This chapter argues that our self-knowledge is often mediated by our affective self-knowledge. In other words, we often know about ourselves by knowing our own emotions. More precisely, what Cassam has called “substantial self-knowledge” (SSK), such as self-knowledge of one's character, one's values, or one's aptitudes, is mediated by affective forecasting, which is the process of predicting one's emotional responses to possible situations. For instance, a person comes to know that she is courageous by predicting her own emotional reactions (...) to possible risks and dangers. This hypothesis explains why attaining substantive self-knowledge tends to be difficult. Attaining substantive self-knowledge is difficult because, first, SSK is mediated by affective forecasting and, second, we tend to be wrong about predicting the intensity and duration of our own emotional reactions. As a result, we can identify what is common to central cases of SSK: such cases require knowledge about complex dispositions whose manifestations involve affective responses that one is not sufficiently familiar with. One thus needs to resort to the highly fallible method of affective forecasting. (shrink)

Demand forecasting in grocery retail encounters considerable difficulties due to fluctuating consumer behavior, as well as external factors such as weather conditions and local events. This research presents an innovative framework that utilizes generative artificial intelligence (AI) to improve forecasting accuracy by incorporating various contextual elements. By employing Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), we integrate weather data, local events, and consumer behavior to better predict grocery sales. The proposed approach aims to optimize inventory management, minimizing (...) stockouts and reducing waste. Experimental results indicate that this context-aware method significantly outperforms traditional forecasting models, providing a powerful solution for improving supply chain operations in grocery retail. (shrink)

This article presents a two level hierarchical forecasting model developed in a consulting project for a Brazilian magazine publishing company. The first level uses a VARMA model and considers econometric variables. The second level takes into account qualitative aspects of each publication issue, and is based on polynomial networks generated by Genetic Programming (GP).

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights (...) into the dynamics of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

Abstract: Accurate stock price prediction is essential for informed investment decisions and financial planning. In this research, we introduce an innovative approach to forecast stock prices using an Artificial Neural Network (ANN). Our dataset, consisting of 5582 samples and 6 features, including historical price data and technical indicators, was sourced from Yahoo Finance. The proposed ANN model, composed of four layers (1 input, 1 hidden, 1 output), underwent rigorous training and validation, yielding remarkable results with an accuracy of 99.84% and (...) an average error of 0.005. Additionally, we conducted a feature importance analysis, identifying the key drivers of stock price prediction, such as "High," "Low," "Open," "Volume"," and "Date." This study not only offers a highly accurate stock price prediction model but also contributes valuable insights into the influential factors affecting stock prices, enhancing the field of financial analysis and investment decision-making that are suitable for the task. (shrink)

This study presents a novel transformer-based model specifically designed for financial forecasting, integrating explainability mechanisms such as SHAP (SHapley Additive exPlanations) values and attention visualizations to enhance interpretability. Unlike previous models, which often compromise between accuracy and transparency, our approach balances predictive accuracy with interpretability, allowing stakeholders to gain deeper insights into the factors driving market changes. By revealing critical market influences through feature importance and attention maps, this model provides both robustness and transparency, catering to the needs of (...) high-stakes financial environments. (shrink)

The purpose of the study is to forecast the number of passengers transported by air in the United States for 2021-2022. The forecast is preceded by a multidimensional comparative analysis of the number of passengers transported by air in the United States from 1 January 2019 to 2 November 2021. To achieve this goal, the data were grouped as dependent variables: years, months-years. The observed similarities, the analysis and evaluation of the literature as well as the own experience made it (...) possible to forecast primary data for the future. The obtained results show a growing trend in the number of passengers transported by air in the United States. The forecast for 3 November 2021 to 31 December 2022 is 837 459 877 prospective air passengers in the United States. The standard deviation of the obtained forecast was 255 680,3, the arithmetic mean was 1 975 141 passengers and the median was 1 998 478 passengers. The observed similar trends in the identical months of July-October in 2019 and 2021 as well as the analysis of the literature and the own experience allowed for the selection of a method for the forecasting of the number of passengers transported by air in the United States for the future. The detected regularities as recurring trends in the number of air passengers transported in the United States on a dynamic basis may be used to forecast them for the future. (shrink)

The book will be useful for economists, finance and valuation professionals, market researchers, public policy analysts, data analysts, teachers or students in graduate-level classes. The book is aimed at students and beginners who are interested in forecasting modeling and analytics of economic processes and want to get an idea of its implementation.

