Forecasting Air Temperature on Edge Devices with Embedded AI

With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402∘C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.04%, and a coefficient of determination (R2) not smaller than 0.997. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible.

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Using BPNN to Predict Number of Low-Income Households in Taiwan Based on Seasonally Adjusted Annualized Rates for Real GDP

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1044-1045.1824 ◽

2014 ◽

Vol 1044-1045 ◽

pp. 1824-1827

Author(s):

Yi Ti Tung ◽

Tzu Yi Pai

Keyword(s):

Neural Network ◽

Low Income ◽

Mean Squared Error ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Percentage Error ◽

Real Gdp ◽

Squared Error ◽

Input Variables ◽

Low Income Households

In this study, the back-propagation neural network (BPNN) was used to predict the number of low-income households (NLIH) in Taiwan, taking the seasonally adjusted annualized rates (SAAR) for real gross domestic product (GDP) as input variables. The results indicated that the lowest mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and highest correlation coefficient (R) for training and testing were 4.759 % versus 19.343 %, 24429972.268 versus 781839890.859, 4942.669 versus 27961.400, and 0.945 versus 0.838, respectively.

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Forecasting Brazilian Ethanol Spot Prices Using LSTM

Energies ◽

10.3390/en14237987 ◽

2021 ◽

Vol 14 (23) ◽

pp. 7987

Author(s):

Gustavo Carvalho Santos ◽

Flavio Barboza ◽

Antônio Cláudio Paschoarelli Veiga ◽

Mateus Ferreira Silva

Keyword(s):

Short Term Memory ◽

Mean Squared Error ◽

Percentage Error ◽

Spot Prices ◽

Statistical Measures ◽

Squared Error ◽

Proposed Model ◽

Price Trends ◽

Long Short Term Memory ◽

Crisis Scenarios

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

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COVID-19 Infection Forecasting based on Deep Learning in Iran

10.1101/2020.05.16.20104182 ◽

2020 ◽

Cited By ~ 3

Author(s):

Mehdi Azarafza ◽

Mohammad Azarafza ◽

Jafar Tanha

Keyword(s):

Short Term Memory ◽

Mean Squared Error ◽

Moving Average ◽

National Level ◽

Absolute Error ◽

Percentage Error ◽

Autoregressive Integrated Moving Average ◽

Error Metrics ◽

Squared Error ◽

Better Than

Since December 2019 coronavirus disease (COVID-19) is outbreak from China and infected more than 4,666,000 people and caused thousands of deaths. Unfortunately, the infection numbers and deaths are still increasing rapidly which has put the world on the catastrophic abyss edge. Application of artificial intelligence and spatiotemporal distribution techniques can play a key role to infection forecasting in national and province levels in many countries. As methodology, the presented study employs long short-term memory-based deep for time series forecasting, the confirmed cases in both national and province levels, in Iran. The data were collected from February 19, to March 22, 2020 in provincial level and from February 19, to May 13, 2020 in national level by nationally recognised sources. For justification, we use the recurrent neural network, seasonal autoregressive integrated moving average, Holt winter's exponential smoothing, and moving averages approaches. Furthermore, the mean absolute error, mean squared error, and mean absolute percentage error metrics are used as evaluation factors with associate the trend analysis. The results of our experiments show that the LSTM model is performed better than the other methods on the collected COVID-19 dataset in Iran

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PREDIKSI DATA TRANSAKSI PENJUALAN TIME SERIES MENGGUNAKAN REGRESI LSTM

Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) ◽

10.23887/janapati.v9i1.19140 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1

Author(s):

Marie Luthfi Ashari ◽

Mujiono Sadikin

Keyword(s):

