Efficient evolutionary modeling in solving maximization of lifetime of wireless sensor healthcare networks

Marappan, Raja; Vardhini, P. A. Harsha; Kaur, Gaganpreet; Murugesan, San; Kathiravan, Muthu K.; Bharathiraja, N.; Venkatesan, R.

doi:10.1007/s00500-023-08623-w

Cited by 7 publications

(5 citation statements)

References 39 publications

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“…The Figure 6 illustrates the temperature at each node against the Celsius and Time parameters. Figures 7,8,9,10,and 11 illustrates the information gathered by these illuminations for every node, correspondingly. After completing the experience, users can launch Wireshark and then access the generated file ".…”

Section: 𝑇𝐷𝑆 = 𝑇𝐷𝑅 + 𝑃𝐿mentioning

confidence: 99%

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IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

Arulkumar V.,

Kavin F.,

Arulkumar D.

et al. 2024

ARASET

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The Internet of Things (IoT) brings new products to everyone to improve daily life. Concurrently other emerging technologies, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. This research work explores the synergies among four systems to identify their shared functionalities and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cornerstone of the proposed system is the utilization of cloud services as the foundational technology for a schema management platform. This platform efficiently gathers data generated by sensors within smart industrial units. Leveraging the capabilities of IoT technology, the data can be remotely managed and accessed using mobile devices with network connectivity. The proposed work addresses the challenges related with the smart city perception and supports for revolutionary approaches in the collection and manipulation of IoT Sensor data. By doing so, it imagines the creation of green, smart schemes that contribute to sustainable urban development.

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Section: 𝑇𝐷𝑆 = 𝑇𝐷𝑅 + 𝑃𝐿mentioning

confidence: 99%

“…This involves a behavioural planning technique [9]. A method for minimizing the overall structures that create forecasts of this usage [10]. Researchers also use a standard structure for which they have a year of reliable information to validate the recommended alternative.…”

Section: Introductionmentioning

confidence: 99%

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

Arulkumar V.,

Kavin F.,

Arulkumar D.

et al. 2024

ARASET

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“…Electronic health records which would include the patient's medical records, findings, prescriptions data, pharmacist data, client insurance details, and social networking entries like blogs and tweets, were examples of health information [21]. An efficient flow processing process that can manage and evaluate the huge quantities of health records was needed [22]. A main edge screening and order technique was utilized to make the methodology.…”

Section: Related Workmentioning

confidence: 99%

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Talari,

Kaur

et al. 2024

PLoS ONE

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Diabetes prediction is an ongoing study topic in which medical specialists are attempting to forecast the condition with greater precision. Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. Various worldwide learning procedures, including casting a ballot, supporting, and sacking, have been applied in this review. The Engineered Minority Oversampling Procedure (Destroyed), along with the K-overlay cross-approval approach, was utilized to achieve class evening out and approve the discoveries. Pima Indian Diabetes (PID) dataset is accumulated from the UCI Machine Learning (UCI ML) store for this review, and this dataset was picked. A highlighted engineering technique was used to calculate the influence of lifestyle factors. A two-phase classification model has been developed to predict insulin resistance using the Sequential Minimal Optimisation (SMO) and SMOTE approaches together. The SMOTE technique is used to preprocess data in the model’s first phase, while SMO classes are used in the second phase. All other categorization techniques were outperformed by bagging decision trees in terms of Misclassification Error rate, Accuracy, Specificity, Precision, Recall, F1 measures, and ROC curve. The model was created using a combined SMOTE and SMO strategy, which achieved 99.07% correction with 0.1 ms of runtime. The suggested system’s result is to enhance the classifier’s performance in spotting illness early.

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“…The performance of various machine learning models like Random forest, SVM, gradient boosting, neural network and RBF are analyzed for their performance against various data sets in [9]. A machine learning model is presented in [10], which uses step-based impaired gait features and conventional FoG detection features to predict the FoG.…”

Section: Related Workmentioning

confidence: 99%

Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

Ezhilarasi Jegadeesan,

Senthil Kiumar Thillaigovindhan

2024

ARASET

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The problem of predicting Freeze of Gait (FoG) on Parkinson diseased patients has been well studied. There exists number of approaches in predicting FoG, which uses sensory features, EEG data and so on. However, the methods suffer to achieve higher performance. To handle this issue, an efficient Time Orient Acceleration Gait pattern based FoG prediction model (TOAGP-FoG) is presented in this paper. The model designed to attach accelerometer sensors at different ankle and joints of the body. The sensor signals are recorded at different gait movement in long term. The sensory signals are passed to the central data server which tracks the movement signals. With the time variant signals stored by the model, the method generates Acceleration Gait Pattern with number of features. At each movement, the method analyses the patterns to compute FOG Risk Support (FoGRS) towards various gait movement. The FoG Risk Support is measured according to the movement forces produced by the patient for various gait movement in different time stamp and computes minimum gait force to be produced. Based on the FoGRS value, the method performs FoG prediction. The proposed method improves the performance of FoG prediction with higher accuracy. Other notable aspects of the suggested model include comparable performance, resiliency, real-time prediction capabilities, FOG-specific integration of data, and advanced deep learning methodologies for accurate prediction. The Special Features of TOAGP-FoG include the Multi-Sensor Configuration, Temporal Analysis, Adaptive Thresholding, Dynamic FoG Risk Support (FoGRS), and Enriched Feature Extraction. The TOAGP-FoG model offers an important breakthrough in the predictive modelling of Parkinson's disease since it integrates several features such as temporal flexibility, dynamic FoGRS computation, adaptive thresholding, enriched feature extraction, and multi-sensor configurations.

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Efficient evolutionary modeling in solving maximization of lifetime of wireless sensor healthcare networks

Cited by 7 publications

References 39 publications

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

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