Algorithmic model

This paper proposes a hybrid ensemble learning framework to enhance the transparency and accuracy of anaemia risk prediction. By combining multiple machine learning classifiers through a soft-voting mechanism, the system provides a robust diagnostic tool that moves beyond traditional "black-box" models. Experimental results from the implemented framework demonstrate high predictive performance, offering a scalable solution for early clinical intervention in iron-deficiency cases.

In the paper, on the basis of auditory analysis of English spoken anecdotes the authors come up with the system of prosodic means that serve to create the text humorous effect. To define the specificity of a complex interaction of emotional, pragmatic, structural and semantic factors of prosodic means ’ functioning in English anecdotes, we substantiated two algorithmic models presenting the text story-line development: one being similar to the structure of the fairy tale (introduction → commentary → code), and the other one resembling the riddle (topic → commentary → code). By way of using these models as well as the traditional method of linguistic interpretation of the auditory analysis results, the authors substantiate the specificity of prosodic, lexico-grammatical and stylistic means interaction of an English anecdote oral actualisation functioning within its structural components. It has been found out that realisation of the anecdote humorous effect is ensured by the predominance of the unidirectional functioning of the language means of all levels with the leading role of prosodic means aimed at drawing the listeners’ attention to the anecdote’s two-plane semantics and its key lexical units, thus stimulating their thinking activities while decoding the humour of the anecdote. The authors come to the conclusion that the application of a functional-and-energetic approach to the study of a complex interaction of emotional, pragmatic, semantic and structural factors makes it possible to present a comprehensive  description  of  invariant  and
variant prosodic patterns of any type of texts.

Robust decision-making strategies are necessary for the long term. These strategies have to work
with changing market conditions and limit the influence of human bias. This paper presents a fully automated
system for long-term investing using Proximal Policy Optimization (PPO). The aim is to build a system to
'Optimize Portfolio' (OP) and assist in balancing buying and selling. The proposed system captures the
investment environment using historical data, technical indicators, and reward functions sensitive to the risk
to address trade-offs between returning and reducing drawdowns. Through the use of a clipped objective
function, investment policies are to be learned with the aid of the PPO algorithm and to provide stable
investments for long periods. The investment system on diverse equity datasets will be analyzed on metrics,
such as cumulative return, Sharpe ratio, maximum drawdown, and turnover, in the long run. The results from
the experiments show that the investment agent using PPO is better than the conventional strategies of 'buy
and hold' and 'do nothing' (rules) in the overall capital preservation and the risk-adjusted return. The results
show that reinforcement learning captures the long-term and changing dynamics of the market. The system
presented is one of the first long-term, fully automated, and intelligent systems using reinforcement learning.

Trading robots or systems have to be effective in different market conditions, including markets going up, markets going down, or even when the market is not moving much for a long time, called choppy or sideways. The paper presents a full-fledged, reinforcement learning-based, automatic trading system that changes its behavior to different market environments without needing human intervention or marketspecific setting adjustments. Essentially, the model is working out a trading system where an agent is enabled to get the best moving in and moving out and staying in the market tactics by means of market interaction and at the same time meet the criterion of maximum, risk, adjusted, long, long-term profitability. This system first identifies the market condition with the help of price indicators and price volatility indicators, along with volume, based measures for trend strength and momentum and the phases of market consolidation. A reward function has been designed which incorporates three main components: profit, drawdown control, and transaction cost penalties to incentivize traders who exhibit stable and genuine trading behavior. The entire setup is put to test through the use of historical datasets covering a bull market, a bear market, and a sideways market and the authors employed trend and volatility filters to separate the different market regimes.

Stock market trading shows unpredictable price movements together with its nonlinear nature,
which makes standard rule-based methods less effective. The research develops an intelligent stock trading
system which uses Simple Moving Average (SMA) crossover signals together with support resistance zone
analysis to operate through Proximal Policy Optimization (PPO) reinforcement learning system. The SMA
crossover method identifies both trend beginnings and trend ending points, while support and resistance zones
identify essential price points which show different market behaviors during consolidation and breakout and
reversal periods. The technical attributes of the system are transformed into a special trading platform which
allows a PPO agent to discover the best times for buying, selling, and holding through ongoing market activity.
The PPO algorithm enables stable policy development through its system which connects exploration with
exploitation in environments that have multiple random elements. The reward function aims to create
maximum total profit while considering both transaction expenses and risk sensitivity, which helps the agent
to flexible adapt to different marketplace situations. The experiments use historical stock price data to show
that the PPO-based strategy achieves better results than traditional SMA crossover and support-resistance
trading techniques because it produces higher profits while maintaining lower drawdowns and better riskadjusted
returns. The findings show that combining trend-following indicators with price-level structures
through a PPO reinforcement learning system creates effective automated trading systems which can adapt to
changing market conditions.

