scholarly journals Financial Instrument Forecast with Artificial Intelligence

2021 ◽  
Vol 11 (2) ◽  
pp. 16-24
Author(s):  
Furkan Kayım ◽  
Atınç Yılmaz
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Ensemble Classification ◽  
Training Data ◽  
New Method ◽  
Financial Instruments ◽  
Successful Result ◽  
Financial Instrument ◽  
Analysis Methods ◽  
Boosting Method

In ancient times, trade was carried out by barter. With the use of money and similar means, the concept of financial instruments emerged. Financial instruments are tools and documents used in the economy. Financial instruments can be foreign exchange rates, securities, crypto currency, index and funds. There are many methods used in financial instrument forecast. These methods include technical analysis methods, basic analysis methods, forecasts carried out using variables and formulas, time-series algorithms and artificial intelligence algorithms. Within the scope of this study, the importance of the use of artificial intelligence algorithms in the financial instrument forecast is studied. Since financial instruments are used as a means of investment and trade by all sections of the society, namely individuals, families, institutions, and states, it is highly important to know about their future.  Financial instrument forecast can bring about profitability such as increased income welfare, more economical adjustment of maturities, creation of large finances, minimization of risks, spreading of ownership to the grassroots, and more balanced income distribution. Within the scope of this study, financial instrument forecast is carried out by applying a new methods of Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Autoregressive Integrated Moving Average (ARIMA) algorithms and Ensemble Classification Boosting Method. Financial instrument forecast is carried out by creating a network compromising LSTM and RNN algorithm, an LSTM layer, and an RNN output layer. With the ensemble classification boosting method, a new method that gives a more successful result compared to the other algorithm forecast results was applied. At the conclusion of the study, alternative algorithm forecast results were competed against each other and the algorithm that gave the most successful forecast was suggested. The success rate of the forecast results was increased by comparing the results with different time intervals and training data sets. Furthermore, a new method was developed using the ensemble classification boosting method, and this method yielded a more successful result than the most successful algorithm result.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

scholarly journals Learn to Train: Improving Training Data for a Neural Network to Detect Pecking Injuries in Turkeys

Animals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2655
Author(s):  
Nina Volkmann ◽  
Johannes Brünger ◽  
Jenny Stracke ◽  
Claudius Zelenka ◽  
Reinhard Koch ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Automated Detection ◽  
Training Data ◽  
High Quality

This study aimed to develop a camera-based system using artificial intelligence for automated detection of pecking injuries in turkeys. Videos were recorded and split into individual images for further processing. Using specifically developed software, the injuries visible on these images were marked by humans, and a neural network was trained with these annotations. Due to unacceptable agreement between the annotations of humans and the network, several work steps were initiated to improve the training data. First, a costly work step was used to create high-quality annotations (HQA) for which multiple observers evaluated already annotated injuries. Therefore, each labeled detection had to be validated by three observers before it was saved as “finished”, and for each image, all detections had to be verified three times. Then, a network was trained with these HQA to assist observers in annotating more data. Finally, the benefit of the work step generating HQA was tested, and it was shown that the value of the agreement between the annotations of humans and the network could be doubled. Although the system is not yet capable of ensuring adequate detection of pecking injuries, the study demonstrated the importance of such validation steps in order to obtain good training data.

Using Convolution and Deep Learning in Gomoku Game Artificial Intelligence

2018 ◽  
Vol 28 (03) ◽  
pp. 1850011
Author(s):  
Peizhi Yan ◽  
Yi Feng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Model ◽  
Neural Network Model ◽  
Traditional Approach ◽  
Training Data ◽  
Game Tree ◽  
The Neural Network ◽  
Testing Data ◽  
Game Board

Gomoku is an ancient board game. The traditional approach to solving the Gomoku game is to apply tree search on a Gomoku game tree. Although the rules of Gomoku are straightforward, the game tree complexity is enormous. Unlike many other board games such as chess and Shogun, the Gomoku board state is more intuitive. That is to say, analyzing the visual patterns on a Gomoku game board is fundamental to play this game. In this paper, we designed a deep convolutional neural network model to help the machine learn from the training data (collected from human players). Based on this original neural network model, we made some changes and get two variant neural networks. We compared the performance of the original neural network with its variants in our experiments. Our original neural network model got 69% accuracy on the training data and 38% accuracy on the testing data. Because the decision made by the neural network is intuitive, we also designed a hard-coded convolution-based Gomoku evaluation function to assist the neural network in making decisions. This hybrid Gomoku artificial intelligence (AI) further improved the performance of a pure neural network-based Gomoku AI.

