBN: (纸本)9798331521691

Recent advances in signal processing and machine learning have significantly advanced EEG-based applications, especially in emotion detection and cognitive analysis. Using Intrinsic Mode Functions (IMFs) developed from Ensemble Empirical Mode Decomposition (EEMD) for improved feature extraction, this work investigates an EEG-based emotional stress identification system. The EEG signals were collected in EDF format, pre-processed by segmenting them into 10-second chunks, ensuring consistent and manageable data windows across all channels. IMFs were extracted from these segments to capture complex frequency components, which were then used as input features for machine learning classifiers. Support Vector Machine (SVM) and Twin Support Vector Machine (Twin SVM) classifiers were used to assess the system. The SVM classifier achieved an accuracy of 80.75%, while the Twin SVM classifier demonstrated a higher training accuracy of 89. 75% and Twin SVM shows an improvement in test accuracy of 93.75%. These results suggest that, while SVM offers good training performance, Twin SVM provides more reliable generalization to unseen data. The findings offer valuable insight into effective pre-processing techniques and the potential of these classifiers for real-time stress detection systems. © 2025 IEEE.

关键词： Arousal EEG signals Machine Learning signal processing stress detection Valence

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Monitoring Human Muscle signal Using Electromyography Sensor Based on Internet of Things 3

Monitoring Human Muscle Signal Using Electromyography Sensor...

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3rd IEEE international Biomedical Instrumentation and Technology conference, IBITeC 2023

作者： Hanapi, Roby Hikmah, Irmayatul Hidayat, Anjar Taufik Institut Teknologi Telkom Purwokerto Faculty of Telecommunication and Electrical Engineering Telecommunication Engineering Purwokerto Indonesia Institut Teknologi Telkom Purwokerto Faculty of Telecommunication and Electrical Engineering Biomedical Engineering Purwokerto Indonesia

ISBN: (纸本)9798350302424

Muscles serve as a system measure of motion, relaxation, and contraction. The electrical signals generated by muscles during contraction are measured using electrodes connected to electromyography sensors. Electrodes are placed on the surface of the skin to detect electrical signals, which are then used to store condition data based on their placement. Electromyography devices still do not have direct integration through websites or apps, nor are they capable of storing digital patient records. Instead, data collection is done manually or on-site. However, with the Internet of Things-based muscle signal detection device, this process becomes easier for medical personnel. Medical personnel can check the patient's medical record results through a laptop and save the digital record data. The advantage is that the monitoring process can be done online, so that medical personnel can easily see the patient's progress from day to day through the available digital data records. The electromyography sensor output is processed by the NodeMCU ESP8266 microcontroller and displayed through the blynk platform integrated with the internet. Electromyograph testing was conducted on the gastrocnemius muscle and 5 different subjects. The test results show that the value of muscle tension is greater when the load treatment has been carried out, both in the biceps muscle and in the gastrocnemius muscle with a large average value in the biceps muscle when the muscle extension condition is 9.746 Volt and 17.296 Volt when the muscle flexion condition. And in the gastrocnemius muscle it is 10.685 Volt when the muscle is in extension and 18.028 Volt when the muscle is in flexion. The value of the voltage is influenced by several factors, one of which is muscle strength and body weight. © 2023 IEEE.

关键词： Electrodes

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Investigation on BPSK Modulated LoRaWAN for data Rate Enhancement

Investigation on BPSK Modulated LoRaWAN for Data Rate Enhanc...

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international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Divyasree K Manoj G Mansi A Sindhukavi S Parameswaran Ramesh Bino J Shabana Parveen M Bhuvaneswari P.T. V Department of Electronics Engineering Madras Institute of Technology Anna University Chennai India Department of Electronics and Communication Engineering St Josephs's Institute of Technology Anna University Chennai India Department of Electronics and Communication Engineering Sri Sairam Engineering College Anna University Chennai India

The Internet of Things (IoT) is an upcoming and promising technology that involves efficient transmission techniques to communicate data obtained from sensory devices in the developed application. The role played by the channel characteristics between these devices is significant, as quality of service metrics are vital factors in determining the performance of the application. To support long-distance communication, LoRaWAN technology is being used in most of the IoT-enabled automation systems. It provides a wider coverage area and encodes the information using Chirp Spread Spectrum (CSS). A low data transmission rate is found to be its major limitation. This research investigates the bit error rate (BER) behaviour of LoRa when the sensed data are modulated with BPSK under different signal-to-noise ratio (SNR). The impact of the spreading factor on data rate is also analyzed. From the MATLAB simulation results, it is found that an increase in the spreading factor reduces the data rate and vice versa. Hence, to obtain a better data rate, a lesser spreading factor is suggested.

