Artficial Neural Networks

There has been a growing interest in applying neural networks and technical analysis indicators for predicting future stock behavior. However, previous studies have not practically evaluated the predictive power of technical indicators by employing neural networks as a decision maker to uncover the underlying nonlinear pattern of these indicators. The objective of this paper is to investigate if using these indicators as the input variables to a neural network will provide more accurate stock trend predictions, and whether they will yield higher trading pro¢ts than the traditional technical indicators. Three neural networks are examined in the study to predict the short-term trend signals of three stocks across different market industries. The overall results indicate that the proportion of correct predictions and the pro¢tability of stock trading guided by these neural networks are higher than those guided by their benchmarks.

Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.

Industrial control systems are nowadays exposed in environments with rapid and unstable parameter changes and uses measuring equipments with critical output sensitivity. In the case of thermal gas analyzer, measurement errors are contributed by temperature, gas flow, and pressure. Error compensation is a key problem for these control systems. In recent years, it has been proven in the literature that artificial neural network (ANN) is a reliable and low cost solution to manage errors. Among all the algorithms of ANN, the back propagation is commonly used because of its simplicity and learning methodology is easy to realize. However, it has two notable drawbacks: (a) it is likely to run into local minimum, and (b) convergence is slow. Thermal conductivity gas analyzer often works in adverse surroundings, which requires fast and accurate measurements. Therefore, a strong learning network is needed. This paper proposes a novel thermal gas analyzer using adaptive neuro-fuzzy inference system. The effectiveness and validity of the proposed method is verified by simulation studies using MATLAB. Fuzzy membership rules are created to allow regulation of learning parameters. Further, the fuzzy adaptive network model is constructed to train large data samples while the high precision compensation of sensor error is realized by the improved flow. Simulation results reveal that the convergence speed and output accuracy is improved and the learning parameters in thermal gas analyzer are automatically corrected by the proposed method in comparison with the back propagation algorithm of artificial neural network.

Speech recognition techniques are one of the most important modern technologies. Many different systems have been developed in terms of methods used in the extraction of features and methods of classification. Voice recognition includes two areas: speech recognition and speaker recognition, where the research is confined to the field of speech recognition.

This paper presents the application of 'Winner Takes All' (WTA) and Ant Colony Optimization (ACO) principles in Wang"s Recurrent Neural Network to solve the Traveling Salesman Problem. Each competing neuron is updates with a part of the value of decision variable using WTA principle. The choice of each city of the route, represented by the Neural Network neurons, is made using the ACO transition rule. The pheromones used in this new technique are the values of the decision variables of the model. The traditional WTA method uses the best value of decision matrix for each iteration. The results are compared with traditional WTA principle and others heuristics with instances of the TSPLIB (Traveling Salesman Problem Library) and 3 instances of spatial missions of debris removal. The presented results show that this new hybrid method assure equal or better results in most of the problems tested compared with traditional WTA.

In the world, energy demand continues to grow incessantly. At the same time, there is a growing need to reduce CO 2 emissions, greenhouse effects and pollution in our cities. A viable solution consists in producing energy by exploiting renewable sources, such as solar energy. However, for the efficient use of this energy, accurate estimation methods are needed. Indeed, applications like Demand/Response require prediction tools to estimate the generation profiles of renewable energy sources. This paper presents an innovative methodology for short-term (e.g. 15 minutes) forecasting of Global Horizontal Solar Irradiance (GHI). The proposed methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of solar radiation samples collected for four years by a real weather station. Then GHI forecast, the output of the neural network, is given as input to our Photovoltaic simulator to predict energy production in short-term time periods. Finally, experimental results for both GHI forecast and Photovoltaic energy prediction are presented and discussed.

This program can be used in any pharmaceutical shops having a database to main-
tain. The software used can generate reports, as per the user's requirements. The
software can print invoices, bills, receipts, etc. It can also maintain the record of
supplies sent in by the supplier The main aim of the project is the management of
the database of the pharmaceutical shop. This is done by creating a database of
the available medicines in the shop. The database is then connected to the main
program by using the interconnection of the Visual Basic program and the database
already created. It's so Costly and time being to gather information About records about Online Shop about medicine invoice or any other information.