scholarly journals Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Amin Daryasafar ◽  
Arash Ahadi ◽  
Riyaz Kharrat
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Equation Of State ◽  
Oil Recovery ◽  
Steam Distillation ◽  
Network Models ◽  
Steam Injection ◽  
Training Data ◽  
Neural Network Models ◽  
Thermal Methods

Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods.

scholarly journals Detection of Rice Plants Diseases Using Convolutional Neural Network (CNN)

2020 ◽  
Vol 3 ◽  
pp. 393-396
Author(s):  
Achmad Ramadhanna’il Rasjava ◽  
Aditya Wisnugraha Sugiyarto ◽  
Yori Kurniasari ◽  
Syaifullah Yusuf Ramadhan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Rice Plant ◽  
Leaf Blight ◽  
Rice Production ◽  
Training Data ◽  
Brown Spot ◽  
Bacterial Leaf Blight ◽  
Rice Plants

As a rice-producing plant, rice plant (Oryza sativa L.) is one of the most important crops in Indonesia. Rice production is increasing every year along with an increase in rice demand and population.The amount of rice production is affected by the condition of the rice plants. The worse the condition of rice plants, the rice production will also lower. Rice plant is very susceptible to diseases or pests that can reduce its productivity, including brown spot disease, leaf smut and bacterial leaf blight. As the development of science and technology, currently known as Artificial Intelligence. Artificial intelligence is a combination of several scientific disciplines such as mathematics, statistics, computer science, and even social science. Using artificial intelligence, the system now have the ability to interpret external data correctly to learn from the data and then use the learning to achieve certain goals through flexible adaptation. The artificial intelligence fields consists of several branches, such as machine learning and deep learning. Neural Network (NN) is one of the methods used in the deep learning.NN has many types, one of which is the Convolutional Neural Network (CNN). CNN is the best-knownmethod used for processingimages data compared to other types of NN. Therefore, in this study the identification of rice plants diseases was carriedout using CNN method. From this study,better results were obtained compared to other methods, obtaining 100% accuracy for training data and 86,67% for testing data. The model obtained by the CNN method can be used for detecting 3 different types of rice plants diseases, there are brown spots, leaf smuts, or bacterial leaf blight disease based on the physical images of rice plant leaves.

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Semi-supervised Multi-task Network For Image Aesthetic Assessment

2020 ◽  
Vol 2020 (8) ◽  
pp. 188-1-188-7
Author(s):  
Xiaoyu Xiang ◽  
Yang Cheng ◽  
Jianhang Chen ◽  
Qian Lin ◽  
Jan Allebach
Keyword(s):  
Neural Network ◽  
Semantic Content ◽  
Training Data ◽  
Score Distribution ◽  
Generalization Ability ◽  
Subjective Preference ◽  
Ranking Tasks

Image aesthetic assessment has always been regarded as a challenging task because of the variability of subjective preference. Besides, the assessment of a photo is also related to its style, semantic content, etc. Conventionally, the estimations of aesthetic score and style for an image are treated as separate problems. In this paper, we explore the inter-relatedness between the aesthetics and image style, and design a neural network that can jointly categorize image by styles and give an aesthetic score distribution. To this end, we propose a multi-task network (MTNet) with an aesthetic column serving as a score predictor and a style column serving as a style classifier. The angular-softmax loss is applied in training primary style classifiers to maximize the margin among classes in single-label training data; the semi-supervised method is applied to improve the network’s generalization ability iteratively. We combine the regression loss and classification loss in training aesthetic score. Experiments on the AVA dataset show the superiority of our network in both image attributes classification and aesthetic ranking tasks.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network

2018 ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin S. Smith ◽  
Jerzy Leszczynski ◽  
Olexandr Isayev
Keyword(s):  
Neural Network ◽  
Molecular System ◽  
Computational Cost ◽  
Chemical Properties ◽  
The State ◽  
Molecular Properties ◽  
Training Data ◽  
Dft Methods ◽  
Benchmark Datasets ◽  
Quantum Phenomena

<p>Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database.</p>

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