Markov Chain Model Research Articles

SAARC super smart grid: Navigating the future - unleashing the power of an energy-efficient integration of renewable energy resources in the saarc region

The major problems in the power system network of the South Asian Association for Regional Cooperation (SAARC) region include the utilization of substantial fossil fuels, which leads to significant emissions of CO2. Moreover, due to the depletion of fossil fuels, it is very difficult to manage the demand requirement of the SAARC regions, which leads to significant power shortage issues. These problems would be resolved through the integration of the substantial amount of renewable energy resources (RERs) that are available in excess in the SAARC regions into the power system networks. This will provide a futuristic roadmap to a sustainable and green energy environment in the SAARC region. In this regard, the SAARC region has devised a new plan to transform its smart grid infrastructure into the super smart grid (SSG), which would connect the SAARC countries using the high voltage transmission lines utilizing each country's substantial RERs. To accelerate this ecofriendly effort, the key objective of this review study is to evaluate the potential of the SAARC SSG for demand management and emissions reduction. This study provides a comprehensive overview of SSG technology, covering its technical background, management, power flow improvement techniques, power stability techniques, simulation models, and future steps for development in the SAARC region. First, the regional conflicts, energy deficiency, and potential of the SAARC region have been discussed. Second, a potential architecture of the SAARC SSG has been simulated. Third, a novel Markov chain modeling has been presented for demand management in the SAARC regions. The results and discussions section presents findings from simulations and models. The paper also discusses CO2 reduction techniques, including methods for capturing, storing, and utilizing carbon in biofuel. The study also discusses futuristic optimization techniques in SSG, including blockchain, federated learning, reinforcement learning, the metaverse, digital twin technology, artificial intelligence, and cloud computing. The SSG challenges and limitations section summarizes the challenges and limitations faced by SSG. Finally, a roadmap to a sustainable and green energy environment for the SAARC power grids has been identified to support the implementation of the futuristic SAARC SSG. The results exhibited that there is a good potential for successful demand management and emissions reduction through the SAARC SSG.

Growth Dynamics and Export Potential of India's Poultry Sector: A Comprehensive Analysis

India ranks as the third largest egg producer and fifth largest producer of poultry meat globally. Commercial poultry farms accounts about eighty-five percent of countries' egg production, the remaining comes from household/backyard poultry. Poultry production plays a significant part in the socio-economic life of India's rural population. This research focused on growth trends, instability in egg and poultry meat production and export trend, instability and Markov chain model was used to identify the market share of importing countries and stable importers of Indian poultry products. The state wise analysis revealed that the Uttar Pradesh registered highest growth rate among the top ten egg producing states for the study period. The export of poultry products has positive growth. Oman and Maldives are reliable market partners for India’s poultry product over the period.

Estimating future bathymetric surface of Kainji Reservoir using Markov Chains and Cellular Automata algorithms

The menace of sedimentation to reservoirs has a significant implication for water quality, storage capacity and reservoir lifetime. Rainfall patterns and other anthropogenic and environmental impacts alter the erosion rate and, by extension, directly affect sedimentation rates if left unchecked. This research focused on using the integration of Markov Chains and Cellular Automata (MC – CA) models to estimate and forecast the future bathymetric surface of the Kainji reservoir in Nigeria for the year 2050. The bathymetric datasets used for this research comprise two different epochs (1990 and 2020). The datasets were acquired using a Single Beam Echosounder at Low and High frequencies of 20 kHz and 200 kHz. The preliminary investigation revealed that sedimentation is exacerbating a greater danger to the reservoir functionality. The results show that the maximum observed depth is 71.2 m, indicating a 7.53% loss in depth from the 1990 archived data and a 16.24% depth loss to sedimentation from 1968 to 2020 and 22.35% depth loss in the year 2050 as shown on the projected surface. Consequently, the integrated model (MC and CA) efficiently predicted the future bathymetric surface of the Kainji reservoir for the year 2050 based on the data characteristics. However, the proven techniques for analysing spatial data, such as the Markov Chain and Cellular Automata, best suited for analysing categorical transition data, show some artefacts (black spots) on the projected generated map which is subject to further investigation.

