International Journal of Computational Science and Engineering (61 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.
  
  
• Statistical analysis for predicting residents travel mode based on random forest  
  by Lei Chen, Zhengyan Sun, Shunxiang Zhang, Guangli Zhu, Subo Wei 
  Abstract: Random forest has achieved good results in the prediction task, but due to the complexity of travel mode and the uncertainty of random forest, the prediction accuracy of travel mode is low. To improve the accuracy of prediction, this paper proposes a residents travel modes prediction method based on the random forest. To extract valuable feature information, the questionnaire survey data is collected, which is preprocessed by three kinds of appropriate methods. Then, each feature is analysed by the statistical learning method to obtain the important feature of transportation selection. Finally, a random forest is constructed to predict the travel mode of residents selection of transportation. The parameters of random forests are modified and improved to achieve higher prediction accuracy of travel mode. The experimental results show that the method proposed in this paper effectively improves the prediction accuracy of the travel mode.
  Keywords: random forest; residents’ travel mode; statistical analysis.
  
  
• Wireless optimisation positioning algorithm with the support of node deployment  
  by Xudong Yang, Chengming Luo, Luxue Wang, Hao Liu, Lingli Zhang 
  Abstract: Position is one of the basic attributes of an object, which is one of the key technologies for its collaborative operation. As a distributed sensing method, Wireless Sensor Networks (WSNs) have become a feasible solution especially in satellite signal denied environments. Considering that the node deployment is the basis of target positioning in WSNs, this paper first researches the optimal deployment of wireless nodes, and then researches the optimal positioning of mobile targets. Based on the least squares equation, a feature matrix that can characterise the positioning error is derived so that the positioning error caused by wireless node deployment is minimised. Following that, the positioning results are refined using particle swarm optimisation, which makes the mobile target have a coarse to fine accuracy. The results indicate that the proposed algorithm can reduce the influence of network topology on positioning error, which is critical for some location-based applications.
  Keywords: distributed sensing; wireless positioning; node deployment; matrix eigenvalues; particle swarm.
  
  
• Investigation on the optimisation of Cholesky decomposition algorithm based on SIMD-DSP  
  by Huixiang Li, Huifu Zhang, Anxing Xie, Yonghua Hu, Wei Liang 
  Abstract: With the development of high-performance SIMD-DSP processors, corresponding highly efficient algorithms for matrix decomposition play an important role in the hardware performance of such processors. Cholesky decomposition is a fast decomposition method for symmetric positive definite matrices, which is widely used in matrix inversion and linear equation solving. According to the hardware characteristics of the FT-M7002 processors, in this paper, we optimise the algorithm in several ways. If the hardware has on-chip double-buffered memory, the parallel process of DMA transmitting and calculating is specially designed, which can hide most of the time cost of data movement and further improve the algorithms performance. The experimental results based on the FT-M7002 processor show that the performance of the optimised algorithm is 3.8~5.64 times that of the serial algorithm, and 1.39~2.14 times that of the TI library function.
  Keywords: Cholesky decomposition; DSP; SIMD.
  
  
• JALNet: joint attention learning network for RGB-D salient object detection  
  by Xiuju Gao, Jianhua Cui, Jin Meng, Huaizhong Shi, Songsong Duan, Chenxing Xia 
  Abstract: The existing RGB-D saliency object detection (SOD) methods mostly explore the complementary information between depth features and RGB features. However, these methods ignore the bi-directional complementarity between RGB and depth features. From this view, we propose a joint attention learning network (JALNet) to learn the cross-modal mutual complementary effect between the RGB images and depth maps. Specifically, two joint attention learning networks are designed, namely, a cross modal joint attention fusion module (JAFM) and a joint attention enhance module (JAEM), respectively. The JAFM learns cross-modal complementary information from the RGB and depth features, which can strengthen the interaction of information and complementarity of useful information. At the same time, we utilize the JAEM to enlarge receptive field information to highlight salient objects. We conducted comprehensive experiments on four public datasets, which proved that the performance of our proposed JALNet outperforms 16 state-of-the-art (SOTA) RGB-D SOD methods.
  Keywords: salient object detection; depth map; bi-directional complementarity; cross-modal features.
  
  
• An improved blind/referenceless image spatial quality evaluator algorithm for image quality assessment  
  by Xuesong Li, Jinfeng Pan, Jianrun Shang, Alireza Souri, Mingliang Gao 
  Abstract: Image quality assessment (IQA) methods are generally studied in the spatial or transform domain. Due to the BRISQUE algorithm evaluating the quality of an image only based on its natural scene statistics of the spatial domain, the frequency features that are extracted from the modulation transfer function (MTF) are applied to improve its performance. MTF is estimated based on the slanted-edge method. The two-dimensional grey fitting algorithm is utilized to estimate the edge slope more accurately. Then the three-order Fermi function is utilized to match the preliminary estimated edge spread function to reduce the aliasing influence on MTF estimation. The features such as crucial frequency and the MTF value at Nyquist frequency are calculated and adopted to the BRISQUE method to assess the image quality. Experimental results on the image quality assessment databases illustrated that the proposed method outperforms the BRISQUE method and some other common methods, based on the linear and nonlinear correlation between the image quality assessed by the methods and their subjective value.
  Keywords: image assessment; modulation transfer function; Fermi function; feature extraction.
  DOI: 10.1504/IJCSE.2022.10051266
   