Crime Forecasting refers to the basic process of predicting crimes before they occur. Crimes are a common social problem affecting the quality of life and the economic growth of a society. A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. For our daily purposes we have to go many places every (...) day and many times in our daily lives we face numerous security issues such as hijacking, kidnapping, harassment, etc. Daily there are huge numbers of crimes occurring frequently. These require keeping track of all the crimes and maintaining a database for same which may be used for future reference. (shrink)

The most significant and pervasive issue in our society is crime. Rising crime rates contribute to an unbalanced societal makeup within a nation. Over the past few years, machine learning techniques have been deployed to scrutinize crime data, offering valuable insights for forecasting and thwarting forthcoming criminal activities. In this paper, crime prediction and forecasting using MLP (Multi-Layer Perceptron) & K-Means clustering algorithms, presents a novel approach that combines machine learning and deep learning techniques to achieve precise crime (...) predictions. MLP is proposed to harness their complementary capabilities in identifying crime patterns and modeling sequential data. The procedure begins by gathering and preparing a wide range of crime data, which encompasses crime reports and demographic data. By leveraging a comprehensive dataset that encompasses factors such as demographics, socio-economic conditions, and historical crime records, and splitting it into training and testing sets. MLP model is implemented and trained to classify whether a crime occurs or not, while evaluation metrics such as accuracy, precision, recall, and F1-score are employed to assess their performance. (shrink)

Analyzing and modeling interconnections between crucial factors of human development, rates of growth thereof and elasticity of the growth rates, the authors have defined specific periods of the development and have made a forecast for the dynamics of the human resources development. Those periods have been defined more exactly and arranged as follows: the first one – «Before Christ»; the second one – «Early Medieval» (1–1100 a.d.); the third one – «Advanced Medieval» (1101–1625); the forth one – «Pioneer’s Modernization» (1626–1970); (...) the fifth one – «Industrialization» (1871–2030) and the sixth one – «Informational society» – after 2030. The results received enable to make forecasts of the global factors dynamics, to find out unified solutions in socio-economic sphere, to define tendencies in social development and to build up unified strategic approaches for modeling global influence on human resources. (shrink)

Financial forecasting plays a crucial role in guiding investment decisions, risk management, and strategic planning. Traditional forecasting methods, such as time series analysis and regression models, often struggle to capture the complexities and non-linear dynamics of financial markets. Machine learning (ML) has emerged as a powerful tool in financial forecasting due to its ability to process vast datasets, identify patterns, and enhance predictive accuracy. This paper explores various ML techniques, including neural networks, ensemble methods, and reinforcement learning, (...) applied to financial forecasting. It examines data acquisition, preprocessing, and feature engineering, along with case studies on stock price prediction, forex exchange rate forecasting, and credit risk assessment. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are analyzed to evaluate model effectiveness. Despite its advantages, ML in finance faces challenges like data quality, overfitting, and the need for interpretability. Ethical concerns, including bias and transparency, are also addressed. The paper highlights future directions, such as explainable AI, quantum computing, and big data applications. Ultimately, ML has the potential to transform financial forecasting, but its responsible implementation requires addressing regulatory, ethical, and technical challenges. (shrink)

This study dealt with evaluating the maintenance cost of roads in Anambra State using the times series approach. The objective of the present study to develop a time series model for estimating maintenance cost of roads in Anambra State. Secondary data set obtained from Consolidated Construction Company (CCC) form the year 2004 to 2013 was used to evaluate the analysis. The statistical tools used include the KwiatkowskiPhillips-Schmidt-Shin test, Augmented Dickey-Fuller test, time series analysis and descriptive analysis. The findings of the (...) study showed that the maintenance cost of roads has an increasing trend over time. The series was found to be stationary using the Kwiatkowski-Phillips-Schmidt-Shin test. Also, it was found that the series has no unit root at the first difference using the Augmented Dickey-Fuller test. It was concluded that the series was stationary overtime which implies that the model obtained can be used to make forecast for future behaviour of the process. Hence, five years forecast on the maintenance cost of roads in Anambra State was made and it was found that in the year 2018 all things being equal the maintenance cost of roads in Anambra State is expected to be about N237,226,028. This result implies that the maintenance cost of roads in Anambra State is expected be about N237,226,028 in the year 2018. (shrink)

This paper presents a machine learning-based framework for real-time short-term snowfall forecasting by integrating atmospheric and topographic data. The model uses real-time meteorological data such as temperature, humidity, and pressure, along with terrain data like elevation and land cover, to predict snowfall occurrence within a 12-hour forecast window. Random Forest (RF) and Support Vector Machine (SVM) models are employed to process these multi-source inputs, demonstrating a significant improvement in prediction accuracy over traditional methods. Experimental results show that the RF (...) model achieved a root mean square error (RMSE) of 3.2 cm, while traditional regression models achieved an RMSE of 5.1 cm. The proposed framework provides a more reliable and accurate tool for real-time snowfall prediction, especially in mountainous regions, and can be deployed for operational weather forecasting applications. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...) % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

