International Journal of Computational Science and Engineering (31 papers in press)

Regular Issues

• 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;.
  
  
• 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
   
  
• A green pattern-based data encryption solution in the cloud  
  by Farah Abdmeziem, Saida Boukhedouma, Mourad Chabane Oussalah 
  Abstract: In today's business landscape, there's a growing interest in Cloud computing, particularly for extensive data storage. However, a significant security concern arises from entrusting data to cloud providers and relinquishing control to customers. To address this, data encryption on the customer's side is a viable solution, though it can be resource-intensive, especially for large data volumes. This can lead to performance issues and environmental impacts, including increased server carbon footprints. In this work, we propose a solution based on customized encryption/decryption patterns, categorizing data into three sensitivity levels and considering access/update frequency. We also assess environmental implications and cost metrics to highlight the positive impact of our approach compared to other state-of-the-art methods. Experimental results demonstrate that our approach not only facilitates encryption adoption but also effectively balances data confidentiality with practical resource and energy constraints.
  Keywords: data; security; cloud computing; green encryption; encryption-decryption patterns; cost metrics.
  DOI: 10.1504/IJCSE.2024.10066044
   
  
• Constructing stock portfolio with transformer  
  by Li Jinyuan, Linkai Luo 
  Abstract: Machine learning methods have been applied to quantitative investing, yet the application of Transformer models remains limited. Stock prices are influenced by both long-term and short-term features. Existing methods usually treat the influencing factors as a whole and do not distinguish them. In this paper, we introduce a Transformer encoder-decoder architecture tailored for the capture of long-term and short-term features. By partitioning historical data into long-term and short-term parts, the encoder module concentrates on extracting long-term features, while the decoder concentrates on short-term features and the integration of long and short-term features. Portfolios are then constructed from the top N predicted stocks. Experimental results show that the proposed Transformer model outperforms the existing state-of-the-art methods, LSTM, RNN, and GRU models, with improvements of 26%, 19%, and 14% in annualised returns for long-short portfolio combinations, respectively. It indicates the benefits of extracting long-term and short-term features separately.
  Keywords: stock portfolio; transformer; factor model.
  DOI: 10.1504/IJCSE.2024.10066373
   
  
• Multilingual language classification model for offensive comments categorisation in social media using HAMMC tree search with enhanced optimisation technique  
  by B. Aarthi, Balika J. Chelliah 
  Abstract: The exponential rise of social media platforms has led to a surge in offensive content, highlighting the necessity for effectively detecting and managing such comments. This necessitates precise and advanced online social networks (OSN) categorisation and optimisation methods. This study introduces and assesses a novel technique for automatically categorising texts, supporting over 60 languages, without relying on a pre-annotated data set. The technique employs multilingual methods based on the randomised explicit semantic analysis (ESA) strategy. To combat the inherently multilingual nature of social media content, the paper introduces an innovative classification and optimisation strategy named hybrid adaptive Markov chain Monte Carlo tree search (HAMCMTS) with enhanced eagle Aquila optimiser (EEAO). The study uses three publicly available datasets to identify negative or offensive comments in various languages, offering a comprehensive analysis in this field. The proposed approach holds potential for diverse applications, particularly in multilingual categorisation tasks such as monitoring disaster-related communications on social media to improve visibility and trust. Moreover, it incorporates a sophisticated mechanism to bolster the dependability of its recommendations.
  Keywords: negative or offensive comments; multilingual languages; explicit semantic analysis; ESA; enhanced eagle Aquila optimiser; EEAO.
  DOI: 10.1504/IJCSE.2024.10066586
   
  
• A verifiable and secure DNN classification model over encrypted data  
  by Weixun Li, Guanghui Sun, Yajun Wang, Long Yuan, Minghui Gao, Yan Dong, Chen Wang 
  Abstract: Outsourcing deep neural networks (DNNs) offers relief for client overhead but sparks concerns over sensitive data privacy. Current methods, like homomorphic encryption and federated learning, aim to safeguard privacy but often falter in preserving it and verifying gradient integrity. In response, we introduce DNNCM-ED, a novel verifiable and secure DNN classification model operating on encrypted data. Our approach establishes a jointly trained DNN model between client and server, with the client encrypting local gradients via double masking before aggregation at the server. We also devise secure communication protocols for fundamental operations crucial in constructing the DNN classification model. Additionally, we craft verifiable algorithms tailored to DNNCM-ED, ensuring privacy of local gradients and integrity verification. Extensive property and performance analyses underscore DNNCM-ED's superiority in accuracy, time efficiency, and communication overhead. Through these advancements, DNNCM-ED addresses critical shortcomings in existing privacy-preserving outsourcing methods while providing robust confidentiality and integrity verification.
  Keywords: deep neural networks; encrypted data; homomorphic encryption.
  DOI: 10.1504/IJCSE.2024.10066642
   
