International Journal of Intelligent Information and Database Systems (31 papers in press)

Regular Issues

• Hybrid optimised deep residual network with trust parameters for intrusion detection in IoT  
  by Asha Rawat, Harsh Namdev Bhor, Jayprabha Terdale, Varsha Bhole, Anuradha Thakare, Vishal Ratansing Patil 
  Abstract: Security issues are still challenging due to the availability of brilliant skills and hacking tools. Thus, detecting the intrusion in the IoT environment is crucial. Hence, this research introduces a novel optimised deep residual network based on the trust and KDD parameters. Here, an efficient mayfly spider monkey optimisation (MSMO) is proposed for tuning the adjustable parameters of the intrusion detector named deep residual network (DRN), which is modelled by hybridising the social behaviour of the mayfly in the mayfly optimisation algorithm (MA) with the foraging behaviour of the spider monkey based on the fission property of the spider monkey optimisation (SMO) to obtain the global best solution. Here, the trust factors and the KDD Cup features are considered for learning the classifier. The proposed model obtained better performance in accuracy of 0.913, precision of 0.919, false alarm rate of 0.084, and recall of 0.958.
  Keywords: intrusion detection; deep residual networks; optimisation; trust factors; KDD Cup features.
  
  
• Convolution-based adaptive ResUNet3 + with attention-based ensemble convolution networks for COVID-19 segmentation and classification  
  by S. Salini, B. SelvaPriya 
  Abstract: In 2018, residual U-shaped network (Res-UNet) and dense U-shaped network (Dense-UNet) were born based on the U-Net architecture. Inspired by dense and residual connections, respectively, Res-UNet and Dense-UNet substitute a kind of dense or residual connection for each U-Net sub-module. The community of artificial intelligence has produced a variety of deep learning models with the intention of recognising COVID-19 based on the visual features of chest X-rays. It is unfortunate that this is the case since constructing really deployable clinical models often requires segmentation as a crucial precursor step. Other applications in radiology typically need segmentation. It might be difficult to differentiate COVID-19 from other pulmonary disorders due to the fact that many lung diseases have similar visual characteristics with COVID-19. Using a segmentation module and an ensemble classifier, we have constructed our deep learning pipeline with the intention of assisting in the clarification of the diagnosis of individuals who are suspected of having COVID-19. Following the completion of an exhaustive comparison investigation, we are able to show that our most advantageous model is capable of effectively achieving an accuracy of 91% and a sensitivity of 92%.
  Keywords: COVID-19 classification; visualisation check; dataset description.
  
  
• A novel hybrid model integrating 1DCNN and WSVM for enhanced chronic disease prediction  
  by Fatma Zohra Tassadit Ait Mesbah, M’hamed Bilal Abidine, Belkacem Fergani 
  Abstract: Chronic diseases require ongoing care and are often diagnosed late, leading to complications and even death. An effective predictive system for rapid and intelligent diagnosis of these pathologies is crucial. This study proposes a hybrid 1DCNN-LDA-WSVM model that combines a 1D convolutional neural network (CNN), linear discriminant analysis (LDA), and weighted support vector machine (WSVM). This model explores the joint application of 1DCNN and LDA for the extraction and selection of pertinent deep features from datasets. The WSVM is employed as a binary classifier to address the issue of minority class overweighting in SVM modeling. Evaluation across four medical datasets demonstrates enhanced performance with predictive accuracy rates of 95%, 99%, 98%, and 99% on the CHDD, PIDD, WBCD, and CKDD datasets, respectively. These results underscore the model's capability to increase precision in forecasting chronic diseases.
  Keywords: disease prediction; dimensionality reduction; deep learning; DL; machine learning; ML; weighted support vector machine; WSVM.
  DOI: 10.1504/IJIIDS.2025.10071706
   
  
• A New Feature Selection Approach Based on New Multi-Exhaustive Search  
  by Fatma Zohra Debba, Lynda Dib, Khaled Berrahil 
  Abstract: Feature selection (FS) is the mechanism of selecting a smaller subset of informative features from the entire dataset in order to improve the performance of the classification model and the result comprehensibility. It contains two essential aspects: feature evaluators and search methods to find the appropriate features in the search space. Among the search methods already proposed, there is the Exhaustive Search (ES) which, unlike other search methods, guarantees to find the optimal subset because all possible combinations of features are tested against a predetermined criterion. But the major drawback of this search method is that it becomes computationally unfeasible, especially with large data sets. To overcome this drawback and take advantage of its optimality, we propose in this paper a new search method called Multi-Exhaustive Search (MES) in which we adjust the use of the ES in order to find the best subset in very low computational time. As
  Keywords: Feature Selection; Search Methods; Exhaustive Search; Multi-Exhaustive Search; Classification; Machine Learning.
  