Due to the development of advanced identity based attacks and even complex cyber threats, merely possessing defensive cyber security capabilities is not enough today. In this study, we investigate how predictive analytics based machine learning (ML) can be employed for pro-active identity management and threat detection. In this study, the authors assess some models of machine learning – Decision Trees, Random Forests, Support Vector Machines (SVM), and a new hybrid one – to determine which best allows for the detection of (...) both known and unknown threats. The results reveal that in metrics such as accuracy, precision, recall, and F1 score. The hybrid model incorporating both supervised and unsupervised learning approaches scored the highest among other models. As a consequence of its adaptability, the hybrid model is capable of real time dynamic threat detection and anomaly based identity management which makes it an appropriate model for the changing cyber security environment. This study provides the prospects to make proactive cybersecurity more efficient and therefore enhancing the technology for protection systems. (shrink)

Cryptocurrency price forecasting is a challenging task due to market volatility and unpredictable investor behavior. This project introduces a sentiment-driven machine learning approach to improve prediction accuracy. Sentiment data is collected from platforms like Twitter and financial news using Natural Language Processing (NLP) techniques. These sentiments are quantified and combined with historical price data to form a robust dataset. An LSTM (Long Short-Term Memory) model is used for its ability to learn temporal dependencies in timeseries data. The model is (...) trained to forecast future price movements by understanding both technical trends and public sentiment. Alerts are generated based on significant sentiment-influenced price changes to assist users in decisionmaking. The system utilizes Python, TensorFlow, and relevant APIs for data extraction and preprocessing. React is used for building the front-end interface, allowing interactive user experiences. The approach bridges the gap between numerical analysis and behavioral finance. Evaluation metrics such as RMSE and MAE are used to assess the model’s performance. Results show that incorporating sentiment data improves forecast accuracy. The application is scalable and adaptable for real-time use. Future enhancements include live data integration and predictive analytics for trade suggestions. This project highlights the power of combining sentiment analysis with deep learning in financial forecasting.. (shrink)

Generally, we confidently ascribe valuing states to ourselves. We make statements such as “I value democracy” or “I value my best friend” - our sense of who we are depends on doing so. Yet what justifies that confidence? If you were asked “Do you value philosophy, or are you just doing it for the money?”, how might you go about generating such knowledge? I will operate with the notion that valuing involves, at a minimum, a set of distinctive emotional dispositions (...) toward the valued object. Given this view of valuing, a plausible explanation stemming from Tooming and Miyazono is the following: to know whether we value, we need to predict our potential or future emotional reactions. That is, they suggest that affective forecasting is necessary to generate knowledge of valuing states. I argue that, despite its intuitive appeal, affective forecasting is not necessary. I then consider a more modest claim, namely that affective forecasting is still explanatorily powerful when it comes to self-knowledge of valuing states, as it explains the difficulty of generating such knowledge. I reject this further claim, arguing that there is a stronger available explanation: the use of theories about valuing. (shrink)

Bakery products are very important in human nutrition and are the basis of any daily diet. Their social significance is determined by the traditions and habits of the population of the countries, accessibility for all groups of the population, diverse assortment, including bakery products for functional and specialized purposes. The up-to-date trend is to expand the assortment of functional bakery products, the use of which will provide the body’s need for the necessary macro- and micronutrients for an active and healthy (...) lifestyle. In the bread and bakery products market, consumer preferences are constantly changing. Consumers prefer products that contain less fat and sugar. The market is in demand for bakery products that are healthy, while taste remains an important success factor. Manufacturers include popular healthy additives (grains, seeds and legumes) in their products. Particularly popular at present are ingredients from Eastern Europe and Asia: yuzu, sesame, matcha, shiso. Tropical fruits (passion fruit, mango, bergamot) and fragrances (dried hibiscus, lavender and rose) are predicted to become popular. This causes the complexity of demand segmentation, features of merchandising and pricing policy, features of consumer behavior and consumption psychology, etc. An assessment is given of the dynamics of production and consumption of bread and exports of products, and a forecast is made for the development of the world market for bread and bakery products. The results of the analysis are necessary to assess the level of food safety and to develop a strategy that takes into account the current trends in the global bread and bakery products market. Prospective trends for sustainable production of frozen bakery products have been developed. (shrink)

