An Advanced CNN-LSTM Model for Cryptocurrency Forecasting

Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing financial market is characterized by significant volatility and strong price fluctuations over a short-time period therefore, the development of an accurate and reliable forecasting model is considered essential for portfolio management and optimization. In this research, we propose a multiple-input deep neural network model for the prediction of cryptocurrency price and movement. The proposed forecasting model utilizes as inputs different cryptocurrency data and handles them independently in order to exploit useful information from each cryptocurrency separately. An extensive empirical study was performed using three consecutive years of cryptocurrency data from three cryptocurrencies with the highest market capitalization i.e., Bitcoin (BTC), Etherium (ETH), and Ripple (XRP). The detailed experimental analysis revealed that the proposed model has the ability to efficiently exploit mixed cryptocurrency data, reduces overfitting and decreases the computational cost in comparison with traditional fully-connected deep neural networks.

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A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study

Algorithms ◽

10.3390/a13110273 ◽

2020 ◽

Vol 13 (11) ◽

pp. 273

Author(s):

Ioannis E. Livieris ◽

Spiros D. Dafnis ◽

George K. Papadopoulos ◽

Dionissios P. Kalivas

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Computational Cost ◽

Economic Benefits ◽

Mixed Data ◽

Cotton Production ◽

Proposed Model ◽

Multiple Input ◽

Fully Connected

Cotton constitutes a significant commercial crop and a widely traded commodity around the world. The accurate prediction of its yield quantity could lead to high economic benefits for farmers as well as for the rural national economy. In this research, we propose a multiple-input neural network model for the prediction of cotton’s production. The proposed model utilizes as inputs three different kinds of data (soil data, cultivation management data, and yield management data) which are treated and handled independently. The significant advantages of the selected architecture are that it is able to efficiently exploit mixed data, which usually requires being processed separately, reduces overfitting, and provides more flexibility and adaptivity for low computational cost compared to a classical fully-connected neural network. An empirical study was performed utilizing data from three consecutive years from cotton farms in Central Greece (Thessaly) in which the prediction performance of the proposed model was evaluated against that of traditional neural network-based and state-of-the-art models. The numerical experiments revealed the superiority of the proposed approach.

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Interval Forecasting in Supply Chain with Small Sample

Mathematical Problems in Engineering ◽

10.1155/2018/1737840 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10

Author(s):

Lifeng Wu ◽

Yan Chen

Keyword(s):

Supply Chain ◽

Small Sample ◽

Decision Makers ◽

Small Samples ◽

Forecasting Model ◽

Future Trends ◽

Human Judgment ◽

Proposed Model ◽

Grey Models ◽

Short Time

To deal with the forecasting with small samples in the supply chain, three grey models with fractional order accumulation are presented. Human judgment of future trends is incorporated into the order number of accumulation. The output of the proposed model will provide decision-makers in the supply chain with more forecasting information for short time periods. The results of practical real examples demonstrate that the model provides remarkable prediction performances compared with the traditional forecasting model.

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An Efficient Dimension Reduction based Fusion of CNN and SVM Model for Detection of Abnormal Incident in Video Surveillance

Journal of Soft Computing Paradigm - September 2019 ◽

10.36548/jscp.2021.2.001 ◽

2021 ◽

Vol 3 (2) ◽

pp. 55-69

Author(s):

Rajesh Sharma ◽

Akey Sungheetha

Keyword(s):

Dimensionality Reduction ◽

Video Surveillance ◽

Background Subtraction ◽

Video Stream ◽

Image Features ◽

Learning Framework ◽

Video Frames ◽

Time Period ◽

Proposed Model ◽

Short Time

Performing dimensionality reduction in the camera captured images without any loss is remaining as a big challenge in image processing domain. Generally, camera surveillance system is consuming more volume to store video files in the memory. The normally used video stream will not be sufficient for all the sectors. The abnormal conditions should be analyzed carefully for identifying any crime or mistakes in any type of industries, companies, shops, etc. In order to make it comfortable to analyze the video surveillance within a short time period, the storage of abnormal conditions of the video pictures plays a very significant role. Searching unusual events in a day can be incorporated into the existing model, which will be considered as a supreme benefit of the proposed model. The massive video stream is compressed in preprocessing the proposed learning method is the key of our proposed algorithm. The proposed efficient deep learning framework is based on intelligent anomaly detection in video surveillance in a continuous manner and it is used to reduce the time complexity. The dimensionality reduction of the video captured images has been done by preprocessing the learning process. The proposed pre-trained model is used to reduce the dimension of the extracted image features in a sequence of video frames that remain as the valuable and anomalous events in the frame. The selection of special features from each frame of the video and background subtraction process can reduce the dimension in the framework. The proposed method is a combination of CNN and SVM architecture for the detection of abnormal conditions at video surveillance with the help of an image classification procedure. This research article compares various methods such as background subtraction (BS), temporal feature extraction (TFE), and single classifier classification methods.

