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Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Qusai Ramadan Zeyd Boukhers Muath Alshaikh Christoph Lange Jan Jurjens Institute of Software Engineering University of Koblenz Germany Fraunhofer Institute for Applied Information Technology FIT Germany and University Hospital of Cologne Germany College of Computing and Informatics Saudi Electronic University Kingdom of Saudi Arabia Fraunhofer Institute for Applied Information Technology FIT Germany and RWTH Aachen University Germany University of Koblenz Germany and Fraunhofer-Institute for Software and Systems Engineering ISST Germany

ISBN: (纸本)9798400709036

Traditional data monetization approaches face challenges related to data protection and logistics. In response, digital data marketplaces have emerged as intermediaries simplifying data transactions. Despite the growing establishment and acceptance of digital data marketplaces, significant challenges hinder efficient data trading. As a result, few companies can derive tangible value from their data, leading to missed opportunities in understanding customers, pricing decisions, and fraud prevention. In this paper, we explore both technical and organizational challenges affecting data monetization. Moreover, we identify areas in need of further research, aiming to expand the boundaries of current knowledge by emphasizing where research is currently limited or lacking.

关键词： Data Exchange

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AI-driven intrusion prevention system: leveraging mathematical foundations for advanced threats detection

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International Journal of Information technology (Singapore) 2025年 1-6页

作者： Vashistha, Avneesh Alghamdi, Tami Abdulrahman Almalki, Sultan Ahmed Ead, Waleed M. Kaur, Gaganpreet Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Ghaziabad India Computer Science Department Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Computer Department Applied College Najran University Najran Saudi Arabia Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef Egypt Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

The given paper introduces a multi-staged AI-driven intrusion prevention system (IPS) that utilizes mathematical foundations like statistical analysis, probabilistic models, and optimization techniques to design strategic algorithms that recognize patterns as well as detect anomalies & potential attack vectors, which usually get missed out on by traditional security tools. The study focuses on the hybrid behavior that combines supervised and reinforcement learning (RL) methods to provide useful analysis of network traffic, which improves early detection of threats, whether they are known or unknown. This research demonstrates how the proposed AI-driven model can achieve a substantial decrease in the rate of false positives and improve threat detection accuracy. Thus, the given framework acts as a proactive component against today’s evolving cybersecurity threats and hence allows the creation of an efficient and responsive enterprise security framework. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Feedback loop Intrusion prevention Network traffic Reinforcement learning Threat detection

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ANN Based Novel Approach to Detect Node Failure in Wireless Sensor Network

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Computers, Materials & Continua 2021年第11期69卷 1447-1462页

作者： Sundresan Perumal Mujahid Tabassum Ganthan Narayana Suresh Ponnan Chinmay Chakraborty Saju Mohanan Zeeshan Basit Mohammad Tabrez Quasim Faculty of Science and Technology Universiti Sains Islam MalaysiaNilai71800Malaysia Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of ECE Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology600062TamilnaduIndia Department Electronics&Communication Engineering Birla Institute of TechnologyMesraJharkhandIndia Faulty of Information Technology University of Technology and Applied SciencesMuscat133Oman College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless *** node failure is considered as one of the main issues in the WSN which creates higher packet drop,delay,and energy consumption during the *** the node failure occurred mostly due to persistent energy exhaustion during transmission of data *** this paper,Artificial Neural Network(ANN)based Node Failure Detection(NFD)is developed with cognitive radio for detecting the location of the node *** ad hoc on-demand distance vector(AODV)routing protocol is used for transmitting the data from the source node to the base ***,the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node *** performance of the proposed ANN-NFD method is analysed in terms of throughput,delivery rate,number of nodes alive,drop rate,end to end delay,energy consumption,and overhead ***,the performance of the ANN-NFD method is evaluated with the header to base station and base station to header(H2B2H)*** packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H ***,the ANN-NFD method provides data consistency during data transmission under node and battery failure.

关键词： AODV artificial neural network artificial intelligence Mahalanobis distance node failure throughput wireless sensor network

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Neural Operator Modeling of Platelet Geometry and Stress in Shear Flow

arXiv

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arXiv 2025年

作者： Laudato, Marco Manzari, Luca Shukla, Khemraj FLOW Research Center Department of Engineering Mechanics KTH Royal Institute of Technology StockholmSE-10044 Sweden PDC Center for High Performance Computing KTH Royal Institute of Technology StockholmSE-11428 Sweden Division of Applied Mathematics Brown University ProvidenceRI02906 United States

