International Journal of Computational Science and Engineering (45 papers in press)

Regular Issues

• Aerial remote sensing image registration based on dense residual network of asymmetric convolution  
  by Ying Chen, Wencheng Zhang, Wei Wang, Jiahao Wang, Xianjing Li, Qi Zhang, Yanjiao Shi 
  Abstract: The existing image registration frameworks pay less attention to important local feature information and part of global feature information, resulting in low registration accuracy. However, asymmetric convolution and dense connection can pay more attention to the key information and shallow information of the image. Therefore, this paper propose a novel feature extraction module to improve the feature extraction ability and registration accuracy of the model. Asymmetric convolution and dense connection are used to improve the residual structure to focus on both local and global information in the feature extraction stage. In the feature matching stage, bidirectional matching is used to alleviate asymmetric matching results by fusing two outcomes. Furthermore, a secondary affine transformation is proposed to estimate the real transformation between two images adequately. In contrast with several popular algorithms, the proposed method has a better registration effect on two public datasets, which has practical significance.
  Keywords: remote sensing image registration; residual network; asymmetric convolution; dense connection; transfer learning; regularization; affine transformation.
  
  
• Self-supervised learning with split batch repetition strategy for long-tail recognition  
  by Zhangze Liao, Liyan Ma, Xiangfeng Luo, Shaorong Xie 
  Abstract: Deep neural networks cannot be well applied to balance testing when the training data present a long tail distribution. Existing works improve the performance of the model in long tail recognition by changing the model training strategy, data expansion, and model structure optimisation. However, they tend to use supervised approaches when training the model representations, which makes the model difficult to learn the features of the tail classes. In this paper, we use self-supervised representation learning (SSRL) to enhance the model's representations and design a three-branch network to merge SSRL with decoupled learning. Each branch adopts different learning goals to enable the model to learn balanced image features in the long-tail data. In addition, we propose a Split Batch Repetition strategy for long-tailed datasets to improve the model. Our experiments on the Imbalance CIFAR-10, Imbalance CIFAR-100, and ImageNet-LT datasets outperform existing similar methods. The ablation experiments prove that our method performs better on more imbalanced datasets. All experiments demonstrate the effectiveness of incorporating the self-supervised representation learning model and split batch repetition strategy.
  Keywords: long-tail recognition; self-supervised learning; decoupled learning; image classification; deep learning; neural network; computer vision;.
  
  
• Integrated power information operation and maintenance system based on D3QN algorithm with experience replay  
  by Yang Yu, Heting Li, Dongsheng Jing 
  Abstract: Owing to the expanding scale of the power system and the variety of equipment, the operation and maintenance tasks require more efficient scheduling algorithms to accommodate large-scale data. To solve the problems of delays and interruptions in scheduling, an integrated operation and maintenance task scheduling algorithm based on D3QN with experience replay is designed. The proposed method performs Markov modelling of the operation and maintenance scheduling scenario, which takes the work order queue and the current total amount of resources as the state, selects tasks in each discrete time period as the action, minimises the average waiting time as the goal. Considering the different levels of urgency of faults, the reward function is designed according to the fault level. In addition, the five-tuple attributes of work orders and their constraints for different tasks such as execution time, maximum load and resource requirements are also taken into account. Finally, the action with the highest Q value in the current state is always selected as the optimal policy of the operation and maintenance task. Experiments on several tasks demonstrated that the proposed method significantly improves the efficiency of overhaul tasks.
  Keywords: orthogonal architecture; reinforcement learning; maintenance; experience replay; integrated operation and maintenance.
  DOI: 10.1504/IJCSE.2023.10058662
   
  
• Time series models for web service activity prediction  
  by Mukta Kambhampati, Sandhya Harikumar 
  Abstract: Web service providers have to be very vigilant in offering their services to their clients and ensure that there are no glitches. In this research, we propose a method for anticipating the load of user activities at various intervals of time that can help in identifying the best time for maintaining the various software upgrades. Machine learning approach for predicting the user traffic is leveraged based on various time series analysis techniques and long short-term memory (LSTM). Further, we provide a graphical visualisation of user traffic at regular intervals and notify the stake holders if the traffic is more than the threshold. The contribution of the work lies in embedding time series analysis with good visualisation for Real-time monitoring, querying the traffic condition, and predictive analysis. The results are validated using the evaluation metric mean absolute percentage error (MAPE) and the visualisations are rendered using Grafana visualisation tool.
  Keywords: machine learning; time series analysis; ARIMA; SARIMA; Grafana; Prometheus; long short-term memory; LSTM.
  DOI: 10.1504/IJCSE.2023.10058966
   
  
• Adjustable rotation gate-based quantum evolutionary algorithm for energy optimisation in cloud computing systems  
  by Jyoti Chauhan, Taj Alam 
  Abstract: The widespread adoption of cloud computing and a rapid rise in capacity and scale of data centres results in a significant increase in electricity usage, rising data centre ownership costs, and increased carbon footprints. One of the challenging research problems of this point in time is to reduce the energy consumption in cloud data centres which leads motivation of green cloud computing. This article presented an energy efficient improved quantum evolutionary algorithm to provide optimal solution for NP-hard task scheduling problem in cloud computing. An adjustable quantum rotation gate operator with adaptively dynamic adjustment of the rotation angle is developed according to the evolutionary generations and change in fitness values in each generation which serves as a major operation to update the population of the conventional QEA. The efficiency and accuracy of proposed QEA are greatly improved to measure the energy efficiency, makespan and resource use in heterogeneous CC environment. Simulation results of the CloudSim shows that proposed QEA is superior to traditional PSO and GA to solve NP-hard task scheduling problem in green CC.
  Keywords: cloud computing; scheduling; quantum evolutionary algorithm; rotation gate; particle swarm optimisation; genetic algorithm; energy efficiency; task scheduling problem.
  DOI: 10.1504/IJCSE.2023.10059058
   
  
• Research on assessment of hybrid teaching mode in colleges stems from deep learning algorithm  
  by Jinghui Xiu, Yingnan Ye 
  Abstract: The blended learning model combines traditional classroom instruction with online learning and has shown significant impact in higher education. Analysis of its effectiveness reveals a decrease in the root-mean-square deviation and the smallest mean squared error, indicating optimal training results. The network intrusion detection model has the lowest mean absolute error compared with other models. The SecRPSO-SVM model has the smallest average absolute percentage error. This innovative teaching model promotes personalised and autonomous learning, cooperative learning, and interactive communication. The use of deep learning algorithms provides new methods for educational assessment and personalised learning, positively impacting the future development of higher education.
  Keywords: deep learning; mixed teaching; evaluation model; SecRPSO-SVM; principal component analysis.
  DOI: 10.1504/IJCSE.2023.10059160
   
