scholarly journals Prediction of quality attributes and maturity classification of pear fruit using laser imaging and Artificial Neural Network

Food Research ◽  
2021 ◽  
Vol 5 (S1) ◽  
pp. 144-151
Author(s):  
S.E. Adebayo ◽  
N. Hashim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optical Properties ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Classification Models ◽  
Pear Fruit ◽  
Laser Imaging

In this study, the application of laser imaging technique was utilized to predict the quality attributes (firmness and soluble solids content) of pear fruit and to classify the maturity stages of the fruit harvested at different days after full bloom (dafb). Laser imaging system emitting at visible and near infra-red region (532, 660, 785, 830 and 1060 nm) was deployed to capture the images of the fruit. Optical properties (absorption ma and reduced scattering ms ʹ coefficients) at individual and combined wavelengths of the laser images of the fruit were used in the prediction and classifications of the maturity stages. Artificial neural network (ANN) was employed in the building of both prediction and classification models. Root mean square error of calibration (RMSEC), root mean square error of crossvalidation (RMSECV), correlation coefficient (r) and bias were used to test the performance of the prediction models while sensitivity and specificity were used to evaluate the classification models. The results showed that there was a very strong correlation between the ma and ms ʹ with pear development. This study had shown that optical properties of pears with ANN as prediction and classification models can be employed to both predict quality parameters of pear and classify pear into different (dafb) non-destructively.

scholarly journals Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent

2019 ◽  
Vol 20 (17) ◽  
pp. 4206 ◽  
Author(s):  
Seef Saadi Fiyadh ◽  
Mohamed Khalid AlOmar ◽  
Wan Zurina Binti Jaafar ◽  
Mohammed Abdulhakim AlSaadi ◽  
Sabah Saadi Fayaed ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Root Mean Square Error ◽  
Adsorption Capacity ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Deep Eutectic Solvent ◽  
Mean Square ◽  
Mercury Ions ◽  
Narx Model

Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10−3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10−3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10−3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.

scholarly journals KAJIAN PREDIKSI FRAGMENTASI BATUAN HASIL KEGIATAN PELEDAKAN MENGGUNAKAN ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 7 (2) ◽  
pp. 145
Author(s):  
Eko Santoso ◽  
Romla Noor Hakim ◽  
Fransisikus Agung Bimantoro
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Relative Weight ◽  
Mean Square ◽  
Total Data ◽  
Artificial Neural

Kegiatan peledakan banyak digunakan sebagai cara pemberaian batuan yang ekonomis dalam proses penambangan, dan memegang perananan penting dalam kemajuan produksi tambang. Ledakan yang optimal mampu menghasilkan fragmentasi batuan yang diinginkan. Fragmentasi batuan sangat penting karena menentukan sebagian besar penggunakan peralatan dan produktivitas. Oleh karena itu, perlu dilakukan kajian prediksi fragmentasi batuan hasil kegiatan peledakan. Teknik artificial neural network lebih disukai daripada rumus empiris karena mampu menggabungkan berbagai faktor yang mempengaruhi hasil peledakan. Penelitian ini bertujuan membandingkan hasil prediksi menggunakan artificial neural network dengan rumus empiris Kuz-Ram untuk memprediksi fragmentasi batuan dengan memperhatikan nilai error dari data yang dihasilkan. Pengembangan model artificial neural network menggunakan sistem neural network tool berbasis MATLAB R2016a dengan metode backpropagation. Pelatihan, validasi dan pengujian dilakukan dengan 99 total data peledakan yang diambil dari lapangan. Seperti burden, spasi, kedalaman, relative weight strenght bahan peledak, faktor batuan, jumlah bahan peledak per lubang ledak, dan fragmentasi batuan. Berdasarkan hasil yang diperoleh dari penelitian, model artificial neural network dengan arsitektur 15-6-1 lebih optimal dalam memprediksi fragmentasi batuan dibandingkan dengan rumus empiris Kuz-Ram. Nilai error yang dihasilkan dari artificial neural network sum square error 422.60, mean square error 0.60, root mean square error 0.78. Kata-kata kunci: Peledakan, Fragmentasi Batuan, Artificial Neural Network, Rumus Empiris Kuz-Ram.