  
• Enhancing multi-view ensemble learning with zig-zag pattern-based feature set partitioning  
  by Aditya Kumar, Jainath Yadav 
  Abstract: This study suggests a novel approach called Zig-Zag Pattern-Based Feature Set Partitioning. The method involves two steps: first, calculating feature correlations using Pearson's coefficient, and second, ranking features based on mean correlation and arranging them in a zig-zag pattern. The zig-zag pattern ensures diverse and balanced feature subsets, improving model generalization and reducing over- fitting. Experimental results on ten high-dimensional datasets show the practical significance of the suggested strategy, which show that it outperforms previous strategies in accuracy and generalization. This approach advances multi-view ensemble learning, offering a practical solution for improving ensemble model performance in complex data analysis tasks.
  Keywords: feature set partitioning; views construction; ensemble learning; zig-zag partitioning; classification; multi-view ensemble learning.
  DOI: 10.1504/IJCSE.2024.10066742
   
  
• EIUAPA: an efficient and imperceptible universal adversarial attack on audio classification models  
  by Huifeng Li, Pengzhou Jia, Weixun Li, Bin Ma, Bo Li, Dexin Wu, Haoran Li 
  Abstract: The domain of Audio Classification Models is emerging as a significant paradigm, albeit susceptible to universal adversarial attacks. These attacks involve the insertion of a single optimal perturbation into all audio samples, leading to incorrect predictions. Nonetheless, existing attack methodologies are hindered by inefficiencies and imperceptibility challenges. In order to streamline the attack process effectively, we propose a two-step strategy EIUAPA that offers an optimal initiation point for the perturbation optimization process, resulting in a notable decrease in generation time. To maintain imperceptibility, we present a range of metrics focusing on perturbation concealment, serving as benchmarks for optimization. These metrics ensure that perturbations are concealed not only in the frequency and time domains but also remain statistically indistinguishable. Experimental results demonstrate that our method generates UAPs 87.5% and 86.8% faster than baseline methods, with improved Signalto-Noise Ratio (SNR) and Attack Success Rate (ASR) scores.
  Keywords: adversarial attack; artificial intelligence; security and privacy; audio classification; deep learning.
  DOI: 10.1504/IJCSE.2024.10066803
   
  
• Using generative adversarial network for music transformation  
  by Cheng-Han Wu, Yu-Cheng Lin, Pimpa Cheewaprakobkit, Wan-Chin Ting, Timothy K. Shih 
  Abstract: In this study, we propose a generative adversarial network (GAN) framework for music style transfer. Initially, a dataset of traditional Jiangnan songs is pre-processed into two categories: complete compositions and corresponding musical phrases (starting and ending notes), which are then converted into piano-roll images. The CycleGAN model is then used to train these images until the model converges to establish a music style transfer model. The goal is to allow users to input only the starting and ending notes of each measure as a musical phrase, and the system will convert this phrase into complete musical compositions in the Jiangnan style. Then we use a deep learning framework and music expertise for data processing, enhancing the quality and utility of our conversions. At the same time, we have established music style assessment metrics based on the statistical data of the dataset, providing an effective method for evaluating music styles.
  Keywords: music transformation; generative adversarial network; GAN; automatic music generation; music style transfer.
  DOI: 10.1504/IJCSE.2024.10066865
   
  
• Scalable malicious URL detection technique for smishing attacks  
  by Razvan Stoleriu, Catalin Negru, Bogdan-Costel Mocanu, Emil-Andrei Constantinescu, Alexandra-Elena Mocanu, Florin Pop 
  Abstract: Nowadays, smartphones are used daily and use sensitive information making people more vulnerable to cyber-security attacks. The easiest way for attackers to access a smartphone is through SMS phishing (smishing) using URL shortening services. In this paper, we propose a scalable technique to detect malicious URLs in smishing attacks based on a Cloud-Edge architecture, using threat intelligence platforms (e.g., VirusTotal, PhishTank), and Machine Learning algorithms that classify the URLs based on their features. We used a public dataset for training and proposed new features to improve it. We evaluated our proposed ML model against JRip, PART, J48, and Random Forest algorithms. Our model has improved performance compared to similar solutions, obtaining an accuracy of approximately 97%. To showcase the effectiveness of our solution, we implement an Android application that detects malicious short URLs in SMS messages and notifies the user concerning their legitimacy.
  Keywords: smishing attacks; malicious URLs; edge-cloud computing; threat intelligence; machine learning.
  DOI: 10.1504/IJCSE.2024.10067011
   
