Robust recognition technique for handwritten Kannada character recognition using capsule networks

<p>Automated reading of handwritten Kannada documents is highly challenging due to the presence of vowels, consonants and its modifiers. The variable nature of handwriting styles aggravates the complexity of machine based reading of handwritten vowels and consonants. In this paper, our investigation is inclined towards design of a deep convolution network with capsule and routing layers to efficiently recognize Kannada handwritten characters. Capsule network architecture is built of an input layer, two convolution layers, primary capsule, routing capsule layers followed by tri-level dense convolution layer and an output layer. For experimentation, datasets are collected from more than 100 users for creation of training data samples of about 7769 comprising of 49 classes. Test samples of all the 49 classes are again collected separately from 3 to 5 users creating a total of 245 samples for novel patterns. It is inferred from performance evaluation; a loss of 0.66% is obtained in the classification process and for 43 classes precision of 100% is achieved with an accuracy of 99%. An average accuracy of 95% is achieved for all remaining 6 classes with an average precision of 89%.</p>

Artificial Neural Network (ANN) Analysis of Co-pyrolysis of Waste Coconut Husk and Laminated Plastic Packaging

Co-pyrolysis of plastic with biomass was used in the possible mitigation of environmental health problems associated with plastic waste. The pyrolysis method possessed the highest solution in the reduction of waste problems. Fuel oil can be produced through the pyrolysis of plastic and biomass waste. Many researchers used pyrolysis technology to produce a suitable amount of pyrolytic oil through different optimization techniques. This study will predict the percentage mass oil yield using an artificial neural network. It uses an input layer, hidden layer and an output layer. Three input factors for the input layer were (i) temperature, (ii) particle size, and (iii) percentage coconut husk. The structure has one hidden layer with two neurons. The artificial neural network was designed to predict the percentage oil yield after 15 pyrolysis runs set by the Box-Behnken design of the experiment. Percentage oil yields after pyrolysis were calculated. Results showed that temperature and percentage of coconut husk significantly influenced the percentage oil yield. Predicted values from simulation in the artificial neural network showed a good agreement through a correlation coefficient of 99.5%. The actual percentage oil yield overlaps the predicted values, which ANN demonstrates as a viable solution.

Using artificial neural networks to determine the prospects of using hybrid tree clones for plantation reforestation

Оценка перспективности использования клонов гибридных пород древесины является одной из актуальных задач для повышения эффективности плантационного лесовыращивания. Одним из перспективных путей решения данной задачи является применение искусственных нейронных сетей (ИНС). Настоящая научная работа является одной из немногих, где применяется ИНС для решения подобных задач в лесном хозяйстве. Для обучения нейронных сетей и определения перспективности использования клонов гибридных пород древесины для плантационного лесовыращивания были взяты биометрические данные клонов гибридной осины 2018 г. В ходе выполнения работы были построены две ИНС, где архитектура первой сети включает входной слой из 3 нейронов, 1 скрытый слой с 6 нейронами и выходной слой из 1 нейрона; архитектура второй сети включает в себя входной слой из 3 нейронов, 2 скрытых слоя по 6 нейронов и выходной слой из 1 нейрона, в которые были загружены нормализованные исходные биометрические данные для обучения определения перспективности использования клонов гибридных пород древесины для плантационного лесовыращивания. По результатам данного исследования была составлена сравнительная характеристика точности ИНС 1 и ИНС 2, которая показала, что ИНС 1 более точная, так как её отклонение на 3,49% меньше ИНС 2. Результаты настоящей работы подтвердили перспективность применения ИНС для оценки использования клонов гибридных пород древесины для плантационного лесовыращивания. По оценке расчётной перспективности ИНС 1 для плантационного лесовыращивания были выявлены клоны гибридных пород древесины VTI, ESCH3, ESCH5. Внедрение ИНС в отрасль лесного хозяйства упрощает оценку результатов биометрических показателей древесины, особенно для начинающих специалистов, что обеспечивает последующую точную оценку перспективности пород древесины. Assessing the prospects of using hybrid wood clones is one of the urgent tasks to improve the efficiency of plantation silviculture. One of the promising ways to solve this problem is the use of artificial neural networks (ANN). This research work is one of the few where ANN are used to solve such problems in forestry. Biometric data from 2018 hybrid aspen clones were taken to train neural networks and determine the potential use of hybrid wood clones for plantation silviculture. During this work, two ANNs were constructed where the architecture of the first network includes an input layer of 3 neurons, 1 hidden layer with 6 neurons and an output layer of 1 neuron, the architecture of the second network includes an input layer of 3 neurons, 2 hidden layers of 6 neurons and an output layer of 1 neuron, into which the normalized input biometric data were loaded for learning to determine the prospective use of hybrid wood species clones for plantation silviculture. Based on the results of this study, a comparison of the accuracy of ANN 1 and ANN 2 was made, which showed that ANN 1 was more accurate because its bias was 3,49% less than ANN 2. The results of this work confirmed the promise of using ANN to evaluate the use of hybrid wood clones for plantation reforestation. According to the evaluation of the calculated promisingness of ANN 1 for plantation silviculture, VTI, ESCH3 and ESCH5 hybrid wood clones were identified. The introduction of ANN in the forestry industry simplifies the evaluation of wood biometric results, especially for beginners, which provides a subsequent accurate assessment of the perspective of wood species.

