As the Internet of Things (IoT) health monitoring systems expand, measures must be taken to ensure the safe, real-time handling of data, particularly for highly infectious diseases which limit the ability for human interaction. Blockchain provides the necessary transparency and immutability so that patient families and hospitals can align on trust with health data. Hyperledger Fabric is the most commonly used solution for these scenarios, as it operates on X.509 certificates for the authentication of device identity. Yet, with the frequent transmission of the IoT sensors, the need for repeated certificate authentication causes considerable computational lags, which can hinder critical real-time assessments in the care continuum. We propose CAVIC (Certificate Authentication Verification Intelligent Cache), which is aimed at simplifying the verification and caching processes with the goal of diminishing the effects of redundant cryptographic activities. CAVIC tracks the high-traffic IoT devices in a network, including temperature, heart rate, and oxygen saturation sensors, and creates a secured cache for their authenticated certificates. Further processes from these devices are verified via instant cache lookups rather than comprehensive certificate re-evaluations, which drastically reduce verification overhead. In situations where a patient's condition can worsen to an extent requiring immediate action from a doctor, CAVIC guarantees that the system's time is dedicated to the analysis of patient data instead of the continual validation of trusted sensors. Incorporating CAVIC into a Hyperledger-based healthcare testbed, we show reduced latency, lower computational costs, and better responsiveness, all of which add to the safety and efficiency of managing patients remotely.

Field service management (FSM) has been faced with the need to efficiently assign tasks to technicians in field service operations (FSOs). The problem of inefficient task allocation has led to either an over utilization or an under utilization of the organization’s workforce and resources across cities and states. This study focuses on developing a resource-efficient multi-criteria algorithm (Proximity-Skill-Priority, PSP algorithm) for optimized field service task management. A mathematical modelling approach was used to design a unified score that takes into consideration the proximity of the technician to the task location, the skillset of each technician and the priority level of the task at hand while ensuring effective workload balance to ensure unbiased task assignment. The study used adaptive weighting coefficients to ensure that real-time adjustments are made when there are varying time conditions. The model was evaluated through a simulation experiment and benchmarked against three single-rule baselines: Proximity-First, Skill-First, and Priority-First assignment. The performance of the algorithms was measured in a 100-run simulation conducted on a synthetic dataset that is parameterized with the attributes of technicians, task priorities, and geospatial information.  The lowest average response time and travel distance were achieved by Proximity-First and Priority-First rules, whereas Skill-First produced the highest first-time-fix-rate surrogate at the cost of substantially higher travel and response values. PSP produced a more balanced trade-off: it improved first-time-fix performance when compared to the proximity-first and priority-first heuristics. At the same time, it balances the severe travel penalty observed under Skill-First assignment. Statistical analysis using Friedman and Wilcoxon signed-rank tests shows that there are significant differences in operation across the different algorithms for the evaluation metrics. These findings show that PSP is best interpreted as a compromise strategy that balances service quality and operational efficiency rather than maximizing any single metric in isolation.

Electroencephalogram (EEG)-based seizure detection is a challenging time-series classification problem because EEG signals are nonlinear, non-stationary, and temporally heterogeneous. This study presents a unified comparison of classical machine-learning, neural and deep-learning, convolutional-kernel time-series, and probability-level ensemble models using the Epileptic Seizure Recognition benchmark dataset. The 11,500 EEG segments were reconstructed into 500 recording groups, and recording-grouped nested cross-validation was applied to prevent segments from the same recording from crossing training and test partitions. Depending on the modelling paradigm, segments were represented either by 67 handcrafted time-, frequency-, and nonlinear-domain features or by ordered 178-sample EEG sequences. Recording-level average precision was used as the primary evaluation metric, supported by ROC-AUC, threshold-dependent measures, Brier score, bootstrap confidence intervals, ablation analysis, explainability, and computational-efficiency benchmarking. RBF-SVM achieved the highest average precision of 0.997985 and ROC-AUC of 0.999475. MultiRocket produced the lowest family-representative Brier score of 0.006148, while MLP achieved the lowest standalone inference latency after feature extraction. The selected meta-ensemble produced strong threshold-dependent performance but did not improve overall precision–recall ranking. Paired bootstrap analysis found no statistically significant differences among the family representatives after multiple-comparison correction. These findings demonstrate that handcrafted-feature and time-series models can both achieve strong recording-level performance when evaluated under a consistent, leakage-controlled protocol. Because verified patient identifiers were unavailable, the results should not be interpreted as evidence of patient-independent clinical generalization.

