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MsMorph: An Unsupervised pyramid learning network for brain image registration

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arXiv 2024年

作者： Nan, Jiaofen Fan, Gaodeng Zhang, Kaifan Zhao, Chen Zhu, Fubao Zhou, Weihua School of Computer Science and Technology Zhengzhou University of Light Industry Henan Province Zhengzhou450000 China Department of Computer Science Kennesaw State University MariettaGA United States Department of Applied Computing Michigan Technological University HoughtonMI United States

In the field of medical image analysis, image registration is a crucial technique. Despite the numerous registration models that have been proposed, existing methods still fall short in terms of accuracy and interpretability. In this paper, we present MsMorph, a deep learning-based image registration framework aimed at mimicking the manual process of registering image pairs to achieve more similar deformations, where the registered image pairs exhibit consistency or similarity in features. By extracting the feature differences between image pairs across various aspects using gradients, the framework decodes semantic information at different scales and continuously compen-sates for the predicted deformation field, driving the optimization of parameters to significantly improve registration accuracy. The proposed method simulates the manual approach to registration, focusing on different regions of the image pairs and their neighborhoods to predict the deformation field between the two images, which provides strong interpretability. We compared several existing registration methods on two public brain MRI datasets, including LPBA and Mindboggle. The experimental results show that our method consistently outperforms state of the art in terms of metrics such as Dice score, Hausdorff distance, average symmetric surface distance, and non-Jacobian. The source code is publicly available at https://***/GaodengFan/MsMorph. © 2024, CC BY.

关键词： Image registration

A systematic review of machine learning approaches for detecting deceptive activities on social media: methods, challenges, and biases

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International Journal of Data Science and Analytics 2025年 1-26页

作者： Liu, Yunchong Shen, Xiaorui Zhang, Yeyubei Wang, Zhongyan Tian, Yexin Dai, Jianglai Cao, Yuchen School of Engineering and Applied Science University of Pennsylvania Philadelphia USA Khoury College of Computer Science Northeastern University Seattle USA Center for Data Science New York University New York USA College of Computing Georgia Institute of Technology Bellevue USA Department of EECS University of California Berkeley Berkeley USA

The growing prevalence of misinformation, spam, and fake accounts on social media platforms such as Twitter, Facebook, and Instagram presents a serious threat to public discourse and safety. To address this, machine learning (ML) and deep learning (DL) models have been widely applied for detecting deceptive content. This systematic review analyzes 36 peer-reviewed studies published between January 2010 and July 2024 that apply ML/DL techniques to combat deception across major platforms. Among these, random forest and support vector machines were the most frequently used ML models (17 and 16 studies, respectively), while deep learning models such as artificial neural networks (8 studies) and LSTM (2 studies) also showed promising results. Performance metrics, including F1 scores and AUROC, were extracted when available, with several studies reporting F1 scores exceeding 90% for specific tasks like fake account detection. Despite these advances, 86% of studies inadequately addressed class imbalance, and only 39% consistently tuned hyperparameters. Furthermore, over 70% did not mention strategies for handling linguistic challenges such as negations. This review identifies key methodological limitations—including sample representativeness, inconsistent preprocessing, and reliance on accuracy in imbalanced settings—and offers recommendations to enhance generalizability, bias mitigation, and real-world applicability of ML-based misinformation detection.

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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT)

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Exploring Sensor-Based Smart Farming Technologies in the Int...

computing and Data Science (ICCDS), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Thenmozhi K C. Balakumar T Sujatha Wilfred Blessing N. R Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India Department of Compute Science Kristu Jayanti College (Autonomous) Bengaluru India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field.

关键词： Smart agriculture Soil measurements Soil moisture Sociology Phosphorus Internet of Things Statistics

Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation

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arXiv 2025年

作者： Cheng, Chun-Wun Zhao, Yining Cheng, Yanqi Montoya, Javier Schönlieb, Carola-Bibiane Aviles-Rivero, Angelica I. Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States Lucerne School of Computer Science and Information Technology Lucerne University of Applied Sciences and Arts United States Yau Mathematical Sciences Center Tsinghua University China

Image segmentation is a fundamental task in both image analysis and medical applications. State-of-the-art methods predominantly rely on encoder-decoder architectures with a U-shaped design, commonly referred to as U-Net. Recent advancements integrating transformers and MLPs improve performance but still face key limitations, such as poor interpretability, difficulty handling intrinsic noise, and constrained expressiveness due to discrete layer structures, often lacking a solid theoretical *** this work, we introduce Implicit U-KAN 2.0, a novel U-Net variant that adopts a two-phase encoder-decoder structure. In the SONO phase, we use a second-order neural ordinary differential equation (NODEs), called the SONO block, for a more efficient, expressive, and theoretically grounded modeling approach. In the SONO-MultiKAN phase, we integrate the second-order NODEs and MultiKAN layer as the core computational block to enhance interpretability and representation power. Our contributions are threefold. First, U-KAN 2.0 is an implicit deep neural network incorporating MultiKAN and second order NODEs, improving interpretability and performance while reducing computational costs. Second, we provide a theoretical analysis demonstrating that the approximation ability of the MultiKAN block is independent of the input dimension. Third, we conduct extensive experiments on a variety of 2D and a single 3D dataset, demonstrating that our model consistently outperforms existing segmentation networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