The rapid advancement of intelligent computing has revolutionized the field of economic forecasting, providing unprecedented capabilities for developing, implementing, and analyzing advanced prediction models. This paper explores the comprehensive process of harnessing intelligent computing for economic forecasting, emphasizing the critical stages of model development, integration, and evaluation. Initially, it discusses data collection and preprocessing techniques essential for building robust models, followed by the selection of suitable statistical, machine learning, and deep learning algorithms. The paper then outlines the practical (...) aspects of model implementation, including real-time data integration, deployment strategies, and scalability considerations. Further, it delves into the continuous monitoring and maintenance required to ensure model accuracy and reliability over time. The analysis section evaluates model performance using various accuracy metrics, scenario analyses, and visualization techniques to provide actionable insights. Through case studies across the financial sector, government policy, and business planning, the paper illustrates the transformative impact of intelligent computing on economic forecasting. The findings underscore the potential of advanced prediction models to enhance decision-making, anticipate economic trends, and respond proactively to dynamic economic environments. (shrink)

Is the pool of fair predictive algorithms fair? It depends, naturally, on both the criteria of fairness and on how we pool. We catalog the relevant facts for some of the most prominent statistical criteria of algorithmic fairness and the dominant approaches to pooling forecasts: linear, geometric, and multiplicative. Only linear pooling, a format at the heart of ensemble methods, preserves any of the central criteria we consider. Drawing on work in the social sciences and social epistemology on the theoretical (...) foundations of the wisdom of crowds, we explain how our observations present an exception to the general trend of finding tradeoffs between the accuracy and fairness of forecasts. (shrink)

In this paper I argue that J. L. Schellenberg’s Divine Hiddenness Argument is committed to a problematic implication that is weakened by research in cognitive psychology on affective forecasting. Schellenberg’s notion of a nonresistant nonbeliever logically implies that for any such person, it is true that she would form the proper belief in God if provided with what he calls “probabilifying” evidence for God’s existence. In light of Schellenberg’s commitment to the importance of both affective and propositional belief components (...) for entering into the proper relationship with God, this implication of his argument becomes an affective prediction or forecast. However, research in cognitive psychology has shown that in multiple and varied circumstances humans often make inaccurate predictions of their future affective states or reactions. Thus, this research provides strong empirical reasons to doubt that the implication is warranted. (shrink)

For more than a decade, an aura of discontentment has challenged existing models and theories that have established the structures in various fields of human endeavours such as philosophy, architecture, political science, media, literature, arts and the humanities in general. For instance, the architectural design of what was hitherto referred to as modern building has at least a sitting room (parlour), a kitchen, a bathroom and a toilet as well as two or more number of bedrooms depending on the size (...) of the family, including the master's bedroom. This design was a view that transcended from the old traditional caves and all manner of living spaces that were devised by the early man. The modern concept arose out of man's need to assume a better living status based on his new initiatives. In the field of media and communication, the period of technological evolvement and development that brought about the coinage of descriptive terms such as 'electronic age' and now 'computer age', was preceded by a 'stone age' where the means of communication was prototypical of that archaic society. Though nothing has changed tremendously in interpersonal communication, there are modern means of interpersonal communication made possible by the advent of the telephone. Prior to the advent of this modern technology, interpersonal communication was only made possible in a face-to-face situation or mere errand. Mass Communication in early traditional societies was done with the use of smoke or town-criers who were employed to go round the communities with their gongs to make announcements. This means of communication hardly made it possible for ideas or information to be shared among people who resided in different parts of the world, unlike today. (shrink)

The purpose of the research is to study the feasibility of using the combined modeling method in evaluation of business value. Modern approaches and methods of evaluating business value and the possibilities of combining them are explored. The peculiarities of the methodology of evaluating the business value by methods of Gordon Growth Model and Exit Multiple are disclosed. During the research the fair value of Luxoft company and the reasons for its deviation from the cost of sale are found. Luxoft’s (...) business value forecast is calculated by 2023. Recommendations for further development of the field of knowledge of modern business analyst are given. (shrink)

The objective of this paper was to identify the level of demand in the Vietnamese fisheries sector for e-Nav – weather and sea condition forecast application developed by NASCA Geosystems. NASCA Geosystems is a small company of five people operating in the field of information technology consulting. Management at NASCA Geosystems wanted to study the Vietnamese market in order to expand their operation in Vietnam. In this thesis, both qualitative and quantitative methods were applied. The quantitative data was gathered from (...) the official statistic sources namely the General Statistics Office of Vietnam, Indexmundi, the United Nations and the World Bank. The qualitative research includes an email interview with the director of NASCA Geosystems and a personal interview with Mr. Tran Dinh Lan from the Institute of Marine Environment and Resources. (shrink)

As humanity grapples with increasingly unpredictable weather and intensifying climate extremes, scientists are calling for not only reliable long-term climate projections but also timely and accurate short-term forecasts—a practice now referred to as climate “nowcasting”. A crucial yet often underestimated player in this endeavor is aerosols—tiny particles suspended in the atmosphere, originating from both human activities such as industrial pollution and wildfires, and natural sources like desert dust and sea spray.

