Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Raza, Ahmad; Ali, Mohsin; Ehsan, Muhammad Khurram; Sodhro, Ali Hassan

doi:10.3390/s23177456

Cited by 9 publications

(7 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address this issue, advancements in artificial intelligence (AI) and machine learning could be explored. Currently, AI is implemented in several fields of medicine and assists in better identifying patients who need extra support and personalized protocols [14][15][16]. By leveraging these technologies, it may be possible to develop algorithms that can better differentiate between true alarms and false alarms, reducing the number of unnecessary alerts.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring

Mayer,

Jose,

Kurgansky

et al. 2023

Signals

View full text Add to dashboard Cite

In the field of modern healthcare, technology plays a crucial role in improving patient care and ensuring their safety. One area where advancements can still be made is in alert systems, which provide timely notifications to hospital staff about critical events involving patients. These early warning systems allow for swift responses and appropriate interventions when needed. A commonly used patient alert technology is nurse call systems, which empower patients to request assistance using bedside devices. Over time, these systems have evolved to include features such as call prioritization, integration with staff communication tools, and links to patient monitoring setups that can generate alerts based on vital signs. There is currently a shortage of smart systems that use sensors to inform healthcare workers about the activity levels of patients who are confined to their beds. Current systems mainly focus on alerting staff when patients become disconnected from monitoring machines. In this technical note, we discuss the potential of utilizing cost-effective sensors to monitor and evaluate typical movements made by hospitalized bed-bound patients. To improve the care provided to unaware patients further, healthcare professionals could benefit from implementing trigger alert systems that are based on detecting patient movements. Such systems would promptly notify mobile devices or nursing stations whenever a patient displays restlessness or leaves their bed urgently and requires medical attention.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring

Mayer,

Jose,

Kurgansky

et al. 2023

Signals

View full text Add to dashboard Cite

show abstract

“…The SVM is a nonlinear and non-parametric method that relies on kernel functions (mathematical functions) [88][89][90]. The kernel functions transform the input data into the required format using SVM algorithms [91].…”

Section: Support Vector Machines (Svms)mentioning

confidence: 99%

Field-Scale Winter Wheat Growth Prediction Applying Machine Learning Methods with Unmanned Aerial Vehicle Imagery and Soil Properties

Nduku,

Munghemezulu,

Mashaba-Munghemezulu

et al. 2024

Land

View full text Add to dashboard Cite

Monitoring crop growth conditions during the growing season provides information on available soil nutrients and crop health status, which are important for agricultural management practices. Crop growth frequently varies due to site-specific climate and farm management practices. These variations might arise from sub-field-scale heterogeneities in soil composition, moisture levels, sunlight, and diseases. Therefore, soil properties and crop biophysical data are useful to predict field-scale crop development. This study investigates soil data and spectral indices derived from multispectral Unmanned Aerial Vehicle (UAV) imagery to predict crop height at two winter wheat farms. The datasets were investigated using Gaussian Process Regression (GPR), Ensemble Regression (ER), Decision tree (DT), and Support Vector Machine (SVM) machine learning regression algorithms. The findings showed that GPR (R2 = 0.69 to 0.74, RMSE = 15.95 to 17.91 cm) has superior accuracy in all models when using vegetation indices (VIs) to predict crop growth for both wheat farms. Furthermore, the variable importance generated using the GRP model showed that the RedEdge Normalized Difference Vegetation Index (RENDVI) had the most influence in predicting wheat crop height compared to the other predictor variables. The clay, calcium (Ca), magnesium (Mg), and potassium (K) soil properties have a moderate positive correlation with crop height. The findings from this study showed that the integration of vegetation indices and soil properties predicts crop height accurately. However, using the vegetation indices independently was more accurate at predicting crop height. The outcomes from this study are beneficial for improving agronomic management within the season based on crop height trends. Hence, farmers can focus on using cost-effective VIs for monitoring particular areas experiencing crop stress.

show abstract

“…SS plays a crucial role in enhancing healthcare applications across various scenarios by ensuring reliable and efficient wireless communication. Here are different scenarios where spectrum sensing benefits healthcare [7][8][9] :…”

Section: Motivationmentioning

confidence: 99%

“…Second, it increases the effectiveness of spectrum usage. 10 Implementing a proposed hybrid approach such as the ED-CS and DMF techniques for 6G-based smart hospital applications offers several valuable contributions to the healthcare industry.…”

Section: Contributionsmentioning

confidence: 99%

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Kumar,

Kaur,

Gaur

et al. 2024

F1000Res

View full text Add to dashboard Cite

Background Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world’s rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients’ health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. Methods In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. Results In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. Conclusions The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

show abstract

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Cited by 9 publications

References 73 publications

Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring

Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring

Field-Scale Winter Wheat Growth Prediction Applying Machine Learning Methods with Unmanned Aerial Vehicle Imagery and Soil Properties

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Contact Info

Product

Resources

About