International Journal of Reasoning-based Intelligent Systems (55 papers in press)

Regular Issues

• Open Access

  Big data intelligent analysis modelling for predicting tourist behaviour in heritage sites ( Free Full-text Access )

  by Huihui Luo 
  Abstract: Cultural heritage sites, as invaluable carriers of human civilisation, attract large numbers of visitors. Accurate prediction of visitor behaviour is crucial for effective site management. However, existing research struggles to fully capture the complex dynamic changes in visitor behaviour, resulting in suboptimal prediction accuracy. To address these challenges, this paper first analyses visitor travel preferences based on improved term frequency-inverse document frequency and hierarchical clustering. Then, a spatio-temporal multi-scale graph is constructed to characterise the dynamic evolution of visitor behaviour across temporal and spatial dimensions. Next, graph neural networks are employed to extract and fuse features from multidimensional behavioural preference data. Finally, the transformer captures key spatio-temporal factors to achieve precise visitor behaviour prediction. Experimental results demonstrate that the proposed model achieves a weighted F1-score at least 4.14% higher than baseline models, providing scientific decision support for efficient heritage site management.
  Keywords: heritage site; visitor behaviour prediction; big data analysis; graph neural network; transformer model.
  DOI: 10.1504/IJRIS.2026.10077114
   
  

• Open Access

  Collaborative optimisation of multi-agent reinforcement learning in enterprise digital supply chain ( Free Full-text Access )

  by Hui Zhou 
  Abstract: With the deepening of the digital transformation of enterprise supply chain, multi-agent system collaborative optimisation faces core challenges such as low communication efficiency and strategy instability. This paper innovatively introduces cognitive load theory into this field, constructs a quantitative model of collaborative cognitive load, and proposes a collaborative optimisation framework based on hierarchical attention communication and distributed policy distillation. The framework minimises the external load through structured communication, and improves the utility of associated load through policy distillation to achieve efficient collaboration. Experiments based on Amazons real supply chain data show that the proposed method significantly outperforms the current optimal baseline method in terms of order fulfilment rate (increased to 94.8%) and total logistics cost (decreased by 8.3%), and statistical tests confirm the significance of its performance improvement. This study provides a new theoretical perspective and practical tool for distributed intelligent collaboration.
  Keywords: digital supply chain; multi-agent reinforcement learning; cognitive load theory; CLT; collaborative optimisation; attention mechanism.
  DOI: 10.1504/IJRIS.2026.10077115
   
  

• Open Access

  A deep learning-based model for noise reduction and audio quality optimisation in music transmission signals ( Free Full-text Access )

  by Yang Song 
  Abstract: To address noise contamination, spectral compression, and reconstruction distortion encountered in wireless transmission, remote collaboration, and embedded audio devices, this paper proposes a deep learning-based framework for audio denoising and audio quality enhancement of music-oriented transmission signals. A dual-path convolutional network incorporating frequency-domain attention and perceptually guided composite loss is then designed to model long-term noise and transient musical details simultaneously. Distinct from speech-oriented models like DCCRN, SEGAN and U-Net, the proposed method fully leverages music-specific spectral dynamics and phase consistency for high-fidelity restoration under complex distortions. Evaluated using real and simulated noise scenarios with metrics including PESQ, STOI, SI-SNR and MOS-LQO, the model achieves a mean PESQ of 3.21 and an average SI-SNR improvement of 16.4 dB, outperforming baselines. Ablation and spectral visualisation validate the key modules. With strong adaptability, the framework is applicable to real-time remote music communication, collaborative systems and high-fidelity acoustic acquisition.
  Keywords: dual-path convolution; frequency-domain attention; perceptual loss optimisation; musical signal restoration; STFT-based feature modelling.
  DOI: 10.1504/IJRIS.2026.10077140
   
  

