Mobile Health App for Adolescents: Motion Sensor Data and Deep Learning Technique to Examine the Relationship between Obesity and Walking Patterns

With the prevalence of obesity in adolescents, and its long-term influence on their overall health, there is a large body of research exploring better ways to reduce the rate of obesity. A traditional way of maintaining an adequate body mass index (BMI), calculated by measuring the weight and height of an individual, is no longer enough, and we are in need of a better health care tool. Therefore, the current research proposes an easier method that offers instant and real-time feedback to the users from the data collected from the motion sensors of a smartphone. The study utilized the mHealth application to identify participants presenting the walking movements of the high BMI group. Using the feedforward deep learning models and convolutional neural network models, the study was able to distinguish the walking movements between nonobese and obese groups, at a rate of 90.5%. The research highlights the potential use of smartphones and suggests the mHealth application as a way to monitor individual health.

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Recurrent disease progression networks for modelling risk trajectory of heart failure

PLoS ONE ◽

10.1371/journal.pone.0245177 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0245177

Author(s):

Xing Han Lu ◽

Aihua Liu ◽

Shih-Chieh Fuh ◽

Yi Lian ◽

Liming Guo ◽

...

Keyword(s):

Heart Failure ◽

Deep Learning ◽

Disease Progression ◽

Recurrent Events ◽

Network Models ◽

Medical Intervention ◽

Learning Models ◽

Neural Network Models ◽

Real Patient

Motivation Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-dimensional data. However, very few studies have explored the ability of RNN in predicting long-term trajectories of recurrent events, which is more informative than predicting one single event in directing medical intervention. Methods In this study, we focus on heart failure (HF) which is the leading cause of death among cardiovascular diseases. We present a novel RNN framework named Deep Heart-failure Trajectory Model (DHTM) for modelling the long-term trajectories of recurrent HF. DHTM auto-regressively predicts the future HF onsets of each patient and uses the predicted HF as input to predict the HF event at the next time point. Furthermore, we propose an augmented DHTM named DHTM+C (where “C” stands for co-morbidities), which jointly predicts both the HF and a set of acute co-morbidities diagnoses. To efficiently train the DHTM+C model, we devised a novel RNN architecture to model disease progression implicated in the co-morbidities. Results Our deep learning models confers higher prediction accuracy for both the next-step HF prediction and the HF trajectory prediction compared to the baseline non-neural network models and the baseline RNN model. Compared to DHTM, DHTM+C is able to output higher probability of HF for high-risk patients, even in cases where it is only given less than 2 years of data to predict over 5 years of trajectory. We illustrated multiple non-trivial real patient examples of complex HF trajectories, indicating a promising path for creating highly accurate and scalable longitudinal deep learning models for modeling the chronic disease.

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DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

Applied Sciences ◽

10.3390/app10041504 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1504 ◽

Cited By ~ 5

Author(s):

Imam Mustafa Kamal ◽

Hyerim Bae ◽

Sim Sunghyun ◽

Heesung Yun

Keyword(s):

Deep Learning ◽

Network Models ◽

Neural Network Models ◽

Business Decisions ◽

Term Prediction ◽

Short And Long Term ◽

The Baltic ◽

Long Term Prediction ◽

Baltic Dry Index

The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders’ and ship-owners’ decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.

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Deriving equations from sensor data using dimensional function synthesis

Communications of the ACM ◽

10.1145/3465216 ◽

2021 ◽

Vol 64 (7) ◽

pp. 91-99

Author(s):

Vasileios Tsoutsouras ◽

Sam Willis ◽

Phillip Stanley-Marbell

Keyword(s):

