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.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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 A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

2020 ◽  
Vol 11 (9) ◽  
pp. 442-449 ◽  
Author(s):  
Sandy C. Lauguico ◽  
◽  
Ronnie S. Concepcion II ◽  
Jonnel D. Alejandrino ◽  
Rogelio Ruzcko Tobias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Urban Farming ◽  
K Nearest Neighbor ◽  
Lettuce Growth

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

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%.

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.

Improved Elastic Model Matching Algorithm for Facial Expression Recognition

2013 ◽  
Vol 397-400 ◽  
pp. 2143-2147
Author(s):  
Wen Dong Zhao ◽  
Jie Zhang ◽  
You Dong Zhang
Keyword(s):  
Nearest Neighbor ◽  
Detection Algorithm ◽  
Gabor Wavelet ◽  
Similar Degree ◽  
Elastic Model ◽  
Expression Recognition ◽  
Model Matching ◽  
K Nearest Neighbor ◽  
Matching Algorithm ◽  
Expression Template

Key frames detected in Video stream contain sufficient expression information. In order to classify and recognize these expression information, a new elastic model matching algorithm is proposed in this paper. Firstly, expression template is transformed by Gabor wavelet , and the detection algorithm of the key expression in the template image is used. According to the feature information of the key expression, structure expression elastic graph, then by changing the key location in the expression template graph and doing non-rigid match of the expression template and expression elastic graph which is measured, so that similar degree between them is got. Finally, by improving the K-nearest neighbor classification strategy, the effective classification and recognition of measured image expression is achieved.

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 Early detection algorithm for alzheimer’s disease using autonomous learning

2021 ◽  
Vol 10 (11) ◽  
pp. 608-621
Author(s):  
Jorge Eduardo Aguilar Obregón ◽  
Octavio José Salcedo Parra ◽  
Juan Pablo Rodríguez Miranda
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nearest Neighbor ◽  
Complete Information ◽  
Research Problem ◽  
Detection Algorithm ◽  
Autonomous Learning ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Information Bank

The current document describes the approach to a research problem that aims to generate an algorithm that allows detecting the probable appearance of Alzheimer’s disease in its first phase, using autonomous learning techniques or Machine Learning, more specifically KNN (K- nearest Neighbor) with which the best result was obtained. This development will be based on a complete information bank taken from ADNI (Alz- heimer’s Disease NeuroImaging Initiative), with all the necessary parameters to direct the inves- tigation to an algorithm that is as efficient as pos- sible, since it has biological, sociodemographic and medical history data, biological specimens, neural images, etc., and in this way the early de- tection of the aforementioned disease was con- figured. A complete guide to the process will be carried out to finally obtain the KNN algorithm whose efficiency is 99%, and then discuss the obtained results. 

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