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A novel dominating set and centrality based graph convolutional network for node classification

Multimedia Tools and Applications 2025年第14期84卷 12979-13004页

作者： Garg, Neeraj Garg, Sneha Jadeja, Mahipal Meena, Yogesh Kumar Gopalani, Dinesh Chauhan, Ganpat Singh Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Information Technology School of IT Manipal University Jaipur Rajasthan Jaipur302034 India

To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are particularly designed to record dependencies and relations in large and complicated networks such as social networks, molecular structures, citation networks and recommendation systems. The main idea behind GNN is the message-passing mechanism that allows sharing of information between nodes and edges in a graph network, enabling the model to learn about graph structures and perform well even with limited training data. GNNs have three different architectures: Graph Convolutional Network (GCN), GraphSAGE, Graph Attention Model (GAT) and other hybrid models. In this paper, we have proposed novel GCN models namely dominating Set with Degree Centrality based GCN (DS_DC_GCN) and dominating Set with Betweenness Centrality based GCN (DS_BWC_GCN) model, for node classification task. In the traditional GNN models, training nodes are selected randomly. In our proposed models, we first identify a subset of the nodes with the help of a dominating set (D) of the underlying network. Then we consider final training nodes (say D′) from set D based on the centrality scores (degree or betweenness). We give preference to nodes having high centrality scores for training. To evaluate the performance of the proposed models, we have used three standard citation network datasets - Cora, Citeseer and Pubmed. Our models obtain improved node classification performance as compared to other state-of-the-art-methods. The consideration of high centrality nodes of dominating set in training helps to improve the feature availability in the underlying computational graphs, which in turn improves learning and hence, model performance. These models also have the potential to offer better results for other network tasks, including link prediction and community detection. © The Author(s), under

关键词： Graph neural networks

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WSO-KELM: War Strategy Optimization-Based Kernel Extreme Learning Machine for Automatic Software Fault Prediction Model

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 145-163页

作者： Elci, J. Brundha Nandagopalan, S. Department of Computer Science and Engineering Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India Department of Information Science and Engineering Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India

The software development projects’ testing part is usually expensive and complex, but it is essential to gauge the effectiveness of the developed software. Software Fault Prediction (SFP) primarily serves to detect faults within software components. The scale of software projects is getting larger, making it impossible and time-consuming to analyze the flaws manually. Artificial Intelligence (AI) techniques are mainly incorporated to solve this problem. The massive data retrieved from the software repositories helps in identifying the features that are highly correlated with fault prediction and the features that are not. This paper introduces a Kernel Extreme Learning Machine (KELM) for SFP. To optimize the hyperparameters of the classifier and also enhance the classification accuracy, the War Strategy Optimization (WSO) algorithm is employed here. The experimental evaluations are conducted using different baseline actual-world datasets of software programs such as Java-coded open-source and PROMISE datasets. The experimental outcomes are conducted using different performance metrics that include sensitivity, Accuracy, and F1-score with values of 0.96%, 0.97%, and 0.96%, respectively. The results show that KELM is beneficial, and the optimization provided by the WSO algorithm also helps us obtain higher classification accuracy for the SFP problem and improve the fault detection process. © The Institution of Engineers (India) 2024.

关键词： Software reliability

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"Survey and analysis of blockchain technologies with respect to: properties, algorithms, architecture, models, evolution and framework"

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Multimedia Tools and Applications 2025年第14期84卷 13571-13615页

作者： Tiwari, Nikita Ranjan, Prakash Biswal, Pradeep Kumar Barde, Chetan Sinha, Neha Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India

The growing realm of blockchain technology has captivated researchers and practitioners alike with its promise of decentralized, secure, and transparent transactions. This paper presents a comprehensive survey and analysis of various blockchain technologies, focusing on their fundamental properties, architectural designs, operational models, evolutionary trends, and overarching frameworks. Through an extensive review of existing literature and empirical studies, this research synthesizes the multifaceted landscape of blockchain, elucidating key concepts such as consensus mechanisms, smart contracts, scalability, interoperability, and privacy protocols. By examining the evolution of blockchain from its inception to its current state and beyond, this paper delineates the trajectory of technological advancements and identifies emerging paradigms. Furthermore, it critically evaluates the strengths and limitations of diverse blockchain architectures and models, offering insights into their applicability across different domains and use cases. Finally, this study culminates in the proposal of a comprehensive framework that encapsulates the essential elements necessary for understanding, implementing, and harnessing the transformative potential of blockchain technologies. Through this holistic analysis, this research contributes to a deeper understanding of blockchain ecosystems and provides valuable guidance for future research directions and practical implementations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Multilevel chaotic encryption model with cyclic redundancy check for medical data

