In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional...
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The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered ...
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Individuals with Cerebral Palsy (CP) are impacted lifetime barriers in their everyday activities, especially in writing phrase, which results from innate neural motor in co-ordination. Numerous studies have focus...
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Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science, community detection typically relies ...
Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing f...
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Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all ***,existing methods have various...
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Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all ***,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all ***,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive *** observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping *** validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and *** approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.
Hereditary Spastic Paraplegia (HSP) comprises a group of neurodegenerative disorders causing progressive lower limb symptoms primarily affecting gait functions. Progressive symmetric gait spasticity poses challenges f...
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ISBN:
(数字)9798350383409
ISBN:
(纸本)9798350383416
Hereditary Spastic Paraplegia (HSP) comprises a group of neurodegenerative disorders causing progressive lower limb symptoms primarily affecting gait functions. Progressive symmetric gait spasticity poses challenges for gait analysis. Traditional motion capture systems offer precise gait parameters but are confined to laboratory settings. In contrast, wearable sensor technology allows for gait analysis settings in real-world environments. Yet, accurate segmentation of gait cycles and ex-traction of temporal parameters remain challenging, particularly in the context of HSP. This paper introduces a comprehensive approach for gait analysis in patients with HSP, comprising two main components: stride segmentation utilizing a Hidden Markov Model (HMM), enabling robust identification of gait cycles from sensor data, and event detection and temporal gait parameter extraction. By leveraging the inherent temporal dynamics of gait patterns captured by wearable sensors, our method aims to overcome the limitations of existing techniques and provide reliable insights into the gait characteristics of HSP patients. Validation of this approach reveals promising results, with an F1 score of 89% for segmentation achieved through to-fold cross-validation. Additionally, our method demonstrates a mean absolute error of 0.008 seconds for stride time estimation compared to a gold standard motion capture system indicating the validity of our approach and its potential utility in hospital and real-world settings.
Hyperbolic deep learning has become a growing research direction in computer vision due to the unique properties afforded by the alternate embedding space. The negative curvature and exponentially growing distance met...
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In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional...
In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machinelearning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machinelearning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.
This study introduces a Generative Artificial Intelligence (GenAI) assistant designed to address key challenges in Remote Patient Monitoring (RPM) for hypertension. After a comprehensive needs assessment from clinicia...
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