Allocation Scheme Research Articles

Minimizing Delay and Power Consumption at the Edge

Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations. These prior approaches have two shortcomings: (a) there is no guarantee that optimum solutions are achieved, and (b) they do not provide an explicit formula for the fraction of tasks that are allocated to the different servers to achieve a specified optimum. This paper offers a radically different and mathematically based principled method that explicitly computes the optimum fraction of jobs that should be allocated to the different servers to (1) minimize the average latency (delay) of the jobs that are allocated to the edge servers and (2) minimize the average energy consumption of these jobs at the set of edge servers. These results are obtained with a mathematical model of a multiple-server edge system that is managed by a task distribution platform, whose equations are derived and solved using methods from stochastic processes. This approach has low computational cost and provides simple linear complexity formulas to compute the fraction of tasks that should be assigned to the different servers to achieve minimum latency and minimum energy consumption.

Effects of retaining different number of mother bamboo on the growth and NSCs allocation of new-born seedlings in Phyllostachys edulis at different age

BackgroundNon-structural carbohydrates (NSCs) are key substances for metabolic processes in plants, providing energy for growth, development, and responses to environmental stress. Pruning mother bamboo in a clump can significantly affect the NSCs allocation of new shoots, thereby affecting their growth. Moso bamboo (Phyllostachys edulis) is an important economic bamboo species with a highest planting area in China. However, it remains unclear how many mother bamboo left maximize the influence in moso bamboo seedlings at different age.ResultsThis study investigated the effects of retaining different number of mother bamboo on the morphological characterization and NSCs allocation of new-born seedlings in moso bamboo at two-year-old and three-year-old, respectively. Retaining more mother bamboo significantly promoted the plant height and diameter of new shoots, particularly in the two-year-old clumps. The growth rate of new shoots increased with the number of mother bamboo in the two-year-old clumps, while it remained relatively stable in the three-year-old clumps. The allocation strategy of NSCs also showed significant differences when retaining different number of mother bamboo across growth stages. NSCs content continuously increased with the growth of new shoots during the rapid growth phase, which reached peak in all parts at the end of the rhizome elongation stage. In two-year-old bamboo seedlings, the NSCs content increased with the number of retained mother plants. Significant differences were found between different treatments, particularly between those with one and four mother bamboos left. It was similar in the three-year-old clumps. However, there were no significant differences in NSCs content among the treatments.ConclusionReducing the number of mother bamboo appropriately can promote the growth of new shoots in moso bamboo. The two-year-old clumps were more dependent on the number of mother bamboo, while it was less pronounced for three-year-old clumps. In production practice, it is recommended that three mother bamboos are retained for two-year-old clumps and two to three mother bamboos for three-year-old clumps. This approach can expand the afforestation area while ensuring the survival rate of new seedlings in moso bamboo.

Parametric insurance for climate adaptation in fisheries and aquaculture

Abstract Ensuring resilience and adaptability of the seafood sector in the face of accelerating climate change involves risk management and risk reduction. In a world where climate patterns are rapidly evolving, innovative financial instruments have a role to play in managing environmental risks and reducing exposure to these risks through climate adaptation. Here, we consider the application of parametric insurance—when a predetermined amount is paid out based on parameters chosen based on expected losses associated with extreme events. We offer insights into its utility, implementation, and limitations. We emphasise the role of ocean forecasting in developing and pricing parametric insurance products to help mitigate and adapt to climate-related risks in aquaculture and fisheries. Forecasting is essential for anticipating changing environmental conditions at a range of spatial and temporal scales and thus informing decision-making regarding resource allocation and adaptation strategies. Through both climate-aware insurance policy design and the provision of education, capacity-building programs, and financial support for industry transition, parametric insurance has the potential to help seafood producers navigate the challenges posed by climate change. Use of parametric insurance can facilitate a managed retreat from vulnerable or non-viable areas to more sustainable alternatives or livelihoods by reducing climate risk in a measured environment. This approach can also contribute to the long-term resilience of seafood industries and coastal communities while protecting the health of marine ecosystems.

