International Research Journal of Multidisciplinary Technovation

Adaptive Komodo Mlipir–Optimized Spatiotemporal Graph Neural Network for Dataset-Specific Physiological-State Classification

2026-05-05T07:46:24+00:00

Physiological-state classification requires models that can represent dependencies among sensor channels while preserving temporal variation. This study presents a dataset-specific evaluation of an Adaptive Komodo Mlipir Algorithm-optimized spatiotemporal graph neural network (AKMA-ST-GNN) using four public physiological datasets with different modalities and target definitions: STEW for cognitive workload, SEED for emotion, DEAP for affect, and WESAD for stress and amusement. The datasets were analyzed independently. No EEG signal from one dataset was combined with EMG or peripheral signals from another dataset, and workload, emotion, affect, and stress labels were not collapsed into a common target. The proposed workflow included signal filtering, training-set standardization, frequency-domain characterization where physiologically appropriate, within-dataset channel graph construction, spatiotemporal graph learning, and dataset-specific classification. AKMA was used as a hyperparameter-search wrapper rather than as an additional predictive layer. Under the preliminary 80% training and 20% testing protocol preserved in the source manuscript, the reported accuracies were 98.60% for STEW, 98.31% for WESAD, 98.06% for DEAP, and 98.12% for SEED. Precision, F1-score, specificity, and sensitivity were available only for selected datasets. Prediction-level outputs, class supports, confusion matrices, and decision scores were not retained, therefore, macro F1, weighted F1, balanced accuracy, Matthews correlation coefficient, Cohen's kappa, and ROC/AUC could not be reconstructed. Repeated-seed results and fold- or participant-level scores were also unavailable, preventing calculation of standard deviations, confidence intervals, and statistical significance tests. The reported values are consequently interpreted as preliminary evidence of technical feasibility rather than proof of stable or subject-independent generalization.

Dual Cloud - Sturnus Optimized Intent-BERT with Randomized Secure Anonymization based Bibliographic Network Recommender for Citation Recommendation System

2026-02-23T05:10:08+00:00

Citation recommendation (CR) systems for manuscripts employ automated systems to identify and suggest relevant scientific publications for effectively citing specific text passages; however, they face challenges related to the promotion of unreliable sources and the privacy of sensitive data. To address these limitations, a novel Dual Cloud Sturnus Optimized Intent Learning BERT with Randomized-Adversarial Secure Anonymization (SIL-BERT) based Bibliographic Network Recommender (BNR) is proposed in this study to enable secure citation recommendation. Among these issues, shilling attacks are particularly important because malicious actors exploit the mathematical foundations of collaborative filtering and manipulate the algorithms. To address this issue, a novel Sturnus Optimized Self-Intent BERT (SS-IBERT) is employed and it effectively reduces artificial inflation caused by algorithmic rhetoric and thereby mitigates the suppression of emerging researchers. Moreover, domain-specific interactions combined with re-identification through linkable attributes generate a distinctive chronological activity fingerprint, which allows anonymized citation logs to be cross-referenced with public metadata. Therefore, to deal with this, a Randomized-Style Adversarial Anonymization (R-SAA) is used, and it effectively suppresses the Small-N Behavioral Vulnerability (S-NBV) that creates elevated re-identification risks. The experimental results demonstrate that the proposed Dual Cloud SIL-BERT achieves a citation-recommendation accuracy of approximately 99%.

Composition-Dependent Magnetic Reversal and Partial Exchange Coupling in SrFe12O19/FeCo Nanocomposites

2026-07-22T09:23:40+00:00

SrFe₁₂O₁₉/FeCo hard–soft magnetic nanocomposites with different hard-to-soft magnetic phase ratios were successfully synthesized using a combination of sol–gel autocombustion and wet-chemical methods, followed by physical mixing. X-ray diffraction analysis confirmed the formation and coexistence of the hexagonal SrFe₁₂O₁₉ hard magnetic phase and body-centred cubic FeCo soft magnetic phase, with no detectable crystalline impurity phases. The structural parameters showed a composition-dependent variation in crystallite size and lattice strain, indicating changes in crystallinity and structural distortion with FeCo incorporation. The magnetic properties exhibited a strong dependence on the hard–soft phase ratio. With increasing FeCo content, the coercivity decreased from 1795 to 866 Oe, which can be attributed to the increasing contribution of the magnetically soft FeCo phase and its easier magnetization reversal. The differential magnetization (dM/dH) curves displayed a dominant central switching peak accompanied by symmetric secondary features, suggesting the coexistence of different magnetization-reversal processes and supporting the presence of partial exchange coupling between the hard and soft magnetic phases. Furthermore, the effective magnetic anisotropy decreased with increasing FeCo content due to the reduced relative contribution of the high-anisotropy SrFe₁₂O₁₉ phase. Despite the decrease in coercivity, the maximum energy product increased from 0.09 to 0.18 MGOe, demonstrating that appropriate adjustment of the hard-to-soft phase ratio can improve the overall magnetic performance. These findings demonstrate that FeCo incorporation provides an effective approach for balancing saturation magnetization and coercivity, highlighting the potential of SrFe₁₂O₁₉/FeCo nanocomposites as rare-earth-free materials for permanent magnet applications.

Deep Learning Methods for Multimodal Fake News Classification Combining Textual and Visual Information

2025-11-21T13:44:14+00:00

The multimodal nature of fake news propagating in the social media has necessitated the need to have strong detection systems that can detect textual and visual discrepancies. In this work, the authors suggest a computationally efficient multimodal deep learning framework using Bidirectional Encoder Representations of Transformers (BERT) to extract textual features and convolutional neural networks (ResNet50, MobileNet and VGG16) to learn visual representations. It uses a feature-level fusion approach that involves the integration of contextual text embeddings and deep visual features, and a softmax-based classification layer. The Fakeddit dataset is experimented on a six-class classification configuration, with unequal data distribution. The suggested multimodal model (BERT + ResNet50) is more effective with an accuracy of 94.7% and a macro F1-score of 0.91, and recall, as compared to unimodal baselines. Image-only models are performing moderately (75-78% accuracy) and the text-only BERT model is at 88.3 percent accuracy which shows the significance of multimodal integration. The findings show that feature-level fusion is effective to capture cross-modal discrepancies, minimizing false positives and enhancing generalization. The presented framework offers a computational scaling alternative to attention-based models, which is computationally expensive, and forms a solid basis in future multimodal misinformation detection studies.

Managing Climate-Change Impacts through Predictive Analytics and AI-Driven Automated Irrigation

2025-10-22T06:15:03+00:00

Climate change poses a major challenge to the agricultural sector because it disrupts weather patterns and reduces water availability. Effective irrigation-water management and accurate climate prediction are essential for mitigating these effects and supporting sustainable farming. This study analyzes the combined use of convolutional neural networks (CNNs) and artificial intelligence (AI) to predict future climate variations and automate irrigation. The proposed system uses a CNN to analyze historical climate data, satellite imagery, and weather forecasts and to generate accurate regional climate predictions. These forecasts are then supplied to an AI-based irrigation system that allocates water according to predicted weather conditions, soil-moisture levels, and crop requirements. The AI system uses real-time sensor data and dynamically adjusts irrigation schedules to improve water-use efficiency and minimize waste. Experimental findings indicate that integrating CNN-based prediction with AI-driven control can improve water management under farming conditions by providing farmers with insight into climate variability and an automated mechanism for reducing water loss. The approach demonstrates a sustainable and scalable method for improving agricultural productivity while mitigating climate-change risks.