Journal articles on the topic 'Analytic Network Proce'
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Haryadi, Haryadi, Citra Amalia, Doddy Teguh Yuwono, Fitri Amalia Sholehah, and Santika Santika. "SISTEM PENDUKUNG KEPUTUSAN SELEKSI BANTUAN DANA HIBAH PENELITIAN DENGAN METODE ANALYTIC NETWORK PROCE (ANP)." Jurnal Informatika dan Rekayasa Elektronik 4, no. 1 (April 19, 2021): 1–11. http://dx.doi.org/10.36595/jire.v4i1.293.
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At the end of 2019, the ranking for research was issued by the Ministry of Research, Technology and Higher Education at that time, UM Palangkaraya moved up the rankings from the fostered cluster to MADYA. This encourages the institution to further improve the quality of research quality from lecturers. One of them is by providing Penelitian Penelitian Kompetitif Dosen Interna (PKDI). For this reason, research programs carried out in tertiary institutions are required to produce high quality and useful products. The manifestation of this openness is that program proposals received by LP2M will be reviewed by the assessment team (peer review) and then declared accepted or rejected for funding. However, the current assessment process is still carried out manually, namely by looking at certain criteria only, regardless of other assessment criteria. Of course, the manual assessment process is very likely to make mistakes. Therefore, a Web-based Decision Support System (DSS) was built using the Analytic Network Process (ANP) method. Based on the results of the research, it can be seen that the developed SPK can make it easier to assess the feasibility of proposing proposals at UM-Palangkaraya effectively and objectively in obtaining Grants based on the weights and predetermined criteria.
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Melo Brentan, Bruno, Gustavo Meirelles Lima, Antonio Carlos Zuffo, and Edevar Luvizotto Junior. "Dimensionamento de redes de distribuição de água por meio de análise multicriterial." Revista DAE 221, no. 68 (2019): 118–30. http://dx.doi.org/10.36659/dae.2020.010.
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O dimensionamento de redes de distribuição de água (RDAs) é feito para que as restrições operacionais de velo- cidade e pressão se mantenham dentro de limites que garantam a eficiência operacional. Assim, buscam-se as tubulações de menor custo para que essas condições sejam satisfeitas. Entretanto, ao adotar essa prática, ou- tras características da rede podem ser afetadas negativamente, como sua resiliência e capacidade de expansão. Dessa forma, este trabalho apresenta um estudo de caso em que a análise multicriterial é utilizada para realizar o dimensionamento de uma RDA. Primeiramente, um mapa cognitivo é feito para identificar os principais cri- térios a serem considerados na solução do problema. Em seguida, o método Delphi é usado em conjunto com o Analytic Hierarchy Process (AHP) para determinar os pesos relativos de cada critério. Por fim, três diferentes métodos de análise multicritérios são utilizados para a solução do problema: AHP, Electre e Promethee. Palavras-chave: Rede de distribuição. Otimização. Análise multicritério. Abstract The design of water distribution networks (WDNs) is made to match velocity and pressure constraints that guaran- tee operational efficiency. Thus, pipes with lower cost are selected to attend these conditions. However, this proce- dure can harm other characteristics of the network, as for example its resilience and expansion capacity. Therefore, this paper presents a case study in which the multicriterial analysis is used to design a WDN. First, a cognitive map is built to identify the main criteria to be considered during the design process. Then, the Delphi method is used jointly with the Analytic Hierarchy Process (AHP) to define the relative weights of each criteria. Finally, three different methods for multicriterial analysis are used to solve the problem: AHP, Electre and Promethee. Keywords: Water distribution network. Optimization. Multicriterial analysis.
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Liu, Chen. "Prediction and Analysis of Artwork Price Based on Deep Neural Network." Scientific Programming 2022 (March 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/7133910.
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The use of deep learning methods to solve problems in the field of artwork prices has attracted widespread attention, especially the superiority of long short-term memory network (LSTM) in dealing with time series problems. However, the potential for deep learning in the prediction of artwork price has not been fully explored. This paper proposes a deep prediction network structure that considers the correlation between time series data and the combination of two-way LSTM as well as one-way LSTM networks to predict the price of artworks. This paper proposes a deep-level two-way and one-way LSTM to predict the price of artworks in the art market. Taking into account the potential reverse dependence of the time series, the bidirectional LSTM layer is used to obtain bidirectional time correlation from historical data. This research uses a matrix to represent the artwork price data and fully considers the spatial correlation characteristics of the artwork price. Simultaneously, this paper uses the two-way LSTM network to correlate the potential contextual information of the historical data of the artwork price stream and fully perform feature learning. This study applies the two-way LSTM network layer to the building blocks of the deep architecture to measure the inverse dependence of the price fluctuation data. The comparison with other prediction models shows that the LSTM neural network fused with one-way and two-way proposed in this paper is superior to other neural networks for predicting price of artworks in terms of prediction accuracy.
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Cui, Xiaodong, Jun Hu, Yiming Ma, Peng Wu, Peican Zhu, and Hui-Jia Li. "Investigation of stock price network based on time series analysis and complex network." International Journal of Modern Physics B 35, no. 13 (May 20, 2021): 2150171. http://dx.doi.org/10.1142/s021797922150171x.
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Complex network is now widely used in a series of disciplines such as biology, physics, mathematics, sociology and so on. In this paper, we construct the stock price trend network based on the knowledge of complex network, and then propose a method based on information entropy to divide the stock network into some communities, that is, a gathering study of stock price trend. We construct time series networks for each stock in Chinese A-share market based on time series network model, and then use these networks to divide the stock market into communities. We find that the average trend of stocks in the same community is the same as the trend of market value weighting, but the average trend of stocks in different communities is quite different and the sequence correlation is low. This conclusion shows that stocks in the same community share the same price trend, while the stock trend in different communities varies. This paper is a successful application of complex network and information entropy in stock trend analysis, which mainly includes two contributions. First, the success of the visibility graph algorithm provides a new perspective for enriching stock price trend modeling. Second, our conclusion proves that the clustering based on information entropy theory is effective, which provides a new method for further research on stock price trend, portfolio construction and stock return prediction.
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Janda, Karel, Ladislav Krištoufek, Barbora Schererová, and David Zilberman. "Price transmission in biofuel-related global agricultural networks." Agricultural Economics (Zemědělská ekonomika) 67, No. 10 (October 26, 2021): 399–408. http://dx.doi.org/10.17221/223/2021-agricecon.
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This article investigates the connections among the prices of biofuels, agricultural commodities and other relevant assets in Europe, the US, and Brazil. The analysis includes a comprehensive data set covering price data for 38 traded titles during the period from 2003 to 2020. We used the minimum spanning tree (MST) approach to identify price connections in a complex trading system. Our analysis of mutual price connections reveals the major defining features of world-leading biofuel markets. We provide the characteristics of the main bioethanol and biodiesel markets with respect to government policies and technical and local features of the production and consumption of particular biofuels. Despite a relatively long and dynamically evolving history of biofuels, the biofuel systems in the US, Brazil and Europe do not converge toward the same pattern of relations among fossil fuels, biofuels, agricultural commodities and financial assets.