  
• Deep learning-based hybrid optimisation for multiclass plant disease detection using leaf images in a distributed environment  
  by Bandi Ranjitha, Arpakkam Karuppan Sampath 
  Abstract: A novel module is designed for multi-class plant disease detection named fractional geese jellyfish search optimisation enabled deep convolutional neural network (FGJSO_DCNN). The input plant leaf image is partitioned utilising enhanced fuzzy c-means clustering (FCM). In the mapper phase, pre-processing is performed by an adaptive Kalman filter (AKF), and leaf disease segmentation is carried out by Link-net, which is trained to employ FGJSO. The augmentation progress is conducted to alter the provided image. The progression of feature extraction is carried out and it is given to the reducer phase. On the other hand in the reducer phase, plant disease classification is accomplished in terms of first level classification using DCNN tuned by FGJSO and second level classification that is detection progress is performed by FGJSO_DCNN. The suggested FGJSO_DCNN model achieved a maximum accuracy of 0.915, TPR of 0.908, FPR of 0.080, F1-score of 0.918, and precision of 0.928.
  Keywords: plant disease detection; MapReduce framework; wild geese migration optimisation; GMO; jellyfish search optimisation; JSO; deep convolutional neural network; deep CNNs.
  DOI: 10.1504/IJIIDS.2025.10072687
   
  
• Document-level sentiment analysis using Jaya chimp optimisation algorithm-enabled deep residual network  
  by Manoj L. Bangare, Sampath Arpakkam Karuppan, Debarati Ghosal, Ashwin Perti, Sanjay Nakharu Prasad Kumar 
  Abstract: Document-level sentiment classification automates the process of categorising text reviews on a single topic as representing negative or positive sentiments. Users and customers are intended to share comments and reviews about their products on various social network sites. One of these processing steps is the classification of emotions associated with the reviews. Therefore, this research paper introduces a robust sentiment analysis method, named Jaya chimp optimisation algorithm-enabled deep residual network (JayaChOA-enabled DRN) for document-level sentiment classification. The input is pre-processed and tokenised, and then the key features are extracted. Moreover, the DRN classifier is used for the sentiment classification where the optimal weights are computed using the JayaChOA. Meanwhile, the introduced JayaChOA is implemented by the incorporation of Jaya optimiser and chimp optimisation algorithm (ChOA). The JayaChOA-based DRN obtained the highest precision of 0.914, F-measure of 0.919, and recall of 0.925 using K-fold.
  Keywords: sentiment analysis; deep learning; chimp optimisation algorithm; Jaya optimiser; natural language processing; NLP.
  
  
• Data aggregation in wireless sensor networks using Bayesian-based data encryption and fragmentation modelling  
  by L. Rajesh, H.S. Mohan, M.K. Bindiya 
  Abstract: This paper proposes a model for data fragmentation and modelling in wireless sensor networks (WSNs) using the Bayesian fuzzy clustering (BFC) technique. Initially, WSN nodes are simulated, and the shuffled shepherd squirrel search optimisation algorithm (SSSSOA) is accomplished for choosing the cluster head (CH). Later, route maintenance operation is performed using the link quality metrics. Hierarchical fractional bidirectional least-mean-square (HFBLMS) is employed for data reduction and data aggregation. After that, the security of the nodes is ensured during data transmission using trust metrics. Besides, the proposed BFC approach is used in the data fragmentation and modelling phase where the elliptic curve cryptography (ECC) encryption and the adaptive data partitioning process are performed. Finally, the decryption and the de-blocking operations are performed. The introduced BFC approach achieved a detection ratio of 81.13%, delay of 0.105 s, packet deliver ration (PDR) of 98.13%, and energy of 2.502 J.
  Keywords: wireless sensor networks; data aggregation; shuffled shepherd optimisation; squirrel search algorithm; Bayesian fuzzy clustering; BFC.
  DOI: 10.1504/IJIIDS.2025.10072688
   