Navigating uncertainty: A dynamic Bayesian network-based risk assessment framework for maritime trade routes

Maritime safety is crucial for international seaborne trade and the global economy. Acknowledging the inevitable and multifaceted risks present in maritime navigation, we introduce a novel approach to evaluate the time-dependent risk performance of various routes, with the objective to mitigate vulnerabilities and fortify resilience. To achieve this goal, we have developed an effective framework using a Dynamic Bayesian network (DBN) model to capture the interactive relationships among the influential factors in probabilistic terms. This framework employs a combination of the synthetic minority over-sampling technique and edited nearest neighbor approach to address the issue of imbalanced maritime accident reports. Furthermore, the gradient descent algorithm is utilized to calculate the conditional probabilities among the influential factors, while the Markov chain model is applied to develop the DBN model. The framework enables dynamical consideration of the risk performance of the maritime trade routes and identifies the key influential factors during different time intervals. Our research scientifically identifies the devastating impact of terrorism on shipping routes, followed by other risk factors such as piracy, military conflicts, and various induced risks, ranked in descending order of severity. The case study on the Indian Ocean shipping route, which applies the proposed methodology, reveals an initial phase of fluctuating increases, succeeded by a prevailing downward trend over the span of 2009–2019. The methodology and results provide valuable support for maritime stakeholders in making informed decisions.

Research on hydrological survey and forecasting model based on rolling sampling Markov chain

Abstract Hydrological survey is of great significance in flood disaster warning, hydropower engineering construction, and efficient utilization of watershed resources. Due to the seasonal and random fluctuations of hydraulic resources, it is difficult to accurately predict waterway runoff. Based on this, this article proposes a hydrological survey and prediction method based on rolling sampling Markov chain. Firstly, based on historical hydrological and meteorological data, establish a watershed runoff model and summarize and analyze the characteristics of the patterns; Then, based on the first-order Markov chain model, the relationship between adjacent days is considered. According to seasonal and meteorological factors, the historical data is rolling sampled in a 10 day sampling interval, and a multi state transition probability matrix is established to construct a year-round time-series runoff forecasting model; Finally, taking the Jinsha River Basin as an example, the annual runoff situation of the waterway was simulated. The calculation results have verified the effectiveness of the proposed method, indicating that it can simulate runoff well and provide certain guidance for watershed management and construction.

Integrated hydrological modeling and water resource assessment in the Mayurakshi River Basin: A comprehensive study from historical data to future predictions

This study employs the SWAT hydrologic model to integrate climatological and hydrological processes for an in-depth analysis of the Mayurakshi River Basin. Utilizing the Markov Chain Model, the study evaluates water availability, flow patterns, and the basin's response to various climatic and land-use scenarios. Over 30 years of daily observed river discharge data were rigorously calibrated, validated, and analyzed for uncertainty, with critical data from the Massanjore Dam and Tilpara Barrage gauge stations characterizing the river's hydrological behavior. The result suggests the watershed received an average annual precipitation of 1432.4 mm, with evapotranspiration accounting for 40% of total water loss (578.4 mm). Surface runoff constituted over 90% of the total discharge, highlighting its importance for agricultural practices, particularly during the dry season. However future projections (2021-2031) indicate a significant decrease in mean annual precipitation (1404.7 mm) and a drop in evapotranspiration (542.1 mm or 38% of mean precipitation), attributed to reduced vegetation cover and increased settlement, leading to enhanced surface runoff. By quantifying internal renewable blue water, evapotranspiration, and soil water, this research provides crucial data for long-term water resource planning and assessment. The findings are valuable for national, regional, and transboundary water management agencies, offering insights into sustainable water resource management under changing climatic and different land-use conditions.

Cost-Effectiveness of Adjuvant Abemaciclib and Ribociclib in High-Risk Hormone Receptor-Positive Early Breast Cancer: An Indian Perspective.

