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A robust Poisson multi-Bernoulli filter for multi-target tracking based on arithmetic average fusion

Chinese Journal of Aeronautics 2023年第2期36卷 179-190页

作者： Zhenzhen SU Hongbing JI Cong TIAN Yongquan ZHANG School of Computer Science and Technology Xidian UniversityXi'an 710071China School of Electronic Engineering Xidian UniversityXi'an 710071China

The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally difficult to handle the above problems simultaneously,such as the Track-Oriented marginal Multi-Bernoulli/Poisson(TOMB/P)and Measurement-Oriented marginal Multi-Bernoulli/Poisson(MOMB/P)*** on the Arithmetic Average(AA)fusion rule,this paper proposes a novel fusion framework for the Poisson Multi-Bernoulli(PMB)filter,which integrates both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with *** order to fuse the different PMB distributions,the Bernoulli components in different Multi-Bernoulli(MB)distributions are associated with each other by Kullback-Leibler Divergence(KLD)***,an adaptive AA fusion rule is designed on the basis of the exponential fusion weights,which utilizes the TOMB/P and MOMB/P updates to solve these difficulties in ***,by comparing with the TOMB/P and MOMB/P filters,the performance of the proposed filter in terms of accuracy and efficiency is demonstrated in three challenging scenarios.

关键词： Arithmetic average fusion Kullback-Leibler divergence Poisson multi-Bernoulli filter Random finite set Target tracking

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Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis

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computers, Materials & Continua 2023年第9期76卷 2649-2665页

作者： Jingyao Liu Qinghe Feng Jiashi Zhao Yu Miao Wei He Weili Shi Zhengang Jiang School of Computer and Information Engineering Chuzhou UniversityChuzhou239000China School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China Zhongshan Institute of Changchun University of Science and Technology ZhongshanChina School of Intelligent Engineering Henan Institute of TechnologyXinxiang453003China

The coronavirus disease 2019(COVID-19)has severely disrupted both human life and the health care *** diagnosis and treatment have become increasingly important;however,the distribution and size of lesions vary widely among individuals,making it challenging to accurately diagnose the *** study proposed a deep-learning disease diagnosismodel based onweakly supervised learning and clustering visualization(W_CVNet)that fused classification with ***,the data were *** optimizable weakly supervised segmentation preprocessing method(O-WSSPM)was used to remove redundant data and solve the category imbalance ***,a deep-learning fusion method was used for feature extraction and classification recognition.A dual asymmetric complementary bilinear feature extraction method(D-CBM)was used to fully extract complementary features,which solved the problem of insufficient feature extraction by a single deep learning ***,an unsupervised learning method based on Fuzzy C-Means(FCM)clustering was used to segment and visualize COVID-19 lesions enabling physicians to accurately assess lesion distribution and disease *** this study,5-fold cross-validation methods were used,and the results showed that the network had an average classification accuracy of 85.8%,outperforming six recent advanced classification models.W_CVNet can effectively help physicians with automated aid in diagnosis to determine if the disease is present and,in the case of COVID-19 patients,to further predict the area of the lesion.

关键词： Classification COVID-19 deep learning segmentation unsupervised learning weakly supervised

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Online multi-target intelligent tracking using a deep long-short term memory network

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Chinese Journal of Aeronautics 2023年第9期36卷 313-329页

作者： Yongquan ZHANG Zhenyun SHI Hongbing JI Zhenzhen SU School of Electronic Engineering Xidian UniversityXi’an 710071China School of Computer Science and Technology Xidian UniversityXi’an 710071China

Multi-target tracking is facing the difficulties of modeling uncertain motion and observation *** tracking algorithms are limited by specific models and priors that may mismatch a real-world *** this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM ***,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,***,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement ***,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and *** results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.

关键词： Data association Deep long-short term memory network Historical sequence Multi-target tracking Target tuple set Track management

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A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

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computers, Materials & Continua 2024年第3期78卷 3001-3019页

作者： Nannan Wu Xianyi Chen James Msughter Adeke Junjie Zhao School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China

Recently,deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information ***,these approaches have some *** example,a cover image lacks self-adaptability,information leakage,or weak *** address these issues,this study proposes a universal and adaptable image-hiding ***,a domain attention mechanism is designed by combining the Atrous convolution,which makes better use of the relationship between the secret image domain and the cover image ***,to improve perceived human similarity,perceptual loss is incorporated into the training *** experimental results are promising,with the proposed method achieving an average pixel discrepancy(APD)of 1.83 and a peak signal-to-noise ratio(PSNR)value of 40.72 dB between the cover and stego images,indicative of its high-quality ***,the structural similarity index measure(SSIM)reaches 0.985 while the learned perceptual image patch similarity(LPIPS)remarkably registers at ***,self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces,making it suitable for diverse computer vision tasks.