  
• An investigation of CNN-LSTM music recognition algorithm in ethnic vocal technique singing  
  by Fang Dong 
  Abstract: A HPSS separation algorithm considering time and frequency features is proposed to address the issue of poor performance in music style recognition and classification. A CNN network structure was designed and the influence of different parameters in the network structure on recognition rate was studied. A deep hash learning method is proposed to address the issues of weak feature expression ability and high feature dimension in existing CNN, which is combined with LSTM networks to integrate temporal dimension information. The results show that, compared with other models such as GRU+LSTM, the double-layer LSTM model used in the study had higher recognition results, with a size of over 75%. This indicates that combining feature learning with hash encoding learning can achieve higher accuracy. Therefore, this model is more suitable for music style recognition technology, which helps in music information retrieval and improves the classification accuracy of music recognition.
  Keywords: music recognition; ethnic vocal music; LSTM; CNN; hash layer.
  DOI: 10.1504/IJCSE.2023.10059161
   
  
• Classification of hyperspectral images by using a lightweight cascaded deep convolutional network  
  by Sandhya Shinde, Hemant Patidar 
  Abstract: In recent years, deep learning frameworks have been increasingly used to address hyperspectral image classification (HIC) challenges, with outstanding results. Existing network models, on the other hand, are more sophisticated and require longer computing. Traditional HIC algorithms frequently ignore the relationships among local spatial factors. This paper introduces a novel lightweight cascaded deep convolutional neural network (LC-DCNN) that represents the spatial and spectral properties of hyperspectral pictures. The proposed LC-DC's performance is tested for several spectral band reduction approaches used to reduce the computational cost of HIC, such as principal component analysis (PCA) and linear discriminant analysis (LDA). The suggested algorithm's efficacy is validated on the Indian Pines and Salinas datasets using accuracy, recall, precision, and F1-score. On the datasheet for IPs, the LC-DCNN+LDA and LC-DCNN+PCA provide overall accuracy of 99.00% and 98.01%, respectively. In the SD, however, LC-DCNN+LDA and LC-DCNN+PCA both provide overall accuracy of 99.6% and 98.62%, respectively. The proposed approach provides superior results (99.6% accuracy) compared with traditional state of arts employed previously for the HIC.
  Keywords: hyperspectral image classification; deep learning; convolutional neural network; principal component analysis.
  DOI: 10.1504/IJCSE.2023.10059162
   
  
• Convolutional neural network optimization for discovering plant leaf diseases with particle swarm optimizer  
  by Vishakha A. Metre, S.D. Sawarkar 
  Abstract: The agriculture industry contributes most to expanding economies and populations, but plant diseases restrict the food production. Utilising an automatic detection method, early diagnosis of plant diseases can improve food production quality and reduce financial losses. The scope of research using deep learning and swarm intelligence in the conventional plant disease identification process is explored. Convolutional neural network (CNN) is precise for image classification problems, but its efficiency is hyper parameter selection dependent. Hence, proposed work utilises particle swarm optimisation algorithm in tuning five influential hyper parameters of a CNN architecture that optimises the process of identification and classification of plant diseases. Experimentation is conducted on 10,567 images for 10 classes including healthy and diseased plant leaves of five species from PlantVillage dataset, covering bacterial, fungal and viral diseases. An optimised CNN prototype is attained, providing 98.52% of accuracy with less parameters and training time as compared to pre-trained models.
  Keywords: convolutional neural network; deep learning; hyper-parameter optimisation; particle swarm optimisation; plant leaf disease detection.
  DOI: 10.1504/IJCSE.2023.10059723
   
  
• Conjugate gradient with Armijo line search approach to investigate imprecisely defined unconstrained optimisation problem  
  by Paresh Kumar Panigrahi, Sukanta Nayak 
  Abstract: The main focus of the study is to investigate nonlinear systems with uncertainties. Here the epistemic type of uncertainties is considered as fuzzy. As such, the present study analyses fuzzy nonlinear systems. In order to solve the fuzzy nonlinear systems, one of the ways is to transform the system into a fuzzy unconstrained optimisation problem. In this context, the concept of conjugate gradient descent optimisation method is used with fuzziness, and the fuzzy parameters were utilised to develop the fuzzy conjugate gradient descent optimisation (FCGDO) algorithm to solve the fuzzy unconstrainted nonlinear optimisation problem. The significance of the FCGDO algorithm is used to fuzzy parameter the Armijo-type line search perform to guarantee that it possesses fast convergence for large-scale problems. Then, the convergence study and comparison are done with seven other approaches with the same case studies and found that the proposed algorithm performs well. Further, to quantify the uncertainties, the system is investigated with fuzzy and fully fuzzy through the case study. The impact of this algorithm is expected to handle various real-life application problems where uncertainty exists, this advancement and improvement can be implemented in future direction of research in both academia and industry.
  Keywords: fuzzy set; triangular fuzzy number; unconstrained optimisation problem; fuzzy conjugate gradient descent optimisation technique; convergence analysis.
  DOI: 10.1504/IJCSE.2023.10059724
   
  
• A deep learning based automated phenotyping for identification of overuse of synthetic fertilisers in Amaranthus crop  
  by J. Dhakshayani, B. Surendiran, J. Jyothsna, A.S. Syed Shahul Hameed, Narendran Rajagopalan 
  Abstract: Amaranth (Amaranthus spp.) is a significant leafy vegetable and cereal crop with high nutrient benefits that is widely consumed worldwide. To maximise its yield, farmers massively rely upon synthetic fertilisers to enhance the quality of the crop. However, this obsessive usage of inorganic fertiliser leads to severe ecosystem damage. For agricultural and ecological sustainability, it is essential to comprehend the process underlying this environmental degradation. This paper analyses the effect of inorganic fertilisers on the growth and yield of Amaranthus. By identifying the productivity and adaptability of Amaranthus in different chemically treated soil conditions and automatically phenotyping its traits using image-based deep learning models, this study aims to determine the overuse of synthetic fertilisers. A comparative evaluation of different state-of-art CNN models was carried out, and the experimental result proves that DenseNet-121 could be a more appropriate learning algorithm for the proposed system with 84% accuracy. It is believed that the proposed deep learning based automated phenotyping framework could greatly assist farmers in understanding the actual requirement of soil, thus avoiding the residual impact of fertiliser abuse in the environment.
  Keywords: Amaranthus; automated phenotyping; fertiliser overuse; deep learning; DenseNet-121.
  DOI: 10.1504/IJCSE.2023.10059782
   