Digital communication technologies have greatly influenced and expanded the way humans interact. The progress of information technology has opened wider opportunities for communication. Social networks have become the modern-day social communities connecting people from different parts of the globe, sharing images and videos on these platforms. By creating virtual communities, digital communication has expanded the scope of communication eliminating barriers. We aim to make further progress in this arena by describing an image in the form of audio to visually impaired people. A certain section of differently abled people is unfortunately isolated from this world. Inorder to combat this issue we have come up with a system that describes an image shown in the form of plain text using an encoder-decoder architecture and is integrated with an end-to-end lexical articulator which produces a vocal description of the given image.

Behavioural Science is the study of human behaviour in different contexts, situation and time. Investigating about past human behaviour can help us calculate human behaviour in the future. In this paper we are analysing the public opinion of a product, as available on social media sites specifically Twitter. Our end goal is to visually represent the vital business insights that cannot be gathered from a plain dataset that can assist in developing further intelligent solutions. Sentence sentiment classification is a predictive modelling task achieved through supervised learning. Here, the extracted sentence is segregated into two target variables i.e. positive and negative stances using Natural Language Processing (NLP), through the utilization of neural networks. Categorization of the public sentiment will help in market testing, public anticipation of the product and public sentiment analysis. Market testing involves assessing the risks involved, gathering the bias people cradle and determining our prospective customers. Once the product is launched it is essential to understand how people judge the product. This will provide a platform for developers to improve in the future. As text is a sequence of information and not simply a discrete representation we need an iterative process to train the model, thus the application of RNN. For training the model, we mined our own dataset from twitter API so that the model got accustomed to the natural trend of writing, as in tweets. Using specific keywords, we gathered all posts and tweets related to our product. After data collection and cleaning, the polarity of sentences is found. A special kind of neural network called as the convolution LSTM-RNN is used to train the machine. Live social media data is given as an input to the model. Data is processed and distributed in its respective class. This result is stored in MSSQL server to which the Tableau's dashboard is connected. Using Tableau, we perform visual analytics on the collected data, where we can classify tweets geographically to understand location wise reaction. Having gathered this data a company might reach out to dissatisfied customers with solution to their predicament, thereby improving customer relation. Not only can we gather the notion people hold about a product but also about competing products.

In recent years, we have witnessed the rapid development of deep neural networks and distributed representations in natural language processing. However, the applications of neural networks in resume parsing lack systematic investigation. In this study, we proposed an end-to-end pipeline for resume parsing based on neural networks-based classifiers and distributed embeddings. This pipeline leverages the position-wise line information and integrated meanings of each text block. The coordinated line classification by both line type classifier and line label classifier effectively segment a resume into predefined text blocks. Our proposed pipeline joints the text block segmentation with the identification of resume facts in which various sequence labelling classifiers perform named entity recognition within labelled text blocks. Comparative evaluation of four sequence labelling classifiers confirmed BLSTM-CNNs-CRF's superiority in named entity recognition task. Further comparison among three publicized resume parsers also determined the effectiveness of our text block classification method.

Many teachers and students in Nigerian institutions of learning find teaching and learning a herculean task and very boring because of the obsolete methods of teaching and learning in this sector of the Nigerian economy. New innovative and IT-driven approach will be used in this research to help improve the quality of teaching and learning in Nigerian schools. This research will try to find a solution to this problem, where both students and teachers will enjoy teaching and learning respectively in an environment where effective teaching and learning is the target objective.

Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of machine learning (ML) IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems.

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

DNNs (Deep Neural Networks) are widely employed in advanced applications including image and audio processing. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DNNs that have been popular for industrial applications in recent years. RNNs are well-suited to time variation problems due to their recursive structure. RNNs are ideally suited for temporal variation concerns due to their recursive structure, whereas CNNs are often employed in computer vision applications such as object recognition. Despite the fact that CNNs and RNNs are both DNNs, their implementation differs significantly. Recurrent Neural Networks (RNN) can be used to solve the sequence to sequence problem when both the input and output have sequential structures. There are always some unseen links between the structures. The traditional RNN technique, on the other hand, has trouble examining the links between the sequences appropriately. This survey introduces some attentionbased RNN models, applications, types, and DNN algorithms that can focus on different features of the input for each output item in order to investigate and exploit the implicit links between the input and output items.

Wireless home automation systems have drawn considerable attentions of the researchers for more than a decade. The major technologies used to implement these systems include Z-Wave, Insteon, Wavenis, Bluetooth, WiFi, and ZigBee. Among these technologies the ZigBee based systems have become very popular because of its low cost and low power consumption. In this paper ZigBee based wireless home automation systems have been addressed. There are two main parts of this paper. In the first part a brief introduction of the ZigBee technology has been presented and in the second part a survey work on the ZigBee based wireless home automation system has been presented. The performances of the ZigBee based systems have also been compared with those of other competing technologies based systems. In addition some future opportunities and challenges of the ZigBee based systems have been listed in this paper.