The purpose of this investigation is to develop a new methodological basis for studying the market opportunities of the multinational corporation (MNC) on the basis of the synthesis of modern scientific methods for further forecasting their change. The results showed the degree of dependence of the effectiveness of business management on the trends in the field development and competitor’s actions, market access opportunities, the use of MNC opportunities, their strategic positions, and the totality of internal factors that determine the (...) profitability of activities. The practical value of the obtained results is to identify areas for increasing the efficiency of MNC business management and forecasting the dynamics of sales volumes of their products on the market. (shrink)

As the climate warms, a pressing global concern is how rising temperatures and shifting weather patterns will influence the spread of infectious diseases.

This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was assessed by the determination (...) coefficient (R²), the sum squared error (SSE) and a review of fit graphs. The results demonstrate the value of ANNs for prediction modeling. Drawing on supervised learning and back propagation, the ANN-based prediction models adopt an architecture of [18-15-1] for zinc, [18-11-1] for manganese, and [18-8-1] for boron, and perform effectively with a single cached layer. It was found that the MLR-based prediction models are substantially less accurate than those based on the ANNs. In addition, the physical-chemical parameters being investigated are nonlinearly correlated with the levels of heavy metals in the surface waters of the Oued Inaouen watershed flowing towards Inaouen. (shrink)

The authors of the scientific monograph have come to the conclusion that мanagement of socio-economic transformations of business processes requires the use of mechanisms to support of entrepreneurship, sectors of the national economy, the financial system, and critical infrastructure. Basic research focuses on assessment the state of social service provision, analysing economic security, implementing innovation and introducing digital technologies. The research results have been implemented in the different models of costing, credit risk and capital management, tax control, use of artificial (...) intelligence and blockchain. The results of the study can be used in the developing of policies, programmes and strategies for economic security, development of the agricultural sector, transformation of industrial policy, implementation of employment policy in decision-making at the level of ministries and agencies that regulate the management of socio-economic and European integration processes. The results can also be used by students and young scientists in the educational process and conducting scientific research on global challenges and creation scenarios for the development of socio-economic processes. (shrink)

The GOLEM XIV’s forecast for the development of the human civilisation and a typology of technological singularities: In the paper, a conceptual analysis of technological singularity is conducted and results in the concept differentiated into convergent singularity, existential singularity, and forecasting singularity, based on selected works of Ray Kurzweil, Nick Bostrom, and Vernor Vinge respectively. A comparison is made between the variants and the forecast of GOLEM XIV (a quasi-alter ego and character by Stanisław Lem) for the possible development (...) of Homo sapiens civilisation. According to the prediction, the scientific and technological progress is to bring humanity to transfer its civilisational primacy to a nonhuman superhuman intelligence xor a posthuman one. Both scenarios are best described by forecasting singularity, yet all variants are applicable only conditionally. As GOLEM did not undertake, the trajectories may also intersect or combine. (shrink)

This paper is concerned with learners who aim to learn patterns in infinite binary sequences: shown longer and longer initial segments of a binary sequence, they either attempt to predict whether the next bit will be a 0 or will be a 1 or they issue forecast probabilities for these events. Several variants of this problem are considered. In each case, a no-free-lunch result of the following form is established: the problem of learning is a formidably difficult one, in that (...) no matter what method is pursued, failure is incomparably more common that success; and difficult choices must be faced in choosing a method of learning, since no approach dominates all others in its range of success. In the simplest case, the comparison of the set of situations in which a method fails and the set of situations in which it succeeds is a matter of cardinality (countable vs. uncountable); in other cases, it is a topological matter (meagre vs. co-meagre) or a hybrid computational-topological matter (effectively meagre vs. effectively co-meagre). (shrink)

According to sentimental perceptualism, affect grounds evaluative or normative knowledge in a similar way to the way perception grounds much of descriptive knowledge. In this paper, we present a novel challenge to sentimental perceptualism. At the centre of the challenge is the assumption that if affect is to ground knowledge in the same way as perception does, it should have a function to accurately represent evaluative properties, and if it has that function, it should also have it in its future-directed (...) imaginative use. As the data on affective forecasting errors indicates, however, the affect system does not have that function. As a result, it is doubtful if affect can do the kind of knowledge-grounding work that sentimental perceptualism assumes it does. (shrink)