  
• A generalised incomplete no-equilibria transformation method to construct a hidden multi-scroll system with no-equilibrium  
  by Lihong Tang, Zongmei He, Yanli Yao, Ce Yang 
  Abstract: At present, there is a lot of research on multi-scroll chaotic systems with equilibrium points. However, there are few studies on no-equilibrium multi-scroll chaotic systems. This paper proposes a generalised incomplete no-equilibrium transformation method to design no-equilibrium multi-scroll chaotic systems. Firstly, a no-equilibrium chaotic system is constructed by adopting the proposed method. Phase plots and Lyapunov exponents show that the constructed no-equilibrium chaotic system can generate hidden hyperchaotic attractors. Then, a no-equilibrium multi-scroll hyperchaotic system is realized by introducing multi-level logic pulse signals. Theoretical analysis and numerical simulation show that the designed no-equilibrium multi-scroll hyperchaotic system can generate hidden multidirectional multi-double-scroll attractors including 1-D, 2-D, and 3-D hidden multi-scroll hyperchaotic attractors. Finally, an analogue circuit of the no-equilibrium multi-scroll hyperchaotic system is implemented by using commercial electronic elements. Various typical hidden multi-scroll attractors are verified on MULTISIM platform.
  Keywords: no-equilibrium; hidden attractors; multi-scroll; multi-level pulse.
  
  
• SAPNN: self-adaptive probabilistic neural network for medical diagnosis  
  by Yibin Xiong, Jun Wu, Qian Wang, Dandan Wei 
  Abstract: Medical diagnosis has always been a hot topic of great concern in the medical field. For this purpose, a self-adaptive probabilistic neural network (SAPNN) is proposed in this paper. Firstly, a hybrid cuckoo search (HCS) algorithm is proposed. Secondly, HCS is used in probabilistic neural networks for adapting the smoothing factor parameters. In order to accurately evaluate SAPNN proposed in this paper, the disease data sets of breast cancer, diabetes and Parkinsons disease were used for testing. Finally, comparison with several other methods yielded that the accuracy of SAPNN was the best in all cases, where the accuracy was 97.51%, 96.53%, 75.74%, 96.61%; recall was 97.6%, 99.12%, 79.74%, 88.24%; specificity was 96.15%, 88.88%, 59.03%, 95.31%; the precision was 97.85%, 94.32%, 80.12%, 85%, respectively. The results of various evaluation indexes show that the proposed SAPNN in this paper is a new method that can be applied to medical diagnosis.
  Keywords: ancillary diagnosis of disease; cuckoo search; information sharing; mutation strategy; probabilistic neural network.
  DOI: 10.1504/IJCSE.2022.10053093
   
  
• Minimum redundancy maximum relevance and VNS-based gene selection for cancer classification in high-dimensional data  
  by Ahmed Bir-Jmel, Sidi Mohamed Douiri, Souad Elbernoussi 
  Abstract: DNA microarray is a technique for measuring the expression levels of a huge number of genes. These levels have a significant impact on cancer classification tasks. In DNA datasets, the number of genes exceeds the number of samples that make the presence of irrelevant or redundant genes possible, which penalises the performance of classifiers. For that, the development of new methods for gene selection represents an active subject for researchers. In this paper, two-hybrid multivariate filters for gene selection, named VNSMI and VNSCor, are presented. The two methods surpass the univariate filters by considering the possible interaction between genes through the search for an optimal subset of genes that contains the minimum redundancy and the maximum relevance (MRMR). In the first stage of our proposed methods, we use a univariate filter by selecting the best-ranked genes based on information theory and the Pearson Correlation Coefficient (PCC). Then, we apply the Variable Neighbourhood Search (VNS) metaheuristic coupled with an innovative Stochastic Local Search (SLS) algorithm to find the final subset of genes that maximise the MRMR objective function. Evaluating the proposed method, the experiments were performed on six well-replicated microarray datasets. The obtained results show that the proposed approach leads to encouraging results in terms of accuracy and the number of selected genes. Also, improvements are observed consistently using the classifiers 1NN and SVM.
  Keywords: gene selection; feature selection; cancer classification; VNS; stochastic local search; normalised mutual information; MRMR; DNA microarray.
  