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Neural Computing and Applications 2025年第5期37卷 3581-3603页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

In telemedicine applications, it is crucial to ensure the authentication, confidentiality, and privacy of medical data due to its sensitive nature and the importance of the patient information it contains. Communication through open networks is insecure and has many vulnerabilities, making it susceptible to unauthorized access and misuse. Encryption models are used to secure medical data from unauthorized access. In this work, we propose a bit-level encryption model having three phases: preprocessing, confusion, and diffusion. This model is designed for different types of medical data including patient information, clinical data, medical signals, and images of different modalities. Also, the proposed model is effectively implemented for grayscale and color images with varying aspect ratios. Preprocessing has been applied based on the type of medical data. A random permutation has been used to scramble the data values to remove the correlation, and multilevel chaotic maps are fused with the cyclic redundancy check method. A circular shift is used in the diffusion phase to increase randomness and security, providing protection against potential attacks. The CRC method is further used at the receiver side for error detection. The performance efficiency of the proposed encryption model is proved in terms of histogram analysis, information entropy, correlation analysis, signal-to-noise ratio, peak signal-to-noise ratio, number of pixels changing rate, and unified average changing intensity. The proposed bit-level encryption model therefore achieves information entropy values ranging from 7.9669 to 8.000, which is close to the desired value of 8. Correlation coefficient values of the encrypted data approach to zero or are negative, indicating minimal correlation in encrypted data. Resistance against differential attacks is demonstrated by NPCR and UACI values exceeding 0.9960 and 0.3340, respectively. The key space of the proposed model is 1096, which is substantially mor

关键词： Telemedicine

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CLASSIFICATION OF ROYAL DELICIOUS APPLES USING HYBRID FEATURE SELECTION AND FEATURE WEIGHTING METHOD BASED ON SVM CLASSIFIER

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Scalable Computing 2025年第2期26卷 940-949页

作者： Kaur, Sandeep Sachan, Manoj Kumar Aggarwal, Ashwani Kumar Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

Fruit safety is a critical component of the global economy, particularly within the agricultural sector. There has been a recent surge in the incidence of diseases affecting fruits, leading to economic setbacks in agriculture worldwide. Conventional manual assessment methods are laborious, prompting the exploration of automated computerized techniques for evaluating fruit quality. This research presents a novel method for assessing the quality of golden delicious apples. A dataset comprising 1256 apple images was gathered under controlled conditions. Afterward Feature extraction focuses on texture features like LBP, GLCM, GLDM, DTF, and Gabor features, color features, and shape and size features. A total of 18654 features are extracted and normalized using z-score. A hybrid method for feature selection and weighting involves the mRMR algorithm to eliminate redundant features and the Sine Cosine Optimization Algorithm for feature weighting, enhancing classification performance. The SVM machine learning technique, augmented with optimized features, yielded a 10.53% improvement in accuracy compared to SVM alone. Validation against state-of-the-art methods using Friedman’s mean rank test underscored the statistical significance of this approach across various metrics. © 2025 SCPE.

关键词： Fruits

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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GENOCARE PROGNOSTICATOR MODEL: HOST GENETICS PREDICT SEVERITY OF INFECTIOUS DISEASE

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Scalable Computing 2025年第2期26卷 924-939页

作者： DUBEY, SHIVENDRA SINGH, SHWETA VERMA, DINESH KUMAR LODHI, SUDHEER KUMAR DUBEY, SAKSHI Department of Artificial Intelligence and Machine Learning Manipal University Rajasthan Jaipur303007 India School of Engineering and Technology Jagran Lakecity University Madhya Pradesh Bhopal462042 India Department of Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Gujarat Vadodara391760 India

