Method of Entrepreneurship Program Education for College Students Based on Deep Learning

The Sixth International Symposium on Neural Networks (ISNN 2009)

It is undoubtful that artificial intelligence (AI) is being the trend of computer science and this trend is still ongoing in the far future even though technologies are being developed suddenly fast because computer science does not reach the limitation of approaching biological world yet. Machine learning (ML), which is a branch of AI, is a spearhead but not a key of AI because it sets first bricks to build up an infinitely long bridge from computer to human intelligence, but it is also vulnerable to environmental changes or input errors. There are three typical types of ML such as supervised learning, unsupervised learning, and reinforcement learning (RL) where RL, which is adapt progressively to environmental changes, can alleviate vulnerability of machine learning but only RL is not enough because the resilience of RL is based on iterative adjustment technique, not based on naturally inherent aspects like data mining approaches and moreover, mathematical fundamentals of RL lean forwards swing of stochastic process. Fortunately, artificial neural network, or neural network (NN) in short, can support all three types of ML including supervised learning, unsupervised learning, and RL where the implicitly regressive mechanism with high order through many layers under NN can improve the resilience of ML. Moreover, applications of NN are plentiful and multiform because three ML types are supported by NN; besides, NN training by backpropagation algorithm is simple and effective, especially for sample of data stream. Therefore, this study research is an introduction to NN with easily understandable explanations about mathematical aspects under NN as a beginning of stepping into deep learning which is based on multilayer NN. Deep learning, which is producing amazing results in the world of AI, is undoubtfully being both spearhead and key of ML with expectation that ML improved itself by deep learning will become both spearhead and key of AI, but this expectation is only for ML researchers because there are many AI subdomains are being invented and developed in such a way that we cannot understand exhaustedly. It is more important to recall that NN, which essentially simulates human neuron system, is appropriate to the philosophy of ML that constructs an infinitely long bridge from computer to human intelligence.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

RoseNNa: A performant, portable library for neural network inference with application to computational fluid dynamics

In terms of electronic nose algorithms, data pre-processing and classifier type are the two main factors affecting gas classification results. In the early stage, data pre-processing mostly takes specific information from gas-reaction waveforms as features and uses machine learning algorithms, such as K-Nearest Neighbor(KNN) and Support Vector Machine(SVM), to classify the gas data. In recent years, some research has been done on using deep learning for gas classification. The data pre-processing takes the overall process of the gas reaction as a feature map, and the classifier uses Convolutional Neural Network(CNN) architecture to classify the gases, resulting in classification accuracy being significantly higher than those of traditional machine learning algorithms. In addition, external factors such as wind speed, and distance from the gas source are also important factors affecting gas classification. The objectives of this study are as follows: 1) improving the data pre-processing method and classifier structure in deep learning for gas analysis and 2) using hybrid deep neural networks with Multilayer Perceptron (MLP) for environment compensation to improve the sensor drift problem caused by external factors. This study used one open-source gas dataset, applied three data pre-processing methods and two deep learning architectures (GasNet, SimResNet-9) for gas analysis and comparison, selected the method with the best classification accuracy and used it in Deep Neural Networks with MLP environmental compensation to promote the accuracy of classification further by learning the relationship between external factors and gas data. The proposed SimResNet-10_X_MLP was used for data training and classification in this study, achieving a 95% classification accuracy.

1 - Congruence of deep learning in biomedical engineering: future prospects and challenges

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca2+), magnesium ions (Mg2+), sodium ions (Na+), chloride ions (Cl−), sulfate ( SO 42−), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination (R2) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R2 between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.

The application of artificial neural networks to the estimation of bioprocess variables will be discussed. In fermentation processes, direct on-line measurements of primary process variables usually are unavailable. The state of the cultivation, therefore, has to be inferred from measurements of secondary variables and any previous knowledge of process dynamics. This research investigates the learning, recall and generalization characteristics of neural networks trained to model the nonlinear behavior of a fermentation process. Two different neural network methodologies are discussed, namely, feed-forward and recurrent neural networks, which differ in their treatment of time dependence. The neural networks are trained by backpropagation using a conjugate gradient technique, which provides a dramatic improvement in the convergence speed. The objective is to use environmental and physiological information available from on-line sensors to estimate concentrations of species in a bioreactor. Results of the neural network estimators are presented, based on experimental data available from the ethanol production by Zymomonas mobilis fermentation. The feed-forward and recurrent neural network methodologies are demonstrated to perform suitably as unmeasurable state estimators. Both networks offer comparable abilities of recall, but recurrent networks perform better than feed-forward networks in generalization.

This thesis considers the application of neural networks in cost estimating in project management and whether they lead to more accurate estimates. It strikes two areas of research, namely neural networks and project management; an introductory chapter on both subjects is included. The statistical problem of parametric cost estimating is described and an explanation of the general principles is given. The Multi-Layer Perceptron with the Backpropagation learning algorithm is determined to be the most appropriate network and a selection of available software programs is reviewed. A Multi-Layer Perceptron neural model is used to determine one of the most important cost estimating relationships of the PRICE model. A comparison of the outputs of the neural network and the PRICE model shows that the Backpropagation algorithm is able to find the underlying estimating relationships used by PRJCE. To investigate whether other underlying functions can be found with artificial intelligence methods, other input parameters are selected and the costs generated by the PRICE model and by the neural network are compared with each other. Further experiments were undertaken in order to improve the performance of the neural network. The neural networks were applied to real data. and their output compared with the PRICE model. The processes of achieving better results are analogous to those used for the artificial data. A neural network was created which performs better than the PRICE model in terms of the accuracy of the estimates produced. The results are discussed and the collection of significant and accurate information and then deciding on which type of network is the best network to be used are identified as the major problems in the application of artificial intelligence for cost estimation in project management. The limitations and restrictions of the implementation of neural networks are examined and the scope and topics of further research are suggested.