  
• Canopy centre-based fuzzy C-means clustering for enhancement of soil fertility Prediction  
  by M. Sujatha, C.D. Jaidhar 
  Abstract: For plants to develop, fertile soil is necessary. Estimating soil parameters based on time change is crucial for enhancing soil fertility. Sentinel-2s remote sensing technology produces images that can be used to gauge soil parameters. In this study, values for soil parameters such as electrical conductivity, pH, organic carbon, and nitrogen are derived using Sentinel-2 data. In order to increase the clustering accuracy, this study suggests using canopy centre-based fuzzy C-means clustering and comparing it with manual labelling and other clustering techniques, such as canopy density-based, expectation maximisation, farthest-first, k-Means, and fuzzy C-means clustering. The proposed clustering achieved the highest clustering accuracy of 78.42%. Machine learning-based classifiers were applied to classify soil fertility, including naive Bayes, support vector machine, decision trees, and random forest (RF). A dataset labelled with the proposed RF clustering classifier achieves a high classification accuracy of 99.69% with 10-fold cross-validation.
  Keywords: clustering; classification; machine learning; remote sensing; soil fertility.
  DOI: 10.1504/IJCSE.2022.10058486
   
  
• Texture-based superpixel segmentation algorithm for classification of hyperspectral images  
  by Subhashree Subudhi, Ramnarayan Patro, Pradyut Kumar Biswal 
  Abstract: To increase classification accuracy, a variety of feature extraction techniques have been presented. A preprocessing method called superpixel segmentation divides an image into meaningful sub-regions, which simplifies the image. This substantially reduces single-pixel misclassification. In this work, a texture-based superpixel segmentation technique is developed for the accurate classification of Hyperspectral Images (HSI). Initially, the local binary pattern and Gabor filters are employed to extract local and global image texture information. The extracted texture features are then provided as input to the Simple Linear Iterative Clustering (SLIC) algorithm for segmentation map generation. The final classification map is constructed by using a majority vote strategy between the superpixel segmentation map and the pixel-wise classification map. The proposed method was validated on standard HSI datasets. In terms of classification performance, it outperformed other state-of-the-art algorithms. Furthermore, the algorithm may be incorporated into the UAV's onboard camera to automatically classify HSI.
  Keywords: hyperspectral image classification; superpixel segmentation; SLIC; spatial-spectral feature extraction.
  
  
• FedCluster: a global user profile generation method based on vertical federated clustering  
  by Zheng Huo, Ping He, Lisha Hu 
  Abstract: Federated learning can serve as a basis to solve the data island problem and data privacy leakage problem in distributed machine learning. This paper proposes a privacy-preserving algorithm referred to as FedCluster, to construct a global user profile via vertical federated clustering. The traditional k-medoids algorithm was then extended to the federated learning architecture to construct the user profiles on vertical segmented data. The main interaction parameter between the participants and the server was the distance matrix from each point to the k medoids. Differential privacy was adopted to protect the privacy of the participant data during the exchange of training parameters. We conducted experiments on a real-world dataset. The results revealed that the precision of FedCluster reached 81.87%. The runtime exhibited a linear increase with an increase in the dataset size and the number of participants, which indicates a high performance in terms of precision and effectiveness.
  Keywords: federated learning; footrule distance; k-mediods clustering; order preserving encryption.
  
  
• A search pattern based on the repeated motion vector components for the fast block matching motion estimation in temporal coding  
  by Awanish Mishra, Narendra Kohli 
  Abstract: To reduce the amount of unnecessary data in a video's timeline, block-based motion estimate is routinely used. However, a significant reduction in the computational complexity of motion estimation remains a significant problem. In this paper, a search pattern approach is proposed to efficiently estimate the motion of blocks. The proposed algorithm estimates the motion based on the maximum frequency of magnitude and direction of the available motion vector components. Motion vector components with higher frequency have greater probability to provide early estimation of matching block. In this iterative process, searching for the matching block is terminated on getting the matched block. To demonstrate the enhanced performance of the proposed approach, a comprehensive analysis is carried out, and when the results are compared, the novel approach outperforms recent motion estimation approaches. The proposed approach improves the best case complexity until it finds one search per block for dynamic blocks. It improves the average case complexity because of the early termination of the process.
  Keywords: motion estimation; block matching; search parameter; source frame; reference frame.
  
  
• Pyramid hierarchical network for multispectral pan-sharpening  
  by Zenglu Li, Xiaoyu Guo, Songyang Xiang 
  Abstract: Pan-sharpening aims to fuse high spatial-resolution panchromatic images (PAN) and low spatial-resolution multispectral images (MS) into high spatial-resolution multispectral images (HRMS). We propose a pyramid hierarchical multi-spectral fusion network, called PH-Net, which can automatically fuse MS images and PAN images to generate corresponding HRMS images. The architecture is based on the U-Net network. First, a multi-level receptive field is realised by constructing an input pyramid. Then, hierarchical features are extracted from the encoder, decoder, and input pyramid. Finally, the rich hierarchical features are used to calculate the residual error between the MS image and the corresponding HRMS image. The learned residual error is inserted into the MS image to obtain the final high spatial-resolution multispectral image. To demonstrate the effectiveness of each component in the network architecture, we conducted an ablation study. In addition, thanks to the design of the multi-layer architecture, model training does not require a large dataset, which greatly improves the training speed and significantly improves the generalizability and ease of deployment of this work in the field of remote sensing images. Through qualitative and quantitative experiments, we proved that the proposed method is superior to current advanced methods.
  Keywords: pan-sharpening; image fusion; pyramid Attention; multispectral image; deep learning.
  DOI: 10.1504/IJCSE.2022.10053377
   