scholarly journals The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach

2017 ◽  
Vol 76 (9) ◽  
pp. 2413-2426 ◽  
Author(s):  
Seef Saadi Fiyadh ◽  
Mohammed Abdulhakim AlSaadi ◽  
Mohamed Khalid AlOmar ◽  
Sabah Saadi Fayaed ◽  
Ako R. Hama ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Back Propagation ◽  
Deep Eutectic Solvents ◽  
Lead Removal ◽  
Mean Square ◽  
Feed Forward Back Propagation ◽  
Artificial Neural Network Ann

Abstract The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10−4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

scholarly journals Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 53
Author(s):  
Joohwan Sung ◽  
Sungmin Han ◽  
Heesu Park ◽  
Hyun-Myung Cho ◽  
Soree Hwang ◽  
...  
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Recurrent Neural Network ◽  
Joint Angle ◽  
Low Frequency ◽  
Measurement Unit ◽  
Mean Square ◽  
Overground Walking

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.

scholarly journals ARTIFICIAL NEURAL NETWORK APPLIED IN FORECASTING THE COMPOSITION OF MUNICIPAL SOLID WASTE IN IASI, ROMANIA

2021 ◽  
Vol 29 (3) ◽  
pp. 368-380
Author(s):  
Cristina Ghinea ◽  
Petronela Cozma ◽  
Maria Gavrilescu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Root Mean Square Error ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Ann Model

Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.

scholarly journals IMPLEMENTASI METODE NEURAL NETWORK BACKPROPAGATION DALAM PREDIKSI INDEKS HARGA SAHAM GABUNGAN (IHSG)

SEMINASTIKA ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 79-85
Author(s):  
Okky Barus ◽  
Christopher Wijaya
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Composite Index ◽  
Mean Square

Pada era saat ini, Investasi saham di pasar modal merupakan aset yang sangat penting bagi beberapa golongan masyarakat dan juga bagi perusahaan. Dengan adanya investasi, secara langsung maupun tidak langsung dapat memberikan dampak bagi perusahaan maupun bagi masyarakat. Penelitian ini bertujuan untuk memprediksi Indeks Harga Saham Gabungan (IHSG) dengan indeks saham: Jakarta Composite Index (JKSE). Metode yang digunakan pada penelitian ini adalah Neural Network Backpropagation. Pengumpulan dataset melalui website finance.yahoo.com dengan periode 8 Mei 2018 sampai dengan 7 Mei 2021 sebanyak 757 data. Setelah melakukan proses pengolahan data, data yang tersisa adalah 724 data. Kemudian data akan dibagi menjadi 70% data training dan 30% data testing yang akan digunakan pada proses pengolahan data. Hasil pengujian menggunakan metode Neural Newtwork Backpropagation mendapatkan hasil terbaik menggunakan Kondisi ke-10 dengan nilai Root Mean Square Error (RMSE) senilai 0.010. Kemudian akan didapatkan hasil perbandingan antara harga Close aktual dengan harga Close prediksi dengan akurasi sebesar 63.06% yang dapat membantu dalam pengambilan keputusan para investor.

scholarly journals Prediksi Permintaan Kargo pada Cargo Service Center Tangerang City Menggunakan Metode Gated Recurrent Unit

Repositor ◽  
2020 ◽  
Vol 2 (8) ◽  
Author(s):  
Rifky Ahmad Saputra
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Recurrent Neural Network ◽  
Mean Square ◽  
Service Center ◽  
Gated Recurrent Unit

Pada saat ini persaingan bisnis dalam bidang layanan kargo khususnya di Indonesia semakin ketat. Terdapat beberapa perusahaan layanan kargo di Indonesia, salah satunya yaitu Cargo Service Center Tangerang City. Untuk mengantisipasi persaingan bisnis tersebut, Cargo Service Center Tangerang City harus dapat menentukan strategi manajemen usaha, baik dalam jangka menengah maupun jangka panjang. Salah satunya hal yang dapat dilakukan yaitu prediksi permintaan kargo. Pada Cargo Service Center Tangerang City terdapat data transaksi kargo mulai dari Januari 2016 hingga Septermber 2019, oleh karena itu dilakukanlah penelitian yaitu mengimplementasikan metode Gated Recurrent Unit untuk melakukan prediksi permintaan kargo. metode Gated Recurrent Unit merupakan model pengembangan dari Recurrent Neural Network yang biasa digunakan untuk melakukan prediksi pada data sekuens. Pengujian model prediksi dalam penelitian ini dilakukan dengan mencari nilai Root Mean Square Error terkecil dari beberapa percobaan. Hasil dari penelitian ini menunjukkan bahwa model cukup baik dalam melakukan prediksi permintaan kargo, namun terdapat beberapa hasil prediksi metode Gated Recurrent Unit yang masih belum maksimal mendekati nilai aktual misalnya pada nilai aktual yang berada di titik puncak.

