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International Journal of Reasoning-based Intelligent Systems
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International Journal of Reasoning-based Intelligent Systems (64 papers in press) Regular Issues
Abstract: In cross-platform music communication, routine audio format conversion is directly affected by coding methods and parameters on listening quality. This paper proposes an SVM-based quality evaluation method for music transmission audio conversion. Select high-quality audio such as WAV and FLAC as reference sources, construct a sample set in common transmission formats such as MP3, AAC, OGG and the bit rate of 96-256 kbps, extract multi-dimensional features such as MFCC, spectral centroid and zero-crossing rate, and form a supervision label by combining subjective listening results. In model construction and verification, prediction performance of different kernel functions is compared and analysed. Experimental results verify that this method precisely captures quality differences under different formats and parameters, with minimal deviation between predicted MOS and subjective scores and stable performance across music types. The results offer technical support for music platform transcoding strategy formulation and transmission quality control. Keywords: support vector machine algorithm; music transmission; audio; quality evaluation; MOS. DOI: 10.1504/IJRIS.2026.10077309
Abstract: Accidents frequently occur at construction sites, and traditional video surveillance relies on manual inspections, which have problems of delayed response and high false alarm rate. This paper addresses the challenge of precise identification of small and occluded targets in complex environments and proposes a real-time detection framework based on the improved you only look once version 8. By integrating multi-scale features and optimising the network structure, the system achieves an average precision of 92.3% on the public safety helmet dataset, which is 8.5 percentage points higher than the benchmark model you only look once version v5; the processing speed on edge devices reaches 45 frames per second, meeting the requirements of real-time monitoring. This method enables automatic identification and warning of behaviours such as wearing safety equipment and intrusion into dangerous areas, providing effective technical support for building an intelligent safety defense line. Keywords: construction site safety monitoring; target detection; YOLOv8; real-time system. DOI: 10.1504/IJRIS.2026.10077310
Abstract: Against the evolving global trade structure, international trade supply chains have scattered nodes, long cycles and high uncertainty. Traditional management lacks collaboration and dynamic response. This paper proposes an AI-driven supply chain optimisation model using reinforcement learning to integrate cross-border transport, inventory and fulfilment for multi-node coordination. The primary contribution lies in the systemic integration of AI for collaborative decision-making rather than the proposal of a novel algorithm. The results show that under the premise of stable order scale, the collaborative optimisation model has improved in terms of total fulfilment cost, order completion cycle and inventory turnover efficiency. The total cost has decreased by more than 20%, the average fulfilment time has been controlled within 30 days, and the inventory turnover cycle has been shortened to about 32 days. An AI-driven collaborative mechanism reduces multi-node imbalances and provides a feasible technical pathway for international trade supply chain optimisation. Keywords: artificial intelligence; international trade; collaborative optimisation algorithm of supply chain. DOI: 10.1504/IJRIS.2026.10077411
Abstract: To overcome the limitations of traditional models in capturing temporal characteristics and fitting nonlinear relationships in stock prediction and selection, and to improve prediction accuracy and return risk balance, LSTM-BPNN stock trend prediction model and PCA-BPNN multi factor stock selection model were developed. The experiment showed that the prediction model test set MAE was 0.287 CNY, RMSE was 0.392 CNY, MAPE 1.76%, significantly lower than ARIMA, and stable under different market conditions; the stock selection model extracts 6 principal components (with a cumulative variance contribution rate of 87.28%), resulting in an annualised return rate of 18.7% and a cumulative return rate of 64.3% for the portfolio from 2022 to 2024. The Sharpe ratio is 1.62, better than benchmarks such as the Shanghai and Shenzhen 300 Index. The two models provide new methods for quantitative stock analysis and assist in investment decision-making. Keywords: BP neural network; LSTM; PCA; multi-factor quantification; stock prediction; stock selection strategy. DOI: 10.1504/IJRIS.2026.10077534
Abstract: This paper innovatively proposes an adaptive target-music generation model: it employs a controllable variational autoencoder (C-VAE) to construct decoupled structure/control latent variables, incorporates transformer-XL for modelling long-term dependencies, and combines a semantically guided modified variational autoencoder (S-GMVAE) to embed mode-emotion relationships into the latent space for controllable generation. On the MAESTRO and LMD datasets, the model achieves F1 = 93.76% and style matching = 91.84%. It maintains coherence = 90.16% even at 30% missing notes while exhibiting the lowest generation latency. Subjective evaluations reveal melody fluency, emotional authenticity, and semantic consistency all exceeding 4.6/5. Compared to PRNN, POP909-BART, MTR-VAE, and others, the model excels in both accuracy and real-time performance. Results from the experiment demonstrate that the proposed framework offers significant advantages in emotion-controlled style transfer and robust generation under missing information, providing effective support for intelligent composition, emotional soundtrack creation, and human-computer interaction music systems. Keywords: music generation; adaptive; variational autoencoder; VAE; guided modified variational autoencoder; GMVAE; transformer. DOI: 10.1504/IJRIS.2026.10077812
Abstract: In the face of the regulatory failure problem caused by blockchain hidden addresses, existing solutions often fall into a dilemma where privacy protection and compliance review are either one or the other. This paper proposes an innovative integration framework that transforms the behavioural elements in anti-money laundering and other legal provisions (such as high-frequency and small-scale transactions) into computable logic. Based on zero-knowledge proof technology, it generates verifiable credentials to determine whether the transaction behaviour is compliant without revealing the true identity of the address. Experiments on a public blockchain transaction dataset (elliptic) show that this framework achieves an average improvement of over 15% in core identification performance compared to traditional non-private rule-based methods, while maintaining an acceptable performance overhead. As a proof-of-concept validation conducted on a transparent dataset with simulated concealment, the actual performance may differ in native privacy-preserving chains. This research provides a new approach that combines legal rigor with technical feasibility for achieving effective on-chain behaviour supervision while protecting user privacy. Keywords: identification of legal elements; zero-knowledge proof; ZKP; privacy computing; blockchain regulation; regulatory technology. DOI: 10.1504/IJRIS.2026.10077855
Abstract: In view of the low efficiency of data analysis and the lack of accuracy of association mining in the traditional study of consumption intention, this study uses adjustment intermediary statistical model, regression analysis, propensity score matching (PSM) and other methods to explore the complex correlation mechanism between college students lifestyle, perceived value, neurotic personality and their online music purchase intention. The results show that fashion taste, perfectionism tendency and freedom proposition are positively correlated with college students online music purchase intention; perceived value plays a partial mediating role in the relationship between fashion taste, perfectionism, freedom proposition and purchase intention, and the mediating effect value is 0.176. Neurotic personality plays a positive regulatory role in the relationship between perceived value and purchase intention. The regulation intermediary statistical model provides analysis tool for consumer behaviour research, and its conclusion can provide data reference for the refined operation of online music platform. Keywords: moderated mediation statistical model; online music purchase intention; lifestyle; perceived value. DOI: 10.1504/IJRIS.2026.10078082
