A Model for Predicting the Class of Illicit Drug Suspects and Offenders

In this study, the artificial neural network was deployed to develop a classification model for predicting the class of a drug-related suspect into either the drug peddler or non-drug peddler class. A dataset consisting of 262 observations on drug suspects and offenders in central Nigeria was used to train the model which uses parameters such as exhibit type, suspect’s age, exhibit weight, and suspect’s gender to predict the class of a suspect, with a predictive accuracy of 83%. The model sets the pace for the implementation of a full system for use at airports, seaports, police stations, and by security agents concerned with drug-related matters. The accurate classification of suspects and offenders will ensure a faster and correct reference to the sections of the drug law that correspond to a particular offence for appropriate actions such as prosecution or rehabilitation.

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Automated Classification of Atrial Fibrillation Using Artificial Neural Network for Wearable Devices

Mathematical Problems in Engineering ◽

10.1155/2020/9159158 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Fengying Ma ◽

Jingyao Zhang ◽

Wei Liang ◽

Jingyu Xue

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Artificial Neural Network ◽

Real Time ◽

Classification Model ◽

Ecg Monitoring ◽

Rr Intervals ◽

Ecg Signals ◽

Artificial Neural

Atrial fibrillation (AF), as one of the most common arrhythmia diseases in clinic, is a malignant threat to human health. However, AF is difficult to monitor in real time due to its intermittent nature. Wearable electrocardiogram (ECG) monitoring equipment has flourished in the context of telemedicine due to its real-time monitoring and simple operation in recent years, providing new ideas and methods for the detection of AF. In this paper, we propose a low computational cost classification model for robust detection of AF episodes in ECG signals, using RR intervals of the ECG signals and feeding them into artificial neural network (ANN) for classification, to compensate the defect of the computational complexity in traditional wearable ECG monitoring devices. In addition, we compared our proposed classifier with other popular classifiers. The model was trained and tested on the AF Termination Challenge Database and MIT-BIH Arrhythmia Database. Experimental results achieve the highest sensitivity of 99.3%, specificity of 97.4%, and accuracy of 98.3%, outperforming most of the others in the recent literature. Accordingly, we observe that ANN using RR intervals as an input feature can be a suitable candidate for automatic classification of AF.

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Classification of Cardiac Signals Using Artificial Neural Network and Fuzzy Equivalence Relation

i-manager’s Journal on Software Engineering ◽

10.26634/jse.2.4.496 ◽

2008 ◽

Vol 2 (4) ◽

pp. 48-56

Author(s):

B. Anuradha ◽

V.C. Veera Reddy

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Equivalence Relation ◽

Artificial Neural ◽

Cardiac Signals

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Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/4/8 ◽

2020 ◽

Vol 17 (4) ◽

pp. 497-506

Author(s):

Sunil Patel ◽

Ramji Makwana

Keyword(s):

Neural Network ◽

Optical Flow ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Classification Model ◽

Hand Gesture ◽

Flow Data ◽

Dynamic Hand Gesture Recognition ◽

Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

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Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

10.36652/0042-4633-2020-8-61-64 ◽

2020 ◽

pp. 61-64

Author(s):

Yu.G. Kabaldin ◽

A.A. Khlybov ◽

M.S. Anosov ◽

D.A. Shatagin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Acoustic Emission ◽

Acoustic Emission Signal ◽

Low Temperatures ◽

Cold Resistance ◽

Emission Signal ◽

Impact Bending ◽

Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

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