scholarly journals Development of a prediction model based on linear regression to estimate the success rates of seafood caught from different catching centers

2021 ◽  
Vol 8 (12) ◽  
pp. 110-116
Author(s):  
Sherimon et al. ◽  
Keyword(s):  
Mean Square Error ◽  
Least Square ◽  
Percentage Error ◽  
Mean Square ◽  
Success Rates ◽  
Total Quality ◽  
Absolute Deviation ◽  
Safety Standards ◽  
Intelligent Software ◽  
Seafood Processing

For businesses and organizations that aim to be efficient and competitive on a worldwide basis, food quality assurance is extremely important. To maintain constant quality, global markets demand high food hygiene and safety standards. Intelligent software to assure fish quality is uncommon in the fishing industry. Most seafood processing industries utilize Total Quality Management (TQM) systems to ensure product safety and quality. These protections ensure that significant quality risks are kept within acceptable tolerance limits. However, there are no ways for calculating the success rates of seafood obtained from different catching centers. The purpose of this study is to develop algorithms for predicting the success rates of seafood caught at different catching centers. To determine the best model to match the data, the algorithms employ the Least-Square Curve Fitting approach. The success rates are predicted using the best-fit model that results. The bestFitModelFinder algorithm is used to find the best model for the input data, while the prediction of quality algorithm is used to predict the success rate. The algorithms were tested using data obtained from a seafood company between January 2000 and December 2019. Statistical metrics such as mean absolute deviation (MAD), mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to evaluate the prediction accuracy of the presented algorithms. The algorithms' performance analysis resulted in lower error levels. The proposed algorithms can assist seafood enterprises in determining the quality of seafood items sourced from various fishing areas.

scholarly journals COMPARISON OF SOME STATISTICAL FORECASTING TECHNIQUES WITH GMDH PREDICTOR: A CASE STUDY

2018 ◽  
Vol 47 (1) ◽  
pp. 16-21 ◽  
Author(s):  
Syed Misbah Uddin ◽  
Aminur Rahman ◽  
Emtiaz Uddin Ansari
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Absolute Percentage Error

Demand forecasts are extremely important for manufacturing industry and also needed for all type of business and business suppliers for distribution of finish products to the consumer on time. This study is concerned with the determination of accurate models for forecasting cement demand. In this connection this paper presents results obtained by using a self-organizing model and compares them with those obtained by usual statistical techniques. For this purpose, Monthly sales data of a typical cement ranging from January, 2007 to February, 2016 were collected. A nonlinear modelling technique based on Group Method of Data Handling (GMDH) is considered here to derive forecasts. Forecast were also made by using various time series smoothing techniques such as exponential smoothing, double exponential smoothing, moving average, weightage moving average and regression method. The actual data were compared to the forecast generated by the time series model and GMDH model. The mean absolute deviation (MAD, mean absolute percentage error (MAPE) and mean square error (MSE) were also calculated for comparing the forecasting accuracy. The comparison of modelling results shows that the GMDH model perform better than other statistical models based on terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square error (MSE).

scholarly journals PERBANDINGAN METODE FTS DAN MA PADA PERAMALAN PERSEDIAAN BERAS

2020 ◽  
Vol 6 (3) ◽  
pp. 29-36
Author(s):  
Deddy Kusbianto ◽  
Agung Pramudhita ◽  
Nurhalimah
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Absolute Percentage Error

Dalam memenuhi kebutuhan masyarakat Kabupaten Malang dan menjaga stabilitas ketersediaan beras pemerintah setempat perlu melakukan proses peramalan. Dimana dalam melakukan proses peramalan menggunakan metode peramalan, salah satunya dengan menggunakan metode Fuzzy Time Series dan Moving Average yaitu dengan menangkap pola dari data yang telah lalu kemudian digunakan untuk memproyeksikan data yang akan da¬¬tang. Dari hasil implementasi dua metode tersebut menghasilkan perbandingan jumlah persediaan beras. hasil perbandingan tersebut akan dipakai untuk mengukur tingkat error dari masing – masing metode dengan menggunakan MAD (Mean Absolute Deviation), MSE (Mean Square Error), RMSE ( Root Square Error ) dan MAPE (Mean Absolute Percentage Error). Kesimpulannya adalah metode fuzzy time series cocok digunakan untuk studi kasus peramalan persediaan beras dibandingkan menggunakan metode moving average. Sehingga untuk proses peramalan selanjutnya dan untuk mendapatkan hasil dengan tingkat error sedikit dapat menggunakan metode fuzzy time series