  
• Performance analysis and comparison of jellyfish optimisation-based maximum power point tracking controller for partial shading condition  
  by Dilip Yadav, Nidhi Singh 
  Abstract: This paper addresses the critical challenge of partial shading conditions (PSC) in photovoltaic systems, which significantly affect the efficiency of PV panels. Conventional methods often fail to optimise output under partial shading condition, prompting the need for innovative approaches. The study proposes the jelly-fish optimisation algorithm for maximum power point tracking, comparing its effectiveness with various existing MPPT controllers including incremental conductance, modified incremental conductance, perturbance and observation, particle swarm optimisation, cuckoo search algorithm, grey wolf optimisation, and whale search optimisation techniques. The study reveals the limitations of conventional techniques in optimising power output under PSC. The findings highlight the superiority of the jellyfish-based MPPT, achieving an impressive efficiency of 99.89% with a minimal tracking time of 0.14 seconds, surpassing other MPPT controllers. This work advances the field by highlighting the jellyfish algorithms effectiveness and guiding future research toward more efficient MPPT methods.
  Keywords: cuckoo search; jellyfish optimisation algorithm; maximum power point tracking; partial shading condition; particle swarm optimisation.
  DOI: 10.1504/IJCSE.2024.10067209
   
  
• Hybrid predictive modelling for insurance premium retention: integrating statistical and AI techniques  
  by Ahmed A. Khalil, Zaiming Liu 
  Abstract: This research highlights the critical role of forecasting in the insurance industry and emphasises the premium retention ratio (PRR) as a key internal performance indicator for evaluating insurance company operations. Traditional time series models like ARIMA and Exponential Smoothing face limitations in capturing complex data patterns. To address this, the study proposes a hybrid predictive model that combines statistical time series models (ARIMA, EXP) with advanced AI techniques (ANN, SVR) to enhance PRR prediction accuracy in Egypts Fire, Marine, and Aviation insurance sectors. Using 80% of data for training (19892015) and 20% for testing (20162021), the study demonstrates that hybrid models, particularly ARIMA-ANN and EXP-ANN, outperform conventional models. The findings suggest that incorporating ANN into these models significantly improves prediction accuracy. This research offers a novel approach to forecasting in the Egyptian insurance market and provides publicly accessible datasets for further comparative studies across different countries.
  Keywords: artificial neural network; ANN; artificial intelligence; autoregressive integrated moving average; ARIMA; exponential smooth; Egyptian insurance market; statistical time series; support vector machine; SVM; insurance.
  DOI: 10.1504/IJCSE.2024.10067258
   
  
• GraphBiGRU model for anti-money laundering based on preference-based reinforcement learning via the label filtering loop mechanism  
  by Meng Li, Xinqiao Su, Lu Jia, Rongbo You 
  Abstract: Anti-Money Laundering (AML) in Bitcoin transactions remains challenging since Bitcoin data has a complex graph structure and sequential nature, with many unknown labels and an imbalanced distribution of licit and illicit transactions. To address these challenging issues, we propose a novel reinforcement learning-based GraphBiGRU model via the label filtering loop mechanism to detect illicit transactions in the Bitcoin blockchain. Specifically, we first constructed the GraphBiGRU network to learn the graph structure and temporal information of Bitcoin data. Then, we introduced the label filtering loop mechanism, which encouraged the GraphBiGRU to select reliable pseudo-labeled samples that reduced data noise interference. In addition, we investigated a preference-based reinforcement learning strategy that enabled the GraphBiGRU to better identify illicit transactions, thereby improving performance on imbalanced datasets. Finally, we conducted experiments on the Elliptic dataset, demonstrating that our method achieved state-of-the-art performance, especially with a limited labelled dataset.
  Keywords: anti-money laundering; illicit transactions; GraphBiGRU; label filtering loop mechanism; pseudo-labeled samples; preference-based reinforcement learning; elliptic dataset.
  DOI: 10.1504/IJCSE.2024.10068029
   