A risk-based model for allocating post-earthquake reconstruction finance in medium-size Iranian Cities

Introduction and BackgroundThis study explores effective strategies for allocating financial resources in postearthquake urban reconstruction. Given the financial complexities of such projects, it is essential to have a structured plan to identify challenges and address potential gaps. The research underscores the importance of predisaster planning in managing financial resources efficiently.MethodologyDistrict 2 of Tehran municipality was chosen as the case study due to its significant risk exposure and vulnerability, making it a suitable representation of a medium-sized urban area. The study includes two key components: seismic risk assessment and financial resource allocation. Seismic risk was modeled using computational methods and risk analysis techniques. Strategies for budget allocation were formulated based on insights from interviews, historical case studies, and computational modeling.ResultsScenarios ranging from optimistic to pessimistic were analyzed to assess their impact on reconstruction timelines. Results show that reconstruction for an urban area like District 2 could be completed in 4.3 years in the optimistic scenario, 9.2 years in the probable scenario, and 11.3 years in the pessimistic scenario.ConclusionThe study reveals that efficient resource allocation minimizes delays, while insufficient planning significantly prolongs recovery. These findings demonstrate the importance of pre-disaster planning and effective resource allocation in expediting recovery and improving resilience in medium-sized cities.

Costs of outpatient services at selected primary healthcare centers in Bangladesh: A cross-sectional study.

Upazila Health Complexes (UzHC) serve as the backbone of primary healthcare (PHC) at the sub-district level in Bangladesh, delivering comprehensive healthcare services including both inpatient and outpatient services to the grassroots levels. However, not all the prescribed medicines and diagnostics services are always available at these facilities for outpatient care. This results in out-of-pocket expenditure (OOPE) to the patients for getting prescribed medicines and diagnostics services which has not been properly explored. Thus, we aimed to estimate the overall provider and user costs for outpatient care services at selected UzHCs in Bangladesh. An ingredient-based costing approach was applied to estimate the costs for the most commonly reported illnesses at outpatient of UzHCs from a societal perspective. We conducted a health facility survey at four purposively selected UzHCs to estimate provider costs and a patient exit survey among 452 patients of selected illnesses to estimate the user costs. Commonly reported illnesses were identified in consultation with healthcare providers of these facilities. The difference between costs of prescribed and provided medicines at UzHCs was estimated using the market prices. Data was collected between February to March 2021. The societal costs of the common outpatient illness or symptoms varied significantly, ranging from BDT 642 to BDT 1,384 per episode. Antenatal care had the highest cost burden at BDT 1,384, followed by respiratory illness at BDT 783 and urinary tract infection at BDT 670. On average, the provider spent BDT 289 for treating an outpatient, while a patient incurred BDT 446 as OOPE. Further, a patient was expected to spend an average of BDT 341 for purchasing medicines not provided from UzHCs. Our study found significant gaps between prescribed and provided medicines at UzHCs, leading to higher OOPE for patients. The current healthcare resource allocation strategy does not consider the outpatient load and healthcare demand at PHC facilities, which further exacerbates this gap. Addressing this gap requires a fundamental shift towards a demand-driven resource allocation model within the healthcare financing strategy to improve healthcare access and achieve health for all.

Application of Particle Swarm Optimization in Optimal Asset Allocation

Particle Swarm Optimization (PSO) is an effective tool for solving nonlinear, non-convex optimization problems, offering a quick and efficient way to identify rational asset allocations. In PSO, each particle represents a specific asset allocation and moves within the search space to optimize the defined criteria. Particles update their positions based on personal experience (individual best) and the collective experience of the swarm (global best), gradually converging toward an optimal solution. Research on applying PSO to asset allocation demonstrates that this algorithm not only optimizes expected returns but also minimizes risk in investment portfolios. With its adaptability and computational speed, PSO can become a valuable tool for investors in formulating flexible and effective asset allocation strategies in a volatile financial environment.

Current Status of Man and Animal Conflict and e-Parihara in Different Forest Ranges of Karnataka