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

scholarly journals PEMODELAN JUMLAH PENDUDUK MISKIN DI JAWA TENGAH MENGGUNAKAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR)

2019 ◽  
Vol 4 (2) ◽  
pp. 10
Author(s):  
Sugi Haryanto ◽  
Gilang Axelline Andriani
Keyword(s):  
Sustainable Development ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Geographically Weighted Regression ◽  
Sustainable Development Goals ◽  
Weighted Regression ◽  
Percentage Error ◽  
Mean Square ◽  
Development Goals

Kemiskinan merupakan sesuatu yang sering menjadi ukuran keberhasilan kepemimpinan seorang kepala daerah. Selain itu juga sebagai tujuan pertama Sustainable Development Goals (SDG’s) untuk dientaskan. Kebijakan yang tepat sangat penting dibuat demi tercapainya tujuan pembangunan berkelanjutan. Pemodelan Geographically Weighted Regression (GWR) penting digunakan untuk menyusun model di setiap kabupaten/kota sebagai dasar pembuat kebijakan. Peubah yang digunakan dalam penelitian ini yaitu jumlah penduduk miskin, Indeks Pembangunan Manusia (IPM), Tingkat Pengangguran Terbuka (TPT), dan Upah Minimum Kabupaten/kota (UMK). Tujuan penelitian ini yaitu menentukan faktor-faktor yang berpengaruh terhadap jumlah penduduk miskin di setiap kabupaten/kota di Jawa Tengah. Pemodelan GWR lebih efektif dalam menggambarkan jumlah penduduk miskin di kabupaten/kota di Jawa Tengah tahun 2018. Hal ini ditunjukkan dengan adanya penigkatan nilai R2 serta penurunan nilai Root Mean Square Error (RMSE) dan Mean Absolute Percentage Error (MAPE).

scholarly journals FORECASTING INTERNATIONAL TOURIST ARRIVALS IN MALAYSIA USING SARIMA AND HOLT-WINTERS MODEL

2020 ◽  
Vol 5 (18) ◽  
pp. 41-51
Author(s):  
Norliana Mohd Lip ◽  
Nur Shafiqah Jumery ◽  
Fatin Amira Ahmad Termizi ◽  
Nurul Atiqa Mulyadi ◽  
Norhasnelly Anuar ◽  
...  
Keyword(s):  
United Kingdom ◽  
Mean Square Error ◽  
Time Series Data ◽  
Tourism Industry ◽  
Series Data ◽  
Percentage Error ◽  
Mean Square ◽  
Public And Private ◽  
The United Kingdom ◽  
Tourism Flows

Tourism can be described as the activities of visitors who make a visit to the main destination outside their usual environment for less than a year for any purpose. The tourism industry has become one of the influential sectors in global economic growth. Thus, tourism forecasting plays an important role in public and private sectors concerning future tourism flows. This study is an attempt to determine the best model in forecasting the international tourist's arrival in Malaysia based on Box-Jenkins and Holt-Winters model. The comparison of the accuracy of the techniques between Box-Jenkins SARIMA and Holt-Winters model was done based on the value of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The secondary time series data were obtained from the Tourism Malaysia Department, which consists of a number of tourist arrivals from Singapore, Korea, and the United Kingdom from the year 2013 until the year 2017. The findings of this study suggest that the SARIMA and Holt-Winters model are suitable to be used in forecasting tourist arrivals. This study found that the Holt-Winters model is the appropriate model to forecast tourist arrivals from the United Kingdom (UK) and Korea. While SARIMA (1,1,1) (1,1,1)12 is the appropriate model for forecasting tourist arrivals from Singapore.