  
• Clustering ensemble by clustering selected weighted clusters  
  by Arko Banerjee, Suvendu Chandan Nayak, Chhabi Rani Panigrahi, Bibudhendu Pati 
  Abstract: Owing to the fact that no single clustering approach is capable of producing the optimal result for any given data, the notion of clustering ensembles has emerged, which attempts to extract a novel and robust consensus clustering from a given ensemble of base clusterings of the data. While forming the consensus, weights can be assigned to the base clusterings or their constituent clusters to prioritise those that accurately represent the underlying structure of the data. In this paper, we present a novel method of cluster selection from base clusterings and subsequently merging selected clusters into the desired number of clusters in order to build a high-quality consensus clustering without gaining access to the internal distribution of data points. The method has been shown to work well with a wide range of data and to be better than many well-known clustering methods.
  Keywords: clustering ensemble; weighted clustering; entropy; cluster selection.
  
  
• Cell counting via attentive recognition network  
  by Xiangyu Guo, Jinyong Chen, Guisheng Zhang, Guofeng Zou, Qilei Li, Mingliang Gao 
  Abstract: Accurate cell counting in biomedical images is a fundamental yet challenging task for disease diagnosis. The early manual cell counting methods are mainly based on detection and regression, which are time-consuming and prone to errors. Benefitting from the advent of deep learning, convolutional neural network (CNN)-based cell counting has become the mainstream method. Despite the outstanding performance of CNN-based cell counting methods, the complex tissue background in medical images still hinders the accuracy of cell counting. In this paper, to solve the problem of complex tissue background and improve the performance of cell counting, an attentive recognition network (ARNet) is built. Specifically, the ARNet is composed of five convolution blocks and a channel attention (CA) module. The convolution blocks are employed to extract the basic features, and the CA module is introduced to suppress the complex background by recalibrating the weight of each channel to pay more attention to cells. Subjective and objective experiments on synthetic bacterial cells dataset and modified bone marrow dataset prove that the proposed ARNet outperforms the mainstream methods in accuracy and stability.
  Keywords: healthcare; cell counting; attention mechanism; convolutional neural network.
  DOI: 10.1504/IJCSE.2022.10055133
   
  
• Information fusion and emergency knowledge graph construction of urban rail transit  
  by Guangyu Zhu, Rongzheng Yang, Jiaxin Fan, Wei Yun, Bo Wu, Qi Wu 
  Abstract: The core of an intelligent emergency system of urban rail transit (URT) is to build a knowledge system for operation and emergency management. This paper proposes a construction model of emergency knowledge graph of URT combined with information fusion. Firstly, the scheme layer of the knowledge graph is designed in the top-down style, which defines the knowledge framework, entity types and entity relationships of the knowledge graph. Secondly, the entity extraction model based on adversarial training and Bert is proposed to extract knowledge from the emergency record text. The information fusion method is used to normalise the knowledge extracted from multi-source data to complete the construction of the data layer. Finally, Neo4j graph database is used to store and manage the data, and then the emergency knowledge graph of URT is constructed. Experiments show that the extraction model proposed in this paper has a better extraction effect than the mainstream models in terms of F1 value, which is increased by 7.52%, 1.87% and 1.31%, respectively. In addition, the emergency knowledge graph of URT based on this method can better fuse multi-source information and provide better basic support for the construction of URT intelligent emergency system.
  Keywords: intelligent emergency; knowledge graph; knowledge extraction; information fusion; urban rail emergency knowledge graph.
  DOI: 10.1504/IJCSE.2022.10053044
   
  
• A bibliometric analysis of the application of deep learning in economics, econometrics, and finance  
  by Arash Salehpour, Karim Samadzamini 
  Abstract: This research looked at the deep learning applications in economics, econometrics, and finance. Two hundred and fifty articles from the Scopus database's index of journals published between 2013 and 2022 were gathered using a bibliometric technique. The data was analysed using many programs (R studio, Excel, and Biblioshiny), and in terms of countries, organisations, publications, papers, and authors, the most prominent scientific players were highlighted. Our research found that as of 2019, the quantity of publications has increased. The literature analysis received the most contributions from China and the United States. The most significant findings and discussions came from the following analyses: estimation of share prices, asset management price fluctuations and liquidity, forecast of bankruptcies, evaluation of credit risk, risk assessment, commodity prices top trend analysis, citation analysis, thematic evolution, and thematic map. Our findings offer practical recommendations on how deep learning may be implemented into decision-making processes for market participants, particularly those working in fintech and finance.
  Keywords: deep learning; bibliometrics; economics; econometrics; finance.
  
  
• 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;.
  