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Laptev, Pavel, Sergey Litovkin, Sergey Davydenko, Anton Konev, Evgeny Kostyuchenko, and Alexander Shelupanov. "Neural Network-Based Price Tag Data Analysis." Future Internet 14, no. 3 (March 13, 2022): 88. http://dx.doi.org/10.3390/fi14030088.
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This paper compares neural networks, specifically Unet, MobileNetV2, VGG16 and YOLOv4-tiny, for image segmentation as part of a study aimed at finding an optimal solution for price tag data analysis. The neural networks considered were trained on an individual dataset collected by the authors. Additionally, this paper covers the automatic image text recognition approach using EasyOCR API. Research revealed that the optimal network for segmentation is YOLOv4-tiny, featuring a cross validation accuracy of 96.92%. EasyOCR accuracy was also calculated and is 95.22%.
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SAWITRI, MADE NITA DWI, I. WAYAN SUMARJAYA, and NI KETUT TARI TASTRAWATI. "PERAMALAN MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK." E-Jurnal Matematika 7, no. 3 (September 2, 2018): 264. http://dx.doi.org/10.24843/mtk.2018.v07.i03.p213.
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The purpose of the study is to forecast the price of rice in the city of Denpasar in 2017 using backpropagation neural network method. Backpropagation neural network is a model of artificial neural network by finding the optimal weight value. Artificial neural networks are information processing systems that have certain performance characteristics similar to that of human neural networks. This analysis uses time series data of rice prices in the city of Denpasar from January 2001 until December 2016. The results of this research, concludes that the lowest rice price is predicted in July 2017 at Rp9791.5 while the highest rice price in April 2017 for Rp9839.4.
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Baboshkin, Pavel P., Alexey Yu Mikhaylov, and Zaffar Ahmed Shaikh. "Sustainable Cryptocurrency Growth Impossible? Impact of Network Power Demand on Bitcoin Price." Financial Journal 14, no. 3 (June 2022): 116–30. http://dx.doi.org/10.31107/2075-1990-2022-3-116-130.
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Due to the youth of the cryptocurrency sphere, the logic of interaction between investors, users and protocols is not always precisely defined. Analysis of the impact of ESG on cryptocurrencies proves that the demand for bitcoin network capacity (occupies the main market share) is the main factor in predicting the price of this cryptocurrency and the cryptocurrency market as a whole. The choice of the statistical method of analysis is determined by the purpose of statistically justified determination of the relationship of the data under consideration, and the reliability of the analysis is checked using Fischer and Student tests. In this paper, several innovations are proposed to solve the problem of energy dependence of cryptocurrencies: firstly, the analysis of cryptocurrencies in the paradigm of sustainable development (taking into account the consumption of a huge amount of energy for the functioning of cryptocurrency systems); secondly, feedback logic to explain the interaction of subjects, including the following parties: users, developers, network infrastructure and their interaction; thirdly, statistical analysis with the creation of artificial variables from real data and iterative improvement of the model. This paper proves that sustainable cryptocurrency growth is impossible when viewed from the perspective of “Green Economics” by Molly Scott Cato. The author's approach is relevant compared to other methods of linear transformations for creating artificial variables by selecting data using the VIF test. As a result, several versions of models were obtained using various combinations of the initially proposed factors, on the basis of which the nature of the greatest influence on the price of bitcoin was established in the form of technical factors and energy infrastructure needs.
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Grange, Louis de, Carlos Melo-Riquelme, Cristóbal Burgos, Felipe González, and Sebastián Raveau. "Numerical Bounds on the Price of Anarchy." Journal of Advanced Transportation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5062984.
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Theoretical upper bounds for price of anarchy have been calculated in previous studies. We present an empirical analysis for the price of anarchy for congested transportation networks; different network sizes and demand levels are considered for each network. We obtain a maximum price of anarchy for the cases studied, which is notably lower than the theoretical bounds reported in the literature. This result should be carefully considered in the design and implementation of road pricing mechanisms for cities.
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Jawad, Shafqat, and Junyong Liu. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends." Energies 13, no. 13 (July 1, 2020): 3371. http://dx.doi.org/10.3390/en13133371.
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The growing trend in electrical vehicle (EV) deployment has transformed independent power network and transportation network studies into highly congested interdependent network performance evaluations assessing their impact on power and transportation systems. Electrified transportation is highly capable of intensifying the interdependent correlations across charging service, transportation, and power networks. However, the evaluation of the complex coupled relationship across charging services, transportation, and power networks poses several challenges, including an impact on charging scheduling, traffic congestion, charging loads on the power grid, and high costs. Therefore, this article presents comparative survey analytics of large-scale EV integration’s impact on charging service network scheduling, transportation networks, and power networks. Moreover, price mechanism strategies to determine the charging fares, minimize investment profits, diminish traffic congestion, and reduce power distribution constraints under the influence of various factors were carried out. Additionally, the survey analysis stipulates the interdependent network performance index, ascertaining travel distance, route selection, long-term and short-term planning, and different infrastructure strategies. Finally, the limitations of the proposed study, potential research trends, and critical technologies are demonstrated for future inquiries.
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Blocher, Jordan, and Frederick C. Harris. "An Equilibrium Analysis of a Secondary Mobile Data-Share Market." Information 12, no. 11 (October 20, 2021): 434. http://dx.doi.org/10.3390/info12110434.
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Internet service providers are offering shared data plans where multiple users may buy and sell their overage data in a secondary market managed by the ISP. We propose a game-theoretic approach to a software-defined network for modeling this wireless data exchange market: a fully connected, non-cooperative network. We identify and define the rules for the underlying progressive second price (PSP) auction for the respective network and market structure. We allow for a single degree of statistical freedom—the reserve price—and show that the secondary data exchange market allows for greater flexibility in the acquisition decision making of mechanism design. We have designed a framework to optimize the strategy space using the elasticity of supply and demand. Wireless users are modeled as a distribution of buyers and sellers with normal incentives. Our derivation of a buyer-response strategy for wireless users based on second price market dynamics leads us to prove the existence of a balanced pricing scheme. We examine shifts in the market price function and prove that our network upholds the desired properties for optimization with respect to software-defined networks and prove the existence of a Nash equilibrium in the overlying non-cooperative game.
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Xie, Feng Jie, and Jing Shi. "The Evolution of Price Competition Game on Complex Networks." Complexity 2018 (July 9, 2018): 1–13. http://dx.doi.org/10.1155/2018/9649863.