  
• Prediction of cardiovascular disease using intelligent nextgen machine learning algorithmic breakthroughs  
  by M. Rohini, S. Oswalt Manoj 
  Abstract: Cardiovascular disease (CVD) is a prevalent and life-threatening condition that affects middle-aged and elderly individuals, leading to severe complications due to unhealthy lifestyles. The goal of the study was to train the prospective machine learning model that introduces a novel approach by integrating generative pre-trained models, combined with attention mechanisms, to refine feature selection to detect cardiac disease at an early stage. The analysis of clinical data demands distinct challenges in the context of generative model learning due to data complexity and the diverse nature of disease markers. Addressing these, the proposed study significantly improved the predictive accuracy by refining the models ability to recognise patterns specific to cardiac conditions. The gradient boosting (GB) algorithm emerged as the most effective optimal predictor, with 97.86% accuracy, 98.52% sensitivity, and 99.72% ROC for CVD classification.
  Keywords: machine learning; ML; gradient boosting; GB; logistic regression; LR; cardiovascular disease; CVD; gene prediction.
  DOI: 10.1504/IJIIDS.2025.10072096
   
  
• Automated organised tour planning with blockchain: an innovative solution  
  by Asmaa Boughrara, Nourhene Aicha Daoud, Nedjma Louiza Harkati 
  Abstract: The traditional travel planning process often involves hiring a travel agency, which can be costly for budget-conscious travellers. Additionally, the tourism industry faces challenges such as fraud, lack of transparency, and inefficient data management. To address these issues, we propose a decentralised platform that automates the creation of organised tours by combining the services of registered stakeholders. Our approach leverages blockchain technology to ensure transparency, security, and data integrity throughout the travel planning and booking process. Additionally, we implement a customised proof-of-reputation mechanism to encourage responsible behaviour among stakeholders.
  Keywords: organised tour; blockchain; proof-of-reputation; node weight; automated planning; tourism efficiency.
  
  
• A context-enriched dataset for recommender systems  
  by Rim Dridi 
  Abstract: Recommender systems play a key role in modern applications. Recognising the importance of user context, researchers have developed context-aware recommender systems (CARS) to generate more personalised and relevant recommendations. Even though there are several approaches working for this kind of recommendation, suitable and publicly available datasets including users contextual ratings are limited, and generally, even these are not large enough to assess CARS properly. To mitigate the contextual datasets availability problem, we propose an enrichment methodology to generate large datasets to be used for context-aware recommender systems evaluation. Our work aims to enrich existing large recommendation datasets by including contextual information to describe users expressed preferences linked to their corresponding contexts. Our assessment with the generated large contextual datasets has revealed promising findings when compared to publicly available contextual datasets.
  Keywords: recommender system; context; dataset; enrichment.
  
  
• Design of an English translation computer intelligent scoring system based on block chain  
  by Lingzhi Xu, Xinxin Zhang 
  Abstract: This paper aims to design an intelligent English translation scoring system based on blockchain technology to improve the efficiency and accuracy of translation scoring. In the intelligent scoring system, blockchain provides a secure, transparent and tamper-proof way to record data. By recording each translation result as a transaction record in the blockchain, the system can ensure the integrity and reliability of the data in the scoring process. The fairness and credibility of the scoring results are guaranteed. At the beginning of this paper, the theoretical knowledge and process of block chain are introduced closely following the theme, and then the block chain model is studied, and the Bayesian formula, conditional probability, N-ary model and HMM model are introduced, and finally proposed BLEU evaluation method. Based on these research methods, this paper designs and implements an intelligent English translation computer scoring system.
  Keywords: English translation; block chain; intelligent scoring system; BLEU evaluation method; system design.
  
  
• Few-shot learning-based zero-day anomaly detection in vehicular networks using conditional GANs  
  by Haewon Byeon, Mohammed E. Seno, Aadam Quraishi, Azzah AlGhamdi, Mukesh Soni, Ihtiram Raza Khan, Mohammad Shabaz 
  Abstract: Detecting zero-day anomalies in vehicular networks poses significant challenges due to the lack of attack data. Anomaly-based detection methods are commonly used; however, complex and dynamic environments in vehicular ad hoc networks (VANETs) lead to diverse behavioural patterns, increasing the likelihood of high false alarm rates. This study proposes a few-shot learning-based zero-day anomaly detection method for vehicular networks using conditional generative adversarial networks (GANs). The proposed approach introduces a conditional GAN model with multiple generators and discriminators to enhance anomaly detection capabilities. To address data imbalance caused by the limited availability of attack samples, a collaborative focal loss function is incorporated into the discriminator to focus on hard-to-classify anomalies. Extensive experiments conducted on the F2MD vehicular network simulation platform demonstrate that the proposed method outperforms existing approaches in terms of detection accuracy and latency for zero-day anomalies. This provides an effective solution for enhancing anomaly detection in vehicular networks.
  Keywords: VANETS; anomaly detection; zero-day anomalies; few-shot learning; conditional generative adversarial networks; GANs.
  