The increasing demand for forest land, habitat disturbance, and fodder shortages have intensified Man-Animal Conflicts (MAC) worldwide. This study analyzes both qualitative and quantitative data to assess the status of MAC in Karnataka, leveraging 2,03,297 geo-tagged conflict cases are reported by the Karnataka Forest Department's (KFD) since 2014 to 2024. Earlier all the MAC records were manually generated and stored as hardcopies, a traditional method of documentation. To implement advance technologies, which makes data tracking easier, KFD decided to design a digital platform, under e-Governance for timely response on conflict issue and its validation. As a result in 2019, a online database was generated, tested its functioning, and launched as e-Parihara website, and a mobile app for the management of MAC, helps to provide ex-gratia relief for various types of damages caused by wildlife. This paper comprehend the real-time data available since 2019, also digitised data from 2014. The e-Parihara mobile app allows forest officials to document geo-tagged conflict cases with photos and geo-stamped damage details. The limitation of this web portal is access restricted to forest officials for proper documentation. The platform ensures transparency and efficiency by integrating real-time SMS notification alerts for both Forest officials and the applicants about claim progress. The study employs a flowchart on automated workflows to process the ex-gratia compensation claims, based on government directives, and generate the official memorandums (OMs). Data analysis includes percentage calculations of compensation claims across species, litmaps supports the constant rise of MAC since 1928–2024. Majority of conflicts caused, involved in various damage types’ viz. crops, properties, livestock and human related issues are elephants. Elephants are responsible for 74.82% of claims and 68.18% of reported incidents, followed by wild boars (11.61% of claims, 10.86% of incidents) and leopards (10.30% of claims, 8.09% of incidents). Gaur (3.69% of claims, 3.27% of incidents), Tigers (3.41% of claims, 1.34% of incidents), and sloth bears (2.5% of claims, 1.18% of incidents), contributed less. This type of information on MAC indicates a pioneering approach to state-wide MAC management, facilitating targeted resource allocation and actionable strategies. It underscores the need for collaboration between local communities, wildlife experts, and policymakers to ensure sustainable wildlife conservation.

Association analysis of T and B-cell epitopes with humoral alloimmunisation in kidney transplantation: A Tunisian cohort study.

HLA matching is critical for successful kidney transplantation. This study aimed to investigate the impact of eplet mismatches and Predicted Indirectly Recognizable HLA Epitopes (PIRCHE-II) scores on the development of de novo donor-specific antibodies (dnDSA) and graft survival in a Tunisian cohort, characterized by a high prevalence of living donors and significant genetic diversity in HLA profiles. This retrospective study included 112 adult kidney transplant recipients who underwent transplantation between 2012 and 2018. Donor and recipient HLA typing was performed using One Lambda Labtype PCR-SSO and PCR-SSP kits, with high-resolution typing inferred using validated tools and reference datasets. Luminex DSA screening (One Lambda) was conducted at transplantation, during follow-up for graft dysfunction or proteinuria, and at the study's conclusion. Eplet mismatches and PIRCHE-II scores were calculated using EpVix and PIRCHE-II software. Nineteen patients (17%) developed dnDSA, predominantly targeting HLA-DQ, with a median detection timeframe of 50months (range: 11-106months) post-transplantation. Male donor sex was negatively associated with dnDSA development, while prolonged cold ischemia time was a significant risk factor. Molecular HLA-DQ mismatches and high PIRCHE-II scores were significantly associated with dnDSA. Factors independently associated with reduced death-censored graft survival included history of transfusion after transplantation, allelic DRB1 mismatch, dnDSA development, and elevated PIRCHE-II score. Our findings highlight the critical role of HLA-DQ compatibility in kidney transplantation and suggest that molecular HLA-DQ matching should be considered in kidney allocation strategies to minimise alloimmune responses and improve long-term graft outcomes.

Engineering Management and Modular Design: A Path to Robust Manufacturing Processes

Manufacturing environments, characterized by dynamic changes and uncertainties, demand effective strategies to minimize disruptions. This study introduces an innovative approach that integrates engineering management principles with modular design to prioritize risk mitigation and enhance robustness in manufacturing processes. From a systems engineering perspective, all manufacturing activities are perceived as interconnected components within a unified system. Leveraging the Axiomatic Design (AD) theory and the Design Structure Matrix (DSM) method, the study modularizes manufacturing process architecture to effectively curb risk propagation and manage system complexity. This study identifies the most optimal design as a pivotal architectural configuration, significantly improving the structural robustness and stability of the System of Interest (SOI). Empirical evidence supports this design’s capability to reduce complexities, thereby enhancing robustness within the broader system architecture. Notably, the proposed approach results in a substantial reduction in complexity, with the most optimal design exhibiting an approximately 82.79 percent reduction in work volume compared to the original design. Our research underscores the critical relationship between manufacturing and engineering management. Effective collaboration between these domains optimizes resource allocation, decision-making processes, and overall organizational strategy, leading to improved production processes and increased efficiency. Importantly, the study demonstrates a significant enhancement in modularization, resulting in elevated overall robustness in manufacturing processes. This highlights the proactive involvement of engineering management in the design phase to address production challenges, ultimately optimizing system performance. Thus, this research contributes to both practical applications and academic discourse by offering a novel approach to enhancing the robustness in manufacturing processes. By integrating engineering management principles and modular design strategies, organizations can fortify their processes against disruptions and effectively adapt to evolving circumstances.

