Prediction-based Decisions & Planning

Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. For predicting of share price using ANN, there are two modules, one is training session and other is predicting price based on previously trained data. We used Backpropagation algorithm for training session and Multilayer Feedforward network as a network model for predicting price. In this paper, we introduce a method which can predict share market price using Backpropagation algorithm and Multilayer Feedforward network.

How and when do we learn to understand other people’s perspectives and possibly divergent beliefs? This question has elicited much theoretical and empirical research. A puzzling finding has been that toddlers perform well on so-called implicit false belief (FB) tasks but do not show such capacities on traditional explicit FB tasks. I propose a navigational approach, which offers a hitherto ignored way of making sense of the seemingly contradictory results. The proposal involves a distinction between how we navigate FBs as they relate to 1) our current affordances (here & now navigation) as opposed to 2) presently non-actual relations, where we need to leave our concrete embodied/situated viewpoint (counterfactual navigation). It is proposed that whereas toddlers seem able to understand FBs in their current affordance space, they do not yet possess the resources to navigate in abstraction from such concrete affordances, which explicit FB tests seem to require. It is hypothesized that counterfactual navigation depends on the development of ‘sensorimotor priors’, i.e. statistical expectations of own kinestetic re-afference, which evidence now suggests matures around age four, consistent with core findings of explicit FB performance.

A central problem in underwater archaeology relates to the discipline’s ability to locate under water the submerged Stone Age settlements it is obliged to protect or otherwise manage in relation to laying of cables or pipelines at sea, construction of harbours or oil installations, dredging operations and so on. An important precondition is the efficient mapping of these sites even when they are concealed within the sea-floor sediments.

Contracts are designed to govern the relations between business partners and allocate risk among them, yet they cannot mitigate all risks; hence, dispute resolution mechanisms have been developed to assist. According to research, arbitration is considered one of the most efficient legally binding dispute resolution mechanisms. Since initiating an arbitration case against business partners often severs the business relationship between them, predicting the arbitrator's decision becomes valuable to the arbitrating parties. To do so, arbitration prediction models are suggested in this paper through modeling the arbitrator's award in two stages: evidence admissibility and evidence evaluation. While arbitrators currently admit evidence in a relatively unsystematic way, this proposed admissibility framework systemizes the current practice by preserving its subjectivity, yet aligning it with the widely deployed rules of arbitration. This research further proposes an evidence evaluation model, providing a rapid answer to the famous commercial arbitration battle, discovery. While the current arbitrators' practice in allowing discovery is not systematic, the proposed optimization model is a tool that could systematically assist arbitrators trading between the arbitration key characteristic, efficiency, and justice. This is achieved by assigning weights to evidence parameters such as sufficiency and credibility, which in turn provide numerical measures to reduce the decision's subjectivity.

Forecasting strategies are numerous and diverse, but only some are successful. We investigated various forecasting methods and evaluated their success and found that certain principles in forecasting, or strategies that have proven to be successful, are in contradiction with general assumptions about the qualities of successful forecasting methods.

— Failure rates in online higher education raises as a major problem for the modal instruction, thus, prediction of student performance is proposed as a preventive strategy to diminish student failure. Research in student performance prediction is of a wide variety that complicates the replication of studies in order to take advantage of research results. This study tackles this issue by proposing three types of neural networks for student performance prediction, which are built from standardized variables more easily obtained than those used in most of studies identified. By the statistical comparison of the prediction accuracies of the neural networks with that of a statistical linear regression, the three neural networks obtained higher prediction accuracy. As conclusion, neural networks are proposed as techniques for an early prediction and identification of students in risk of failure.

With this product, my objective was to make cryptocurrency investments simple & profitable. This product operates via an easy to use website allowing customers to buy, sell, and invest in cryptocurrencies, and in stock markets, all in one single place. The vision was to make a product for the global financial system that is easily accessible to everyone. The costumer will first have to make investments in cryptocurrencies, then he/she can invest in share markets as per their needs. This product also has a built-in machine learning based stock prediction model which helps it’s users to decide which stock to invest in & when it is the right time to sell such stocks.

