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Reliable Task Offloading for 6G-Based IoT Applications

computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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uFOIL: An Unsupervised Fusion of Image Processing and Language Understanding

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IEEE Access 2025年 13卷 31683-31700页

作者： Rahman, Md. Abdur Hasan, Md. Tanzimul Farooq Howlader, Umar Kader, Md. Abdul Islam, Md. Motaharul Hung Pham, Phuoc Mehedi Hassan, Mohammad United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh Providence College Department of Mathematics and Computer Science ProvidenceRI02918 United States King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

In academic institutions, processing and evaluating documents such as exam scripts remains a labor-intensive process susceptible to human error. Traditional digitization systems face significant challenges in handling the complexities of mixed handwritten and printed text and varying document structures. These challenges are exacerbated by the absence of annotated datasets due to privacy concerns, particularly in contexts involving sensitive exam evaluations. To address these issues, this study introduces uFOIL, an unsupervised ensemble-based framework that integrates advanced image and language processing techniques to automate the extraction and validation of key information. The framework employs a majority voting mechanism that combines four state-of-the-art optical character recognition systems. Furthermore, a transformer architecture is incorporated to enhance contextual understanding and the structured formatting of extracted text that follows a post-processing confidence scoring mechanism. The proposed framework achieves high performance, with accuracies of 95.77% and 96.48% for student names and IDs, respectively;and 95.07% for total mark validation based on a dataset of exam script samples. Additionally, experiments on the benchmark ICDAR2013 dataset suggest the framework’s strong applicability achieving precision, recall, and F1-scores of 95.89%, 97.86%, and 96.87%, respectively. © 2025 The Authors.

关键词： Optical character recognition

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Contract-Based Incentive Mechanisms for UAV Networks in Covert Communications

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7378-7391页

作者： Tian, Wen Fan, Hao Liu, Guangjie Du, Miao Liu, Yinqiu Niyato, Dusit Han, Zhu Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing University of Information Science and Technology Key Laboratory of Intelligent Support Technology In Complex Environment Ministry of Education Nanjing210044 China Southeat University School of Computer Science and Engineering China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point covert communication method cannot effectively guarantee the reliability of covert communication. In order to solve this problem, unmanned aerial vehicle (UAV) are widely adopted in covert communication as common relay equipment. However, the UAV maximizes its benefits by taking advantage of the information asymmetry between itself and the covert communication sender. Therefore, it is necessary to encourage UAV-assisted potential users (UPU) to transmit covert data honestly by designing an information asymmetry-based incentive mechanism. In this paper, we propose a UAV-assisted contract-theoretic (UACT) model to improve the covert communication performance, where UPU honestly transmits covert data and the throughput of UACT can be increased. Specifically, we first divide UPU into finite types and model the data transfer contract between the sender, UPU, and receiver. Then, the contract feasibility of UACT is derived with sufficient and necessary conditions. Finally, we analyze the contract optimization in the continuous case of UPUs. Numerical simulations show that the UACT can incentivize UPUs to transmit covert data through UAV assistance and make covert communication performance close to the information symmetry case. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Video forgery localization using inter-frame denoising and intra-frame segmentation

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Multimedia Tools and Applications 2025年 1-17页

作者： Banerjee, Debanik Chittaragi, Nagaratna B. Koolagudi, Shashidhar G. Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India Department of Information Science and Engineering Siddaganaga Institute of Technology Karnataka Tumkur572103 India

Video forgery detection has been necessary with recent spurt in fake videos like Deepfakes and doctored videos from multiple video capturing devices. In this paper, we provide a novel technique of detecting fake videos by creating an ensemble network, based on statistical and deep learning methods to detect interframe forgery and intraframe forgery in forged videos separately. In this paper, Noise signature extraction of a particular image capturing sensor and an Autoencoder-based Convolutional Neural Network model (CNN) are used to localize the forged regions. We have trained the model to localize Deepfake video forgeries as well as copy-paste forgeries with effective results in the test data. The proposed fake video detector can be applied at the back-end of on-line video aggregating services and check their authenticity to verify the genuineness of videos. The results achieved have shown better performances in detecting fake videos compared to existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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A New Pairwise Key Scheme Based on Deployment Knowledge in Multiphase Sensor Networks

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IEEE Internet of Things Journal 2025年第10期12卷 14124-14135页

作者： Li, Sujun Zhou, Boqing Miao, Decheng Cheng, Yun Wu, Jie Shaoguan University School of Information Engineering Shaoguan512005 China Hunan University of Humanities Science and Technology Department of Information Science and Engineering Hunan Loudi417000 China Temple University Department of Computer and Information Sciences PhiladelphiaPA19122 United States