Reinforcement Learning-Based Resource Allocation Scheme of NR-V2X Sidelink for Joint Communication and Sensing

Joint communication and sensing (JCS) is becoming an important trend in 6G, owing to its efficient utilization of spectrums and hardware resources. Utilizing echoes of the same signal can achieve the object location sensing function, in addition to the V2X communication function. There is application potential for JCS systems in the fields of ADAS and unmanned autos. Currently, the NR-V2X sidelink has been standardized by 3GPP to support low-latency high-reliability direct communication. In order to combine the benefits of both direct communication and JCS, it is promising to extend existing NR-V2X sidelink communication toward sidelink JCS. However, conflicting performance requirements arise between radar sensing accuracy and communication reliability with the limited sidelink spectrum. In order to overcome the challenges in the distributed resource allocation of sidelink JCS with a full-duplex, this paper has proposed a novel consecutive-collision mitigation semi-persistent scheduling (CCM-SPS) scheme, including the collision detection and Q-learning training stages to suppress collision probabilities. Theoretical performance analyses on Cramér–Rao Lower Bounds (CRLBs) have been made for the sensing of sidelink JCS. Key performance metrics such as CRLB, PRR and UD have been evaluated. Simulation results show the superior performance of CCM-SPS compared to similar solutions, with promising application prospects.

Imbalance in nitrogen and phosphorus allocation between tree roots and leaves induced by nitrogen addition.

The allocation of limiting elements, such as nitrogen (N) and phosphorus (P), in plant organs is essential for nutrient cycling between soil and plants (soil-plant nutrient cycling) and functional optimization in plant communities. Unprecedented inputs of anthropogenic N have caused drastic N and P imbalances in terrestrial ecosystems. However, the effects of N addition on the allocation strategies of N and P between plant organs remain unclear. In this study, we conducted a long-term, multilevel N addition experiment to investigate the allocation strategies for N and P in plant leaves and fine roots. We found that N addition significantly increased leaf N concentration, leaf P concentration, and leaf N:P ratios, while significantly decreasing fine root N concentration, fine root P concentration, and fine root N:P ratios. Additionally, we demonstrated a higher proportional increase of N in leaves and a lower proportional decrease of P in fine roots with N addition. Furthermore, our analyses revealed that N addition influenced the allocation of N and P between plant leaves and fine roots through changes in plant growth patterns and nutrient distribution strategies. These changes were driven by a significant increase in soil inorganic N concentration, a decrease in soil N cycling and a reduction in mycorrhizal symbiosis. Our findings suggest that N addition will likely lead to an imbalance between the N and P cycles in temperate forest ecosystems, due to the unequal allocation of N and P between tree roots and leaves. This imbalance may, in turn, have negative implications for the provision of ecosystem services.

Control of scabies fluctuation during COVID-19 pandemic

This study investigates the dynamics of scabies infestation using a mathematical model, analyzing its stability, sensitivity, and optimal control. We analyze the model for stability, and the results demonstrate the system’s stable behavior under specific conditions, suggesting the possibility of effective control. The model is subjected to sensitivity analysis, revealing the most influential parameters affecting the infestation’s dynamics. These parameters are identified as key targets for intervention. The optimal control strategy is derived using Pontryagin’s Maximum Principle, minimizing the cost of intervention while effectively controlling the infestation. The results demonstrate the importance of early intervention and sustained control efforts. This study provides a comprehensive understanding of scabies infestation dynamics, highlighting the critical factors influencing its spread and the most effective control strategies. The findings inform public health policy and guide resource allocation to combat scabies infestations effectively. The major contributions include improved understanding of scabies transmission dynamics, identification of critical parameters, development of evidence-based control strategies, evaluation of resource allocation effectiveness, and assessing intervention impact. It may also contribute to public health policy, resource allocation, disease surveillance, and intervention strategies. Numerical simulations are carried out to support our analytical findings.

