Inverse Kinematic Solution for 6-R Industrial Robot Manipulator Using Convolution Neural Network

An artificial neural network (ANN) using backpropagation algorithm is applied to solve inverse kinematics problems of industrial robot manipulator. 6R robot manipulator with offset wrist was chosen as industrial robot manipulator because geometric feature of this robot does not allow solving inverse kinematics problems analytically. In other words, there is no closed form solution for this problem. In order to define orientation of robot end-effector, three different representations are used here: homogeneous transformation matrix, Euler angles and equivalent angle axis. These representations were compared to obtain inverse kinematics solutions for 6R robot manipulator with offset wrist. Simulation results show that prediction performance from the approximation accuracy point of view is satisfactory with low effective errors based on 10 degrees data resolution

Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.

Nowadays, the number of patients with brain tumors is steadily increasing, diagnosis and isolation of the tumor play an important role in the process of treatment and surgery. Due to the high error of manual segmentation of the tumor, algorithms that perform this operation with less error are of great importance. Convolutional neural networks have made great progress in the field of medical imaging. The use of imaging techniques and pattern recognition in the diagnosis and automatic determination of brain tumors by MRI imaging reduces errors, human error and speeds up detection. The artificial convolutional neural network (CNN) has been widely used in the diagnosis of intelligent cancers and has significantly reduced the error rate. Therefore, in this paper, we present a new method using a combination of convolutional and multi-scale artificial neural network that has significantly increased the accuracy of tumor diagnosis. This study presents a multidisciplinary convolution neural network (MCNN) approach to classifying tumors that can be used as an important part of automated diagnosis systems for accurate cancer diagnosis. Based on the MCNN structure, which presents the MRI image to several deep convolutional neural networks of varying sizes and resolutions, the stage of extracting classical hand-made features is avoided. This approach proposes better classification rates than the classical methods. This study uses a multi-scale convolution technique to achieve a detection accuracy of 95/4%, which shows the efficiency of the proposed method.

Digital Image Processing has become a foundation for developing Intelligent Systems, particularly those used in image classification & recognition. CNN (Convolutional Neural Network) are well known for their ability to extract hierarchical spatial features from images; however, they are very resource intensive. ANN (Artificial Neural Network), on the other hand, have a much simpler structure as well as are generally more resource-efficient than CNN. However, ANN is not nearly as successful in identifying spatial relationships in images. Therefore, this paper uses a balanced comparison between ANN and CNN using 100,000 photographs of faces to evaluate both methods. A confusion matrix was used to measure the performance of each method across 5 different epochs and 3 different data partition scenarios. The results indicate that ANN provided relatively stable performance but were not capable of producing significant improvements in accuracy. On the other hand, CNN produced significantly higher and more consistent performance, albeit more sensitive to the specific research configurations. CNN also provided accuracy values that were approximately 2.74% greater than the values obtained by ANN. In fact, the average accuracy value for CNN was 84.27% while the average accuracy value for ANN was 81.53%. These results further validate the trade-off between accuracy and computational resources, in which CNN are optimal for achieving high levels of precision, and ANN are optimal for achieving acceptable performance within systems with limited resources.

paper, we focus on exploring the relationship between weave patterns and their mechanical properties in woven fiber composites through Machine Learning. Specifically, we explore the interactions between woven architectures and in-plane stiffness properties through Deep Convolutional Neural Network (DCNN) and Generative Adversarial Network (GAN). Our research is important for exploring how woven composite’s pattern is related to its mechanical properties and accelerating woven composite design as well as optimization. We focus on two tasks: (1) Stiffness prediction: Predicting in-plane stiffness properties for given weave patterns. Our DCNN extracts high-level features through several convolutional and fully connected layers to determine the final predictions. (2) Weave pattern prediction: Predicting weave patterns for target stiffness properties, which can be treated as the reverse task of the first one. Due to many-to-one mapping between weave patterns and the composite properties, we utilize a Decoder Neural Network as our baseline model and compare its performance with GAN and Genetic Algorithm. We represent the weave patterns as 2D checkerboard models and use finite element analysis (FEA) to determine in-plane stiffness properties, which serve as input data for our ML framework. We show that: (1) for stiffness prediction, DCNN can predict stiffness values for a given weave pattern with relatively high accuracy (above 93%); (2) for weave pattern prediction, the GAN model gives the best prediction accuracy (above 92%) while Decoder Neural Network has the best time efficiency. HAOTIAN FENG

The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.