Abstract: To address scattered data sources and inconsistent rule interpretation in tennis match officiating, this paper constructs an intelligent officiating system based on multi-source heterogeneous data fusion, with rule compliance as its core contribution rather than mere performance enhancement. The system integrates match video, on-court sensor data, and referee records to support not replace human officials by providing evidence-based decision recommendations. Four key metrics evaluate system performance: decision accuracy (agreement with official final calls), consistency rate (agreement with review panel judgements), misjudgement rate (proportion of incorrect in/out calls), and rule conflict rate (deviations from ITF rule interpretations). In sample verification from professional events, the complete fusion model achieved a decision accuracy above 0.96 in hard-court events, a consistency rate of approximately 0.93 with review referees, a misjudgement rate below 0.03, and a rule conflict rate under 0.02. Results demonstrate that prioritising rule-aligned decision-making through multi source collaboration enhances both penalty stability and regulatory conformity, offering empirical support for compliant intelligent officiating in tennis competitions. Keywords: multi-source heterogeneous data fusion; tennis tournament; intelligent officiating system; rule compliance. DOI: 10.1504/IJRIS.2026.10078083 Enhancing Critical Thinking Skills through Generative AI Models: Mechanisms and Educational Impacts by Vincent Raj, Eronimus Jeslin Renjith, S.Silvia Priscila, C.Sathish Kumar, S. Suman Rajest Abstract: Enhancing Critical Thinking Skills has even been considered to revolutionize the future of artificial intelligence and has such huge impacts across sectors, especially education. This study looks at the way generative AI models enhance critical thinking in learners based on recent studies; it debates applying them to actual education and their influence. The study adopted a mixed-method approach. One would be to carry out an analysis of learners’ performance using a quantitative method and also obtain a subjective assessment of the development of learners’ critical thinking skills. The results show that generative AI improves scholastic performance; personalised learning tools raise critical thinking scores from 50-80 to 70-100. The conclusion shows that models increase engagement and positive attitudes towards enhanced learning outcomes. AI integration into higher education faces various hurdles, including privacy issues over higher education data and educator training. The study also discusses how educators and governments might use focused Keywords: Generative AI; Critical Thinking; Educational Technology; Personalized Learning; Interactive Learning; AI in Education; Pedagogical Strategies; Cognitive Development. DOI: 10.1504/IJRIS.2025.10068827 Reinforcement Learning-Driven Collective Intelligence for Prioritized Spectrum Reservation in Cognitive Radio Network by Meetu Nag, Bhanu Pratap Abstract: In the realm of cognitive radio networks, research aims to enhance spectrum usage by enabling access for more users through different spectrum allocation policies. The dynamic and rapid changes in the communication environment pose challenges in making correct decision for spectrum allocation. To facilitate dynamic spectrum allocation, intelligence is integrated into the cognitive system to analyze environmental parameters, various known parameters have to be analyzed to know about the nature of the radio node. In this paper a novel method is discussed for spectrum allocation by involving a multiple decision system that works on priority-based allocation approach. This system collects environmental data for decision-making, ensuring efficient service in this adaptive communication scenario. Keywords: Reinforcement Learning; Collective Intelligence; Spectrum Reservation in Cognitive Radio Network; Spectrum Sensing; Cognitive Radio Network. DOI: 10.1504/IJRIS.2025.10069324 Predicting Chronic Obstructive Pulmonary Disease (COPD) using Machine Learning with Bio-Inspired Hyperparameter Optimization by Yalin Song Abstract: Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory condition for which early detection is crucial to effective patient management. With LGBM and DTC as the foundational models, this study explores the predictive capability of ML approaches for COPD. Two bio-inspired optimizers, the TSA and ROA, were employed to enhance their performance. These optimizers mimic the collective behavior of biological systems, such as tunicates’ foraging patterns and jellyfish’s pulsating movements, to achieve optimal solutions within the model training process. Relevant features are extracted from patient data, potentially including demographics, medical history, lung function tests, and lifestyle factors. Among the metrics used to evaluate the performance of the optimized models are their accuracy and precision. The DTTS model’s excellent performance shows how well the DTC model predicts COPD. The greatest accuracy and precision scores of 0.907 and 0.911 support its COPD prediction accuracy. These findings demonstrate the DTTS model’s reliability and Keywords: Chronic Obstructive Pulmonary; Decision Tree Classification (DTC); Light Gradient Boosting Classification (LGBM); Rhizostoma Optimization Algorithm (ROA); Tunicate Swarm Algorithm (TSA); Machine Learn. DOI: 10.1504/IJRIS.2025.10070424 Efficient deep mood-based Hindustani raga music recommendation using facial emotion expressions by Yogesh Prabhakar Pingle, Lakshmappa K. Ragha Abstract: Music recommendation is considered as a solution, and the performance is degraded with prediction error. A novel approach for music recommendation based on facial emotions with the objective of extracting better feature information without loss is required. In this paper, an efficient cross-dense network model with multi-pooling is used to detect basic emotions from the face image. The complex cross-dense connections are provided for the extraction of most discriminate feature information. After recognising the emotion from the face, a new attention-based deep collaborative filtering recommendation system is proposed, with a list of Hindustani raga music to improve users moods. The proposed framework is invoked with the Facial Expression Recognition 2013 (FER-2013) dataset, and the recommendation is provided for happy and sad emotions from the ragas. The performance is compared with existing deep learning-based approaches. The proposed approach improves accuracy, precision and recall by 0.9972, 0.9896, and 0.9906. Keywords: facial emotion recognition; CrossDenseNet; multi-pooling layer; AttentionNet; collaborative recommendation; Hindustani music. DOI: 10.1504/IJRIS.2025.10070903 Enhancing academic success: a deep dive into students' performance prediction using decision tree classification models by Tingting Du, Linglanxuan Kong Abstract: Education, a fundamental human right, plays a pivotal role in personal and societal advancement, cultivating critical thinking and problem-solving skills, fostering social integration, and contributing to global progress, with a focus on innovative strategies to elevate education standards and prioritise students' performance. Educational data mining (EDM) is a burgeoning field within DM that investigates patterns in education, covering analysis of student knowledge and behaviour, teacher curriculum planning, and course scheduling, all with the primary goal of enhancing student learning performance and achieving efficiency in education systems. This paper addresses the task of predicting and categorising students' performance in the Portuguese language, emphasising decision tree classification (DTC) models, along with 2 hybrid models optimised using aquila optimiser (AO) and honey badger algorithm (HBA), for a cohort of 649 students. The results underscore the exceptional predictive capabilities of the DTHB model, outperforming the DTAO model in G2 prediction with an impressive F1-score of 0.9428 compared to 0.9381. Additionally, the DTHB model continues to excel in G3 prediction, boasting the best performance at an F1-score of 0.9275. Keywords: Student performance; decision tree; aquila optimiser; AO; honey badger algorithm; HBA; teacher curriculum planning; educational data mining; EDM; course scheduling; decision tree classification; DTC. DOI: 10.1504/IJRIS.2025.10072219 Comprehensive study on digital image encryption using magic square by Vybhavi. Balasundar, K. Mani, Uma Devi, S.Kumar Chandar Abstract: Digital image encryption plays a vital role in safeguarding sensitive images from unauthorised access. Among the emerging methodologies, magic square-based encryption has gained significant attention due to its simplicity, flexibility, and capacity to generate diverse encryption keys. This review provides a detailed analysis of magic square-based techniques for image encryption, emphasising their unique properties and applications. The paper examines several recent algorithms, exploring their design, strengths, and limitations. Furthermore, it highlights the potential of hybrid encryption approaches that integrate magic square techniques with other cryptographic methods to enhance security and efficiency. Finally, the review discusses the current advancements in magic square-based image encryption and identifies key challenges, such as scalability, robustness, and adaptability, clearing the path for additional study and advancement in this area. Keywords: digital image encryption; magic square methodology; hybrid encryption; algorithms; techniques. DOI: 10.1504/IJRIS.2025.10072799 A novel method for solving probabilistic programming problem in interval type-2 fuzzy environment by Babita Chaini, Narmada Ranarahu Abstract: This paper introduces a novel mathematical model for stochastic programming in a type-2 fuzzy environment, addressing the dual uncertainties of fuzziness and randomness through fuzzy normal random variables. The proposed model innovatively converts fuzzy stochastic problems into deterministic ones using a two-step process: the -cut technique to remove fuzziness and the chance-constrained technique to handle randomness. This approach, involving perfectly normal interval type-2 triangular fuzzy numbers, is illustrated with a numerical example. The critical finding is the effective transformation of complex fuzzy stochastic problems into more manageable deterministic forms, enhancing computational efficiency and solution accuracy. The industrial implications are significant, offering a robust decision-making framework for sectors like manufacturing, logistics, and finance, where uncertainty is a critical factor. This methodology improves accuracy and reliability in operational and strategic planning, making it highly relevant for practical applications. Keywords: stochastic programming; normal random variables; optimisation techniques; type-2 fuzzy set; T2FS. DOI: 10.1504/IJRIS.2025.10073067 Multi-scale semantic awareness fusion transformer for sentiment analysis in electricity marketing by Chunlei Liu, Wei Ge, Yanan Cai, Jinghui Chen Abstract: In the context of electricity market marketing, facial recognition-based emotion analysis systems can help enterprises better understand customers emotional feedback, thereby enhancing service experience and improving the precision of marketing strategies. To address these challenges, this paper proposes a multiscale semantic perception and attention fusion model (MSPAF) aimed at improving the accuracy and robustness of customer emotion recognition in the power industry. During the multimodal feature fusion