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Informatica (Slovenia) 2025年第10期49卷 191-204页

作者： Sharma, Neha Dhiman, Pankaj Department of Computer Science and Engineering Jaypee University of Information Technology Waknaghat H. P. Solan India

The rapid proliferation of Internet of Things (IoT) devices, coupled with the rollout of advanced 5G networks, has generated significant concerns regarding security breaches. These concerns stem from the expanded attack surfaces that come with improved connectivity, making IoT systems more vulnerable to malicious threats. A crucial strategy to counter these security challenges is the implementation of robust user authentication methods. Despite numerous proposals for multi-factor authentication mechanisms, many of these systems exhibit weaknesses, particularly in their susceptibility to user impersonation attacks and the risks posed by stolen mobile devices. Furthermore, several schemes fail to incorporate essential features such as session key agreements or backup solutions for instances of lost or stolen devices and compromised private keys. To address urgent security challenges in IoT environments, we developed a three-factor user authentication system tailored for low-cost IoT devices. This system tackles critical vulnerabilities while maintaining low computing and communication costs, enhancing security without compromising usability and efficiency. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 5G mobile communication systems

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CPT:A Configurable Predictability Testbed for DNN Inference in AVs

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Tsinghua science and Technology 2025年第1期30卷 87-99页

作者： Liangkai Liu Yanzhi Wang Weisong Shi Department of Computer and Information Science University of DelawareNewarkDE 19713USA Department of Electrical&Computer Engineering Northeastern UniversityBostonMA 02115USA

Predictability is an essential challenge for autonomous vehicles(AVs)’*** neural networks have been widely deployed in the AV’s perception ***,it is still an open question on how to guarantee the perception predictability for AV because there are millions of deep neural networks(DNNs)model combinations and system configurations when deploying DNNs in *** paper proposes configurable predictability testbed(CPT),a configurable testbed for quantifying the predictability in AV’s perception *** provides flexible configurations of the perception pipeline on data,DNN models,fusion policy,scheduling policies,and predictability *** top of CPT,the researchers can profile and optimize the predictability issue caused by different application and system *** has been open-sourced at:https://***/Torreskai0722/CPT.

关键词： predictability deep neural network autonomous driving

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Open-Space Emergency Guiding with Individual Density Prediction Based on Internet of Things Localization

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第1期9卷 785-797页

作者： Chen, Lien-Wu Huang, Hao-Wei Chen, Yi-Ju Tsai, Ming-Fong Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan National United University Department of Electronic Engineering Miaoli360 Taiwan

This article proposes an open-space emergency guiding (OSEG) framework that explores deep learning techniques to predict individual densities for evacuation based on Internet of Things localization. The OSEG framework adopts Densely Connected Convolutional Networks that can reserve fine-grained features in earlier convolutional layers and extract them in later convolutional layers to efficiently reduce prediction errors. In addition, OSEG can dynamically guide individuals to exits in the shortest time though balancing the evacuation load among exits as individual densities are non-uniform in indoor spaces. According to our review of relevant research, this is the first framework that integrates deep learning for individual density prediction with load-balancing evacuation for emergency guiding in open indoor spaces, where the distance to each exit, capacities of all exits, concurrent moving of evacuees, and distribution of indoor individuals/groups are considered simultaneously. Simulation results show that our approach outperforms existing methods and can balance the evacuation load of each exit for individuals/groups to minimize total evacuation time, which can reduce 50% of total evacuation time at most compared with existing methods. © 2017 IEEE.

关键词： Internet of things

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BAAP-FIoT: Blockchain Assisted Authentication Protocol for Fog-enabled Internet of Things Environment

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IEEE Internet of Things Journal 2025年第11期12卷 15681-15696页

作者： Ponnuru, Raveendra Babu Kumar, Sathish A.P. Azab, Mohamed Alavalapati, Goutham Reddy Virginia Tech Virginia Military Institute and Computer Science Department of Computer and Information Science BlacksburgVA United States Cleveland State University Department of Electrical Engineering and Computer Science ClevelandOH44115 United States Virginia Military Institute Department of Computer Science and Information Sciences LexingtonVA24450 United States University of Illinois Springfield One University Plaza Department of Computer Science SpringfieldIL62703 United States

The proliferation of Internet of Things (IoT) devices across multiple domains has heralded an era of unprecedented connectivity and data exchange. Fog computing enhances edge-network processing, enabling real-time data analysis and prompt responses. However, ensuring secure and trustworthy communication among these devices remains a paramount concern. In fog-enabled IoT environments, securing communication among users,IoT devices, gateways, and fog nodes is of paramount importance to prevent unauthorized access and ensure data integrity and confidentiality. Additionally, users can control and deliver instructions to IoT devices remotely. Hence, we propose a blockchain-assisted authentication protocol tailored specifically for fog-enabled IoT environments to verify the user's identity prior to accessing the IoT devices. The proposed protocol leverages cutting-edge crypto primitives like elliptic curve cryptography, hash functions, and blockchain to establish secure communication between users and IoT devices. Furthermore, we evaluate the proposed scheme through formal (scyther) and informal analysis, demonstrating its efficacy in mitigating well-known attacks. On the other hand, the proposed protocol exhibits robustness against relevant protocols in terms of communication and computational aspects, as well as reliability for real-world fog-enabled IoT applications. © 2014 IEEE.

关键词： Authentication Protocol

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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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Multimedia Tools and Applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;LGXP and GLCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes LSTM and AML-CNN models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-Communications Information Technology Center Nagoya University National Institute of Informatics(NII) Hohhot Minzu College Toyota Motor Corporation Department of Computer Science and Engineering Shanghai Jiaotong University

Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity of traditional communication methods. To tackle this, we propose a novel framework using semantic communications, through a region of interest semantic segmentation method, to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data. To solve the knowledge base inconsistencies inherent in semantic communications, we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases. This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management. Additionally, the implementation of blockchain sharding handles differentiated knowledge bases for various tasks, thus boosting overall blockchain efficiency. Experimental results show a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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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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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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