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Artificial Neural Networks architectures for stock price prediction: comparisons and applications

Artificial Neural Networks architectures for stock price prediction: comparisons and applications

Artificial Neural Networks look like electronic models based on the neural structure of the brain, which ba- sically learns from experience. The simplest kind of neural network is a single-layer perceptron network, which consists of a single layer of output nodes, while the inputs are fed directly to the outputs via a series of weights. In this way, it can be considered the sim- plest kind of feed-forward network. Multilayer neural network consists of multiple layers of computational units, usually interconnected in a feed-forward way. Each neuron in one layer has directed connections to the neurons of the subsequent layer. In many applica- tions the units of these networks apply a sigmoid func- tion as an activation function. The universal approx- imation theorem for neural networks states that every continuous function that maps intervals of real num- bers to some output interval of real numbers can be approximated arbitrarily closely by a multi-layer per- ceptron with just one hidden layer. This result holds for a wide range of activation functions as in the case of sigmoidal functions.
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Development of a Machine Vision system for quality control of fruits

Development of a Machine Vision system for quality control of fruits

The study was conducted with the basis of six theories namely: Spatial imaging, rough set theory, electronic sensors, logic scoring of preference, artificial neural network, database monitoring, and data and information tracker. Spatial imaging was crucial for identifying the smallest detail of the image in order to determine the fruit. Rough set theory was used to make approximations for the data gathered in the research. Since the focus of this topic is machine vision, electronic sensors are one of the key factors in the study. Since the program relies on the image being processed, machine vision systems should have a form of electronic sensor to feed the program the image it will process. For logic scoring of preference, the decision-making methods were used to further make the verdict of the system as accurate as possible. Since quality control is a repeated process of checking the produce, the artificial neural network helps the system become more accurate when exposed to more and more inputs. This helps make the decision making faster and more efficient. Database monitoring is closely
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Artificial Neural Network

Artificial Neural Network

language program and then into machine code that the computer can understand. These machines are totally predictable; if anything goes wrong is due to a software or hardware fault. Neural networks and conventional algorithmic computers are not in competition but complement each other. There are tasks are more suited to an algorithmic approach like arithmetic operations and tasks that are more suited to neural networks. Even more, a large number of tasks, require systems that use a combination of the two approaches (normally a conventional computer is used to supervise the neural network) in order to perform at maximum efficiency. What is Artificial Neural Network? Artificial Neural Networks are relatively crude electronic models based on the neural structure of the brain. The brain basically learns from experience. It is natural proof that some problems that are beyond the scope of current computers are indeed solvable by small energy efficient packages. This brain also models promises a less technical way to develop machine solutions. This new approach to computing also provides a more graceful degradation during system overload than its more traditional counterparts. These biologically inspired methods of computing are thought to be the next major advancement in the computing industry. Even simple animal brains are capable of functions that are currently impossible for computers.
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Study of Artificial Neural Network

Study of Artificial Neural Network

Artificial neuron is a basic building block of every artificial neural network. Its design and functionalities are derived from observation of a biological neuron that is basic building block of biological neural networks (systems) which includes the brain, spinal cord and peripheral ganglia. Similarities in design and functionalities can be seen in Fig. 3. whereas the left side of a figure represents a biological neuron with its soma, dendrites and axon and where the right side of a figure represents an artificial neuron with its inputs, weights, transfer function, bias and outputs [3].
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Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine

Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine

One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as “ machine/deep learning ” and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100 – 150 per year in 2007 – 2008 to 700 – 800 per year in 2016 – 2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6 – 9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare . Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient ’ s values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.
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Soft Computing Stock Market Price Prediction for the Nigerian Stock Exchange

Soft Computing Stock Market Price Prediction for the Nigerian Stock Exchange

Abstract— Forecasting the price movements in stock market has been a major challenge for common investors, businesses, brokers and speculators because Stock Prices are considered to be very dynamic and susceptible to quick changes. As more and more money is being invested, the investor gets anxious of the future trends of the stock prices in the market and thus, creates a high desirable need for a more’ intelligent’ prediction model. Two soft computing models- Artificial Neural Network (ANN) and Fuzzy Artificial Neural Network (FANN) hybrid model were used to forecast the next day’s closing price. The historical trading data was obtained from the Nigerian Stock Exchange for Dangote Sugar Refinery Plc . The results showed the power of Soft Computing techniques (SC) in stock Price Prediction.
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Vol 5, No 1 (2013)