Cluster Allocation Strategy of Multi-electrolyzers in Wind-hydrogen System Considering Electrolyzer Degradation Under Fluctuating Operating Conditions

Cluster Allocation Strategy of Multi-electrolyzers in Wind-hydrogen System Considering Electrolyzer Degradation Under Fluctuating Operating Conditions

Coupling data-driven agent-based and hydrological modelling to explore the effect of collective water allocation strategies in environmental flows

Coupling data-driven agent-based and hydrological modelling to explore the effect of collective water allocation strategies in environmental flows

Task Search and Allocation Strategy for Heterogeneous Multiagent Systems Under Communication Constraints

Task Search and Allocation Strategy for Heterogeneous Multiagent Systems Under Communication Constraints

Analyzing market plans for enhanced energy hub efficiency: strategies for integrating multiple energy sources and collaboration

ABSTRACT In this study, we introduce the Archimedes optimization algorithm as a key component of our methodology. The Archimedes algorithm, renowned for its robustness and efficiency, is specifically tailored to handle complex optimization problems encountered in energy management systems. By leveraging its unique capabilities, we ensure precise solutions and rapid convergence even under challenging solution conditions characterized by uncertain energy demand forecasts. The proposed algorithm operates by iteratively refining solutions based on probabilistic load predictions and system constraints. It employs sophisticated optimization techniques to navigate the multi-dimensional solution space efficiently, thereby identifying optimal strategies for energy resource allocation within EHs. By incorporating the Archimedes algorithm into our framework, we can effectively capture the dynamic interactions between various energy carriers and adapt to changing market conditions in real-time. Furthermore, the Archimedes algorithm’s ability to provide near-optimal solutions without the need for exhaustive computational resources makes it particularly well-suited for practical implementation in energy management systems. Its efficiency ensures that decision-makers can obtain actionable insights and make informed decisions quickly, thereby enhancing the operational efficiency and economic viability of EHs. The proposed optimization algorithm outperforms alternatives like Archimedes optimization algorithm (APO) and genetic algorithm (GA). On average, it requires 25% fewer iterations for convergence. Moreover, it achieves a 90% convergence rate within just 50 iterations, compared to 70 iterations for APO and 60 iterations for GA, based on 30 distinct runs.

BASEL III LIQUIDITY REQUIREMENTS AND BANK PROFITABILITY

This paper examines the impact of Basel III liquidity requirements, specifically the Liquidity Coverage Ratio (LCR) and the Net Stable Funding Ratio (NSFR), on the profitability of banks in Nigeria, using regression discontinuity design (RDD) to analyze data from 14 banks over the period from 2009 to 2020. The study provides empirical insights into how these regulations influence bank performance, focusing on return on assets (ROA) and return on equity (ROE). The results reveal that the LCR has a significant positive impact on ROA, indicating that holding adequate high-quality liquid assets improves asset efficiency. However, its effect on ROE is insignificant, suggesting that enhanced liquidity does not necessarily translate into higher returns for shareholders. Similarly, the NSFR shows a significant positive impact on ROA but an insignificant effect on ROE, highlighting the benefits of long-term funding stability for asset profitability without significantly boosting shareholder returns. These findings suggest that while Basel III liquidity requirements contribute to enhancing bank stability and operational performance, they may not immediately result in higher equity returns in the Nigerian context. The study recommends that banks optimize their capital allocation and liquidity management strategies to achieve a balance between regulatory compliance and shareholder value. Additionally, regulators, particularly the Central Bank of Nigeria (CBN), should periodically review liquidity requirements to reflect the unique challenges of the Nigerian banking environment. This study contributes to the existing literature by providing empirical evidence on the impact of Basel III liquidity requirements on bank profitability in the Nigerian context. The use of Regression Discontinuity Design offers a robust approach to identifying causal effects, enriching the understanding of how liquidity management practices affect financial performance, particularly in emerging markets where liquidity risks are more pronounced.