Incorporating adjuvant cyclin-dependent kinase (CDK) 4/6 inhibitors abemaciclib and ribociclib along with endocrine therapy has been shown to improve invasive disease-free survival (iDFS) for hormone receptor-positive (HR+) human epidermal receptor 2-negative (HER2-) early breast cancer (EBC). This study assesses the cost-effectiveness of this strategy, along with adjuvant aromatase inhibitors from an Indian perspective. A Markov chain model evaluated the cost-effectiveness of abemaciclib and ribociclib with letrozole compared with letrozole alone for HR+/HER2- EBC from a payer perspective in India. Key measures included lifetime quality-adjusted life-years (QALY), life-years (LY), and total costs. This study explores two scenarios for effectiveness: a best-case (BC) scenario, where the benefit of CDK4/6 inhibitors in improving iDFS lasts a lifetime, and a worst-case (WC) scenario, where benefits disappear after 5 years. Probabilistic sensitivity analyses (PSA) were used to account for simulation uncertainty. In the BC scenario, abemaciclib added 2.17 QALY and 4.96 LY, incurring ₹2,317,957.7 ($27,756.65 in US dollars [USD]) in additional costs. However, the incremental cost-effectiveness ratio (ICER) for abemaciclib exceeded India's willingness-to-pay threshold in the BC and WC scenarios. In the BC scenario, ribociclib added 0.98 QALY and 2.58 LY with added costs of ₹1,711,504.32 ($20,494.6 USD). The ICER for ribociclib also surpassed India's threshold in both scenarios. PSA showed that neither drug was cost-effective at the current market prices in either BC/WC scenario. The cost of abemaciclib and ribociclib needs to be reduced by at least 78.61% and 87.19%, respectively, to be cost-effective in the BC scenario. The combination of adjuvant abemaciclib or ribociclib with letrozole is not cost-effective for HR+/HER2- EBC in India in either the BC or WC scenario.

Forecasting Price of Crude Oil Using the Weight Markov Chain (WMC) and ARIMA Model Techniques

Forecasting Price of Crude Oil Using the Weight Markov Chain (WMC) and ARIMA Model Techniques

Mapping benthic sediment types and composition in a turbid Jamaican bay using hydroacoustic data and different spatially explicit interpolation techniques

Hydroacoustic sensors are better suited for mapping shallow turbid coastal waters, but surveys must take place along transects, necessitating the interpolation of processed, large, spatially dependent hydroacoustic datasets to produce continuous maps. The aim of this study was to evaluate the performance of interpolation techniques that can handle data obtained from a hydroacoustic survey (covering 23 km2) conducted in 2016 in a shallow bay in southwestern Jamaica. Independent data or a 5-fold cross validation were used to compare the accuracies of continuous maps of benthic sediment percentage composition and submerged aquatic vegetation cover (SAV), interpolated from the survey data using random forest (RF) regression (RFr), RFr kriging (RFrk) and Ensemble Machine Learning (EML) with rotated/oblique coordinates. Categorical/thematic maps of sediment types interpolated using RF (RFc) and EML classifiers and a spatial Markov chain model with indicator kriging predictor/simulation (Mc-IK) were also compared. RFr, RFc, RFrk and EML covariates included coordinates and auxiliary data. The following maps were more accurate than corresponding interpolated maps: EML - mud, sand, silt, clay and SAV; RFr - loss on ignition [LOI]; RFrk - calcium carbonate; Mc-IK – sediment types with 3 and 4 classes. The Mc-IK maps were also the most suitable maps for assessing spatial and quantitative changes. The predominant substrate type in the bay was muddy sand covering 15.7 km2. The highest percentage composition of mud, silt, clay, and organic matter [LOI] was found close to the mouth of the Black River where sandy mud was found, indicating a high input of terrigenous sediment. Calcium carbonate composition showed an opposite spatial trend and was positively associated with higher SAV percentage cover. The bay is now part of a protected area, as such, these maps will be used as a baseline to gauge the impact of increased protection/management on benthic dynamics.

Effects of beef semen and beef embryo strategies on profitability: Economics of using beef semen and beef in vitro–produced embryo transfer in Jersey herds