Stock market trading becomes unpredictable because of constant price changes that occur together with different levels of market activity and shifting trend patterns. Traders who depend on traditional rule-based methods which use one technical indicator face problems because these strategies produce their first signals after a delay. To overcome these challenges, the researchers developed a reliable hybrid trading system which combines Simple Moving Average Crossover (SMAC) and On-Balance Volume (OBV) with a Proximal Policy Optimization (PPO) reinforcement learning framework. The SMAC indicator helps to determine the current market trend, while OBV serves as a volume-based confirmation tool to track cumulative buying and selling activities, which prove that price changes occurred through actual market activity. This combination reduces the likelihood of false breakouts and improves signal reliability. The PPObased reinforcement learning agent achieves better trading execution skills through its continuous analysis of historical market data. The agent evaluates current market conditions by analyzing three factors, which include trend direction and volume strength and portfolio position, in order to achieve maximum long-term returns while managing risk. The proposed SMAC-OBV-PPO framework proves its superiority over traditional SMAC-based strategies through three performance metrics, which include trade accuracy, profitability and stability. The results demonstrate that automated trading systems achieve better performance in unstable financial markets when operators use trend-following indicators which combine with volume confirmation and reinforcement learning techniques.

This study develops a computationally efficient and interpretable model for predicting volatility in multicommodity exchange (MCX) markets. It addresses the limitations of complex black-box models by proposing a finite-state automaton framework that represents market volatility as discrete states and model transitions based on key market indicators. The methodology involves collecting real-time and historical MCX commodity data, extracting technical indicators, such as the Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD), and designing a finite-state machine where state transitions are governed by threshold-based rules. The model was implemented in Python, calibrated using historical data, and validated using unseen real-time data. Experimental results demonstrate that the finite-state automaton framework achieves competitive accuracy in forecasting volatility with enhanced interpretability and reduced computational overhead compared to traditional statistical and machine learning approaches. The rule-based model provides transparency and adaptability, rendering it suitable for practical use in real-time trading environments. In conclusion, this study illustrates the viability of applying the finite automata theory to commodity market volatility prediction, offering a novel, transparent, and efficient alternative to existing methods.

The digital era has revolutionized architectural practices, with Building Information Modeling (BIM) emerging as a pivotal tool for architects, engineers, and construction professionals. However, the transition from traditional methods to BIM-centric approaches poses significant challenges, particularly in the context of existing structures. This research presents a technical approach to bridge this gap through the development of algorithms that facilitate the automated transformation of 3D point cloud data into detailed BIM models. The core of this research lies in the application of algorithmic modeling and computational design methods to interpret and reconstruct point cloud dataa collection of data points in space, typically produced by 3D scannersinto comprehensive BIM models. This process involves complex stages of data cleaning, feature extraction, and geometric reconstruction, which are traditionally timeconsuming and prone to human error. By automating these stages, our approach significantly enhances the efficiency and accuracy of creating BIM models for existing buildings. The proposed algorithms are designed to identify key architectural elements within point clouds, such as walls, windows, doors, and other structural components, and to translate these elements into their corresponding BIM representations. This includes the integration of parametric modeling techniques to ensure that the generated BIM models are not only geometrically accurate but also embedded with essential architectural and structural information. This research contributes significantly to the field of architectural technology by providing a scalable and efficient solution for the integration of existing structures into the BIM framework. It paves the way for more seamless and integrated workflows in renovation and heritage conservation projects, where the accuracy of existing conditions plays a critical role. The implications of this study extend beyond architectural practices, offering potential benefits in urban planning, facility management, and historical preservation.

A behavioral model of spiral inductor is developed in the present paper realized using the possibilities of the Ansoft Simplorer simulator. The basic possibilities and advantages of Ansoft Simplorer for behavioral modeling and simulation of electronic circuits are presented. An example is presented illustrating the modeling and simulation of spiral inductors. A comparison of the results with a corresponding PSpice model is given. The designed VHDL-AMS spiral inductor model is simple, algorithmic and realized to be easily implemented as a module in mixed signal simulation of large analog-digital projects.