  
• Triple attention-enhanced transformer-based federated meta-learning for epileptic seizure detection  
  by Ashwini Patil, Megharani Patil 
  Abstract: In neurological healthcare, accurately identifying seizure occurrences from the electroencephalogram (EEG) signals is essential, implying the epileptic seizure detection task. data privacy, epileptic knowledge scarcity, non-independent and identically distributed (non-IID) characteristics, and inter-patient variability pose research constraints to the traditional centralised learning systems. To develop a reliable and private-preserving model for patient-specific knowledge-aware epileptic seizure detection, this paper suggests a unique model that combines meta-learning with federated learning (FL). The proposed approach applies the ternary feature extraction and hybrid augmentation methods to enhance the comprehensive learning of EEG features over data scarcity. Subsequently, the design of the transformer-based model in federated meta-learning architecture significantly captures the intricate relationships with long-range dependencies in the sequential EEG signals of each federated client while performing task-specific learning within each local EEG data. Thus, the stacked transformer encoder with triple attention in the local model inherently learns the discriminative ictal and non-ictal EEG patterns with the updates of patient-specific learning through the collaborative training across the patients by meta-learning and federated clients by FL, improving the epileptic seizure detection performance.
  Keywords: epileptic seizure detection; ternary feature extraction; hybrid augmentation; stacked transformer encoder with triple attention; federated learning; FL; meta-learning; transformer; and multi-head self-attention.
  DOI: 10.1504/IJIIDS.2025.10072488
   
  
• Enhanced imputation genetic algorithm: a novel approach for data intelligent imputation  
  by V. Amala Deepa, T. Lucia Agnes Beena 
  Abstract: The imputation of missing data in multivariate datasets has been used to enhance the accuracy and reliability of statistical analyses and the machine learning model, especially when the integrity of data can directly impact their decisions; healthcare and finance are basic examples. Methods become biased or inaccurate in these traditional imputation methods as they are not complex enough for multivariate data. It introduces an entirely novel data imputation method called the enhanced imputation genetic algorithm. Such enables dynamic control over genetic operators with the means that crossover and mutation rates may contribute toward achieving some balancing of exploration and exploitation and further enhancing this by embedding higher-level statistical distances in an improved fitness version, thereby providing EIGA the necessary tool for upholding statistical property for the datasets involved. Due of its genetic diversity, EIGA avoids time convergence, unlike many classic genetic algorithms. Iris, adults, and cardio benchmark datasets show that EIGA reduces RMSE and MAD best. RMSE improved from 0.1668 to 0.1654 and MAD from 0.0479 to 0.0455 on iris with 60% missing data. EIGA, however computationally expensive, is another good alternative for complicated datasets that need more precise imputation.
  Keywords: data intelligent imputation; dynamic crossover; dynamic mutation; genetic algorithm; fitness function; missing data; multivariate data; imputed data; root mean square error; RMSE; mean absolute deviation; MAD; enhanced imputation genetic algorithm; EIGA.
  DOI: 10.1504/IJIIDS.2025.10070762
   
  
• Optimisation enabled deep learning model for data privacy protection in blockchain networks using federated learning  
  by T. Premkumar, D.R. Krithika 
  Abstract: Recently, federated learning (FL) has been employed in blockchain networks to protect users data privacy. This paper proposes gradient beluga whale optimisation-deep residual network (GBWO-DRN) for data privacy protection in blockchain networks. Data privacy protection in the blockchain network is performed by nodes and servers. The process is performed in local and global training models, where distributed data is fed into a local model. The data is normalised and Laplace noise is added to it. Paillier homomorphic encryption is applied and the result is classified by the DRN. The GBWO is used to train DRN to improve DRNs performance. Finally, local updation and aggregation are done in the global training model and the data is stored in cloud. The GBWO-DRN recorded false positive rate (FPR), root mean squared error (RMSE), mean squared error (MSE), accuracy, loss function, and mean average precision (MAP) of 6.64%, 37.98%, 14.42%, 93.52%, 6.48%, and 92.10%.
  Keywords: deep residual network; gradient beluga whale optimisation; GBWO; gradient descent optimisation algorithms; GDOA; beluga whale optimisation; BWO; federated learning.
  DOI: 10.1504/IJIIDS.2025.10070801
   