  
• Efficient traffic management in the internet of vehicles through enhanced routing and deep learning  
  by Arundhati Sahoo, Asis Kumar Tripathy 
  Abstract: In the Internet of Vehicles (IoV), vehicles are treated as sophisticated smart devices with robust communication systems. IoV uses cellular technology and internet access for vehicle-to-vehicle communication. However, traditional routing algorithms struggle with rapid vehicle movements and varying road conditions, leading to instability and inefficiency, especially in congested traffic. This study proposes a unique approach called the Improved Greedy-Bi directional Long Short-Term Memory (I-GBiLSTM) predictor, which integrates an Improved Greedy Perimeter Stateless Routing Algorithm to enhance link stability within 5G networks by incorporating real-time data on vehicle movements and road conditions and traffic patterns. Additionally, a BiLSTM neural network has been enhanced by incorporating a 1-dimensional Convolutional Autoencoder (1D-CNNAE) and a Temporal Transformer Encoder (TTE) for monitoring and predicting traffic data, enabling unique feature extraction. Experimental results demonstrate that I-GBiLSTM is superior to the other existing protocols, achieving a 99% delivery ratio, 100 routing overhead, 180 ms end-to-end delay, and 98.2% prediction accuracy.
  Keywords: traffic management; internet of vehicles; IoV; routing; deep learning; network traffic prediction.
  DOI: 10.1504/IJCSE.2024.10068349
   
  
• Social network perspective on false information detection in vehicular ad hoc networks: combining spatial inference with historical behaviour analysis  
  by Youke Wu 
  Abstract: Rapid advancements in vehicular ad hoc networks (VANETs), which create a unique social network ecosystem, have heightened concerns over false information dissemination. To safeguard road safety and VANET integrity, sophisticated data analytics are vital to counteract such misinformation effectively. This necessitates an innovative hybrid detection method, merging spatial inference with historical behaviour analysis, enabling precise identification of false reports in both environmental and human-generated incidents. By refining data gathering and computational processes, this approach eases communication loads while evaluating spatial inference models' efficacy across various VANET scales. A historical vehicle behaviour model bolsters detection accuracy significantly. Through nuanced event categorisation and tailored detection strategies, the hybrid model's effectiveness is reinforced. Findings confirm this strategy enhances detection efficiency and precision, fitting VANET social network monitoring needs. Consequently, policy suggestions involve reinforcing data privacy, upgrading communication infrastructure, and instituting specialised regulatory frameworks to bolster VANETs' resilience against false traffic information.
  Keywords: VANET; false information; hybrid detection; security.
  DOI: 10.1504/IJCSE.2024.10068606
   
  
• ADBSCSL: adaptive DBSCAN-SMOTE with cost-sensitive learning to enhance diagnostic accuracy for imbalanced medical datasets  
  by M. Kavitha, M. Kasthuri 
  Abstract: Medical diagnosis is complicated by imbalanced datasets, which biased models cannot distinguish minority class cases like rare diseases. To improve diagnosis accuracy, the research introduces ADBSCSL, which stands for Adaptive DBSCAN-SMOTE with cost-sensitive learning. Adaptive DBSCAN, SMOTE, and cost-sensitive learning handle skewed data well. Adaptive DBSCAN clusters minority class occurrences. It changes parameters to dataset density change. The diversity of the density condition cannot have caused the minority class to misidentify. SMOTE is then applied to these clusters to increase synthetic examples and class balance. It reduces misclassification costs using cost-sensitive learning. This pushes the model toward minority class priority and avoids majority class bias. The approach was evaluated on Brain Stroke, Cerebral Stroke, and Autism Spectrum Disorder datasets. ADBSCSL F1-scores of 91.8% and 90.6% indicate accuracy over 90% on Brain Stroke and Cerebral Stroke datasets. On ASD datasets, it had 100% accuracy, precision, recall, and F1-score. Results show that the ADBSCSL increases classification performance, making it a powerful and efficient tool for medical diagnosis with highly imbalanced datasets.
  Keywords: imbalanced datasets; DBSCAN; SMOTE; cost-sensitive learning; machine learning; diagnostic accuracy; imbalanced medical datasets.
  DOI: 10.1504/IJCSE.2024.10068671
   
