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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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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Geometry-Aware Self-Supervised Indoor 360° Depth Estimation via Asymmetric Dual-Domain Collaborative Learning

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IEEE Transactions on Multimedia 2025年 27卷 3224-3237页

作者： Wang, Xu He, Ziyan Zhang, Qiudan Yang, You Zhao, Tiesong Jiang, Jianmin Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Huangzhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Fuzhou University College of Physics and Information Engineering Fuzhou350108 China

Being able to estimate monocular depth for spherical panoramas is of fundamental importance in 3D scene perception. However, spherical distortion severely limits the effectiveness of vanilla convolutions. To push the envelope of accuracy, recent approaches attempt to utilize Tangent projection (TP) to estimate the depth of 360° images. Yet, these methods still suffer from discrepancies and inconsistencies among patch-wise tangent images, as well as the lack of accurate ground truth depth maps under a supervised fashion. In this paper, we propose a geometry-aware self-supervised 360° image depth estimation methodology that explores the complementary advantages of TP and Equirectangular projection (ERP) by an asymmetric dual-domain collaborative learning strategy. Especially, we first develop a lightweight asymmetric dual-domain depth estimation network, which enables to aggregate depth-related features from a single TP domain, and then produce depth distributions of the TP and ERP domains via collaborative learning. This effectively mitigates stitching artifacts and preserves fine details in depth inference without overspending model parameters. In addition, a frequent-spatial feature concentration module is devised to simultaneously capture non-local Fourier features and local spatial features, such that facilitating the efficient exploration of monocular depth cues. Moreover, we introduce a geometric structural alignment module to further improve geometric structural consistency among tangent images. Extensive experiments illustrate that our designed approach outperforms existing self-supervised 360° depth estimation methods on three publicly available benchmark datasets. © 1999-2012 IEEE.

关键词： Self-supervised learning

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Infrared Single Pixel Imaging Based on Generative Adversarial Network

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Journal of Shanghai Jiaotong University (science) 2023年 1-11页

作者： Jiang, Yilin Zhang, Yilong Zhang, Fangyuan College of Information and Communication Engineering Harbin Engineering University Harbin150001 China College of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

In the field of imaging, the image resolution is required to be higher. There is always a contradiction between the sensitivity and resolution of the seeker in the infrared guidance system. This work uses the rosette scanning mode for physical compression imaging in order to improve the resolution of the image as much as possible under the high-sensitivity infrared rosette point scanning mode and complete the missing information that is not scanned. It is effective to use optical lens instead of traditional optical reflection system, which can reduce the loss in optical path transmission. At the same time, deep learning neural network is used for control. An infrared single pixel imaging system that integrates sparse algorithm and recovery algorithm through the improved generative adversarial networks is trained. The experiment on the infrared aerial target dataset shows that when the input is sparse image after rose sampling, the system finally can realize the single pixel recovery imaging of the infrared image, which improves the resolution of the image while ensuring high sensitivity. © 2023, Shanghai Jiao Tong University.

关键词： Generative adversarial networks

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Optimized graph transformer with molecule attention network based multi class attack detection framework for enhancing privacy and security in WSN

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Multimedia Tools and Applications 2025年第15期84卷 14273-14304页

作者： Anoop, M. Mary, L. William Wilson, Allan J. Kiran, W.S. Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram campus Chennai600089 India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India

In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high computational demands, diminished accuracy rates, making them susceptible to a wide array of cyber threats. To tackle these challenges, this research introduced a novel method known as Optimized Graph Transformer with Molecule Attention Network (OGTMAN) on the fusion of Secure Multi-party Computation (SMC) and differential privacy aimed at enhancing privacy and security. This approach starts with data preprocessing, employing min–max normalization to address dataset imbalances. N-Tuple Contrastive Learning with Generative transformer(N-TCLG)feature extractor distinguish between different classes of data instances and effectively learns representations that capture relevant information about network traffic patterns Further, feature selection by enhanced chi-square statistics with kepler algorithm, is utilized to assess the significance of each feature in characteristic between normal network behavior and malicious activities further refines the feature selection method by optimizing the selection criteria based on the statistical properties of the dataset. Finally, the proposed OGTMAN classifies various attacks based on their behavioural patterns with the help of enhanced ant aphid optimization with adaptive step factor. Ultimately, this fine-tuning yield superior model training outcomes, resulting more accurate attack classification outcomes. To evaluate the effectiveness of this approach, datasets encompassing with KDD Cup 99 datasets are employed. The efficiency of the introduced method is demonstrated when compared with prevailing attack detection methods in terms of accuracy, recall, precision and F-measure of 99.98%, 99.5%.,99.9% and 99.5% respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer N

关键词： Molecules

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ANovel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

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computer Modeling in engineering & sciences 2023年第7期136卷 87-106页

作者： Bejoy Abraham Jesna Mohan Linu Shine Sivakumar Ramachandran Department of Computer Science and Engineering College of Engineering MuttatharaThiruvananthapuramKerala695008India Department of Computer Science and Engineering Mar Baselios College of Engineering and TechnologyThiruvananthapuramKerala695015India Department of Electronics and Communication Engineering College of Engineering TrivandrumKerala695016India

Tear film,the outermost layer of the eye,is a complex and dynamic structure responsible for tear *** tear film lipid layer is a vital component of the tear film that provides a smooth optical surface for the cornea and wetting the ocular *** eye syndrome(DES)is a symptomatic disease caused by reduced tear production,poor tear quality,or excessive *** diagnosis is a difficult task due to its multifactorial *** of several clinical tests available,the evaluation of the interference patterns of the tear film lipid layer forms a potential tool for DES *** instrument known as Tearscope Plus allows the rapid assessment of the lipid layer.A grading scale composed of five categories is used to classify lipid layer *** reported work proposes the design of an automatic system employing light weight convolutional neural networks(CNN)and nature inspired optimization techniques to assess the tear film lipid layer patterns by interpreting the images acquired with the Tearscope *** designed framework achieves promising results compared with the existing state-of-the-art techniques.