Data Streams ◽

Laboratory Experiments ◽

Network Models ◽

Sensor Data ◽

Neural Network Models ◽

Physical Systems ◽

Computing Platform ◽

Comparable Accuracy ◽

Two Phases ◽

The Relationship

We present a new method for deriving functions that model the relationship between multiple signals in a physical system. The method, which we call dimensional function synthesis , applies to data streams where the dimensions of the signals (e.g., length, mass, etc.) are known. The method comprises two phases: a compile-time synthesis phase and a subsequent calibration using sensor data. We implement dimensional function synthesis and use the implementation to demonstrate efficiently summarizing multimodal sensor data for two physical systems using 90 laboratory experiments and 10,000 synthetic idealized measurements. The results show that our technique can generate models in less than 300 ms on average across all the physical systems we evaluated. This is a marked improvement when compared to an average of 16 s for training neural networks of comparable accuracy on the same computing platform. When calibrated with sensor data, our models outperform traditional regression and neural network models in inference accuracy in all the cases we evaluated. In addition, our models perform better in training latency (up to 1096X improvement) and required arithmetic operations in inference (up to 34X improvement). These significant gains are largely the result of exploiting information on the physics of signals that has hitherto been ignored.

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Domain randomization-enhanced deep learning models for bird detection

Scientific Reports ◽

10.1038/s41598-020-80101-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xin Mao ◽

Jun Kang Chow ◽

Pin Siang Tan ◽

Kuan-fu Liu ◽

Jimmy Wu ◽

...

Keyword(s):

Deep Learning ◽

Continuous Monitoring ◽

Bird Species ◽

Training Data ◽

Learning Models ◽

Fine Grained ◽

Bird Detection ◽

Relationship Of ◽

The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

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Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

Concurrent Engineering ◽

10.1177/1063293x211003194 ◽

2021 ◽

pp. 1063293X2110031

Author(s):

Maolin Yang ◽

Auwal H Abubakar ◽

Pingyu Jiang

Keyword(s):

Deep Learning ◽

Complex Network ◽

Network Models ◽

Neural Network Models ◽

Complex Network Theory ◽

New Type ◽

Manufacturing Resource ◽

Manufacturing Resources ◽

Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

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Feature extraction-based image steganalysis using deep learning

WEENTECH Proceedings in Energy ◽

10.32438/wpe.182021 ◽

2021 ◽

pp. 188-198

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Secure Communication ◽

Information Technologies ◽

Network Models ◽

Error Rates ◽

Multimedia Data ◽

Secret Message ◽

Neural Network Models

The innovations in advanced information technologies has led to rapid delivery and sharing of multimedia data like images and videos. The digital steganography offers ability to secure communication and imperative for internet. The image steganography is essential to preserve confidential information of security applications. The secret image is embedded within pixels. The embedding of secret message is done by applied with S-UNIWARD and WOW steganography. Hidden messages are reveled using steganalysis. The exploration of research interests focused on conventional fields and recent technological fields of steganalysis. This paper devises Convolutional neural network models for steganalysis. Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. The Convolutional neural network is used to extract spatio-temporal information or features and classification. We have compared steganalysis outcome with AlexNet and SRNeT with same dataset. The stegnalytic error rates are compared with different payloads.

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Deep Network Guided Proof Search

10.29007/8mwc ◽

2018 ◽

Cited By ~ 1

Author(s):

Sarah Loos ◽

Geoffrey Irving ◽

Christian Szegedy ◽

Cezary Kaliszyk

Keyword(s):

Deep Learning ◽

Program Analysis ◽

Network Models ◽

Theorem Prover ◽

Neural Network Models ◽

Two Phase ◽

First Order ◽

Proof Search ◽

System Verification ◽

Theory Reasoning

Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go.Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification.Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved.Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.

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SEALing Neural Network Models in Encrypted Deep Learning Accelerators

10.1109/dac18074.2021.9586199 ◽

2021 ◽

Author(s):

Pengfei Zuo ◽

Yu Hua ◽

Ling Liang ◽

Xinfeng Xie ◽

Xing Hu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Models ◽

Neural Network Models

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Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps

Machine Learning and Knowledge Extraction ◽

10.3390/make1010027 ◽

2019 ◽

Vol 1 (1) ◽

pp. 450-465 ◽

Cited By ~ 8

Author(s):

Abhishek Sehgal ◽

Nasser Kehtarnavaz

Keyword(s):

Deep Learning ◽

Real Time ◽

Open Source Software ◽

Mobile Applications ◽

Network Models ◽

Software Tools ◽

Learning Tools ◽

Smartphone Apps ◽

Learning Models ◽

Neural Network Models

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption, and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models, and the validation results based on the benchmarking metrics are reported.

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