Vol 5, No 1 (2013)

The use of back propagation and cross validation in neural network with optimization by genetic algorithm. The results show that this method is better that random topology [1][3]. One serious problem in neural networks to avoid overfitting is a generalization of the network inputs is high. The solution to this problem is to avoid non-useful data on the network is using best practices. In fact, the use of a validation set can detect any irregularities in the data and prevents the optimal weights for the network. Balance between genetic programming and neural networks, the network topology are an interesting topic. In advance of his generation program using appropriate structure for the network gets updated. Performance results on some math functions show that the algorithm has several training and testing compared to the mean value of 90.32% is reached[1][4]. Combinations of genetic algorithms and neural networks in another two problems in NN that are permutation and convergence are discussed. This method tested on Cloud Classification. By weights of neural network by Genetic Algorithm optimization amounts to about 3% error is reached. A combination of genetic algorithms and multi-layer perceptron neural network to predict the performance of stroke patients with different data sets is equivalent to 89.67% [1][5]. An article comparing three Bayesian methods, neural networks and decision trees for problem solving is a predictor of stroke patients. The best accuracy achieved for the 91% Bayesian methods for neural networks and decision trees versus 92% against 94% [1][6].
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The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Di erent Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Di erent Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

Abstract: The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68–99.6% and a detection accuracy between 80.6–94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.
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Blind Navigation System using Artificial Intelligence

Blind Navigation System using Artificial Intelligence

The field of creation of intelligent machines that work like humans and respond quickly, in computer science is known as Artificial intelligence. The core part of AI research is Knowledge engineering. Machines can react and act like humans only when they have abundant information related to the world. To implement knowledge engineering, Artificial intelligence should have access to objects, categories, properties, and relations. To initiate common sense, reasoning and problem-solving power in machines, it is a difficult and tedious task. Machine learning is another one of the core parts of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.
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Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks

Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks

multi-model automatic diagnosis system for brain tumour detection and localization. In the first phase, the system structure consists of preprocessing, feature extraction using a convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) approach. The purpose of the first system phase is to detect brain tumour by classifying the MRIs into normal and abnormal images. The aim of the second system phase is to localize the tumour within the abnormal MRIs using a fully designed five-layer region-based convolutional neural network (R-CNN). The performance of the first phase was assessed using three CNN models, namely, AlexNet, Visual Geometry Group (VGG)-16, and VGG-19, and a maximum detection accuracy of 99.55% was achieved with AlexNet using 349 images extracted from the standard Reference Image Database to Evaluate Response (RIDER) Neuro MRI database. The brain tumour localization phase was evaluated using 804 3D MRIs from the Brain Tumor Segmentation (BraTS) 2013 database, and a DICE score of 0.87 was achieved. The empirical work proved the outstanding performance of the proposed deep learning-based system in tumour detection compared to other non-deep-learning approaches in the literature. The obtained results also demonstrate the superiority of the proposed system concerning both tumour detection and localization.
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Review on Transformer Protection through Arti...

Review on Transformer Protection through Arti...

The demand for a reliable supply of electrical energy for the need of modern world in each and every field has increased considerably requiring nearly a no-fault operation of power systems. The crucial objective is to mitigate the frequency and duration of unwanted outages related to power transformer puts a high pointed demand that includes the requirements of dependability associated with no false tripping, and operating speed with short fault detection and clearing time. The second harmonic restrain principle is widely used in industrial application for many years, which uses discrete Fourier transform (DFT) often encounters some problems such as long restrain time and inability to discriminate internal fault from magnetizing inrush condition. Hence, artificial neural network (ANN), a powerful tool for artificial intelligence (AI), which has the ability to mimic and automate the knowledge, has been proposed for detection and classification of faults from normal and inrush condition.
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Comparison of Artificial Intelligence Methods on the Example of Tea Classification Based on Signals from E nose Sensors