Dairy herds have adopted sexed semen (SS) and beef semen (BS) to control heifer inventory and increase calf sales revenue. Beef in vitro-produced embryo transfer (beef IVP-ET) may be an alternative to increase calf sales revenue. Additionally, raising those Jersey-beef crossbred or pure beef animals in a dairy system may be a new source of revenue. We aimed to evaluate the effects of breed strategies combining dairy conventional semen (CS), SS, BS, and beef IVP-ET on herd dynamics and profitability with strategies of marketing those animals at 1 d old or raising them to a weight of 180 kg. A Markov chain model was developed to maximize the profitability of Jersey herds by changing the number of dairy heifers sold at birth and the culling rate of cows in their third parity or greater. The model presents inputs on the reproductive and productive performance of heifers and cows over time. The last year's data (year 10, steady state) was used to calculate accrual operational cost and revenue per cow per year. We varied the breeding strategy by breeding order and parities, the embryo transfer cost ($85 or $170), the pure beef calf market price ($200 or $300), and the marketing strategy. A total of 8 scenarios plus a default scenario were simulated. Overall, the proportion of SS use was 47.3 ± 0.6% (mean ± SD). For the scenarios replacing all CS breedings with BS breedings, the percentage of BS used was 52.3 ± 0.6%. When beef IVP-ET was used, the percentages of BS and beef IVP-ET used were 22.4 ± 0.1% and 31.0 ± 0.1%, respectively. We observed that when we compared the combination of BS and dairy SS with the default scenario, the production of purebred Jersey male calves was reduced by 83.5%, and profit per cow per year was increased from $113.50 to $203.30. When a beef IVP-ET cost of $85 per transfer was used (scenarios 2 and 3), profit per cow per year was $145.50 and $176.20 for pure beef calf prices of $200 and $300, respectively. In scenario 4, with a beef IVP-ET cost of $170, the lowest profit ($52.90 per cow per year) was found when marketing 1-d-old pure beef calves at $200. The highest profit was achieved for the scenario of raising the Jersey-beef crossbred animals to 180 kg ($232.90, scenario 6), followed by scenario 7 ($222.90, using the combination of BS, dairy SS, and beef IVP-ET) with an embryo transfer cost of $85. Under the current market conditions, combining SS and BS in the reproductive program was a feasible economic opportunity for Jersey herds, yielding the highest net return. The adoption of beef IVP-ET in a reproductive program can potentially increase profit per cow per year, but its profitability will depend on the beef IVP-ET pregnancy cost, the pure beef market price, calf performance, and herd reproductive performance. In conclusion, raising the Jersey-beef crossbred calves may be a profitable strategy, and dairy producers need to evaluate the best option to invest in, as it will take extra risk to produce high-quality animals.

Integrated analysis of reliability, power, and performance for IoT devices and servers

The Internet of Things (IoT) is currently widely used in various sectors and spaces. IoT devices are becoming small yet powerful servers and perform server-like functions. Reliability is a critical aspect in both IoT devices and servers, as they work together to create a robust and dependable IoT ecosystem. Power and performance are two other major considerations of an IoT system. Modeling, analysis, evaluation, and optimization of reliability, power, and performance for IoT devices and servers are major components in IoT systems development and deployment. In this paper, we conduct an integrated study of reliability, power, and performance for IoT devices and servers by mathematically rigorous modeling and analysis. The contributions of the paper can be summarized as follows. We establish a continuous-time Markov chain (CTMC) model that incorporates server failure rate, server repair rate, task arrival rate, and task processing rate. Using such an analytical model, we can calculate the server availability, the average task response time, and the average power consumption. We point out that there is an optimal server speed that minimizes the power-time product and a combined cost-performance metric of power, performance, and reliability. We show the impact of server reliability on response time, power consumption, server utilization, and the power-performance tradeoff. To the best of the author’s knowledge, this is the first paper that takes a combined approach to modeling and analysis of reliability, power, and performance for IoT devices and servers. It has been noticed that there has been little such theoretically solid investigation in the existing literature. Therefore, this paper has made tangible contributions and significant advances in the joint understanding of reliability, power, performance, and their interplay in IoT devices and servers quantitatively and mathematically.

Markov Chains Slash Inventory Costs in Indonesia

PT. Varia Usaha Beton, facing an 8% decline in 2022 targets due to uncontrolled inventory and a 15,150-ton overstock of sand at its BSP Sayung plant, needed an effective solution. This study applied a Markov chain model with perfect enumeration to optimize sand inventory management. Qualitative data from observations, expert interviews, and company records (2021-2022) and quantitative analysis identified Policy 58 as optimal, reducing excess costs to Rp. 32,463,360 monthly. Implementing improvements in specific overstock states (3, 4, 5, and 6) provides a strategic approach to minimize sand overstock and associated costs. Highlight: Inventory Decline: 8% target drop due to uncontrolled inventory in 2022. Optimal Policy: Policy 58 reduced monthly excess costs to Rp. 32,463,360. Strategic Improvements: Implement changes in states 3, 4, 5, and 6 to manage overstock. Keywoard: Inventory Control, Markov Chain, Perfect Enumeration, Overstock Management, Construction Materials