  
• A secure consensus mechanism for IoT-based energy internet using post-quantum blockchain  
  by Yousra Angague, Hadil Sahraoui, Chahrazed Benrebbouh, Houssem MANSOURI, Al-Sakib Khan Pathan 
  Abstract: The Energy Internet (EI) is a cutting-edge technology with a vision to integrate diverse energy sources into an efficient and flexible grid However, there are several critical security challenges for its real-life implementation due to its interconnected nature. Again, the advancement of another cutting-edge technology, Quantum computing heightens these concerns as it threatens the traditional cryptographic defenses, making them vulnerable to quantum attacks In this work, we introduce a secure consensus mechanism for IoT-based EI systems by integrating Post-Quantum Cryptography (PQC) and Blockchain technology In our approach, we combine the strengths of two state-of-the-art protocols: a mutual authentication framework and a post-quantum consensus mechanism. We propose two secure consensus algorithms, leveraging the Dilithium and Falcon PQC schemes. Simulation studies, demonstrate that our proposed approach significantly improves transaction throughput and reduce latency, providing a resilient framework for secure energy data management in post-quantum world.
  Keywords: blockchain; consensus; energy internet; EI; internet of things; IoT; post-quantum cryptography; PQC; quantum attack; security.
  DOI: 10.1504/IJCSE.2024.10068855
   
  
• Air pollution prediction by using long-short-term memory neural network  
  by Qinghua Xu, Jiankang Shen, Meng Gao 
  Abstract: High ground-level ozone concentrations affect air quality, plant growth, and human health. This study uses an LSTM model to predict 1-h, 8-h, and 24-h ozone concentrations. We tested models with various hidden layer neurons and sequence lengths. Sensitivity to parameters rose with longer prediction intervals. After optimizing hyperparameters, LSTM outperformed traditional methods like Random Forest and MLP in predicting ozone concentrations, with satisfactory predictive capability and pollution event warning rates.This validates the feasibility of LSTM models for predicting environmental ozone levels across different time intervals and confirms their effective ability to forecast air pollution incidents effectively.
  Keywords: ozone; long-short-term memory; hyperparameters; time interval.
  DOI: 10.1504/IJCSE.2025.10069211
   
  
• VMO-HNIA: virtual machine optimisation using hybrid nature inspired algorithm for cloud resources efficiency  
  by Ruaa Ali 
  Abstract: Optimisation for cloud data centres virtual machine (VM) consolidation is advised, however performance trade-offs are difficult. VM optimisation utilising the hybrid nature inspired algorithm (VMO-HNIA) is a new VM consolidation framework. The HNI-based VM consolidation system uses a multi-resources aware decision algorithm (MADA) to identify host overload or underload dynamically. To enhance the optimisation of VM resources and load balancing, the MADA calculates numerous resources to inform decision-making. The correct classification of each host further boosts VM consolidation processes like VM selection, migration, and placement. To improve the process of selecting and placing VMs in a VM consolidation architecture, we suggest using a new technique called the hybrid whale optimisation technique (HWOA) for VM selection and placement. To improve VM consolidation, the HWOA places the best host utilising several objective functions. Experimental findings show the VMO-NHI framework employing CloudSim outperforms underlying solutions.
  Keywords: cloud computing; decision-making; host placement; nature-inspired; VM selection; VM placement; resources optimisation.
  DOI: 10.1504/IJCSE.2025.10069245
   
  
• Pedestrian head detection based on improved YOLOv5  
  by Yong Ren, Tian Qiu, Jian Shen 
  Abstract: This paper presents an improved YOLOv5 model for the detection of pedestrian heads in crowded scenes. By incorporating FasterNet, the C2f module, Soft- NMS and Optimal Transport Assignment (OTA), the proposed model achieves significant performance improvements over the baseline YOLOv5s model, with a recall of 75.21%, AP50 of 84.31%, and AP50-95 of 57.29%, while maintaining a reduced computational complexity of 14.2 GFLOPs. In comparison with other YOLO series models, the proposed model demonstrates a higher AP50-95 score while maintaining competitive recall and AP50 values. The effectiveness of the model has been demonstrated in diverse scenarios, including various crowd densities, lighting conditions, pedestrian orientations, image resolutions, and pedestrian sizes. The results indicate that the improved YOLOv5 model exhibits robustness, adaptability, and generalization capabilities in challenging pedestrian head detection tasks.
  Keywords: pedestrian head detection; YOLOv5; FasterNet; soft non-maximum suppression; Soft-NMS; optimal transport assignment; OTA.
  DOI: 10.1504/IJCSE.2025.10069246
   