  
• Honey badger optimisation based recurrent neural network for COVID-19 classification  
  by S. Salini, B. SelvaPriya 
  Abstract: A new research has begun to look into the sounds of people breathing, coughing, and recording their voices using hospital-confirmed COVID-19 tools. The sounds these things make are different from the sounds healthy people make. When COVID-19 was identified through coughing, the data on non-respiratory and respiratory noises that were linked to all the different situations that were given were also taken into account. The virus that causes the lung illness known as COVID-19 is called Severe Acute lung Syndrome Coronavirus-2 (SARS-CoV-2). The number of COVID-19 cases has been slowly rising, which more important to find safe ways to find people who have the virus. The Gaussian filtering model brings out the sensitivity of the method that was made better. The specificities of DCNN are 0.9125, BI-AT-GRU are 0.8926, and XGBoost are 0.9014. In k-fold value 9, a new DNFN based on JHBO has a precision of 0.9219.
  Keywords: honey badger optimisation; COVID-19 classification; feature extraction.
  DOI: 10.1504/IJIIDS.2025.10071128
   
  
• ECC based authentication protocol for IoT and untrusted cloud computing environments  
  by Sheetal Kalra, Jyoti Saroj 
  Abstract: Over recent years, password-based remote user authentication schemes using smart cards have become increasingly popular in internet of things (IoT) and cloud computing (CC) environment. However, implementing these smart card-based systems is costly due to the necessary infrastructure for smart card utilisation. In this research paper a dynamic password based remote user authentication protocol for common storage devices based on elliptic curve cryptography (ECC) has been proposed. This protocol not only preserves all merits of a smart card based authentication schemes rather reduces the computational cost as well. The comparison shows that the protocol is robust in comparison to other protocols and achieves all the security requirements. The security analysis confirms that the proposed protocol offers robust security and is impervious to network threats.
  Keywords: authentication; elliptic curve cryptography; ECC; smart card; mobile devices; internet of things; IoT; cloud computing; CC.
  DOI: 10.1504/IJIIDS.2025.10071134
   
  
• Quantum convolutional neural networks with new SVM-FE feature selection for robust intrusion detection  
  by Yogesh Gurav, Mukil Alagirisamy, Sathish Kumar Selvaperumal 
  Abstract: Intrusion detection is a vital component of cybersecurity, requiring effective methods to identify anomalies in complex datasets. Traditional machine learning models face challenges with high-dimensional data and intricate feature relationships. To address these, we propose an Improved quantum convolutional neural network (QCNN) combined with a novel SVM-RFE-based feature selection technique for enhanced detection. The process begins with data collection and pre-processing using min-max normalisation. Feature extraction captures various aspects of the dataset, including correntropy, statistical, and raw features. CASC-SVM-RFE is then applied for feature selection, reducing dimensionality while retaining key attributes. Anomaly detection is performed using the eigen value decomposition integrated convolutional layer within the QCNN, utilising quantum computing principles for accurate identification. Our method achieves 0.949 accuracy, outperforming traditional models like LSTM, Bi-LSTM, QNN, SVM, CNN, RNN, and QSVM+QCNN, showcasing the potential of quantum computing for tackling complex, high-dimensional intrusion detection tasks.
  Keywords: intrusion detection system; EDIC-QCNN; CASC-SVM-RFE; min-max normalisation and anomaly detection.
  DOI: 10.1504/IJIIDS.2025.10071348
   
  
• BioASQ-Ispec16: a novel resource for advancing specialty-based organisation in medical texts  
  by Walid Benaouda, Siham Ouamour, Halim Sayoud 
  Abstract: To improve specialty-specific classification of biomedical texts, we introduce BioASQ-Ispec16, a novel corpus of 844,481 medical abstracts spanning 16 specialties. This dataset undergoes rigorous filtering and cleaning to ensure high-quality annotations. We evaluate three main approaches: zero-shot learning, retrieval-based classification, and fine-tuned transformer models. Our results highlight the limitations of zero-shot learning, which, despite its flexibility, fails to capture domain-specific nuances, achieving an accuracy of 62.51% and an F1-score of 62.55%. In contrast, retrieval-based methods, particularly bge-large-en-v1.5, show improved performance, with an F1-score of 84.32%, demonstrating its effectiveness in medical abstract classification. However, fine-tuning BERT-like models on domain-specific data proves to be the most effective strategy, with accuracy ranging from 90.77% to 92.54%, and with PubMedBERT achieving an F1-score of 92.68%, outperforming other models. These results provide insights into enhancing medical text classification systems, highlighting the value of domain-specific data annotation and the effectiveness of transformer-based approaches.
  Keywords: medical text classification; retrieval-based models; bidirectional encoder representations from transformers; BERT; zero-shot learning; ZSL; large language models; LLMs.
  DOI: 10.1504/IJIIDS.2025.10071884
   
