This paper proposes a drift-aware off-policy deterministic actor–critic framework for constrained continuous resource allocation in non-stationary environments. Feasible allocations are ensured by a simplex-parameterized policy using softmax normalization with budget scaling, avoiding projection or Lagrangian tuning. The reward integrates Nash social welfare via mean log-utility, efficiency, fairness, and constraint-violation penalties with adaptive weights. To improve sample efficiency, we adopt prioritized experience replay based on TD error and state novelty. Non-stationarity is detected by KL divergence between recent and historical state-visitation distributions; detected drift triggers buffer refresh and incremental fine-tuning, while Elastic Weight Consolidation mitigates catastrophic forgetting. Experiments across six application-motivated domains (food, medical, housing, education services, employment support, and elderly care) demonstrate improved utilization and welfare with reduced inequality and low decision latency compared with optimization, heuristic, and DRL baselines. Results are reported over multiple runs with mean ± standard deviation and corrected significance tests.

The rapid advancements in deep learning and classifier fusion techniques offer promising solutions to enhance the accuracy and robustness of biometric authentication systems in this paper we propose the integration of these methodologies, specifically in multimodal biometric systems that utilize face and fingerprint recognition. The research investigates various deep learning architectures, highlighting their effectiveness in processing diverse biometric datasets. Additionally, it examines classifier fusion techniques, which combine multiple classifiers to improve person identification performance. A significant focus of this research is on spoofing and anti-spoofing measures. Biometric systems, especially those involving facial and fingerprint recognition, are vulnerable to spoofing attacks such as the use of photographs, videos, or artificial fingerprints to impersonate legitimate users. We developed various anti-spoofing strategies that are integrated into the biometric authentication process to mitigate these risks. These include techniques like texture analysis, motion analysis, and liveness detection, which help differentiate between genuine biometric traits and spoofed samples. We benchmarked a comparative analysis of deep learning models and classifier fusion, demonstrated their strengths, weaknesses, and best practices. Additionally, performance evaluations focus on key metrics such as accuracy, computational efficiency, scalability, and the system’s ability to resist spoofing attacks. Ultimately, the paper emphasizes the potential of these advanced techniques to revolutionize biometric systems, with a particular focus on future research directions for optimizing these methodologies, particularly in the context of improving robustness against spoofing and enhancing the overall security of biometric authentication systems. overall system Equal Error Rate (EER), the True Acceptance Rate at a specified False Acceptance Rate (e.g., TAR @ 0.1% FAR), and the accuracy of the anti-spoofing module.

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. 

Accurate prediction of cardiovascular disease (CVD) is essential for timely intervention and improved patient outcomes. This paper presents a hybrid model, BPSO-RAF-CNN that integrates Binary Particle Swarm Optimization (BPSO) with a Regularized Accuracy-Based Fitness Function (RAF) and a Convolutional Neural Network (CNN) to improve prediction performance through optimized feature selection. The approach begins with feature engineering on cardiovascular data, followed by BPSO-RAF to identify the most important, predictively salient and compact feature subset, lowering dimensionality and improving generalization. These selected features are then fed into a CNN for final classification. Extensive experiments demonstrate that BPSO-RAF-CNN outperforms traditional classifiers (Logistic Regression, SVM, Naive Bayes, Decision Tree, Random Forest) achieving an accuracy of 87.05%, Precision 89.71%, Recall 83.77%, F1-score of 86.05%. And Specificity 90.22%, all with a standard deviation 0.5%. The model also shows good performance across 10-fold cross-validation, indicating strong generalization. 

Modern machine learning models typically require large amounts of precisely labeled data to perform effectively. However, obtaining such labels is time-consuming and costly, especially in specialized domains such as medical image analysis and document classification, where unlabeled data is abundant but expert annotation is scarce. This paper addresses the problem of learning from very few labeled examples by jointly leveraging weak supervision, active learning (AL), and semi-supervised learning (SSL). A hybrid framework is proposed in which a small set of informative samples is actively selected for manual annotation using an entropy-based acquisition function combined with weak label disagreement scoring, while a large pool of unlabeled or weakly labeled data is exploited through SSL based on the FixMatch algorithm. The approach iteratively corrects noisy labels and refines the model with minimal human involvement. The framework is evaluated using a ResNet-18 classifier on the CIFAR-10 benchmark dataset and is compared against two baselines: pure active learning and pure semi-supervised learning. Each method is run independently across three random seeds at the key active learning rounds, and accuracy is reported as mean ± standard deviation. Across three independent seeds, the hybrid framework consistently leads both baselines at intermediate labelling budgets, with the largest absolute gap at Round 15 (+1.27 percentage points over pure active learning, +1.35 percentage points over pure SSL). The framework also offers a clear label-efficiency advantage: at Round 15, with |D_L | = 6500 labels, the hybrid method already reaches 0.6792 ± 0.0097 test accuracy – exceeding the accuracies that pure active learning (0.6730 ± 0.0139) and pure SSL (0.6687 ± 0.0056) attain only at Round 20 with |D_L | = 7000. By Round 20 all three methods saturate near a common data ceiling, indicating that the integrated use of weak supervision, active learning, and consistency-based SSL is most valuable when the annotation budget is genuinely constrained.