  
• Feature selection using war strategy optimisation algorithm for software fault prediction  
  by Pradeep Kumar Rath, Roshan Samantaray, Susmita Mahato, Sushruta Mishra, Sanat Kumar Patro, Himansu Das 
  Abstract: Identifying problematic software modules early on in development process can help programmers create software that is highly efficient and dependable. In this paper, a novel FS approach using war strategy optimization (FSWSO) have been proposed that applies ancient war strategy planning principles to the selection of features or variables in SFP. This approach seeks to identify the most relevant features for SFP by simulating army operations and evaluating the performance of different feature subsets in a simulated war space. In this experiment, we have compared the proposed FSWSO algorithms performance to that of other FS techniques including FSACO, FSDE, FSGA, and FSPSO in order to assess the algorithms accuracy. In the majority of cases, FSWSO has provided better performance with fewer chosen features. The suggested approach has been validated and proven to be superior to prior approaches in choosing an optimal selection of features using the Friedman and Holm tests.
  Keywords: software fault prediction; war strategy optimization; metaheuristic; machine learning; feature selection; classification.
  DOI: 10.1504/IJCSE.2025.10069977
   
  
• Face spoofing detection using noise-based random feature and Fisher vector encoding  
  by Fang Xu, Na Yang, Xiaochao Zhao, Hao Chen, Manzoor Ahmed, Yi Ma, Zhen Liu, Yuquan Zhang 
  Abstract: With the vast application of face recognition technology, its security risks have increased as systems are vulnerable to spoofing attacks with falsified faces, attracting many researchers attention. In this paper, we proposed to make use of noise information in colour space to detect spoofing attacks. Firstly, we extracted frame-based noise from face videos in multiple colour spaces. Then local random features are extracted via random projection. Finally, Fisher vector encoding is employed to aggregate these features into global feature vectors, and a classification model is trained for detection. Experimental results on three standard face spoofing databases demonstrate the effectiveness of the approach. The equal error rate on the replay attack database is 0%. On the CASIA and MSU databases, the equal error rates are 3.52% and 0%, respectively. By combining noise-based random features and Fisher vector encoding, this method effectively resists photo and video-based spoofing attacks.
  Keywords: face recognition; face spoofing detection; noise; random projection; feature extracting.
  DOI: 10.1504/IJCSE.2025.10070188
   
  
• Scene text detection using robust masks and cascaded classifiers  
  by Houssem Turki, Mohamed Elleuch, Monji Kherallah, Alima Damak 
  Abstract: Detecting text in scenes poses a significant and challenging problem due to the complex character shapes and the diverse degradations present in natural images. It represents the initial and crucial phase that must be successfully completed before the text recognition stage. In this study, we suggest a hybrid approach to tackle this issue, leveraging the maximally stable extremal regions (MSER) algorithm, which gained significant attention in recent research. Despite its popularity, it remains very sensitive to the shape, size, scale and background noise of text characters. To tackle its limits and refine the final result, we focus on an extended MSER based method. Overall flowchart of the suggested system is divided into three steps: 1) robust masks generation to identify the text candidate regions; 2) feature extraction and selection based on VGG16 deep learning architecture; 3) employing classifiers in a cascaded structure to differentiate between text and non-text areas based on enhanced geometrical pattern characteristics. The effectiveness of the proposed method is demonstrated through an experimental study conducted on various benchmarks, such as ICDAR2013, ICDAR2015, MSRA-TD500, and RRC-MLT.
  Keywords: scene text; text detection; maximally stable extremal regions; MSER; VGG16 deep learning model; random forest; SVM; NCA-based feature selection.
  DOI: 10.1504/IJCSE.2024.10066146
   
  
• Solving overhead transmission line engineering problems with elastic catenary equations  
  by Hervé Ducloux 
  Abstract: Most methods used to solve overhead line sag-tension problems assume that the conductor is inextensible. This assumption leads to the classical catenary curve. In this article, the conductor is assumed to be extensible, which leads to the more accurate elastic catenary curve. First, an original mathematical approach of establishing the equations of this curve is proposed: it uses the slope to connect the abscissa and the ordinate of each point on the curve. Then, it is used to solve sag-tension problems for perfectly linear elastic conductor or composite conductor with nonlinear inelastic behaviour. In each case, all steps of the algorithms proposed are explained. The numerical examples included in this paper show that the number of iterations needed to solve a sag-tension problem is quite small. Comparisons with previous studies are also made to assess the accuracy of the proposed method.
  Keywords: equations of elastic catenary curves; overhead transmission lines; sag-tension problems.
  DOI: 10.1504/IJCSE.2024.10066037
   