This paper considers the problem of oscillations in a synchronous generator connected to infinite bus through transmission lines. Two on-line control techniques, namely, artificial neural networks (ANN) and simulated annealing (SA) are utilized to cancel the oscillations in synchronous generators (SG). Simulation results of applying external disturbances to the synchronous generator controlled by the proposed simulated annealing controllers are compared to results obtained by using neural network controllers. These control schemes contribute to preventing system instability by suppressing the low-frequency oscillations arising from power grid fault disturbances. The proposed on-line SA and NN integrate a voltage regulator and a power system stabilizer to obtain near-optimal solutions of the problem through utilizing functions evaluation. They can be adopted to replace the conventional automatic voltage regulator (AVR) with power system stabilizer (PSS) of the generator. Simulation results show that algorithms can efficiently and effectively solve such optimization problems within short time. In addition, they are presented to demonstrate the effectiveness and advantage of the control system of synchronous generator (SG) in comparison with the conventional control scheme so as to allow the generator to operate closely to its steady state stability limits.

Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.

Most studies focused on the introduction of new technologies have not investigated the psychological factors affecting the willingness to use them or conducted empirical studies to explore whether willingness and actual construction safety knowledge-sharing behavior are associated with fewer construction incidents. We conducted face-to-face and LinkedIn open-ended interviews as well as a global survey to study the willingness and actual behavior to share construction knowledge via social software Web 2.0, Internet of Things (IoT) and mobile apps. Then, the Partial Least Square-Structural Equation Model (PLS-SEM) for willingness and actual knowledge-sharing behavior, as well as the Multilayer Perceptron (MLP) Neural Network were used to illustrate the effect of various factors on predicting the willingness to share knowledge via Web 2.0, mobile apps and IoT. Results of the interviews found that practitioners use IoT for knowledge sharing, mainly because they do not want to fall behind the curve. PLS-SEM and MLP revealed that practitioners share construction safety knowledge are not driven by safety-related reasons such as safety awareness enhancement but perceived organization support from their companies. Employees who agree that their organization cared about their employees' well-being was the strongest predictor in influencing people's decision to use tools for knowledge sharing. Moreover, many respondents claimed that factors such as monetary rewards have little impact on motivating people to use tools for knowledge sharing.

The quality control of the agricultural products, which in many cases is through intuitive observation of the visible features of the product, plays a key role in the survival of the agricultural industry. From a long times ago, the qualitative categorization of these products have been performed by trained people who search for products for the specific characteristics. On the other hand, hard and repetitive working in the humankind workers can make them some mistakes in computing quality control errors. Became Hence, nowadays, by entering the machine vision systems into this subject, they turned into a reliable, low-cost and real-time technology. Despite the existence of machine vision systems in this process, there are still major challenges in categorizing agricultural products in terms of quality, size, shape, and examination of defects. Potato is one of the most important agricultural products that is produced and has a high application. Unfortunately, it suffers from various types of diseases and defects. Hence, its quality control has a particular importance. In general, image quality control algorithms for agricultural products such as citrus and potatoes are comprised of two parts of product visualization and defect detection. Machine vision is one of the newest methods in automated potato sorting; this technique consists of two stages including separating the image of potato from the background and then examining the presence of a defect in the chopped potato image. In this chapter, different image segmentation methods have been studied and analyzed which are applied to the potato images as a first step technique for quality inspection of this kind of vegetables.

Stock market price index prediction is a challenging task for investors and scholars. Artificial neural networks have been widely employed to predict financial stock market levels thanks to their ability to model nonlinear functions. The accuracy of backpropagation neural networks trained with different heuristic and numerical algorithms is measured for comparison purpose. It is found that numerical algorithm outperform heuristic techniques.

— Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as aneural network. Use neural network in forecasts time series can be agood solution, but the problem is network architecture and the training method in the right direction. General Regression Neural Network (GRNN) is one of the network model radial basis that used to approach a function. GRNN including model neural network model with a solution that quickly, because it is not needed each iteration in the estimation weight. This model has a network architecture that wasa number of units in pattern layer in accordance with the number of input data. One of the application GRNN is to predict the crude oil by using a model GRNN.From the training and testing on the data obtained by the RMSE testing 1.9355 and RMSE training 1.1048.Model is good to be used to give aprediction that is quite accurate information that is shown by the close target with the output

The field of automatic music composition has seen great progress in the last few years, much of which can be attributed to advances in deep neural networks. There are numerous studies that present different strategies for generating sheet music from scratch. The inclusion of high-level musical characteristics (e.g., perceived emotional qualities), however, as conditions for controlling the generation output remains a challenge. In this paper, we present a novel approach for calculating the valence (the positivity or negativity of the perceived emotion) of a chord progression within a lead sheet, using pre-defined mood tags proposed by music experts. Based on this approach, we propose a novel strategy for conditional lead sheet generation that allows us to steer the music generation in terms of valence, phrasing, and time signature. Our approach is similar to a Neural Machine Translation (NMT) problem, as we include high-level conditions in the encoder part of the sequence-to-sequence architectures used (i.e., long-short term memory networks, and a Transformer network). We conducted experiments to thoroughly analyze these two architectures. The results show that the proposed strategy is able to generate lead sheets in a controllable manner, resulting in distributions of musical attributes similar to those of the training dataset. We also verified through a subjective listening test that our approach is effective in controlling the valence of a generated chord progression.

Beton yüksek sıcaklık etkisinde kaldığında önemli ölçüde
hasara uğrar. Bu durum istenilmeyen yapısal kusurlara neden
olabilir. Polipropilen liflerin ilavesi bu hasarın azaltılmasında
kullanılan yöntemlerden biridir. Bu çalışmada lif katkısız, 0.9,
1.35 ve 1.8 kg/m3
polipropilen lif katkılı beton numuneler
üretilmiş, numuneler laboratuar ortamında olgunlaştırılmış, 28.
günün sonunda tüm numuneler 20, 400, 600 ve 800 ºC sıcaklık
etkisinde bırakılmıştır. Yüksek sıcaklık etkisinde kalan
numunelerin basınç dayanımları test edilmiştir. Deneysel
olarak bulunan test sonuçlarının yapay sinir ağları (YSA)
kullanılarak bulunması amaçlanmıştır. YSA yaklaşımı ile
deneysel olarak elde edilmiş veriler karşılaştırıldığında
değerlerin birbirine en çok % 3.5 en az % 0.0 hata ile yakın
olduğu görülmüştür.

The role of Nitrate as one of the qualitative parameters of groundwater and its sensitivity on social health requires accurate periodic measurement .The goal of this article is to introduce an efficient, precise and inexpensive method in comparison with the regression methods. This method is based on Artificial Intelligence. By extracting and using the quantitative and qualitative information of groundwater wells, we can estimate the values of nitrate. The groundwater qualitative data of Birjand plain were gathered from 35 wells and aqueducts twice a year (every 6 months) from 2008 until 2010. As a result the artificial neural network was optimized by genetic algorithm and can predict data with the correlation coefficient of 0.83 and gives values between lab results and actual results which shows reliability of this method for predicting nitrate values in Birjand plain. By drawing the distribution plot we can see the alteration of nitrate values in plain of Birjand during the study and get that the western parts are more sensitive to nitrate than other parts of plain.

This is a user’s guide for the Spice-SOM - a Self-Organizing Map (SOM) application. It doesnot intend to introduce about SOM theories. You can find more knowledge about SOM and Neural Network (NN) in other textbooks.Depending on the version, some contents of this material may be different with your downloaded Spice-SOM.The purpose of this program is to get you started quickly with Neural Network without havingto go through lengthy theory of the Neural Network background. Once you understand these programs you will be able to consult the Neural Network materials on a need basis.Spice-SOM's arm is to introduce NN and SOM to graduated students studying ComputationalIntelligence. Currently Spice-SOM has been using by many students around the world. Spice-SOM has interfaces in Vietnamese, English and Japanese.Spice-SOM was written by CAO THANG when he did researches in the Soft IntelligenceLaboratory, Ritsumeikan University, Japan, 2003-2007.Spice-SOM and Spice-Neuro can be downloaded at download.cnet.com