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The well-known “Bertrand paradox” describes a price competition game in which two competing firms reach an outcome where both charge a price equal to the marginal cost. The fact that the Bertrand paradox often goes against empirical evidences has intrigued many researchers. In this work, we study the game from a new theoretical perspective—an evolutionary game on complex networks. Three classic network models, square lattice, WS small-world network, and BA scale-free network, are used to describe the competitive relations among the firms which are bounded rational. The analysis result shows that full price keeping is one of the evolutionary equilibriums in a well-mixed interaction situation. Detailed experiment results indicate that the price-keeping phenomenon emerges in a square lattice, small-world network and scale-free network much more frequently than in a complete network which represents the well-mixed interaction situation. While the square lattice has little advantage in achieving full price keeping, the small-world network and the scale-free network exhibit a stronger capability in full price keeping than the complete network. This means that a complex competitive relation is a crucial factor for maintaining the price in the real world. Moreover, competition scale, original price, degree of cutting price, and demand sensitivity to price show a significant influence on price evolution on a complex network. The payoff scheme, which describes how each firm’s payoff is calculated in each round game, only influences the price evolution on the scale-free network. These results provide new and important insights for understanding price competition in the real world.
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Wankhade, Sunil B., Divyesh Surana, Neel J. Mansatta, and Karan Shah. "Hybrid Model based on unification of Technical Analysis and Sentiment Analysis for Stock Price Prediction." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 9 (December 5, 2013): 3025–33. http://dx.doi.org/10.24297/ijct.v11i9.3415.
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Stock price forecasting phenomenon has been majorly made on the basis of quantitative information. Over the time, with the advent of technology, stock forecasting used technical analysis to get more accurate predictions. Until recently, studies have demonstrated that sentiment information hidden in corporate reports can be effectively incorporated to predict short-run stock price returns. Soft computing methods, like neural networks, fuzzy models and support vector regression, have shown great results in the forecasting of stock price due to their ability to model complex non-linear systems.In this paper we propose a hybrid method for stock price predication, which is combinational feature from technical analysis and sentiment analysis (SA). The features of sentiment analysis are based on a Point wise Mutual Information (PMI) and we apply neural network and ε-support vector regression models to predict the yearly change in the stock price.
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Li, Fali, Wenjing Peng, Yuanling Jiang, Limeng Song, Yuanyuan Liao, Chanlin Yi, Luyan Zhang, et al. "The Dynamic Brain Networks of Motor Imagery: Time-Varying Causality Analysis of Scalp EEG." International Journal of Neural Systems 29, no. 01 (January 10, 2019): 1850016. http://dx.doi.org/10.1142/s0129065718500168.
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Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which involves a large-scale network that spans multiple brain areas. The corresponding cortical activity reflected on the scalp is characterized by event-related desynchronization (ERD) and then by event-related synchronization (ERS). However, the network mechanisms that account for the dynamic information processing of MI during the ERD and ERS periods remain unknown. Here, we combined ERD/ERS analysis with the dynamic networks in different MI stages (i.e. motor preparation, ERD and ERS) to probe the dynamic processing of MI information. Our results show that specific dynamic network structures correspond to the ERD/ERS evolution patterns. Specifically, ERD mainly shows the contralateral networks, while ERS has the symmetric networks. Moreover, different dynamic network patterns are also revealed between the two types of MIs, in which the left-hand MIs exhibit a relatively less sustained contralateral network, which may be the network mechanism that accounts for the bilateral ERD/ERS observed for the left-hand MIs. Similar to the network topologies, the three MI stages also appear to be characterized by different network properties. The above findings all demonstrate that different MI stages that involve specific brain networks for dynamically processing the MI information.
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Lahmiri, Salim. "A Technical Analysis Information Fusion Approach for Stock Price Analysis and Modeling." Fluctuation and Noise Letters 17, no. 01 (January 23, 2018): 1850007. http://dx.doi.org/10.1142/s0219477518500074.
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In this paper, we address the problem of technical analysis information fusion in improving stock market index-level prediction. We present an approach for analyzing stock market price behavior based on different categories of technical analysis metrics and a multiple predictive system. Each category of technical analysis measures is used to characterize stock market price movements. The presented predictive system is based on an ensemble of neural networks (NN) coupled with particle swarm intelligence for parameter optimization where each single neural network is trained with a specific category of technical analysis measures. The experimental evaluation on three international stock market indices and three individual stocks show that the presented ensemble-based technical indicators fusion system significantly improves forecasting accuracy in comparison with single NN. Also, it outperforms the classical neural network trained with index-level lagged values and NN trained with stationary wavelet transform details and approximation coefficients. As a result, technical information fusion in NN ensemble architecture helps improving prediction accuracy.
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Ji, Yu, Xiaogang Hou, Lingfeng Kou, Ming Wu, Ying Zhang, Xiong Xiong, Baodi Ding, Ping Xue, Junlong Li, and Yue Xiang. "Cost–Benefit Analysis of Energy Storage in Distribution Networks." Energies 12, no. 17 (September 1, 2019): 3363. http://dx.doi.org/10.3390/en12173363.
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Due to the challenges posed to power systems because of the variability and uncertainty in clean energy, the integration of energy storage devices (ESD) has provided a rigorous approach to improve network stability in recent years. Moreover, with the rapid development of the electricity market, an ESD operation strategy, which can maximize the benefits of ESD owners as well as the contribution to the electricity network stability, plays an important role in the marketization of ESDs. Although the benefits for ESD owners are discussed in many studies, the economic impact of ESD operation on distribution networks has not been commendably taken into account. Therefore, a cost–benefit analysis method of ESD which quantifies the economic impact of ESD operation on distribution networks is proposed in this paper. Considering the time-of-use (TOU) price and load demand, the arbitrage of ESD is realized through a strategy with low price charging and high price discharging. Then, the auxiliary service of ESD is realized by its capability of peak shaving and valley filling. In this paper, the long-run incremental cost (LRIC) method is adopted to calculate the network price based on the congestion cost. Based on the dynamic cost–benefit analysis method, the cost–benefit marginal analysis model in the ESD life cycle is proposed through the calculation of the present value of benefit. Subsequently, the optimal ESD capacity and charge/discharge rate is obtained to get the shortest payback period by analyzing different operation parameters. Finally, a case study is undertaken, where the ESD operation model mentioned above is simulated on a two-bus system and a 33-bus system, and the ESD cost–benefit analysis and the analysis of corresponding influence factors are carried out adequately.
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Tsai, Pei-Hsuan, Jun-Bin Zhang, and Meng-Hsun Tsai. "An Efficient Probe-Based Routing for Content-Centric Networking." Sensors 22, no. 1 (January 4, 2022): 341. http://dx.doi.org/10.3390/s22010341.
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With the development of new technologies and applications, such as the Internet of Things, smart cities, 5G, and edge computing, traditional Internet Protocol-based (IP-based) networks have been exposed as having many problems. Information-Centric Networking (ICN), Named Data Networking (NDN), and Content-Centric Networking (CCN) are therefore proposed as an alternative for future networks. However, unlike IP-based networks, CCN routing is non-deterministic and difficult to optimize due to frequent in-network caching replacement. This paper presents a novel probe-based routing algorithm that explores real-time in-network caching to ensure the routing table storing the optimal paths to the nearest content provider is up to date. Effective probe-selections, Pending Interest Table (PIT) probe, and Forwarding Information Base (FIB) probe are discussed and analyzed by simulation with different performance measurements. Compared with the basic CCN, in terms of qualitative analysis, the additional computational overhead of our approach is O(NCS + Nrt + NFIB ∗ NSPT) and O(NFIB) on processing interest packets and data packets, respectively. However, in terms of quantitative analysis, our approach reduces the number of timeout interests by 6% and the average response time by 0.6 s. Furthermore, although basic CCN and our approach belong to the same Quality of Service (QoS) category, our approach outperforms basic CCN in terms of real values. Additionally, our probe-based approach performs better than RECIF+PIF and EEGPR. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when accounting for router failures. In summary, the results demonstrate that compared to basic CCN, our probe-based routing approach raises FIB accuracy and reduces network congestion and response time, resulting in efficient routing.