  
• Deep learning architectures for detection of acute myeloid leukemia  
  by Niteesh Kempusagara Ramesh, Vijayakumar Kadappa, Rajeshwari Devi Doddapoojari Veerabhaskar, Divijendranatha Reddy Sirigiri, Pooja Tekal Sreedhar 
  Abstract: Acute myeloid leukaemia (AML) involves rapid growth of immature blood cells, impairing normal immune functions. AML represents nearly 80% of adult blood cancers. Despite various treatments and therapies, prognosis remains poor and there are critical but unmet needs. Artificial intelligence (AI) offers potential diagnostic options yet existing models often rely on traditional algorithms and small datasets, limiting effectiveness in AML detection. We propose two deep learning architectures using large blood cell image datasets to detect AML. These models use diverse kernel shapes to identify complex patterns, requiring up to 97% fewer parameters while achieving high accuracy (99.7% and 99.6%). Compared to other networks like AlexNet, MobileNet, ResNet50, and InceptionV3, which show accuracy of 99.5%, 96.4%, 54.6%, and 95.9%, our models are better or competitive. Improved generalisation is confirmed by learning curves and feature maps. These models can help diagnosis of AML more accurately and efficiently.
  Keywords: artificial intelligence; deep learning; artificial neural networks; acute myeloid leukemia; healthcare.
  DOI: 10.1504/IJCSE.2024.10065883
   
  
• Image-text multimodal sentiment analysis method integrating multi-themes and multi-labels  
  by Shunxiang Zhang, Longhui Hu, Shuyu Li, Wenjie Duan, Xiaolong Wang 
  Abstract: The current image-text sentiment analysis models only focus on the content of text and image, and ignore the synergistic effect of themes and labels information on the semantic features of image and text. Therefore, we propose a multi-modal sentiment analysis method integrating multi-themes and multi-labels. Firstly, the global features and local features of the image are obtained by CNN and Faster-RCNN. Bi-LSTM is used to obtain word-level features and sentence-level features of the text, and the Bert is responsible for extracting the theme-label features. Then, the attention network for feature interaction to generate the word-local correlation features, and the text's sentence features are combined with the image's global features to generate the joint features of the image-sentence. Finally, these two features are fused with the theme-label features to obtain the results of the sentiment analysis. The experimental results demonstrate that the proposed method can improve the accuracy of image-text sentiment analysis.
  Keywords: multimodal sentiment analysis; multi-theme labels; modal fusion; target detection; attention network.
  DOI: 10.1504/IJCSE.2024.10065952
   
  
• IQ-RRT*: a path planning algorithm based on informed-RRT* and quick-RRT*  
  by Afroze Rahman, Anindita Kundu, Sumanta Banerjee 
  Abstract: Optimal path planning algorithms such as the RRT* and its variants seek to generate the best feasible path from an initial state to a goal state in the least possible time. Prior work on RRT* has focused on improving the convergence rate of the algorithm while keeping its computational complexity unchanged. Informed-RRT* and quick-RRT* are two such variants that, in certain scenarios, converge to the optimal path faster than RRT* does. This work focuses on the novel addition of informed sampling to quick-RRT* to enhance its convergence rate. The resultant algorithm provides initial solutions with costs comparable to quick-RRT* and convergence rates at par with quick-RRT* in the worst case. The authors have concluded that this new algorithm, named IQ-RRT*, outperforms informed-RRT* and quick-RRT* in a multitude of scenarios. IQ-RRT*, unlike quick-RRT*, is a faster alternative to informed-RRT* even in cluttered environments and mazes with long corridors.
  Keywords: path planning; informed sampling; fast convergence; rapidly-exploring random tree; RRT*.
  DOI: 10.1504/IJCSE.2025.10069763
   