Adsorption of dyes present in textile industry waste water is an important process for environmental remediation and pollution control. Dyes are complicated organic compounds used in textile dyeing processes. The presence of dyes in wastewater is of major environmental concern because of their recalcitrance and potential toxicity. Adsorption technology is a promising solution for the removal of dyes from textile wastewater. It uses adsorbent materials to capture and immobilize dye molecules from aqueous solution. Machine learning techniques have been used to reduce the analytical efforts for prediction of concentration of dye solution in parts per million using a UV spectrophotometer.
In the research work, a prediction technique using Machine Learning and image processing is used to identify the color of images of samples and corresponding adsorption of the dye. The suggested model has achieved correct dye concentration estimates using retrieved image attributes with 93% accuracy with experimental data.

Proactive prediction based channel allocation for Cognitive Radio Networks (CRNs) remains a challenging area of research. In Internet of Things (IoT) scenarios, prediction algorithms must balance resource efficiency with high accuracy. The proposed algorithm, ProCLAMUS, a Matrix Completion (MC) Based Proactive Spectrum Allocation Protocol for CRNs, couples nuclear norm matrix completion with short horizon sliding window prediction. By reconstructing sparse spectrum sensing data and down weighting inconsistent reports, ProCLAMUS enables infrequent sensing and decentralized decisions without a fusion center, reducing energy and attack surface. In a network of 40 Secondary Users (SUs), 400 Primary Users (PUs) and 400 channels with malicious data ranging from 5 to 50%, ProCLAMUS sustains the highest channel utilization (avg 89.96%) with the lowest backoff rate (3.81 s^(-1)) and sensing delay (0.58 channels/success). ProCLAMUS achieves the lowest radio energy (16.82×10^(-3) J/s), a 44–53% reduction when compared with recently proposed techniques, while using 33–43% less memory. These gains arise from sparse sensing, fusion (majority voting) and conservative allocation. The results demonstrate superior energy efficiency and robust prediction under malicious data conditions during the Spectrum Sensing Data Falsification Attack.

Predicting Carbon dioxide (CO₂) emissions is essential for guiding environmental policies and climate strategies, yet accurate local-scale prediction remains challenging due to complex spatial and temporal factors. Existing approaches often focus on regional or global models and do not fully exploit high-resolution satellite imagery for local trend analysis. To overcome these limitations, Conditional Pine Cone Optimization enabled Attention-Based Long Short-Term Memory (CPCO_ALSTM) is devised for CO2 Emission prediction for Trend Analysis in Satellite Image. In this approach, satellite images of the query region acquired at different time intervals are used as input. Initially, a Kalman Filter is employed for noise reduction and temporal smoothing during preprocessing. Then, segmentation of satellite image is done using Dense-Res Recurrent Prototypical Network (DRRP-Net), wherein DRRP-Net is developed by combining Dense-Res-Inception Network (DRINet) and Recurrent Prototypical Network (RP-Net). Based on the segmented regions and extracted features, carbon emission estimation is carried out. The estimated CO₂ emissions are then validated against ground-truth emission data obtained from a reference database to assess prediction accuracy. For temporal trend analysis, an Attention-Based Long Short-Term Memory (ALSTM) model is utilized to capture long-range dependencies in emission patterns. The ALSTM parameters are optimally tuned using the proposed CPCO algorithm, which combines Pine Cone Optimization (PCO) with Conditional Autoregressive Value at Risk (CAViaR) to enhance convergence stability and prediction robustness. Additionally, CPCO_ALSTM has attained 96.35% of accuracy, 0.087 of normalized MSE, and 0.957 of Jaccard Coefficient.

