scholarly journals Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence

2020 ◽  
Vol 10 (14) ◽  
pp. 4886 ◽  
Author(s):  
Mohammed Ali Mohammed Al-hababi ◽  
Muhammad Bilal Khan ◽  
Fadi Al-Turjman ◽  
Nan Zhao ◽  
Xiaodong Yang
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Nearest Neighbor ◽  
Wireless Channel ◽  
Weight Lifting ◽  
K Nearest Neighbor ◽  
Post Surgery ◽  
Contact Sensing

Non-contact health care monitoring is a unique feature in the emerging 5G networks that is achieved by exploiting artificial intelligence (AI). The ratio of the number of health care problems and patients is increasing exponentially and creating burgeoning data. The integration of AI and Internet of things (IoT) systems enables us to increase the huge volume of data to be generated. The approach by which AI is applied to the IoT systems enhances the intelligence of the health care system. In post-surgery monitoring of the patient, timely consultation is essential before further loss. Unfortunately, even after the advice of the doctor to the patient, he/she may forget to perform the activity in the correct way, which may lead to complications in recovery. In this research, the idea is to design a non-contact sensing testbed using AI for the classification of post-surgery activities. Universal software-defined radio peripheral (USRP) is utilized to collect the data of spinal cord operated patients during weight lifting activity. The wireless channel state information (WCSI) is extracted by using orthogonal frequency division multiplexing (OFDM) technique. AI applies machine learning to classify the correct and wrong way of weight lifting activity that was considered for experimental analysis. The accuracy achieved by the proposed testbed by using a fine K-nearest neighbor (FKNN) algorithm is 99.6%.

scholarly journals Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1558
Author(s):  
Muhammad Bilal Khan ◽  
Mubashir Rehman ◽  
Ali Mustafa ◽  
Raza Ali Shah ◽  
Xiaodong Yang
Keyword(s):  
Psychological Health ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Vital Signs ◽  
Wireless Channel ◽  
Machine Learning Algorithms ◽  
Breathing Patterns ◽  
Connected Health ◽  
Innovative Solution

The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.

Data Mining Approach to Analyze COVID-19 Clinical Dataset

2021 ◽  
Vol 15 (6) ◽  
pp. 1812-1819
Author(s):  
Azita Yazdani ◽  
Ramin Ravangard ◽  
Roxana Sharifian
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Clinical Signs ◽  
Study Data ◽  
Mining Machine ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Approach

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

scholarly journals SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1323 ◽  
Author(s):  
Donald L. Hall ◽  
Ram M. Narayanan ◽  
David M. Jenkins
Keyword(s):  
Deep Learning ◽  
Software Defined Radio ◽  
High Performance ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Propagation ◽  
Multipath Channel ◽  
K Nearest Neighbor ◽  
Positioning Systems ◽  
Channel Characteristics

Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.

DIMENSIONALITY REDUCTION FOR SENSORY DATASETS BASED ON MASTER–SLAVE SYNCHRONIZATION OF LORENZ SYSTEM

2013 ◽  
Vol 23 (05) ◽  
pp. 1330013 ◽  
Author(s):  
REZA GHAFFARI ◽  
IOAN GROSU ◽  
DACIANA ILIESCU ◽  
EVOR HINES ◽  
MARK LEESON
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Electronic Nose ◽  
Lorenz System ◽  
Nearest Neighbor ◽  
Performance Testing ◽  
K Nearest Neighbor ◽  
Classification Rate ◽  
Benchmark Datasets ◽  
Novel Method

In this study, we propose a novel method for reducing the attributes of sensory datasets using Master–Slave Synchronization of chaotic Lorenz Systems (DPSMS). As part of the performance testing, three benchmark datasets and one Electronic Nose (EN) sensory dataset with 3 to 13 attributes were presented to our algorithm to be projected into two attributes. The DPSMS-processed datasets were then used as input vector to four artificial intelligence classifiers, namely Feed-Forward Artificial Neural Networks (FFANN), Multilayer Perceptron (MLP), Decision Tree (DT) and K-Nearest Neighbor (KNN). The performance of the classifiers was then evaluated using the original and reduced datasets. Classification rate of 94.5%, 89%, 94.5% and 82% were achieved when reduced Fishers iris, crab gender, breast cancer and electronic nose test datasets were presented to the above classifiers.

scholarly journals KNN Algorithm Used for Heart Attack Detection

2021 ◽  
Vol 11 (1) ◽  
pp. 7-19
Author(s):  
Ibrahima Bah
Keyword(s):  
Artificial Intelligence ◽  
Heart Attack ◽  
Nearest Neighbor ◽  
Attack Detection ◽  
K Nearest Neighbor ◽  
Knowing That ◽  
Knn Classifier ◽  
Patients At Risk ◽  
Artery Disease ◽  
First Time

Machine Learning, a branch of artificial intelligence, has become more accurate than human medical professionals in predicting the incidence of heart attack or death in patients at risk of coronary artery disease. In this paper, we attempt to employ Artificial Intelligence (AI) to predict heart attack. For this purpose, we employed the popular classification technique named the K-Nearest Neighbor (KNN) algorithm to predict the probability of having the Heart Attack (HA). The dataset used is the cardiovascular dataset available publicly on Kaggle, knowing that someone suffering from cardiovascular disease is likely to succumb to a heart attack. In this work, the research was conducted using two approaches. We use the KNN classifier for the first time, aided by using a correlation matrix to select the best features manually and faster computation, and then optimize the parameters with the K-fold cross-validation technique. This improvement led us to have an accuracy of 72.37% on the test set.

scholarly journals Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Ijaz Ahmad ◽  
Inam Ullah ◽  
Wali Ullah Khan ◽  
Ateeq Ur Rehman ◽  
Mohmmed S. Adrees ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Comparative Analysis ◽  
Target Detection ◽  
Nearest Neighbor ◽  
Detection Algorithm ◽  
Vital Role ◽  
Computational Time ◽  
K Nearest Neighbor ◽  
Mri Scan ◽  
Video File

Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time.