Objectives: The paper presents application of convolution neural network and artificial neural network for image classification problem for clothing dataset along with their performance comparison against different optimizers. The major objective of this paper is to perform image classification on fashion-mnist clothing dataset images. Methods: The methods used here are, the traditional ANN and CNN. Here image classification is performed on Fashion-mnist, clothing dataset using CNN and ANN with different optimizers. The performance of the working of ANN and CNN in classifying images from fashion-mnist dataset is compared against different optimizers namely stochastic gradient Descent, Adagrad, RMS prop and Adam optimizer. Findings: The study found that CNN worked better than ANN yielding training accuracy of 95%, 93% and testing accuracy of 91%, 89% when used with Adam and RmsProp respectively. Novelty: The novelty of this work is to present a comparative study of image classification using CNN, ANN using different optimizers, since not many studies or research articles showed the performance comparison of traditional and convolution neural networks in image classification along with different optimizers. Since the real-world scenarios of today require enormous data to be processed, CNN can fit well to diversify applications since they highly reduce the number of parameters to be trained that speeds up the training process. Moreover, to be specific on image classification problems they require the best and most prominent features to be detected and uncovered; this can be achieved using CNN since it has the concept of convolution using filters at its Core. Hence, CNN is highly recommended for such image classification applications than the traditional artificial-neural-networks because of the aforementioned reasons. Keywords: ANN (Artificial NeuralNetworks); CNN (ConvolutionNeural Networks); Optimization; SGD (Stochastic Gradient Descent); RmsProp (Root mean Square propagation); Adam (Adaptive moment estimation); AdaGrad (Adaptive Gradient)

Artificial convolution neural network for medical image pattern recognition

Dementia is a progressive disease that worsens over time as cognitive abilities deteriorate. Effective preventive interventions require early detection. However, there are no reports in the literature concerning apps that have been developed and designed to predict patient dementia classes (DCs). This study aimed to develop an app that could predict DC automatically and accurately for patients responding to the clinical dementia rating (CDR) instrument. A CDR was applied to 366 outpatients in a hospital in Taiwan, with assessments on 25 and 49 items endorsed by patients and family members, respectively. The 2 models of convolutional neural networks (CNN) and artificial neural networks (ANN) were applied to examine the prediction accuracy based on 5 classes (i.e., no cognitive decline, very mild, mild, moderate, and severe) in 4 scenarios, consisting of 74 (items) in total, 25 in patients, 49 in family, and a combination strategy to select the best in the aforementioned scenarios using the forest plot. Using CDR scores in patients and their families on both axes, patients were dispersed on a radar plot. An app was developed to predict patient DC. We found that ANN had higher accuracy rates than CNN with a ratio of 3:1 in the 4 scenarios. The highest accuracy rate (=93.72%) was shown in the combination scenario of ANN. A significant difference was observed between the CNN and ANN in terms of the accuracy rate. An available ANN-based app for predicting DC in patients was successfully developed and demonstrated in this study. On the basis of a combination strategy and a decision rule, a 74-item ANN model with 285 estimated parameters was developed and included. The development of an app that will assist clinicians in predicting DC in clinical settings is required in the near future.

Neural Networks, with their remarkable capacity to get significance from convoluted information can be utilized to remove patterns that are too composite to be in any way seen by humans. A prepared neural network can be thought of as a specialist in the classification of data which is given to analyze. There are different kinds of Neural Networks like Artificial Neural Network (ANN), Feedforward Neural Network, Recurrent Neural Network(RNN), Recursive Recurrent Neural Network (RRNN), Convolutional Neural Network(CNN), Modular Neural Network (MNN), Restricted Boltzmann Machine (RBM) etc. In this paper, we have discussed the performance of ANN, CNN, RNN, and RBM where CNN has outplayed the remaining with accuracy of 97.81%.

With the development of artificial intelligence, artificial neural network (ANN) has been widely used in recent years. In this paper, the method is applied to the prediction of the fluid force exerted on the bluff body when flow passes around. Firstly, back propagation (BP) model and convolutional neural network (CNN) model are introduced; then the mapping relation between the shape of bluff body and the fluid force, which is calculated by computational fluid dynamics (CFD), is established by sample training. Finally, it is used to predict the fluid force of the new shape bluff body. By taking the CFD results as benchmark, CNN model is capable of predicting both the resistance and lift force, while BP model is incompetent to predict lift force. Furthermore, both CNN and BP models have a significant advantage in prediction efficiency, compared by CFD calculation method.