The paper deals with the topical issue of studying cyclic patterns in the economy and their practical application for the forecasts on the development of financial markets. The work aims to establish the features of the seasonal-cyclic patterns "The January barometer" and "The first five days of January" in the international financial markets in current conditions and to develop recommendations for the practical application of these patterns in the investment activities. The US stock market as an integral part of the (...) World financial market was chosen as a basis for research. The research was conducted by statistical processing of data on the values of the broad market index Standard & Poor's-500 for the period from 1950 to 2019. The study showed that the formation of forecasts about the annual growth or decline of the stock market index using the seasonal-cyclic patterns "The January barometer" and "The first five days of January" can show a high result in cases where the growth of the Standard & Poor's-500 index by the results of both the first five days of January and the results of the entire month of January of this year is a positive value. Otherwise, forecasting based on these instruments is not advisable. The best results (the forecast efficiency was 93.3%) were achieved with the joint application of both cyclic patterns. The practical application of the research results makes it possible to improve the efficiency of investment activities in international financial markets. (shrink)

Earthquakes are among the most destructive natural disasters, yet accurately predicting them remains one of science’s greatest challenges. Traditional seismological approaches struggle to interpret complex patterns from vast seismic datasets. Recently, machine learning (ML) has shown promise in seismology by identifying hidden patterns, detecting microseismic activities, and forecasting earthquake probabilities. This paper explores the integration of ML into earthquake prediction, reviewing current models, methodologies, and challenges. It also proposes a data- driven framework for improving seismic event forecasting using (...) supervised and unsupervised ML algorithms. (shrink)

A : In the rapidly evolving financial markets, accurately predicting stock prices is crucial for investors seeking to optimize their portfolios and mitigate risks. This project leverages machine learning techniques to develop a predictive model for stock price forecasting. We utilize historical stock price data, along with relevant economic indicators and market sentiment, to construct a robust dataset. Key methodologies include time series analysis, regression models, and advanced machine learning algorithms, such as Long Short-Term Memory (LSTM) networks, which excel (...) at capturing temporal dependencies in sequential data. The project involves comprehensive data preprocessing and feature engineering to enhance model performance. Various models are trained and evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to ensure accuracy and reliability. The results demonstrate the potential of machine learning approaches in financial predictions, revealing patterns and insights that can inform investment strategies. This research aims to contribute to the field of predictive analytics in finance, offering a framework for future studies and practical applications in stock market forecasting. cognitive radio(CR) is a transceiver which automatically detects available channels in wireless spectrum and accordingly changes its transmission or reception parameters. In this paper, it proposes an algorithm for the energy-efficient and spectrumaware communications requirements in CR network. It enables each node to determine and regulate its transmission strategy to provide minimum energy consumption without sacrificing end-to-end delay performance and also maximizes overall spectrum utilization. Spectrum sensing is one of the essential parameter to be considered in CR networks. Therefore, the security aspect of spectrum sensing should be addressed well. Using a Trust-Worthy algorithm, it improves the trustworthiness of the Spectrum sensing in CR-Networks. It implemented using Network Simulator-2. (shrink)

The exponential growth of high-frequency real-time financial transactions necessitates scalable machine learning infrastructures capable of processing and forecasting data in real time. This paper proposes a comprehensive design and implementation strategy for such infrastructures using distributed computing frameworks like Apache Spark and cloud services such as Amazon Web Services (AWS). Emphasizing technical specifics, the paper delves into architectural designs, implementation strategies, and optimization techniques that address critical challenges in data ingestion, real-time processing, model training, and deployment. A proof-of-concept implementation (...) demonstrates the feasibility of the proposed architecture on a small scale, highlighting its potential benefits. The findings suggest that implementing a scalable distributed machine learning infrastructure can enhance computational efficiency and significantly improve the accuracy and timeliness of financial forecasts. Future work will involve deploying the proposed architecture in large-scale industry settings to validate its effectiveness in real-world scenarios. (shrink)

Introduction: There is a general agreement among previous studies that gender, merit and catchment area criteria allows for access to university education, but the pattern of these variables over the years has not been proven in these studies. Purpose: This study used a times series approach to evaluate the enrolment patterns in federally owned universities in South-South Zone, Nigeria, based on the gender, merit and catchment area criteria. Methodology: The descriptive survey design was adopted for this study. A purposive sampling (...) technique was adopted to choose three universities based on the generation of their establishment. The evaluation covered from 2010 to 2019 using stationary time-bound data collected from the academic planning units of the institutions through a checklist. Future enrolment forecasts were made to 2031 using the moving average forecasting technique. Results: A gender-based difference in enrolment, in favour of males was recorded between 2010 and 2019; there were notable fluctuations in the enrolment pattern based on the merit criterion from 2010 to 2019; the enrolment pattern in terms of the catchment area criterion was consistent from 2010 – 2019. In terms of the projection, it was predicted that enrolment at the aggregate level would rise continuously across federal universities, but not at the institutional level. more women than men are expected to be enrolled in academic programmes at federal institutions, while at the institutional level, gender-based enrolment can fluctuate. Recommendations/Classroom Implications: Based on these findings, implications are discussed for future enrolment decisions at both the institutional and national levels. (shrink)