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Lin, Haiyan. "Network Consumers’ Reference Price Formation Analysis." Open Journal of Business and Management 06, no. 03 (2018): 696–706. http://dx.doi.org/10.4236/ojbm.2018.63053.
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Lee, Se-Yul, and Yong-Soo Kim. "Design and Analysis of Probe Detection Systems for TCP Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 369–72. http://dx.doi.org/10.20965/jaciii.2004.p0369.
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Advanced computer network technology enables the connectivity of computers in an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and cannot detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We propose a network-based intrusion detection model using fuzzy cognitive maps (FCM) that detects intrusion by Denial of Service (DoS) attack detection using packet analysis. A DoS attack typically appears as a Probe and Syn Flooding attack. Syn Flooding Preventer using Fuzzy cognitive maps (SPuF) model captures and analyzes packet information to detect Syn flooding attacks. Using the result of decision module analysis, which used FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. The result of simulation using the "KDD’99 Competition Data Set" for the SPuF model shows that Probe detection exceeded 97%.
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Qin, Xiao Wei, and Feng Chen. "Multicommodity-Based Delay Analysis for Heterogeneous Wireless Services in Core Network." Applied Mechanics and Materials 198-199 (September 2012): 1733–38. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1733.
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With the explosive growth of wireless applications, the subscribers’ requirements of QoS (Quality of Service) are increasing as well. In this paper, the upper bound of the tolerant delay of services in wireless access network is investigated, by mapping core network onto a cost-variable directed graph, where the cost is construed as the average service delay of the flows traveling in core network that depends on the current load. A multicommodity minimal cost flow mathematics problem is then derived and solved by Price-directive Decomposition and Lagrangian Relaxation. Simulations are carried out in two typical core networks and some valuable conclusions are gained.
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Hervás, C., A. Garrido, B. Lucena, N. García, and E. De Pedro. "Near Infrared Spectroscopy for Classification of Iberian Pig Carcasses Using an Artificial Neural Network." Journal of Near Infrared Spectroscopy 2, no. 4 (October 1994): 177–84. http://dx.doi.org/10.1255/jnirs.44.
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Artificial neural networks (ANNs) have demonstrated their usefulness in near infrared (NIR) reflection and transmittance spectroscopy for quantitative prediction. The new approach presented here considers the use of ANNs for qualitative classification. Four forms of neural networks (a competitive network using the learning vector quantisation, LVQ learning rule; a backpropagation network using the extended delta-bar-delta, EDBD rule; a network with direct random search, DRS; and a simple competitive linear network, CL) have been tested for classification of 118 fat samples from Iberian pig carcasses into three different price groups. An ANN using the LVQ learning rule has been found to be the best in terms of classification error size. The classification ability of the LVQ network has been evaluated against discriminant analysis, one of the most used methods for NIR spectroscopic qualitative analysis.
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Kalampakas, Argyrios, and Georgios C. Makris. "Statistical Analysis to Bitcoin Transactions Network." International Journal of Statistics and Probability 9, no. 5 (August 28, 2020): 85. http://dx.doi.org/10.5539/ijsp.v9n5p85.
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There is abundantly documented scientific evidence that the financial transactions that have grown rapidly recently, in conjuction with the interest of the public, were due to the sharp rise in the price of Bitcoin in December 2017. As a consequence, a freshly emerging dataset in the research community has emerged. Therefore, the aim of the present investigation was to examine the analyses of data in this newly emerging dataset in the research community. In order to achieve the extraction of data, their conversion to network and finally their fragmentation, the studied variables were analyzed by using two parts of analysis, namely, statistical network analyses and economic activity analyses. Network statistical analyses was employed aiming to analyze, in a holistic approach, the complex systems of modern times which are represented as networks, as it is impossible to analyze them partially, in order to avoid incorrect conclusions. Additionally, the analyses of economic activity, which is related to indicators from the stock market and the economics of science, was used, after it had been transferred and matched with the economic model represented by Bitcoin. The results distinguished the extent of the data generated by the statistical analyses of the networks and the analyses of economic activity. With respect to data presented, we established that the daily transaction networks were scale free networks which were not evolving like ER random networks and they were not defined as the small world. Also, it was demonstrated that daily transaction networks cannot be reproduced in a random way like ER random networks. Furthermore, the opportunities and problems encountered in conducting the present research were briefly presented.
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BIKSHAM, V., B. VISHAL KUMAR, C. RAHUL, G. VENU, and M. BHARGAV SAI. "STOCK PRICE PREDICTION." YMER Digital 21, no. 05 (May 2, 2022): 1–6. http://dx.doi.org/10.37896/ymer21.05/01.
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Machine learning has many important applications in the stock price prediction. Here, we will discuss about predicting the returns on stocks. This has uncertainties and it is a very complex task. This project will be developed into two parts: First, we will learn how to predict stock price using the Long Short-Term Memory neural networks. Predicting stock market prices involves human-computer interaction. For stock market analysis, conventional batch processing methods cannot be utilized efficiently due to the correlated nature of stock prices. We suggest an algorithm that utilizes a kind of recurrent neural network (RNN) called Long Short-Term Memory (LSTM), where using stochastic gradient descent the weights are adjusted for individual data points
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Pavićević, Milutin, and Tomo Popović. "Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks." Sensors 22, no. 3 (January 28, 2022): 1051. http://dx.doi.org/10.3390/s22031051.
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As artificial neural network architectures grow increasingly more efficient in time-series prediction tasks, their use for day-ahead electricity price and demand prediction, a task with very specific rules and highly volatile dataset values, grows more attractive. Without a standardized way to compare the efficiency of algorithms and methods for forecasting electricity metrics, it is hard to have a good sense of the strengths and weaknesses of each approach. In this paper, we create models in several neural network architectures for predicting the electricity price on the HUPX market and electricity load in Montenegro and compare them to multiple neural network models on the same basis (using the same dataset and metrics). The results show the promising efficiency of neural networks in general for the task of short-term prediction in the field, with methods combining fully connected layers and recurrent neural or temporal convolutional layers performing the best. The feature extraction power of convolutional layers shows very promising results and recommends the further exploration of temporal convolutional networks in the field.
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Mahmoudabadi, Abbas, Mehdi Kanaani, and Fatemeh Pourhossein Ghazimahalleh. "Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price." HighTech and Innovation Journal 3, no. 1 (March 1, 2022): 45–55. http://dx.doi.org/10.28991/hij-2022-03-01-05.