Deep neural networks such as convolutional neural networks (CNNs) have achieved a great success in a broad range of fields. Spiking neural networks (SNNs) are designed as a solution for realizing ultra-low power consumption using spikebased neuromorphic hardware. To enable the mapping between conventional artificial neural networks (ANNs) and spike-based neuromorphic hardware, direct conversion from conventional ANNs into SNNs has recently been proposed. However, performance loss is hard to avoid after conversion. To reduce the loss, we analyze the encoding methods of SNNs and the optimization methods for converting. We use the rate coding and approximate the output of an activation function of CNNs by the number of spikes produced by a neuron within a given time window in SNNs. We propose a method for generating fixed uniform spike train whose number of spikes is as exact as we expect, and present an optimization method to reduce the loss by rescaling the threshold of each layer in SNNs. For evaluation, we train three different CNNs with MNIST and SVHN datasets separately and convert all of them into SNNs. The networks achieve a max accuracy of 99.17% on MNIST and of 93.36% on additional training dataset of SVHN. The results show the proposed fixed and uniform spike train not only outperforms the Poisson distributed spike train but also costs less time to generate. Our threshold rescaling method greatly improves the performance of SNNs.

In the field of big data analytics, deep learning has garnered a lot of interest due to the fact that it is capable of doing feature extraction and classification. The categorization of big data has traditionally been carried out by academics via the use of Machine Learning methods; nonetheless, the process of feature extraction has traditionally been carried out manually. Deep learning is a method developed by researchers that allows for the extraction of features via the use of various algorithms. This research made use of an algorithm known as "Convolutional Neural Network," or CNN for short, in order to get a deeper comprehension of the dependability of big data analytics. Primary research has been conducted in order to get an understanding of how hidden nodes and layers influence the accuracy of this neural network. The "Recurrent Neural Network" (also known as RNN) and the "Artificial Neural Network" (also known as ANN) are the two other neural network algorithms that have been selected for comparison with CNN in order to evaluate whether or not CNN is more effective than the other algorithms. In the first research, the reliability of CNN as well as other methods was analyzed in relation to the number of nodes, the number of hidden layers, the amount of training time, and the amount of validating time. Control variables were employed in both correlation and regression investigations. These variables included Hidden Nodes, Training time, Valid time, and Secret Layers. CNN, ANN, and RNN were significant variables. According to the findings, CNN is more dependable than other neural networks for analyzing huge amounts of data, scoring 92%, whereas other neural networks scored less than 90%. The inclusion of hidden nodes results in a significant increase in CNN's reliability.

Some features of cognitive modeling are presented, including the prerequisites for a cognitive approach to solving complex problems. Cognitive modeling involves the use of various artificial neural networks, including convolutional neural networks. The classification of artificial neural networks according to various characteristics is given. The features of self-organizing neural networks and networks using deep learning methods are considered. The artificial neural network, which is a three-layer unidirectional direct propagation network, the interface of a computer program used to approximate functions using the specified neural network, as well as the solution of the image recognition problem using an artificial convolutional neural network, in which the neural network parameters are adjusted for each recognizable image fragment in order to adaptively filter the image, are considered in detail. The analysis of images in video surveillance systems in order to detect fires allows them to be detected at an early stage and, thus, prevent the fire propogation.

Balancing selection is an important adaptive mechanism underpinning a wide range of phenotypes. Despite its relevance, the detection of recent balancing selection from genomic data is challenging as its signatures are qualitatively similar to those left by ongoing positive selection. In this study, we developed and implemented two deep neural networks and tested their performance to predict loci under recent selection, either due to balancing selection or incomplete sweep, from population genomic data. Specifically, we generated forward-in-time simulations to train and test an artificial neural network (ANN) and a convolutional neural network (CNN). ANN received as input multiple summary statistics calculated on the locus of interest, while CNN was applied directly on the matrix of haplotypes. We found that both architectures have high accuracy to identify loci under recent selection. CNN generally outperformed ANN to distinguish between signals of balancing selection and incomplete sweep and was less affected by incorrect training data. We deployed both trained networks on neutral genomic regions in European populations and demonstrated a lower false-positive rate for CNN than ANN. We finally deployed CNN within the MEFV gene region and identified several common variants predicted to be under incomplete sweep in a European population. Notably, two of these variants are functional changes and could modulate susceptibility to familial Mediterranean fever, possibly as a consequence of past adaptation to pathogens. In conclusion, deep neural networks were able to characterize signals of selection on intermediate frequency variants, an analysis currently inaccessible by commonly used strategies.