  
• Cost-sensitive budget adaptive label thresholding algorithms for large-scale online multi-label classification.  
  by Rui Ding, Tingting Zhai 
  Abstract: Kernel-based methods have proven effective in addressing nonlinear online multi-label classification tasks. However, their scalability is hampered by the curse of kernelisation when handling large-scale tasks. Additionally, the class-imbalance of multi-label data can significantly impact their performance. To mitigate both challenges, we propose two cost-sensitive budget adaptive label thresholding algorithms. Firstly, we introduce a cost-sensitive strategy to assign varying costs to the misclassification of different labels, building upon the first-order adaptive label thresholding algorithm. Furthermore, we present two merging budget maintenance strategies: 1) a global strategy where all predictive models share one support vector pool and undergo simultaneous budgeting; 2) a separate strategy that utilises two independent support vector pools
  Keywords: kernel-based methods; large-scale online multi-label classification; curse of kernelisation; class-imbalance; cost-sensitive; budget maintenance.
  DOI: 10.1504/IJCSE.2023.10060217
   
  
• Digital twin-based fault detection for intelligent power production lines  
  by You Zhou, Xuefeng Qian, Dan Xu, Can Zhao, Kejun Qian 
  Abstract: Digital twin technology realises real-time capture of system operation status, real-time monitoring and prediction of potential risks. In view of this, a fault detection method based on digital twin of power production line is proposed, where the attention technology required by virtual production line fault capture technology and model establishment combined with deep reinforcement learning model is used to analyse the power production line and realise fault detection. The method takes the node-related features of the visualisation equipment and power production line as input, analyses the possible production line faults through computer vision, and performs image recognition on all the collected pictures. The feature data collected by installing inspection equipment has rich information and spatiotemporal accompanying information of intelligent power production line, and the fault detection model of intelligent power production line constructed by digital twin has high confidence. The experimental results verified the effectiveness of the proposed method.
  Keywords: digital twin; intelligent production line; deep learning; attention technology; power production line fault detection.
  DOI: 10.1504/IJCSE.2023.10060218
   
  
• Alliance: a makespan delay and bill payment cost saving-based resource orchestration framework for the blockchain and hybrid cloud enhanced 6G serverless computing network  
  by Mahfuzul H. Chowdhury 
  Abstract: Serverless computing technology with the function-as-a-service and backend-as-a-service platforms provides on-demand service, high elasticity, automatic scaling, service provider-based server/operating system management, and no idle capacity charges facilities to the users. Owing to limited resources, the traditional research articles on public/private cloud-based serverless computing cannot meet the user's IoT application requirements. The current works are limited only to a single job type and objectives. There is a lack of appropriate resource orchestration schemes for low latency, bill payment, and energy-cost-based serverless computing job execution networks with hybrid cloud and blockchain. To overpower these issues, this paper instigates a low latency, energy-cost, and bill payment-based multiple types of job scheduling, resource orchestration, network, and mathematical model for the blockchain and hybrid cloud-enhanced serverless computing network. The experimental results delineated that up to 70% makespan delay and 30.23% bill payment gain are acquired in the proposed alliance scheme over the baseline scheme.
  Keywords: serverless computing; job scheduling; worker selection; resource orchestration; blockchain; cloud computing; 6G; job execution time; user bill payment.
  DOI: 10.1504/IJCSE.2023.10060388
   
  
• An improved continuous and discrete Harris Hawks optimiser applied to feature selection for image steganalysis  
  by Ankita Gupta, Rita Chhikara, Prabha Sharma 
  Abstract: To attack advanced steganography, high-dimensional rich feature set such as spatial rich model (SRM) (34671-dimensional) is extracted for image steganalysis. To address the dimensionality curse, researchers utilised feature selection techniques and developed efficient steganalysers. In this study, the Harris Hawks optimiser (HHO) is combined with particle swarm optimisation (PSO) and differential evolution (DE) to increase the exploitation and exploration capabilities of HHO respectively. This hybridised HHO is called DEHHPSO and is giving good results on continuous optimisation problems as well as on feature selection problems. Initially, the Fisher filter method is used to discard some irrelevant features and the resultant features are passed to the proposed DEHHPSO feature selection method. The combined approach removes more than 94% features of the SRM feature set with improved detection accuracy when compared with state-of-the-art methods. The classification performance using the selected features is also superior to the several deep learning networks of steganalysis.
  Keywords: steganalysis; steganography; spatial rich model; SRM; Harris Hawks optimiser; HHO; particle swarm optimisation; PSO; differential evolution.
  DOI: 10.1504/IJCSE.2023.10060447
   
  
• A novel DenseNet-based architecture for liver and liver tumour segmentation  
  by Deepak Jayaprakash Doggalli, B. S. Sunil Kumar 
  Abstract: The segmentation of the liver and lesions plays an important role in the clinical interpretation and therapeutic planning of hepatic disorders. Analysing volumetric computed tomography scans physically is time-consuming and imprecise. In this work, we propose an automatic segmentation technique based on DenseNet, in which each layer receives feature maps from all layers before it and uses summation instead of concatenation for combining the features. The architecture uses the U-Net model as its basis, and all blocks in each tier of the U-Net architecture are replaced with DenseNet blocks. Utilising DenseNets improves the gradient flow throughout the network, allowing each layer to recognise more diverse and low-complexity features. Other U-Net-based hybrid models use two-dimensional filters that might overlook contextual information. In contrast, the proposed model accepts as input a stack of five adjacent slices and returns a segmentation map for the middle slice. Consequently, this method is highly parameter-efficient and enables enhanced feature extraction. On the LiTs dataset, the experimental findings reveal a liver segmentation accuracy of 94.9% dice score and a tumour segmentation accuracy of 79.2% dice score. On the LiTs competition website, the result indicated that the accuracy was comparable to that of the most effective techniques.
  Keywords: liver lesion segmentation; U-Net; DenseNet; computed tomography images; convolutional neural networks; Kaiming initialisation; leaky Relu activation function.
  DOI: 10.1504/IJCSE.2023.10060448
   