Detecting and classifying brain tumours is essential for early diagnosis and effective treatment planning, significantly enhancing patient outcomes. This research presents a deep learning-based approach that utilizes T1-weighted MRI data to automatically identify and classify brain tumours, distinguishing between normal and abnormal cases. The proposed methodology consists of four key steps: pre-processing, segmentation, feature extraction, and classification. In the pre-processing stage, image quality is enhanced using Contrast Limited Adaptive Histogram Equalization (CLAHE) to boost contrast, along with a Gaussian filter to reduce noise. Tumour segmentation is achieved through thresholding, which effectively isolates the tumour regions. For feature extraction, a Convolutional Neural Network (CNN) captures high-dimensional features that are vital for classification. To accurately differentiate between normal and abnormal tumours, an Artificial Neural Network (ANN) is employed for classification. The effectiveness of the proposed technique is evaluated based on performance metrics such as time, accuracy, and peak signal-to-noise ratio (PSNR). The obtained parameters are compared with existing techniques to highlight improvements in detection and classification performance. Among the tested images, the best result achieved a PSNR of 13.015 dB, an accuracy of 99.231%, and a computational time of 1.267 ms, demonstrating the efficiency and reliability of the proposed method for brain tumor detection and classification. Overall, this approach provides an effective and automated method for detecting brain tumours, aiding in clinical decision-making and diagnosis.

This paper presents a hybrid machine learning model for the classification of DNA sequences by combining different machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree, Random Forest, Light Gradient Boosting Machine (LGBM), and XGBoost (XGB). This model has been developed using the stacking ensemble method, associated with a majority voting mechanism to achieve improved overall classification accuracy. In this study, the Promoter Gene Sequences dataset from the UCI Machine Learning Repository was used to concentrate on classifying promoter versus non-promoter sequences. The results indicated an accuracy of 96.25%, showcasing the hybrid model’s ability to classify DNA sequences effectively. This research provides valuable insights into ensemble machine-learning techniques in DNA classification, with possible applications in genomics research, medical diagnostics, agricultural biotechnology, and forensic science. The hybrid model’s thriving implementation demonstrates the potential for more accurate and reliable DNA sequence classification methods.

Agriculture remains the primary occupation for a majority of the Indian population, yet granting much emphasis to subjective decision-making of traditional farming texts will lead to inefficiency, wastage of resources, and decrease in crop yields. To mitigate these problems, we are in an acute need for technology-based and data-oriented methods that may optimize agricultural practices for sustainable development. The growing demand for sustainable agricultural practices in the face of climate change, soil degradation, and rising food demand presents a significant challenge in India. Small and marginal farmers are almost never given timely and accurate advice on crops and fertilizers, for which the farmers suffer low productivity and the environment its degradation. Herein is outlined a complete suite of machine learning-driven systems to satisfy crop recommendation, fertilizer optimization, and yield prediction needs. The main objective is to generate intelligent, data-driven recommendations based on historical crop data, soil properties, weather data, and crop measurements so that farmers may use these data to make best possible decisions. Random Forest models are utilized to enhance the precision of recommendations, achieving an accuracy of 62.67% for crop and fertilizer recommendation and 98.6% for yield prediction. By giving recommendations based on data and practice, this study hopes to revolutionize traditional agricultural methods and hence improve the farmer's living standards, create employment for others, and push the economy ahead in rural areas, visualizing sustainable agricultural development.