Machine Learning ◽

Time Series ◽

Short Term Memory ◽

Mean Squared Error ◽

Percentage Error ◽

Short Term ◽

Term Memory ◽

Root Mean Squared Error ◽

Squared Error ◽

Long Short Term Memory

Sebagai upaya untuk memenangkan persaingan di pasar, perusahaan farmasi harus menghasilkan produk obat – obatan yang berkualitas. Untuk menghasilkan produk yang berkualitas, diperlukan perencanaan produksi yang baik dan efisien. Salah satu dasar perencanaan produksi adalah prediksi penjualan. PT. Metiska Farma telah menerapkan metode prediksi dalam proses produksi, akan tetapi prediksi yang dihasilkan tidak akurat sehingga menyebabkan tidak optimal dalam memenuhi permintaan pasar. Untuk meminimalisir masalah kurang akuratnya proses prediksi tersebut, dalam penelitian yang disajikan pada makalah ini dilakukan uji coba prediksi menggunakan teknik Machine Learning dengan metode Regresi Long Short Term Memory (LSTM). Teknik yang diusulkan diuji coba menggunakan dataset penjualan produk “X” dari PT. Metiska Farma dengan parameter kinerja Root Mean Squared Error (RMSE) dan MAPE (Mean Absolute Percentage Error). Hasil penelitian ini berupa nilai rata – rata evaluasi error dari pemodelan data training dan data testing. Di mana hasil menunjukan bahwa Regresi LSTM memiliki nilai prediksi penjualan dengan evaluasi model melalui RMSE sebesar 286.465.424 untuk data training dan 187.013.430 untuk data testing. Untuk nilai MAPE sebesar 787% dan 309% untuk data training dan data testing secara berurut.

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Analysis of canopy phenology in man-made forests using near-earth remote sensing

Plant Methods ◽

10.1186/s13007-021-00803-9 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Peng Guan ◽

Yili Zheng ◽

Guannan Lei

Keyword(s):

Remote Sensing ◽

Color Index ◽

Short Term Memory ◽

Mean Squared Error ◽

Absolute Error ◽

Percentage Error ◽

Earth Remote Sensing ◽

Squared Error ◽

Highly Sensitive ◽

Different Color

Abstract Background Forest canopies are highly sensitive to their growth, health, and climate change. The study aims to obtain time sequence images in mix foresters using a near-earth remote sensing method to track the seasonal variation in the color index and select the optimal color index. Three different regions of interest (RIOs) were defined and six color indexes (GRVI, HUE, GGR, RCC, GCC, and GEI) were calculated to analyze the microenvironment difference. The key phenological phase was identified using the double logistic model and the derivative method, and the phenology forecast of color indexes was performed based on the long short-term memory (LSTM) model. Results The results showed that the same color index in different RIOs and different color indexes in the same RIO present a slight difference in the days of growth and the days corresponding to the peak value, exhibiting different phenological phases; the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the LSTM model was 0.0016, 0.0405, 0.0334, and 12.55%, respectively, indicating that this model has a good forecast effect. Conclusions In different areas of the same forest, differences in the micro-ecological environment in the canopies were prevalent, with their internal growth mechanism being affected by different cultivation ways and the external environment. Besides, the optimal color index also varies with species in phenological response, that is, different color indexes are used for different forests. With the data of color indexes as the training set and forecast set, the feasibility of the LSTM model in phenology forecast is verified.

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Estimating Daily Solar Radiation Using Hargreaves Model in Eastern Malaysia

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.699.564 ◽

2014 ◽

Vol 699 ◽

pp. 564-569 ◽

Cited By ~ 1

Author(s):

Mohd Irwan Yusoff ◽

Muhamad Irwanto ◽

Safwati Ibrahim ◽

Gomesh Nair ◽

Syed Idris Syed Hassan ◽

...

Keyword(s):

Solar Radiation ◽

Mean Squared Error ◽

Coefficient Of Determination ◽

Maximum Temperature ◽

Percentage Error ◽

Model Estimation ◽

Estimation Model ◽

Radiation Data ◽

Squared Error ◽

Hargreaves Model

This paper presents the forecasting of solar radiation in Kelantan, Eastern Malaysia for the year of 2011 using Hargreaves model. This estimation is based on latitude and daily minimum and maximum temperature in Kelantan. The measured and estimated solar radiation data were compared for the year 2011 and analyzed using coefficient of residual mass (CRM), root mean squared error (RMSE), coefficient of determination (R2) and percentage error (e). The results showed that the value ofCRMis 0.09, it indicates the tendency of the estimation model to under-estimate the measure solar radiation. Meanwhile, the value ofRMSEis 8.21% and the value ofR2is 0.8661, closed to 1 indicates that about 86.61% of the total variation is explained in the data. For thee, the value is 7.98%, it indicates that the model estimation is good.