Abstract PR013: Detecting pairwise and higher-order antagonistic epistatic effects among somatic cancer genotypes to discover synthetic lethality

Abstract The mutual exclusivity of somatic variants in cancer suggests that current somatic variants can exert antagonistic epistatic effects on the selection of new mutations, contributing to a complex landscape of evolutionary trajectories. However, quantifying pairwise and higher-order epistatic effects—which are essential to estimation of the trajectory of likely cancer genotypes—has been a challenge. We have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation, dependent on somatic cancer genotype. We coupled the continuous-time Markov chain model with an approach that deconvolutes the underlying mutation rates and selection intensities across trajectories of oncogenesis. By elucidating the routes of variant evolution and the strengths of selective forces at play, our approach lays the groundwork for quantifying antagonistic epistatic effects of specific somatic genotypes. We applied our approach to report the variant mutation rates, selection intensities, and consequent substitution rates for lung adenocarcinoma, comparing smoker and nonsmoker tumor cohorts. We describe the distinct influences of smoking on underlying mutation rates and on physiology affecting the selective regime of lung tissue. Inference of the most likely routes of site-specific variant evolution, as well as estimation of the selection strength operating on each step along the route represents a key component for prioritization, development, and implementation of personalized cancer therapies that target synthetic lethality. Citation Format: Jorge A. Alfaro-Murillo, Krishna Dasari, Jeffrey P. Townsend. Detecting pairwise and higher-order antagonistic epistatic effects among somatic cancer genotypes to discover synthetic lethality [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR013.

Markov Chains to Explore the Nanosystems for the Biophysical Studies of Cancers

The immune response is essential for the human body to function well and to survive against the sudden and chronic diseases such as viral & bacterial infections and cancers. In the immunosurveillance process, Natural Killer (NK) cells are one of the main elements in controlling the development of such infections and, for this reason, they have become the subject of “in-depth” studies especially for the application of new forms of immunotherapy. NK cells can rapidly destroy both autologous and tumor cells in vitro and for this reason the interest in their function is increasingly growing. Their presence in the tumor micro-environment (TME) also assumes prognostic value since the repertoire of NK cell receptors has been linked to anti-tumor function. In this work, a Markov chain modeling approach is proposed to analyze the network of interactions that NK cells carry out with other immune elements in the defense against cancer such as CD4+ cells and CD8+ cells and dendritic cells (DCs) that activate and enhance immune responses. The probabilistic approach used is promising since it helps to understand the balance and the communication in the micro-environment, in a realistic manner. The advantage of discrete time Markov chain approach is that, it can be further extended to complex networks using the state-of-the-art algorithms and can also be translated for the novel AI tools for the cytokines and protein databases.

Stochastic occupancy modeling for spaces with irregular occupancy patterns using adaptive B-Spline-based inhomogeneous Markov Chains

This paper presents a discrete time, discrete state-space in-homogeneous Markov Chains model for stochastic occupancy modeling in spaces with irregular occupancy patterns. The goal of the model is to provide accurate predictions of occupancy numbers, enabling appropriate actions to be taken for HVAC system to maintain optimal indoor environment. The proposed Markov Chain model incorporates time in-homogeneity by coupling the time-varying model parameters using a Periodic B-Spline expansion with adaptive knots, which effectively captures patterns in occupancy activity. This method optimizes the distribution of knots based on specific occupancy characteristics observed in different types of rooms. To evaluate the effectiveness of the proposed method, six months of occupancy data collected from a meeting room are utilized. A comprehensive comparison is conducted between the proposed adaptive B-Spline method and other approaches, including the counting method and uniform B-Spline method. The comparison considers both model accuracy and complexity, using metrics such as the Akaike Information Criterion and Bayesian Information Criterion. Results indicate that the proposed model achieves more accurate predictions with fewer model parameters compared to other methods. These forecasts are particularly useful in optimizing the control of HVAC systems, where accurate predictions of future occupancy numbers are essential.

