scholarly journals A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 273
Author(s):  
Ioannis E. Livieris ◽  
Spiros D. Dafnis ◽  
George K. Papadopoulos ◽  
Dionissios P. Kalivas
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Computational Cost ◽  
Economic Benefits ◽  
Mixed Data ◽  
Cotton Production ◽  
Proposed Model ◽  
Multiple Input ◽  
Fully Connected

Cotton constitutes a significant commercial crop and a widely traded commodity around the world. The accurate prediction of its yield quantity could lead to high economic benefits for farmers as well as for the rural national economy. In this research, we propose a multiple-input neural network model for the prediction of cotton’s production. The proposed model utilizes as inputs three different kinds of data (soil data, cultivation management data, and yield management data) which are treated and handled independently. The significant advantages of the selected architecture are that it is able to efficiently exploit mixed data, which usually requires being processed separately, reduces overfitting, and provides more flexibility and adaptivity for low computational cost compared to a classical fully-connected neural network. An empirical study was performed utilizing data from three consecutive years from cotton farms in Central Greece (Thessaly) in which the prediction performance of the proposed model was evaluated against that of traditional neural network-based and state-of-the-art models. The numerical experiments revealed the superiority of the proposed approach.

scholarly journals Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yang Zhou ◽  
Rui Fu ◽  
Chang Wang
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Maximum Entropy ◽  
Network Model ◽  
Neural Network Model ◽  
Percentage Error ◽  
Inverse Reinforcement Learning ◽  
Car Following ◽  
Proposed Model ◽  
Fully Connected

The present study proposes a framework for learning the car-following behavior of drivers based on maximum entropy deep inverse reinforcement learning. The proposed framework enables learning the reward function, which is represented by a fully connected neural network, from driving data, including the speed of the driver’s vehicle, the distance to the leading vehicle, and the relative speed. Data from two field tests with 42 drivers are used. After clustering the participants into aggressive and conservative groups, the car-following data were used to train the proposed model, a fully connected neural network model, and a recurrent neural network model. Adopting the fivefold cross-validation method, the proposed model was proved to have the lowest root mean squared percentage error and modified Hausdorff distance among the different models, exhibiting superior ability for reproducing drivers’ car-following behaviors. Moreover, the proposed model captured the characteristics of different driving styles during car-following scenarios. The learned rewards and strategies were consistent with the demonstrations of the two groups. Inverse reinforcement learning can serve as a new tool to explain and model driving behavior, providing references for the development of human-like autonomous driving models.

KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

2019 ◽  
Vol 24 (3) ◽  
pp. 220-228
Author(s):  
Gusti Alfahmi Anwar ◽  
Desti Riminarsih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Input Layer ◽  
Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

scholarly journals Neural network model for multimodal recognition of human aggression

2020 ◽  
pp. 132-149
Author(s):  
М.Ю. Уздяев
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Space ◽  
Smart Environments ◽  
High Fidelity ◽  
Cyberphysical Systems ◽  
Human Aggression ◽  
Proposed Model ◽  
User Actions

Увеличение количества пользователей социокиберфизических систем, умных пространств, систем интернета вещей актуализирует проблему выявления деструктивных действий пользователей, таких как агрессия. При этом, деструктивные действия пользователей могут быть представлены в различных модальностях: двигательная активность тела, сопутствующее выражение лица, невербальное речевое поведение, вербальное речевое поведение. В статье рассматривается нейросетевая модель многомодального распознавания человеческой агрессии, основанная на построении промежуточного признакового пространства, инвариантного виду обрабатываемой модальности. Предлагаемая модель позволяет распознавать с высокой точностью агрессию в условиях отсутствия или недостатка информации какой-либо модальности. Экспериментальное исследование показало 81:8% верных распознаваний на наборе данных IEMOCAP. Также приводятся результаты экспериментов распознавания агрессии на наборе данных IEMOCAP для 15 различных сочетаний обозначенных выше модальностей. Growing user base of socio-cyberphysical systems, smart environments, IoT (Internet of Things) systems actualizes the problem of revealing of destructive user actions, such as various acts of aggression. Thereby destructive user actions can be represented in different modalities: locomotion, facial expression, associated with it, non-verbal speech behavior, verbal speech behavior. This paper considers a neural network model of multi-modal recognition of human aggression, based on the establishment of an intermediate feature space, invariant to the actual modality, being processed. The proposed model ensures high-fidelity aggression recognition in the cases when data on certain modality are scarce or lacking. Experimental research showed 81.8% correct recognition instances on the IEMOCAP dataset. Also, experimental results are given concerning aggression recognition on the IEMOCAP dataset for 15 different combinations of the modalities, outlined above.

