Neurotic Fuzzy-Data-Envelopment Analysis to Forecast Efficiency of Bank Branches

In this study the prediction of efficiency of four different Bank Branches have been done by using Neurotic Fuzzy Data Envelopment Analysis approach. In the first stage of the study, Artificial Neural Network (ANN) model has been modelled and trained using the last five years data. The data belonging any year has been taken as input of ANN, next year data has been defined as output of ANN. Fuzzyfication process has been applied to obtained predictions based on asking managers of bank branches, after Fuzzy Data Envelopment Analysis process has been applied to fuzzy values. As a result, the bank branches parameters belonging to 2021 year have been obtained. The efficiency of 2021 for bank branches have been calculated based on Fuzzy Data Envelopment Analysis (FDEA).

Predictability of temperature and pressure for issuing aircraft take-off forecast over Madras airport

Forecasting surface temperature and pressure to a reasonable degree of accuracy atleast 3 hours ahead of the scheduled departure of an aircraft helps the aircrew to make the optimum planning for the payload and cargo load. The method of generalised Adaptive Filter (AF) algorithm as suggested by Makridakis and Wheelright (1978) has been used to forecast temperature and pressure over Madras airport and the forecast efficiency is compared with that obtained through method of persistency, auto regressive processes and other statistical techniques. The dimensions of attractors of the phase space trajectories of these variables have been estimated using the Grassberger and Procaccia (1983) algorithm of correlation fractal dimension with a view to find out the predictability of these variables and the minimum and maximum number of parameters needed for modelling these variables.

Assessing Earthquake Forecast Performance Based on b Value in Yunnan Province, China

Many studies have shown that b values tend to decrease prior to large earthquakes. To evaluate the forecast information in b value variations, we conduct a systematic assessment in Yunnan Province, China, where the seismicity is intense and moderate–large earthquakes occur frequently. The catalog in the past two decades is divided into four time periods (January 2000–December 2004, January 2005–December 2009, January 2010–December 2014, and January 2015–December 2019). The spatial b values are calculated for each 5-year span and then are used to forecast moderate-large earthquakes (M ≥ 5.0) in the subsequent period. As the fault systems in Yunnan Province are complex, to avoid possible biases in b value computation caused by different faulting regimes when using the grid search, the hierarchical space–time point-process models (HIST-PPM) proposed by Ogata are utilized to estimate spatial b values in this study. The forecast performance is tested by Molchan error diagram (MED) and the efficiency is quantified by probability gain (PG) and probability difference (PD). It is found that moderate–large earthquakes are more likely to occur in low b regions. The MED analysis shows that there is considerable precursory information in spatial b values and the forecast efficiency increases with magnitude in the Yunnan Province. These results suggest that the b value might be useful in middle- and long-term earthquake forecasts in the study area.

An Analysis on Forecasting Inflation Rate in the Philippines: A Recurrent Neural Network Method Approach

Inflation rate is the proceeding rise within the common level of costs of products and services in an economy over a certain span of time. In 2018, the Philippines has the highest inflation rate among the 10 South East Asian countries. The objective of this research is to forecast the inflation rate of the Philippines for the next five years (2019-2023). Also, the researchers compared the results obtained from the Multiple Linear Regression and Recurrent Neural Network (RNN) performed in MATLAB to determine which of these two models will be the better model in forecasting inflation rate. In this study, the researchers observed the behavior of the Inflation Rate(y) and its economic factors such as Import (x1), Export (x2), Money Supply (x3), Gross Domestic Product (x4), Gross National Product (x5), Expenditure (x6) and Exchange Rate (x7). Using Multiple Linear Regression, this study determined that the significant predictors are Money Supply (x3) and Expenditures (x6). By evaluating the forecast efficiency of the two methods, the researchers concluded that Multilayered Recurrent Neural Network outperforms Multiple Linear Regression in predicting inflation rate of the Philippines. This paper can be useful to the Philippine Government on their decisions about monetary policy making since forecasting the inflation rate has a huge importance and impact in conducting monetary policy.

