This study introduces a hybrid machine learning framework for Wireless Sensor Networks (WSNs) designed to enhance energy efficiency and extend network longevity. The model integrates Q-learning for adaptive routing, hybrid clustering through Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), and decision tree regression for predictive energy depletion analysis. By dynamically balancing energy consumption and rerouting data to circumvent nodes approaching exhaustion, the framework improves reliability and operational stability. Simulation results demonstrate notable improvements over conventional protocols such as LEACH and PEGASIS, achieving a 40% reduction in energy consumption and a 37.76% extension of network lifespan. Statistical validation (t-test, p < 0.0001) confirms the significance of these results. The proposed approach holds promise for deployment in real-world WSN and IoT applications, where optimized energy utilization and extended network lifetime can reduce maintenance costs and ensure continuous, reliable data acquisition.

One of the effects of the rapid adoption of the cashless policy in Nigeria and the introduction of new naira notes is operational difficulties among financial institutions, which have led to a significant increase in ATM card theft and fraud among clients. Absence of real-time analysis of access points, combined with the intermittent and simultaneous quality of fraudulent dealings, are two major factors that make conventional fraud detection systems fail regularly. Towards reducing ATM fraud, this paper will present a high-performance, intelligent based, AI-based model to integrate three factors of biometric authentication, spending pattern analysis, and password verification into a three-factor model. Results of experiments based on real banking data prove that the proposed solution is superior to traditional models in terms of accuracy, precision, recall, and F1-score. The model uses an optimized Bi -Directional Long Short-Term Memory (BiLSTM) network to analyze historical ATM transaction records and identify behavioral abnormalities that could point to fraud. A Cuttlefish Optimization (MCFA) algorithm that is based on mapping is used to fine-tune the parameters, thus improving the reliability and accuracy of the classification. Biometric verification combined with behavioral modeling using AI stands out as a scalable and dependable framework of minimizing ATM card fraud and instilling confidence within the banking industry.

The article presents a model of dynamic processes occurring in non-isolated populations that differ in their habitat and mode of nutrition. The results of theoretical studies carried out on the basis of this model show the decisive influence of the ratio of the coefficients of inter-subpopulation competition on qualitative changes in the behavior of the system and individual subpopulations. This ratio is also the main factor influencing the formation of the dominant subpopulation in the system. It has been shown that the system-wide dynamics of subpopulation processes significantly depends on the reproductive potential of all subpopulations and on the mass fraction of individuals that, according to their phenotypic properties, are related to the parents. In this case, the mass fractions of individuals (transition coefficients) must correspond to the condition of closed system and be in specified intervals. It has been established that subpopulations in real life can exchange descendants, which, in turn, can significantly affect the numerical and qualitative aspects of the dynamics. Using the example of a two-dimensional system, the relationship between the sum of the main elements of the transition coefficient matrix and the mutual dependence of subpopulations, as well as their transition to qualitatively different levels, is shown. The bifurcation properties of the model of subpopulation dynamics with a Lotka–Voltaire type function in basic quality have been studied. An approximate justification of possible bifurcations of the system allows us to evaluate the factors that qualitatively influence the dynamics of the system and develop a number of recommendations to prevent the occurrence of catastrophes and collapses in the system.

Automated respiratory disease classification from auscultation sounds holds transformative potential for early clinical screening, yet existing approaches remain constrained by the quadratic complexity of Transformer-based sequence encoders, the limited expressiveness of conventional multi-layer perceptron classifiers, and the persistent challenge of scarce annotated medical audio data. This paper presents MambaResp-KAN, a novel architecture that unifies Bidirectional Mamba state space models, Kolmogorov–Arnold Network classifiers with learnable B-spline activation functions, multi-modal gated cross-attention fusion of WavLM, BEATs, and handcrafted spectral features, and class-conditional denoising diffusion probabilistic model augmentation into a single end-to-end framework for explainable respiratory sound analysis. The Bidirectional Mamba encoder achieves linear-time sequence modeling through input-dependent selective state space discretization, processing forward and reverses temporal streams with gated aggregation to capture both causal and anti-causal dependencies in respiratory waveforms. The Kolmogorov–Arnold Network classifier replaces fixed-activation neurons with learnable univariate B-spline functions on each network edge, directly grounded in the Kolmogorov–Arnold representation theorem, yielding a classifier that is both more parameter-efficient and intrinsically interpretable than standard multi-layer perceptrons. A gated cross-modal attention mechanism fuses embeddings from the self-supervised WavLM and BEATs audio encoders with handcrafted MFCC and spectral features, while a class-conditional denoising diffusion probabilistic model synthesizes high-fidelity respiratory audio to alleviate class imbalance. Integrated Gradients attribution and KAN concept bottleneck analysis provide clinician-interpretable explanations of model decisions. Evaluated on two benchmark datasets, Asthma Detection V2 (five classes, 1,211 samples) and KAUH (four classes, 940 samples), MambaResp-KAN achieves classification accuracies of 99.6% and 99.4%, respectively, surpassing the prior state-of-the-art E-RespiNet by 0.7 and 0.6 percentage points while using 62% fewer parameters and reducing inference latency by 56.3%. Cross-dataset evaluation yields an average accuracy of 84.0% with a generalization gap of 15.8%, compared to 23.3% for E-RespiNet, confirming improved transferability across clinical institutions.

Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. In Cloud Computing (CC) environments, efficient Task Scheduling (TS) plays a critical role in minimizing operational expenses and enhancing system reliability. This paper presents a novel task scheduling approach that uses the Coati Optimization Algorithm (COA) to address two pivotal challenges: reducing the total cost (sum of computational cost and communication cost) and minimizing Virtual Machine (VM) failure rates. Inspired by the cooperative foraging and adaptive behavior of coatis in dynamic environments, the proposed algorithm leverages intelligent exploration and exploitation strategies to identify optimal task-to-VM mappings under fluctuating workloads. The COA incorporates cost-awareness and failure probability metrics into its fitness function to ensure robust scheduling decisions that align with budgetary constraints and fault tolerance requirements. To assess the performance of the proposed model, comprehensive simulations were conducted using the CEA-Curie real-world workload. The results were compared against three state-of-the-art approaches, MoHHOTS, RTATSA2C, and TS-GWO. Experimental evaluations demonstrate that COA significantly outperforms these existing methods by achieving a 19.8% reduction in overall cost and a 22.5% decrease in VM failure rate. These findings demonstrate that COA offer a promising pathway toward sustainable, cost-effective, and resilient task execution in large-scale cloud infrastructures, particularly under diverse and realistic workload scenarios.

Machine learning (ML) has made it much easier to find and estimate the risk of early stage of cardiovascular illnesses by making it possible to analyses massive, various clinical datasets quickly and easily. In these kinds of datasets, demographic information, lifestyle characteristics, medical history, and diagnostic measurements are all included. These are all things that may not be easy to see through standard clinical examination. This study examines heart disease prediction through a series of hybrid ML models that integrate neighborhood-based classifiers, swarm intelligence-driven optimization, and ensemble learning, motivated by existing obstacles. There are four hybrid models being proposed: MSMO-KE and MSMO-KM, which combine Modified Spider Monkey Optimization (MSMO) with K-Nearest Neighbour classifiers that use Euclidean and Minkowski distance measures, respectively. There are also two ensemble variants, MSMO-KECB and MSMO-KMCB, which add CatBoost as a final prediction layer. To make sure it is strong and can be used in other situations, the proposed framework is tested on three separate cardiovascular datasets using a cross-validation method. The experimental findings show that the performance is always better than the baseline and the best models that are already used. The MSMO-KMCB model performs the best overall out of all the approaches tested. It has a cross-validated accuracy of 98.2% on Dataset-3 while keeping a high sensitivity. The comparative research demonstrates that the proposed MSMO-based ensemble models surpass current methodologies in predictive accuracy and recall, underscoring their promise for dependable and efficient heart disease risk prediction in clinical decision-support systems.

Plant diseases adversely affect the quantity and quality of food production, contributing to food insecurity. Prompt identification, diagnosis, and intervention can significantly minimize economic and ecological losses. By reducing the use of agrochemicals through timely disease detection, the environmental impact can be mitigated. Traditional manual methods for recognizing plant diseases are prevalent but are often limited, time-consuming, costly, and ineffective. Convolutional Neural Network (CNN) architectures have demonstrated excellent capabilities in detecting plant diseases and other complex images, but they lack spatial or rotational invariance and require extensive data in various forms to be effective. This is typically achieved by applying data augmentation, as the datasets in the field of agriculture are often limited. Capsule Networks address CNN's limitations, but their encoder network is inefficient at feature extraction, hence does not perform well on complex images. This study seeks to modify and improve CapsNet by combining a Color Difference Histogram (CDH) with a Capsule Network that includes extra two convolutional, three max pooling layers, three batch normalization layers, and reduced the primary capsule channels in the original CapsNet to 16 from 32 for efficient plant disease detection in apples, bananas, grapes, corn, mangoes, pepper, potatoes, rice, tomato, and on the CIFAR-10 dataset. This approach improved the original CapsNet in terms of validation accuracies by 5.83%, 14.82%, 5.9%, 4.42%, 20.87%, 40.12%, 4.41%, 0.76%, 9.49%, and 13.97% on apple, banana, grape, corn, mango, pepper, potato, rice, tomato, and CIFAR-10 datasets respectively. The CDH-CapsNet achieved better results in terms of accuracy, sensitivity, F1-Score, precision, specificity, Receiver Operating Characteristic (ROC), Precision-Recall (PR) values, parameter count, and disk size, surpassing the original CapsNet and CapsNet models presented in available research. The original CapsNet and CDH-CapsNet exhibited strong performance on datasets such as the Rice dataset, possibly because of high-quality images and low intra-class variance. The findings suggest that this efficient and computationally less demanding supportive tool can significantly enhance plant disease classification by offering a lightweight, scalable solution that can be adapted for field use in resource-constrained settings, contributing to efforts aligned with the SDG 2 goal. However, environmental factors such as inconsistent lighting and complex backgrounds encountered in practical  
scenarios may affect the model's effectiveness.  Subsequent studies will aim to overcome these issues and broaden the model's applicability. 