  
• Fake content detection on benchmark dataset using various deep learning models  
  by Chetana Thaokar, Jitendra Kumar Rout, Himansu Das, Minakhi Rout 
  Abstract: The widespread use of social media and its development have offered a medium for the propagation of fake contents quickly among the masses. Fake contents frequently misguide individuals and lead to erroneous social judgments. Individuals and society have been harmed by the dissemination of low-quality news content on social media. In this paper, we have worked on a benchmark dataset of news content and proposed an approach comprising basic natural language processing techniques with different deep learning models for categorising content as real or fake. Different deep learning models employed are LSTM, bi-LSTM, LSTM and bi-LSTM with an attention mechanism. We compared the outcomes by using one hot word embedding and pre-trained GloVe technique. On benchmark LIAR dataset, the LSTM achieved a better accuracy of 67.2%, while the bi-LSTM with GloVe word embedding reached an accuracy of 67%. An accuracy of 98.22% is achieved using bi-LSTM and 97.98% using LSTM on Real-Fake dataset. Fake news can be a menace to society, so if it is detected early, harmony can be maintained in society and individuals can avoid being misled.
  Keywords: fake news; word embedding; global vectors; GloVe; LIAR dataset; deep learning models; bidirectional encoder representations from transformer; BERT.
  DOI: 10.1504/IJCSE.2023.10060449
   
  
• Performance assessment of multi-unit web and database servers distributed system  
  by Muhammad Salihu Isa, Jinbiao Wu, Ibrahim Yusuf, U.A. Ali, Tijjani W. Ali, Abubakar Sadiq Abdulkadir 
  Abstract: The present paper focuses on evaluating the performance of a complex multi-unit computer networking system. Understanding the performance of such systems is crucial for ensuring efficient operation and identifying areas for improvement. The networking system being analysed consists of four interconnected subsystems. This architecture with subsystems connected in a series-parallel is commonly found in real-world computer networks. The present paper considers two types of system failures: degraded failure and total failure. Understanding failure scenarios and their consequences helps in designing resilient systems and developing effective fault-tolerant mechanisms. The study utilises supplementary variables techniques, Laplace transforms, Copula and general distribution to analyse and model system’s behaviour. The findings can inform the design and optimisation of similar complex networking systems, leading to improved performance, fault tolerance and overall system reliability. The insights gained from this study may also aid in decision-making processes related to network architecture, load balancing strategies and system resilience.
  Keywords: performance; web server; database server; multi-unit; k-out-of-n: G policy and availability.
  DOI: 10.1504/IJCSE.2023.10060450
   
  
• Sparse landmarks for facial action unit detection using vision transformer and perceiver  
  by Duygu Cakir, Gorkem Yilmaz, Nafiz Arica 
  Abstract: The ability to accurately detect facial expressions, represented by facial action units (AUs), holds significant implications across diverse fields such as mental health diagnosis, security, and human-computer interaction. Although earlier approaches have made progress, the burgeoning complexity of facial actions demands more nuanced, computationally efficient techniques. This study pioneers the integration of sparse learning with vision transformer (ViT) and perceiver networks, focusing on the most active and descriptive landmarks for AU detection across both controlled (DISFA, BP4D) and in-the-wild (EmotioNet) datasets. Our novel approach, employing active landmark patches instead of the whole face, not only attains state-of-the-art performance but also uncovers insights into the differing attention mechanisms of ViT and perceiver. This fusion of techniques marks a significant advancement in facial analysis, potentially reshaping strategies in noise reduction and patch optimisation, setting a robust foundation for future research in the domain.
  Keywords: action unit detection; sparse learning; vision transformer; perceiver.
  DOI: 10.1504/IJCSE.2023.10060451
   
  
• Enhancing e-commerce product recommendations through statistical settings and product-specific insights  
  by Onur Dogan 
  Abstract: In the e-commerce industry, effectively guiding customers to select desired products poses a significant challenge, necessitating the use of technology and data-driven solutions. To address the extensive range of product varieties and enhance product recommendations, this study improves upon the conventional association rule mining (ARM) approach by incorporating statistical settings. By examining sales transactions, the study assesses the statistical significance of correlations, taking into account specific product details such as product name, discount rates, and the number of favourites. The findings offer valuable insights with managerial implications. For instance, the study recommends that if a customer adds products with a high discount rate to their basket, the company should suggest products with a lower discount rate. Furthermore, the traditional rules are augmented by incorporating product features. Specifically, when the total number of favourites is below 7,500 and the discount rate is less than 75%, the similarity ratio of the recommended products should be below 0.50. These enhancements contribute significantly to the field, providing actionable recommendations for e-commerce companies to optimise their product recommendation strategies.
  Keywords: association rules; basket analysis; statistical tests; e-commerce.
  DOI: 10.1504/IJCSE.2023.10060975
   
  
• ORNAIC-TDNSI optimal RetinaNet with artificial immune classification for text detection on natural scene images  
  by Sharfuddin Waseem Mohammed, Brindha Murugan 
  Abstract: Text detection and recognition from natural scene images is helpful in many industrial, surveillance, and security applications. Text detection in natural scenes is a vital but challenging issue due to differences in line orientation, text fonts and size. This study introduces an optimal RetinaNet with artificial immune classification for text detection on natural scene images (ORNAIC-TDNSI). The ORNAIC-TDNSI model encompasses two major processes namely textual region detection and text recognition from detected regions. At the initial stage, the RetinaNet object detector is applied for detection of textual regions in the natural scene images. For enhancing the detection efficiency of the RetinaNet model, group teaching optimisation algorithm (GTOA) is utilised. Next, artificial immune classification (AIC) model is applied for accurate text recognition. The experimental validation of the ORNAIC-TDNSI model is tested on ICDAR-2015, ICDAR-2017, and Total-Text datasets. The comparison study reported that the ORNAIC-TRNSI model outperforms the other DL models.
  Keywords: natural scene images; text recognition; deep learning; RetinaNet; artificial immune classification; AIC.
  DOI: 10.1504/IJCSE.2023.10061549
   
  
• Value chain for smart grid data: a brief review  
  by Feng Chen, Huan Xu, Jigang Zhang, Guiyu Li 
  Abstract: Smart grids are now crucial infrastructures in many countries. They build two-way communication between customers and utility enterprises. Since power and energy are associated with human activities, smart grid data are extremely valuable. At present, these data are currently being used in some areas. As a novel asset, the value of smart grid data needs quantitative measurement for evaluation and pricing. To achieve this, it is essential to analyze the overall process of value creation, which can help calculate costs and discover potential applications. The process can be effectively revealed by building a data value chain for smart grid data, which illustrates the data flow and clarifies the data sources, analytics, utilization, and monetization. This article provides a three-step data value chain for smart grid data and expounds on each step. This article also reviews various methods and some challenges with smart grid data.
  Keywords: smart grid; data value chain; data collection; data analysis; data monetisation.
  DOI: 10.1504/IJCSE.2024.10061602
   