Multi-criteria decision making (MCDM) often faces challenges in dealing with data with different scales and units, as well as ensuring the stability of ranking results against changes in criterion weights. This article proposes a new framework called multi-objective hybrid normalization for ideal solution-based alternative ranking (MONAS) that integrates hybrid normalization techniques and ideal solution approaches to improve the accuracy and consistency of alternative rankings. Weight uncertainty in MCDM reflects the variability or ambiguity in determining the relative importance among criteria, which can affect the stability and reliability of the final decision outcomes; however, this condition can be minimized through the MONAS approach, which adaptively integrates multi-objective normalization, thereby balancing the influence among criteria and improving the consistency of ranking results. The normalization hybrid used in this study combines three approaches: min-max normalization, vector normalization, and sum normalization. These are integrated by calculating the average of the results of these three normalization methods for each data value.  
The data domain used in the MONAS method for evaluating supplier performance includes seven alternatives and six assessment criteria. These criteria represent a combination of benefit and cost attributes, with value distributions that vary across them. Experimental results show that MONAS is capable of producing more reliable rankings that are resilient to data uncertainty compared to conventional methods. Testing results using 10 popular MCDM methods indicate that MONAS can provide more stable and reliable alternative rankings compared to those conventional methods. This framework offers effective and adaptive solutions for various complex decision-making applications across different domains, thereby improving the quality and trust in the decision-making process.

This paper establishes a linear profile decomposition for bounded sequences in the homogeneous Dirichlet–Sobolev space H ̇_D^1 (Ω), where Ω=R³/O is the exterior of a smooth, compact obstacle O⊂R³. Given a bounded sequence {f_n}⊂H₃¹ (Ω), we prove that, after passing to a subsequence, it decomposes as f_n=∑_(j=1)^Jϕ_n^j +w_n^J, where the profiles {ϕ_n^j} are asymptotically orthogonal and the remainder w_n^J vanishes in all Strichartz spaces L_t^q L_x^r as J→∞. The decomposition satisfies an exact energy identity ∥∇f_n ∥_(L^2)^2=∑_j^∥ ∇ϕ_n^j ∥_(L^2)^2+∥∇w_n^J ∥_(L^2)^2+o(1). Four distinct geometric concentration regimes are identified according to the behaviour of the scale sequence {λ_n^j} relative to the distance d(x_n^j ) to ∂Ω: profiles localised inside Ω; profiles dispersing to spatial infinity (limiting domain R³); profiles concentrating deep inside Ω away from the boundary; and profiles concentrating near ∂Ω (limiting domain: a half-space). As an application, we prove small-data scattering in H ̇_D^1 (Ω) for the defocusing, energy-subcritical NLS i∂_t u+Δ_Ω u=|u|^(p-1) u with 1<p<5 and Dirichlet boundary condition. We emphasise that the NLS is a Hamiltonian (conservative) system: its energy is conserved, not decaying, and the scattering result follows from Strichartz estimates rather than from any dissipative mechanism. 