A Remote Sensing Image Segmentation Method Based on Fusion Mechanism

A U-Net convolutional network structure is fully capable of completing the end-to-end training with extremely little data, and can achieve better results. When the convolutional network has a short link between a near input layer and a near output layer, it can implement training in a deeper, more accurate and effective way. This paper mainly proposes a high-resolution remote sensing image change detection algorithm based on dense convolutional channel attention mechanism. The detection algorithm uses U-Net network module as the basic network to extract features, combines Dense-Net dense module to enhance U-Net, and introduces dense convolution channel attention mechanism into the basic convolution unit to highlight important features, thus completing semantic segmentation of dense convolutional remote sensing images. Simulation results have verified the effectiveness and robustness of this study.

Research on Mnist Handwritten Numbers Recognition based on CNN

In view of the increasing demand for handwritten digit recognition, a handwritten digit recognition model based on convolutional neural network is proposed. The model includes 1 input layer and 2 convolutional layers (5*5 convolution Core), 2 pooling layers (2*2 pooling core), 1 fully connected layer, 1 output layer, and use the mnist data set for model training and prediction. After a lot of training and participation, the accuracy rate of the training set was finally reached to 100%, and the accuracy rate of 99.25% was also achieved on the test set, which can meet the requirements of recognizing handwritten digits.

Selection of features system and network parameters for hyperspectral images classification using convolutional neural networks

A classification system for hyperspectral images using convolutional neural networks is described. A specific network was selected and analyzed. The network parameters, ensured the maximum classification accuracy: dimension of the input layer, number of the layers, size of the fragments into which the classified image is divided, number of learning epochs, are experimentally determined. High percentages of correct classification were obtained with a large-format hyperspectral image, and some of the classes into which the image is divided are very close to each other and, accordingly, are difficult to distinguish by hyperspectra.

Chaos-generalized regression neural network prediction model of mine water inflow

Artificial neural network (ANN) provides a new way for mine water inflow prediction. However, the effectiveness of prediction using ANN model would not be guaranteed if the influencing factors of water inflow are difficult to quantify or there are only a few observation data. Chaos theory can recover the rich dynamic information hidden in time series. By reconstructing water inflow time series in phase space, the multi-dimensional matrix could be obtained, with each column representing an influencing factor of water inflow and its value representing the change of the influencing factor with time. Therefore, a new prediction model of mine water inflow can be established by combining ANN with chaos theory when lacking data on the influencing factors of water inflow. In the present study, the No. 12 coal mine of Pingdingshan China was selected as the study site. The Chaos-GRNN model and Chaos- BPNN model of mine, water inflow were established by using the water inflow data from February 1976 to December 2013. The model was verified by using the water inflow values in the 24 months from 2014 to 2015. The number embedded dimension (M) of influencing factors of water inflow determined by phase space reconstruction was 7, meaning that there were 7 influencing factors of water inflow and 7 neurons in GRNN input layer, and the time delay was 13 months. The value of GRNN input layer neurons was determined accordingly. The maximum Lyapunov index was 0.0530, and the prediction time of GRNN was 19 months. The two models were evaluated by using four evaluation indices (R, RMSE, MAPE, NSE) and violin plot. It was found that both models can realize the long-term prediction of water inflow, and the prediction effectiveness of Chaos-GRNN model is better than that of Chaos-BPNN model.