  
• SLIC-SSA: an image segmentation method based on superpixel and sparrow search algorithm  
  by Hao Li, Hong Wen, Jia Li, Lijun Xiao 
  Abstract: Clustering algorithms are widely used in image segmentation owing to their universality. However, the methods based on clustering algorithms are sensitive to noise and easily fall into local optimum. To address these issues, we propose an image segmentation method (SLIC-SSA) based on superpixel method and sparrow search algorithm. Firstly, the presegmentation result is obtained by superpixel method. Owing to the use of local spatial information, the influence of noise can be reduced. Then, the clustering algorithm based on sparrow search algorithm is performed on superpixel image to complete the segmentation. To improve the quality of the results, the chaotic strategy is used to initialise the population. A fitness function is proposed to ensure the similarity within the cluster and the difference between the clusters. Experiments on real images show that the proposed method can obtain better results than comparative methods. Meanwhile, time consumption can be reduced.
  Keywords: clustering; image segmentation; sparrow search; superpixel; swarm intelligence optimisation.
  DOI: 10.1504/IJCSE.2023.10053888
   
  
• Integration of statistical parameters based color-texture descriptors for radar remote sensing image retrieval applications  
  by Naushad Varish, Sambidi Rohan Reddy, Nadimpalli Gautham Sashi Varma, Priyanka Singh 
  Abstract: In this paper, a novel image retrieval method based on colour-texture contents for radar remote sensing applications is proposed, where global properties-based colour contents are extracted from different number of groups of histograms of colour image planes and local properties-based texture contents have been derived from block level GLCM of an image plane. The integration of colour-texture contents represents the low dimensional feature which reduces overall computational overhead and increases the retrieval speed. To give importance to the feature components, suitable weights are imposed to both colour-texture contents appropriately. The obtained feature information is describing the radar image effectively and also similarity measures play a significant role for better performance. This work compares eight similarity metrics to select the best one in the retrieval process. To validate the suggested method, experiments on two image datasets are performed and decent retrieval results have been attained with rich colour-texture contents.
  Keywords: remote sensing image retrieval; statistical parameters; grey-level co-occurrence matrix; feature descriptors; min-max; similarity measures.
  
  
• Optimising group-by and aggregation on the coupled CPU-GPU architecture  
  by Hua Luan, Yan Fu 
  Abstract: The coupled CPU-GPU architecture as an emerging heterogeneous environment has attracted much attention from researchers. On this kind of architecture, the GPU is built on the same chip as the CPU. Different from the discrete GPU, there is no data transfer via the PCIe bus between the CPU and the integrated GPU, and the two processors share the same memory. Grouping and aggregation is an important and time-consuming operator in a DBMS. Whether the coupled GPU could be used to increase its performance is an interesting problem. In this paper, we study how to optimise grouping and aggregation based on chained hashing on the coupled CPU-GPU architecture. Two flexible co-processing strategies are proposed to take advantage of the hybrid computing resources effectively. A thorough set of experiments are conducted and the results show that the coupled GPU could help obtain better performance for group-by and aggregation.
  Keywords: hash grouping; coupled CPU-GPU architecture; co-processing.
  
  
• Distilling object detectors with mask-guided feature and relation-based knowledge  
  by Liang Zeng, Xiangfeng Luo, Liyan Liyan Ma, Yinsai Guo, Xue Chen 
  Abstract: Knowledge distillation(KD) is an effective technique for network compression and model accuracy enhancement in image classification, semantic segmentation, pre-trained language model, and so on. However, existing KD methods are specialized for image classification and cannot be used effectively for object detection tasks, with the following two limitations: the imbalance of foreground and background instances and the neglect distillation of relation-based knowledge. In this paper, we present a general Mask-guided Feature and Relation-based Knowledge distillation framework (MAR) consisting of two components, mask-guided distillation, and relation-based distillation, to address the above problems. The mask-guided distillation is designed to emphasize students' learning of close-to-object features via multi-value masks, while relation-based distillation is proposed to mimic the relational information between different feature pixels on the classification head. Extensive experiments show that our methods achieve excellent AP improvements on both one-stage and two-stage detectors. Specifically, Faster RCNN with ResNet50 backbone achieves 40.6% in mAP under 1x schedule on the COCO dataset, which is 3.2% higher than the baseline and even surpasses the teacher detector.
  Keywords: knowledge distillation; multi-value mask; object detection.
  