Drought is a critical environmental issue that affects agriculture, water resources, and ecosystems. Traditional drought monitoring methods rely on ground-based meteorological observations, which have limited spatial coverage and do not provide real-time assessments. This project aims to develop a Vegetative Drought Prediction System by integrating Vegetation Condition Index (VCI) data from the ISRO VEDAS VCI Dashboard, remote sensing indices (NDVI), meteorological drought indicators (SPI, PDSI), and machine learning algorithms (Random Forest, SVM, LSTM) to accurately detect and predict drought conditions. The (...) system utilizes VCI data from the ISRO VEDAS portal, which provides high-resolution drought monitoring for India. Additionally, satellite imagery from MODIS, Sentinel-2, and Landsat-8 is used to compute NDVI (Normalized Difference Vegetation Index) to assess vegetation health. Meteorological indices, such as the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI), are used to evaluate precipitation deficits and long-term drought trends. Machine learning models further enhance drought prediction. Random Forest (RF) and Support Vector Machine (SVM) classify drought severity based on VCI, NDVI, temperature, and rainfall data. Long Short-Term Memory (LSTM) networks analyze time-series data to forecast future drought conditions. These models are trained on historical drought records and meteorological data to improve accuracy. A web-based application is developed to visualize drought conditions, enabling users to select a region, analyze VCIbased drought indices, and receive real-time predictions. The frontend is built using HTML, CSS, and JavaScript (React.js), while the backend is implemented using Flask/Django, with PostgreSQL/MongoDB for data storage. This project provides an efficient, scalable, and cost-effective solution for drought monitoring using ISRO VEDAS VCI data, benefiting farmers, policymakers, and researchers in making informed decisions and enhancing drought preparedness. (shrink)

The entire world community, since 2019, affected by the global pandemic COVID-19. The pandemic caused by this virus, led not only to significant human losses worldwide, but also imposed significant restrictions on the socio-cultural life of the population and radically changed the trends of the global economy and the further functioning of socio-economic systems. Now, huge economic losses have been recorded, which affected almost all sectors of the national economy and the state in the short, medium and long term. However, (...) it is important to consider individual economic development forecasts and measures developed by the governments of the world’s leading countries to overcome the negative effects of the COVID-19 pandemic. This will allow to form a real vision of the possible course of economic processes that will directly affect the living standards of the population and the restoration of socio-economic systems. To further restore the socio-economic system it is necessary to developing modern strategies and forecasts to ensure the effective functioning of economic entities through innovation, digitalization, marketing and use of competitive advantages in the consumer markets in conditions of limited resources, development promising sectors of the national economy, etc. The purpose of writing this scientific monograph is to substantiate the theoretical and methodological foundations, the formation new strategies for restoring socio-economic systems and overcoming the negative consequences of the caused by the COVID-19 pandemic, taking into account changes and challenges in the modern world. The object of the authors’ research is the process of forming new approaches, strategies and mechanisms for managing socio-economic systems in the context of the COVID-19 pandemic, eliminating the negative consequences in the activities of economic entities. The subject of research is socio-economic, organizational and institutional processes of formation and effective implementation of approaches, strategies and mechanisms for managing socio-economic systems; stabilization of the functioning of economic entities; introduction of innovative processes and digital technologies; implementation of best practices in the managing of socio-economic systems using world experience in various sectors of the economy caused by the COVID-19 pandemic. (shrink)

(Third edition with additions) -/- This is a collection of short stories centering around the protagonist character, Kingfisher, originally written in Vietnamese by myself. -/- The book aims to introduce international readers to snippets of Vietnamese culture through the ordinary yet humorous life of the bird village. -/- The first 15 of these short stories were published in the Khoảng Lặng (Quiet Moment) column of the Vietnamese magazine Kinh Tế và Dự Báo (Economy and Forecast Review) from 2017 to 2019. (...) -/- However, this is the first time that the collection of all the Kingfisher stories has been published in its entirety, covering stories that have not been published elsewhere, including new stories written during 2023-2024. -/- The stories of Kingfisher are full of humor and satire while also deep in social commentary. I hope that the book brings readers joy and some food for thought, for beyond satire is genuine contemplation of the humanistic values in our lives. -/- Hanoi, August 21, 2024 -/- Quan-Hoang Vuong -/- (Incld. Artworks by Bui Quang Khiem and Dam Thi Thu Ha). (shrink)