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Investment experts, who deal with stock price estimation, commonly look for the most accurate and appropriate statistical techniques to make decisions on investment. The aim of this study is to improve the accuracy of stock price prediction models through modifying the structure of a combined neural network model with time-series data, in which the main contribution is to insert the time-series analysis prediction into the hidden layer of the neural network. The proposed structure is made up of neural networks and time-series analysis, with variable reduction used to remove attributes with inter-correlations. Data has been collected over six years (72 months) from the Iranian stock market, including the number of trades, new-coin price, gold-18 price, US Dollar and Euro equivalent currencies, oil-index price, Brent-oil price, industry index, and balanced stock index, followed by developing the prediction models. Comparing the performance criteria of the proposed structure to the traditional ones in terms of the mean square and mean absolute errors revealed that inserting time-series estimated variables into hidden layers would improve the performance of neural network models to estimate stock prices for making investment decisions. Doi: 10.28991/HIJ-2022-03-01-05 Full Text: PDF
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Savita, Savita, Anjali Anjali, and Gurpal Singh. "Analysis of MAC Protocol for Reliable Broadcast." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (June 21, 2018): 536–43. http://dx.doi.org/10.24297/ijct.v4i2c1.4180.
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In wireless communication It is important to find a reliable broadcasting protocol that is especially designed for an optimum performance of public-safety and data travelling related applications. Using RSU and OBU, there are four novel ideas presented in this research work, namely choosing the nearest following node as the network probe node, headway-based segmentation, non-uniform segmentation and application adaptive. The integration of these ideas results in a protocol that possesses minimum latency, minimum probability of collision in the acknowledgment messages and unique robustness at different speeds and traffic volumes. Wireless communications are becoming the dominant form of transferring information,and the most active research field. In this dissertation, we will present one of the most applicable forms of Ad-Hoc networks; the Vehicular Ad-Hoc Networks (VANETs). VANET is the technology of building a robust Ad-Hoc network between mobile vehicles and each other, besides, between mobile vehicles and roadside units.
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Xu, Xiaojie, and Yun Zhang. "Network analysis of corn cash price comovements." Machine Learning with Applications 6 (December 2021): 100140. http://dx.doi.org/10.1016/j.mlwa.2021.100140.
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Bilò, Vittorio, Michele Flammini, and Luca Moscardelli. "On Nash Equilibria in Non-Cooperative All-Optical Networks." Algorithms 14, no. 1 (January 9, 2021): 15. http://dx.doi.org/10.3390/a14010015.
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We consider the problem of determining a routing in all-optical networks, in which some couples of nodes want to communicate. In particular, we study this problem from the point of view of a network provider that has to design suitable payment functions for non-cooperative agents, corresponding to the couples of nodes wishing to communicate. The network provider aims at inducing stable routings (i.e., routings corresponding to Nash equilibria) using a low number of wavelengths. We consider three different kinds of local knowledge that agents may exploit to compute their payments, leading to three corresponding information levels. Under complete information, the network provider can design a payment function, inducing the agents to reach a Nash equilibrium mirroring any desired routing. If the price to an agent is computed only as a function of the wavelengths used along connecting paths (minimal level) or edges (intermediate level), the most reasonable functions either do not admit Nash equilibria or admit very inefficient ones, i.e., with the largest possible price of anarchy. However, by suitably restricting the network topology, a constant price of anarchy for chains and rings and a logarithmic one for trees can be obtained under the minimal and intermediate levels, respectively.
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Bilò, Vittorio, Michele Flammini, and Luca Moscardelli. "On Nash Equilibria in Non-Cooperative All-Optical Networks." Algorithms 14, no. 1 (January 9, 2021): 15. http://dx.doi.org/10.3390/a14010015.
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We consider the problem of determining a routing in all-optical networks, in which some couples of nodes want to communicate. In particular, we study this problem from the point of view of a network provider that has to design suitable payment functions for non-cooperative agents, corresponding to the couples of nodes wishing to communicate. The network provider aims at inducing stable routings (i.e., routings corresponding to Nash equilibria) using a low number of wavelengths. We consider three different kinds of local knowledge that agents may exploit to compute their payments, leading to three corresponding information levels. Under complete information, the network provider can design a payment function, inducing the agents to reach a Nash equilibrium mirroring any desired routing. If the price to an agent is computed only as a function of the wavelengths used along connecting paths (minimal level) or edges (intermediate level), the most reasonable functions either do not admit Nash equilibria or admit very inefficient ones, i.e., with the largest possible price of anarchy. However, by suitably restricting the network topology, a constant price of anarchy for chains and rings and a logarithmic one for trees can be obtained under the minimal and intermediate levels, respectively.
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Zhao, Jumin, Hao Tian, and Deng-ao Li. "Channel Prediction Based on BP Neural Network for Backscatter Communication Networks." Sensors 20, no. 1 (January 5, 2020): 300. http://dx.doi.org/10.3390/s20010300.
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Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing methods share common drawbacks; that is, spatial and frequency diversity cannot be considered at the same time or channel probe is expensive. In this paper, we propose a channel prediction scheme for backscatter networks. The scheme consists of two parts: the monitoring module, which uses the data of the acceleration sensor to monitor the movement of the node itself, and uses the link burstiness metric β to monitor the burstiness caused by the environmental change, thereby determining that new data of channel quality are needed. The prediction module predicts the channel quality at the next moment using a prediction algorithm based on BP (back propagation) neural network. We implemented the scheme on readers. The experimental results show that the accuracy of channel prediction is high and the network goodput is improved.
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Muzakir, Ari, and Usman Ependi. "ANALYSIS OF THE USE OF CELLULAR OPERATORS USING THE ANALYTIC HIERARCHY PROCESS METHOD." Journal of Information Systems and Informatics 1, no. 1 (March 5, 2019): 29–38. http://dx.doi.org/10.33557/isi.v1i1.5.
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The development of technology in the telecommunications sector is now easier to be enjoyed by the community. Almost all regions have been able to enjoy the ease of access to information through telecommunications networks. Telecommunications service providers certainly have competed in such a way as to be able to hold users to remain loyal to the provider used. From several surveys conducted, there are still many users who cannot be loyal to use the services of these providers. The increasing number of communication network providers makes users more choices. Various ways are carried out by communication network providers to maintain their users such as giving bonuses, providing cheap rates, increasing services to the regions. In this study will focus on the level of customer satisfaction with cellular operators with a variety of criteria that have been prepared which will be a consideration for users in using these cellular operators. This research will use the AHP method (Analytic Hierarchy Process) to find out which cellular operators are the most superior based on the judgment of the user with various criteria each. So that in the end it will be known which cellular operators will be selected and purchased by consumers according to their respective needs. The results of this study show that based on the bonus, IM3 is the best with a weight of 0.21712. Then based on the price rate, a tri card with a weight of 0.16565. Furthermore, the service criteria show a better Sympathy card with a weight of 0.21311
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Muzakir, Ari, and Usman Ependi. "ANALYSIS OF THE USE OF CELLULAR OPERATORS USING THE ANALYTIC HIERARCHY PROCESS METHOD." Journal of Information Systems and Informatics 1, no. 1 (March 5, 2019): 29–38. http://dx.doi.org/10.33557/journal-isi.v1i1.5.