Comparison of Artificial Intelligence Methods on the Example of Tea Classification Based on Signals from E nose Sensors

Abstract The data collected from electronic nose systems are multidimensional and usually contain a lot of redundant information. In order to extract only the relevant data, different computational techniques are developed. The article presents and compares selected pattern recognition algorithms in application to qualitative determination of different brands of tea. The measured responses of an array of 18 semi- conductor gas sensors formed input vectors used for further analysis. The initial data processing consisted on standardization, principal component analysis, data normalization and reduction. Soft computing one can divide into single method systems using neural networks, fuzzy systems, and hybrid systems like evolutionary-neural, neuro-fuzzy, evolutionary-fuzzy. All the presented systems were evaluated based on accuracy (generated error) and complexity (number of parameters and training time) criteria. A novel method of forming input data vector by aggregation of the first three principal components is also presented.
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Artificial Neural Networks Approach to the Forecast of Stock Market Price Movements

Artificial Neural Networks Approach to the Forecast of Stock Market Price Movements

During recent years, Artificial Neural Networks (ANN) have been interested by a renewed interest by academicians, as well as by practitioners of various type. The turning point is represented by the win- ning solution for ImageNet challenge in 2012, which was based on deep convolutional network trained on GPUs, see [1]. Then, ANN approaches to deep learn- ing have been used in lot of different areas such, e.g., autonomic car driving, medicine, bots, playing games, finance, physics, etc. Within the financial arena, ma- chine learning is already applied to, e.g., credit scor- ing, portfolio management, algorithmic trading, auto- mated underwriting of loans, automated financial doc- ument classification, separation systems, etc. In the present work we are dealing with the ANN-machine learning approach to the study of stock price move- ments prediction.
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Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning

Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning

Once the neural network was created, trained, validated, and tested on the calibration data, in a further step, it was tested on our data set of clinical data (described in detail above under the “Data” subsection in “Methods”) consisting of a subset of strabismic eyes and a control subset of normal eyes, all obtained with the pediatric vision screener. Four normalized spectral powers from a total of 78 eyes were organized as an input matrix of 4 rows and Q columns (Q = 78). The target vector was a vector of length 78, each element of which was either 1 (CF) or 0 (para-CF). The four inputs for each subject were fed to the ANN, and the output was compared each time with the target, which in fact was the doctor’s decision. This allowed the calculation of the sensitivity and speci- ficity of the ANN when applied to the clinical data. Further, these results permitted a comparison between the performance of the ANN and the statistical methods reported earlier [18], such as the simple adaptive threshold that minimized the overall error, or 2-, 3- and 4-way linear discriminant analysis. The results are summarized in Table  1, col- umns SBJ (human subjects). The two new patients (4 eyes) were quite “tricky” adding two false negative decisions to the “Standard Threshold” method and just one false nega- tive decision to the neural network’s results. Again, the ANN performed slightly bet- ter than the other methods, with sensitivity of 0.9851 and specificity of 1.0000, with no false positive decisions and only one false negative decision. Generally, the discriminant- analysis-based methods showed lower sensitivity. Specificity on the clinical data was 1.0000 for all methods except for the 2-way discriminant analysis. Note that the only other method that used all four inputs separately is the 4-way discriminant analysis, giv- ing a sensitivity of 0.9417 for the calibration data, and only 0.8507 for the clinical data. The excellent performance of the ANN is obviously due to the two-layer structure and to the nonlinear (sigmoid) transfer function at the output of each neuron, giving more flexibility, while the performance of all discriminant functions used in the previous study was strictly linear, resembling just one layer of neurons with a linear transfer function, not ideal for pattern recognition.
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DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network

A CNN is a form of deep neural network (DNN) with special convolution structure, which can reduce the amount of memory occupied by the deep network and the number of parameters in the network. In the convo- lution layer, a feature map in which hidden layers are connected to each other is used to extract pixel-level ab- stracted image features via convolution operations of one or more convolution kernels (also referred to as a filter) [36]. Each convolution kernel applies a sliding window mechanism to traverse the entire feature map, and thereby gathers and fuses the information of each small area to complete the representation of a partial feature of the input image. In a CNN, the filter parame- ters used in each convolution layer are ordinarily con- sistent for two reasons: (i.) sharing allows the image content to be unaffected by location; and (ii.) this consistency can dramatically reduce the optimization pa- rameters. The mechanism of parameter sharing is a very important and attractive property of the CNN algorithm.
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An approach of curing Depression by Artificial Neural          Networks (ANN)

