In the paper, a new machine-learning technique is proposed to recognize movement patterns. The efficient system designed for this purpose uses an artificial neural network (ANN) model implemented on a microcontroller ...
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ISBN:
(纸本)9783031530357;9783031530364
In the paper, a new machine-learning technique is proposed to recognize movement patterns. The efficient system designed for this purpose uses an artificial neural network (ANN) model implemented on a microcontroller to classify boxing punches. Artificial intelligence (AI) enables the processing of sophisticated and complex patterns, and the X-CUBE-AI package allows the use of these possibilities in portable microprocessor systems. The input data to the network are linear accelerations and angular velocities read from the sensor mounted on the boxer's wrist. By using simple time-domain measurements without extracting signal features, the classification is performed in real-time. An extensive experiment was carried out for two groups with different levels of boxing skills. The developed model demonstrated high efficiency in the identification of individual types of blows.
The proceedings contain 29 papers. The special focus in this conference is on Language Processing and Knowledge Management. The topics include: machinelearning Serving the School Orientation Process;improving Ar...
ISBN:
(纸本)9783031850660
The proceedings contain 29 papers. The special focus in this conference is on Language Processing and Knowledge Management. The topics include: machinelearning Serving the School Orientation Process;improving Arabic Fake News Detection Across Context-Aware Attention Deep Model Based on Natural Language Processing;towards a Maude-Based Approach for Formal Modeling Deep Neural Networks;Anti-pattern Based IoRT-Aware Business Process Structure Verification Approach;opinion Analysis Based on a Sentiment Lexical Ontology and Deep learning Models: Tunisian Dialect Case;disfluent-to-Fluent Tunisian Dialect Speech Translation with Fine-Tuning Pre-trained Language Models;BERT-Based Model for Sarcasm Detection in Arabic Texts;typology of Event data Imperfections;towards Sentiment Analysis for Libyan Dialect;text Categorization Can Enhance Domain-Agnostic Stopword Extraction;normalized Orthography for Tunisian Arabic;deep learning Approach for Early Prediction of Depression on Social Network;adapting Large Language Models to Biomedical Domain: A Survey of Techniques and Approaches;tunisian Arabic Understanding: Resources Analysis and Evaluation;from data to Decisions: An Ontology-Driven Method for Opinion mining;LLMs for Cyberbullying Detection in Political Social Media;Assessing BERT Models for Arabic Named Entity recognition in a Multi-dialectal Context;tunisian Normalized Pronunciation;traffScOnto: Ontology for Traffic Management in the Context of Smart City Domain;SERTUS dataset Collection from Spontaneous Environments;an Agricultural Sentiment dataset for Pest Control and Crop Diseases;deep learning Approach to Identify and Classify Arabic Verbal Multi-word Expressions;a Design pattern-Based Approach for Analyzing MapReduce Applications;analyzing the Impact of Big data in Mental Health;The Impact of AI on Knowledge Management;a Rule-Based System for Translating Libyan Dialect Dual Forms to Modern Standard Arabic.
作者：
Peng, LiXinjiang Univ
Ctr Innovat Management Res Xinjiang Urumqi Xinjiang Peoples R China Xinjiang Univ
Sch Econ & Management Urumqi Xinjiang Peoples R China
To further improve the management level of tourists in scenic spots, a prediction method of tourist volume of scenic spots based on combined model is proposed. Among them, extreme learningmachine, support vector regr...
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ISBN:
(纸本)9798400709777
To further improve the management level of tourists in scenic spots, a prediction method of tourist volume of scenic spots based on combined model is proposed. Among them, extreme learningmachine, support vector regression and gated recurrent neural network are used as the single prediction models. Then, the combination prediction model is constructed based on the combination rules. On this basis, denoising processing is introduced to further improve the prediction effect. The experimental results show that compared with the denoised prediction model, the prediction model with the introduction of empirical mode decomposition has higher prediction accuracy, indicating that the introduction of denoising is necessary for the prediction model. Compared with the single prediction models, the combined prediction model has lower error in predicting tourist volume, and it can accurately predict the changing trend of tourist volume. In summary, the prediction method of prediction method of tourist volume of scenic spots based on combined model based on combined model has good performance, and it can accurately predict the tourist volume of scenic spots, which helps scenic spots to carry out better daily management and has certain practical application value.
The traditional performance evaluation of metal materials depends on trial and error, which is expensive and time-consuming. machinelearning algorithm, especially deep learning, has brought revolutionary changes to t...