stage, the model applies a multi-level attention pooling strategy to effectively capture emotional correlations between different modalities while reducing feature dimensionality, thereby improving efficiency and generalization. When using generic image encoding features combined with global semantics and local syntax fusion, the models accuracy drops by 1.64% and 2.34%, respectively. Keywords: multiscale semantic awareness; transformer; electricity marketing; sentiment analysis; multihead attention mechanism. DOI: 10.1504/IJRIS.2025.10073068 Cognitive driving: harnessing machine learning to understand driver behaviour by Deepika Arunachalavel, Pandeeswari Nagarajan Abstract: This study presents an innovative approach to enhancing road safety and optimising transportation efficiency by leveraging advanced machine learning techniques. The primary focus is on analysing telematics and sensor data collected from vehicles to model, predict, and classify various aspects of driver behaviour. By utilising a combination of supervised and unsupervised learning methods, the research aims to develop a robust, real-time system capable of detecting patterns associated with safe, aggressive, and distracted driving. Supervised learning techniques are employed to train classification models using a diverse set of features extracted from telematics data, including speed variations, acceleration and braking patterns, steering behaviours, lane discipline, and spatial-temporal characteristics. Emphasis is placed on model interpretability to ensure transparency, reliability, and trust in real-world applications, especially for law enforcement and insurance industries. Additionally, unsupervised learning approaches, such as anomaly detection, are explored to identify deviations from normal driving behaviour without relying on predefined labels. By integrating these techniques, this study contributes to intelligent transportation systems, reducing accidents and improving overall road safety. Keywords: road safety; feature extraction; vehicle telematics; analysing telematics; sensor data. DOI: 10.1504/IJRIS.2025.10073243 Face expression recognition for electricity marketing based on multiscale feature fusion with swin transformer by Yanan Cai, Jinghui Chen, Wei Ge Abstract: In this method, the proposed lightweight SPST module replaces the swin transformer blocks in the fourth stage of the original swin transformer model, significantly reducing the number of parameters and enabling lightweight and efficient inference. Subsequently, an EMA module is embedded after the second stage of the improved model to enhance the perception of subtle facial expression details through multi-scale feature extraction and cross-spatial information aggregation, thereby improving the accuracy and robustness of facial expression recognition in power marketing scenarios. Experimental results show that the proposed method achieves recognition accuracies of 97.56%, 86.46%, 87.29%, and 70.11% on the JAFFE, FERPLUS, RAF-DB, and FANE public facial expression datasets, respectively. Compared with the original swin transformer model, the improved model reduces the number of parameters by 15.8% and increases FPS by 9.6%, demonstrating significantly enhanced real-time performance while maintaining high recognition accuracy. Keywords: power marketing; face expression recognition; swin transformer; ST; multiscale feature fusion. DOI: 10.1504/IJRIS.2025.10073378 Research on neural network-based UAV distribution grid line defect detection methods by Bin Feng, Keke Lu, Shuang Fu, Jun Wei, Yu Zou Abstract: This study presents a neural framework for UAV-based insulator defect detection in power distribution systems, addressing critical challenges in real-time operation, multi-scale defect recognition, and computational efficiency. Extensive experiments on a custom dataset (2,721 images, 6,812 instances) demonstrate state-of-the-art performance with 97.3% mAP@0.5:0.95 and 31.4 FPS on embedded GPUs, outperforming YOLOv5 (89.1% mAP), Faster R-CNN (93.4%), and DETR (89.8%). Ablation studies confirm the complementary nature of proposed components, showing cumulative improvements from 91.1% (baseline) to 97.3% mAP through progressive integration. The framework particularly excels in challenging scenarios with 91.4% AP for sub-10px defects and maintains <5.1% false positive rate under complex backgrounds. Keywords: embedded systems; multi-scale attention; power line defects; UAV inspection; wise-IoU loss; YOLOv8. DOI: 10.1504/IJRIS.2025.10073667 Research on a method for assessing the status of electric power metering assets based on neural network federated learning by Mingxin Jin, Shanshan Li, Guanna Lu, Yanguo Lv, Huinan Wang Abstract: This approach not only avoids the security risks associated with third-party coordination but also enhances the models performance in practical applications such as fault diagnosis and electricity bill recovery risk prediction. Additionally, an incentive mechanism based on multi-dimensional contribution assessment and a block chain-based smart contract implementation scheme is designed to provide a sustainable motivational guarantee for multi-party collaboration. Specifically, by exchanging encrypted intermediate parameters (such as gradients or weight updates) during model training, the method achieves effective integration and joint modelling of multi-party data values. Keywords: federated learning; information security; machine learning; neural networks; electricity metering. DOI: 10.1504/IJRIS.2025.10074388 Research on a deep learning-based coordinated optimisation and control technology for source-load-storage in new-type distribution networks by Xiaomeng Yan, Peng Wang, Tao Liang, Wei Jiang, Yang Liu, Jun Guo, Zhebin Sun Abstract: This paper proposes an intelligent multi-timescale optimisation and control method for active distribution networks based on deep reinforcement learning, taking into account the accuracy of generation-load power forecasting and the operational characteristics of devices. In the day-ahead stage, control plans for energy storage systems and flexible loads are formulated to achieve economic operation of the distribution network and reduce the peak-shaving pressure on the upper-level grid. A corresponding feature extraction method is designed for the multi-node, multi-period state space. In the intraday stage, the optimisation scheduling problem is transformed into a Markov decision process. Keywords: active distribution networks; optimised regulation; source-load-storage synergy; deep reinforcement learning; DRL; power prediction. DOI: 10.1504/IJRIS.2025.10074597 Research on online error estimation method for station gate metering devices based on dynamic bus topology unit energy conservation by Qiang Song, Zhiyi Qu, Jing Yang, Qingqing Fu, Tiejun Cheng Abstract: This enables the establishment of a mapping between metering device errors and deviations in system energy conservation, forming a dynamic error modelling framework that reflects actual operating conditions. Then, a fading memory mechanism is introduced, and the FMRLS algorithm is employed to recursively estimate model parameters, thereby realising online and adaptive estimation of metering device errors. Simulation results demonstrate that, compared with the Levenberg-Marquardt (LM) algorithm and the limited memory recursive least squares (LMRLS) algorithm, the proposed method significantly improves the accuracy and dynamic responsiveness of error estimation while maintaining convergence stability. Keywords: error estimation; dynamic line loss; fading memory recursive least squares; online estimation. DOI: 10.1504/IJRIS.2025.10074598 MIV-3: modified inception V3 architecture for enhancing periodontal diagnostic accuracy with SE attention module by R. Kausalya, J. Anitha Ruth Abstract: Over the recent decades, real-world applications and research which use AI (Artificial Intelligence) have evolved significantly, exclusively in dental and healthcare sectors. Our research discusses the utilisation of AI in X-ray imaging to detect periodontal diseases at an early stage. MIV-3 (modified inception V-3) is a model which enhances feature extraction and accuracy in diagnosis by combining an attention module and a squeeze-and-excitation (SE) module. Separable convolutions are utilised by MIV-3 model for increasing computational efficiency without impacting accuracy. A NPV and sensitivity of 98.37% and 94.68% respectively were depicted in the experimental data. Having a sensitivity of 94.68%, NVP of 98.85%, ROC-AUC of 99.14% and a specificity of 97.65% will help the model in predicting dental caries in a more accurate manner. The results indicate that the detection of periodontal disease happens at a faster pace and more accurately with the proposed AI-driven method. For developing the model, MATLAB program is utilised which offers a strong and dependable tool for diagnosis in clinical applications. Keywords: periodontal diagnosis; dental care; deep learning; inception V3; squeeze and excitation. DOI: 10.1504/IJRIS.2025.10074670 Breast cancer classification refined using ResNet50 parameter tuning with lyre bird optimisation by Sabura Banu Urundai Meeran Abstract: Breast cancer remains a major cause of mortality among women, highlighting the need for accurate and efficient diagnostic methods. Deep learning, particularly CNNs, has improved medical image analysis, yet further optimisation is required for better precision and faster inference. This study optimises ResNet50 using the lyrebird optimisation (LBO) algorithm for hyperparameter tuning. A histopathological image dataset with cancer and non-cancer classes was used for training and evaluation. LBO fine-tuned key parameters such as learning rate, significantly enhancing model performance. The LBO-optimised ResNet50 outperformed standard ResNet50, Inception V3, and VGG16, achieving 98.85% accuracy along with high precision, recall, F1 score, and specificity (98.6%). The model also achieved an AUC-ROC of 99.98%, low log loss (0.0267), and reduced inference time (0.1377 seconds). Confusion matrix results showed fewer misclassifications. While promising for improving diagnostic reliability, additional clinical validation is recommended. Keywords: histopathological