scholarly journals ANALISA KOMPARASI NEURAL NETWORK BACKPROPAGATION DAN MULTIPLE LINEAR REGRESSION UNTUK PERAMALAN TINGKAT INFLASI

2019 ◽  
Author(s):  
Amrin Amrin
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Root Mean Square Error ◽  
Multiple Linear Regression ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation

Tingkat inflasi tidak dapat dianggap remeh dalam sistem perekonomian suatu negara dan pelaku bisnis pada umumnya. Jika inflasi dapat diramalkan dengan akurasi yang tinggi, tentunya dapat dijadikan dasar pengambilan kebijakan pemerintah dalam mengantisipasi aktivitas ekonomi di masa depan. Pada penelitian ini akan digunakan metode prediksi neural network backpropagation dan multiple linear regression untuk memprediksi tingkat inflasi bulanan di indonesia, selanjutnya membandingkan manakah yang terbaik dari kedua metode tersebut. Data inflasi yang digunakan bersumber dari Badan Pusat Statistik dari tahun 2006-2015, dimana 80% sebagai data training dan 20% sebagai data testing. Dari hasil analisis data yang dilakukan disimpulkan bahwa Performa model multiple linear regression lebih baik dibandingkan dengan metode neural network backpropagation dengan nilai mean absolute deviation (MAD) sebesar 0.0380, mean square error (MSE) sebesar 0.0023, dan nilai Root Mean Square Error (RMSE) sebesar 0.0481

Prediction of Significant Wave Height Using Neural Network in the Java Sea (North of Surabaya)

2017 ◽  
Vol 862 ◽  
pp. 72-77
Author(s):  
Wimala L. Dhanistha ◽  
R.A. Atmoko ◽  
P. Juniarko ◽  
Ridho Akbar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Wave Height ◽  
Mean Square ◽  
The North ◽  
Java Sea ◽  
The City

Indonesia is an archipelago, Surabaya is the second crowded city in Indonesia. So the shipping lane and the city is comparable. Neural network is models inspired by biological neural networks and used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Neural network is used to predict the wave height in Java Sea (The North of Surabaya). The Root Mean Square Error average for the next one hour is 0.03 and the Root Mean Square Error average for the next six hours is 0.09. That’s mean the longest the prediction, the biggest Root Mean Square error.

scholarly journals O OPTIMALISASI NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK MEMPREDIKSI LAMA PENYINARAN MATAHARI DALAM MEMENUHI KEBUTUHAN ENERGI

2019 ◽  
Vol 4 (2) ◽  
pp. 52-62
Author(s):  
Wahyudin Hasyim ◽  
Alter Lasarudin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Particle Swarm ◽  
Mean Square ◽  
Swarm Optimization

Tingginya beban listrik  yang mencapai 325 MegaWatt, hal ini merupakan perhatian penting bagi pemerintah Provinsi Gorontalo dalam  kebutuhan energi listrik, maka perlu memprediksi lama penyinaran matarahari pada suatu daerah, Energi sel surya salah satunya bergantung pada lamanya penyinaran cahaya matahari. Diantaranya dengan melakukan perancangan model prediksi. Metode prediksi yang mimiliki nilai error terkecil adalah Neural Network, akan tetapi masih adanya kelemahan pada waktu pelatihan untuk mencapai konvergen dan overfitting. Maka  perlu dilakukan optimalisasi pada bobot jaringan dengan menggunakan Particle Swarm Optimazition, yang merupakan salah satu metode terbaik dalam optimasi. Dengan penggunaan optimasi yang diukur melalui hasil peroleha Root Mean Square Error (RMSE). Hasil pengujian terhadap algoritma menunjukkan bahwa nilai RMSE mengunakan Neural Network 0,131, sedangkan dengan penerapan optimasi dengan particle swarm optimization  hasil RMSE  0,127. Dengan penerapan metode optimasi terserbut dapat mengurangi nilai error

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