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Information extraction has become a research hotspot. According to the division of ACE (Automatic Content Extraction) conference evaluation tasks, the main research focuses on four areas: named entity recognition, ent...
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The proceedings contain 53 papers. The topics discussed include: exploring the capacity of a generative design software application based on AI applied to the design of sports foot prostheses;AI-enhanced IoT system fo...
ISBN:
(纸本)9798331529222
The proceedings contain 53 papers. The topics discussed include: exploring the capacity of a generative design software application based on AI applied to the design of sports foot prostheses;AI-enhanced IoT system for efficient traffic management: leveraging black data in smart cities;enhancing real-time clinical decision-making through AI-integrated FHIR solutions: a Medplum implementation;machinelearning analysis of conductivity relative salinity seawater;optimization of machinelearning models for diabetes prediction in Senegal;exploring the intersection of generative ai and cognitive science: insights and implications;and instant fingerprint recognition using optimized machinelearning models by corona virus optimization algorithm.
Implantable cardiac devices (ICDs) are often used as an effective treatment for arrhythmia. Although these devices have access to a live Electrocardiogram (ECG) stream, currently they do not offer on-device classifica...
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ISBN:
(纸本)9798350372977;9798350372984
Implantable cardiac devices (ICDs) are often used as an effective treatment for arrhythmia. Although these devices have access to a live Electrocardiogram (ECG) stream, currently they do not offer on-device classification of arrhythmia due to the limited computing capability and severe power constraints. In this paper we propose a low-energy computing method for extracting shape-based features from ECG in combination with machinelearning techniques for classifying nine different cases of arrhythmia. This is achieved by using a Localized Longest Common Subsequence (LLCS) algorithm which has low computational requirements that allows on-device execution. The proposed method strongly focuses on maintaining minimal energy and computational footprint, in line with the operating constraints of implantable devices. To demonstrate the energy efficiency and low computation load of the proposed method we implement the classification pipeline on a low-power RISC microcontroller and compare its performance with existing classification techniques. The classification accuracy and energy of the proposed method is compared with state-of-the art research in arrhythmia classification.
The proceedings contain 31 papers. The special focus in this conference is on Applied Intelligence and Informatics. The topics include: Speech Emotion recognition: An Empirical Analysis of machinelearning Algori...
ISBN:
(纸本)9783031686382
The proceedings contain 31 papers. The special focus in this conference is on Applied Intelligence and Informatics. The topics include: Speech Emotion recognition: An Empirical Analysis of machinelearning Algorithms Across Diverse data Sets;A BERT-Based Chatbot to Support Cancer Treatment Follow-Up;Optimizing Medical Imaging Quality: An In-Depth Examination of Preprocessing Methods for Brain MRIs;Minddata for Enhanced Entertainment: Building a Comprehensive EEG dataset of Emotional Responses to Audio-Visual Stimuli;a Survey on Tools and Techniques of Classification in Educational datamining;a Robust and Explainable Deep learning Method for Cervical Cancer Screening;classifying Depressed and Healthy Individuals Using Wearable Sensor data: A Comparative Analysis of Classical machinelearning Approaches;performance Analysis of a Single-Input Thermal Image Classifier with Patient Information for the Detection of Breast Cancer;Investigation of HR and QT Variability for Monitoring Sleep Apnea: An Interpretable machinelearning Approach;transfer learning-Based Ensemble of Deep Neural Architectures for Alzheimer’s and Parkinson’s Disease Classification;classifying Emotions of Parkinsonian Patients from Electroencephalogram Signals Using Efficient Attention Capsule Network;early Prediction of Chronic Kidney Disease Using machinelearning Algorithms with Feature Selection Techniques;a Survey on Heart Disease Prediction Using machinelearning Techniques;a Fuzzy Logic-Based Framework for Accurate Detection of Infectious Diseases;A Media-Pipe Integrated Deep learning Model for ISL (Alphabet) recognition and Converting Text to Sound with Video Input;a Driver Fatigue Detection Framework with Convolutional Neural Network and Long Short-Term Memory Network;FallGuardian: Wear OS-Based machinelearning Fall Detection Framework;Classification of Cancer Types Based on RNA HI-SEQ data Using Dimensionality Reduction;YOLO-V4 Based Detection of Varied Hand Gestures in Heterogeneous Settin
Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a ...
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Oracle Bone Inscriptions (OBI) are the earliest Chinese characters found so far, they are the treasures of Chinese traditional culture and have high research value. However, the machinerecognition of OBI is a difficu...
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