The Elliptic dataset is widely used in Bitcoin anti-money laundering research, yet its original anonymized features have limited forensic interpretability. Much of the existing Elliptic-based literature relies on these opaque benchmark variables, leaving insufficient attention to semantically explicit and interpretable graph representations for illicit transaction detection. To address this gap, this article proposes a combined approach that integrates transaction-level feature reconstruction with interpretable forensic descriptor engineering. First, the benchmark’s original feature space is replaced with a semantically explicit reconstructed representation derived from public on-chain transaction data and metadata after resolving benchmark node identifiers to transaction hashes. Second, the proposed approach extends this reconstructed representation with interpretable forensic descriptors that capture local transaction abnormality, outgoing value redistribution behavior, and deviations from upstream transaction history. The empirical design isolates the contribution of the proposed descriptors by comparing the reconstructed representation against its descriptor-augmented variant. Across eight classifiers evaluated under a whole-snapshot train-test protocol that preserves within-snapshot graph structure, the descriptor-augmented representation consistently improves illicit class retrieval. CatBoost achieves the best results, increasing the area under the precision recall curve for the illicit transaction class from 85.10% to 90.27%, precision from 77.49% to 87.04%, recall from 75.57% to 81.11%, and F1-score from 76.44% to 83.90%. The article also discusses how the predictive component can be embedded into a hybrid analytical framework that separates machine learning classification from address-level forensic interpretation. This structure supports explainable prioritization and expert review while preserving the distinction between predictive evidence and forensic interpretation. Overall, the findings demonstrate that semantically explicit and forensically interpretable representations can substantially improve illicit transaction retrieval while supporting transparent post hoc analysis in Bitcoin anti-money laundering research.

The fields of augmented engineering are confronted with formidable obstacles because of the absence of chances for self-paced learning, the wide coverage of undergraduate curricula, uneven academic content standards, and shortages in teacher knowledge. This study suggests a thorough strategy to overcome these drawbacks. In order to enhance current course offerings through bridge or add-on courses, we want to integrate the NPTEL Swayam and NIELIT platforms as additional resources of Self-Paced Learning. This plan will improve students’ knowledge acquisition, give them various learning options, and promote a continuous learning culture reinforced by certification processes. The project intends to solve issues with skill development, student engagement, and standardized academic material by incorporating various online platforms as supplemental or add-on courses which are used for Curriculum Enhancement. To test the efficacy of this strategy, a pilot deployment encompassing course selection, curriculum integration, and student enrollment was carried out. Positive student results in terms of knowledge acquisition and skill enhancement are indicated by preliminary studies. Nonetheless, issues with workload management and technical difficulties were noted.

The results of studies on an innovative technology for complex presowing seed treatment, which includes the application of protective and nutrient substances with subsequent drying, are presented. Traditional apparatuses with mechanical agitators or rotating bottoms have substantial limitations, such as a high risk of mechanical damage to seeds, the formation of agglomerates, and a low intensity of heat and mass transfer. To solve these problems, the application of an inhomogeneous jet-pulsating fluidization in a self-oscillating mode is put forward as a viable solution. A physical model of the interaction of a gas coolant with granular material of non-spherical (ellipsoidal) shape is theoretically and experimentally validated using wheat grains as an example. It has been experimentally proven that in a gas flow, seeds orient themselves with minimal projection along the flow direction. This reduces hydrodynamic resistance and leads to a local increase in bed porosity to 0.5-0.9. It was established that the introduction of a coolant forms an intense asymmetric spouting motion. At the same time, cyclic entrainment of about 40 % of the bed mass into the freeboard space occurs with a frequency of more than 1.5 hertz. The ratio of the gas bubble volume to the initial bed volume increases to 37 %. This specific hydrodynamic condition provides active volumetric mixing with a significant increase in the interfacial contact area and intensification of heat and mass transfer processes at low temperatures (not exceeding 40 °C), which significantly mitigates the risk of thermal degradation and mechanical impact to the seeds compared to traditional methods.

Melanoma skin disease is a major concern for skin cancer-related deaths worldwide. Early diagnosis and detection are crucial for improving patient outcomes. However, existing detection methods often result in false alarms, highlighting the need for more accurate and reliable approaches. This paper proposes a Dual-Stream Semi-Supervised Melanoma Network (DS-MelNet) for melanoma detection. The DS-MelNet utilizes a semi-supervised learning framework to incorporate both labeled and unlabeled data, enhancing detection accuracy. The model's performance is evaluated on the SIIM-ISIC Melanoma Classification Challenge dataset. The dataset undergoes hair detection and removal from skin lesion images using three algorithms proposed in literature viz. Modified Dull Razor, Modified E-shaver and Adaptive principle curvature with Modified dull razor fusion. Performance of the proposed models is assessed through commonly used metrics that include Accuracy, Recall, Precision, and F1-score. Comparative analysis of the DS-MelNet is performed against two benchmarks: Simple Convolutional Neural Network (SCNN) and a Fine-tuned VGG-16 model proposed in this paper. The results clearly indicate that the DS-MelNet demonstrates superior performance, achieving an accuracy of 86% and outperforming both SCNN (76%) and VGG-16 (82%) models. This exceptional performance underscores the potential of the DS-MelNet for effective melanoma classification. The study highlights the promise of semi-supervised learning frameworks and sophisticated neural networks in enhancing melanoma diagnostics. The ability of the proposed model to learn from a small set of labeled data makes it highly suitable for real-world applications where annotated datasets are limited.

