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IoT-CDS:Internet of Things Cyberattack Detecting System Based on Deep Learning Models

computers, Materials & Continua 2024年第12期81卷 4265-4283页

作者： Monir Abdullah Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT *** learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT *** rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT *** DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT *** BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal *** experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy *** are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack *** method,without feature selection,demonstrates advantages in training time and detection ***,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.

关键词： Cyberattacks intrusion detection system deep learning internet of things

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Quantum self-attention neural networks for text classification

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science China(information sciences) 2024年第4期67卷 301-313页

作者： Guangxi LI Xuanqiang ZHAO Xin WANG Institute for Quantum Computing Baidu Research Centre for Quantum Software and Information University of Technology Sydney QICI Quantum Information and Computation Initiative Department of Computer ScienceThe University of Hong Kong Thrust of Artificial Intelligence Information HubHong Kong University of Science and Technology(Guangzhou)

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing(NLP). Although some efforts based on syntactic analysis have opened the door to research in quantum NLP(QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets. In this paper, we propose a new simple network architecture, called the quantum selfattention neural network(QSANN), which can compensate for these limitations. Specifically, we introduce the self-attention mechanism into quantum neural networks and then utilize a Gaussian projected quantum self-attention serving as a sensible quantum version of self-attention. As a result, QSANN is effective and scalable on larger data sets and has the desirable property of being implementable on near-term quantum devices. In particular, our QSANN outperforms the best existing QNLP model based on syntactic analysis as well as a simple classical self-attention neural network in numerical experiments of text classification tasks on public data sets. We further show that our method exhibits robustness to low-level quantum noises and showcases resilience to quantum neural network architectures.

关键词： quantum neural networks self-attention natural language processing text classification parameterized quantum circuits

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Knowledge Discovery of the Internet of Things (IoT) Using Large Language Model

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International Journal of Advanced computer science and Applications 2025年第4期16卷 993-998页

作者： Alsulami, Bassma Saleh Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Internet of Things (IoT) technology quickly transformed traditional management and engagement techniques in several sectors. This work explores the trends and applications of the Internet of Things in industries, including agriculture, education, transportation, water management, air quality monitoring, underground mining, smart retail, smart home systems, and weather forecasting. The methodology involves a comprehensive review of the literature, followed by data extraction and analysis using BERT to identify key insights and patterns in IoT applications. The findings show that IoT significantly impacts the improvement of real-time monitoring, increasing efficiency, and encouraging innovative solutions in various sectors. Despite its transformative potential, cybersecurity threats, data privacy concerns, and the need for strong policy frameworks persist. The study emphasizes the necessity of multidisciplinary approaches to address these difficulties and optimize IoT implementation. Future research should focus on establishing secure IoT systems, maintaining data integrity, and encouraging collaboration between disciplines to realise the benefits of IoT technology. © (2025), (science and information Organization). All rights reserved.

关键词： Data integrity

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Research on stock trend prediction method based on optimized random forest

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CAAI Transactions on Intelligence technology 2023年第1期8卷 274-284页

作者： Lili Yin Benling Li Peng Li Rubo Zhang School of Computer and Science and Technology Harbin University of Science and TechnologyHarbinChina Department of Computer Science and Engineering Dalian Nationalities UniversityDalianChina

As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical *** in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock *** study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend *** study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random *** the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as ***,the parameter combination of the model is optimized through random parameter *** experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine *** with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

关键词： ensemble learning finance random forest random search technical indicator

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EPDTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image Diagnosis

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Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： Data quality

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Enhanced photocatalytic performance of K-doped NaNbO3 correlated with structural defects, optical and polar properties

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Environmental science and Pollution Research 2025年第14期32卷 9030-9049页

作者： Nain, Rajbala Dwivedi, Rakesh Kumar Department of Physics and Materials Science and Engineering Jaypee Institute of Information Technology Noida201309 India

The swift pace of industrialization in the modern world has intensified the global energy crisis while exacerbating the challenges of water pollution, posing significant threats to the environmental sustainability and human well-being. Substantial endeavors have been undertaken to protect the environment from the detrimental impacts of methylene blue in the wastewater. This study shows the photocatalytic performance of eco-friendly K-modified NaNbO3 catalysts in the degradation of methylene blue dye. Compositions with x = 0, 0.05, 0.10, 0.15, and 0.20 have been synthesized using solid-state reaction method. The XRD patterns for all the compositions demonstrate the formation of a single-phase orthorhombic structure with Pbcm space group symmetry. Analysis of the UV–Visible spectra indicates a trend of decreasing band gap from 3.21 eV for x = 0 to 3.01 eV for x = 0.20. High-resolution XPS scans show the presence of additional Nb4+ state along with Nb5+ state for doped samples. The edges of the valence and conduction bands using XPS and UV–Visible spectra, have been determined for each of the samples. The composition with x = 0.10 has shown a maximum degradation efficiency of 96% under UV radiations (300 W Xenon lamp) that aligns with the lowest photoluminescence intensity observed for this sample. An appropriate defect model with the help of band diagrams of these compositions for MB dye degradation and a suitable electron transfer mechanism have been proposed. The scavenger test has been found to validate the role of superoxide ions in the photocatalytic degradation of MB dye. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Sustainable development

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DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s

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computers, Materials & Continua 2025年第5期83卷 1929-1949页

作者： Honglin Wang Yaolong Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border ***,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV *** address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object *** leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small *** the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere *** components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference ***,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object *** results on the VisDrone 2019 dataset demonstrate the effectiveness *** to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter *** results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.

关键词： YOLOv8 UAV-based object detection AFPN small-object detection head SPPELAN DualConv loss function

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ANNDRA-IoT:A Deep Learning Approach for Optimal Resource Allocation in Internet of Things Environments

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computer Modeling in engineering & sciences 2025年第3期142卷 3155-3179页

作者： Abdullah M.Alqahtani Kamran Ahmad Awan Abdulaziz Almaleh Osama Aletri Department of Electrical and Electronic Engineering College of Engineering and Computer ScienceJazan UniversityJazan45142Saudi Arabia Department of Information Technology The University of HaripurHaripur22620Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Department of Computing College of Engineering and ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse *** study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing *** to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real *** training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent *** simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.

关键词： Internet of things resource optimization deep learning optimal resource allocation neural network efficiency

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