Scientific community understanding of the variance in severity of infectious disease like COVID-19 across patients is an important area of focus. The article presents an innovative voting ensemble GenoCare Prognosticator (GCP) model that incorporates XGBoost and Random Forest classifiers, two cutting-edge machine learning approaches. A large dataset that incorporates medical covariates like gender and age along with biological WES (Whole Exome Sequencing) data was used to train these models. Five-fold stratified cross-validation was used to process the dataset in order to improve model stability and avoid overfitting. Two medical covariates and sixteen recognized candidate gene variants were among the eighteen major features on which our GCP model had been verified using data from earlier studies. Specific post-hoc clarification of the model's predictions was provided by ExplainerDashboard, a Python open-source library, to improve interpretability. Furthermore, we utilized OpenTarget and Enrichr, two bioinformatic resources, to establish connections between the discovered variations in genetics and pertinent ontologies, biological pathways, and possible drug/disease relationships. Unsupervised clustering of SHAP key feature values was included in the analysis, which revealed intricate genetic interactions that affect the severity of the disease. Our results show that although gender and age are the main factors influencing the severity of COVID-19, complex genetic interactions cause severe symptoms in a specific subset of patients. This work contributes to our comprehension of the biological variables influencing the severity of COVID-19 and offers a reliable, comprehensible model that can help recognize patients at high risk and guide individualized treatment plans. © (2025), (West University of Timisoara). All rights reserved.

关键词： COVID-19

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Salp swarm algorithm using lens opposition-based learning and local search for constrained optimization problems

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Iran Journal of computer science 2025年 1-28页

作者： Majumdar, Parijata Mitra, Sanjoy Department of Computer Science and Engineering Indian Institute of Information Technology Agartala Jirania 799046 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh 799009 India

The Salp swarm algorithm (SSA) simulates how salps forage and travel in the ocean. SSA suffers from low initial population diversity, improper balancing of exploration and exploitation, and slow convergence speed. Thus, we have proposed a novel variant of SSA (LOLSSA) by integrating lens opposition learning with the fundamental evolutionary techniques of SSA to increase the initial population variety. During exploration, the information about the local best position for each individual is effectively shared with the follower, using the salp position update process to avoid getting stuck at the local optimum. At the end of each iteration, the local search algorithm is utilized to increase exploitation. Additionally, the leader’s salp position incorporates inertia weight to speed up the convergence speed. Swarm intelligence algorithms have proven their efficacy in feature selection, since they search for an optimal set of features in a large search area that will have the best impact on the accuracy of the training system. LOLSSA is thus employed for feature selection to select informative features in 11 high-dimensional datasets, where it outperforms well-known metaheuristics in average fitness, accuracy, and average number of feature reductions. In the software-as-a-service cloud computing model, a task scheduling problem is solved to satisfy the QoS requirement of both the service provider and the user. Experiments show that LOLSSA optimizes this problem in terms of different performance metrics. LOLSSA also outperforms other metaheuristics regarding 23 well-known benchmark functions, IEEE CEC-C06 2019, and CEC-06 2020 benchmark functions, and in solving engineering design problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Convergence speed Feature selection Global optimization Lens opposition learning Salp swarm algorithm Task scheduling problem

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FLAG: frequency-based local and global network for face forgery detection

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Multimedia Tools and Applications 2025年第2期84卷 647-663页

作者： Zhou, Kai Sun, Guanglu Wang, Jun Wang, Jiahui Yu, Linsen School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Department of Information Engineering and Mathematics University of Siena Siena53100 Italy

Deepfake detection aims to mitigate the threat of manipulated content by identifying and exposing forgeries. However, previous methods primarily tend to perform poorly when confronted with cross-dataset scenarios. To address the above issue, we propose an innovative hybrid network called the Frequency-based Local and Global (FLAG) network to explore local and global information with the help of frequency-domain cues for better generalization capability. In consideration of the fact that forged faces often exhibit flaws in the frequency domain, we design a Frequency-based Attention Enhancement Module (FAEM) to enhance the aggregation of CNN and Vision Transformer (ViT). In this design, local features from CNN are attentively enhanced by selected frequency coefficients in FAEM, facilitating generalizable global features learning by the ViT module. The effectiveness of the proposed method is validated via numerous experiments and the generalization performance is improved under cross-dataset scenarios. Especially, the proposed method have obtained an AUC of 99.26% and an ACC of 96.56% using intra-dataset experimental results on FaceForensics++ (C23). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Face recognition

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