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Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks

Applied Sciences ◽

10.3390/app9153136 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3136 ◽

Cited By ~ 2

Author(s):

Jesus Lopez-Ballester ◽

Adolfo Pastor-Aparicio ◽

Jaume Segura-Garcia ◽

Santiago Felici-Castell ◽

Maximo Cobos

Keyword(s):

Deep Neural Networks ◽

Computational Cost ◽

Direct Calculation ◽

Audio Signals ◽

Acoustic Parameters ◽

Quadratic Error ◽

Acoustic Sensor Networks ◽

Proposed Model ◽

Iot Devices ◽

Interesting Solution

Psycho-acoustic parameters have been extensively used to evaluate the discomfort or pleasure produced by the sounds in our environment. In this context, wireless acoustic sensor networks (WASNs) can be an interesting solution for monitoring subjective annoyance in certain soundscapes, since they can be used to register the evolution of such parameters in time and space. Unfortunately, the calculation of the psycho-acoustic parameters involved in common annoyance models implies a significant computational cost, and makes difficult the acquisition and transmission of these parameters at the nodes. As a result, monitoring psycho-acoustic annoyance becomes an expensive and inefficient task. This paper proposes the use of a deep convolutional neural network (CNN) trained on a large urban sound dataset capable of efficiently predicting psycho-acoustic annoyance from raw audio signals continuously. We evaluate the proposed regression model and compare the resulting computation times with the ones obtained by the conventional direct calculation approach. The results confirm that the proposed model based on CNN achieves high precision in predicting psycho-acoustic annoyance, predicting annoyance values with an average quadratic error of around 3%. It also achieves a very significant reduction in processing time, which is up to 300 times faster than direct calculation, making CNN designed a clear exponent to work in IoT devices.

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Dynamically Optimizing Network Structure Based on Synaptic Pruning in the Brain

Frontiers in Systems Neuroscience ◽

10.3389/fnsys.2021.620558 ◽

2021 ◽

Vol 15 ◽

Author(s):

Feifei Zhao ◽

Yi Zeng

Keyword(s):

Neural Networks ◽

Network Architecture ◽

Network Performance ◽

Computational Cost ◽

Deep Convolutional Neural Networks ◽

Learning Speed ◽

Proposed Model ◽

The Cost ◽

Fully Connected ◽

Synaptic Pruning

Most neural networks need to predefine the network architecture empirically, which may cause over-fitting or under-fitting. Besides, a large number of parameters in a fully connected network leads to the prohibitively expensive computational cost and storage overhead, which makes the model hard to be deployed on mobile devices. Dynamically optimizing the network architecture by pruning unused synapses is a promising technique for solving this problem. Most existing pruning methods focus on reducing the redundancy of deep convolutional neural networks by pruning unimportant filters or weights, at the cost of accuracy drop. In this paper, we propose an effective brain-inspired synaptic pruning method to dynamically modulate the network architecture and simultaneously improve network performance. The proposed model is biologically inspired as it dynamically eliminates redundant connections based on the synaptic pruning rules used during the brain's development. Connections are pruned if they are not activated or less activated multiple times consecutively. Extensive experiments demonstrate the effectiveness of our method on classification tasks of different complexity with the MNIST, Fashion MNIST, and CIFAR-10 datasets. Experimental results reveal that even for a compact network, the proposed method can also remove up to 59–90% of the connections, with relative improvement in learning speed and accuracy.

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World Economy: Prospects and Barriers for Recovery

Voprosy Ekonomiki ◽

10.32609/0042-8736-2012-12-61-83 ◽

2012 ◽

pp. 61-83 ◽

Cited By ~ 1

Author(s):

M. Ershov

Keyword(s):

World Economy ◽

Western World ◽

Short Time Period ◽

Market Segments ◽

Junk Bonds ◽

The Past ◽

Time Period ◽

The Us ◽

Global Liquidity ◽

Short Time

According to the latest forecasts, it will take 10 years for the world economy to get back to “decent shape”. Some more critical estimates suggest that the whole western world will have a “colossal mess” within the next 5–10 years. Regulators of some major countries significantly and over a short time‑period changed their forecasts for the worse which means that uncertainty in the outlook for the future persists. Indeed, the intensive anti‑crisis measures have reduced the severity of the past problems, however the problems themselves have not disappeared. Moreover, some of them have become more intense — the eurocrisis, excessive debts, global liquidity glut against the backdrop of its deficit in some of market segments. As was the case prior to the crisis, derivatives and high‑risk operations with “junk” bonds grow; budget problems — “fiscal cliff” in the US — and other problems worsen. All of the above forces the regulators to take unprecedented (in their scope and nature) steps. Will they be able to tackle the problems which emerge?