  
• Integrating rich event-level and schema-level information for script event prediction  
  by Wei Qin, Xiangfeng Luo, Hao Wang 
  Abstract: A script is consists of a series of structured event sequences extracted from the texts.Given historical scripts, script event prediction aims to predict the subsequent event.The critical aspect in script event prediction is how to effectively represent events, which plays an important role in making accurate predictions. Most existing methods describe events through verbs and a few core independent arguments (i.e. subject, object, and indirect object), which lack the capability to deal with complex and sparse event data. In this paper, we propose a Hierarchical Event Prediction (HEP) model, which integrates information from both event-level and schema-level. At the event level, HEP enriches the existing event representation with extra arguments (i.e., time, place) and modifiers, which provides in-depth event information. At the schema level, it induces the sparse events into conceptual schema, which improves the model’s generalization ability to make more reasonable predictions. To more effectively integrate these two...
  Keywords: script event prediction; SEP; event-level; schema-level; contrast learning; bimodal cross attention; BCA.
  DOI: 10.1504/IJCSE.2024.10061887
   
  
• An efficient stitching algorithm for aerial images with low-overlap  
  by Qingshan Tang, Huang Jiang, Sijie Li 
  Abstract: In fields such as military reconnaissance, the overlap between images captured by UAVs is limited to 15%-30%. To achieve larger perspective panoramic images from low-overlap aerial images, this study proposes an efficient algorithm for image stitching. Specifically, the algorithm utilises the oriented fast and rotated brief (ORB) and grid-based motion statistics (GMS) algorithms. Next, image alignment is achieved by calculating location-dependent homographies based on the grid. For seamless integration, the algorithm combines optimal seam blending and gradient-domain fusion techniques. Experimental results demonstrate that the proposed algorithm outperforms the scale-invariant feature transform (SIFT) and the affine-scale invariant feature transform (ASIFT) algorithms in terms of feature point matching accuracy. Moreover, comparisons with other algorithms, such as the as-projective-as-possible (APAP), adaptive as-natural-as-possible (AANAP), and single-perspective-warps (SPW), it is proved that the proposed algorithm can obtain high-quality stitched images. While the problem of slow stitching speed, poor alignment, and ghosting is effectively solved.
  Keywords: aerial image; image stitching; motion grid statistics; optimal stitching; gradient fusion.
  DOI: 10.1504/IJCSE.2023.10061946
   
  
• A quantum evolutionary algorithm inspired by manta ray foraging optimisation  
  by Shikha Gupta, Naveen Kumar 
  Abstract: Manta Ray Foraging Optimisation (MRFO) algorithm is a relatively recent bio-inspired technique that has been tested for optimisation problems and proven to be effective in several ways, such as better accuracy, enhanced performance, and lower computational cost. Quantum-motivated computing has been aimed at improving our ability to solve complex combinatorial optimisation problems. The present work proposes a novel continuous space optimization approach inspired by quantum computing and the MRFO algorithm. The performance of the proposed algorithm is examined vis-a-vis the standard MRFO algorithm for optimizing the value of 20 benchmark functions. While both algorithms compete well in finding the best fitness values, the proposed approach shows better convergence for 16 out of 20 functions. Wilcoxon signed ranks test is used to evaluate the significance of the improved convergence for the proposed algorithm. Results show that introducing the quantum computing mechanism is effective in improving the convergence of the MRFO algorithm.
  Keywords: angle-coded; bio-inspired; qubits encoding; Bloch coordinates; meta-heuristic approach.
  DOI: 10.1504/IJCSE.2024.10062570
   
  
• TSALSHADE: improved LSHADE algorithm with tangent search  
  by Abdesslem Layeb 
  Abstract: DE algorithm is among the most successful algorithm for numerical optimization However, like other metaheuristics, DE suffers from several weaknesses like weak exploration and local minimum stagnation problems Besides, most DE variants including the most efficient ones like LSHADE variants, suffer in presence of hard composition functions containing global optima hard to reach On the other hand, Tangent Search Algorithm (TSA) has shown an effective capacity to deal with hard optimization functions thanks to the tangent flight operator This one offers a good way to escape from local optima of hard test functions while preserving good exploration capacity In this scope, a hybrid TSA and LSHADE algorithm called TSALSHADE is proposed The main advantage of the new proposed algorithm is its capacity to deal with hard composite functions The experimental study on the latest CEC 2022 benchmark functions has shown that TSALSHADE provides very promising and competitive results.
  Keywords: differential evolution; LSHADE; tangent search algorithm; optimisation.
  DOI: 10.1504/IJCSE.2024.10062887
   
  
• Multi-label classification and fuzzy similarity-based expert identification techniques for software bug assignment  
  by Rama Ranjan Panda, Naresh Kumar Nagwani 
  Abstract: In software development, bug fixing is a time-consuming and labor-intensive process. A bug can occur due to multiple failures in software, and it may require multiple developers to fix it. Machine learning approaches belong to discriminative learning, and a developer is assigned to a software bug with an agreed level of opinion from the assigner. But instances of software bugs are textual and fuzzy. Hence, it cannot be classified as having a clear-cut outcome. Furthermore, the bug assigner faces lots of difficulties as the bug belongs to multiple categories. This has motivated the authors to devise two fuzzy system-based automatic software bug assignment techniques, namely, fuzzy bug assignment technique for software developers and unique term relationships (FDUR) and fuzzy bug assignment technique for software developers and category relationships (FDCR). To measure and compare the performance of both techniques with other techniques, experiments are carried out on the benchmark software repositories.
  Keywords: bug assignment; expert finding; decision making; fuzzy logic; mining bug repositories; machine learning; fuzzy similarity.
  DOI: 10.1504/IJCSE.2024.10063031
   