  
• An automated segmentation and classification model for leaf disease using the multiscale residual LSTM  
  by R.C. Dyana Priyatharsini, G. Rosline Nesa Kumari 
  Abstract: Plant leaf diseases are significant for reducing crop failure as well as the transferring of harmful viruses. In this work, we designed a unique plant disease classification model using deep learning models. Initially, the images are gathered from publicly available datasets. Then, the accumulated images are put into the segmentation stage done by the adaptive Bayesian clustering (ABC) model; here several parameters are tuned by the improved one-to-one-based optimiser (IOOBO) to enhance the effectuality of the designed approach. Then, the feature extraction procedure is conducted in the segmented images. The features like shape, colour, texture, and vision transformer (ViT)-aided features are extracted. Further, the multiscale residual long short-term memory (MR-LSTM) model is adopted for leaf disease classification with the help of extracted features and the classified images as the outcome. Finally, the experimental analysis of the recommended approach is executed and validated among various traditional models.
  Keywords: leaf disease segmentation; leaf disease classification; adaptive Bayesian clustering; ABC; multiscale residual long short-term memory; MR-LSTM; improved one-to-one-based optimiser; IOOBO.
  DOI: 10.1504/IJIIDS.2025.10072338
   
  
• Research on smart phone app interface interaction design based on smart city internet of things  
  by Xiao Chen, Yi Yu 
  Abstract: As the functions of mobile phone software become stronger and the corresponding industry development trends become more and more perfect, the design of mobile APP pages has also risen to the height of a special study. This article intends to conduct in-depth research on the human-computer interaction design of the smart phone APP interface through the smart city internet of things. This article first introduces the smart city under the internet of things. The QoS indicator of the internet of things is modelled, and the QoS calculation process based on the internet of things is given. The construction of smart cities was explored based on the internet of things environment, and the interactive design of smart phones was researched on the basis of the internet of things environment.
  Keywords: smart city; smart phone; interactive design; QoS indicator modelling.
  DOI: 10.1504/IJIIDS.2025.10072467
   
  
• Android malicious application detection technology based on deep learning  
  by Jing Liu, Mingwei Sun, Ying Meng 
  Abstract: With the widespread use of mobile phones, they have become essential tools in everyday life. Androids open architecture and commercial potential have made it a frequent target for malware and security threats. At present, the detection mode and technology of Android malware have a low detection rate and low efficiency and cannot effectively detect the currently rapidly growing malware. In the detection technology test, when using deep learning algorithms to detect malicious programmes, its recognition accuracy reaches 97.2%. Under the same dataset and input features, it is significantly better than the detection performance of traditional classifiers such as support vector machines, decision trees, naive Bayes, K-nearest neighbour algorithms, and mainstream deep learning models such as RNN, GRU and transformer. Experiments show that the detection effect of Bi-LSTM algorithm on malicious programmes is better than the above detection methods.
  Keywords: application detection technology; Android malicious application; deep learning; bidirectional long short-term memory neural network.
  DOI: 10.1504/IJIIDS.2025.10072577
   
  
• Parallel image filtering algorithm with adaptive dynamic load balancing  
  by Xiangjiao Liu, Rui Cui 
  Abstract: To solve the problem of waste of computing resources and low efficiency due to uneven load in the parallel image filtering process, this paper introduces an adaptive dynamic load balancing mechanism, combines the block parallel convolution filtering algorithm, dynamically adjusts task allocation, optimises the use of computing resources, and improves the filtering performance and real-time performance in large-scale image processing. A dynamic task allocation strategy is implemented in parallel image filtering to ensure that each processing unit can balance the load. A dynamic adjustment task scheduling strategy is introduced to balance the computing load of each processing unit, and the image is filtered using a block parallel filtering algorithm. The experimental results show that the adaptive load-balanced block parallel convolution filtering algorithm in this paper has an acceleration ratio, computing resource utilisation, memory efficiency and energy consumption ratio of 2.5, 85%, 220 MB/s and 1.35 J/s respectively, and is superior to other parallel image filtering algorithms of different models, demonstrating the high efficiency and real-time performance of this study in image filtering. This study presents a novel approach to mitigate waste of computational resources and inefficiencies in large-scale image processing.
  Keywords: adaptive dynamic; load balancing; parallel image; filtering algorithm; convolution filtering.
  DOI: 10.1504/IJIIDS.2025.10072999
   
