Kinematics approach with neural networks for early detection of sepsis (KANNEDS)

Abstract Background Sepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes. In recent years, researchers have used models to classify positive patients or identify the probability for sepsis using vital signs and other time-series variables as input. Methods In our study, we analyzed patients’ conditions by their kinematics position, velocity, and acceleration, in a six-dimensional space defined by six vital signs. The patient is affected by the disease after a period if the position gets “near” to a calculated sepsis position in space. We imputed these kinematics features as explanatory variables of long short-term memory (LSTM), convolutional neural network (CNN) and linear neural network (LNN) and compared the prediction accuracies with only the vital signs as input. The dataset used contained information of approximately 4800 patients, each with 48 hourly registers. Results We demonstrated that the kinematics features models had an improved performance compared with vital signs models. The kinematics features model of LSTM achieved the best accuracy, 0.803, which was nine points higher than the vital signs model. Although with lesser accuracies, the kinematics features models of the CNN and LNN showed better performances than vital signs models. Conclusion Applying our novel approach for early detection of sepsis using neural networks will prove to be an invaluable, more accurate method than considering only simple vital signs as input variables. We expect that other researchers with similar objectives can use the model presented in this innovative approach to improve their results.

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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽

10.3390/s21082852 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2852

Author(s):

Parvathaneni Naga Srinivasu ◽

Jalluri Gnana SivaSai ◽

Muhammad Fazal Ijaz ◽

Akash Kumar Bhoi ◽

Wonjoon Kim ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Skin Disease ◽

Network Architecture ◽

Large Scale ◽

Short Term Memory ◽

Convolutional Networks ◽

Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

Frontiers in Research Metrics and Analytics ◽

10.3389/frma.2021.673928 ◽

2021 ◽

Vol 6 ◽

Author(s):

Tahani Aljohani ◽

Alexandra I. Cristea

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Methodological Approach ◽

High Accuracy ◽

Directional Model ◽

Textual Representations ◽

The One

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

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Part-of-Speech Tagging via Deep Neural Networks for Northern-Ethiopic Languages

Information Technology And Control ◽

10.5755/j01.itc.49.4.26808 ◽

2020 ◽

Vol 49 (4) ◽

pp. 482-494

Author(s):

Jurgita Kapočiūtė-Dzikienė ◽

Senait Gebremichael Tesfagergish

Keyword(s):

Neural Network ◽

Neural Networks ◽

Language Processing ◽

Deep Neural Networks ◽

Short Term Memory ◽

Parameter Tuning ◽

Feed Forward Neural Network ◽

Pos Tagging ◽

Part Of Speech ◽

Pos Tagger

Deep Neural Networks (DNNs) have proven to be especially successful in the area of Natural Language Processing (NLP) and Part-Of-Speech (POS) tagging—which is the process of mapping words to their corresponding POS labels depending on the context. Despite recent development of language technologies, low-resourced languages (such as an East African Tigrinya language), have received too little attention. We investigate the effectiveness of Deep Learning (DL) solutions for the low-resourced Tigrinya language of the Northern-Ethiopic branch. We have selected Tigrinya as the testbed example and have tested state-of-the-art DL approaches seeking to build the most accurate POS tagger. We have evaluated DNN classifiers (Feed Forward Neural Network – FFNN, Long Short-Term Memory method – LSTM, Bidirectional LSTM, and Convolutional Neural Network – CNN) on a top of neural word2vec word embeddings with a small training corpus known as Nagaoka Tigrinya Corpus. To determine the best DNN classifier type, its architecture and hyper-parameter set both manual and automatic hyper-parameter tuning has been performed. BiLSTM method was proved to be the most suitable for our solving task: it achieved the highest accuracy equal to 92% that is 65% above the random baseline.

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Music generation and human voice conversion based on LSTM

MATEC Web of Conferences ◽

10.1051/matecconf/202133606015 ◽

2021 ◽

Vol 336 ◽

pp. 06015

Author(s):

Guangwei Li ◽

Shuxue Ding ◽

Yujie Li ◽

Kangkang Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Short Term Memory ◽

Human Life ◽

Piano Music ◽

Training Model ◽

Voice Conversion ◽

Neural Network Models ◽

Human Voice ◽

Music Generation

Music is closely related to human life and is an important way for people to express their feelings in life. Deep neural networks have played a significant role in the field of music processing. There are many different neural network models to implement deep learning for audio processing. For general neural networks, there are problems such as complex operation and slow computing speed. In this paper, we introduce Long Short-Term Memory (LSTM), which is a circulating neural network, to realize end-to-end training. The network structure is simple and can generate better audio sequences after the training model. After music generation, human voice conversion is important for music understanding and inserting lyrics to pure music. We propose the audio segmentation technology for segmenting the fixed length of the human voice. Different notes are classified through piano music without considering the scale and are correlated with the different human voices we get. Finally, through the transformation, we can express the generated piano music through the output of the human voice. Experimental results demonstrate that the proposed scheme can successfully obtain a human voice from pure piano Music generated by LSTM.

