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 308 papers. The topics discussed include: machine vision-driven semantic segmentation for autonomous navigation;face recognition based automated smart attendance using hybrid machine learning a...
ISBN:
(纸本)9798350375190
The proceedings contain 308 papers. The topics discussed include: machine vision-driven semantic segmentation for autonomous navigation;face recognition based automated smart attendance using hybrid machine learning algorithms and computer vision;DDoS attacks detection in IoT networks using Naive Bayes and random forest;data analysis and visualization of master sample management and due date alert tool;recent developments in designing internet of things architectures;a study on hybrid electric vehicles (HEV) safety and industrial control network security;detection of cyber-attacks in network traffic using machine learning algorithm;smart grid protection with AI and cryptographic security;deep learning based cotton plant pest detection and fertilizer recommendation system;and interactive tactile book framework for visually challenged community.
The proceedings contain 11 papers. The topics discussed include: improved YOLOv5-based method for date palm leaf disease recognition;three real-time pathfinding strategies in games: from a ideas to machine learning ap...
ISBN:
(纸本)9798331532864
The proceedings contain 11 papers. The topics discussed include: improved YOLOv5-based method for date palm leaf disease recognition;three real-time pathfinding strategies in games: from a ideas to machine learning approaches;software metrics selection– the case of C#;automated seismic horizon picking using vision transformer;a regionally generalized machine learning framework towards census-enabled multi-factor non-communicable disease analyses;and enhancing the customer experience in Philippine food court establishments with an integrated self-service ordering system.
Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice a...
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In a medical emergency situation, such as a traffic accident, effective and efficient identification of patients can improve the chances of survival for critically injured patients. In some cases, the patients may be ...
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ISBN:
(纸本)9798350372977;9798350372984
In a medical emergency situation, such as a traffic accident, effective and efficient identification of patients can improve the chances of survival for critically injured patients. In some cases, the patients may be unconscious when paramedics arrive at the scene. Timely and successful identification of patient in varying states of injury and/or consciousness can provide the attending paramedics with vital information about the patients' medical records, thereby allowing them to provide the patients with the most appropriate life-saving on-scene medical interventions. Major improvements in miniaturized biometric hardware, low-power telecommunication devices, efficient biometric identification platforms, and energy-efficient artificial intelligent-enhanced data processing techniques all contribute towards the next generation of smart on-scene patient identification, even for paramedics attending at emergency sites in remote locations. This paper focuses specifically on fingerprint acquisition and processing, as fingerprints are among the most popular biometrics used for identification. In addition to fingerprint identification for patients, it is also useful for quick and effective identification of medical personnel for access to resources like ambulances. Specifically, the purpose of this research is to investigate factors that can enhance the reliability of such identification systems in practical scenarios involving some form of telecommunications backbone, which are typically much more unforgiving than comparable laboratory settings.
Radar target recognition technology is an important research direction in the field of radar information processing. With the rapid development of aerospace industry, the requirements for space target recognition are ...
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ISBN:
(纸本)9798350352634;9798350352627
Radar target recognition technology is an important research direction in the field of radar information processing. With the rapid development of aerospace industry, the requirements for space target recognition are constantly increasing. The fusion of multiple features of the radar information of space targets, e.g., HRRP, time-frequency map, can extract useful information in various complex and uncertain situations, and improve recognition accuracy. This paper introduces the fusion of convolutional neural networks and recurrent neural networks. The CNN-LSTM network model is used for simulation, and the simulation results are analyzed. To verify the performance of the proposed method, training datasets and test datasets of targets with different SNRs (18dB, 8dB and - 2dB) are created. On the above dataset, the classification accuracy can reach 93.94%, which outperforms methods based on single feature.
This article delves into an innovative radar working patternrecognition algorithm based on multi-layer perceptron (MLP). Through carefully designed optimization algorithms, we systematically searched and determined t...
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ISBN:
(纸本)9798400716959
This article delves into an innovative radar working patternrecognition algorithm based on multi-layer perceptron (MLP). Through carefully designed optimization algorithms, we systematically searched and determined the optimal MLP network structure to solve the radar operating patternrecognition problem. In a detailed simulation experiment, we carefully analyzed the effects of various network parameters, including the number of network layers, number of neurons, learning rate, and batch rate. The experimental results show that the MLP network can exhibit optimal performance when the number of layers is 5, the number of neurons is 512, the learning rate is 0.006, and the batch rate is 10. This discovery provides us with a highly promising solution to the problem of radar working patternrecognition.
With several deep learning approaches, the domain of automatic speech recognition (ASR) has seen notable advancements in recent times. The domains of intelligent human- computer interaction and machine translation gre...
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To advance the application of artificial intelligence in complex environments, this study focuses on weed recognition in spinach fields and proposes an intelligent recognition method based on an improved YOLOv8 model....
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ISBN:
(纸本)9798350352634;9798350352627
To advance the application of artificial intelligence in complex environments, this study focuses on weed recognition in spinach fields and proposes an intelligent recognition method based on an improved YOLOv8 model. The process begins with image preprocessing and data augmentation to enhance the dataset. Then, the Large Separable Kernel Attention (LSKA) mechanism is introduced to improve target recognition in complex backgrounds. To simplify the network structure and reduce computational demands, the Deformable Convolutional Network (DCN) is employed. The original loss function is replaced with the Minimum Point Distance IoU loss function to accelerate convergence. Comparing the performance of several popular object detection models, we find experimental results demonstrated that the improved model achieved excellent performance, with accuracy, recall, and average precision reaching 83.4%, 70.2%, and 73.6%, respectively. The model's performance across various configurations confirmed the effectiveness of the proposed improvements in enhancing response speed and handling complex scenes. The comprehensive application of these strategies improved various metrics while maintaining an acceptable increase in model complexity. This study provides a new technical method and theoretical foundation for applying artificial intelligence in real-time, efficient target recognition.
The proceedings contain 266 papers. The topics discussed include: prediction of Parkinson’s disease with various ML and DL techniques on speech data;iterative imputation of incomplete wireless network traffic using a...
ISBN:
(纸本)9798350372120
The proceedings contain 266 papers. The topics discussed include: prediction of Parkinson’s disease with various ML and DL techniques on speech data;iterative imputation of incomplete wireless network traffic using adversarial learning and a two-stage approach;photoplethysmogram (PPG)-based blood pressure estimation using vision transformer networks: a deep learning approach;error identification in translation look-aside buffer;exploring Jukebox: a novel audio representation for music genre identification in MIR;computer vision based algorithms for detecting and classification of activities for fall recognition on real time video;advancements in neurological health: predicting brain age with machine learning;planar u-shaped slot multiresonator-based chipless RFID tags;and design and analysis of helical antenna for GPS systems.
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