An Efficient Method for Coronavirus Detection Through X-rays using deep Neural Network

2021 ◽  
Vol 17 ◽  
Author(s):  
P. Srinivasa Rao ◽  
Pradeep Bheemavarapu ◽  
P. S. Latha Kalyampudi ◽  
T. V. Madhusudhana Rao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Chronic Respiratory Disease ◽  
Automated System ◽  
X Rays ◽  
Medical Problems ◽  
X Ray ◽  
Chest X Ray ◽  
Fully Connected

Background: Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of infection caused by a coronavirus in the respiratory tract can vary from mild to lethal. A serious illness can be developed in old people and those with underlying medical problems like diabetes, cardiovascular disease, cancer, and chronic respiratory disease. For the diagnosis of the coronavirus disease, due to the growing number of cases, a limited number of test kits for COVID-19 are available in the hospitals. Hence, it is important to implement an automated system as an immediate alternative diagnostic option to pause the spread of COVID-19 in the population. Objective: This paper proposes a deep learning model for classification of coronavirus infected patient detection using chest X-ray radiographs. Methods: A fully connected convolutional neural network model is developed to classify healthy and diseased X-ray radiographs. The proposed neural network model consists of seven convolutional layers with rectified linear unit, softmax (last layer) activation functions and max pooling layers which were trained using the publicly available COVID-19 dataset. Results and Conclusion: For validation of the proposed model, the publicly available chest X-ray radiograph dataset consisting COVID-19 and normal patient’s images were used. Considering the performance of the results that are evaluated based on various evaluation metrics such as precision, recall, MSE, RMSE & accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%.

scholarly journals An Efficient Neural Network for Shape from Focus with Weight Passing Method

2018 ◽  
Vol 8 (9) ◽  
pp. 1648 ◽  
Author(s):  
Hyo-Jong Kim ◽  
Muhammad Mahmood ◽  
Tae-Sun Choi
Keyword(s):  
Neural Network ◽  
Convergence Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Image Sequences ◽  
Depth Maps ◽  
The Neural Network ◽  
Proposed Model ◽  
Real Objects ◽  
Shape From Focus

In this paper, we suggest an efficient neural network model for shape from focus along with weight passing (WP) method. The neural network model is simplified by reducing the input data dimensions and eliminating the redundancies in the conventional model. It helps for decreasing computational complexity without compromising on accuracy. In order to increase the convergence rate and efficiency, WP method is suggested. It selects appropriate initial weights for the first pixel randomly from the neighborhood of the reference depth and it chooses the initial weights for the next pixel by passing the updated weights from the present pixel. WP method not only expedites the convergence rate, but also is effective in avoiding the local minimization problem. Moreover, this proposed method may also be applied to neural networks with diverse configurations for better depth maps. The proposed system is evaluated using image sequences of synthetic and real objects. Experimental results demonstrate that the proposed model is considerably efficient and is able to improve the convergence rate significantly while the accuracy is comparable with the existing systems.

THE RANDOM NEURAL NETWORK MODEL FOR TEXTURE GENERATION

1992 ◽  
Vol 06 (01) ◽  
pp. 131-141 ◽  
Author(s):  
VOLKAN ATALAY ◽  
EROL GELENBE ◽  
NESE YALABIK
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Markov Random Fields ◽  
Random Network ◽  
Computational Cost ◽  
Image Synthesis ◽  
Field Equations ◽  
Random Neural Network ◽  
Texture Generation

The generation of artifical textures is a useful function in image synthesis systems. The purpose of this paper is to describe the use of the random neural network (RN) model developed by Gelenbe to generate various textures having different characteristics. An eight parameter model, based on a choice of the local interaction parameters between neighbouring neurons in the plane, is proposed. Numerical iterations of the field equations of the neural network model, starting with a randomly generated gray-level image, are shown to produce textures having different desirable features such as granularity, inclination, and randomness. The experimental evaluation shows that the random network provides good results, at a computational cost less than that of other approaches such as Markov random fields. Various examples of textures generated by our method are presented.