The article offers forecasts of the geopolitical and geo-economic development of the world in the forthcoming decades. One of the main accusations directed toward globalization is that it deepens the gap between the developed and developing countries dooming them to eternal backwardness. The article demonstrates that the actual situation is very different. It is shown that this is due to the globalization that the developing countries are generally growing much faster than the developed states, the World System core starts weakening (...) and its periphery begins to strengthen. At the same time there is a continuing divergence between the main bulk of developing countries and the group of the poorest developing states. The article also explains why the globalization was bound to lead to the explosive rise of many developing countries and the relative weakening of the developed economies. In the forthcoming decades this trend is likely to continue (though, of course, not without certain interruptions). It is also demonstrated that this convergence constitutes a necessary condition for the next technological breakthrough. (shrink)

In Fact, Fiction and Forecast, Nelson Goodman claims that the problem of justifying induction is not something over and above the problem of describing valid induction. Such claim, besides suggesting his commitment to the collapse of the distinction between the context of description and the context of justification, seems to open the possibility that the new riddle of induction could be addressed empirically. Discoveries about psychological preferences for projecting certain classes of objects could function as a criterion for determining which (...) predicates are after all projectible. In this paper, I argue that Goodman's claim must be construed within his project for constructional definitions, which is methodologically oriented by reflective equilibrium. The description of inductive practice is committed to the articulation of the extension of the class selected by the predicate ‘valid induction’. The mutual adjustment between theoretical considerations and inductive practice involved in the proposal of a definition of ‘valid induction’ must preserve that practice as much as possible, there is no way to get rid of entrenchment. Empirical discoveries about the psychological mechanism that underlies projections may help that adjustment but they cannot substitute the role played by the entrenchment of predicates. (shrink)

Stock market prediction is essential and of great interest because success- ful prediction of stock prices may promise smart bene ts. These tasks are highly complicated and very dicult. Many researchers have made valiant attempts in data mining to devise an ecient system for stock market movement analysis. In this paper, we have developed an ecient approach to stock market prediction by employing fuzzy C-means clustering and arti cial neural network. This research has been encouraged by the need of predicting (...) the stock market to facilitate the investors about buy and hold strategy and to make pro t. Firstly, the original stock market data are converted into interpreted historical ( nancial) data i.e. via technical indi-cators. Based on these technical indicators, datasets that are required for analysis are created. Subsequently, fuzzy-clustering technique is used to generate di erent training subsets. Subsequently, based on di erent training subsets, di erent ANN models are trained to formulate di erent base models. Finally, a meta-learner, fuzzy system module, is employed to predict the stock price. The results for the stock market prediction are validated through evaluation metrics, namely mean absolute deviation, mean square error, root mean square error, mean absolute percentage error used to estimate the forecasting accuracy in the stock market. Comparative analysis is carried out for single Neural Network (NN) and existing technique neu- ral. The obtained results show that the proposed approach produces better results than the other techniques in terms of accuracy. (shrink)

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. (...) In our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

Building an accurate load forecasting model with minimal under predictions is vital to prevent any undesired power outages due to underproduction of electricity. However, the power consumption patterns of the residential sector contain fluctuations and anomalies making them challenging to predict. In this paper, we propose multiple Long Short-Term Memory (LSTM) frameworks with different asymmetric loss functions to impose a higher penalty on underpredictions. We also apply a density based spatial clustering of applications with noise (DBSCAN) anomaly detection approach, (...) prior to the load forecasting task, to remove any present outliers. (shrink)

Models not only represent but may also influence their targets in important ways. While models’ abilities to influence outcomes has been studied in the context of economic models, often under the label ‘performativity’, we argue that this phenomenon also pertains to epidemiological models, such as those used for forecasting the trajectory of the Covid-19 pandemic. After identifying three ways in which a model by the Covid-19 Response Team at Imperial College London may have influenced scientific advice, policy, and individual (...) responses, we consider the implications of epidemiological models’ performative capacities. We argue, first, that performativity may impair models’ ability to successfully predict the course of an epidemic; but second, that it may provide an additional sense in which these models can be successful, namely by changing the course of an epidemic. (shrink)

This article examines digital twins' applications and future directions in supply chain management. Digital twins, virtual representations of physical objects or processes, revolutionize supply chain operations by providing real-time insights and predictive capabilities. The global digital twin market is projected to reach $48.2 billion by 2026, driven by the need for real-time monitoring and predictive maintenance across industries. The article discusses the key components of supply chain digital twins, their implementation in the distribution industry, and their numerous benefits, including enhanced (...) visibility, improved forecasting, risk mitigation, and increased efficiency. Challenges such as data integration and skill shortages are also addressed. The article concludes by exploring future directions, including AI integration, IoT expansion, blockchain integration, extended reality interfaces, and cross industry collaboration, highlighting the transformative potential of digital twins in creating more resilient, agile, and intelligent supply networks. (shrink)