This study was conducted to predict contact area (A) of bias-ply tire based on contact area index (CAI), inflation pressure (P) and vertical load (W). For this purpose, contact area of four bias-ply tires with different contact area index were measured at three levels of inflation pressure and four levels of vertical load. Results of contact area measurement for bias-ply tires No. 1, 2 and 3 were utilized to determine regression model and three-variable linear regression model A = 113.33-0.0023 CAI-2108.6 P + 33.429 W with R = 0.901 was P 2 obtained. Also, results of contact area measurement for bias-ply tire No. 4 were used to verify model. The paired samples t-test results indicated that the contact area values predicted by model were more than the contact area values measured by test apparatus. To check the discrepancies between the contact area values predicted by model with the contact area values measured by test apparatus, RMSE and MRPD were calculated. The amounts of RMSE and MRPD were 30.4 cm and 33.1%, respectively. Amounts of RMSE and MRPD confirmed 2 that the three-variable linear regression model may be used to predict contact area of bias-ply tire based on contact area index, inflation pressure and vertical load. On the other hand, to calculate actual contact area values or contact area values measured by test apparatus (A) based on contact area values predicted by model M (A) the linear regression model A = 0.920 A-20.08 with R = 0.916 can be strongly recommended. P M P 2

Anomaly detection refers to the problem of finding patterns in data that do not confirm to expected behaviour. The survey tries to provide a structured and comprehensive overview of the research on anomaly detection. There are grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category there are identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behaviour. Further, for each category, identifying the advantages and disadvantages of the techniques in that category is a part of the projects goal. And find out the model which is best suited for detecting and prediction of anomaly from the telemetry data. The aim is to identify a simple automated process which only verifies if a certain parameter crosses upper or lower thresholds and is robust enough to detect the various types of anomalies that may occur in the telemetry data. We will be analysing data and use machine learning algorithms to attempt a realistic prediction, gain experience in machine learning algorithms and their applicability to satellite telemetry data analysis. Combining satellite data and machine learning algorithms to predict the dynamic characteristics of a satellite, such that an outcome is obtained with good accuracy rate.

This study establishes a theoretical framework for predicting the American College Testing (ACT) Mathematics sub-score and AP Calculus AB and BC scores from the Precalculus Concept Assessment (PCA) exam results and suggests a total of 16 different regression based models to actually perform the prediction. The strong positive correlation between the actual and predicted values confirm that the PCA is a powerful tool for identifying students who are at the risk of not passing AP Calculus AB or BC tests and thus helping teachers, parents and students take the necessary measures in a timely manner.

According to our results, increase in productivity of personal prediction has a positive influence on life satisfaction. The present research explores what role the various types of future prediction play in personal life satisfaction. The study focuses on some psychological time phenomena: desires, life choices, aspirations, motivations. To influence personal well-being the desires might be mature, productive, strong and adequately assessed the current life situation. For high level of well-being intrapersonal desires are more important than interpersonal ones. The life choice determining the horizon prediction leads to the transformation of meanings due to the development of new causal sequences of life-satisfaction. The satisfaction with own self and own life world grows when a person makes life-changing decisions from some alternatives. There are the criteria of readiness for significant choices to increase life-satisfaction: self-organizing; psychosemantic; subjective; functional; operational. The stages of life aspirations include communicative semiotization for defining unconscious ambitions and the registration of the forecast as an underway to the desired plot of worthy life, the narration activation leads to the verbal form of the forecast as a desirable plot of a worthy life, and purposeful creation of the story about decent future life, known as setting the concrete tasks. Motivation-semantic future structuring determines the personal life-satisfaction while taking the narrative form in different contexts of communication. The future structuring which is directly related to the effort to optimize the quality of life is self-creating and re-assessment one; in the 1-st model a person re-assesses adopted values and the source of life changes as changes of own self through the integration of new experience; in the 2-and model the values are already adopted, interpreted, and the movement is due to the changes in life situation.

En adoptant l’approche complexe pour l’étude des phénomènes politiques, nous pourrons, de nouveau, obtenir une capacité de prédiction et d’explication fiable. En d’autre mots, si nous pouvions montrer l’utilité des sciences politiques, alors un progrès réel, se mesurant notamment en nombre de morts évitées et de souffrances épargnées, aurait lieu.

Nous illustrerons ce propos en montrant ici comment le « nationalisme » et le génocide peuvent mieux se comprendre en utilisant les sciences de la Complexité, et nous donnerons des exemples d’application pratique issus de la théorie. Le cas d’école que nous suivrons, de façon résumée, porte sur les liens existant entre « nationalisme » et génocide, le but étant d’élucider pourquoi un processus génocidaire eut lieu au Cambodge entre 1970 et 1979 et comment le sentiment national Kampuchéen de la période 1970-1979 interagit avec ce processus génocidaire.

