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MOHSQO: Multi objective harmony search algorithm based QoS optimization in delay tolerant networks

Multimedia Tools and Applications 2025年 1-26页

作者： Sonkar, Nidhi Pandey, Sudhakar Kumar, Sanjay Department of Computer Science and Engineering National Institute of Technology Warangal Hanamkonda India Department of Information Technology National Institute of Technology Raipur Raipur India

Delay Tolerant Networks (DTNs) have the ability to make communication possible without end-to-end connectivity using store-carry-forward technique. Efficient data dissemination in DTNs is very challenging problem due to compromising in Quality-of-Service (QoS) metrics such as delivery ratio, end-to-end delay and overhead ratio. That means, if an algorithm is design to find highest delivery ratio, then the algorithm does not bother about delay and overhead ratio. Researchers tried to improve the performance of network but, none of them had given satisfactory results to make a balance between all QoS metrics. In this paper, we have proposed a novel approach to optimize the performance of the network using Multi objective harmony search algorithm by creating a balance between all QoS metrics. Harmony search algorithm considers number of QoS parameters simultaneously. We have used characteristics of the node (Speed, residual energy, buffer capacity and encounters) to select the optimum forwarder node so that we can balance the performance metrics for network optimization. We have used ONE (Opportunistic Network Environment) simulator to simulate the proposed method and compared with exiting techniques. By analyzing the results, we can say that proposed method has the optimum performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Delay tolerant networks

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DMF: A Deep Multimodal Fusion-Based Network Traffic Classification Model

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computers, Materials & Continua 2025年第5期83卷 2267-2285页

作者： Xiangbin Wang Qingjun Yuan Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu Henan Key Laboratory of Network Cryptography Technology Information Engineering UniversityZhengzhou450001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.

关键词： Deep fusion intrusion detection multimodal learning network traffic classification

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Integration of data science with the intelligent IoT(IIoT): Current challenges and future perspectives

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Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon University University of Electronic Science and Technology of China College of Information Science and Engineering Hohai University Department of Computer Science University of Salerno Graduate School of Informatics and Engineering The University of Electro-Communications

The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence(AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most state-of-the-art research focuses independently on either data science or IIoT, rather than exploring their integration. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions.

关键词： Data science Internet of things (IoT) Big data Communication systems Networks Security Data science analytics

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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computer Modeling in engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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An Integrated Framework with Enhanced Primitives for Post-Quantum Cryptography: HEDT and ECSIDH for Cloud Data Security and Key Exchange

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第11期49卷 165-176页

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Durga, V. Sathya Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Computer Science&Engineering Hindustan Institute of Technology and Science Chennai India

If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must be developed. Current PQC schemes primarily focus on data security or key exchange, and further improvement towards enhanced PQC primitives is required. Our proposal in this research is an innovative paradigm for PQC-focused cloud data security. The proposed HEDT approach achieves encryption and decryption with significantly lower latency (20% improvement) and higher reliability than AES, DES, and RSA, as demonstrated through experimental results. Furthermore, ECSIDH, a hybrid key exchange mechanism combining SIDH and ECDH, improves security strength by 50% while maintaining computational costs within 1.13x of SIDH. Compared to individual key exchange schemes like SIDH, ECSIDH offers superior security as a PQC candidate. These results confirm the robustness and efficiency of the proposed framework in ensuring secure data outsourcing and key exchange in cloud environments. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Quantum computers

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Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

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Alexandria engineering Journal 2025年 126卷 262-276页

作者： Rahman, Md. Atiqur Bhuiyan, Touhid Ali, M. Ameer Department of Computer Science and Engineering East West university Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system's performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen's kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems. © 2025 The Authors

关键词： Risk management

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A Generative Image Steganography Based on Disentangled Attribute Feature Transformation and Invertible Mapping Rule

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computers, Materials & Continua 2025年第4期83卷 1149-1171页

作者： Xiang Zhang Shenyan Han Wenbin Huang Daoyong Fu School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Engineering Research Center of Digital Forensics Nanjing University of Information Science and TechnologyMinistry of EducationNanjing210044China

Generative image steganography is a technique that directly generates stego images from secret *** traditional methods,it theoretically resists steganalysis because there is no cover ***,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information ***,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping ***,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,***,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute *** noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference ***,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed *** results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden ***,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.

关键词： Image information hiding generative information hiding disentangled attribute feature transformation invertible mapping rule steganalysis resistance

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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A Semantic-Based Approach for Self-Configuring Smart Home Systems Using Feedback-Based Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 17729-17741页

作者： Ran, Weizhi Nantogma, Sulemana Yang, QingZhuo Zhang, Shangyan Wang, Yan Xu, Yang University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Xi'an University of Architecture and Technology School of Information and Control Engineering China

As urban populations grow, smart home technology has become a key enabler for enhancing energy efficiency, comfort, and convenience in residential environments. However, existing smart home implementations often struggle to adapt to complex and dynamic user needs due to issues like device heterogeneity, rigid pre-set solutions, and limited interoperability. This paper presents a novel semantic-based self-configuring framework that captures the functional, operational, state, and characteristic perspectives of smart home devices, facilitating seamless interconnection and interoperability across heterogeneous devices. The proposed framework uses service entity, that is semantically modeled to act as a linker and mediator between applications and devices to enable dynamic scheduling and control based on application requests and user preferences. To enable continuous adaptation and learning, the proposed approach formulates the self-configuration problem as a Markov Decision Process (MDP), with parameters instantiated using semantic information and rules. A novel Feedback-based Reinforcement Learning approach is then proposed to adjust the reward function and state transition parameters based on user feedback. The performances of the proposed approach against a bench-marked fixed and rule-based mechanisms are demonstrated through simulation. © 2014 IEEE.

关键词： Smart homes

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HDEL: a hierarchical deep ensemble approach for text-based emotion detection

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Multimedia Tools and Applications 2025年第4期84卷 1799-1820页

作者： Vora, Shivani Mehta, Rupa G. Information Technology Department C.K.Pithawala College of Engineering and Technology Gujarat Surat India Department of Computer Science and Engineering SVNIT Gujarat Surat India

Emotion detection from social media data plays a crucial role in studying societal emotions concerning different events, aiding in predicting the reactions of specific social groups. However, it is complex to automatically extract implicit emotional information from noisy social media text data. This study introduces the Hierarchical Deep Ensemble Learning (HDEL) system to identify emotions in text data. The proposed HDEL model utilizes BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), BiGRU (Bidirectional Gated Recurrent Unit), and RCNN (Recurrent Convolutional Neural Network) in the first level of its hierarchy. The predicted probabilities of the four models are embedded with input data to prepare the intermediate hybrid data. This hybrid data is fed to the next layer of the proposed system, which utilizes a Random Forest (RF) algorithm to predict the emotion. The proposed approach is tested using three emotion datasets: the WASSA-2017 Emotion Intensity (EmoInt) dataset, the International Survey on Emotion Antecedents and Reactions (ISEAR) dataset, and the CrowdFlower (CF) dataset. EmoInt and ISEAR are clean and balanced, while CF is noisy and imbalanced. The results are compared with various state-of- the-art Machine Learning models. The outperforming results depict the superiority of the proposed approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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