Thrombosis involves processes spanning large-scale fluid flow to sub-cellular events such as platelet activation. Traditional CFD approaches often treat blood as a continuum, which can limit their ability to capture these microscale phenomena. In this paper, we introduce a neural operator-based surrogate model to bridge this gap. Our approach employs DeepONet, trained on high-fidelity particle dynamics simulations performed in LAMMPS under a single shear flow condition. The model predicts both platelet membrane deformation and accumulated stress over time, achieving a mode error of ∼ 0.3% under larger spatial filtering radii. At finer scales, the error increases, suggesting a correlation between the DeepONet architecture’s capacity and the spatial resolution it can accurately learn. These findings highlight the importance of refining the trunk network to capture localized discontinuities in stress data. Potential strategies include using deeper trunk nets or alternative architectures optimized for graph-structured meshes, further improving accuracy for high-frequency features. Overall, the results demonstrate the promise of neural operator-based surrogates for multi-scale platelet modeling. By reducing computational overhead while preserving accuracy, our framework can serve as a critical component in future simulations of thrombosis and other micro-macro fluid-structure problems. © 2025, CC BY.

关键词： Computational fluid dynamics

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Extraction of Abnormal Skin Lesion from Dermoscopy Image using VGG-SegNet 7

Extraction of Abnormal Skin Lesion from Dermoscopy Image usi...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Taniar, David Damasevicius, Robertas Rajinikanth, Venkatesan Lawal, Isah A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Monash University Faculty of Information Technology ClaytonVIC3800 Australia Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

Skin is one of the vital and well-known sensory organs in human physiology and due to various reasons, the abnormality in skin arises. Skin Melanoma (SM) is one of the medical crisis in humans and the untreated SM will cause various abnormalities, such as skin irritation, spreading the cancerous cells through the blood stream, etc. Efficient assessment of the SM is essential to identify the severity of the disease and hence the proposed work implemented a Convolutional-Neural-Network (CNN) based approach to support the automated SM examination. This work employed the VGG-SegNet scheme to extract the SM section from the Digital-Dermoscpy-Image (DDI). After the extraction, a relative assessment between the segmented SM and the Ground-Truth (GT) is executed and the essential performance indices are then computed. The proposed scheme is tested and validated using the benchmark ISIC2016 database and the average result attained with the proposed study helped to achieve a better values of Jaccard-Index, Dice, and Accuracy for the DDI with and without the artifacts. These results confirm that, proposed technique is significant in evaluating the clinical grader DDI. © 2021 IEEE.

关键词： Extraction

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U-Net Supported Segmentation of Ischemic-Stroke-Lesion from Brain MRI Slices 7

U-Net Supported Segmentation of Ischemic-Stroke-Lesion from ...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Damasevicius, Robertas Taniar, David Rajinikanth, Venkatesan Lawal, Isah A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Monash University Clayton Faculty of Information Technology VIC3800 Australia St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

The brain abnormality is one of the major sicknesses in human's health and the untreated brain defect will cause major illness. Ischemic stroke is one of the major medical emergencies and the timely diagnosis and treatment will save the patient from serious sickness. The proposed research employs the U-Net scheme to extort the Ischemic-Stoke-Lesion (ISL) from the brain MRI slices of ISLES2015 database. In this work, a pre-trained U-Net encoder-decoder system is employed to extort the ISL fragment from the chosen test image. After the extraction, a relative assessment is performed with the ground-truth available along with consequent test image. In this work, 20 patients' images (20 patient x 25 slices = 500 images) are adopted for the assessment and the general result achieved with the executed methodology helped to achieve a better value of Jaccard (>90%), Dice (>95%) and Accuracy (>98%) on the considered image dataset. © 2021 IEEE.

关键词： Diagnosis

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Automated Detection of Schizophrenia from Brain MRI Slices using Optimized Deep-Features 7

Automated Detection of Schizophrenia from Brain MRI Slices u...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Taniar, David Damasevicius, Robertas Rajinikanth, Venkatesan Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Monash University Faculty of Information Technology ClaytonVIC3800 Australia Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

In humans, the incident rate of mental illness is gradually rising due to various causes. Schizophrenia is one of the chronic mental illness and its happing rate also rising in the current era. The patient with Schizophrenia will experience a confused mental condition and a timely recognition and treatment is essential to reduce the risk. The proposed work aims to implement a methodology to support the automated detection of Schizophrenia from the brain MRI slices of T1 modality (T1W). The assessment of brain MRI is executed using the pre-trained VGG16 system and the deep-features extracted are optimized with the Slime-Mould-Algorithm (SMA) and the reduced features are then considered to train, test and validate the binary classifiers employed in this work. This research is implemented using 500 images of each case (healthy/abnormal) and the attained result with the SVM-Cubic is superior compared to other classifiers considered in the automated disease detection system. © 2021 IEEE.

关键词： Magnetic resonance imaging

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Customized Hybrid Deep Learning Model for Road Accident Detection Based on CCTV Images

Customized Hybrid Deep Learning Model for Road Accident Dete...