image analysis; deep learning models; hyperparameter tuning; diagnostic accuracy; medical image classification; confusion matrix analysis; performance optimisation; computer-aided diagnosis. DOI: 10.1504/IJRIS.2025.10075375 Research on error optimisation algorithm for station gate electric energy metering devices based on triplet Siamese networks by Qiang Song, Zhiyi Qu, Jing Yang, Qingqing Fu, Tiejun Cheng Abstract: The triplet Siamese network not only extracts features from the training samples themselves but also learns the similarities among samples of the same class and differences among samples of different classes, significantly enhancing the clustering effect and discriminative ability of the feature vectors. Simulation results demonstrate that the proposed algorithm achieves high accuracy and superior performance under small-sample conditions, significantly outperforming traditional machine learning methods and other deep learning models. It can effectively support error optimisation for station gate electric energy metering devices and contribute to enhancing the intelligence and security stability of power grid operations. Keywords: ternary twin network; Gram’s corner field; plant-station gateway; power metering device error. DOI: 10.1504/IJRIS.2025.10075376 Research on an online voltage unbalance mitigation method for distribution networks based on deep reinforcement learning by Lin Xu, Chang Liu, Houdong Xu, Yan Gong, Fuxin Li, Yi Zheng Abstract: Key innovations include: achieving model-free online decision-making, eliminating dependence on precise network parameters; possessing dynamic environment adaptability to respond in real-time to load fluctuations and distributed generation (DG) output variations; and simultaneously enhancing voltage balance and governance cost-effectiveness through reward function optimisation. Simulation results demonstrate that this method can effectively suppress voltage unbalance (reduced by over 30% in typical scenarios) within seconds, significantly decrease violation duration, and optimise compensation device switching frequency. It provides crucial technological support for constructing an intelligent and agile new-generation distribution network voltage governance system. Keywords: distribution grid; distributed resources; three-phase imbalance; intensive learning. DOI: 10.1504/IJRIS.2025.10075377 Research on data-driven methods for evaluating and predicting the health status of energy storage cell packs by Ning Li, Pengcheng Wei, Mingyang Wang, Yuan Liang, Dengyou Lei Abstract: To address the limitations of traditional scheduling methods in modelling multi-variable coupling relationships and dynamic response delays, this paper proposes an attention mechanism-based multi-layer neural network (AMNN) optimisation framework. By employing a bidirectional long short-term memory (bi-LSTM) network, a multidimensional time-series prediction model incorporating electricity price fluctuations, battery aging, and meteorological features is constructed to achieve precise perception of the energy storage system's operational status. Validation using real-world operational data demonstrates that compared to the PSO optimisation algorithm, this method reduces scheduling errors by 19.7% during sudden load fluctuations and lowers the lifetime cost per kilowatt-hour by 12.3%. Keywords: deep learning; electric energy metering; fault diagnosis; smart grid monitoring. DOI: 10.1504/IJRIS.2025.10075378 Simulation and dispatch optimisation of electricity spot markets considering renewable energy uncertainty by Xuanyuan Wang, Xu Gao, Zhen Ji, Wei Sun, Bo Yan, Bohao Sun Abstract: This paper aims to develop an integrated electricity spot market simulation and dispatch optimisation model that incorporates the characteristics of renewable energy. First, by establishing probabilistic models for wind and solar power output and employing stochastic programming or robust optimisation methods, the impact of their uncertainty on market clearing is characterised. Second, the objective function comprehensively considers factors such as minimising total system operating costs and maximising renewable energy integration, while the constraints rigorously account for grid security, unit technical limits, and power balance requirements, forming a complex mathematical optimisation problem. Keywords: simulation; distribution network; electricity spot markets; hierarchical planning. DOI: 10.1504/IJRIS.2026.10076057 Research on wide-area protection algorithms for power grids based on fault charge quantity comparison and distributed computing by Yu Sui, Xun Lu, Xiaoyu Deng, Wei Xu Abstract: The method employs fault charge comparison, where local integration is used to extract the positive and negative characteristics of fault currents on both sides, enabling distributed retention and transmission of key features. For switching information, it relies only on the stage II and III starting signals of distance protection, calculates the fault correlation coefficient combined with threshold criteria, and aggregates the basic probability assignments of adjacent lines, thereby reducing the need for centralised transmission across the network. The algorithm applies an improved evidence theory within the distributed framework to fuse multi-source information and reliably identify faulted lines. Keywords: fault charge quantity; wide-area protection; fault identification; fault tolerance; distributed computing. DOI: 10.1504/IJRIS.2026.10076502 Research on low-latency communication network methods for power system automation based on 5G technology by Yabin Chen, Wei Xu, Xiaoyu Deng, Yu Sui Abstract: This paper focuses on the stringent requirements for real-time performance, reliability, and security in communication transmission for power system automation, and researches low-latency communication network methods based on 5G technology. Traditional communication methods face challenges in meeting the low-latency and high-reliability demands of new services such as distributed intelligent control, wide-area protection, and precise load monitoring. It emphasises the in-depth integration of 5G technology with power automation services and the design of end-to-end communication solutions to support the future smart grid, thereby enhancing its rapid response and handling capabilities for renewable energy integration and complex faults. Keywords: 5G technology; power system automation; low latency; communication networks. DOI: 10.1504/IJRIS.2026.10076503 Research on modelling and anomaly analysis methods for metering errors at factory stations by Zhiyi Qu, Qiang Song, Pengcheng Li, Qinghui Chen, Tiejun Cheng Abstract: A norm-feedback algorithm is introduced to optimise the distribution of weights during training, thereby improving the convergence and predictive performance of the model. Based on the extracted error features, statistical methods are employed to derive confidence intervals for metering errors, enabling early identification and assessment of abnormal metering behaviours. Experimental results demonstrate that the method can accurately model the characteristics of gateway metering errors and identify multiple extreme conditions that may lead to significant deviations, providing theoretical support and technical means for enhancing the reliability and anomaly monitoring capability of substation gateway metering. Keywords: electricity metre; confidence interval of measured values; convolutional neural network: norm feedback algorithm. DOI: 10.1504/IJRIS.2026.10076504 Research on load dispatch and grid restoration in disaster-resilient emergency response for distribution networks based on nonlinear programming by Hao Dai, Guowei Liu, Lisheng Xin, Longlong Shang, Qingmiao Guo, Hao Deng Abstract: This paper addresses large-scale blackout scenarios in distribution networks following extreme disasters and investigates post-disaster emergency restoration strategies based on nonlinear programming. By constructing a multi-objective optimisation model that aims to maximise the total restored load and minimise switching operations, it comprehensively considers security constraints such as line capacity, voltage deviation, and radial operation, forming a mixed-integer nonlinear programming problem. The model effectively coordinates flexible resources like distributed generation and soft open points to achieve the coordinated optimisation of load transfer and network reconfiguration. Simulation results demonstrate that the proposed strategy significantly enhances the efficiency and resilience of post-disaster restoration. Keywords: deep learning; disaster-resilient; grid restoration; prediction model. DOI: 10.1504/IJRIS.2026.10076505 Research on source-load cooperative planning of distribution network based on carbon emission of distributed power sources by Hanyun Wang, Jianjie Jiang, Yang Liu, Tao Wang, Jiaqian Chen, Wei Zheng, Hai Liu Abstract: The paper proposes a source-load cooperative planning method for distribution networks based on load carbon emission characteristics. Based on the carbon potential and net load curve of the distribution network, a quantitative model of load carbon emission characteristics considering carbon emissions from electricity consumption, distance low carbon, trend low carbon and low carbon electricity consumption rate is established to obtain the low carbon planning priority of each distribution network within the regional grid. The carbon emission reduction benefits of the proposed method are analysed and verified by simulation on the improved IEEE30 node system. Keywords: carbon intensity; load carbon emission characteristics; low-carbon demand response; source-load synergy; low carbon planning. DOI: 10.1504/IJRIS.2026.10076684 Research on sparse prediction training technology for brain-like models based on pulse neural networks by Guoliang Zhang, Peng Zhang, Fei Zhou, Zexu Du, Jiangqi Chen, Zhisong Zhang, Qingyu Kong Abstract: This paper proposes a sparsity-prediction-based SNN training method, which introduces a predictive sparsity mechanism into the network structure to effectively reduce redundant computations and unnecessary synaptic updates. Specifically, during the training phase, an improved global recursive partitioning optimisation