• Open Access

  Spatio-temporal big data-based rural economic situation awareness and decision support ( Free Full-text Access )

  by Xiaoyun Chen, Dan Yuan 
  Abstract: This study builds a situation awareness and decision support framework for the rural economy using spatio-temporal big data, collecting data from 2013 to 2023 on macroeconomics, population, land environment, and traffic flow. After data cleaning and processing, reliable samples are formed. The study uses clustering methods to reveal the spatio-temporal distribution of rural economy and identifies development rhythm differences with slow and fast variable models. A decision-making model is developed with input variables and strategy simulations for different scenarios. A multi-dimensional evaluation system, incorporating comprehensive weighting and grade classification, is used for dynamic early warning and feedback. The results show increasing economic activity in the eastern region, fluctuating activity in the central region, and slow growth in the western region, with the model predictions fitting actual values well. The study highlights current bottlenecks in rural economic development and offers policy suggestions for regional coordination, labour support, resource optimisation, and data governance.
  Keywords: spatio-temporal big data; rural economy; situation awareness; decision support.
  DOI: 10.1504/IJRIS.2026.10076926
   
  

• Open Access

  Development of deep learning combined with spectrum analysis to separate human voice from musical instrument sound in music production ( Free Full-text Access )

  by Fei Xue 
  Abstract: This study proposes a deep learning-based system for separating human voice from musical instrument sound in music production. A time-frequency fusion framework is constructed by integrating constitutional neural networks with long-short-term memory networks to model overlapping spectral and temporal features. An attention mechanism and multi-scale spectral analysis enhance stability under reverberation and multi-source interference. Experiments on MUSDB18 and MIR-1K show that the method achieves an SNR of 14.2 dB, a PESQ of 3.12, and a speech intelligibility score of 0.91, outperforming traditional approaches by about 35%. With GPU acceleration, system latency remains below 85 ms, meeting real-time monitoring requirements. The results demonstrate that deep learning-driven time-frequency fusion improves separation fidelity and provides an effective technical basis for music production, speech enhancement, and intelligent audio engineering.
  Keywords: deep learning; spectrum analysis; sound source separation; vocal extraction; music making.
  DOI: 10.1504/IJRIS.2026.10077120
   
  

• Open Access

  ANS-REA algorithm for dynamic corporate financial distress prediction ( Free Full-text Access )

  by Bo Wang 
  Abstract: Traditional prediction models often overlook the complexity of enterprise data, resulting in unstable prediction results. This study proposes a dynamic corporate financial distress prediction model based on the adaptive neighbour synthesis minority oversampling technique-recursive integration method. The area under the receiver operating characteristic curve (AUC) is adopted as the primary metric for evaluating prediction accuracy. The sample covered 2,850 listed companies, and the data collection period was from 2014 to 2022, involving seven major industries. The results show that the classifier algorithm based on random forest achieves an accuracy of 91.38%. The proposed algorithm achieves an accuracy of 91.96% when dealing with imbalanced data, and the prediction model combined with five time periods achieves an accuracy of 92.5%. The results show that the prediction model based on the adaptive neighbour synthetic minority over-sampling technique-recursive integration approach can provide a potential tool for corporate risk assessment.
  Keywords: ANS-REA; financial distress; random forest; unbalanced data; AUC.
  DOI: 10.1504/IJRIS.2026.10076975
   
  

• Open Access

  Development of a key dimension inspection and error correction system for product design drawings combining computer vision and YOLOv10 ( Free Full-text Access )

  by Liya Li, Fuguo Wu, Haijing Liu 
  Abstract: To address the challenges of large detection errors and difficult error correction in detecting key dimensions in paper product design drawings, we developed a novel object detection method based on YOLO v10. We added coordinate and attention mechanisms, along with a variable convolutional module, to the original method to optimise feature fusion. Furthermore, we optimised the loss function to better suit the specific application of product design drawings. Experimental results show that our proposed model has significant advantages over several classic object detection algorithms, achieving an average accuracy of 0.831 on the test set, significantly outperforming other benchmark models. We also conducted ablation studies on our model, revealing that the backbone network has the most significant impact on the overall model performance. Coordinate attention and variable convolution modules significantly boost performance. Our proposed model focuses on detecting and correcting key drawing dimensions, enhancing real-world applicability by advancing traditional models.
  Keywords: computer vision; YOLOv10; critical dimension detection; error correction; smart manufacturing.
  DOI: 10.1504/IJRIS.2026.10076927
   