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Principal component articial neural network calibration models for the simultaneous spectrophotometric estimation of mefenamic acid and paracetamol in tablets

Journal of the Serbian Chemical Society ◽

10.2298/jsc0611207s ◽

2006 ◽

Vol 71 (11) ◽

pp. 1207-1218

Author(s):

Dondeti Satyanarayana ◽

Kamarajan Kannan ◽

Rajappan Manavalan

Keyword(s):

Neural Network ◽

Neural Networks ◽

Mefenamic Acid ◽

Principal Component Regression ◽

Principal Component ◽

Simultaneous Estimation ◽

Uv Spectrophotometry ◽

Novel Approach ◽

Marquardt Algorithm ◽

Calibration Models

Simultaneous estimation of all drug components in a multicomponent analgesic dosage form with artificial neural networks calibration models using UV spectrophotometry is reported as a simple alternative to using separate models for each component. A novel approach for calibration using a compound spectral dataset derived from three spectra of each component is described. The spectra of mefenamic acid and paracetamol were recorded as several concentrations within their linear range and used to compute a calibration mixture between the wavelengths 220 to 340 nm. Neural networks trained by a Levenberg-Marquardt algorithm were used for building and optimizing the calibration models using MATALAB? Neural Network Toolbox and were compared with the principal component regression model. The calibration models were thoroughly evaluated at several concentration levels using 104 spectra obtained for 52 synthetic binary mixtures prepared using orthogonal designs. The optimized model showed sufficient robustness even when the calibration sets were constructed from a different set of pure spectra of the components. The simultaneous prediction of both components by a single neural network with the suggested calibration approach was successful. The model could accurately estimate the drugs, with satisfactory precision and accuracy, in tablet dosage with no interference from excipients as indicated by the results of a recovery study.

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Bio-Medical Image Processing

Examining Fractal Image Processing and Analysis - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-0066-8.ch007 ◽

2020 ◽

pp. 158-169

Author(s):

Rasmita Lenka ◽

Koustav Dutta ◽

Ashimananda Khandual ◽

Soumya Ranjan Nayak

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Early Detection ◽

Digital Image Processing ◽

Medical Science ◽

Medical Image Processing ◽

The World ◽

Medical Reports

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.

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Automatic Classification of Indian Languages into Tonal and Non-tonal Categories Using Cascade Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) Recurrent Neural Networks

2018 International Conference on Signal Processing and Communications (SPCOM) ◽

10.1109/spcom.2018.8724471 ◽

2018 ◽

Author(s):

Chuya China ◽

Dipjyoti Bisharad ◽

Rabul Hussain Laskar

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Recurrent Neural Networks ◽

Short Term Memory ◽

Indian Languages ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Neural Networks as Classification Mechanisms of Complex Human Activities

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213020500116 ◽

2020 ◽

Vol 29 (05) ◽

pp. 2050011

Author(s):

Anargyros Angeleas ◽

Nikolaos Bourbakis

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Neural Nets ◽

Quality Of Data ◽

Short Term ◽

Video Frames ◽

Data Engineering ◽

Formal Framework

Within this paper, we present two neural nets for view-independent complex human activity recognition (HAR) from video frames. For our study here, we reduce the number of frames produced by a video sequence given that we can identify activities from a sparsely sampled sequence of body poses, and, at the same time, we are able to reduce the processing complexity and response while hardly affecting the accuracy, precision, and recall. To do so, we use a formal framework to ensure the quality of data collection and data preprocessing. We utilize neural networks for the classification of single and complex body activities. More specifically, we consider the sequence of body poses as a time-series problem given that they can provide state-of-the-art results on challenging recognition tasks with little data engineering. Deep Learning in the form of Convolutional Neural Network (CNN), Long Short-Term Neural Network (LSTM), and a one-dimensional Convolutional Neural Network Long Short-Term Memory model (CNN-LSTM) are used as benchmarks to classify the activity.

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Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States

Scientific Reports ◽

10.1038/s41598-019-52152-2 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Laura Gagliano ◽

Elie Bou Assi ◽

Dang K. Nguyen ◽

Mohamad Sawan

Keyword(s):

Neural Networks ◽

Multilayer Perceptron ◽

Recurrent Neural Networks ◽

Short Term Memory ◽

Focal Epilepsy ◽

Single Layer ◽

Epileptic Seizures ◽

Novel Approach ◽

Multilayer Perceptron Networks

Abstract This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.

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Multiple Source Localization in a Shallow Water Waveguide Exploiting Subarray Beamforming and Deep Neural Networks

Sensors ◽

10.3390/s19214768 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4768 ◽

Cited By ~ 2

Author(s):

Zhaoqiong Huang ◽

Ji Xu ◽

Zaixiao Gong ◽

Haibin Wang ◽

Yonghong Yan

Keyword(s):

Neural Network ◽

Neural Networks ◽

Shallow Water ◽

Source Localization ◽

Deep Neural Networks ◽

Short Term Memory ◽

Direction Finding ◽

Multiple Source ◽

Feed Forward Neural Network ◽

Subarray Beamforming

Deep neural networks (DNNs) have been shown to be effective for single sound source localization in shallow water environments. However, multiple source localization is a more challenging task because of the interactions among multiple acoustic signals. This paper proposes a framework for multiple source localization on underwater horizontal arrays using deep neural networks. The two-stage DNNs are adopted to determine both the directions and ranges of multiple sources successively. A feed-forward neural network is trained for direction finding, while the long short term memory recurrent neural network is used for source ranging. Particularly, in the source ranging stage, we perform subarray beamforming to extract features of sources that are detected by the direction finding stage, because subarray beamforming can enhance the mixed signal to the desired direction while preserving the horizontal-longitudinal correlations of the acoustic field. In this way, a universal model trained in the single-source scenario can be applied to multi-source scenarios with arbitrary numbers of sources. Both simulations and experiments in a range-independent shallow water environment of SWellEx-96 Event S5 are given to demonstrate the effectiveness of the proposed method.

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