  
• 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 will provide a continuous power supply. A local dairy farms 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. Approaching this design, the renewable technologies and specifications best suited for the location are chosen to include a horizontal axis wind turbine and a single axis solar panel with active tracking. A 120 kilowatt wind turbine and a 150 kilowatt solar panel kit is 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. An economic analysis is completed to determine an investment recuperation within 8 years and estimated savings of $890,982.52 for the remainder of the system's life expectancy.
  Keywords: wind turbine; solar PV energy; dairy farm; hybrid renewable system.
  DOI: 10.1504/IJCSE.2023.10058164
   
  
• WSN distributed sensing of mobile robot under the irregular topology of underground pipe gallery  
  by Gaifang Xin, Jun Zhu, Jing Tang 
  Abstract: As a long and narrow irregular structure, the underground pipe gallery can reach tens of kilometres. In order to avoid the tedious work by manual handheld or complex installation by wired networks, Wireless sensor networks (WSNs) integrated with mobile robot can be used for monitoring. It is worth noting that mobile robot needs to mark its corresponding position when collecting the WSNs data. Hence, this paper proposes a WSNs distributed sensing model for mobile robots. Within the communicable radius, the anchor node set for distributed sensing is firstly solved by combining the motion characteristics of mobile robot. Then dual mapping between wireless parameters and robot positions is established in irregular topology.
  Keywords: underground pipe gallery; mobile robot; distributed sensing; irregular structure; accuracy evaluation.
  DOI: 10.1504/IJCSE.2023.10055745
   
  
• An energy efficient on-demand multi-path routing protocol for wireless body area network  
  by Qingling Liu, Qi Wang 
  Abstract: Wireless body area network (WBAN) makes a remarkable contribution to healthcare. However, WBAN is faced with problems such as energy shortage and radiation safety. The existing researches on WBAN routing protocol tend to focus on energy saving, but ignore other problems. Therefore, in this paper, an energy efficient on-demand multi-path routing protocol for WBAN is proposed to prolong the network lifetime while ensuring the user’s radiation security and quality of service (QoS). Firstly, a cost function is constructed, which linearly combines the node residual energy ratio, the specific absorption rate (SAR), the buffer remaining space ratio and the node movement metric parameters. Secondly, the analytic hierarchy process (AHP) is adopted to calculate the weight vector of the cost function. Thirdly, the standard ad hoc on-demand multi-path distance vector protocol (AOMDV) is used as the base routing protocol and its routing mechanism is tuned to meet WBAN performance requirements using the cost function provided. Simulation results show that the revised routing protocol has better performance in terms of network throughput, average end-to-end delay and energy efficiency in a variety of simulation scenarios.
  Keywords: wireless body area network; WBAN; quality of service; QoS; specific absorption rate; SAR; analytic hierarchy process; AHP.
  DOI: 10.1504/IJCSE.2023.10055920
   
  
• Supervised learning models to predict mental illness and its severity from Reddit posts  
  by Neha Angadi, Navya Eedula, Kshitij Prit Gopali, Jayashree Rangareddy 
  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 condition, which the model used to determine the mental illness. The final step is to assess the severity of the sickness, which helps to 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
   
  
• Availability assessment and sensitivity analysis of an MBaaS platform  
  by Francisco Airton Silva, Antônio 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%. Owing 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; downtime; mobile cloud.
  DOI: 10.1504/IJCSE.2023.10058179
   
  
• Integer wavelet transform based data hiding scheme for electrocardiogram signals protection  
  by Sayah Moad, Narima Zermi, Khaldi Amine, Redouane Kafi 
  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
   
  
• Attack and defence 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 owing 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 defence simulation platform called MSP, which allows us to customise the distributed LEO satellite network for attack and defence 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; 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 experiment 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
   
  
• 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
   
  
• 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 attack. 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
   
  
• 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
   
  
• A light-weight model with granularity feature representation for fine-grained visual classification  
  by Qiumei Zheng, Tianqi Peng, Ding Huang, Fenghua Wang, Neng Xiang 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 network (CNN), this ability is largely limited. With a lightweight orientation, we here used the advantage of transformer in capturing global features by combining the technically mature CNN proposed a lightweight model MV-GFR based on MobileViT. Further, we also proposed 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
   
  
• 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
   
  
• Classifying blockchain cybercriminal transactions using hyperparameter tuned supervised machine learning models  
  by Rohit Saxena, Deepak Arora, Vishal Nagar 
  Abstract: Bitcoin is a crypto asset with transactions recorded on a decentralised, publicly accessible ledger. The real-world identity of the bitcoin blockchain users is masked behind a pseudonym, known as an address that provides a high level of anonymity, which is one of the reasons for its widespread use in criminal operations such as ransomware attacks, gambling, etc. As a result, the classification of diverse cybercriminal users' activities and addresses in the bitcoin blockchain is demanded. This research work presents a classification of user activities and addresses associated with illicit transactions using supervised machine learning (ML). The labelled dataset samples are trained using decision trees, ensemble, Bayesian, and instance-based learning. Extra Trees emerged as the best classification model, whereas Gaussian naïve Bayes as the worst. GridSearchCV is employed to optimise the CV accuracy of classification models with CV accuracy below 85% which led to an improvement in the CV accuracy.
  Keywords: blockchain; bitcoin; supervised machine learning; classification; pseudo-anonymity; anonymity; GridSearchCV.
  DOI: 10.1504/IJCSE.2022.10056854
   