Full textAbstract:
The development of technology in the telecommunications sector is now easier to be enjoyed by the community. Almost all regions have been able to enjoy the ease of access to information through telecommunications networks. Telecommunications service providers certainly have competed in such a way as to be able to hold users to remain loyal to the provider used. From several surveys conducted, there are still many users who cannot be loyal to use the services of these providers. The increasing number of communication network providers makes users more choices. Various ways are carried out by communication network providers to maintain their users such as giving bonuses, providing cheap rates, increasing services to the regions. In this study will focus on the level of customer satisfaction with cellular operators with a variety of criteria that have been prepared which will be a consideration for users in using these cellular operators. This research will use the AHP method (Analytic Hierarchy Process) to find out which cellular operators are the most superior based on the judgment of the user with various criteria each. So that in the end it will be known which cellular operators will be selected and purchased by consumers according to their respective needs. The results of this study show that based on the bonus, IM3 is the best with a weight of 0.21712. Then based on the price rate, a tri card with a weight of 0.16565. Furthermore, the service criteria show a better Sympathy card with a weight of 0.21311
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Muzakir, Ari, and Usman Ependi. "ANALYSIS OF THE USE OF CELLULAR OPERATORS USING THE ANALYTIC HIERARCHY PROCESS METHOD." Journal of Information Systems and Informatics 1, no. 1 (March 5, 2019): 29–38. http://dx.doi.org/10.33557/journalisi.v1i1.5.
Full textAbstract:
The development of technology in the telecommunications sector is now easier to be enjoyed by the community. Almost all regions have been able to enjoy the ease of access to information through telecommunications networks. Telecommunications service providers certainly have competed in such a way as to be able to hold users to remain loyal to the provider used. From several surveys conducted, there are still many users who cannot be loyal to use the services of these providers. The increasing number of communication network providers makes users more choices. Various ways are carried out by communication network providers to maintain their users such as giving bonuses, providing cheap rates, increasing services to the regions. In this study will focus on the level of customer satisfaction with cellular operators with a variety of criteria that have been prepared which will be a consideration for users in using these cellular operators. This research will use the AHP method (Analytic Hierarchy Process) to find out which cellular operators are the most superior based on the judgment of the user with various criteria each. So that in the end it will be known which cellular operators will be selected and purchased by consumers according to their respective needs. The results of this study show that based on the bonus, IM3 is the best with a weight of 0.21712. Then based on the price rate, a tri card with a weight of 0.16565. Furthermore, the service criteria show a better Sympathy card with a weight of 0.21311
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Wang, Xiao, and Dirk Burghardt. "Building-network: Concept, generation method and centrality analysis." Proceedings of the ICA 2 (July 10, 2019): 1–8. http://dx.doi.org/10.5194/ica-proc-2-141-2019.
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<p><strong>Abstract.</strong> Buildings are among the most important features of cities. In the suburban or rural regions, buildings are normally constructed along the roads, which forms the smooth and consistent patterns so that the building arrangements also can be described with network models. In previous studies, network theory has achieved good performance in cartography and GIS. In this paper, a study of a building-network is proposed, including the concepts, generation methods and centrality analysis. Firstly, with the constraint Delaunay triangulation and the refinement strategy by facing ratio, the building-network is generated by considering the buildings and the proximal segments as the nodes and segments of the network, respectively. Then, centrality analysis is applied on the building-network, aiming to reveal the crucial relationships among buildings, which is useful for understanding the structural properties of the complex network. Four different centrality measures, i.e. degree, closeness, betweenness, and eigenvector centrality, are calculated based on the building-networks. The buildings show different distribution effects and patterns under the four centrality measures. From the results, the degree centrality reveals the local centre of the region; closeness and eigenvector centrality have the ability to cluster buildings into different groups; while betweenness centrality can detect the linear patterns. Therefore, using network theory to analyse buildings can reveal some inner relationships of buildings and has great potential in the application of building pattern detection, classification, clustering and further generalization.</p>
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Pedram, Mehdi, and Maryam Ebrahimi. "The Effects of Economic Variables on Exchange Rate, Modeling and Forecasting: Case of Iran." Business and Management Horizons 3, no. 1 (May 25, 2015): 13. http://dx.doi.org/10.5296/bmh.v3i1.7675.
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This paper investigates the model estimation and data forecasting of exchange rate using artificial neural network. Recent studies have shown the classification and prediction power of the neural networks. It has been demonstrated that a neural network can approximate any continuous function. In this research, ANN is employed in training and learning processes and after modeling, the forecast performance is measured by making use of a loss function (RMSE). By sensitivity analysis, the importance and the weight of each economic variable on exchange rate such as consumer price index, old price, oil price and total value of export and import have been determined. The results show that Iran consumer price index is the most effective factor on exchange rate trend. In addition to, it is possible to estimate a model to forecast the value of exchange rate even by having access to a limited subset of data.
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Dhafer, Ali H., Fauzias Mat Nor, Gamal Alkawsi, Abdulaleem Z. Al-Othmani, Nuradli Ridzwan Shah, Huda M. Alshanbari, Khairil Faizal Bin Khairi, and Yahia Baashar. "Empirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicators." Computational Intelligence and Neuroscience 2022 (March 25, 2022): 1–13. http://dx.doi.org/10.1155/2022/9208640.
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Stock price prediction is one of the major challenges for investors who participate in the stock markets. Therefore, different methods have been explored by practitioners and academicians to predict stock price movement. Artificial intelligence models are one of the methods that attracted many researchers in the field of financial prediction in the stock market. This study investigates the prediction of the daily stock prices for Commerce International Merchant Bankers (CIMB) using technical indicators in a NARX neural network model. The methodology employs comprehensive parameter trails for different combinations of input variables and different neural network designs. The study seeks to investigate the optimal artificial neural networks (ANN) parameters and settings that enhance the performance of the NARX model. Therefore, extensive parameter trails were studied for various combinations of input variables and NARX neural network configurations. The proposed model is further enhanced by preprocessing and optimising the NARX model’s input and output parameers. The prediction performance is assessed based on the mean squared error (MSE), R-squared, and hit rate. The performance of the proposed model is compared with other models, and it is shown that the utilisation of technical indicators with the NARX neural network improves the accuracy of one-step-ahead prediction for CIMB stock in Malaysia. The performance of the proposed model is further improved by optimising the input data and neural network parameters. The improved prediction of stock prices could help investors increase their returns from investment in stock markets.
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Creamer, Germán G., and Tal Ben-Zvi. "Volatility and Risk in the Energy Market: A Trade Network Approach." Sustainability 13, no. 18 (September 13, 2021): 10199. http://dx.doi.org/10.3390/su131810199.
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This paper evaluates the effect of energy trade networks on the price volatility of coal, oil, natural gas, and electricity. This research conducts a longitudinal analysis using a time series of static coal trade networks to generate a dynamic trade network. It uses the component causality index as a leading indicator of the price volatility of the energy market. This research finds out that the component causality index, based on degree centrality, anticipates or moves together with coal volatility and, to a lesser degree, with natural gas and electricity volatility for the period 1998–2014. The proposed index could be integrated into a risk management system for investors and regulators. The broad impact of this research lies in the understanding of mechanisms of the instability and risk of the energy sector as a result of a complex interaction of the network of producers and traders.