关键词： Dry-eye syndrome tearscope plus tear film deep neural networks

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Passive-state preparation for quantum secure direct communication

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 35-44页

作者： Jia-Wei Ying Jin-Yu Wang Yu-Xiang Xiao Shi-Pu Gu Xing-Fu Wang Wei Zhong Ming-Ming Du Xi-Yun Li Shu-Ting Shen An-Lei Zhang Lan Zhou Yu-Bo Sheng College of Electronic and Optical Engineering and College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China College of Science Nanjing University of Posts and TelecommunicationsNanjing 210023China

Quantum secure direct communication(QSDC)can transmit the secret message ***-photon-based QSDC protocol requires the active modulation of the quantum state of the source,potentially introducing various side-channel *** this paper,we propose a passive-state preparation QSDC *** utilizing a passive-state preparation method,we remove the need for active modulation,thus eliminating the side-channel risks associated with the source *** evaluate the performance of the protocol,we develop a system model and improve the decoy state *** parameter optimization,we identify the optimal secrecy message capacity for various communication distances and compare it with active modulation *** a communication distance of 10(15)km,the secrecy message capacity of passive-state preparation QSDC reaches3.894×10^(-4)(3.715×10^(-5))bit/pulse,achieving 95.3%(91.5%)of the active case’s ***,we consider the resource consumption and optimize the secrecy message transmission rate of passive-state preparation *** a coherent light source with a frequency of 10^(6)Hz,at communication distances of 5,10,and 15 km,the secrecy message transmission rates for passive-state preparation QSDC reach 2.370×10^(2),4.218×10,and 2.504 bit/s,respectively.

关键词： quantum secure direct communication single-photon passive-state preparation decoy state

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Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

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Intelligent Automation & Soft Computing 2023年第4期36卷 879-894页

作者： T.Shanmugapriya Dr.K.Kousalya Department of Information Technology SNS College of TechnologyCoimbatore641035India Department of Computer Science and Engineering Kongu Engineering CollegeErode638060India

The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst *** is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things(IoT)and Cyber-Physical Systems(CPS).Data secur-ity,detection of faults,management of energy,collection and distribution of data,network protocol,network coverage,mobility of nodes,and network heterogene-ity are some of the issues confronted by *** is not much published information on issues related to node mobility and management of energy at the time of aggregation of *** the goal of boosting the mobility-based WSNs’network performance and energy,data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy(LEACH-M)and Energy Efﬁcient Heterogeneous Clustered(EEHC)scheme have been exam-ined in this work.A novel Artiﬁcial Bee Colony(ABC)algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station(BS)with least energy *** is avoidance of the local optima problem at the time of solution space search in this proposed *** have been conducted on a large WSN network that has issues with mobility of nodes.

关键词： Wireless sensor network routing clustering mobility low-energy adaptive clustering hierarchy energy efﬁcient heterogeneous clustered artiﬁcial bee colony

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A reputation-based blockchain scheme for sustained carbon emission reduction

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science China(information sciences) 2024年第5期67卷 329-330页

作者： Lixiao ZHOU Changbing TANG Zheng BAO Yang LIU Xinghuo YU College of Physics and Electronics Information Engineering Zhejiang Normal University School of Computer Science and Technology Zhejiang Normal University School of Mathematical Sciences Zhejiang Normal University School of Engineering RMIT University

Climate change is a global issue that necessitates collective responsibility from all nations. Recently, a blockchainenabled emission trading scheme(ETS) has been proposed for reducing carbon emissions to overcome the drawbacks of centralized methods [1, 2]. However, existing consensus protocols in blockchain have not been well prepared for low energy consumption and high incentive compatibility [3],which is contrary to the low carbon goal of ETS.

关键词： reputation emission scheme carbon based blockchain reduction sustained

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Energy efficient quantum-informed ant colony optimization algorithms for industrial internet of things

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1077-1086页

作者： Jannu, Srikanth Dara, Suresh Thuppari, Chaitanya Vidyarthi, Ankit Ghosh, Debjani Tiwari, Prayag Muhammad, Ghulam Department of Computer Science and Engineering Vaagdevi Engineering College Singaram506005 India School of Technology Woxsen University HyderabadTS 502 345 India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India Department of Computer Science and Engineering Bennett University Greater Noida201309 India School of Information Technology Halmstad University Halmstad301 18 Sweden Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

One of themost prominent research areas in information technology is the Internet of Things (IoT) as its applications are widely used, such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-The-Art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, in this article, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics, such as residual energy of the network, network lifetime, and the number of live IoT nodes. Impact Statement-Toward IoT-based applications, here we presented the Quantum-inspired ACO clustering algorithm for network lifetime. IoT nodes in the clustering phase choose theirCH through the distance between cluster member IoT nodes and the residual energy. Thus, CH selection reduces the energy consumption of member IoT nodes. Therefore, our significant contributions are summarized as follows. i. Developing Quantum-informed ACO clustered routing algor

关键词： Energy efficiency

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