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Evaluating data Migrations with Respect to Interoperability in Hybrid Cloud 3rd

Evaluating Data Migrations with Respect to Interoperability ...

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3rd international conference on Machine Learning, Image processing, Network Security and data Sciences, MIND 2021

作者： Barhate, S.M. Dhore, M.P. DECS RTM Nagpur University Nagpur Maharashtra Nagpur440033 India SSESA’S College RTM Nagpur University Nagpur Maharashtra Nagpur440035 India

ISBN: (纸本)9789811958670

Cloud computing is the most popular environment in industry which is gaining popularity due to its enormous powerhouse facilities like scalability, virtualization, ubiquitous computing, on demand access to pool of resources on a pay per basis. It provides a broad access to shared resources which are rapidly provisioned with minimal management efforts. Hybrid cloud is the most chosen cloud model in the recent years with its powerful combination of private and public clouds. While working with hybrid cloud model, the data has to be migrated between the private and public clouds as and when needed. This paper studies the performance of hybrid cloud, while the data is migrated between the clouds. This paper deals with consolidation and data distribution technologies implemented for effective data migration technologies in hybrid cloud. CloudSim simulator has been used to study the performance issues of different data migration techniques while implementing interoperability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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An End-to-end Framework for Graph Clustering: Semantic Fusion of Graph Convolutional Network and Variational Auto-encoder 3

An End-to-end Framework for Graph Clustering: Semantic Fusio...

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3rd international conference on Image processing, Computer Vision and Machine Learning, ICICML 2024

作者： Peng, Yongxin Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350355413

Attribute graph clustering is a fundamental and challenging task in graph data mining, requiring the adequate utilization of both node attributes and graph structure. Recently, a series of graph clustering methods have been proposed, integrating both graph convolutional networks (GCNs) and autoencoders to capture structural information and node attributes, respectively. However, most existing methods either employ a complicated summation mechanism in the unaligned representation space from GCN and auto-encoder, or select a certain module (e.g. results from GCN) as a biased target selftraining signal. To address this, we propose a new end-to-end graph clustering framework that integrates a GCN and a variational autoencoder (VAE) with a more efficient and reasonable fusion mechanism in the semantic level. To better supervise the clustering, we select a high-confidence set of nodes based on the consensus of two encoders, further boosting the performances. Extensive experiments on multiple benchmark datasets demonstrate that our framework significantly outperforms state-of-the-art baselines, highlighting its efficacy in graph clustering. © 2024 IEEE.

关键词： Clustering Graph data Mining Variational Autoencoder

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Detection of Four Class Motor Imagery from EEG signal for Brain-Computer Interface Applications 3

Detection of Four Class Motor Imagery from EEG Signal for Br...

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3rd international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2023

作者： Nambiar, Amrutha Rajagopal, Sivakumar Subramaniam, Shamala Department of Sensor and Biomedical Technology School of Electronics Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India Department of Communication Technology and Networks Universiti Putra Malaysia Selangor 43400 Malaysia

ISBN: (纸本)9781643684581

Brain-Computer Interface (BCI) is an interfacing technology used between humans and machines. Initially, research on BCI was confined only to medical applications such as the detection and quantification of brain states like alertness, emotion, and attention. However, in the recent past, it has been extended to incorporate engineering and industrial applications as well. Electroencephalogram (EEG) based Brain-Computer Interfaces became an energized research area within the field of neural engineering, rehabilitation, and neuroscience. EEG-based BCIs especially those using motor-imagery (MI) data became very useful within the clinical field and entertainment industry as well. MI data is created when someone imagines the motion of his/her limb. Motor-imagery BCI is a sub-category of BCI that has the technique to control an external device through imagined motor movements. This essentially means that an external device will relocate itself to the left or right based on the corresponding movement that the user imagines. This has been considered a brand new method of communication not just for people who have lost motor control or speech abilities but also for healthy users as well. Various signal processing techniques are done on the MI-based EEG datasets, focusing mainly on artefact rejection, feature extraction and selection of significant features to enhance the functionality of the proposed BCI. In this paper, the work is confined to assisting people to control the motion of external devices. By implementing an MI-based BCI, people who have lost motor control functionalities will be put at ease. They will be able to control the motion of external devices by simply imagining motor movements. © 2023 The Authors.

关键词： Feature extraction

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