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Dai, Zhiyi, and Zhengming Zhou. "Research and Forecast Analysis of Financial Stability for Policy Uncertainty." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/8799247.
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The instability of financial market will have a great impact on money, bonds, and stocks and affect the economic development of society and people's lives. Therefore, it is very necessary for us to study and predict the financial stability. According to the forecast results, we will analyze and make a series of preparatory measures. First, we make a series of analyses on the structure and significance of policy uncertainty and financial stability. This paper introduces the advantages and disadvantages of the P/L model, the KLS signal method, and the BP neural network model for financial stability early warning, It is clearly pointed out that the BP neural network is more reliable and accurate, Then, the BP neural network, the ant colony algorithm, and the genetic algorithm are used to predict the opening price, closing price, highest price, and lowest price of KDJ index of Cathay Pacific Group's 5-day data. Compared with the real value, we find that the BP neural network is almost the smallest in forecasting the opening price and closing price, or the lowest price and the highest price, and has good stability, which once again proves the feasibility of applying the BP neural network to the research and prediction of financial stability.
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ROXAS-VILLANUEVA, RANZIVELLE MARIANNE, MAELORI KRISTA NAMBATAC, and GIOVANNI TAPANG. "CHARACTERIZING ENGLISH POETIC STYLE USING COMPLEX NETWORKS." International Journal of Modern Physics C 23, no. 02 (February 2012): 1250009. http://dx.doi.org/10.1142/s012918311250009x.
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Complex networks have been proven useful in characterizing written texts. Here, we use networks to probe if there exist a similarity within, and difference across, era as reflected within the poem's structure. In literary history, boundary lines are set to distinguish the change in writing styles through time. We obtain the network parameters and motif frequencies of 845 poems published from 1522 to 1931 and relate this to the writing of the Elizabethan, 17th Century, Augustan, Romantic and Victorian eras. Analysis of the different network parameters shows a significant difference of the Augustan era (1667–1780) with the rest. The network parameters and the convex hull and centroids of the motif frequencies reflect the adjectival sequence pattern of the poems of the Augustan era.
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Finn, Kelly R. "Multilayer network analyses as a toolkit for measuring social structure." Current Zoology 67, no. 1 (January 11, 2021): 81–99. http://dx.doi.org/10.1093/cz/zoaa079.
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Abstract The formalization of multilayer networks allows for new ways to measure sociality in complex social systems, including groups of animals. The same mathematical representation and methods are widely applicable across fields and study systems, and a network can represent drastically different types of data. As such, in order to apply analyses and interpret the results in a meaningful way the researcher must have a deep understanding of what their network is representing and what parts of it are being measured by a given analysis. Multilayer social networks can represent social structure with more detail than is often present in single layer networks, including multiple “types” of individuals, interactions, or relationships, and the extent to which these types are interdependent. Multilayer networks can also encompass a wider range of social scales, which can help overcome complications that are inherent to measuring sociality. In this paper, I dissect multilayer networks into the parts that correspond to different components of social structures. I then discuss common pitfalls to avoid across different stages of multilayer network analyses—some novel and some that always exist in social network analysis but are magnified in multi-layer representations. This paper serves as a primer for building a customized toolkit of multilayer network analyses, to probe components of social structure in animal social systems.
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Anand, C. "Comparison of Stock Price Prediction Models using Pre-trained Neural Networks." March 2021 3, no. 2 (July 19, 2021): 122–34. http://dx.doi.org/10.36548/jucct.2021.2.005.
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Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures inclusive of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used in this paper for stock price prediction of an organization by using the previously available stock prices. The National Stock Exchange (NSE) of India dataset is used for training the model with day-wise closing price. Data prediction is performed for a few sample companies selected on a random basis. Based on the comparison results, it is evident that the existing models are outperformed by CNN. The network can also perform stock predictions for other stock markets despite being trained with single market data as a common inner dynamics that has been shared between certain stock markets. When compared to the existing linear models, the neural network model outperforms them in a significant manner, which can be observed from the comparison results.
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Li, Hong Wei, Xiao Xiang Gao, and Ke Jun Cheng. "The Application of Wavelet Neural Network in Prediction of the Fish Price." Applied Mechanics and Materials 687-691 (November 2014): 1945–49. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1945.
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The market fish price is an important factor that affects the income of fishermen, so how to accurately analyze and predict the fish pricet o obtain huge profits has caught people's attention. As science advances, various price forecasting and analysis methods have come into being. How to build a prediction theories and models with relatively high success rate has been the study of many scholars over the years. With the development of artificial intelligence, neural networks have become an important tool of predicting and analyzing changes in market prices. Neural networks are important artificial intelligence technology, which have simple structures, but are able to solve complicated problems. They have strong applicability in predicting the mature index fluctuations in a short period. This paper considers some shortcomings and deficiencies the BP network prototype, which tries to use the wavelet Functions to replace the excitation function in the traditional BP algorithm on the basis of a network of neurons and then forms into WNN. We can verify the feasibility of WNN by perch price forecasts, and then this method is used in price forecasts of the three main fish of the Ulungur Lake Aquatic, to provide the basis for the aquatic base decision
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Chaiwuttisak, Pornpimol. "Latent Topic Analysis of the Post Property for Sales to Predict a Selling Price of Second-Hand Condominiums." Journal of Physics: Conference Series 2050, no. 1 (October 1, 2021): 012005. http://dx.doi.org/10.1088/1742-6596/2050/1/012005.
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Abstract This research objective is to study the latent topics analysis in selling post of real estate of second-hand condominium by using Latent Dirichlet Allocation (LDA) and build a price prediction model of second-hand condominium using multiple linear regression and artificial neural networks by measuring and comparing the performance of the second hand condominium price prediction model with root mean square error (RMSE). This experiment included four variables are room size, number of bathroom, number of bedroom and latent topics from LDA. The result of LDA indicated that selling post of real estate can be separated into 4 topics, in which finding the factors that affect the price use the regression analysis method to get five variables are room size, number of bathroom, floors, topic 2 and topic 4. The RMSE based on the multiple linear regression analysis was 1.349, while the RMSE based on artificial neural network was 1.156. Thus, it can be concluded that the predictive model using the artificial neural networks is superior to multiple linear regression.
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Oliver Muncharaz, J. "Hybrid fuzzy neural network versus backpropagation neural network: An application to predict the Ibex-35 index stock." Finance, Markets and Valuation 6, no. 1 (2020): 85–98. http://dx.doi.org/10.46503/alep9985.