On the Use of Artificial Intelligence Techniques in Crop Monitoring and Disease Identification

2021 ◽  
pp. 166-186
Author(s):  
Muzaffer Kanaan ◽  
Rüştü Akay ◽  
Canset Koçer Baykara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Crop Yields ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Disease Identification ◽  
Use Of Technology ◽  
Learning Techniques

The use of technology for the purpose of improving crop yields, quality and quantity of the harvest, as well as maintaining the quality of the crop against adverse environmental elements (such as rodent or insect infestation, as well as microbial disease agents) is becoming more critical for farming practice worldwide. One of the technology areas that is proving to be most promising in this area is artificial intelligence, or more specifically, machine learning techniques. This chapter aims to give the reader an overview of how machine learning techniques can help solve the problem of monitoring crop quality and disease identification. The fundamental principles are illustrated through two different case studies, one involving the use of artificial neural networks for harvested grain condition monitoring and the other concerning crop disease identification using support vector machines and k-nearest neighbor algorithm.

Diagnostic Model of in-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods : Algorithm Development and Validation (Preprint)

2021 ◽  
Author(s):  
Yong Li
Keyword(s):  
Artificial Intelligence ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Diagnostic Model ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Artificial Intelligence Methods ◽  
Neighbor Classification

BACKGROUND Preventing in-hospital mortality in Patients with ST-segment elevation myocardial infarction (STEMI) is a crucial step. OBJECTIVE The objective of our research was to to develop and externally validate the diagnostic model of in-hospital mortality in acute STEMI patients used artificial intelligence methods. METHODS As our datasets were highly imbalanced, we evaluated the effect of down-sampling methods. Therefore, down-sampling techniques was additionally implemented on the original dataset to create 1 balanced datasets. This ultimately yielded 2 datasets; original, and down-sampling. We divide non-randomly the American population into a training set and a test set , and anther American population as the validation set. We used artificial intelligence methods to develop and externally validate the diagnostic model of in-hospital mortality in acute STEMI patients, including logistic regression, decision tree, extreme gradient boosting (XGBoost), K nearest neighbor classification model ,and multi-layer perceptron.We used confusion matrix combined with the area under the receiver operating characteristic curve (AUC) to evaluate the pros and cons of the above models. RESULTS The strongest predictors of in-hospital mortality were age, female, cardiogenic shock, atrial fibrillation(AF), ventricular fibrillation(VF),in-hospital bleeding and medical history such as hypertension, old myocardial infarction.The F2 score of logistic regression in the training set, the test set , and the validation data set were 0.7, 0.7, and 0.54 respectively.The F2 score of XGBoost were 0.74, 0.52, and 0.54 respectively. The F2 score of decision tree were 0.72, 0.51,and 0.52 respectively. The F2 score of K nearest neighbor classification model were 0.64,0.47, and 0.49 respectively. The F2 score of multi-layer perceptron were 0.71, 0.54, and 0.54 respectively. The AUC of logistic regression in the training set, the test set, and the validation data set were 0.72, 0.73, and 0.76 respectively. The AUC of XGoBost were 0.75, 0.73, and 0.75 respectively. The AUC of decision tree were 0.75, 0.71,and 0.74 respectively. The AUC of K nearest neighbor classification model were 0.71,0.69, and 0.72 respectively. The AUC of multi-layer perceptron were 0.73, 0.74, and 0.75 respectively. The diagnostic model built by logistic regression was the best. CONCLUSIONS The strongest predictors of in-hospital mortality were age, female, cardiogenic shock, AF, VF,in-hospital bleeding and medical history such as hypertension, old myocardial infarction. We had used artificial intelligence methods developed and externally validated the diagnostic model of in-hospital mortality in acute STEMI patients.The diagnostic model built by logistic regression was the best. CLINICALTRIAL We registered this study with WHO International Clinical Trials Registry Platform (ICTRP) (registration number: ChiCTR1900027129; registered date: 1 November 2019). http://www.chictr.org.cn/edit.aspx?pid=44888&htm=4.

Diagnostics of Surface Errors by Embedded Vision System and its Classification by Machine Learning Algorithms

2015 ◽  
Vol 669 ◽  
pp. 459-466 ◽  
Author(s):  
Kamil Židek ◽  
Alexander Hošovský ◽  
Ján Dubják
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Error Detection ◽  
Nearest Neighbor ◽  
Vision System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Vision Systems ◽  
Embedded Vision

The Article deals with usability and advantages of embedded vision systems for surface error detection and usability of advanced algorithms, technics and methods from machine learning and artificial intelligence for error classification in machine vision systems. We provide experiments with following classification algorithms: Support Vector Machines (SVM), Random Threes, Gradient Boosted Threes, K-Nearest Neighbor and Normal Bayes Classifier. Next comparison experiment was conducted with multilayer perceptron (MLP), because currently it is very popular for classification in the field of artificial intelligence. These classification approaches are compared by precision, reliability, speed of teaching and algorithm implementation difficulty.

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