This research proposes the implementation of the subjective and objective weighting approach (SOWA) method as a new approach in determining the criteria weights that combines subjective assessments from experts and data-driven objective calculations. The criteria weights generated from the SOWA method are then used in various multi-criteria decision-making (MCDM) methods, such as simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS), multi-objective optimization on the basis of ratio analysis (MOORA), grey relational analysis (GRA), multi-attribute utility theory (MAUT), weighted aggregated sum product assessment (WASPAS), weighted product (WP), simple multi-attribute rating technique (SMART), multi-attributive border approximation area comparison (MABAC), and Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA), to evaluate and rank alternatives. The research results show that the SOWA method is capable of producing balanced and representative weights, as well as consistent alternative rankings across MCDM methods. Sensitivity analysis of the ranking results indicates that all methods yield identical ranking results, signifying a high level of stability and reliability of the generated weights. These findings demonstrate that the SOWA method can serve as a solid foundation in decision support systems, particularly in the context of candidate selection or evaluation based on multiple criteria.

Education is crucial for personal and economic growth, but financial challenges in developing countries hinder equitable academic success. NGOs administer scholarship programs to empower underprivileged individuals, a crucial step towards the attainment of Sustainable Development Goal 4, which aims to provide inclusive and equitable quality education for all. This study proposes a novel Scholarship Award Recommendation System that leverages predictive modelling and ensemble learning to identify deserving students for scholarship awards. The system utilizes a robust ensemble model that combines the strengths of Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Extra Trees (ET) to predict students' academic performance. Additionally, we incorporate answers from the General Mental Health Questionnaire (GHQ-12). The GHQ-12 responses are pre-processed using a binary scoring approach (0-0-1-1) and integrated as predictive variables alongside academic and demographic features. We apply this framework to a case study of Nigerian university students in partnership with Springtime Development Foundation. The results indicate that incorporating GHQ-12 features significantly enhances prediction accuracy, with QDA, RF, and ET achieving accuracy scores of 0.90, 0.86, and 0.89, respectively. Statistical analysis using a t-test confirms the relevance of GHQ-12 features, with a p-value of 0.0013 establishing a significant correlation between student performance and mental health status. The study showed the effectiveness of the ensemble model to accurately predict students’ academic performance. It highlights the significance of incorporating variables from the (GHQ-12) into the predictive model, indicating mental health as a crucial factor for predicting academic performance which in turn enhances the performances of the Classification Models considered.

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.

We study the prime spectrum of idealistic S-algebras, defined via an algebraic structure with a complete lattice of ideals and a suitable notion of prime ideals. The spectrum is equipped with a natural topology and is shown to form a spectral space, possessing key properties such as compactness, separation, and sobriety. We further establish that the spectrum construction is functorial and provides a correspondence between minimal prime ideals and irreducible components under appropriate conditions. Examples are included to illustrate the role of the underlying structure, showing that the existence of minimal primes depends critically on the ideal-theoretic properties.

An accurate and comprehensive assessment of student engagement in classrooms is crucial for enabling data-driven teaching and personalized education. Current approaches primarily rely on teacher observation or student self-reports, which are often subjective, delayed, and unable to capture cognitive engagement. To address these limitations, this study proposes a Multimodal Cognitive-Attention Fusion (MCA Fusion) framework, grounded in Fredricks’ three-dimensional engagement model.  The framework integrates electroencephalography (EEG), facial expressions, and body posture to simultaneously quantify cognitive, emotional, and behavioral engagement.  Built on a Transformer architecture, it employs self-attention to extract temporal features within each modality and introduces a cognition-guided cross-attention mechanism to dynamically integrate multimodal signals. To validate the framework, experiments were conducted with 36 undergraduate students in real classroom settings. The results demonstrate that our framework significantly outperforms all single-modality baselines, achieving an accuracy of 92% and an F1-score of 94.87%. Compared with the best single-modality model (EEG), the F1-score improves by 34.58 percentage points. Ablation studies further confirm the critical role of the cognitive modality (EEG) and the MCA Fusion mechanism, the removal of which leads to F1-score reductions of 62.58 and 56.16 percentage points, respectively. The proposed approach not only provides a theoretically informed and technically evaluated framework for engagement recognition but also provides a methodological foundation for future closed-loop “perception–assessment–feedback” systems in intelligent learning environments. 

This paper proposes an intelligent load balancing framework for distributed big data processing systems that integrates machine learning techniques with adaptive weight-based decision mechanisms. The study addresses limitations of traditional static load balancing methods, which do not account for dynamic workload variations and heterogeneous request characteristics, leading to inefficient resource utilization and bottlenecks in multi-node environments. The proposed approach combines an online learning model for real-time estimation of request complexity with multi-parameter evaluation of node states, including CPU utilization, memory consumption, queue length, response latency, and cache efficiency. A dynamic weighting strategy is used to construct an integrated load indicator for adaptive request distribution across nodes. The framework is deployed within a multi-layer distributed architecture consisting of clustered application servers, distributed databases, caching subsystems, and monitoring components, ensuring scalable and fault-tolerant processing. For evaluation, a three-node simulation environment was used with 10,000 heterogeneous requests, followed by extended testing on semi-realistic workload traces derived from web traffic patterns and database query logs. The dataset included over 1.2 million requests, capturing bursty arrivals, skewed distributions, and heterogeneous complexity. Experimental results show that the proposed method improves load distribution uniformity to 6%, reduces average response time to 210 ms, and increases throughput up to 13,800 requests per second. Statistical validation using confidence intervals and hypothesis testing confirms a 47% (±3.2% at 95% confidence level) reduction in mean response time and throughput improvement up to 14,200 requests per second under realistic workloads.