  
• Synthesis and evaluation of structure of CAM memory by QCA computing technique  
  by Nirupma Pathak, Neeraj Kumar Misra, Santosh Kumar 
  Abstract: The lithographically based CMOS technology revolutions of the past few years are long behind us, but the technology used in today's microelectronics faces significant challenges in terms of speed, area, and power consumption. In the domain of QCA, the aim of this article is to design a novel CAM memory. This article deals with the compact structure of the novel CAM memory, which is based on GDI-CMOS and QCA technology, respectively. Compared with the recently reported designs in the literature, it is observed that the area, latency, majority gate, and cell count of the proposed CAM are decreased by more than 78.57%, 50%, 40%, and 67%, respectively. In addition to this, the clock delay of the CAM cell design is less than the other results that have been reported. This cutting-edge QCA-based CAM structure is not only one of a kind but also very cost-effective in today's nano-devices. This CAM design that has been proposed improves performance while also making the use of modern device development simpler and more cost-effective.
  Keywords: CAM memory; quantum-dot cellular automata; QCA; GDI-CMOS; nano-electronics; computing.
  DOI: 10.1504/IJCSE.2022.10053117
   
  
• Examining the role of likes in follower network evolution based on a dynamic panel data model  
  by Tao Wang, Shuang Fu, Zhiyi Wu 
  Abstract: Posting product recommendation articles by content creators from the consumer group in social shopping communities has become an effective way to connect consumers to products. Content creators with larger follower counts have higher levels of influence. However, little is known about the causes of the evolution of their follower networks. Therefore, we examined the impact of social media likes that content creators received on the follower count and the moderating effect of the previous follower count on the role of likes. We achieve that by crawling real data from China's leading social shopping community. We empirically tested a dynamic panel data model and found that more likes are positively associated with the growth of the follower network size, while the previous follower count negatively moderates this effect. These findings have implications for researchers seeking to understand the antecedents of follower network evolution and for practitioners seeking to attract more followers.
  Keywords: content creator; social media like; follower network evolution; social shopping community; dynamic panel data model.
  DOI: 10.1504/IJCSE.2023.10055525
   
  
• 3DL-PS: an image encryption technique using 3D logistic map, hashing functions and pixel scrambling techniques  
  by Parth Kalkotwar, Rahil Kadakia, Ramchandra Mangrulkar 
  Abstract: This paper presents an efficient image encryption scheme that uses a 3D logistic map, SHA-512, and pixel scrambling techniques. Firstly, two secret keys are generated using two different user-provided keys and the input image. Image pixels are altered using the values obtained upon the iteration of the 3D logistic map to increase the complexity of the algorithm. Further, different fragments of various sizes are swapped depending on the secret keys. Finally, a jumbling technique mixes the pixels horizontally and vertically in a completely dynamic way using secret keys. The keyspace of the algorithm is large enough to resist brute force attacks. The encrypted image has been analysed against classical attacks such as chosen-plaintext, chosen-ciphertext, known-ciphertext, known-plaintext, and statistical attacks. Key sensitivity analysis and noise resistance analysis have also been performed. The results prove that the algorithm is resistant to several renowned attacks, enabling usage in various real-time applications.
  Keywords: image cryptography; chaotic systems; security analysis; pixel scrambling; SHA-512.
  DOI: 10.1504/IJCSE.2022.10049693
   
  
• Comparative study of point matching method with spectral method on numerical solution electromagnetic problems  
  by Mahmoud Behroozifar 
  Abstract: The present study focuses on comparing the point matching method and spectral method for solving the integral equations arising in the electromagnetic domain. The point matching method, which is a traditional method, was based on basis functions which most of the time result in a singular and ill-posed system of nonlinear equations. In order to prevent these inconveniences, the physical structure of the object must be altered in some cases, which yields a high error in the results and requires high CPU time and memory usage. Also in most cases, this method converges slowly and leads to a singular and ill-posed system. Consequently, applying the point matching method for this problem leads to an approximate solution with low accuracy and high computation volume. As an alternative, we present the spectral method based on Bernstein polynomials (BPs) as a robust nominee. Employing the BPs reduces the problem to an algebraic equations system. The other merits of the presented method are faster convergence and avoidance of occurring a singular system.
  Keywords: Bernstein polynomials; electrostatic; microstrip; point matching method; spectral method.
  DOI: 10.1504/IJCSE.2023.10060794
   
  
• Human behaviour analysis based on spatio-temporal dual-stream heterogeneous convolutional neural network  
  by Qing Ye, Yuqi Zhao, Haoxin Zhong 
  Abstract: At present, there are still many problems to be solved in human behaviour analysis, such as insufficient utilisation of behaviour characteristic information and slow operation rate. We propose a human behaviour analysis algorithm based on spatio-temporal dual-stream heterogeneous convolutional neural network (STDNet). The algorithm is improved on the basic structure of the traditional dual-stream network. When extracting spatial information, the DenseNet uses a hierarchical connection method to construct a dense network to extract the spatial feature of the video RGB image. When extracting motion information, BNInception is used to extract temporal features of video optical flow images. Finally, feature fusion is carried out by multi-layer perceptron and sent to Softmax classifier for classification. Experimental results on the UCF101 dataset show that the algorithm can effectively use the spatio-temporal feature information in video, reduce the amount of calculation of the network model, and greatly improve the ability to distinguish similar actions.
  Keywords: human behaviour analysis; STDNet; optical flow; feature extraction; dual-stream network.
  DOI: 10.1504/IJCSE.2022.10048568
   