strategy is employed to enhance inter-cluster communication efficiency. Experimental results on five representative SNN benchmark models demonstrate that the proposed method significantly reduces communication latency and energy consumption while maintaining model accuracy, thereby improving training efficiency. Compared with existing approaches, it also exhibits clear advantages in terms of sparsity utilisation and energy efficiency. Keywords: pulse neural networks; brain-inspired processors; sparse prediction; training techniques. DOI: 10.1504/IJRIS.2026.10076685 Research on organisational forms and operational mechanisms of multimodal industry-education integration platforms by Na Xie Abstract: This study examines multimodal industry-education integration platforms, aiming to systematically analyse their diverse characteristics in organisational structure, constituent entities, and connection methods, along with their applicable scenarios. Through a combination of theoretical and practical analysis, this study aims to construct a more efficient and sustainable organisational and operational framework model for multimodal industry-education integration platforms. This framework seeks to effectively address the structural barriers and functional challenges encountered in the process of deepening industry-education integration, thereby supporting the enhancement of technical and skilled talent cultivation, strengthening industrial innovation momentum, and serving the high-quality development of regional economies. Keywords: multimodal; industry-education integration; organisational structure; operational mechanism. DOI: 10.1504/IJRIS.2026.10076686 Research on task management in English teaching based on multimodal fusion neural networks by Tiantian Tang, Xin Guo Abstract: This paper addresses the issues of insufficient personalisation and efficiency bottlenecks in traditional English teaching task management by proposing a novel management model based on multimodal fusion neural networks. By deeply integrating multimodal data including student learning behaviour videos, voice interactions, text assignments, and classroom expressions the model employs neural networks for feature extraction and collaborative analysis. This enables precise perception of learning states, dynamic adaptation of task difficulty, and intelligent recommendation of teaching resources. Experimental results demonstrate that this framework effectively enhances task planning efficiency and personalisation levels, providing a new technical pathway and practical reference for intelligent English teaching management. Keywords: multimodal; fusion neural networks; English teaching; task management. DOI: 10.1504/IJRIS.2026.10076689 A study on multitask deep learning-based prediction of student dropout risk and analysis of influencing factors by Guohua Sun, Hongxia Jia Abstract: To address the challenge of predicting student attrition risk in higher education institutions, this study proposes a multi-task deep learning-based early warning model for student dropout. This approach enables precise prediction of attrition risk and in-depth analysis of key influencing factors. By jointly learning multi-dimensional data including academic performance, behavioural characteristics, and personal attributes through a shared feature representation layer, the method simultaneously accomplishes attrition classification and factor analysis tasks. Experimental results demonstrate that this model achieves significant improvements in both prediction accuracy and stability compared to traditional single-task models. It effectively identifies key factors influencing student attrition, such as academic performance, attendance rates, and engagement levels, providing data-driven decision support for universities to implement targeted interventions and academic support. Keywords: multitask deep learning; student attrition; risk prediction; influencing factors. DOI: 10.1504/IJRIS.2026.10076691 Fermatean neutrosophic sets and their role in advanced decision-making systems by Prasanta Kumar Raut, K. Saritha, M. Gayathri Lakshmi, R. Rajalakshmi Abstract: In recent years, the need for effective representation and management of uncertain, imprecise, and inconsistent information has grown rapidly, especially in complex decision-making environments. Fermatean neutrosophic sets (FNS), a novel extension of neutrosophic sets, have emerged as a powerful mathematical tool capable of capturing higher degrees of uncertainty by relaxing conventional constraints. This paper presents a comprehensive overview of Fermatean neutrosophic sets, highlighting their foundational structure, key properties, and advantages over classical and intuitionistic fuzzy paradigms. Furthermore, we explore the pivotal role of FNS in advanced decision-making systems, including multi-criteria decision making (MCDM), risk assessment, and data classification problems. Illustrative examples and potential application domains are discussed to showcase the effectiveness of Fermatean neutrosophic models in real-world decision scenarios. Keywords: Fermatean neutrosophic set; FNS; uncertainty modelling; indeterminacy; neutrosophic logic; fuzzy systems. DOI: 10.1504/IJRIS.2026.10076734 Vibration analysis of Fe-based soft magnetic composite core reactor based on improved particle swarm algorithm by Yangyang Ma, Wenle Song, Jie Gao, Yang Liu, Yilei Shang, Weimei Zhao, Fuyao Yang Abstract: Using central composite design combined with finite element simulations, the study investigates the influence of different air-gap structural parameters on vibration responses and establishes an orthogonal polynomial-based response prediction model for accurately estimating core vibration displacement. Taking the minimisation of core vibration as the optimisation objective while maintaining the inductance value nearly constant, the optimal air-gap length of the reactor is obtained. The results show that under the optimised structural parameters, the maximum core vibration displacement is reduced by 10%, while the inductance variation is only 0.051%. This optimisation method provides significant reference value for reducing vibration and noise. Keywords: iron core reactor; electromagnetic-structural force field coupling; air gap structure; core vibration; orthogonal polynomial model. DOI: 10.1504/IJRIS.2026.10077021 An autonomous UAV trajectory optimisation and continuous stitching method for refined inspection of transmission lines by Lin Ao, Teng Ma Abstract: Combined with a spherical-threshold-based spatial density filtering method, redundant trajectory points near shooting locations are removed. Furthermore, a minimum turning radius constraint and arc smoothing are introduced to achieve trajectory smoothness, and flight safety validation is completed through safety distance constraints. Experimental results demonstrate that the proposed method can reduce the number of trajectory points for a single tower inspection by more than 90% while ensuring shooting consistency and flight safety. This significantly enhances the efficiency of UAV autonomous inspections and the reusability of trajectories, providing an engineering-feasible solution for refined and continuous autonomous inspections of transmission lines using UAVs. Keywords: autonomous inspection; trajectory optimisation; Douglas-Peucker algorithm; unmanned aerial vehicle; UAV. DOI: 10.1504/IJRIS.2026.10077022 Exploring the value and pathways of integrating algorithmic ethics education into ideological and political courses in the new era by Ge Chen, Xiaodong Yang Abstract: This paper explores the practical value and implementation pathways of integrating algorithm ethics education into ideological and political courses in the new era. Algorithmic technologies are profoundly transforming social life, and the ethical challenges they pose urgently require educational guidance to address. The study argues that integrating such education into ideological and political courses helps cultivate students correct understanding of technological ethics, sense of social responsibility, and value judgment capabilities, thereby achieving the unity of technological rationality and humanistic spirit. Specific pathways include: developing interdisciplinary teaching cases that integrate topics such as algorithmic transparency, fairness, and privacy with core socialist values; innovating teaching methods through scenario-based discussions and ethical deliberation; strengthening faculty training to enhance teachers technological ethics literacy; and establishing collaborative education mechanisms involving universities, enterprises, and societal stakeholders. Keywords: algorithmic ethics; educational integration; new era; ideological and political education courses. DOI: 10.1504/IJRIS.2026.10077113 Research on English oral classroom instruction design in teacher-AI collaborative models by Tingyu Luan, Zhefan Wang Abstract: This study systematically explored the integration pathways and practical efficacy of artificial intelligence technology in English oral communication instruction by establishing a dual-subject collaborative teaching model featuring teacher-led instruction with AI empowerment. A three-stage instructional framework encompassing pre-class intelligent diagnostics, in-class tiered training, and post-class personalised feedback was designed and implemented through a 16-week empirical teaching experiment. The study also found the collaborative model most significantly benefited intermediate-level students (40% improvement), while advanced and foundational learners still required enhanced teacher intervention. These findings provide actionable design solutions and data support for reforming English speaking instruction in the intelligent era, confirming the immense potential of teacher-AI collaboration in enhancing teaching efficiency and promoting personalised learning. Keywords: AI collaborative mode; English speaking; classroom instruction; deep learning. DOI: 10.1504/IJRIS.2026.10077116 Research on teaching and intelligent management based on multimodal fusion deep learning behaviour analysis by Wenjing Sun, Weisong Wang, Teng Ma Abstract: Addressing bottlenecks in traditional classroom teaching evaluations such as high subjectivity and