  
• A predictive model based on the LSTM technique for the maintenance of railway track system  
  by Sharad Nigam, Divya Kumar 
  Abstract: Maintenance is a substantial process to sustain the operations of transportation system. Railway is a major way of transportation, defect and failure in track side equipment and track itself may causes major loss of human lives. So, an effective maintenance technique is needed which saves the passenger lives and maximise the utilisation of railway track equipment, just before it fails, that cause casualty. There are various types of track defects to be inspected, but this paper only deals with surface defects, cross level, and DIP. By examining the track condition and expecting the RUL, Railway industry can schedule maintenance of railway tracks. In this research paper, we have tried to conduct a relative analysis of various machine learning algorithms by comparing their performance for estimation future failure point of railway tracks to inspect the reliability of LSTM technique. Dataset is taken from the trusted source RAS Track geometry analytics (2015).
  Keywords: predictive maintenance; K-nearest neighbour; KNN; machine learning; long short-term memory; LSTM; railway track; support vector machine; SVM.
  DOI: 10.1504/IJCSE.2024.10063100
   
  
• DMRFO-CD: a discrete manta ray foraging inspired optimisation algorithm for community detection in networks  
  by Priyanka Gupta, Shikha Gupta, Naveen Kumar 
  Abstract: Evolutionary algorithms are meta-heuristic approaches that have effectively addressed complex optimization problems. The problem of detecting communities in networks using evolutionary algorithms has received substantial attention from researchers. Manta Ray Foraging Optimization (MRFO), a recently proposed real-valued evolutionary algorithm, has demonstrated superior performance in challenging optimization engineering problems. The present work adapts the MRFO algorithm for the discrete-valued community detection problem while optimizing (maximizing) network modularity, a measure of the density of connections within a community. Experiments on synthetic and real-world benchmark networks show that the proposed approach successfully detects community structures with high modularity. Normalized Mutual Information (NMI) was computed to determine the quality of the detected communities. High NMI values (> 0.75) were obtained for most of the networks. However, since the proposed method maximizes the modularity value at the cost of closeness with the original community structure, one-third of networks exhibited NMI values below 0.5.
  Keywords: evolutionary algorithms; network modularity; normalised mutual information; metaheuristic; swarm-based; partitioning.
  DOI: 10.1504/IJCSE.2024.10063171
   
  
• A food safety traceability system based on trusted double chain  
  by Haoran Chen, Jiafan Wang, Hongwei Tao, Yinghui Hu, Yanan Du 
  Abstract: In recent years, global food safety issues have been increasingly prevalent, posing threats to people’s health and lives. Traditional food traceability systems face significant challenges due to centralised storage, data silos, and the potential for information tampering. This article proposes an improved RAFT consensus algorithm for the first time, applied to trace the production process of food based on private chains. Subsequently, an enhanced PBFT consensus algorithm is introduced for tracing the distribution process of food based on consortium chains. Finally, a reliable dual-chain food quality and safety traceability system based on the Ethereum blockchain platform is presented. This system effectively addresses data reliability issues on the chain by introducing a reliability measurement evaluation module. Moreover, the application of aggregated signature technology enhances the performance of the traceability system, ensuring the authenticity, reliability, and tamper resistance of traceability information. This innovation not only strengthens the privacy protection and data security of food quality and safety tracking but also helps maintain the commercial interests of all parties involved.
  Keywords: food traceability; blockchain; cross-agency traceability system.
  DOI: 10.1504/IJCSE.2024.10063226
   
  
• A multi-agent intrusion detection model based on importance feature extraction  
  by Yang Yu, Ping He, Shengli Xing 
  Abstract: The swift evolution of the internet has delivered convenience to people while also introducing challenges concerning the security of information. Network intrusion detection, which recognizes distinct attack behaviors in the network by gathering and analyzing network data, is a key piece of technology for information security. Therefore, a multi-agent intrusion detection model based on important feature extraction is proposed in this study, named MIDI, that effectively solves the problems of feature redundancy, no consideration of correlation between the data, and the inability to detect various attack modes in traditional intrusion detection methods. MIDI first calculates the importance indicator of features in network traffic by combining attention mechanisms to extract features, and then introduces the reinforcement learning method to build a multi-agent model to learn various attack modes in the network. Ultimately, in contrast to conventional intrusion detection models, the experimental outcomes demonstrate MIDI's efficiency and supremacy in assignments.
  Keywords: intrusion detection; importance index; feature extraction; multi-agent.
  DOI: 10.1504/IJCSE.2024.10063227
   
  
• A content-adaptive video compression method based on transformer  
  by Heting Li, Dongsheng Jing, Ping He, Jiasheng Wu 
  Abstract: Convolutional neural network architectures have been the primary choice for deep learning-based video compression algorithms in recent years, but common convolutional neural networks can only exploit local correlations, while compression is faced with a wide variety of data types, making the compression performance and generalization ability of the model challenging. Therefore, we propose a transformer-based content-adaptive video compression (TAVC) method based on transformer that can effectively achieve better compression while improving the generalization ability of the model. To further enhance compression performance, we specifically take advantage of the nonlocal correlation between features and propose a transformer-based compression network for residual coding and motion information coding. In addition, we design a content-adaptive algorithm to choose the best encoder parameters for various videos. Experiments show that TAVC outperforms current mainstream deep learning-based video compression coding algorithms on the HEVC dataset.
  Keywords: deep learning; video compression; content-adaptive; transformer.
  DOI: 10.1504/IJCSE.2024.10063351
   
  
• Robust link-assessment-based approach for detection and isolation of blackhole attacker in resource constraint internet of things  
  by Himanshu Patel, Devesh Jinwala 
  Abstract: Blackhole is one of the crucial packet-dropping attacks that can be launched on Routing Protocol for Low-power lossy networks (RPL). In this paper, we propose an improved specification-based approach for textit{integrating trust} value in specification-based approach to detect and isolate blackhole attackers, based on a textit{tangible value} of packet-forwarding behaviour of the nodes in the network. To our knowledge, this is the first attempt to integrate packet-forwarding trust with the specification-based approach. In our proposed approach, lightweight-specification data are maintained at each device and shared periodically with the resource-rich edge device. Resource-insensitive computations are carried out at the edge device. The proposed approach uses a robust data collaboration mechanism that ensures detection-data delivery between nodes and edge devices, even in the presence of 10% of attacker nodes. Our simulations on Cooja simulator show that in an environment with 20% data loss, the proposed approach yields more than 80% TPR
  Keywords: internet of things; RPL; blackhole attack; trust.
  DOI: 10.1504/IJCSE.2024.10063354
   