This research uses an advanced intuitionistic fuzzy optimization framework to study the Multi-Objective Emission and Economic Load Dispatch (MEELD) issue under ambiguous and imprecise operational conditions. Because fuel cost and pollution emissions must be minimized simultaneously while carefully adhering to power balancing calculations, generator capacity limitations, and system operational constraints, the MEELD problem is intrinsically complicated. A strong optimization method that can manage uncertainty and decision ambiguity is required because of these competing goals. In order to overcome this difficulty, a mathematical model that incorporates vagueness related to system characteristics and decision variables is developed in both fuzzy and intuitionistic fuzzy contexts. The intuitionistic fuzzy model, in contrast to other fuzzy methods, takes membership, non-membership, and hesitation degrees into account, offering a more thorough depiction of uncertainty. Using intuitionistic fuzzy aggregation operators, a structured solution approach is suggested to convert the multi-objective optimization problem into an equivalent single-objective formulation. A three-unit thermal power generation system, which is frequently used as a benchmark in MEELD research, is used to illustrate the efficacy of the suggested methodology. The intuitionistic fuzzy optimization method effectively accomplishes an ideal trade-off between economic and environmental goals, according to simulation data. When compared to conventional optimization techniques, the resulting solutions show better compromise solutions, increased flexibility, and improved convergence characteristics. In summary, the MEELD problem continues to be a crucial component of contemporary power system operation, especially when considering sustainable energy management and environmental requirements. For large-scale power system applications needing simultaneous economic and emission optimization, the suggested intuitionistic fuzzy optimization approach offers a technically sound and effective framework for decision-making.

This paper presents an information system developed to automate the systems analysis of the quality of technical students’ training using correlation and regression methods. The article considers key problems of quality assessment and outlines the theoretical foundations of correlation and regression analysis in the context of educational data. The structure and algorithm of an information system designed for automated analysis of educational datasets are presented. The system allows to determine pairs of courses for which prediction of grades by means of regression analysis is performed with minimal error. In this study, grades from courses for the previous period were considered as known parameters x, and grades from courses for the next period were considered as predicted results y. The correlation analysis of educational data involved calculating the Pearson correlation coefficient Corr, which quantitatively describes the linear relationship between two parameters, x and y, in the educational dataset. The correlation coefficient Corr allows for a targeted investigation of relationships with high Corr values. The regression analysis of the data involved constructing a regression equation approximated by a polynomial of degree p to establish the relationship between the x and y parameters of the educational dataset. The accuracy of the approximation was evaluated using the root mean square error (Rmse) for the training set and RmseV for the validation set. The automatic selection of the polynomial degree pA, was performed according to the criterion of minimizing the approximation error RmseV on the validation dataset, while also ensuring the monotonicity of the regression equation. Developed in Python, the software performs correlation and regression analysis, prediction, outlier detection, and result visualization. This approach was applied to analyze the semester grades of students in the 'Computer Science' program, covering 12 courses over the first four semesters. Using the constructed regression equations, were forecasted students’ grades in six courses for the 3rd and 4th semesters based on their performance in the same courses during the 1st and 2nd semesters. The developed regression model also allows for evaluating students’ academic achievements through the outlier detection. The proposed correlation and regression analysis models are highly scalable, enabling the processing of educational data for large size. Integrating correlation and regression methods into the systems analysis of technical education quality allows for automated analysis of educational monitoring data, forecasting of student performance, outlier detection, and the recommendation of elective courses to optimize students’ educational trajectories.

This research aims to enhance predictive maintenance and inspection planning in urban construction projects. Recent advances in graph neural networks and graph transformer architecture have demonstrated significant potential for modeling complex lifecycle processes of building systems. However, most existing approaches remain predominantly data-driven and lack integration of physics-informed modeling and real-time data, which limits their applicability in large-scale urban environments. This research addresses this gap by proposing an approach for managing heterogeneous big data in urban construction projects, enabling prediction of the technical condition and inspection needs of structural elements. The core contribution is the development of a physics-informed heterogeneous graph transformer model that integrates domain-specific physical knowledge into the learning process through physics-based features and regularization mechanisms. The results confirm that all validation criteria are simultaneously satisfied: the difference between training and validation accuracy remains within the threshold (=0.05); The overall classification accuracy exceeds 92.06%; area under ROC curve above 0.8; F1-score is above 0.8 for all major classes; Physics-alignment error is lower than 0.15; and a strong Spearman correlation is observed between model predictions and physics-based indicators. The novelty of the proposed approach lies in the development of a physics-informed graph learning paradigm which enables the integration of structural mechanics, degradation processes, and heterogeneous data sources within a unified predictive framework.