  

• Open Access

  Optimised design of commercial building interior spaces and landscapes using enhanced ResNet and virtual reality technology ( Free Full-text Access )

  by Ni Yin 
  Abstract: To address the limitations of traditional commercial building design, this study proposes an optimisation method integrating an improved ResNet model with VR technology. The enhanced ResNet50 model, incorporating a CBAM attention mechanism and multi-scale feature fusion, quantifies key metrics like spatial comfort and traffic efficiency, achieving 94.7% feature recognition accuracy. A VR platform then generates interactive scenes based on model outputs to collect real-time user feedback. Experiments on three commercial buildings showed that after optimisation, space utilisation increased by 15.2%, landscape satisfaction rose by 21.6%, and traffic efficiency improved by 18.9%. This data-driven, user-centred approach offers significant practical and academic value for commercial design.
  Keywords: improving ResNet; virtual reality technology; commercial buildings; indoor space; optimisation landscape design.
  DOI: 10.1504/IJRIS.2026.10076628
   
  

• Open Access

  Application of convolutional neural network in normative detection and error correction of college students' physical exercise actions ( Free Full-text Access )

  by Huan Chen 
  Abstract: To address the issues of experience-dependent movement standard evaluation and delayed error correction feedback in college PE, a bond product neural network-based method is developed for detecting and correcting students' exercise movements. Focusing on squats, push-ups, and lunges, the study builds a skeleton sequence dataset, and adopts a model combining convolutional feature extraction and multi-distribution attention weights for joint movement classification, standard scoring, and deviation correction. Experimental results show that the model's action classification accuracy on the test set is 92.7%-96.8%, with an average absolute error of 2.95 and a reasoning delay of ~11 ms. Normative scores are 77.6-84.1; 28.1% and 25.4% of corrections target insufficient squat depth and hip sinking. The results demonstrate that this method meets the accuracy and real-time demands of college PE classrooms, providing quantitative support for teaching evaluation and training intervention.
  Keywords: coupon product neural network; college students; physical education in colleges and universities; normative score of action; error correction prompt.
  DOI: 10.1504/IJRIS.2026.10076977
   
  

• Open Access

  A general-purpose tourism intent understanding and route planning based on multimodal spatio-temporal pre-training ( Free Full-text Access )

  by Qiong Zhang 
  Abstract: Accurately understanding tourists' travel intentions and planning routes has become a key issue in smart tourism. Traditional methods rely on a single data modality and are unable to cope with multi-dimensional user demands in complex temporal and spatial contexts. Therefore, this paper first explicitly models the multi-modal spatio-temporal relationships by constructing timestamp-aligned heterogeneous graphs, and uses graph convolution networks for semantic interaction. A dynamic weight mechanism based on the Pearson correlation coefficient is introduced to optimise the fusion process of cross-modal features, ultimately achieving the precise identification of travel intentions. Finally, guided by intent understanding results, a multi-objective tourism route planning method is designed. Combining heuristic search and evolutionary strategies, an optimal and balanced route plan is generated. Experimental results show that the recognition accuracy of the proposed method reaches 93.51%, and the reverse generation distance of the route planning is only 1.9, demonstrating strong generalisation ability and practical value.
  Keywords: travel intent recognition; route planning; pre-trained model; spatio-temporal feature extraction; multimodal feature fusion.
  DOI: 10.1504/IJRIS.2026.10076976
   
  
• 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
   
  
• 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