This article presents a set of specialized software tools for educational data mining and describes the integration of the developed tools into the educational process of students of technical specialties. The developed set consists of three programs that implement the main tasks of educational data mining. Program No. 1 provides segmentation, analysis, and visualization of images of educational materials; Program No. 2 performs data clustering; Program No. 3 performs correlation and regression analysis to identify relationships and predict learning outcomes. The developed programs can be used to implement a systematic analysis of education quality, during which automated processing of educational data and the formation of recommendations for improving the educational process are performed. The programs are implemented in Python. A scheme for integrating specialized programs into the educational process has been developed. The scheme reflects the relationships between the programs that process educational materials, analyze the results of the educational process, influence students’ educational trajectories and the educational process.
Program No. 1, “SegmentFuzzy24,” is designed for segmentation, analysis, and visualization of images of educational materials. Such processing makes it possible to highlight the investigated elements of technical devices in order to focus students’ attention, as well as to determine the quantitative and geometric characteristics of multimedia presentation slides, including the number and size of symbols, the area of graphical objects, etc. Segmentation is performed using the region growing method. The determined characteristics are used for the classification of educational materials or are transferred to Programs No. 2 and No. 3 for further cluster, correlation, and regression analysis.
Program No. 2, “ClusterFuzzy23,” is designed for clustering educational data, including students’ learning outcomes and the characteristics of educational materials obtained from Program No. 1. Objects are divided into groups with similar characteristics using the K-means method. During the clustering process, the sum of the squared Euclidean distances between each object and the centroid of its cluster is minimized. Data clustering ensures their differentiated processing and contributes to the adjustment of educational trajectories. The clustering results can be transferred to Program No. 3 for further analysis. Fuzzy membership functions are used to determine the degree of membership of objects in overlapping clusters.
Program No. 3, “CorrelRegres25,” is designed for correlation and regression analysis of educational data obtained directly or as a result of clustering in Program No. 2. Correlation analysis is used to establish relationships between the characteristics of the educational process, while regression analysis is applied to model their dependencies using polynomial functions and to predict learning outcomes. The obtained models are used to adjust educational trajectories, while objects that have significant deviations from regression dependencies are classified as outliers.
The developed programs have been demonstrated on sample data to process real educational data, namely for image segmentation and visualization, clustering of educational data, and their correlation and regression analysis.
Data clustering and correlation–regression analysis were performed using the grades of N0 = 76 students in 12 subjects, assessed on a 100-point scale, for courses completed during the first and second years of study. The initial sample of N0 students was randomly divided into a training set (N = 60, 80% of the data) and a validation set (NV = 16, 20% of the data). The quality of the clustering results was evaluated using the Silhouette coefficient, Calinski–Harabasz index and Davies–Bouldin index. The analysis indicated that three or four clusters provide an appropriate representation of the data. The optimal polynomial degree pA = 3 for the regression model was determined based on the minimum root mean square error RmseV = 12.37458, obtained on the validation set. The relatively high RmseV value can be attributed to the influence of a considerable number of factors affecting students’ academic performance. The resulting regression model was used to predict students’ grades in a subject for the subsequent academic period based on their grades in the same subject during the previous period. A promising direction for further improvement of the developed prediction system is the integration of artificial neural networks.

The growing sophistication of contemporary network infrastructures has increased the pressure on the smart and dynamic intrusion detection systems that can react to the dynamic cyber threats. The machine learning (ML) and deep learning (DL) methods have high classification rates, but they work with fixed decision boundaries which reduces their ability to adapt to dynamic traffic distributions and emerging attack patterns. In order to overcome these issues, the resource-aware Proximal Policy Optimization (PPO)-based adaptive multi-class intrusion detection system (IDS) is suggested in this study. The system characterizes intrusion detection as a sequential decision-making and incorporates computational resource measures into the reinforcement learning (RL) rewarding framework, which allows optimizing detection performance and operational efficiency at the same time. In the ensemble comparison, PPO-Model achieved the highest accuracy (99.4%), recall (98.6%), and Macro AUC (0.998), while reducing CPU utilization by 25.8% and memory consumption by 27.1% compared with the Stacking model. These results demonstrate that the proposed approach can improve detection performance while reducing computational resource requirements. The results suggest that next-generation intrusion detection in the dynamic network environment can be achieved with a scalable and robust solution based on the combination of RL and resource-aware optimization.