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Computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

Neural Operator Modeling of Platelet Geometry and Stress in Shear Flow

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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

Online Flexible Busy Time Scheduling on Heterogeneous Machines

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arXiv 2024年

作者： Calinescu, Gruia Davies, Sami Khuller, Samir Zhang, Shirley Department of Computer Science Illinois Institute of Technology Northwestern University United States Department of EECS Simons Institute for the Theory of Computing UC Berkeley United States Department of Computer Science Northwestern University United States Paulson School of Engineering and Applied Sciences Harvard University United States

We study the online busy time scheduling model on heterogeneous machines. In our setting, unit-length jobs arrive online with a deadline that is known to the algorithm at the job's arrival time. An algorithm has access to machines, each with different associated capacities and costs. The goal is to schedule jobs on machines before their deadline, so that the total cost incurred by the scheduling algorithm is minimized. Relatively little is known about online busy time scheduling when machines are heterogeneous (i.e., have different costs and capacities), despite this being the most practical model for clients using cloud computing services. We make significant progress in understanding this model by designing an 8-competitive algorithm for the problem on unit-length jobs, and providing a lower bound on the competitive ratio of 2. We further prove that our lower bound is tight in the natural setting when jobs have agreeable deadlines. © 2024, CC BY.

关键词： Scheduling algorithms

Ad Hoc Networks: Adaptable Connectivity for Contemporary Business Needs 10

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Ad Hoc Networks: Adaptable Connectivity for Contemporary Bus...

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Dhanik, Saurabh Vijai, C. Reddy, Y Manohar Dutt, Amit Shaaker, Abothar Mahmod Raj, Vijilius Helena School of Computing Graphic Era Hill University Bhimtal Campus Uttarakhand India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Administration Chennai62 India Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India The Islamic University Najaf Iraq New Horizon College of Engineering Department of Applied Sciences Bangalore India

ISBN: (纸本)9798350382471

This article covers the research of ad hoc networks and various variations, such as mobile ad hoc networks, wireless mesh networks, and wireless sensor networks. Ad hoc networks allow wireless devices to connect to each other immediately without the need for a centralized Wireless Access Point (WAP). A base station, also known as a WAP device, often controls and directs data transfer among wireless devices. Since nodes in ad hoc networks are continually moving, the concepts of mobile, intelligent, and automotive ad hoc networks are also introduced. This network is open to any node joining or leaving at any moment. Nodes are portable devices that are a member of the network, such as laptops, MP3 players, personal computers, PDAs, and cell phones. Furthermore, the document compares and evaluates the features, limitations, difficulties, benefits, and drawbacks of routing protocols in wireless mesh, mobile ad hoc, and wireless sensor networks. The safety and effectiveness of the various node kinds are the most crucial aspects of ad hoc networks. © 2023 IEEE.

关键词： Ad Hoc networks mobile routing protocols vehicular wireless

CNNs in Crop Care: A Comparative Analysis of Tomato Disease Detection Models

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CNNs in Crop Care: A Comparative Analysis of Tomato Disease ...

Cyber Resilience (ICCR), International Conference on

作者： Ali M Ali Shadi Nashwan Ahmad Al-Qerem Amjad Aldweesh Mohammad Alauthman Zenab Elgamal Ammar Almomani Communications and Computer Engineering Department Faculty of Engineering AlAhliyya Amman University Amman Jordan Cybersecurity Department Faculty of Information Technology Middle East University (MEU) Amman Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia Department of Information Security Faculty of Information Technology University of Petra Amman Jordan School of Computing Skyline University College University City of Sharjah Sharjah United Arab Emirates School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Timely and accurate detection of plant diseases is critical for sustainable crop production. This paper presents a comparative analysis of convolutional neural network (CNN) models for automated tomato leaf disease detection, including VGG16, VGG19, AlexNet and Google Net. A dataset of tomato leaf images with multiple disease types was utilized to train and test the performance of each CNN architecture. Our results demonstrate that the VGG16 model achieved the highest accuracy of 99.38% in classifying tomato leaf diseases. Compared to AlexNet and VGG19 models which obtained 98.67% and 99.29% accuracy respectively, the superior performance of VGG16 highlights its suitability for deploying robust tomato disease detection systems to enable prompt disease control interventions. Our work provides valuable insights on harnessing deep learning for agricultural disease surveillance, allowing farmers and agronomists to identify crop infections early and implement timely management practices to reduce yield losses.

关键词： Deep learning Plant diseases Analytical models Visualization Surveillance Crops Training data