Assessing the quality of scientific articles being submitted for publishing through digital libraries is an essential step that ensures that the published articles meet the qualifications required by the journal, to maintain the reputations of these journals. Normally, these articles are peer-reviewed by experts in the field the article investigates, which has dramatically increased the time required to assess these articles before publishing. The quality of an article is measured mainly by the quality of the writing and the significance of the field it investigates. In this study, a quality assessment technique is proposed, which uses artificial neural networks to predict the number of citations the article is expected to gain as a measure of its quality. The evaluation is based on three main components of the article, the title, keywords and abstract, as these components can reflect the overall quality of the article, without the need of excessive processing of the entire article. Two approaches are evaluated, the first attempt to measure the quality per each of the components, separately, and fuse the measures into a single overall quality measure. The second approach uses a single hybrid neural network that combines the three networks together, so that, these components are processed simultaneously. Per each approach, three types of neural networks are evaluated, which are the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short-Term Memory (LSTM). The results show that the use of the CNN with the hybrid network has achieved the best predictions, with 4.52 Mean Squared Error (MSE).

Automatic recognition of cardiac arrhythmias is important for diagnosis of cardiac abnormalies. Several algorithms have been proposed to classify ECG arrhythmias; however, they cannot perform very well. Therefore, in this paper, an expert system for ElectroCardioGram (ECG) arrhythmia classification is proposed. Discrete wavelet transform is used for processing ECG recordings, and extracting some features, and the Multi-Layer Perceptron (MLP) neural network performs the classification task. Two types of arrhythmias can be detected by the proposed system. Some recordings of the MIT-BIH arrhythmias database have been used for training and testing our neural network based classifier. The simulation results show that the classification accuracy of our algorithm is 96.5% using 10 files including normal and two arrhythmias.

In this paper, we present a soft computing modelof decision support systems for diagnosing diseases andprescribing herbal prescriptions by oriental medicine.Inputs to the model are severities of observed symptoms onpatients and outputs from the model are a diagnosis of disease states and corresponding herbal prescriptions.First, having used fuzzy inferences, the most seriousdisease state in which the patient appears to be infected isdetermined. Next, an herbal prescription written insuitable herbs with reasonable amounts for treating thatdisease state is given by neural networks. Finally, wedescribe an application of this model in rheumatismdiagnoses and show its evaluations

This paper investigates the global chaos synchronization of identical hyperchaotic Bao systems (Bao and Liu, 2008), identical hyperchaotic Xu systems (Xu, Cai and Zheng, 2009) and non-identical hyperchaotic Bao and hyperchaotic Xu systems. Active nonlinear control is the method adopted for the global chaos synchronization of the hyperchaotic systems addressed in this paper and the synchronization results have been established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the active nonlinear control method is very effective and convenient to achieve global chaos synchronization of identical and non-identical hyperchaotic Bao and hyperchaotic Xu systems. Numerical simulations have been provided to demonstrate the effectiveness of the synchronization results for the hyperchaotic Cao and hyperchaotic Xu systems.

This paper introduces an approach of plant classification which is based on the characterization of texture properties. We used the combined classifier learning vector quantization. We randomly took out 30 blocks of each texture as a training set and another 30 blocks as a testing set. We found that the combined classifier method gave a high performance which is a superior than other tested methods. The experimental results indicated that our algorithm is applicable and its average correct recognition rate was 98.7%.

An experiment on predicting flood flows at each of the upstream and a down stream section of a river network is presented using focused Time Lagged Recurrent Neural Network with three different memories like TDNN memory, Gamma memory and Laguarre memory. This paper focuses on application of memory to the input layer of a TLRN in developing flood forecasting models for multiple sections in a river system. The study shows the Gamma memory has better applicability followed by TDNN and Laguarre memory.

Age estimation of unrestricted imaging circumstances has attracted an augmented recognition as it is appropriate in several real-world applications such as surveillance, face recognition, age synthesis, access control, and electronic customer relationship management. Current deep learning-based methods have displayed encouraging performance in age estimation field. Males and Females have a variable type of appearance aging pattern; this results in age differently. This fact leads to assuming that using gender information may improve the age estimator performance. We have proposed a novel model based on Gender Classification. A Convolutional Neural Network (CNN) is used to get Gender Information, then Bayesian Optimization is applied to this pre-trained CNN when fine-tuned for age estimation task. Bayesian Optimization reduces the classification error on the validation set for the pre-trained model. Extensive experiments are done to assess our proposed model on two data sets: FERET and FG-NET. The experiments’ result indicates that using a pre-trained CNN containing Gender Information with Bayesian Optimization outperforms the state of the arts on FERET and FG-NET data sets with a Mean Absolute Error (MAE) of 1.2 and 2.67 respectively.