  
• MFA: Web API recommendation based on service multiple feature aggregation  
  by Guobing Zou, Chunhua Zeng, Yue Zhu, Pengtao Li, Song Yang, Shengxiang Hu 
  Abstract: As the number of web services continues to increase, it has become a challenging problem to provide developers with accurate and efficient web services that meet Mashup requirements. To solve this problem, various methods have been proposed to recommend APIs to match the needs of Mashups, and have achieved great success. However, due to the uneven quality of service descriptions, there are some challenges in feature extraction, utilisation of service meta-information, and textual requirements understanding. Therefore, we propose a Web API recommendation method (FMA) based on service multiple feature aggregation. FMA uses the attention mechanism model to mine the semantic features of similar services and enhance the features of Mashup services and Web API services. The service category is used as the basis for constructing the graph network, and multiple service features are mined and integrated through the hierarchical feature aggregation algorithm of the graph convolution network to further enhance the service features, thereby significantly improving the Web API recommendation effect. We conduct extensive experiments on a large-scale real-world dataset called ProgrammableWeb, and the results show that FMA outperforms existing baseline methods on multiple evaluation metrics.
  Keywords: Web API; Mashup service; API recommendation; multiple feature aggregation; attention mechanism.
  DOI: 10.1504/IJCSE.2024.10065996
   
  
• Near-lossless medical image compression using wavelet subband thresholding and convolutional autoencoder  
  by Muthalaguraja Venugopal, Kalavathi Palanisamy, Punitha Viswanathan 
  Abstract: In the digital era of healthcare, telemedicine services are continuously evolving. Medical images with massive file sizes increase storage and transmission complexity while providing telemedicine services. To address this, image compression becomes obligatory. Learning-based methods show promising results in image compression tasks. However, the problem of maintaining image reconstruction quality still needs to be addressed. This work proposes a wavelet-based convolutional autoencoder for near-lossless medical image compression. Thresholded wavelet subbands of medical images were used to train the compression model. A convolutional encoder-decoder model with a simple encoder network and an extended decoder network is proposed to achieve a near-lossless image compression standard. A combined loss function is employed to improve the model's reconstruction performance. The combined loss function includes mean squared error and structural similarity index metric, focusing on image reconstruction quality. Extensive experiments show the efficiency of the proposed method over the existing image compression techniques.
  Keywords: image compression; CNN; wavelet; convolutional autoencoder; CAE; deep learning; medical image compression.
  DOI: 10.1504/IJCSE.2025.10069762
   
  
• Agreement window algorithm for user controlled and utility supported personal data privacy  
  by Geocey Shejy, Pallavi Vijay Chavan 
  Abstract: A good privacy-preserving algorithm must keep the trade-off between privacy preservation and the utility of the data for analysis purposes. For fine-tuned protection of individual data privacy, both customers and organisations who are the stakeholders of digital services need more supporting research to ensure legal compliance. The work in this article keeps the European Union's GDPR as a key reference. The agreement window (AW) algorithm ensures ϵ-differential privacy and helps organisations collect legitimate personal data by ensuring user consent. The agreement window is a conceptual space where the data owner and the service-providing organisations agree to share the data. While sharing data in this space, the AW algorithm calculates the sensitivity factor (SF), which is the combined quantity of sensitivity of collecting data by the service provider and is used to decide the noise addition required to be added to the original data.
  Keywords: agreement window; differential privacy; set sensitivity; sensitivity factor; personal identifiable information; PII; personal sensitive information; PSI; quasi identifier; QI.
  DOI: 10.1504/IJCSE.2024.10065997
   
  
• Design of exercise recommendation model based on clustering collaborative filtering adaptability  
  by Chaoyang Shi, Zhen Zhang 
  Abstract: The study explores the issue of insufficient personalised recommendation ability of exercise systems in online teaching. It combines clustering analysis and collaborative filtering algorithms. K-means clustering is used as the basis for clustering analysis. And the collaborative filtering algorithm is optimised from three aspects: the number of learners working together, the difference in exercise scores, and the difficulty of exercise. A clustering collaborative filtering adaptive exercise recommendation model based on similarity improvement is proposed. The study evaluates the application effectiveness of the model through simulation experiments. The experimental results show that the MAE values formed by the designed algorithm under changes of the nearest neighbours are the lowest among the comparison algorithms, proving its superiority. In the comparison of indicators, the accuracy, recall, and F1 values of the algorithm are all the highest among the comparison algorithms, further verifying its effectiveness. Stability analysis shows that in both sets, the accuracy of the research design model reaches above 0.87, indicating that the model has high stability and accuracy. From this, the model designed in the study has advantages in recommendation effectiveness, which can help students improve learning effectiveness and provides a new approach for learning assistance systems.
  Keywords: clustering; collaborative filtering; online learning; exercise; recommendation; similarity.
  DOI: 10.1504/IJCSE.2024.10066045