A Markov chain model of Daphnia motion

A Markov chain model of Daphnia motion

A measure of the importance of moment for ball-strike counts in a baseball plate appearance

ABSTRACT This study constructs a discrete-time Markov Chain (DTMC) model for a baseball plate appearance (PA) employing Major League Baseball’s pitch-by-pitch dataset. Based on the DTMC model, we propose a novel measure for a baseball PA, termed the Importance of Moment (IOM). The IOM quantifies the criticality of each ball-strike count situation, by assessing the probabilistic difference between the pitcher’s and hitter’s favourable outcomes (out vs reaching base). If the favours significantly vary right after a particular ball-strike count, then the count is deemed critical and is assigned a high IOM value. We empirically verify that IOM explains pitchers’ behaviour of fastball speed. We then further investigate whether the behaviour of ace pitchers differs significantly from the majority. Several interesting properties are found from the analysis. Firstly, the path independence assumption generally holds, with the exception of the ball-strike count of 2B1S. Second, pitchers tend to throw the faster fastball at counts with higher IOM values. Lastly, ace pitchers are capable of pitching even faster fastball in two-strike situations in which IOM is high. The DTMC effectively models the probabilistic structure of a baseball PA, and the proposed IOM measure serves as a useful tool for explaining player behaviour.

Forecasting Land Use and Land Cover Changes in the Malaprabha Right Bank Canal Command Area through Cellular Automata and Markov Chain Modeling

Forecasting Land Use and Land Cover Changes in the Malaprabha Right Bank Canal Command Area through Cellular Automata and Markov Chain Modeling

Cost-effectiveness of adjuvant abemaciclib and ribociclib in high-risk, hormone receptor–positive, early breast cancer in India.

e12516 Background: Incorporating adjuvant cyclin-dependent kinase (CDK) 4/6 inhibitors abemaciclib and ribociclib along with endocrine therapy has been shown to improve invasive disease-free survival (iDFS) for hormone receptor-positive (HR+) human epidermal receptor 2 negative (HER2-) early breast cancer (EBC). This study assesses the cost-effectiveness of this strategy, along with adjuvant aromatase inhibitors (AI) from an Indian perspective. Methods: A Markov chain model evaluated the cost-effectiveness of abemaciclib and ribociclib with letrozole compared to letrozole alone for HR+/HER2- EBC from a payer perspective in India. Key measures included lifetime quality-adjusted life years (QALY), life-years (LY), and total costs. This study explores two scenarios for effectiveness: a best-case (BC) scenario, where the benefit of CDK4/6 inhibitors in improving iDFS lasts a lifetime, and a worst-case (WC) scenario, where benefits disappear after 5 years. Probabilistic sensitivity analyses (PSA) were used to account for simulation uncertainty. Results: In the BC scenario, abemaciclib added 2.17 QALY and 4.96 LY, costing $27,756.65 more. Ribociclib in the BC scenario added 0.98 QALY and 2.58 LY, with added costs of $20,494.6. The incremental cost-effectiveness ratio for abemaciclib and ribociclib surpassed the willingness to pay threshold for in India in both scenarios (Table). At current market prices there is 0% probability of cost-effectiveness of abemaciclib and ribociclib. The cost of abemaciclib and ribociclib needs to be reduce by 78.61% and 87.19%, respectively to achieve cost-effectiveness. Conclusions: The combination of adjuvant abemaciclib or ribociclib with letrozole is not cost-effective for HR+/HER2- EBC in India in either the best or worst-case scenario.[Table: see text]

Holistic personas to increase the novice developer productivity

A deeper understanding and integration with system users' thoughts and emotional experiences are required for user-engaged development. User experience (UX) journey integrates user requirements and problem-solving approaches. The integration of data-driven techniques and user-centric approaches in software development is investigated in this study. It focuses on using the Markov chain model to predict developer productivity based on data gathered while creating personas across three projects. Organizations can gain valuable insights into user needs and requirements by conducting purposeful activities such as strength, weaknesses, opportunities, and threats (SWOT) analysis, competitor analysis, hypothesis formulation, identification of behavioral variables, mapping interviews, and defining characteristics and objectives. The model has predictive capabilities that allow for more informed decision-making, more efficient resource allocation, and better project planning. The goal of the activity and the model ensure the development of software products that effectively meet the needs of users, resulting in a higher success rate for software development initiatives. This study emphasizes the importance of integrating quantitative and qualitative analysis to drive successful software development projects and increase productivity while meeting user needs. According to the findings of the research conducted from the three projects completed, the proposed methods have similarities, and predictions using the Markov chain can determine the success of novice developers.