  
• Finite element analysis of the liver subjected to non-invasive indirect mechanical loading  
  by Samar Shaabeth, Amina Kadhem, Hassanain Ali Lafta 
  Abstract: Injuries from non-invasive abdominal blunt trauma represent 75% of no visible bleeding traumas. As a step for additional faster and more reliable diagnostic tool, simulation was performed of karate kick, punch, and pinpoint loading on the front, back, right, and left sides of the abdomen for 31-and 50-years old females and a 45-year-old male 2D segmented computed tomography images. The organs densities and mechanical properties were applied. Mechanical analysis was performed during 0.5 s, 1 s, 1.5 s and 2 s loading time. The results, compatible with previous literature, indicated the affected regions of liver, spleen, pancreas, kidney, and colon by the trauma.
  Keywords: liver; blunt abdominal trauma; BAT; simulation; finite element; indirect loading; non-invasive.
  DOI: 10.1504/IJCSE.2024.10063606
   
  
• Network traffic anomaly detection based on deep learning: a review  
  by Wenjing Zhang, Xuemei Lei 
  Abstract: Network traffic anomaly detection has become an important research topic with the increasing prevalence of network attacks. Deep learning, with its ability to analyse large-scale datasets, has emerged as a powerful tool for network traffic anomaly detection. This paper presents a comprehensive overview of state-of-the-art deep learning-based network traffic anomaly detection models including VAE, BiLSTM, and vision transformer, in terms of dimensional deduction, time dependence and data imbalance. The performance of these models has been evaluated and compared on KDDCUP99 and CICIDS2017 datasets. Finally, we outline challenges and future research aimed at enhancing the performance and practicality of network traffic anomaly detection based on deep learning.
  Keywords: anomaly detection; deep learning; network traffic; network security.
  DOI: 10.1504/IJCSE.2023.10059801
   
  
• Attack and defense simulation platform for satellite networks based on Mininet  
  by Weipeng Liang, Binjie Liao, Peixian Chen, Yipeng Wang, Shan Ai, Yu Wang, Teng Huang, Jianwei He 
  Abstract: The LEO satellite network has attracted a lot of attention due to its low latency and low link loss. However, its security has not received enough attention. In this context, we propose a LEO satellite network attack and defense simulation platform called MSP, which allows us to customise the distributed LEO satellite network for attack and defense simulation. On MSP, we implement four types of possible attacks on LEO satellite networks, namely DDoS, port scanning, FTP blasting and SSH blasting, and provide some mitigations. The experiments show that these four types of malicious attacks can bring risks such as network congestion, service disruption, port explosion, and password leaks to LEO satellite networks.
  Keywords: low earth orbit satellite network; satellite constellations; simulation; malicious attacks; security.
  DOI: 10.1504/IJCSE.2023.10058276
   
  
• Research on econometric safety model for export structure of manufacturing industry  
  by Hongbing Wang, Dongfang Hua, Haoze Feng, Guanran Ben, Jingyuan Tan, Dongjie Zhu 
  Abstract: China's export structure is gradually changing from labour-intensive products to technology-intensive products. It is of great significance to deeply analyse the impact of continuous increases in investment in research and invention on the changes in the manufacturing export structure. The current research on the formation mechanism of manufacturing export structure evolution has essential reference value for the structural evolution analysis in the previous round of development. Still, there is a lack of in-depth research on the export structure of technology-intensive products. This paper empirically studies the factors affecting the structural changes of manufacturing exports and the safety measurement model. In order to ensure the validity of the model estimation results and avoid false regression, all variables are tested for stationarity. The experimental results show that the level of economic development and the degree of openness has a significant positive impact on the changes in the structure of manufacturing exports, but the effect of expenditure on research and invention is not noticeable. On this basis, policy recommendations for further promoting the upgrading of the manufacturing export structure are put forward.
  Keywords: manufacturing industry; export structure; R&D input; econometric safety model.
  DOI: 10.1504/IJCSE.2023.10058661
   
  
• ISWM: an information security system for water surface oil spill monitoring based on NB-IoT  
  by Ye Yang, Jingyuan Tan, Chi Zhang, Jiyang Luo, Rui Zhang, Haotian Zhang, Xiaofang Li, Chuxi Nan, Dongjie Zhu 
  Abstract: The detection of marine oil spills is of great significance for protecting marine water resources and the environment. This paper proposes an oil spill monitoring platform (ISWM) based on multiple technologies. To address the challenges of the large parameter variations in the shape and spreading range of marine oil spills, as well as the issue of system power consumption, we propose a combined approach using fluorescence and image detection technologies. Data transmission is achieved through NB-IoT devices to enable real-time monitoring of marine oil spill situations. To overcome data loss and reduce the risk of data tampering, the transmitted data is encrypted using the AES algorithm, and selective data retransmission is performed. Finally, the analysed data is displayed in real-time on the terminal. Experimental result shows that the system has fast data loading speed, low power consumption, and improved detection accuracy compared to traditional platforms that rely solely on image detection. This platform provides strong real-time data monitoring capability and data reliability, greatly enhancing the ability to monitor marine oil spills.
  Keywords: big data; NB-IoT; oil spill monitoring information platform; intelligent system.
  DOI: 10.1504/IJCSE.2023.10061072
   
  
• An efficient deep learning approach for identifying interstitial lung diseases using HRCT images  
  by Nidhin Raju, D. Peter Augustine 
  Abstract: Interstitial lung disease (ILD) encompasses over 200 fatal lung disorders affecting the interstitium, leading to significant mortality rates. We propose an AI-driven approach to diagnose and classify ILD from high-resolution computed tomography (HRCT) images. The research utilises a dataset of 3,045 HRCT images and employs a two-tier ensemble method that combines various machine learning (ML) models, convolutional neural networks (CNNs), and transfer learning. Initially, ML models achieve high accuracy, with the J48 model at 93.08% accuracy, mainly highlighting the importance of diagonal-wise standard deviation. Deep learning techniques are then applied, with three CNN models achieving test accuracies of 94.08%, 92.04%, and 93.72%. Transfer learning models also show promise, with InceptionV3 at 92.48% accuracy. Ensembling these models further boosts accuracy, with the ensemble of three CNN models reaching 97.42%. This research has the potential to advance ILD diagnosis, offering a robust computational framework that enhances accuracy and ultimately improves patient outcomes.
  Keywords: interstitial lung disease; ILD; deep learning; DL; transfer learning; multi-label classification; high-resolution computed tomography; HRCT.
  DOI: 10.1504/IJCSE.2023.10060792
   