scholarly journals Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

2020 ◽  
pp. 23
Author(s):  
Anggita Rosiana Putri ◽  
Abdul Rohman ◽  
Sugeng Riyanto ◽  
Widiastuti Setyaningsih
Keyword(s):  
Root Mean Square Error ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

scholarly journals A Comprehensive Review on Channel Estimation in OFDM System

2019 ◽  
Vol 5 (3) ◽  
pp. 6 ◽  
Author(s):  
Neha Dubey ◽  
Ankit Pandit
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Orthogonal Frequency Division Multiplexing ◽  
Transmission Rate ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Square ◽  
Mimo Ofdm

In wireless communication, orthogonal frequency division multiplexing (OFDM) plays a major role because of its high transmission rate. Channel estimation and tracking have many different techniques available in OFDM systems. Among them, the most important techniques are least square (LS) and minimum mean square error (MMSE). In least square channel estimation method, the process is simple but the major drawback is it has very high mean square error. Whereas, the performance of MMSE is superior to LS in low SNR, its main problem is it has high computational complexity. If the error is reduced to a very low value, then an exact signal will be received. In this paper an extensive review on different channel estimation methods used in MIMO-OFDM like pilot based, least square (LS) and minimum mean square error method (MMSE) and least minimum mean square error (LMMSE) methods and also other channel estimation methods used in MIMO-OFDM are discussed.

Multivariate analysis of three chemometric algorithms on rapid prediction of some important quality parameters of crude shea butter using Fourier transform-near infrared spectroscopy

2019 ◽  
Vol 27 (3) ◽  
pp. 220-231
Author(s):  
Emmanuel Amomba Seweh ◽  
Zou Xiaobo ◽  
Feng Tao ◽  
Shi Jiachen ◽  
Haroon Elrasheid Tahir ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Saponification Value ◽  
Peroxide Values

A comparative study of three chemometric algorithms combined with NIR spectroscopy with the aim of determining the best performing algorithm for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results showed that genetic-algorithm partial least square models were superior in predicting iodine value and saponification value while partial least squares was excellent in predicting free fatty acids content and peroxide values. The nine-factor genetic-algorithm partial least square iodine value calibration model for predicting iodine value yielded excellent ( R2 cal = 0.97), ( R2 val = 0.97), low (root mean square error of cross-validation = 0.26), low (root mean square error of Prediction = 0.23), and (ratio of performance to deviation = 6.41); for saponification value, the nine-factor genetic-algorithm partial least square saponification value calibration model had excellent R2 cal (0.97), R2 val (0.99); low root mean square error of cross-validation (0.73), low root mean square error of Prediction (0.53), and (ratio of performance to deviation = 8.27); while for free fatty acids, the 11-factor partial least square free fatty acids produced very high R2 cal (0.97) and R2 val (0.97) with very low root mean square error of cross-validation (0.03), low root mean square error of Prediction (0.04) and (ratio of performance to deviation = 5.30) and finally for peroxide values, the 11-factor partial least square peroxide values calibration model obtained excellent R2 cal (0.96) and R2val (0.98) with low root mean square error of cross-validation (0.05), low root mean square error of Prediction (0.04), and (ratio of performance to deviation = 5.86). The built models were accurate and robust and can be reliably applied in developing a handheld quality detection device for screening, quality control checks, and prediction of shea butter quality on-site.

scholarly journals Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

2020 ◽  
Vol 10 (20) ◽  
pp. 7079
Author(s):  
Elias Eze ◽  
Tahmina Ajmal
Keyword(s):  
Dissolved Oxygen ◽  
Mean Square Error ◽  
Short Term Memory ◽  
Sensor Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Mean Square ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Do Concentration

Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an early warning system, for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD)-based LSTM (long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that strongly correlate with the original sensor data, and integrate into both inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. The performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, four statistical performance indices were adopted: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results are presented for the short term (12-h) and the long term (1-month) that are encouraging, indicating suitability of this technique for forecasting DO values.

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