The lifetime of a sensor network is generally longer than that of a single sensor node, so to ensure the connectivity of the network, new nodes need to be deployed into the network in multiphase. Such networks are called multiphase sensor networks. In sensor networks, establishing a pairwise key between two neighboring nodes is a fundamental security requirement. However, for sensor nodes with strictly limited capabilities, there are great challenges in establishing a pairwise key between two neighboring sensor nodes deployed at different phases. In order to solve this problem, some scholars have proposed some solutions based on backward key-chains and forward key-chains, however, how to further improve the networks’ resilience against node capture attacks still needs further research;and some scholars have proposed some solutions based on key pools of each phase are independent, but how to improve the probability of establishing a pairwise key between two neighboring nodes deployed at different phases is still a very challenging problem. In this article, we propose a new pairwise key scheme based on deployment knowledge. Theoretical analysis and simulation show that the proposed model can achieve high probability of establishing a pairwise key between two neighboring nodes and good resilience against node capture attacks by setting appropriate parameters. © 2014 IEEE.

关键词： Sensor nodes

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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L2H-NeRF: low- to high-frequency-guided NeRF for 3D reconstruction with a few input scenes

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Visual computer 2025年 1-12页

作者： Bao, Taoqi Ye, Jiangnan Bao, Zhankong Leow, Chee Siang Hu, Haoji Lu, Jianfeng Fujishiro, Issei Xu, Jiayi School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China College of Information Science & Electronic Engineering Zhejiang University Hangzhou China Department of Mechatronics University of Yamanashi Kofu Japan Department of Information and Computer Science Faculty of Science and Technology Keio University Yokohama Japan

Nowadays, three-dimensional (3D) reconstruction techniques are becoming increasingly important in the fields of architecture, game development, movie production, and more. Due to common issues in the reconstruction process, such as perspective distortion and occlusion, traditional 3D reconstruction methods face significant challenges in achieving high-precision results, even when dense data are used as inputs. With the advent of neural radiance field (NeRF) technology, high-fidelity 3D reconstruction results are now possible. However, high computational resources are usually required for NeRF computations. Recently, a few data inputs are used to ensure the highest quality. In this paper, we propose an innovative low- to high-frequency-guided NeRF (L2H-NeRF) framework that decomposes scene reconstruction into coarse and fine stages. For the first stage, a low-frequency enhancement network based on a vision transformer is proposed, where the low-frequency-based globally coherent geometric structure is recovered, with the dense depth restored in a depth completion way. In the second stage, a high-frequency enhancement network is incorporated, where the high-frequency-related detail is compensated by robust feature alignment across adjacent views using a plug-and-play feature extraction and matching module. Experiments demonstrate that both the accuracy of the geometric structure and the feature detail of the proposed L2H-NeRF outperforms state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Three dimensional computer graphics

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Skeleton-Aware Graph-Based Adversarial Networks for Human Pose Estimation from Sparse IMUs

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ACM Transactions on Multimedia Computing, Communications and Applications 2025年第4期21卷 1-22页

作者： Chen, Kaixin Zhang, Lin Wang, Zhong Zhao, Shengjie Zhou, Yicong School of Software Engineering Tongji University Shanghai China Department of Computer and Information Science University of Macau Macau China

Recently, sparse-inertial human pose estimation (SI-HPE) with only a few IMUs has shown great potential in various fields. The most advanced work in this area achieved fairish results using only six IMUs. However, there are still two major issues that remain to be addressed. First, existing methods typically treat SI-HPE as a temporal sequential learning problem and often ignore the important spatial prior of skeletal topology. Second, there are far more synthetic data in their training data than real data, and the data distribution of synthetic data and real data is quite different, which makes it difficult for the model to be applied to more diverse real data. To address these issues, we propose "Graph-based Adversarial Inertial Poser (GAIP),"which tracks body movements using sparse data from six IMUs. To make full use of the spatial prior, we design a multi-stage pose regressor with graph convolution to explicitly learn the skeletal topology. A joint position loss is also introduced to implicitly mine spatial information. To enhance the generalization ability, we propose supervising the pose regression with an adversarial loss from a discriminator, bringing the ability of adversarial networks to learn implicit constraints into full play. Additionally, we construct a real dataset that includes hip support movements and a synthetic dataset containing various motion categories to enrich the diversity of inertial data for SI-HPE. Extensive experiments demonstrate that GAIP produces results with more precise limb movement amplitudes and relative joint positions, accompanied by smaller joint angle and position errors compared to state-of-the-art counterparts. The datasets and codes are publicly available at https://***/GAIP/. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Network theory (graphs)

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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