Hierarchical Function Approximation with a Neural Network Model

2012 ◽  
pp. 466-479
Author(s):  
Luis F. de Mingo ◽  
Nuria Gómez ◽  
Fernando Arroyo ◽  
Juan Castellanos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Polynomial Equation ◽  
Neural System ◽  
Input Pattern ◽  
Activation Functions ◽  
Proposed Model ◽  
Generate Polynomial

This article presents a neural network model that permits to build a conceptual hierarchy to approximate functions over a given interval. Bio-inspired axo-axonic connections are used. In these connections the signal weight between two neurons is computed by the output of other neuron. Such arquitecture can generate polynomial expressions with lineal activation functions. This network can approximate any pattern set with a polynomial equation. This neural system classifies an input pattern as an element belonging to a category that the system has, until an exhaustive classification is obtained. The proposed model is not a hierarchy of neural networks, it establishes relationships among all the different neural networks in order to propagate the activation. Each neural network is in charge of the input pattern recognition to any prototyped category, and also in charge of transmitting the activation to other neural networks to be able to continue with the approximation.

A Homotopic Algorithm of NN and its Application for the End-Point Prediction Model of BOF Process

2014 ◽  
Vol 513-517 ◽  
pp. 431-434
Author(s):  
Ming Xia Feng ◽  
Ren Chen ◽  
Qiang Li
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Homotopy Theory ◽  
Smelting Process ◽  
Original Result ◽  
New Model ◽  
End Point ◽  
Proposed Model ◽  
Gradient Descent Algorithm

A Homotopic BI neural network model is developed by combining the homotopy theory and the BI neural network model, to improve the defects of the steepest gradient descent algorithm itself, such as low speed converging and liable to be trapped in local minimum. The end-point carbon content and temperature of molten steel in BOF smelting process is predicted by the proposed model and the original. Result shows that the precision of new model is improved significantly. The hit rates are increased by about 5% and 10%, and the forecasting residuals have decreased 16.31% and 8.67% than the conventional ones, respectively. Also, the calculation time of the new model is 10% shorter than BI model.

scholarly journals Filtering Reordering Table Using a Novel Recursive Autoencoder Model for Statistical Machine Translation

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Jinying Kong ◽  
Yating Yang ◽  
Lei Wang ◽  
Xi Zhou ◽  
Tonghai Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Machine Translation ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Statistical Machine Translation ◽  
Learning Problem ◽  
Proposed Model ◽  
Discriminative Model

In phrase-based machine translation (PBMT) systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (+0.28) and Uyghur-Chinese (+0.33) MT.

scholarly journals PROPOSING A NEURAL NETWORK MODEL TO PREDICT TIME AND COST CLAIMS IN CONSTRUCTION PROJECTS

2016 ◽  
Vol 22 (7) ◽  
pp. 967-978 ◽  
Author(s):  
Vahidreza YOUSEFI ◽  
Siamak HAJI YAKHCHALI ◽  
Mostafa KHANZADI ◽  
Ehsan MEHRABANFAR ◽  
Jonas ŠAPARAUSKAS
Keyword(s):  
Neural Network ◽  
Case Studies ◽  
Construction Industry ◽  
Network Model ◽  
Neural Network Model ◽  
Construction Management ◽  
Construction Projects ◽  
New Model ◽  
Proposed Model ◽  
The Cost

Despite broad improvements in construction management, claims still are an inseparable part of many con-struction projects. Due to huge cases of claim in construction industry, this study argues that claim management is a significant factor in construction projects success. In this study, the most possible causes of these emerging claims are identified and statistically ranked by Probability-Impact Matrix. Subsequently, by classifying claims in different cases, the most important ones are ranked in order to achieve a better understanding of claim management in each project. In this regard, a new index is defined, being able to be applied in a variety of projects with different time and cost values, to calculate the amount of possible claims in each project along with related ratios with respect to the cost and time of each claim. This study introduces a new model to predict the frequency of claims in construction projects. By using the proposed model, the rate of possible claims in each project can be obtained. This model is validated by applying it into fitting case studies in Iran construction industry.

Sign in / Sign up

Export Citation Format

Share Document