  
• Simple and compact finite difference formulae using real and complex variables  
  by Yohei Nishidate 
  Abstract: A new set of compact finite difference formulae is derived by simple combinations of the real and the complex Taylor series expansions. The truncation error is fourth-order in derived formulae for approximating first to fourth-order derivatives. Although there exist complex stencil finite difference formulae with better truncation errors, our formulae are computationally cheaper, requiring only three points for first to third-order and four points for fourth-order derivatives. The derived formulae are experimented with for approximating derivatives of relatively simple and highly nonlinear functions used in other literature. Although the new formulae suffer the subtractive cancellation, it is demonstrated that the derived formulae outperform finite difference formulae of comparable computational costs for relatively large step sizes.
  Keywords: Taylor series expansion; approximation in the complex domain; finite difference methods; compact finite difference formula; numerical approximation.
  DOI: 10.1504/IJCSE.2023.10060796
   
  
• Non-parametric combination forecasting methods with application to GDP forecasting  
  by Wei Li, Yunyan Wang 
  Abstract: This work is devoted to constructing non-parametric combination prediction methods, which can improve the forecasting effect and accuracy to some extent. In this paper, in order to forecast the regional gross domestic product, a non-parametric autoregressive method is introduced into the autoregressive integrated moving average model, and a combined method of ARIMA model and non-parametric autoregressive model is established based on the residual correction. Furthermore, the specific prediction steps are proposed. The empirical results show that the new proposed combined model outperforms both the ARIMA model and the non-parametric autoregressive model in terms of regression effect and forecasting accuracy. The combination of parametric model and non-parametric model not only provides a method with better applicability and prediction effect for the establishment of GDP prediction model, but also provides a theoretical basis for the prediction of relevant economic data in the future. The prediction results show that during the China's 14th Five-Year Plan period, the gross domestic product of Jiangxi Province will increase by 7.01% annually.
  Keywords: gross domestic product; GDP; ARIMA model; non-parametric autoregressive model; residual correction; combined model.
  DOI: 10.1504/IJCSE.2023.10060793
   
  
• Joint optimisation of feature selection and SVM parameters based on an improved fireworks algorithm  
  by Xiaoning Shen, Jiyong Xu, Mingjian Mao, Jiaqi Lu, Liyan Song, Qian Wang 
  Abstract: In order to reduce the redundant features and improve the accuracy in classification, an improved fireworks algorithm for joint optimisation of feature selection and SVM parameters is proposed. A new fitness evaluation method is designed, which can adjust the punishment degree adaptively with the increase of the number of selected features. A differential mutation operator is introduced to enhance the information interaction among fireworks and improve the local search ability of the fireworks algorithm. A fitness-based roulette wheel selection strategy is proposed to reduce the computational complexity of the selection operator. Three groups of comparisons on 14 UCI classification datasets with increasing scales validate the effectiveness of our strategies and the significance of joint optimisation. Experimental results show that the proposed algorithm can obtain a higher accuracy in classification with fewer features.
  Keywords: fireworks algorithm; support vector machines; feature selection; parameter optimisation; joint optimisation.
  DOI: 10.1504/IJCSE.2023.10060795
   
  
• Selection of the best hybrid spectral similarity measure for characterising marine oil spills from multi-platform hyperspectral datasets  
  by Deepthi, Deepa Sankar, Tessamma Thomas 
  Abstract: Marine oil pollution causes major economic crises in major industrial sectors like fishing, shipping and tourism. It affects marine life and human even decades after spillage necessitating very quick detection and remediation. Generally, oils are exceedingly difficult to identify from high-resolution images as oil slicks and sea water possess identical spectral characteristics. Therefore a cohesive and synergistic classification method called hybrid spectral similarity measures (HSSM) that discerns the data-rich constituents of hyperspectral images (HSI) is recommended in this paper to classify oil spills. Oil spill HSI procured from spaceborne [earth observation (EO-1) Hyperion] and airborne [airborne visible/infrared imaging spectrometer (AVIRIS)] platforms are employed to discriminate different marine spectral classes. The statistical parameters like overall accuracy (OA), Kappa, ROC/PR curve, AUC/PRAUC, weighted Youden index (J_w), F1score and Noise analysis have identified spectral information divergence-chi square distance (SID-CHI) as the best HSSM promulgating its multi-class, multi-sensor, and multi-platform oil spill classification capability.
  Keywords: hybrid spectral similarity measure; HSSM; hyperspectral image; HSI; ROC curve; weighted Youden index; F1 score; optimal cut-off value; OCV; signal to noise ratio; SNR; Hyperion; AVIRIS; oil spill.
  DOI: 10.1504/IJCSE.2022.10056248