Utilization of IoT-assisted computational strategies in wireless sensor networks for smart infrastructure management

Raghunath, K. M. Karthick; Koti, Manjula Sanjay; Sivakami, R.; Kumar, V. Vinoth; Jyothi, Grande Naga; Muthukumaran, V.

doi:10.1007/s13198-021-01585-y

Cited by 15 publications

(8 citation statements)

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“…Karthick Raghunath, K. M et al [14] has discussed the Time Series Data Prediction and Feature Analysis of Sports Dance Movements Based on Machine Learning uses artificial intelligence, such as neural networks and support vector machines, to identify patterns in dance movements. These patterns can be used to accurately forecast future sports dance movements and determine correlations between various movements.…”

Section: Introductionmentioning

confidence: 99%

IoT based Dance Movement Recognition Model based on Deep Learning Framework

Ji,

Tian

2024

SCPE

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Deep Learning is becoming an emerging field in the Internet of Things (IoT) due to its ability to provide a comprehensive approach to automatic feature extraction and predictive modeling for analysis and decision-making. This paper introduces an IoT-based Dance Movement Recognition Model based on a Deep Learning Framework. The framework consists of a convolutional neural network (CNN) with a data-centric architecture to identify dance movements from the acquired data gathered by an IoT device. The IoT device collects 3D motion data captured by three accelerometers. Feature extraction is then done with the CNN architecture, resulting in a flattened matrix representing the movement. Subsequently, a Multi-Layer Perception (MLP) is used to classify the movements. The proposed system is experimentally evaluated on a standardized dataset of 16 dance steps with three-speed levels. The results show that our model outperforms state-of-the-art approaches in accuracy, evaluation time, and classification accuracy. The proposed model reached 90.74% accuracy, 87.12% precision, 83.78% recall and 84.39% F1-Score. The proposed model can serve as a basis for a reliable and intuitive system that can be used to monitor patient’s dance movements with accuracy.

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Section: Introductionmentioning

confidence: 99%

IoT based Dance Movement Recognition Model based on Deep Learning Framework

Ji,

Tian

2024

SCPE

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“…Feature extraction is the very important activity of the machine learning algorithms, in problems like classification, where the accuracy of the classification lies in extracting more positive features. Feature extraction considered as a low-level process, uses first-order statistics (like mean, median, standard deviation and skewness) are used in the low-level feature extraction [19][20][21][22]. Second order statistics such as shape, Gabor features and gray-level co-occurrence matrix were used to understand the formation of the foreign masses.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Classification of Gray-Scale Images of Brain Tumor using Deep Learning

Kondra,

Vurukonda

2024

SCPE

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Deep Learning using CNN plays a paramount role for the classification methods applied on medical image data. With a crucial role in accurate diagnosis, treatment planning and patient management for medical and healthcare systems, CNNs won accolades in the Deep Learning research. As simple the learning model so precise are the results for decision making. The proposed Sequential model of CNN is built with Parametric ReLU with the values aligned to geometric mean, attains a specific goal of tumor classification. The additional support of ground-truth aid in deciding the shape and severity of tumor in the Grayscale MRI of brain tumor. The simple Sequential model, although a minimal version has proved achieved significant classification goals using the GMP-ReLU. Comparative results with variants of ReLU have been charted in this article standing with the proof of consistent classification model with parametric-ReLU. The proposed design is conducted on images from Kaggle and a model is trained (classifier is built), which can be considered as ideal filter for all the benchmark images. The accuracy of proposed design is considerably improved compared to normal ReLU up to 89.214%.

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“…IoT stands for Internet of Things, a development in computer technology and communication that attempts to link intelligent items together. Anything that environments may communicate with a smart object [10]. Figure 1 shows the WSN network.…”

Section: Introductionmentioning

confidence: 99%