delayed feedback this study explores intelligent teaching-management feedback mechanisms centred on multimodal fusion deep learning for behavioural analysis. By constructing an end-to-end intelligent analysis framework that integrates multi-source data including classroom visuals, audio, and text, and employing attention-based deep fusion strategies, it achieves fine-grained recognition and contextual understanding of teacher-student instructional behaviours. This research not only provides an innovative technical approach for classroom behaviour analysis but also drives a paradigm shift from experiential teaching management toward precision-driven, personalised educational governance through data-driven intelligent feedback mechanisms. Keywords: deep learning; instructional networks; teaching; classroom ecologisation; management system. DOI: 10.1504/IJRIS.2026.10077117 Research on deep learning-driven adaptive course resource recommendation and instructional planning by Jiaxue Liu, Xiaoxian Su Abstract: This study addresses the common challenges faced by online learning platforms, such as resource overload, rigid learning pathways, and lack of personalisation, by constructing an integrated framework for adaptive course resource recommendation and teaching planning based on deep learning. This framework combines a dual-channel learner dynamic perception model based on transformer and knowledge graph embedding, a resource representation approach that integrates knowledge structure and semantic information, and a long-term teaching planner based on deep reinforcement learning. Experiments on the public datasets ASSISTments2012 and EdNet indicate that our model improves recommendation accuracy by 12.5% compared to the best baseline methods, achieves a path rationality score of 4.5/5.0 as evaluated by experts, and enhances learning gains by 15%. The results suggest that the proposed framework effectively enables personalised cognitive navigation and teaching pathway planning, providing a feasible technical path for the development of the next generation of adaptive learning systems. Keywords: deep learning-driven; adaptive; course resource recommendation; teaching planning. DOI: 10.1504/IJRIS.2026.10077118 Research on the ecological management system of English teaching classrooms based on deep learning network technology by Jingshu Wu, Yawei Hu, Haodong Guo Abstract: With the rapid advancement of artificial intelligence technologies such as deep learning, traditional English teaching models are undergoing profound transformation. This study aims to establish an ecological management system for English classrooms. By constructing this ecological management model, the objectives are to achieve precise allocation of teaching resources, dynamic optimisation of teaching processes, intelligent recommendation of personalised learning paths, and diversified comprehensive teaching evaluation. This approach promotes the synergistic evolution and balanced development of all elements within the classroom ecosystem. Establish a theoretical framework and practical pathways for creating a new ecosystem of intelligent and harmonious English teaching. Keywords: deep learning; instructional networks; English teaching; classroom ecologisation; management system. DOI: 10.1504/IJRIS.2026.10077119 Research on the microstructure and magnetic properties of dual-phase composite magnetic materials by Jie Gao, Fuyao Yang, Yang Liu, Cong Wang, Pinpin Zhu, Zhibin Nie Abstract: This study systematically investigates the intrinsic relationship between microstructural characteristics and macroscopic magnetic properties in dual-phase composite magnetic materials. By adjusting preparation process parameters, composite microstructures with varying phase compositions, grain sizes, and interface morphologies were obtained. Combining microscopic analysis with magnetic measurements, the distribution, coupling state, and interface effects between soft and hard magnetic phases were analyzed in detail. Results indicate that exchange coupling between the two phases significantly influences the materials coercivity, remanence ratio, and maximum energy product. Optimising the microstructure effectively enhances magnetic properties, providing crucial theoretical and experimental foundations for designing and fabricating high-performance composite permanent magnet materials. Keywords: dual-phase composite; magnetic materials; microstructure; magnetisation; interphase interface. DOI: 10.1504/IJRIS.2026.10077121 Research on route planning methods based on an improved particle swarm optimisation algorithm by Yanfeng Xu, Yang Wang, Xiaobo Li, Xiang Xu Abstract: Addressing the need for intelligent optimisation of equipment layout and route schemes in complex geographical environments, this paper integrates geographic information systems (GIS) with intelligent optimisation algorithms to propose a route planning method based on an improved particle swarm optimisation (PSO) algorithm. The equipment arrangement and route planning problem is modelled as an optimisation model with multiple constraints. To solve this model efficiently, improvements are made to the standard PSO algorithm by introducing an adaptive inertia weight and a hybrid learning strategy, which effectively balance the algorithms global exploration and local exploitation capabilities, thus preventing premature convergence. Keywords: particle swarm optimisation; PSO; route planning; equipment scheduling; spatial analysis; intelligent algorithms. DOI: 10.1504/IJRIS.2026.10077122 A novel bipolar neutrosophic soft topological model for agricultural decision analysis by Prasanta Kumar Raut, R. Rajalakshmi, K. Saritha, M. Gayathri Lakshmi Abstract: Agricultural decision-making is frequently influenced by uncertainty stemming from environmental dynamics, soil heterogeneity, and varying agronomic conditions, which often lead to incomplete, vague, and even contradictory information. Conventional uncertainty modelling approaches, including fuzzy and intuitionistic frameworks, are not sufficiently equipped to address such complexity in a unified manner. In this paper, a novel analytical framework based on bipolar neutrosophic soft topological structures is proposed to effectively model these challenges. The introduced framework integrates the parameter-driven nature of soft sets with the capability of bipolar neutrosophic logic to simultaneously handle favourable and unfavourable information, together with the organisational advantages of topological structures. This combined model offers a refined representation of uncertainty and indeterminacy in decision environments. The applicability of the proposed methodology is demonstrated through multi-criteria decision-making problems in agriculture, including crop selection and resource management under uncertain scenarios. The results obtained from a detailed case study indicate that the proposed approach produces more robust, transparent, and reliable decisions compared to existing fuzzy-based techniques, highlighting its potential as a valuable tool for agricultural decision support systems. Keywords: bipolar neutrosophic set; BNS; soft topology; agricultural decision-making; uncertainty modelling; multi-criteria analysis; crop management. DOI: 10.1504/IJRIS.2026.10077123 Research on optimising the absorption capacity of feeders in medium and low-voltage distribution networks based on deep learning by Haitao Li, Xinming Xu, Xiaobin Chen Abstract: With the large-scale integration of distributed renewable energy, medium- and low voltage distribution networks face severe challenges regarding feeder hosting capacity. Traditional assessment methods rely on precise physical models and static scenarios, struggling to handle high-dimensional uncertainties and real-time optimisation needs. This paper proposes a deep learning-based method for optimising feeder hosting capacity, constructing a collaborative prediction-assessment-decision framework. Through a hierarchical-coordinated optimisation architecture (centralised upper-layer optimisation and distributed fast-response lower layer), simulation case studies verify that this method significantly enhances system hosting capacity (Power/psi index), smoothes power fluctuations at the point of common coupling, eliminates voltage limit violations, and improves three-phase unbalance and voltage fluctuation ratios. It provides an effective technical pathway for the safe and high-quality operation of distribution networks under high-penetration distributed energy integration. Keywords: absorption capacity; low-voltage; distribution networks; deep learning; DL. DOI: 10.1504/IJRIS.2026.10077602 Research on pre-trained transformer models incorporating domain knowledge in government big data analysis by Yunlong Sun Abstract: This paper explores the application of domain-knowledge-integrated pre-trained Transformer models in government big data analysis. Addressing the specialised nature and complex structure of government data, the study adapts general pre-trained models to specific domains and enhances their knowledge by incorporating domain dictionaries, entity knowledge, and business rules. It proposes training objectives and an integrated architecture tailored to the characteristics of government texts, thereby improving the models comprehension and reasoning capabilities in tasks such as policy analysis, public sentiment assessment, and service recommendation. Experiments demonstrate that this approach effectively improves the accuracy and interpretability of government text classification, information extraction, and decision support, providing robust technical support for smart government development. Keywords: domain knowledge integration; pre-training; transformer; government big data. DOI: 10.1504/IJRIS.2026.10077603 Research on resource aggregation scheduling and optimal control strategies for virtual power plants based on spatio-temporal data mining analysis by Hongtao Li, Dongyu He, Zhengzhe Li, Haoze Zhang, Liguo Zheng, Xingwang Jia, Xia Zhang Abstract: This paper addresses the resource allocation demands for flexibility in new power systems, focusing on the aggregation scheduling and optimisation control of virtual power plants (VPPs). It proposes a strategy framework based