In the paper on the basis of a systemic analysis of the English fairy tales' semantic, functional and structural characteristics the author updates its definition of a small form folk text, outlines typical genre characteristics and substantiates a hierarchical subordination of its functional features. The paper also advances the algorithmic model, representing the steps of unfolding the English fairy tales' plot elements, which serves as a theoretical and methodological ground for the experimental phonetic study of prosodic means that organise the text of a fairy tale. Within the process of model formation, the author performs a matrix analysis of the fairy tale's content and compositional structure, presented as a systemic algorithmic scheme. The evaluation of emotional and pragmatic potentials of the spoken fairy tale made it possible to describe the peculiarities of prosodic features functioning within its structural elements (exposition → development of the events → climax → denouement) and at their junctures as well as substantiate its invariant and variant intonation patterns. The carried out auditory analysis proves that there is a direct correlation between the fairy tale's plot structure and its prosodic means, whose specific interaction triggers creative and cognitive processes in the recipient's mind aimed at conveying the text pragmatic aim.

In the paper on the basis of a systemic analysis of the English fairy tales' semantic, functional and structural characteristics the author updates its definition of a small form folk text, outlines typical genre characteristics and substantiates a hierarchical subordination of its functional features. The paper also advances the algorithmic model, representing the steps of unfolding the English fairy tales' plot elements, which serves as a theoretical and methodological ground for the experimental phonetic study of prosodic means that organise the text of a fairy tale. Within the process of model formation, the author performs a matrix analysis of the fairy tale's content and compositional structure, presented as a systemic algorithmic scheme. The evaluation of emotional and pragmatic potentials of the spoken fairy tale made it possible to describe the peculiarities of prosodic features functioning within its structural elements (exposition → development of the events → climax → denouement) and at their junctures as well as substantiate its invariant and variant intonation patterns. The carried out auditory analysis proves that there is a direct correlation between the fairy tale's plot structure and its prosodic means, whose specific interaction triggers creative and cognitive processes in the recipient's mind aimed at conveying the text pragmatic aim.

The paper is a study of the prosodic features involved in the oral actualisation of English proverbs as influenced by structural, emotional and pragmatic factors, which are defined within the framework of a new energetic approach to the research of phonetic phenomena. On the basis of auditory and acoustic analyses of English proverbs, the authors describe the energetic specificity of their prosodic organisation. This description introduces energy-grams formed with the use of a quantitative K-criterion of the texts' emotional and pragmatic potentials, as well as their typical intonation patterns. Using the traditional method of linguistic interpretation of the results obtained, the authors substantiate invariant and variant intonation patterns of English proverbs, whose parameters are described both within the text's structural elements and at their junctures. The authors come to the conclusion that an energetic approach to the study of a complex interaction of emotional, pragmatic, semantic and structural factors makes it possible to present a comprehensive description of invariant and variant prosodic patterns for any type of texts on the basis of such fundamental linguistic categories as emotion, pragmatics, structure, and meaning.

Risk management is a practical step in handling risk scenarios in an organization, including in the field of information security. There are many techniques used to carry out information security risk assessments. One of them is a combination technique using ISO 27005 and NIST SP 800-30 revision 1. Previous research proved that the combination technique could be implemented in a non-profit organization (government). However, the detailed risk assessment steps are not explained clearly yet. Thus, raising the question of whether this new approach can be utilized in a common organization or not (not only non-profit but also profit organization). This research focuses on information security risk assessment by implementing the combination technique in a profit organization using semi-quantitative methods. The result, the combination technique can be used in common organizations both profit and non-profit with clear step by step translation.

Computer-aided extraction procedure is developed for obtaining the parameters of spiral inductor model. The procedure is of direct type and can be easily realized using the possibilities of standard circuit simulators such as Cadence PSpice and graphical analyzer Cadence Probe. The computer realization of the extraction procedure is performed using macro-definitions in the graphical analyzer Cadence Probe.

A behavioral model of spiral inductor is developed in the present paper realized using the possibilities of the Ansoft Simplorer simulator. The basic possibilities and advantages of Ansoft Simplorer for behavioral modeling and simulation of electronic circuits are presented. An example is presented illustrating the modeling and simulation of spiral inductors. A comparison of the results with a corresponding PSpice model is given. The designed VHDL-AMS spiral inductor model is simple, algorithmic and realized to be easily implemented as a module in mixed signal simulation of large analog-digital projects.

