Search Results - (( data extraction sensor algorithm ) OR ( using function a algorithm ))
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Jogging activity recognition using k-NN algorithm
Published 2022“…Jogging activity recognition using the k-NN algorithm is a system that can help users collect information data of user speed movement using speed sensor and give the classification of jogging activity to the user. …”
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Academic Exercise -
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Smart fall detection by enhanced SVM with fuzzy logic membership function
Published 2023“…Because combining these two algorithms is not an easy task, we leverage SVM with a kernel comprised of a fuzzy membership function and thus build a new model known as FSVM. …”
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Anomaly detection in ICS datasets with machine learning algorithms
Published 2021“…The features of flow-based network traffic are extracted for behavior analysis with port-wise profiling based on the data baseline, and anomaly detection classification and prediction using machine learning algorithms are performed.…”
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REFERENCE-FREE REDUCTION OF BALLISTOCARDIOGRAM ARTIFACT FROM EEG DATA USING EMD-PCA
Published 2015“…The algorithm does not require the recordings of Electrocardiogram (ECG), Electrooculogram (EOG) or the signals from motion sensors to extract the BCG artifact.…”
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Thesis -
5
A comparative study of vibrational response based impact force localization and quantification using radial basis function network and multilayer perceptron
Published 2017“…Impact locations were fixed at four edges of the test rig to simulate impact events at a vehicle's wheels. Time-domain peak-to-peak and peak arrival time features were extracted from accelerometer data to use as network inputs. …”
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Development of rheumatoid arthritis classification from electronic image sensor using ensemble method
Published 2020“…Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. …”
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Google the earth: what's next?
Published 2010“…Considering the exponential growth of data volumes driven by the rapid progress in sensor and computer technologies in recent years, the future of remotely sensed data should ideally be in automated data processing, development of robust and transferable algorithms and processing chains that require little or no human intervention. …”
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Inaugural Lecture -
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Terahertz sensing analysis for early detection of ganoderma boninense disease using near infrared (NIR) spectrometer
Published 2023“…Spectral data are collected from 6 samples of inoculated and non-inoculated oil palm samples at nursery stages using an integrated NIRS sensor. …”
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9
Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid
Published 2012“…In this work however, the fault protection is an additional feature of the control system, where the wavelet based fault tolerant system has been tested and simulated using a 1kW IM, which has a short stator winding and sensor faults, while the primary faults are open stator winding. …”
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Forensic language of property theft genre based on mathematical formulae and machine learning algorithms / Hana' Abd Razak
Published 2020“…Four traits was discovered namely spine, pelvis, knee and ankle. Next, data collection of normal and anomalous behaviour is collected using Kinect sensor. …”
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Thesis -
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Abstract data extraction and reformation for IoT
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Final Year Project / Dissertation / Thesis -
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Correlation-based subset evaluation of feature selection for dynamic Malaysian sign language
Published 2016“…In this study, the images has been captured using a kinect sensor based skeletal algorithms. …”
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Internet of Things (IoT) based activity recognition strategies in smart homes: a review
Published 2022“…In this work, we focus our review on activity recognition implementation strategies by examining various sensors and sensing technologies used to collect useful data from IoT devices, reviewing preprocessing and feature extraction techniques, as well as classification algorithms used to recognize human activities in smart homes. …”
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Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer
Published 2017“…Furthermore, the Lazy-IBK algorithm have been validated to produce the best classifier model, with the machine learning algorithm performance of 65.26%, recall of 65.3%, and 65.4% F-measured as compared to other evaluated machine learning classifier algorithms proposed within the WEKA data mining algorithm. …”
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15
Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer�s Disease
Published 2022“…The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. …”
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Forward scattering radar for real-time detection of human activities and fall classification
Published 2019“…Support Vector Machine (SVM) algorithm modified by kernel linear function was used for classifying the fall event from the other activities. …”
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Thesis -
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A systematic literature review on outlier detection in wireless sensor networks
Published 2020“…Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. …”
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Machine learning in botda fibre sensor for distributed temperature measurement
Published 2023“…To make the sensor more reliable, temperature data must be collected over the length of the cable, or distributed data rather than point data. …”
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Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks
Published 2014“…Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders.…”
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Conference or Workshop Item -
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Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring
Published 2023“…Brillouin scattering; Concretes; Curve fitting; Data handling; Extraction; Fiber optic sensors; Fiber optics; Learning algorithms; Machine learning; Structural health monitoring; BOTDA; Brillouin frequency shift extraction; Brillouin frequency shifts; Brillouin gain spectrum; Correlation techniques; Distributed fiber-optic sensors; Frequency shift; Generalized linear model; Low signal-to-noise ratio; Prediction accuracy; Signal to noise ratio; algorithm; fiber optics; noise; signal noise ratio; Algorithms; Fiber Optic Technology; Noise; Signal-To-Noise Ratio…”
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