  
• Data mining and pattern recognition in intelligent maintenance orders of power systems under knowledge graph  
  by Yin Wu, Yuechao Jin, Wuneng Ling, Jiayi Yang, Yan Qin, Fangling Luo 
  Abstract: The paper studied the entity relationships of power faults based on datasets and extracted entity relationships in the fault domain. Word2Vec can be used to mine textual data for maintenance orders and preprocess the mined data. Support vector machines can be used to construct fault recognition patterns for power systems, improving the accuracy of fault recognition for power system maintenance. It completed the generation of intelligent maintenance orders through the construction of recognition patterns and power knowledge graph. The accuracy rate, recall rate, and F1-values of the power system maintenance fault mode recognition model based on long short-term memory-support vector machine (LSTM-SVM) are 87.23%, 74.39%, 84.23%. The data mining and pattern recognition in intelligent maintenance orders of power systems based on knowledge graph can help improve the maintenance efficiency and resource allocation optimisation of power systems. It can also promote technological innovation and energy security, making positive contributions to the sustainable development of the power industry.
  Keywords: knowledge graph; power system maintenance orders; data mining; support vector machine; SVM; pattern recognition.
  DOI: 10.1504/IJIIDS.2025.10073064
   
  
• Corporate credit economy evaluation based on improved K-means clustering algorithm  
  by Zhifei Yi 
  Abstract: In todays dynamic economic environment, a firms credit status is crucial for financial stability, investment decisions, and business cooperation. This study designs a corporate creditworthiness evaluation method based on an improved K-means clustering algorithm. The process involves constructing an economic clustering index system, reducing dimensionality using factor analysis, and extracting key common factors. A binary classification approach is then applied. To enhance performance, a density- and weight-based K-means algorithm is proposed. Experimental results show that the proposed method outperforms two other algorithms in terms of convergence speed and optimisation accuracy. Specifically, on the vowel dataset, it converged 10.25% and 6.47% faster, and on the glass dataset, 8.67% and 7.45% faster than the other algorithms. These results demonstrate the efficiency and accuracy of the improved K-means algorithm in evaluating corporate credit.
  Keywords: K-means clustering algorithm; KCA; credit economy evaluation; binary classification research; high-density; factor loading matrix.
  DOI: 10.1504/IJIIDS.2025.10073065
   
  

Special Issue on: Knowledge Extraction and Mining to Enhance Intelligent Information Systems

• Artificial intelligence-based visual communication approach for intelligent graphic design  
  by Beiyi Liu 
  Abstract: By leveraging AI technology, the optimisation of image colour features can be significantly enhanced to further improve the visual impact of graphic design images. Through the incorporation of advanced algorithms, we introduce a cutting-edge graphic design method rooted in visual communication technology. Utilising AI-powered algorithms, we meticulously extract the pixel intensity and luminance components of the graphic design image through this visual communication method. These extracted features allow for precise adjustments to the images global brightness using multi-scale retinex techniques, thereby enhancing local contrast. Additionally, the colour of the enhanced image is restored and corrected to achieve an optimised visual effect, ensuring that the final output is both aesthetically pleasing and technically refined. Simulation results demonstrate that the local area colour information and contrast of the graphic design image are significantly improved, leading to a more engaging and visually appealing outcome. Furthermore, the AI-powered method exhibits excellent noise suppression capabilities, enhancing the users willingness to engage with and browse the graphic design image. This integration of AI technology promises to revolutionise the field of graphic design, delivering more impactful and engaging visual experiences.
  Keywords: artificial intelligence; visual communication; intelligent graphic design; multi-scale retinex.
  
  
• Cloud and edge cooperation-based system architecture for multi-layer edge computing  
  by Ruobin Li, Wei Wang, Shenghua Bian, Xinyu Sun, Xiaoguang Yin 
  Abstract: Centered around edge-cloud cooperation, this research tackles the issue where large repositories of model and operational data amassed in distributed cloud systems lead to substantial stress on core cloud transmission networks, while also posing challenges to maintaining real-time data responsiveness. In this work, we devise a holistic edge-cloud cooperation framework applied to distributed cloud environments analogously. Using the improvement in power grid dispatch control data quality as a case study, we systematically analyse parameters such as network bandwidth management and latency minimisation. The performance of collaborative data processing across the edge and cloud tiers have optimised task decomposition schemes and improved central cloud storage mechanisms. Extensive experiments have demonstrated that the integration of edge-cloud cooperation technologies provides a robust solution to the challenges posed by excessive network bandwidth consumption and the diminished relevance of data in traditional centralised computing models.
  Keywords: cloud edge cooperation; edge computing; multi-layer structure; scheduling and control.
  