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Effect of short-term use of oral contraceptive pills on intraocular pressure

Pakistan Journal of Ophthalmology ◽

10.36351/pjo.v35i3.871 ◽

2020 ◽

Vol 35 (3) ◽

Author(s):

Tayyaba Gul Malik ◽

Hina Nadeem ◽

Eiman Ayesha ◽

Rabail Alam

Keyword(s):

Oral Contraceptive ◽

Childbearing Age ◽

Short Term ◽

Oral Contraceptive Pills ◽

Contraceptive Pills ◽

Time Period ◽

Significant Difference ◽

Group A ◽

Group B ◽

Short Time

Objective: To study the effect of short-term use of oral contraceptive pills on intra-ocular pressures of women of childbearing age. Methods: It was a comparative observational study, conducted at Arif memorial teaching hospital and Allied hospital Faisalabad for a period of six months. Hundred female subjects were divided into two groups of 50 each. Group A, included females, who had been taking oral contraceptive pills (OCP) for more than 6 months and less than 36 months. Group B, included 50 age-matched controls, who had never used OCP. Ophthalmic and systemic history was taken. Careful Slit lamp examination was performed and intraocular pressures (IOP) were measured using Goldman Applanation tonometer. Fundus examination was done to rule out any posterior segment disease. After collection of data, we analyzed and compared the intra ocular pressures between the two groups by using ANOVA in SPSS version 21. Results: Average duration of using OCP was 14.9 months. There was no significant difference of Cup to Disc ratios between the two groups (p= 0.109). However, significant difference was noted between the IOP of OCP group and controls. (p=0.000). Conclusion: OCP significantly increase IOP even when used for short time period.

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A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽

10.3390/e21050455 ◽

2019 ◽

Vol 21 (5) ◽

pp. 455 ◽

Cited By ~ 5

Author(s):

Hongjun Guan ◽

Zongli Dai ◽

Shuang Guan ◽

Aiwu Zhao

Keyword(s):

Time Series ◽

Information Entropy ◽

Stock Exchange ◽

Time Series Forecasting ◽

High Order ◽

Stock Index ◽

Neutrosophic Set ◽

Forecasting Model ◽

Forecasting Models ◽

Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

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Wind Farm Cable Connection Layout Optimization with Several Substations

Energies ◽

10.3390/en14123615 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3615

Author(s):

Adelaide Cerveira ◽

Eduardo J. Solteiro Pires ◽

José Baptista

Keyword(s):

Energy Production ◽

Wind Farm ◽

Programming Model ◽

Optimal Solution ◽

Wind Farms ◽

Green Energy ◽

Fossil Energy ◽

Proposed Model ◽

Short Time ◽

Cable Connection

Green energy has become a media issue due to climate changes, and consequently, the population has become more aware of pollution. Wind farms are an essential energy production alternative to fossil energy. The incentive to produce wind energy was a government policy some decades ago to decrease carbon emissions. In recent decades, wind farms were formed by a substation and a couple of turbines. Nowadays, wind farms are designed with hundreds of turbines requiring more than one substation. This paper formulates an integer linear programming model to design wind farms’ cable layout with several turbines. The proposed model obtains the optimal solution considering different cable types, infrastructure costs, and energy losses. An additional constraint was considered to limit the number of cables that cross a walkway, i.e., the number of connections between a set of wind turbines and the remaining wind farm. Furthermore, considering a discrete set of possible turbine locations, the model allows identifying those that should be present in the optimal solution, thereby addressing the optimal location of the substation(s) in the wind farm. The paper illustrates solutions and the associated costs of two wind farms, with up to 102 turbines and three substations in the optimal solution, selected among sixteen possible places. The optimal solutions are obtained in a short time.

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Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽

10.3390/en14041213 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1213

Author(s):

Ahmed Aljanad ◽

Nadia M. L. Tan ◽

Vassilios G. Agelidis ◽

Hussain Shareef

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Solar Irradiance ◽

Particle Swarm ◽

Time Interval ◽

Time Intervals ◽

Swarm Optimization ◽

Backpropagation Neural Networks ◽

Proposed Model ◽

Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

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