Proximal Policy Optimization for Efficient Channel Allocation with Quality of Service (QoS) in Cognitive Radio Networks

A multi-variable relationship exists in Cognitive Radio Networks (CRNs) where factors such as Energy efficiency, Throughput, Delay and Signal Noise Ratio (SINR) are related. The SINR shows the quality of the signal and is defined as the total power of a specific signal over the total power of an inter signal plus noise. This work proposes an effective energy and delay-efficient channel allocation strategy for CRNs (Cognitive Radio Networks) using Q-Learning and actor-criticism algorithms that maximize rewards. We also propose a Proximal Policy Optimization (PPO) algorithm that uses clipping of surrogate objectives to prevent large policy changes and ensure that the other parameters remain stable over time. We study the tradeoff between rewards, energy efficiency and other parameters and compare the algorithms with respect to the same. Results show that the proposed PPO method, while using optimally increased energy consumption, significantly reduces the delay, improves the thought and reduces the packet loss ratio for efficient channel allocation. This is positive with our findings shown in the results section and by comparing the proposed method with other algorithms to identify improved throughput and channel utilization. As the simulation results indicate that the PPO algorithm has very high throughput and significantly minimizes the delay and packet loss, it is suitable for application in all sorts of services such as video, imaging or M2M. The results are also compared with two of the existing channel allocation schemes and they confirm that the proposed algorithm performs better in terms of throughput discussed in one scheme and channel efficiency in the other.

A new gas lift allocation method in the IoT environment using a hybrid optimization algorithm

In the realm of petroleum extraction, well productivity declines as reservoirs deplete, eventually reaching a point where continued extraction becomes economically unfeasible. To counteract this, artificial lift techniques are employed, with gas injection being a prevalent method. Ideally, unrestricted gas injection could maximize oil output. However, gas scarcity necessitates judicious resource management to optimize oil production while minimizing gas usage. Gas injection serves to alleviate hydrostatic pressure within wells, thereby enhancing oil recovery. Conventional gas allocation strategies often prove inadequate when confronted with the complex, non-linear constraints of real-world scenarios, particularly under gas supply limitations. This research introduces an innovative approach to gas allocation optimization, leveraging Internet of Things (IoT) technology in conjunction with advanced computational methods. The study melds two optimization algorithms: Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO). This hybrid technique harnesses IoT capabilities for real-time data acquisition and processing, enabling more precise and adaptive optimization. The proposed methodology incorporates PSO’s individual and collective learning mechanisms into the ASO framework, accelerating the solution refinement process. Additionally, it introduces dynamic parameters to balance broad exploration with focused exploitation of the solution space. The algorithm’s efficacy is further enhanced by implementing an adaptive force constant for each “atom” (solution candidate), which evolves based on the atom’s performance over successive iterations. Empirical evaluation of this novel approach demonstrated significant improvements in both energy efficiency and gas utilization. Specifically, the hybrid method achieved average reductions of 12.12% in energy consumption and 18.05% in gas injection volume compared to existing techniques. Also, the results showed that battery life and cost are better than the other methods and have been improved by an average of 7.67% and 9.48%, respectively.

MACHINE LEARNING APPLICATIONS IN RURAL HEALTHCARE: PREDICTIVE MODELING FOR IMMUNIZATION COMPLETION RATES IN OGUN STATE, NIGERIA

This study investigated the application of machine learning techniques for predicting child immunization completion in Ado-Odo/Ota Local Government Area, Ogun State, Nigeria, utilizing data from 8,808 immunization records across 15 primary healthcare centers. Using a quantitative research methodology with retrospective data analysis, we developed and compared predictive models for immunization completion patterns. Three machine learning algorithms were employed based on their proven effectiveness in healthcare applications: Logistic Regression for its interpretability in clinical settings, Support Vector Machine (SVM) for handling non-linear relationships in health data, and K-Nearest Neighbours (KNN) for processing demographic variables. The study analyzed immunization completion rates using these algorithms within a comprehensive framework incorporating Principal Component Analysis for dimensionality reduction. The Logistic Regression model demonstrated superior performance with 99.77% accuracy and an MSE of 0.0023, outperforming both SVM (99.32% accuracy) and KNN (99.03% accuracy) models. Notably, socioeconomic analysis revealed an unexpected pattern where high-income households showed lower immunization completion rates compared to low and moderate-income groups. The study's findings provide valuable insights for healthcare policy development and resource allocation strategies while demonstrating the practical applicability of machine learning in enhancing immunization program effectiveness in developing nations.