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The use of neural networks has been extended in all areas of knowledge due to the good results being obtained in the resolution of the different problems posed. The prediction of prices in general, and stock market prices in particular, represents one of the main objectives of the use of neural networks in finance. This paper presents the analysis of the efficiency of the hybrid fuzzy neural network against a backpropagation type neural network in the price prediction of the Spanish stock exchange index (IBEX-35). The paper is divided into two parts. In the first part, the main characteristics of neural networks such as hybrid fuzzy and backpropagation, their structures and learning rules are presented. In the second part, the prediction of the IBEX-35 stock exchange index with these networks is analyzed, measuring the efficiency of both as a function of the prediction errors committed. For this purpose, both networks have been constructed with the same inputs and for the same sample period. The results obtained suggest that the Hybrid fuzzy neuronal network is much more efficient than the widespread backpropagation neuronal network for the sample analysed.
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Duan, Keyi, Songyun Xie, Xin Zhang, Xinzhou Xie, Yujie Cui, Ruizhen Liu, and Jian Xu. "Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT)." Brain Sciences 13, no. 2 (January 31, 2023): 247. http://dx.doi.org/10.3390/brainsci13020247.
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The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1–30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks.
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Shang, Wen-Long, Yanyan Chen, Chengcheng Song, and Washington Y. Ochieng. "Robustness Analysis of Urban Road Networks from Topological and Operational Perspectives." Mathematical Problems in Engineering 2020 (August 14, 2020): 1–12. http://dx.doi.org/10.1155/2020/5875803.
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This study comprehensively analyses the robustness of urban road networks through topological indices based on the complex network theory and operational indices based on traffic assignment theory: User Equilibrium (UE), System Optimum (SO), and Price of Anarchy (POA). Analysing topological indices may pin down the most important nodes for URNs from the perspective of connectivity, while more sophisticated operational indices are helpful to examine the importance of nodes for URNs by taking into account link capacity, travel demand, and drivers’ behaviour. The previous way is calculated in a static way, which reduces the computation times and increases the efficiency for quick assessment of the robustness of URNs, while the latter is in a dynamic way, namely, calculating is based on removal of individual nodes, although this way is more likely to capture realistic meanings but consumes huge amount of time. The efforts made in this study try to find the relationship between topological and operational indices so as to assist the assessment of robustness of URNs to local disruptions. Seven realistic urban road networks such as Sioux Falls and Anaheim are used as network examples, and results show that different indices reflect robustness characteristics of urban road networks from different ways, and rank correlations between any two indices are poor although small network such as Sioux Falls have better correlations than others.
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Aggarwal, Kirti, and Anuja Arora. "Detecting Community Structure in Financial Markets Using the Bat Optimization Algorithm." International Journal of Information Technology Project Management 13, no. 3 (July 1, 2022): 1–21. http://dx.doi.org/10.4018/ijitpm.313421.
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A lucid representation of the hidden structure of real-world application has attracted complex network research communities and triggered a vast number of solutions in order to resolve complex network issues. In the same direction, initially, this paper proposes a methodology to act on the financial dataset and construct a stock correlation network of four stock indexes based on the closing stock price. The significance of this research work is to form an effective stock community based on their complex price pattern dependencies (i.e., simultaneous fluctuations in stock prices of companies in a time series data). This paper proposes a community detection approach for stock correlation complex networks using the BAT optimization algorithm aiming to achieve high modularity and better-correlated communities. Theoretical analysis and empirical modularity performance measure results have shown that the usage of BAT algorithm for community detection proves to transcend performance in comparison to standard network community detection algorithms – greedy and label propagation.
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Xiu, Yuxuan, Guanying Wang, and Wai Kin Victor Chan. "Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network." Entropy 23, no. 12 (November 30, 2021): 1612. http://dx.doi.org/10.3390/e23121612.
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This study proposes a framework to diagnose stock market crashes and predict the subsequent price rebounds. Based on the observation of anomalous changes in stock correlation networks during market crashes, we extend the log-periodic power-law model with a metric that is proposed to measure network anomalies. To calculate this metric, we design a prediction-guided anomaly detection algorithm based on the extreme value theory. Finally, we proposed a hybrid indicator to predict price rebounds of the stock index by combining the network anomaly metric and the visibility graph-based log-periodic power-law model. Experiments are conducted based on the New York Stock Exchange Composite Index from 4 January 1991 to 7 May 2021. It is shown that our proposed method outperforms the benchmark log-periodic power-law model on detecting the 12 major crashes and predicting the subsequent price rebounds by reducing the false alarm rate. This study sheds light on combining stock network analysis and financial time series modeling and highlights that anomalous changes of a stock network can be important criteria for detecting crashes and predicting recoveries of the stock market.
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Wu, Yang, Junyong Liu, Furong Li, Zhanxin Yan, and Li Zhang. "Network model of bilateral power markets based on complex networks." International Journal of Modern Physics B 28, no. 22 (July 3, 2014): 1450144. http://dx.doi.org/10.1142/s0217979214501446.
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The bilateral power transaction (BPT) mode becomes a typical market organization with the restructuring of electric power industry, the proper model which could capture its characteristics is in urgent need. However, the model is lacking because of this market organization's complexity. As a promising approach to modeling complex systems, complex networks could provide a sound theoretical framework for developing proper simulation model. In this paper, a complex network model of the BPT market is proposed. In this model, price advantage mechanism is a precondition. Unlike other general commodity transactions, both of the financial layer and the physical layer are considered in the model. Through simulation analysis, the feasibility and validity of the model are verified. At same time, some typical statistical features of BPT network are identified. Namely, the degree distribution follows the power law, the clustering coefficient is low and the average path length is a bit long. Moreover, the topological stability of the BPT network is tested. The results show that the network displays a topological robustness to random market member's failures while it is fragile against deliberate attacks, and the network could resist cascading failure to some extent. These features are helpful for making decisions and risk management in BPT markets.
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Štubňová, Michaela, Marta Urbaníková, Jarmila Hudáková, and Viera Papcunová. "Estimation of Residential Property Market Price: Comparison of Artificial Neural Networks and Hedonic Pricing Model." Emerging Science Journal 4, no. 6 (December 1, 2020): 530–38. http://dx.doi.org/10.28991/esj-2020-01250.
Full textAbstract:
The correct real estate property price estimation is significant not only in the real estate market but also in the banking sector for collateral loans and the insurance sector for property insurance. The paper focuses on both traditional and advanced methods for real estate property valuation. Attention is paid to the analysis of the accuracy of valuation models. From traditional methods, a regression model is used for residential property price estimation, which represents the hedonic approach. Modern advanced valuation methods are represented by the artificial neural network, which is one of the soft computing techniques. The results of both methods in residential property market price estimation are compared. The analysis is performed using data on residential properties sold on the real estate market in the city of Nitra in the Slovak Republic. To estimate the residential property prices, artificial neural networks trained with the Levenberg-Marquart learning algorithm, the Bayesian Regularization learning algorithm, and the Scaled Conjugate Gradient learning algorithm, and the regression pricing model are used. Among the constructed neural networks, the best results are achieved with networks trained with the Regularization learning algorithm with two hidden layers. Its performance is compared with the performance of the regression pricing model, and it can state that artificial neural networks can considerably improve prediction accuracy in the estimation of residential property market price. Doi: 10.28991/esj-2020-01250 Full Text: PDF