We are analyzing https://link.springer.com/article/10.1007/s13042-024-02397-9.

Title:
Adam energy valley optimization-based routing and RF-Spinalnet enabled medical data classification in IoT | International Journal of Machine Learning and Cybernetics
Description:
The progress in big data, especially in Internet of Things (IoT) based healthcare and biomedical categories, attempts to help patients by detecting diseases early by analyzing the medical data. However, the effectiveness of the existing medical data classification schemes needs improvement. Here, a Recurrent Fusion SpinalNet (RF-Spinalnet) is introduced for better classification of medical data in IoT. Initially, IoT is simulated, then, nodes collect the patient’s data, and later, routing is carried out to acquire the medical data at the Base Station (BS) for classification. Here, the routing is done by devised Adam Energy Valley Optimization (AEVO). Afterwards, at BS, input data is subjected to a pre-processing step to remove redundant data using linear normalization. Once the process is completed, feature fusion is done by a Chebyshev distance measure and a Deep Neural Network (DNN). Thereafter, data augmentation is carried out, and finally, classification of medical data is performed using RF-Spinalnet, which is the combination of Deep Recurrent Neural Network (DRNN) and SpinalNet. The efficiency of RF-Spinalnet is evaluated using the COVID-19 Machine Learning Dataset and Covid-19 disease on the Korean dataset based on accuracy, sensitivity, and specificity by Python tool. The RF-Spinalnet demonstrated an accuracy of 91.91%, a sensitivity of 91.89%, and a specificity of 91.45% and the AEVO-routing achieved energy of 0.005 J, distance of 38.516 m, and delay of 0.668 ms.
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Keywords {🔍}

google, scholar, data, covid, iot, math, article, routing, medical, learning, internet, things, neural, machine, network, disease, based, deep, comput, classification, energy, optimization, kumar, recurrent, protocol, rfspinalnet, june, model, algorithm, ieee, authors, privacy, research, adam, valley, khan, spinalnet, dataset, health, trans, wireless, cookies, content, analysis, journal, singh, patients, feature, access, intelligence,

Topics {✒️}

energy-efficient iot e-health recurrent fusion spinalnet iot-fog computing environments energy valley optimizer deep neural network cloud-based iot environment qos-based routing schema month download article/chapter adam optimization algorithm aevo-routing achieved energy cnn-based health model multi-agent feature selection nanotechnology-based sensitive biosensors recurrent neural networks enhanced mayfly clustering-based deep neural networks sdn-based iot networks smart healthcare system iot-based healthcare applications firefly optimization algorithm santhosh kumar balan hybrid classification model artificial intelligence model wireless sensor network privacy choices/manage cookies rf-spinalnet demonstrated things rf-spinalnet feature fusion santhosh kumar designed iot medical data medical things environment support vector machine article international journal smart hospital environment korean dataset based homomorphic secret sharing multi-attribute evaluation full article pdf detecting diseases early fuzzy logic control comput intellig neurosci ieee access 8 remove redundant data conditions privacy policy early disease diagnosis energy-aware data-science-3eb914d2681” remote monitoring holds exclusive rights l-diversity model

Schema {🗺️}

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      description:The progress in big data, especially in Internet of Things (IoT) based healthcare and biomedical categories, attempts to help patients by detecting diseases early by analyzing the medical data. However, the effectiveness of the existing medical data classification schemes needs improvement. Here, a Recurrent Fusion SpinalNet (RF-Spinalnet) is introduced for better classification of medical data in IoT. Initially, IoT is simulated, then, nodes collect the patient’s data, and later, routing is carried out to acquire the medical data at the Base Station (BS) for classification. Here, the routing is done by devised Adam Energy Valley Optimization (AEVO). Afterwards, at BS, input data is subjected to a pre-processing step to remove redundant data using linear normalization. Once the process is completed, feature fusion is done by a Chebyshev distance measure and a Deep Neural Network (DNN). Thereafter, data augmentation is carried out, and finally, classification of medical data is performed using RF-Spinalnet, which is the combination of Deep Recurrent Neural Network (DRNN) and SpinalNet. The efficiency of RF-Spinalnet is evaluated using the COVID-19 Machine Learning Dataset and Covid-19 disease on the Korean dataset based on accuracy, sensitivity, and specificity by Python tool. The RF-Spinalnet demonstrated an accuracy of 91.91%, a sensitivity of 91.89%, and a specificity of 91.45% and the AEVO-routing achieved energy of 0.005 J, distance of 38.516 m, and delay of 0.668 ms.
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         Mechatronics
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