Risk management is a practical step in handling risk scenarios in an organization, including in the field of information security. There are many techniques used to carry out information security risk assessments. One of them is a combination technique using ISO 27005 and NIST SP 800-30 revision 1. Previous research proved that the combination technique could be implemented in a non-profit organization (government). However, the detailed risk assessment steps are not explained clearly yet. Thus, raising the question of whether this new approach can be utilized in a common organization or not (not only non-profit but also profit organization). This research focuses on information security risk assessment by implementing the combination technique in a profit organization using semi-quantitative methods. The result, the combination technique can be used in common organizations both profit and non-profit with clear step by step translation.

While software is essential for all areas of the digital age, and there's no ideal method for software creation either way. The more technologies and procedures are modern, the novel are the risks, which must be monitored and handled accordingly. Focusing on the mechanism of risk assessment in IT projects would then contribute to the relevant outcomes. Increased emphasis on risk control seems important in view of the high rate of failures in IT programme. In this paper we suggest a new model explaining the risk assessment and method of risk incidents in SDLC phases. Furthermore, our model considers the international and non-profit organization regulations and guidelines. The model takes into consideration and assesses the utility of these strategies in commonly recognized and suggested methods of risk analysis. Results were evaluated using SPSS software. The extension of the suggested risk assessment model to any IT SDLC method is a good example.

Risk management has emerged as an important component of projects these days, particularly information technology projects which are complex and dynamic in nature. Risk management constitutes dif erent components and organisations are required to strategies it keeping in mind dif erent dimensions of IT projects. The present study aims to review the types of risk in IT projects and subsequently recommended the innovative and dynamic ways to overcome it. IT projects are generally characterised by scheduling, cost and performance risk. Organizations by having risk management strategies in place such as avoidance, transference, mitigation and acceptance in place can identify, analyze, prioritize and mitigate risk factors. A survey of 100 team members of IT projects and interview with 5 team managers showed that risk management and risk factors such as scheduling risk and cost risk have an impact on firm performance and also there is presence of barriers in the process risk management.

While software is essential for all areas of the digital age, and there's no ideal method for software creation either way. The more technologies and procedures are modern, the novel are the risks, which must be monitored and handled accordingly. Focusing on the mechanism of risk assessment in IT projects would then contribute to the relevant outcomes. Increased emphasis on risk control seems important in view of the high rate of failures in IT programme. In this paper we suggest a new model explaining the risk assessment and method of risk incidents in SDLC phases. Furthermore, our model considers the international and non-profit organization regulations and guidelines. The model takes into consideration and assesses the utility of these strategies in commonly recognized and suggested methods of risk analysis. Results were evaluated using SPSS software. The extension of the suggested risk assessment model to any IT SDLC method is a good example.

Risk management can be defined as a systematic process for risk assessment, analysis and control in projects or disorganization. Risk management in specific a list of ten Risk Factors that is most common in IT and software projects is presented with descriptions and risk diagrams. A structured risk assessment framework, which is accompanied by efficient approaches in specific process phases, is helpful to diverse high-risk programme. Therefore, we incorporate risk assessment strategies that are more or less agreed by stakeholders as well as literature. The paper proposes utilization of a probabilistic Bayesian network model as software risk identification paradigm to reflect on the most important software risk management metrics in software development life cycle. An analytical evaluation based on the evidence obtained from an organization's software development programme was undertaken to analyze the model developed

While software is essential for all areas of the digital age, and there's no ideal method for software creation either way. The more technologies and procedures are modern, the novel are the risks, which must be monitored and handled accordingly. Focusing on the mechanism of risk assessment in IT projects would then contribute to the relevant outcomes. Increased emphasis on risk control seems important in view of the high rate of failures in IT programme. In this paper we suggest a new model explaining the risk assessment and method of risk incidents in SDLC phases. Furthermore, our model considers the international and non-profit organization regulations and guidelines. The model takes into consideration and assesses the utility of these strategies in commonly recognized and suggested methods of risk analysis. Results were evaluated using SPSS software. The extension of the suggested risk assessment model to any IT SDLC method is a good example

While software is essential for all areas of the digital age, and there's no ideal method for software creation either way. The more technologies and procedures are modern, the novel are the risks, which must be monitored and handled accordingly. Focusing on the mechanism of risk assessment in IT projects would then contribute to the relevant outcomes. Increased emphasis on risk control seems important in view of the high rate of failures in IT programme. In this paper we suggest a new model explaining the risk assessment and method of risk incidents in SDLC phases. Furthermore, our model considers the international and non-profit organization regulations and guidelines. The model takes into consideration and assesses the utility of these strategies in commonly recognized and suggested methods of risk analysis. Results were evaluated using SPSS software. The extension of the suggested risk assessment model to any IT SDLC method is a good example.