The so-called "Scary Idea" of an Artificial Intelligence improving itself and taking over the world assumes the possibility of a logic-based intelligence that can improve without limits. But the limits on intelligence are not technological; rather, the usefulness of logic-based predictions, and hence intelligence, is limited by the unpredictability of the world. The debate about how to guarantee that future AIs will not be "Unfriendly" starts from incorrect premises and should be abandoned.

Efforts to build Logic-based Artificial intelligences are misguided. Instead, we should concentrate our efforts on Abduction based AI/AGI. These will become useful tools within our lifetimes.

In order to support and supervise patients, the key detection and estimation of cancer type should establish a compulsion in the cancer research. Many research teams from the biomedical and bioinformatics fields have been advised to learn and evaluate the use of machine learning (ML) methods because of the relevance of classifying cancer patients into high or low risk clusters. To predict breast cancer, the logistic regression method and many classifiers have been proposed to generate profound predictions about breast cancer data in a new environment. This paper discusses the various approaches to data mining using classification to create deep predictions that can be applied to Breast Cancer data. In addition, by testing datasets on different classifiers, this analysis predicts the best model that delivers high efficiency. In this paper, the UCI machine learning repository has 699 instances with 11 attributes collected from the Breast cancer dataset. First, the data set is pre-processed, visualized and fed to different classifiers such as Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. 10-fold cross validation is implemented and testing is carried out in order to create and validate new models. Effective analysis shows that Logistic Regression generates the deep predictions of all classifiers and obtains the best model delivering strong and precise outcomes, followed by other methods: Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. Most models were less reliable compared to the approach of logistic regression.

The majority of organic foods consumed by Americans are sourced internationally, which has global-reaching implications on health, economics, and sustainability. Current research findings show that environmental devastation and negative health outcomes have resulted from unsustainable, nonorganic agricultural practices; including herbicides, pesticides, and overcultivation. However, there is a lack of quantitative research on factors that motivate Americans to consume organic food. Based on the theory of planned behavior, this quantitative
study employed an online survey to examine the role of attitudes, subjective norms, descriptive norms, and perceived behavioral control on the intention and behavior of American consumers to consume organic foods. Additional descriptors of willingness to pay and perceived product attributes were also measured. Theory of planned behavior and American Organic Consumption questionnaires were completed by 276 adult consumers in the United States. Multiple regression analyses were performed to identify relationships and create predictive models between constructs of a modified theory of planned behavior, sociodemographics, and organic consumption. Key findings revealed that a modified theory of planned behavior, which included descriptive norms, predicted intent to consume organics stronger than the nonmodified theory of planned behavior. Attitudes, perceived behavioral control, and descriptive norms were significant predictors of intention to consume organics. Health was perceived as the major product attribute for organic consumption and low willingness to pay was perceived as the major barrier. This study has implications for positive social change such that it contributes to understanding motivational factors behind American’s food choices and consumption, which can be used to modify and target consumer behaviors and market campaigns.

This is the power to analyze systematically the interactions of the environmental movements & systems and interpret the destination. This is the simplest and the hardest science on this earth of ours. It is a day to day science everyone can deal with so it is easy to deceive but not easy to follow. There is no other power behind like spiritual visions other than the skills and the genius of the person responsible.
Predictions deteriorate, change the trend, decline, or disappear depending on the interference of new systems and movements within the mentioned environment. Consider the prophecies of the bible; these have already extinct but were analyzed by intelligence and geniuses of that era before the extraction of earth and universal science.
SCIENTIFIC PREDICTIONS; Whatever accuracy, it is rare to get the results 100% however, digital era prediction is an interdisciplinary task including laboratories.

The suggested method helps predicting vehicles movement in order to give the driver more time to react and avoid collisions on roads. The algorithm is dynamically modelling the road scene around the vehicle based on the data from the on-board camera. All moving objects are monitored and represented by the dynamic model on a 2D map. After analyzing every object's movement, the algorithm predicts its possible behavior.