on spatio-temporal data mining and computational analysis. By mining the spatio-temporal correlation characteristics and operational patterns of distributed energy resources and loads, the study constructs a resource aggregation model that accounts for multiple uncertainties. Subsequently, a collaborative optimisation method integrating forecasting, scheduling, and feedback control is designed to achieve economically efficient scheduling and dynamic real-time control of virtual power plants across multiple time scales from day-ahead to intraday. Simulation results demonstrate that the proposed strategy effectively enhances the aggregation and control capabilities of virtual power plants over distributed resources, significantly improving system operational economics and renewable energy integration levels. This provides theoretical foundations and technical references for the engineering application of virtual power plants. Keywords: spatio-temporal data mining; computational analysis; virtual power plant; VPP; resource aggregation and dispatch. DOI: 10.1504/IJRIS.2026.10077604 Dynamic evolution analysis of relationships between traditional culture and Chinese literary figures based on knowledge graphs and graph neural networks by Li Cai, Qi Wang Abstract: This paper addresses the limitation in traditional culture and Chinese literary studies where character relationship analysis often remains static. It proposes an evolutionary analysis framework integrating dynamic knowledge graphs with graph neural networks (GNNs). By constructing a multimodal knowledge graph with temporal slices to encode characters, relationships, and their semantics, and combining GNNs with long short-term memory (LSTM) networks to capture network structure and temporal dependencies, this framework enables micro-level tracking and macro-level modelling of the entire process of character relationship formation, reinforcement, transformation, and rupture in classic works such as Dream of the Red Chamber and Romance of the Three Kingdoms. The research reveals that the evolution of literary character relationships exhibits a dynamic pattern intertwining event-driven abrupt changes with gradual shifts in emotional and ethical dynamics. This study provides a new semantic-driven computational pathway for digital humanities, advancing paradigm innovation in traditional cultural analysis. Keywords: knowledge graph; graph neural network; GNN; traditional culture; Chinese literary character relationships. DOI: 10.1504/IJRIS.2026.10077605 Research on intelligent accounting decision-making and financial education pathways based on deep reinforcement learning by Jia Feng, Xiaorui Xue Abstract: This paper addresses the limitations of traditional accounting decision-making, which relies heavily on experience and rules, and the lack of personalised pathways in financial education. It explores the application of deep reinforcement learning in the fields of intelligent accounting and financial education. The study constructs an intelligent accounting decision-making model based on deep reinforcement learning, optimising financial decisions through dynamic environmental interaction and reward mechanisms. Concurrently, an adaptive financial education path recommendation system is designed to dynamically adjust teaching content and difficulty levels in real-time based on learner behavioural data. Results demonstrate that this approach effectively enhances the precision and automation of accounting decisions while providing personalised, interactive learning solutions for financial education. It holds both theoretical and practical value for advancing the intelligent transformation of accounting and innovating financial talent cultivation models. Keywords: deep reinforcement learning; DRL; intelligent accounting decision-making; financial education; path analysis. DOI: 10.1504/IJRIS.2026.10077606 Research on uncertainty quantification algorithms based on Bayesian neural networks and a posteriori regularisation by Li Zhang Abstract: In security-sensitive applications of deep learning, accurately quantifying model prediction uncertainty is crucial. Bayesian neural networks, through their probabilistic framework, provide a theoretical foundation for simultaneously quantifying epistemic and aleatic uncertainty. However, traditional approximate inference methods (such as variational inference) often suffer from biased posterior distribution estimates due to approximation errors or inaccurate prior settings, leading to underestimated uncertainty or poor calibration. To address this, this paper proposes a novel Bayesian neural network uncertainty quantification method incorporating posterior regularisation. By introducing regularisation terms based on information theory or task-specific knowledge into the variational objective function, this approach constrains the shape of the posterior distribution, thereby guiding the model to learn more accurate and better calibrated uncertainty estimates. Keywords: Bayesian; neural network; a posteriori regularisation; uncertainty quantification. DOI: 10.1504/IJRIS.2026.10077733 Research on deep learning-based evaluation mechanisms for ideological and political education by Yaxin Li, Xiaojuan Zhang Abstract: As the process of educational informatisation continues to deepen, traditional ideological and political education evaluation mechanisms face challenges in terms of dynamism, precision, and scientific rigor. This study focuses on applying deep learning technology to innovatively construct an evaluation system for ideological and political education. It aims to achieve multidimensional, intelligent analysis of the learning process through methods such as neural networks and natural language processing. The study explores a hybrid evaluation model integrating student behavioural data, textual sentiment analysis, and cognitive feedback to quantitatively assess educational outcomes, dynamically track ideological development, and provide data support for personalised teaching interventions. Results indicate that deep learning assisted evaluation mechanisms effectively enhance objectivity, real-time responsiveness, and predictive capabilities, offering a viable technical pathway and practical reference for advancing the precision and scientific rigor of ideological and political education. Keywords: deep learning; supportive ideological and political education; educational evaluation mechanisms; political education. DOI: 10.1504/IJRIS.2026.10078009 Subset predicate encryption supporting a wholesaler by Kamalesh Acharya Abstract: Predicate encryption (PE) is a public key cryptographic primitive which gives fine-grained access structure in the cryptographic framework. Subset predicate encryption (SPE) is a variant of PE in which the decryption of the ciphertext corresponding to set S will be successful by using a secret key corresponding to set T if T S. In this work, we introduce subset predicate encryption supporting a wholesaler (SPeW), a new variant of SPE that incorporates an additional entity called as the wholesaler who purchases content in bulk and distributes it to a group of subscribers while preserving fine-grained cryptographic access control. Unlike previous SPE, SPeW provides a three-layered security guarantee: group privacy, a bound on group size, and security against illegal users. Our construction achieves adaptive security in the standard model while maintaining constant-size ciphertexts and secret keys, a property not achieved simultaneously in prior work. We implement SPeW using the PBC library and benchmark its performance across subscriber group sizes ranging from 1,200 to 1,800. The results show that setup and key generation, verification, and encryption times remain almost constant with group size, while decryption and group token generation scale linearly with group size. Keywords: subset predicate encryption; SPE; constant-size ciphertext; wholesaler; group privacy; bound on group size; security against illegal users. DOI: 10.1504/IJRIS.2026.10078084 Construction and application research of intelligent analysis model for course management data based on deep learning by Qian Ma, Yunqiao Peng Abstract: This study aims to construct a deep learning-based intelligent analysis model for course management data and explore its practical application value. The research first performs pre-processing and feature engineering on multi-source heterogeneous data within course management, establishing a high-quality data foundation for model training. Subsequently, tailored to the characteristics of educational data, a hybrid deep learning model integrating CNN and LSTM Networks is designed and constructed. This model effectively captures local features and long-term temporal dependencies within the data, enabling precise analysis for critical tasks such as academic performance early warning, learning behaviour pattern recognition, and course teaching quality evaluation. Keywords: deep learning; course management; data intelligence analysis; teaching management. DOI: 10.1504/IJRIS.2026.10078085 Optimising feature selection in educational datasets using an enhanced teaching-learning-based optimisation algorithm by D.I. George Amalarethinam, A. Emima Abstract: Educational data mining (EDM) is an emerging study topic that helps schools improve student performance. Selecting only relevant data reduces model input parameters with feature selection. It reduces dimensionality by selecting a subset of features and removing incorrect, superfluous, or noisy ones. It improves learning accuracy, computational cost, and model interpretability. This impacts the accuracy of performance models used to assess student outcomes. Most optimisation methods, including the genetic algorithm, must optimise many governing parameters for greater performance. Optimisation approaches using wrapper feature selection (WFS) improve classifier prediction. The proposed ETLBO algorithm with WFS techniques uses the Euclidean distance formula to assess fitness value and popular control parameters to select the optimal feature subset. The algorithm above is used on the educational dataset. Classification algorithms evaluate the best features from TLBO ETLBO algorithms: four algorithms classify performance