Increased expression of homeobox 5 predicts poor prognosis: A potential therapeutic target for glioma

Background: The homeobox gene 5 (HOXB5) encodes a transcription factor that regulates the central nervous system embryonic development. Of note, its expression pattern and prognostic role in glioma remain unelucidated. This study aimed to identify the relationship between HOXB5 and glioma by investigating the HOXB5 expression data from the The Cancer Genome Atlas (TCGA) and The Genotype Tissue Expression (GTEx) databases and validating the obtained data using the Chinese Glioma Genome Atlas (CGGA) database. Kaplan-Meier and univariate cox regression analyses were performed to assess the prognostic value of HOXB5. The key functions and signaling pathways of HOXB5 were analyzed using GSEA and GSVA. Immune infiltration was calculated using Microenvironment Cell Populations-counter (MCP-counter), single-sample Gene Set Enrichment Analysis (ssGSEA), and ESTIMATE algorithms.Result: HOXB5 expression was elevated in glioma tissues. The increased levels of HOXB5 were significantly correlated with a higher WHO grade and aggressive cancer phenotypes. HOXB5 overexpression represented a risk factor that was associated with shorter overall survival (OS) while exhibiting a moderate forecast efficiency in most clinical subgroups. These results were validated using the CGGA and Rembrandt datasets. Furthermore, the functional analysis showed enrichment of angiogenesis, the IL6/JAK-STAT3 pathway, and inflammatory response in the tissues that showed high expression of HOXB5. Lastly, the high expression of HOXB5 was associated with enrichment of Tregs and MDSCs, and HOXB5 expression was shown to play a role in several immune checkpoint genes.Conclusions: HOXB5 may serve as a predictive factor of glioma malignancy and prognostic status and represents potential as a molecular treatment candidate.

A fuel sales forecast method based on variational Bayesian structural time series

Fuel prices, which are of broad concern to the general public, are always seen as a challenging research topic. This paper proposes a variational Bayesian structural time-series model (STM) to effectively process complex fuel sales data online and provide real-time forecasting of fuel sales. While a traditional STM normally uses a probability model and the Markov chain Monte Carlo (MCMC) method to process change points, using the MCMC method to train the online model can be difficult given a relatively heavy computing load and time consumption. We thus consider the variational Bayesian STM, which uses variational Bayesian inference to make a reliable judgment of the trend change points without relying on artificial prior information, for our prediction method. With the inferences being driven by the data, our model passes the quantitative uncertainties to the forecast stage of the time series, which improves the robustness and reliability of the model. After conducting several experiments by using a self-collected dataset, we show that compared with a traditional STM, the proposed model has significantly shorter computing times for approximate forecast precision. Moreover, our model improves the forecast efficiency for fuel sales and the synergy of the distributed forecast platform based on an architecture of network.

El Niño Index forecasting using machine learning techniques

<p>El Niño is a large-scale ocean-atmospheric coupling phenomenon in the Pacific. The interaction among marine and atmospheric variables over the tropical Pacific modulate the evolution of El Niño. The latest research shows that machine learning and neural network (NN) have appeared as effective tools to achieve meaningful information from multiple marine and atmospheric parameters. In this paper, we aim to predict the El Niño index more accurately and increase the forecast efficiency of El Niño events. Here, we propose an approach combining a neural network technique with long short-term memory (LSTM) neural network to forecast El Niño phenomenon. The attributes of model are resulted from physical explanation which are tested with the experiments and observations. The neural network represents the connection among multiple variables and machine learning creates models to identify the El Niño events. The preliminary experimental results exhibit that training NN-LSTM model on network metrics time series dataset provides great potential for predicting El Niño phenomenon at lag times of up to more than 6 months.  </p>

Mobilization of innovative and resource factors for development of national outsourcing IT companies

Purpose. To systematize and justify the mobilization optimization of internal resource potential and innovative indicators for development of national outsourcing IT companies. Methodology. To complete this paper work the authors followed the justification of the forecast based on the use of the following methods: trend method (establishing the forecast indicators of development on the basis of determining the average trend of the previous periods) to identify and systematize ways to ensure forecast efficiency of resource potential, innovative development of outsourcing companies in IT; comparative and statistical methods (application of methods for analysis of reporting data, their comparison with forecast) and forecasting method (forecasting according to the main quantitative and qualitative indicators) to determine the comparative characteristics of the given paths; geometric method of direct change dependence (determination of the level of change dependence, calculated by the method of direct geometric progression) to assess the forecast scheme for Ukrainian outsourcing IT subjects of unification in the form of cooperation. Findings. The basic methods of resource potential optimization, innovative indicators for development of outsourcing IT companies in Ukraine are investigated and substantiated. Systematization of scientific approaches, views of researchers allowed forming a list of optimization paths of these companies development. Originality. For the first time the organizational and economic form of integration of the subjects of the IT-outsourcing market in Ukraine is proposed as a way to optimize the resource potential of the association in cooperation, which provides functioning of competitive structures based on cooperation and partnership relation with clients, distribution of functional loads, creation of additional market economic effects, possible solution of social and economic problems of regions. Practical value. The practical significance of the study lies in the development and testing of methodological tools designed for rapid assessment of the development level of innovation and resource potential of IT enterprises. The results of the study can be used by national IT outsourcing companies to increase capital and maintain operational efficiency, to ensure functioning of competitive structures of different categories aimed at increasing the added economic value of their participants.