Clustering for mobile ad hoc networks (MANETs) offers a kind of hierarchical organization by partitioning mobile hosts into disjoint groups of hosts (clusters). However, the problem of changing topology is recurring and the main challenge in this technique is to build stable clusters despite the host mobility. In this paper, we present a novel clustering algorithm, which guarantees longer lifetime of the clustering structure in comparison to other techniques proposed in the literature. The basis of our algorithm is a scheme that accurately predicts the mobility of each mobile host based on the stability of its neighborhood (i.e., how different is its neighborhood over time). This information is then used for creating each cluster from hosts that will remain neighbors for sufficiently long time, ensuring the formation of clusters that are highly resistant to host mobility. For estimating the future host mobility, we use provably good information theoretic techniques, which allow on-line learning of a reliable probabilistic model for the existing host mobility.

Empirical analyses of IPO initial returns are heavily dependent on linear regression models. However, these models can be inefficient due to its sensitivity to outliers which are common in IPO data. In this study, the machine learning method random forest is introduced to deal with the issues the linear regression cannot solve. The random forest is used to predict initial returns of IPOs issued on Borsa Istanbul. The prediction accuracy of the random forest is then tested against methods of robust regression. The prediction results show that random forest has by far outperformed other methods in every category of the comparison. The variable importance measure shows that the IPO proceeds and IPO volume are the most important predictors of IPO initial returns. The results also show that the variables that act as potential proxies for ex-ante uncertainty are more important than variables that are proxies for information asymmetry.

Survival in a fast-changing environment requires animals not only to detect unexpected sensory events, but also to react. In humans, these salient sensory events generate large electrocortical responses, which have been traditionally interpreted within the sensory domain. Here we describe a basic physiological mechanism coupling saliency-related cortical responses with motor output. In four experiments conducted on 70 healthy participants, we show that salient substartle sensory stimuli modulate isometric force exertion by human participants, and that this modulation is tightly coupled with electrocortical activity elicited by the same stimuli. We obtained four main results. First, the force modulation follows a complex triphasic pattern consisting of alternating decreases and increases of force, time-locked to stimulus onset. Second, this modulation occurs regardless of the sensory modality of the eliciting stimulus. Third, the magnitude of the force modulation is predicted by the amplitude of the electrocortical activity elicited by the same stimuli. Fourth, both neural and motor effects are not reflexive but depend on contextual factors. Together, these results indicate that sudden environmental stimuli have an immediate effect on motor processing , through a tight corticomuscular coupling. These observations suggest that saliency detection is not merely perceptive but reactive, preparing the animal for subsequent appropriate actions.

As demand for computer software continually increases, software scope and complexity become higher than ever. The software industry is in real need of accurate estimates of the project under development. Software development effort estimation is one of the main processes in software project management. However, overestimation and underestimation may cause the software industry loses. This study determines which technique has better effort prediction accuracy and propose combined techniques that could provide better estimates. Eight different ensemble models to estimate effort with Ensemble Models were compared with each other base on the predictive accuracy on the Mean Absolute Residual (MAR) criterion and statistical tests. The results have indicated that the proposed ensemble models, besides delivering high efficiency in contrast to its counterparts, and produces the best responses for software project effort estimation. Therefore, the proposed ensemble models in this study will help the project managers working with development quality software.

Are those who are familiar with scientific research on consumer behavior better able to make predictions about phenomena in this field? Predictions were made for 105 hypotheses from 20 empirical studies selected from Journal of Consumer Research. A total of 1,736 predictions were obtained from 16 academics, 12 practitioners, and 43 high school students: The practitioners were correct on 58.2 percent of the hypotheses, the students on 56.6 percent, and the academics on 51.3 percent. No group performed better than chance.

Clustering for mobile ad hoc networks (MANETs) offers a kind of hierarchical organization by partitioning mobile hosts into disjoint groups of hosts (clusters). However, the problem of changing topology is recurring and the main challenge in this technique is to build stable clusters despite the host mobility. In this paper, we present a novel clustering algorithm, which guarantees longer lifetime of the clustering structure in comparison to other techniques proposed in the literature. The basis of our algorithm is a scheme that accurately predicts the mobility of each mobile host based on the stability of its neighborhood (i.e., how different is its neighborhood over time). This information is then used for creating each cluster from hosts that will remain neighbors for sufficiently long time, ensuring the formation of clusters that are highly resistant to host mobility. For estimating the future host mobility, we use provably good information theoretic techniques, which allow on-line learning of a reliable probabilistic model for the existing host mobility.