An approach of curing Depression by Artificial Neural Networks (ANN)

Depression – a word synonymous to today’s world. Every instance we come across various people who have suffered depression at some point of time in their life or suffering from it at this very moment. Is depression fully curable? Can medication fully recover a person of depression or the patient has still traces left in him/her? A big question which still stands not fully answered. Let’s try and find it. Neural Network one of the pillars of Artificial Intelligence is very similar to our central Nervous System (CNS) and depression affects our CNS very badly. Using these powerful tools of Artificial Neural Network we can try to give a permanent cure to depression.
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On the application and design of artificial neural networks for motor fault detection. II.

On the application and design of artificial neural networks for motor fault detection. II.

P of the use of artificial neural networks in motor fault detection applications. In Part I1 of this paper, we will discuss how to design an artificial neural network for[r]

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Prediction of Skin Cancer Using Morphological Neural Network Analysis

Prediction of Skin Cancer Using Morphological Neural Network Analysis

ecently, cancer patients are increasing due to following factors: lifestyle, smoking tobacco, alcohol usage, diet, physical activity, environmental change, sun and other types of radiation, viruses and so on. The most common type cancer includes skin cancer. Unusual swellings of cells for skin can be the skin cancer. There are four kinds of skin cancers: Actinic Keratoses (AK), Basal cell carcinoma (BCC), Squamous cell carcinoma (SCC), and Melanoma. Early diagnosis of the cancer helps to treat it successfully. The late diagnosis of the cancer causes the cancer spread to other nearby organs, and cannot be treat. There are numerous publications are composed in detecting, segmenting, and classifying skin cancers employing different computer vision, machine learning, image processing, MNN, and classification techniques. Esteva et al. developed the skin cancer classification using MNN. Hitoshi et al. presented a quite automatic system for the classification of the melanomas. While Anas et al. composed the melanoma classification by four kinds classification, Almansour et al. demonstrated a classification method for melanoma using the k-means clustering and Support Vector Machine (SVM). Abbas et al., and Capdehourat et al. composed skin lesions, cancers, and dermoscopy image classification methods using AdaBoost MC, separately. Giotis et al., and Ruiz et al. developed decision support systems utilizing image processing and neural network algorithms by lesion texture, color, visual diagnostic attributes, and affected area, degree of damage for the melanoma, respectively. Also, Isasi et al. presented an automatic melanomas diagnosis system. The cutaneous melanoma is diagnosed by Blum et al. Kiran et al. developed an Android application for the melanoma classification. As a subfield of the deep neural network, MNNs has
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Bedload transport predictions based on field measurement data by combination of artificial neural network and genetic programming

Bedload transport predictions based on field measurement data by combination of artificial neural network and genetic programming

Among the numerous ANN structures, the multilayer, feed-forward network is the most widely used in the area of sediment transport (Rumelhart et al., 1985). The Levenberg- Marquardt (LM) algorithm, a standard second-order nonlinear least-squares technique based on the backpropagation process, was used in this study to train the ANN models. The performances of the GP and ANN models, as well as a combination of the ANN and GP were evaluated and the best model was selected for estimating the bedload transport of Kurau River.

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DETECTION AND CLASSIFICATION OF DIABETIC RETINOPATHY USING ADAPTIVE BOOSTING AND ARTIFICIAL NEURAL NETWORK

DETECTION AND CLASSIFICATION OF DIABETIC RETINOPATHY USING ADAPTIVE BOOSTING AND ARTIFICIAL NEURAL NETWORK

Classifiers based on extracted features are trained and are used to classify the images as eyes with No DR and Severity labels of DR. Supervised machine learning algorithm (Adaboost) is implemented along with the Artificial Neural Network. In deep learning, the architecture of convolutional neural network layers [19] is particularly well-adapted for the classification of images. For multi-class classification, this architecture is found to be robust and sensitive to the features present in the images.

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