Accurate detection of ECG arrhythmias plays a critical role in enabling timely diagnosis and treatment of cardiovascular diseases, which remain the leading cause of mortality worldwide. However, achieving high classification performance remains challenging due to class imbalance, signal variability, and resource constraints in real-time deployments. This study aims to enhance ECG arrhythmia detection accuracy through an optimized ensemble approach combining multiple CNN models with a novel stage-wise sieving strategy.
Methodology: Three lightweight CNN models (ShuffleNet, MobileNet-v2, ResNet-18) were integrated into a multi-stage binary classification framework. Each stage systematically eliminated accurately classified arrhythmia classes. The novelty of the proposed approach lies in introducing a stage-wise sieving strategy that incrementally removes well-classified classes, combined with an optimized ensemble fusion of multiple CNN models guided by metaheuristic optimization techniques to boost performance. Optimization techniques, including Particle Swarm Optimization, Whale Optimization Algorithm, Grey Wolf Optimizer, Ant Colony Optimization, and Firefly Algorithm, were applied to improve model fusion. The approach was validated using combined public datasets (PTB-XL, MIT-BIH, and Shaoxing ECG databases). Results: The proposed stage-wise sieving ensemble significantly improved overall classification accuracy by 17.95%, reaching 96.29% accuracy using the Grey Wolf Optimizer. Classes previously misclassified, such as Conduction Disturbance and Hypertrophy, exhibited accuracy improvements of up to 32.44% and 25.19%, respectively.
Conclusion: The proposed optimized ensemble approach significantly enhances ECG arrhythmia detection performance and demonstrates feasibility for real-time deployment on resource-constrained platforms such as Raspberry Pi.

In this study, a novel decision support model integrating spherical fuzzy sets enhanced with autoencoder-based dimensionality reduction, MEREC weighting, and CODAS ranking methods is proposed for high-dimensional, uncertain multi-criteria decision problems. The spherical fuzzy set structure allows decision makers to express their evaluations using three levels of membership (membership, non-membership, and hesitation). Thus, it produces linguistic evaluations appropriately to the nature of uncertainty.  In the numerical analysis, five elective courses, Python Programming, Java Programming, C# Console Programming, Visual Programming with C#, and Web-Based Programming, were evaluated based on 41 selection criteria.  The latent structures among these criteria were analyzed using the Autoencoder architecture, yielding 17 latent features with a reconstruction mean squared error of 0.016 as determined by an elbow-based reconstruction loss analysis, indicating negligible information loss beyond this dimension. The weights for these dimensions were objectively calculated using the MEREC method, which is based on the distinctiveness of each dimension in the decision process. The CODAS method was applied to rank the courses and provide decision support using the calculated weights. In the final stage, a comprehensive sensitivity analysis was performed to test the impact of changes in both dimension weights and decision-maker weights on the results Sensitivity analysis further confirmed the robustness of the proposed framework, with the top-ranked alternative preserved under ±10% criteria weight perturbations. The numerical results illustrate the practical applicability of the proposed framework and validate its effectiveness in handling complex evaluation structures.  Although the proposed framework is demonstrated through a programming course selection problem, the methodology is generic and can be readily applied to other complex decision-making scenarios involving high-dimensional, uncertain, and interrelated criteria.

In this research, a Temporal Convolutional Network (TCN) is combined with a Transformer model with multi-head attention to present a novel approach to stock price forecasting. The primary objective is to address the challenges of recognizing complex patterns and long-term interdependence inherent in the volatility of financial time series data. By fusing the powerful attention mechanisms of Transformers with the sequential processing capabilities of TCNs, the hybrid model provides a powerful solution. This method performs better than conventional deep learning models, including Long Short-Term Memories and standalone TCNs, according to extensive testing on historical stock market data. The outcomes highlight the efficacy of this approach for trustworthy stock market forecasting by demonstrating notable gains in prediction accuracy and model stability.

This study investigates interaction effects among rule sets, sensor fusion strategies, and membership functions on the navigational performance of a nonholonomic wheeled mobile robot in static, unknown environments using fuzzy logic controller. Employing a 3×3×3 factorial design, factors including rule set size (27, 18, and 14 rules), fusion level (minimal, moderate, and dense), and membership function shape (triangular, trapezoidal, and Gaussian) were varied. Each of the 27 configurations were evaluated in triplicate using a MATLAB/CoppeliaSim co‐simulation, with traversal time as the performance metric. An analysis of variance (ANOVA) revealed that each of the three main factors had a significant impact on traversal time (p < 0.001). Notably, there were also meaningful interactions between rule set size and membership function, as well as between rule set size and sensor fusion (p < 0.01), suggesting that system performance is closely tied to how these parameters are combined. Among the tested configurations, setup with a 14-rule base, Level 2 sensor fusion, and a triangular membership function consistently achieved the fastest average traversal times. These interactions likely arise from computational perceptual trade-offs. Increasing rule set size enhances decision granularity but introduces inference delay, whose effects vary depending on how smoothly membership functions partition the input space and how densely sensor data are fused. In practice, this implies that controller performance depends on achieving a balance between linguistic complexity, sensor integration depth, and fuzzification. The findings therefore emphasize the importance of joint parameter tuning and offer design insight for balancing computational cost against navigational precision in embedded fuzzy logic controllers.