A Matching Degree Management Model of Human Body Shape and Fashion Design Based on Big Data Analysis

The existing clothing design model lacks the screening link of the human body part index, and the output clothing data are affected by the high correlation coefficient, resulting in large matching errors. Therefore, based on the analysis of human body shape, a management model of matching degree of human body shape and clothing design based on big data is constructed. After processing with big data methods, human body characteristic data used signals as the input layer of a neural network model and the matching degree management model of human body shape and fashion design. The simulation results show that the built-up model has a matching error of less than 5%, which can effectively improve the matching of human body shape and clothing design.

Design and formulation of microbially induced self-healing concrete for building structure strength enhancement

Self-healing concrete is described as the capability of material to repair their cracks independently. Cracks in concrete are well-known circumstance because of their short tensile strength. Many researchers carried out their research on self-healing concrete using different classification and clustering methods. But the temperature variation and pH variation were not minimized. In order to address these problems, a Multivariate Logistic Regressed Chi-Square Deep Recurrent Neural Network based Self-Healing (MLRCSDRNN-SH) Method is introduced. The main aim of MLRCSDRNN-SH method is to improve building structures strength through inducing the micro-bacteria in concrete. Multiple Logistic Regressed Chi-Square Deep Recurrent Neural Network (MLRCSDRNN) is used to revise bacteria’s stress-strain behaviour towards enhanced material strength in the MLRCSDRNN-SH approach. Initially, the bacteria selection is carried out in alkaline environment like Bacillus subtilis, E. coli and Pseudomonas sps. The data sample is given to the input layer. The input layer transmits sample to the hidden layer 1. The regression analysis is carried out between the multiple independent variables (i.e., parameters) using multivariate logistic function for improving the building structure strength. The regressed value is transmitted to the hidden layer 2. The pearson chi-squared independence hypothesis is performed to identify the probability of crack self-healing property for increasing the building structure strength. When probability value is higher, then the building structure strength is high. Otherwise, the output of second hidden layer is feedback to the input of hidden layer 1. The mixture with higher strength of building structure is sent to the output layer. Several specimens have different sizes used by various researchers for bacterial material study in comparison with the concrete. Depending on experimental results, compressive strength restoration proved higher self-healing ability of the concrete.

PATTERN RECOGNITION AKSARA LAMPUNG MENGGUNAKAN ALGORITMA NEURAL NETWORK

Indonesia merupakan Negara yang terdiri dari berbagai macam suku dan budaya, Indonesia juga memiliki berbagai macam bahasa daerah, salah satunya merupakan bahasa Lampung. Bahasa Lampung merupakan bahasa asli suku lampung, didalam bahasa lampung terdapat aksara yaitu aksara lampung. Aksara lampung memiliki 20 kepala bahasa dan 12 tanda baca. Pada penelitian ini dilakukan analisa pengenalan tulisan berdasarkan perubahan iterasi dengan menggunakan metode neural network. Neural network merupakan jaringan saraf yang terdiri dari unit dasar yang seperti analog dengan neuron, neural network dibagi berdasarkan 3 layer yaitu input layer, hidden layer dan output layer. Dimana setiap node pada masing-masing layer memiliki suatu error rate, yang akan digunakan untuk proses training. Pada penelitian ini akan menggunakan bahasa pemograman python. Percobaan untuk induk surat akan menggunakan 20 huruf aksara lampung dengan masing-masing huruf terdapat 10 pengujian citra, dan percobaan untuk anak surat akan menggunakan 12 huruf anak aksara lampung dengan masing-masing huruf terdapat 10 pengujian citra, sehingga total keseluruhan dataset mejadi 320 citra. Hasil yang diperoleh dari proses pemeriksaan masing-masing adalah 75%, untuk induk surat dengan sebaran 135 citra terdeteksi benar dan 45 citra tidak terdeteksi dengan benar. Untuk anak surat 81 citra terdeteksi dengan benar dan 27 citra tidak terdeteksi dengan benar.