  
• MS-ConvNeXt: a deep-learning method for tomato leaf diseases identification  
  by Yunchao Li 
  Abstract: Existing deep learning methods for tomato leaf disease identification are challenged by the multi-scale disease regions and complex backgrounds in tomato leaf images. A network for tomato leaf disease is proposed. In the proposed network, a cross-channel-and-spatial attention mechanism is first introduced in the ConvNeXt block (called A-ConvNeXt block) to avoid interference of invalid features from the complex backgrounds. Then, a multiscale feature mechanism is integrated into the backbone constructed by the A-ConvNeXt block to extract features across multiscale diseases. The fine multiscale and silence features are extracted to address the limitations on tomato leaf diseases. Experimental results on laboratory and natural datasets show that the identification accuracy reached 95.67%, which outperformed many other existing networks in comparison experiments. The proposed network may effectively improve tomato leaf disease identification and provide decision-making information for practical applications in modern agriculture.
  Keywords: tomato leaf disease identification; attention mechanism; multiscale feature mechanism; deep learning.
  DOI: 10.1504/IJIIDS.2025.10071705
   
  
• A novel multi-sensor fusion approach for enhanced navigation in autonomous driving  
  by Qinghai Liao, Feiyang Cheng, Ji Yu, Zhengguang Ao, Zhiquan Deng, Liang Huang, Huiyun Li 
  Abstract: The limitations of single-sensor SLAM technologies in addressing the intricate requirements of modern intelligent vehicles have prompted a shift towards multi-sensor fusion SLAM as a prominent area of research. In response, this paper proposes a tightly-coupled SLAM system integrating LiDAR, cameras, and IMUs to boost the location accuracy and mapping capabilities. The system processes multi-sensor data upfront to enable effective backend optimisation. Specifically, it integrates LiDAR odometry directly within the vision-inertial framework as inter-frame constraints to streamline computational complexity. Moreover, to counter the progressive error accumulation typical of odometry-based methods, loop closure detection is incorporated, enhancing the quality of localisation and mapping. The effectiveness is substantiated through experiments on public datasets, confirming its proficiency in accurate positioning and navigation. The experimental results demonstrate that the proposed multi-sensor fusion SLAM system maintains high accuracy and reliability across different speeds and environmental conditions, with improvements in trajectory estimation due to loop closure.
  Keywords: autonomous driving; SLAM; multi-sensor fusion; pose estimation; LiDAR odometry.
  DOI: 10.1504/IJIIDS.2025.10072053
   
  
• A comprehensive survey on deep learning based keyphrase generation and extraction for natural language processing  
  by Jimmy Jose, P. Beaulah Soundarabai 
  Abstract: Recent deep learning advances have revolutionised natural language processing through improved computational power and data availability. Keyphrases condensed information representations enhance performance in text summarisation, information retrieval, classification, sentiment analysis, and topic modelling. While previous surveys focused primarily on keyphrase extraction (KPE) and specific techniques, this comprehensive review uniquely analyses recent deep learning, embedding-based, and pre-trained language models in both keyphrase extraction and generation (KPG). The survey examines NLP advancements and their applications, with special focus on pre-trained language models like GPT and BERT and their impact on keyphrase tasks. It analyses strengths and limitations of recent KPG/KPE methods to help researchers improve existing approaches and addresses future research directions, ultimately promoting deeper understanding of KPG/KPE within the broader NLP landscape.
  Keywords: natural language processing; NLP; keyphrase generation; KPG; keyphrase extraction; KPE; pre-trained language model; PLM; bidirectional encoder representations from transformers; BERT.
  DOI: 10.1504/IJIIDS.2025.10071346
   
  
• Support vector machine learning-based green secure supply chain management and optimisation  
  by Mingjiang Zhao, Jiming Dai, Jiyang Kang, He Li 
  Abstract: Green supply chain management (GSCM) is crucial for sustainable business practices, emphasising environmental impact mitigation and resource efficiency. In this context, assessing supply chain risks is vital to ensure resilience and stability. This study explores existing literature to develop a comprehensive framework for supply chain risk assessment that integrates GSCM principles. The aim is to identify and evaluate risks from both environmental and operational angles. Firstly, to implement this framework, data were gathered from industry experts and practitioners via structured questionnaires. Secondly, these data were analysed using a machine learning algorithm based on support vector machines (SVM), enabling the construction of a risk assessment model that encompasses both conventional and green-specific risks. Lastly, experimental analysis is performed to test the models effectiveness. Results indicate that the SVM-based model not only accurately forecasts potential risks but also demonstrates high computational efficiency during training, which underscores the significance of embedding GSCM principles within supply chain risk management.
  Keywords: supply chain; green and risk assessment; support vector machine; SVM; management; optimisation.
  DOI: 10.1504/IJIIDS.2025.10072283