This chapter is devoted to atmospheric and oceanic satellite remote sensing (RS) NN applications. Two major RS problems, forward problem and inverse problem (or satellite retrievals), are introduced and discussed. Applications of forward models (FM) for solving the forward problem in the process of direct assimilation of satellite measurements and for variational retrievals, as well as applications of retrieval algorithms, solutions of the inverse problem, for assimilation of geophysical parameters in data assimilation systems, are discussed. Correspondingly, two neural network (NN) applications, NN FMs and NN retrieval algorithms, are introduced. An intelligent NN retrieval system, which incorporates an automatic quality control of satellite retrievals, is introduced. Previously developed RS NN applications are reviewed. Theoretical considerations are illustrated with real-life applications of the NN approach to the Special Sensor Microwave Imager (SSM/I). SSM/I NN FM and SSM/I NN retrieval algorithms are introduced, discussed, and compared with FMs and retrieval algorithms developed using other techniques elsewhere. Advantages and limitations of NN FMs and retrieval algorithms are discussed. An example of QuikSCAT wind vector retrievals is used to demonstrate great potential of using the NN technique to go beyond the standard point-wise retrieval paradigm. This chapter contains an extensive list of references giving extended background and further detail to the interested reader on each examined topic. It can serve as a textbook and an introductory reading for students and beginning and advanced investigators interested in learning how to apply the NN emulation technique in different RS applications.KeywordsForward ModelSatellite MeasurementSurface Wind SpeedRetrieval AlgorithmNeural Network AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

This paper investigates the estimation of R-values from transient time series using regression outputs of different neural network architectures, specifically a naive multilayer perceptron (MLP) and a physics-informed neural network (PINN). The PINN incorporates a bias correction for steady-state R-value calculations. The ground truth is derived from transient simulations of wall assemblies. A parameter study examines the prediction accuracy of various MLP architectures within the training process. Further evaluations assess the MLP performances on both validation and independent test datasets. The results indicate that neural networks can effectively enhance R-value estimation. However, in the proposed setup, PINNs did not yield significantly better predictions than conventional MLPs. Future research should explore alternative PINN architectures incorporating more temporal context of the transient input data. Additional studies should also investigate the applicability of more context-aware neural networks, such as convolutional or recurrent neural networks.

This paper explores the use and applications of neural networks in the construction of a text markup program. This research paper describes various tasks that require textual data analysis and discusses the problems, issues, and solutions that accompany them. Interest in neural networks has increased in recent years, and they are find­ing applications in a wide variety of fields, such as business, medicine, engineering, geology, and physics. Neural networks have made great strides in forecasting, planning, and management. There are several reasons for this situation. Neural networks are very powerful modeling systems capable of creating complex dependencies. Neural networks can be widely used in areas such as text/speech recognition, semantic search, decision support/expert systems, inventory forecasting, data storage systems and content analysis. The object of the work is the process of functioning of neural networks and an algorithm for text markup recognition. The purpose of the scientific paper is the application of neural networks in the construction of the program for the markup of the text. In order to achieve the goal, the following tasks were put forward: a) study existing neural networks, b) choose a neural network to cre­ate a model and study its structure, c) convert input data to feed it into a neural network model. Representation and analysis of symbolic structures in neural networks seems to be an interesting and useful direction in neural network theory. In this paper, we have reviewed some neural network architectures that may merit further consideration in these circumstances. In what follows, we will focus on specific experiments in this area. Thus, this paper outlines a problem area for further research and testing.

When operating gas transfer units with gas turbine drive a significant share (about 9%) of natural gas is spent on compressor stations own needs. A considerable expense of pumped gas for gas transfer units own needs at compressor stations determines the actuality of realization of resources saving technologies. When performing energy saving measures it is required to control the resulting effect. Control of energy consumption is based on energy efficiency indicators of gas transfer units which include efficiency and gas fuel rate. The aim of this paper was to develop a universal method of calculating energy efficiency indicators using intelligent methods which exclude risks of reducing accuracy of results. In this paper we proposed and justified method of calculating energy efficiency indicators on the base of monitored parameters of gas transfer units work with application of neural networks. The research made it possible to receive and to substantiate the method of determining the specific consumption of fuel gas by the parameters of HPA controlled standard automation system based on the application of intelligent neural networks. Neural network model can be integrated in the station monitoring system compressor station need only advance to pick up army of weights to train a neural network) for a particular type HPA. When training a neural network according to the variation of the parameters in the training set and 20% teaching error does not exceed 1%. When using neural networks for the calculation of energy efficiency indicators for the unit for which there was training for other units of the same type, the mean square error does not exceed 5%.

Deep learning has transformed various sectors, introducing new applications and opportunities. However, the underlying physical mechanisms or mathematical theories responsible for its success remain fundamental questions. This inquiry explores the connection between deep learning algorithms and established scientific principles with the aim of uncovering the mysteries behind their remarkable capabilities. By bridging the gap between deep learning, neural networks, and scientific knowledge, we can develop robust and interpretable models with enhanced capabilities. This ongoing research involves collaboration across diverse fields to unveil the hidden intricacies of deep learning algorithms and their links to physical phenomena. The ultimate goal is to contribute to the potential of the journal by examining the theory, design and application of neural networks and machine learning, focusing on the effectiveness of neural network paradigms for deep learning and their connections to physical events. By examining the intersection of deep learning, neural networks, and physical phenomena, we aim to advance our understanding and use of neural networks and machine learning in many areas of space, pushing the boundaries of excellence in science and engineering