metrics: GNB, LR, SVM, and K-nearest neighbour. Experimental results suggest that the ELTBO algorithm's best feature subset improves classification accuracy for GNB, LR, SVM, and KNN compared to TLBO. Keywords: classification algorithms; feature selection; FS; optimisation technique; Euclidean distance; enhanced teacher learner based optimisation; ETLBO; teacher learner based optimisation; TLBO; Guassian Naive Bayes; GNB; logistic regression; LR. DOI: 10.1504/IJRIS.2024.10068107 Ancient epigraphical monuments' convolution neural network-based skeletonised structural angular morphing character identification intelligent systems by P. Selvakumar Abstract: Tamil is one of the oldest languages, and it is based on several proofs from ancient Kiladhi epigraphic monuments. Tamil texts have various structural styles and projections identified from monuments like palm lead characters, vattezhuthu, and stone inscriptions. By projecting Tamil characters in various angles, the text style may vary due to structural representation, leading the actual character style to differentiate from the old style. Thus, recognition of the specific projection of the old character leads to more features on the dimension level to get the Tamil character and classification. Consider skeletonised structural angular morphing (S2AM) based on a CNN-identified Tamil character from ancient epigraphic monuments for optimum identification. Epigrammatic images will be pre-processed using Gaussian filters, and then SMS will glide the character region using CED. Use the skeletonised angular projection to discover text structural components and extract angular information. The selected features will be trained with a DFCNN to find the Tamil character. The suggested system outperforms other outdated character recognition methods in precision, sensitivity, and false detection accuracy. Keywords: script systems identifying; Tamil character detection; edge detection skeletonisation; character identification intelligent systems; canny edge detection; CED; deep featured convolution neural network; DFCNN; sliding morphological segmentation; SMS; convolution neural network; CNN. DOI: 10.1504/IJRIS.2025.10068690 Leveraging social capital and SIoT for sustainable entrepreneurship development by K.M. Ashifa, Mehdi Safaei, Hina Zahoor, Rehab El Gamil, Nasir Mustafa Abstract: The current research examines the combined effect of integrating social internet of things technology in entrepreneurial skill development programs for the Irula tribal community, Tamil Nadu, toward socio-economic upliftment. LAS and SCAM were adopted to collect data at the household level of 538 households, besides gathering qualitative information through purposive collection through focused group discussion and an in-depth interview of 60 participants. Quantitative results, as shown by paired t-tests and CR analyses, recorded significant increases in social capital and entrepreneurial skills following intervention. In-depth interviews, FGDs, and workshops brought rich qualitative insights into improved networking, innovation, and decision-making. Increasing communities' cohesion and resilience resulted in enhanced livelihood - this approach of the classic stability objective helped its growth, which is deeply credited for such livelihood maintenance and intervention. This study is likely to highlight the promising roles of SIoT in assisting underprivileged region inhabitants by improving resource availability and helping them towards better economic access. This research provides meaningful implications to policymakers and practitioners who are interested in using technology for community development programs to support tribal/indigenous populations. Keywords: social internet of things; SioT; tribal development; indigenous knowledge; community health; entrepreneurial skills; government interventions; livelihood assessment schedule; LAS; social capital assessment matrix; SCAM; focus group discussions; FGDs; critical ratio; CR. DOI: 10.1504/IJRIS.2024.10068109 Advancing healthcare intelligent systems: the critical role of paternity benefits in modern caregiving by R. Swapna Ashmi, P.R.L. Rajavenkatesan Abstract: The Maternity Benefit Act of 1961 ensures that women are entitled to receive payment for maternity leave and leave in the event of a miscarriage. It is also important to note that Indian law has not appropriately recognised paternity leave. Fathers can take paternity leave after the birth of their child or after miscarriage, adoption, or similar circumstances. The legislation regarding paternity leave in India was officially passed in 2017. The execution of this law needs improvement, as dads' paternity leave rights are not regulated. Gender-neutral policy guidelines matter in a global economy. Fathers' contributions to their spouses and children's well-being make paid parental leave crucial. The study examines how paternity benefits affect children's development and growth. The study also compared India's paternity leave policy to many others. Healthcare analysis and kid well-being are also examined. It was given to 317 people from diverse fields. The study evaluates the importance of paternity benefit enforcement in India based on 250-member replies. MS Office was used to draft and organise the research, while Python was used to process and compare data. Keywords: childcare; advancing healthcare; intelligent systems; equality and fatherhood; gender-neutral; maternity leave; miscarriage and paternity leave. DOI: 10.1504/IJRIS.2024.10068108 Intelligent techniques for evaluating organisational agility via contingency theory in dynamic environments by M. Sivakoti Reddy, Seema Bhakuni, Vinayak Anil Bhat, Rameshwaran Byloppilly, Rishi Prakash Shukla, Jayesh Solanki Abstract: The contingency approach to management holds that effective management depends on the context of a situation. This paper discusses how the contingency approach works in different managerial settings, focussing on environmental variables like organisational size, task structure, and leadership style that may affect manager effectiveness. This study will combine an in-depth literature review, interview and survey data, and statistical analysis to understand the contingency strategy in practice. Management approaches combined with these environmental variables improved organisational performance, adaptation, and resilience. With organised contingency practices, larger organisations perform better, adaptive management tactics help difficult tasks, and democratic leadership styles work better in varied situations. Regression models and the coefficient of correlation show strong positive correlations supporting these links. Since the contingency technique usually requires ongoing adjustment and integration of various factors into a system, it also highlights its obstacles and limitations from the study. Long-term contingency practices and their effects or how technological advances affect management techniques are recommended. This may help contingency-based managers improve decision-making, resource allocation, and organisational performance. Keywords: contingency approach; managerial effectiveness; organisational performance; leadership style; environmental variables; task structure; organisational size; adaptability. DOI: 10.1504/IJRIS.2025.10068931 Special Issue on: OA Reasoning-Based Intelligent Systems for the Digital Society Economic Management, Social Behaviour, and Smart Decision-Making
Abstract: This study proposes an intelligent personalised learning system (I-PRA) for English education in the digital society. The system integrates knowledge graphs with a reasoning oriented AI and a rigorous network engineering framework to build an adaptive, trustworthy, and socially interactive learning ecosystem. Knowledge graphs represent the hierarchical and associative structure of English knowledge, while convolutional neural networks and deep reinforcement learning provide dynamic learner analysis and personalised feedback. Emotional engagement is further supported through sentiment analysis, and collaborative filtering combined with natural language processing helps identify cognitive gaps and improve recommendation accuracy. To ensure reliability, the system adopts social collaborative tasks to support peer interaction and communicative competence. Experimental results show that, after deployment, average test scores increased by 10 points, task quality by 8.2 points, task completion rates by 24.7%, and social interaction frequency by 1.5 times. These findings demonstrate the systems effectiveness in improving learning quality and interaction. Keywords: reasoning-based decision support; social learning behaviour; adaptive educational systems; neuro-intelligent analysis; trustworthy network governance. DOI: 10.1504/IJRIS.2026.10077254
Abstract: To address the urgent need for verifiable and intelligent decision making in digital heritage governance, this study proposes a reasoning-based conservation framework for the murals of Shuilong Temple. The framework establishes a neuro-symbolic workflow that integrates laser scanning and high-definition imaging to support accurate digital preservation and restoration. Unlike conventional black-box approaches, the proposed knowledge-guided generative adversarial network (KG-GAN) incorporates domain expert knowledge, particularly constraints related to pigments and brushstrokes, thereby ensuring both transparency and historical fidelity in the restoration process. In addition, the system provides a multimodal decision-support interface that facilitates human-machine collaborative reasoning. Experimental results demonstrate that the proposed framework improves geometric consistency and restoration quality, while significantly enhancing user experience in digital public services. Overall, this work offers a trustworthy and explainable technical paradigm for cultural heritage governance and provides valuable support for the intelligent preservation of shared cultural assets. Keywords: neuro-symbolic restoration; knowledge-guided GAN; cultural heritage management; reasoning-based intelligent systems; human-machine collaboration; smart decision making. DOI: 10.1504/IJRIS.2026.10078081 |
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