  
• Study on the capacity of a hybrid solar PV/wind turbine system using small-scale prototype application for dairy farm power demand in North Texas  
  by Dakota Messer, Hoe-Gil Lee 
  Abstract: Renewable energy systems are increasing in power production and efficiency, as the need for clean energy sources continues to increase. The purpose of this research is to design and analyse an integrated hybrid system that provides a continuous power supply. A local dairy farm's cattle cooling system operates at a power demand of 38,664.00 watts to 128,880.00 watts per day and is chosen as the load that is to be supported for an off-grid application. A 120 kilowatt wind turbine and a 150 kilowatt solar panel kit are determined to be optimal through various analyses. Integrating the subsystems with energy conversion and battery storage systems provides the power demand at all times throughout the day as well as the effectiveness of the system. This investigation incorporates a similar analysis to a small-scale prototype to validate the larger hybrid system's general characteristics and is discussed in detail.
  Keywords: wind turbine; solar PV energy; dairy farm; hybrid renewable systems.
  DOI: 10.1504/IJCSE.2023.10058164
   
  
• A context-aware factorisation machine approach for accurate QoS prediction  
  by Wenyu Tang, Mingdong Tang, Jianguo Xie 
  Abstract: Web services are very popular in constructing software systems on the internet. With the increasing number of web services with similar functionalities, quality of service (QoS) becomes a crucial concern in web service selection. However, QoS values of web services may be unknown to users for service providers used not to publish them. Moreover, QoS values usually depend on the contexts of services and their users, such as locations and network conditions. Therefore, to accurately acquire QoS values of web services is a challenge. By collecting and exploring web services' historical QoS records, this paper proposes an accurate QoS prediction approach based on context-aware factorisation machines (CAFM). The approach adapts the classic factorisation machine model by leveraging the context information of services and users. Experimental results based on a real-world QoS dataset validate the performance of the proposed approach.
  Keywords: QoS prediction; context-aware; factorisation machines; service selection.
  DOI: 10.1504/IJCSE.2024.10063385
   
  
• Availability assessment and sensitivity analysis of an MBaaS platform  
  by Francisco Airton Silva, Antonio Carvalho, José Miqueias, Jorge Macedo, Juliana Carvalho, Gustavo Callou 
  Abstract: The OpenMobster platform offers services for the mobile cloud in a complete way. However, OpenMobster's availability requires attention. Analytical models are usually used to evaluate the dependability of a system, enabling the reduction of downtime and other advantages. This work proposes a set of stochastic Petri net models focused on evaluating the availability and reliability of the MBaaS OpenMobster platform. A sensitivity analysis was performed to identify the system's most critical components. The base model obtained an availability considered low, 96.8%. An extended cold-standby model with server redundancy was implemented, resulting in a better availability, 97.09%. Due to the low significant increase in availability, another redundancy strategy was applied to the MBaaS service model. A self-healing technique was used, which presented the best availability among the three proposed models with 99.91%.
  Keywords: OpenMobster; dependability; MBaaS; cloud; downtime; mobile cloud.
  DOI: 10.1504/IJCSE.2023.10058179
   
  
• A light-weight model with granularity feature representation for fine-grained visual classification  
  by Qiumei Zheng, Tianqi Peng, Ding Huang, Fenghua Wang, Nengxiang Xu 
  Abstract: Fine-grained image recognition can provide a more precise recognition technique for industrial production and applications. However, since it is difficult to capture comprehensive features and discriminative regions in convolutional neural networks (CNN), this ability is largely limited. With a lightweight orientation, we here use the advantage of Transformer in capturing global features by combining the technically mature CNN, and propose a lightweight model MV-GFR based on MobileViT. Further, we also propose three lightweight modules to help the network capture more subtle differences. First, we used the training module to provide the network with richer granularity information while ensuring its global integrity. Second, we used the feature part mask module in combining the diversity of CNN and the saliency of the transformer. Finally, we used the feature fusion module to integrate features of different levels and generate a complement between the global and local features. We then demonstrated the effectiveness of this scheme through experiments on three commonly used datasets.
  Keywords: fine-grained visual classification; FGVC; light-weight; granularity feature; convolutional neural network; CNN.
  DOI: 10.1504/IJCSE.2023.10060612
   
  
• Supervised learning models to predict mental illness and its severity from Reddit posts  
  by Neha Arun Angadi, Navya Eedula, Kshitij Prit Gopali, R. Jayashree 
  Abstract: Given the growing popularity of free dialogue on social media, this paper presents a methodology for identifying mental illnesses from Reddit posts where users describe their experiences with illnesses like bipolar disorder, borderline personality disorder (BPD), depression, eating disorders, obsessive-compulsive disorder (OCD), panic disorder, post-traumatic stress disorder (PTSD), and schizophrenia. After data cleaning and pre-processing with the standard NLP techniques on the posts, hyperparameter tweaking helped evaluate multiple different supervised classification models, from which the LinearSVC model delivered the best results with 78.25% accuracy. CalibratedClassifierCV helped with probabilistic calibration for the model. If the findings revealed that multiple mental diseases had comparable probability, a second step of classification was performed using a questionnaire that described the user's conditions, which the model used to determine the mental illness. The final step is to assess the severity of the sickness, which helps analyse the next plan-of-action to tackle the mental disorder.
  Keywords: supervised machine learning; probability calibration; text pre-processing; mental illness; Reddit posts; severity detection.
  DOI: 10.1504/IJCSE.2023.10058168
   
  
• Integer wavelet transform based data hiding scheme for electrocardiogram signals protection  
  by Moad Med Sayah, Zermi Narima, Khaldi Amine, Kafi Med Redouane 
  Abstract: A significant security and protection concern in telemedicine at the moment is how to secure the confidentiality of sensitive data delivered over the Internet and limit access to designated information. In this paper, we propose medical ECG data privacy protection using secure and blind watermarking. The proposed method converts the ECG signal into a 2D image, from which an integer wavelet transform (IWT) is used to recover the frequency information of the image. The obtained coefficient is then filtered using a singular value decomposition (SVD). Given that the primary issue of IWT-based watermarking systems is the lower hiding capacity; this study suggests a safe high capacity IWT-based watermarking scheme for getting this specific constraint. With an average imperceptibility rate of 41 dB, the suggested approach efficiently preserves a significant quality of watermarked signals. According to experiment results on robustness, the watermark is also resistant to the most common watermarking assaults. The suggested scheme offered a normalised cross correlation rate of more than 0.9 on average for all attacks.
  Keywords: electrocardiogram signals; digital watermarking; blind watermarking; integer wavelet transform; IWT; singular value decomposition; SVD; QR code.
  DOI: 10.1504/IJCSE.2023.10058180