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (C c) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict C c through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e 0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate C c. This study presents a novel prediction model for the C c of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C c based on LL, PL, and e 0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R 2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R 2 , RMSE, and MAE compared to the other models.

Background: Guidelines for prostate cancer (PCa) screening recommend physicians to have an informational discussion with patients. At the time of biopsy, patients should be informed of their heightened PCa risk, particularly African Americans (AA) who have significantly higher diagnostic and mortality risk. We tested predictors of patients' estimation of their likelihood of having PCa at the time of biopsy.

Background: Predictive policing and crime analytics with a spatiotemporal focus get increasing attention among a variety of scientific communities and are already being implemented as effective policing tools. The goal of this paper is to provide an overview and evaluation of the state of the art in spatial crime forecasting focusing on study design and technical aspects. Methods: We follow the PRISMA guidelines for reporting this systematic literature review and we analyse 32 papers from 2000 to 2018 that were selected from 786 papers that entered the screening phase and a total of 193 papers that went through the eligibility phase. The eligibility phase included several criteria that were grouped into: (a) the publication type, (b) relevance to research scope, and (c) study characteristics. Results: The most predominant type of forecasting inference is the hotspots (i.e. binary classification) method. Traditional machine learning methods were mostly used, but also kernel density estimation based approaches, and less frequently point process and deep learning approaches. The top measures of evaluation performance are the Prediction Accuracy, followed by the Prediction Accuracy Index, and the F1-Score. Finally, the most common validation approach was the train-test split while other approaches include the cross-validation, the leave one out, and the rolling horizon. Limitations: Current studies often lack a clear reporting of study experiments, feature engineering procedures, and are using inconsistent terminology to address similar problems. Conclusions: There is a remarkable growth in spatial crime forecasting studies as a result of interdisciplinary technical work done by scholars of various backgrounds. These studies address the societal need to understand and combat crime as well as the law enforcement interest in almost real-time prediction. Implications: Although we identified several opportunities and strengths there are also some weaknesses and threats for which we provide suggestions. Future studies should not neglect the juxtaposition of (existing) algorithms, of which the number is constantly increasing (we enlisted 66). To allow comparison and reproducibility of studies we outline the need for a protocol or standardization of spatial forecasting approaches and suggest the reporting of a study's key data items.

Theory of Planned Behavior Analysis and Organic Food Consumption of American Consumers by Marie Donahue MBA, The Royal University of Agriculture, 2011 BS, Arizona State University, 2009 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Psychology Walden University May 2017

Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. For predicting of share price using ANN, there are two modules, one is training session and other is predicting price based on previously trained data. We used Backpropagation algorithm for training session and Multilayer Feedforward network as a network model for predicting price. In this paper, we introduce a method which can predict share market price using

O presente artigo apresenta o segundo estudo sobre a evolução do novo coronavírus (SARS-CoV-2) no estado do Pará, desde a confirmação do primeiro infectado no dia 18/03/2020 até o dia 28/05/2020, através de mapas. O estudo apresenta também um modelo matemático para estimar o número de infectados até o dia 28/05/2020 e a projeção de pico da epidemia no estado do Pará como um todo, com análises mais detalhadas em dez municípios incluindo a capital Belém. Os resultados mostram que o modelo possui confiabilidade acima de 90% para predições de curto prazo, cuja evolução pode ser de 1 infectado em 18/03/2020 a 33.304 infectados em 28/05/2020.

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (C c) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict C c through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e 0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate C c. This study presents a novel prediction model for the C c of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C c based on LL, PL, and e 0. The performance of the devel...

In the garment industry, one of the most prevalent industries in Sri Lanka, the process of sampling is key to gaining a competitive advantage in the market. The sample rooms in garment factories are considered the most important section in the readymade garments industry. Numerous processes occur in sample rooms, from perfecting patterns and quality checks on certain fabrics to confirming bulk production orders for prospective customers. Therefore, for these vital processes that occur in the sample room to function as efficiently as possible, they require the most accurate data. Research, including an initial pilot study done on a small to medium garment factory, suggests that approximately 35% of sample rooms in garment factories under utilise their capacity in accordance with demand. One reason being that most garment factories are based in developing countries, where the production focus is more labour intensive and less technology intensive. Not being able to accurately worker productivity metrics, coupled with a low merchandiser visibility on sample progression, would not allow the sample room to work on numerous pre-designs prior to the order. This hinders the chance for example, for designers to take on more simultaneous projects and approve bulk productions faster, resulting in potential losses for the organisation (Windmuller, 2020). Keywords: SMV:-Known as Standard Minute Value, it is the value of the calculated accepted time to finish a particular garment job is known as the Standard Minute Value.

Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. For predicting of share price using ANN, there are two modules, one is training session and other is predicting price based on previously trained data. We used Backpropagation algorithm for training session and Multilayer Feedforward network as a network model for predicting price. In this paper, we introduce a method which can predict share market price using Backpropagation algorithm and Multilayer Feedforward network.

Obesity and cardiovascular disease (CVD) can be considered as diseases as a result of unhealthy diet and physical inactivity. The aim of our programmes was to develop a model of efficient intervention intended for high-risk individuals with abdominal fat distribution and increased risk for CVD and diabetes mellitus (T2DM). According to the protocol of work, all tested at the programme four groups of changeable variables were followed and calculation indexes were made. Analysed were 15 anthropometrics, 13 functional, 12 laboratory and 2 ergonomic variables in order to predict the energetic consumption according to the level of the physical activity. Research results confirm a significant therapeutic efficiency of the recommended program of physical activity in combination with a suitable diet therapy prescribe the possibilities for therapy and prevention of obesity connected with increased risk for CVD and T2DM at people with abdominal fat distribution.

As demand for computer software continually increases, software scope and complexity become higher than ever. The software industry is in real need of accurate estimates of the project under development. Software development effort estimation is one of the main processes in software project management. However, overestimation and underestimation may cause the software industry loses. This study determines which technique has better effort prediction accuracy and propose combined techniques that could provide better estimates. Eight different ensemble models to estimate effort with Ensemble Models were compared with each other base on the predictive accuracy on the Mean Absolute Residual (MAR) criterion and statistical tests. The results have indicated that the proposed ensemble models, besides delivering high efficiency in contrast to its counterparts, and produces the best responses for software project effort estimation. Therefore, the proposed ensemble models in this study will help the project managers working with development quality software.

Survival in a fast-changing environment requires animals not only to detect unexpected sensory events, but also to react. In humans, these salient sensory events generate large electrocortical responses, which have been traditionally interpreted within the sensory domain. Here we describe a basic physiological mechanism coupling saliency-related cortical responses with motor output. In four experiments conducted on 70 healthy participants, we show that salient substartle sensory stimuli modulate isometric force exertion by human participants, and that this modulation is tightly coupled with electrocortical activity elicited by the same stimuli. We obtained four main results. First, the force modulation follows a complex triphasic pattern consisting of alternating decreases and increases of force, time-locked to stimulus onset. Second, this modulation occurs regardless of the sensory modality of the eliciting stimulus. Third, the magnitude of the force modulation is predicted by the ...

When making serial predictions in a binary decision task, there is a clear tendency to assume that after a series of the same external outcome (e.g., heads in a coin flip), the next outcome will be the opposing one (e.g., tails), even when the outcomes are independent of one another. This so-called " gambler's fallacy " has been replicated robustly. However, what drives gambler's fallacy behavior is unclear. Here we demonstrate that a run of the same external outcome by itself does not lead to gambler's fallacy behavior. However, when a run of external outcomes is accompanied by a concurrent run of failed guesses, gambler's fallacy behavior is predominant. These results do not depend on how participants' attention is directed. Thus, it appears that gambler's fallacy behavior is driven by a combination of an external series of events and a concurrent series of failure experiences.

Nowadays, people often judge which restaurant is good or bad by looking at the rating of the restaurant. That's why ratings are a critical factor in the restaurant business. Ratings are usually given by people judging by what kind of service restaurants are providing. So, features of restaurants play a very important role in this regard. The main goal of this research is to predict ratings of restaurant business based on features to help new entrepreneurs to set up new business. We used different machine learning algorithms like Decision tree, Support vector machine (SVM), k-nearest neighbors' algorithm (KNN), Stochastic gradient descent (SGD), Gaussian Naive Bayes. We also used a convolutional neural network (CNN) model here. It gives us an accuracy score of 97.2 25 percent which is higher than all other algorithms.