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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： P.C. Sherimon Vinu Sherimon Yousuf Nasser Al Husaini Ambily Merlin Kuruvilla Ajit Mammen Varghese Harikrishnan D Faculty of Computer Studies Arab Open University Muscat Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Sultanate of Oman PG Department of Computer Applications and Artificial Intelligence Saintgits College of Applied Sciences Kerala India

Road accidents have increased with the increase in the vehicle population. There are several studies and research that focus on detecting road accidents. High-performance traffic accident detection systems are becoming ever more necessary because of the rise in traffic accidents. A vehicle accident detection system is developed in this study using the deep learning technique. A customized convolutional neural network is used for this study. The model uses pre-trained customized convolutional models for feature extractions from the images. In this research work a customized base model along with six custom hybrid models such as DenseNet-121, InceptionV3, Xception, MobileNetV2, VGG16 and ResNet-50 is used. The hybridized models use a standalone feature extractor module which uses transfer learning technique for feature extraction. The features from all the training images are extracted using the above six hybrid models. The selected features are then input to the customized base Deep Neural Network architecture. The model is tested using random Google images after being trained on a custom dataset. The performance of all the models were tested using the parameters Precision, Recall, Specificity, F1 Score along with Accuracy. It was found that the custom hybridized Resnet-50 showed the maximum performance compared with the other models in terms of accuracy.

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Towards to Beyond 5G Virtual Environment for Cybersecurity Testing in V2X Systems

Towards to Beyond 5G Virtual Environment for Cybersecurity T...

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Workshop on Communication Networks and Power Systems (WCNPS)

作者： Giovanni A. Santos João Paulo J. da Costa Antonio Arlis S. da Silva Hamm-Lippstadt University of Applied Sciences (HSHL) Lippstadt Germany Graduate School for Applied Research in North Rhine-Westphalia (PKNRW) Bochum Germany Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Graduate Program in Computing (PPGC) UFRGS Porto Alegre Brazil

Vehicle-to-Everything (V2X) communication has an essential role for enhancing safety, effi-ciency, and overall driving experience of autonomous driving. To meet the requirements of autonomous driving, V2X demands high data-rate connectivity, per-ceived zero-latency, and high reliability. These char-acteristics are inherently linked to future-generation mobile communication technologies, such as Beyond 5G (B5G) also known as 6G. However, current so-lutions for simulating 5G communications lack inte-gration between the user and control planes, making the simulation not reliable since the 5G control plane functionalities are not taken into account. In this sense, this paper proposes a first contribution towards to the platform called Beyond 5G Virtual Environment for Cy-bersecurity Testing in V2X Systems (B5GCyberTestV2X) by integrating the 5G control plane from Open5GS and UERANSIM into the Simu5G simulator. The inclusion of the control plane in 5G urban mobility simulations increases the security and reliability of the V2X commu-nication service. Therefore, in order to show the impact of the 5G control plane, we validate a simulation with a scenario in the presence of a spoofer transmitting false warning information. As shown in our simulation, the 5G control plane does not allow the connection of the spoofer into 5G network, making the cybersecurity tests of 5G-based V2X communication more realistic.

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Automatic modeling learner’s personality using learning analytics approach in an intelligent Moodle learning platform

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Interactive Learning Environments 2023年第5期31卷 2529-2543页

作者： Tlili, Ahmed Denden, Mouna Essalmi, Fathi Jemni, Mohamed Chang, Maiga Kinshuk Chen, Nian-Shing Smart Learning Institute of Beijing Normal University Beijing China Research laboratory of Technologies of Information and Communication & Electrical Engineering (LaTICE) Tunis higher school of engineering (ENSIT) University of Tunis Tunis Tunisia School of Computing and Information Systems Athabasca University Athabasca Canada University of North Texas Denton TX United States Department of Applied Foreign Languages National Yunlin University of Science and Technology Douliou Taiwan

The ability of automatically modeling learners’ personalities is an important step in building adaptive learning environments. Several studies showed that knowing the personality of each learner can make the learning interaction with the provided learning contents and activities within learning systems more effective. However, the traditional method of modeling personality is using self-reports, such as questionnaire, which is subjective and with several limitations. Therefore, this study presents a new unobtrusive method to model the learners’ personalities in an intelligent Moodle (iMoodle) using Learning Analytic (LA) approach with Bayesian network. To evaluate the accuracy of the proposed approach, an experiment was conducted with one hundred thirty-nine learners in a public university. Results showed that recall, precision, F-measure and accuracy values are in acceptance range for three personality dimensions including extraversion, openness, and neuroticism. Moreover, the results showed that the LA approach has a fair agreement with the Big Five Inventory (BFI) in modeling these three personality dimensions. Finally, this study provides several recommendations which can help researchers and practitioners develop effective smart learning environments for both learning and modeling. For example, it is needed to help identify more features of the hardest personality traits, such as agreeableness, using gamification courses. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： assessment learner modeling learning analytics Moodle Personality smart learning

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