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An ERBF Network Prediction of Taiwan’s Crude Birth Rate Taking Economic Growth Rate and National Income Indices as Input Variables

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1044-1045.1721 ◽

2014 ◽

Vol 1044-1045 ◽

pp. 1721-1724

Author(s):

Yi Ti Tung ◽

Tzu Yi Pai

Keyword(s):

Economic Growth ◽

Growth Rate ◽

Birth Rate ◽

Mean Squared Error ◽

National Income ◽

Crude Birth Rate ◽

Percentage Error ◽

Economic Growth Rate ◽

Squared Error ◽

Input Variables

In this study, exact radial basis function (ERBF) network was used to predict Taiwan’s crude birth rate (CBR), taking the economic growth rate (EGR) and national income indices (NII) as input variables. To establish the ERBF network model, the EGR and NII were taken as the input variables, and the CBR was taken as the output variable. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 0.00 %, 0.00, 0.00, and 1.00, respectively when training. Those for testing were 52.41 %, 27.88, 5.28, and 0.00, respectively. According to the results, ERBF network could predict CBR by taking the EGR and NII as input variables.

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How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

Epidemiology and Infection ◽

10.1017/s0950268821000091 ◽

2021 ◽

Vol 149 ◽

Author(s):

Junwen Tao ◽

Yue Ma ◽

Xuefei Zhuang ◽

Qiang Lv ◽

Yaqiong Liu ◽

...

Keyword(s):

Variable Selection ◽

Mean Squared Error ◽

Moving Average ◽

Dynamic Bayesian Networks ◽

Environmental Data ◽

Control Measures ◽

Coefficient Of Determination ◽

Percentage Error ◽

Analysis Strategy ◽

Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

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Peramalan Jumlah Populasi Sapi Potong di Kalimantan Selatan Menggunakan Metode Moving Average, Exponential Smoothing dan Trend Analysis

Jurnal Teknologi Agro-Industri ◽

10.34128/jtai.v6i1.88 ◽

2019 ◽

Vol 6 (1) ◽

pp. 41

Author(s):

Jaka Darma Jaya

Keyword(s):

Trend Analysis ◽

Mean Squared Error ◽

Moving Average ◽

Exponential Smoothing ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Absolute Percentage Error ◽

Polynomial Trend ◽

Squared Error

Perkembangan produksi daging sapi di Indonesia selama 30 tahun terakhir secara umum cenderung meningkat. Kebutuhan daging sapi di Indonesia masih belum bisa dicukupi oleh supply domestik, sehingga diperlukan impor daging sapi dari luar negeri. Diperlukan kajian tentang proyeksi ketersediaan populasi sapi potong di masa mendatang agar diambil kebijakan yang tepat dalam menjaga stabilitas dan keterpenuhan supply daging nasional. Penelitian ini bertujuan untuk melakukan peramalan jumlah populasi sapi potong menggunakan 3 (tiga) metode peramalan yaitu metode moving average, exponential smoothing dan trend analysis. Hasil peramalan ini selanjutnya diukur akurasinya menggunakan MAD (Mean Absolud Deviation), MSE (Mean Squared Error) dan MAPE (Mean Absolute Percentage Error). Proyeksi populasi sapi potong pada tahun 2019 (periode berikutnya) menggunakan 3 metode peramalan adalah: 195.100 (moving average); 218.225 (exponential smooting) dan 262.899 (trend analysis). Pengukuran akurasi menggunakan MAD, MSE dan MAPE menunjukkan bahwa metode peramalan jumlah populasi sapi potong yang paling akurat adalah peramalan menggunakan metode polynomial trend analysis (MAD 14.716,12; MSE 327.282.084,17; dan MAPE 0,09) karena memiliki tingkat kesalahan yang lebih kecil dibandingkan hasil peramalan menggunakan metode moving average dan exponential smoothing.

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Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

10.2196/preprints.31709 ◽

2021 ◽

Author(s):

Hangsik Shin

Keyword(s):

Machine Learning ◽

Correlation Coefficient ◽

Age Estimation ◽

Mean Squared Error ◽

Mean Absolute Error ◽

Absolute Error ◽

Coefficient Of Determination ◽

Vascular Aging ◽

Squared Error ◽

Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

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