关键词： Deep image hiding attention mechanism privacy protection data security visual quality

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Trust management in online computing environment: a complete review

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 491-545页

作者： Singh, Ashish Chatterjee, Kakali School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering National Institute of Technology Bihar Patna800005 India

Nowadays, trust management plays a significant role in different applications like commercial applications, Internet of Things (IoT) based applications, social networking applications, cloud computing-based applications, sensor networks, online services, and many more. Thus, it is an important research area for industry and engineering research communities. The concept of trust is mainly derived from long-term sociological and psychological human behaviour. It is used for decision-making purposes in different contexts and perspectives. Many growing research studies address the development of trust computation models using different approaches in different areas, such as cloud computing, P2P networks, online social network, etc. Still, most of the prior work does not provide a comprehensive systematic review of different trust management systems nor provide sufficient details about the computation process. In this paper, we have discussed a systematic review of the existing trust management systems by indicating the limitation of previous works. A systematic review started with research question identification, followed by searching and filtration of relevant works, and ended with a comparison and final report. A detailed comparative study of trust computational models will help understand the security and privacy aspects of trust. This survey provides a detailed study of each trust taxonomy and computation model in different online computing environments. The survey result finds several trust computation techniques. The results also say that trust in the online computing environment provides greater security and protection against several threats and attacks. Despite the potential benefits of trust, the results also included challenges and open research issues that are still opportunities for new researchers. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Cloud computing

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Hybrid deep learning based digital image watermarking using GAN-LSTM and adaptive gannet optimization techniques

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Multimedia Tools and Applications 2024年 1-31页

作者： Shedole, Sambhaji Marutirao Santhi, V. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Katpadi Vellore632014 India

Nowadays, multimedia technology is progressing everyday. It is very easy to duplicate, distribute and modify digital images with online editing software. Image security and privacy are critical aspects of the multimedia revolution. Therefore, digital image watermarking offers an alternative way out for image authentication. Currently, watermarking methods are crucial for safeguarding digital images. Several traditional watermarking approaches have been developed to protect images using spatial domains and transformations. Watermarking techniques that are more traditional are less resistant to repeated attacks. Deep learning-based watermarking has recently gained traction, greatly improving the safety of visual images in a variety of common applications. This study presents a robust and secure digital watermarking method for multimedia content protection and authentication. The watermark image is first transformed using the hybrid wavelet transform, and then it is encrypted using a chaos encryption algorithm. The cover image is simultaneously subjected to neighborhood-based feature extraction. Leveraging these extracted features, a novel Adaptive Gannet Optimization algorithm (AGOA) is employed to determine the optimal embedding location. Subsequently, the watermarked image is seamlessly integrated and extracted using the hybrid Generative adversarial network-based long short-term memory (GAN-LSTM) approach within the identified optimal region. Decryption and Inverse transformation are then used to get the original watermark image. Several previous methods, such as DNN, Deep-ANN, and Deep-CNN, are used to evaluate the performance of the proposed method. This technique improves multimedia content protection and authentication by guaranteeing strong and secure watermarking. The proposed method for digital image watermarking produced a peak signal-to-noise ratio of 46.412 and a mean square error of 24.512. Therefore, the proposed method performs well in digital image wa

关键词： Wavelet transforms

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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A Holistic Secure Communication Mechanism Using a Multilayered Cryptographic Protocol to Enhanced Security

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computers, Materials & Continua 2024年第3期78卷 4417-4452页

作者： Fauziyah Zhaoshun Wang Mujahid Tabassum School of Computer and Communication Engineering University of Science and TechnologyBeijing100000China Noroff School of Technology and Digital Media(Noroff Accelerate) Noroff University CollegeKristiansand4612Norway

In an era characterized by digital pervasiveness and rapidly expanding datasets,ensuring the integrity and reliability of information is *** cyber threats evolve in complexity,traditional cryptographic methods face increasingly sophisticated *** article initiates an exploration into these challenges,focusing on key exchanges(encompassing their variety and subtleties),scalability,and the time metrics associated with various cryptographic *** propose a novel cryptographic approach underpinned by theoretical frameworks and practical *** to this approach is a thorough analysis of the interplay between Confidentiality and Integrity,foundational pillars of information *** method employs a phased strategy,beginning with a detailed examination of traditional cryptographic processes,including Elliptic Curve Diffie-Hellman(ECDH)key *** also delve into encrypt/decrypt paradigms,signature generation modes,and the hashes used for Message Authentication Codes(MACs).Each process is rigorously evaluated for performance and *** gain a comprehensive understanding,a meticulously designed simulation was conducted,revealing the strengths and potential improvement areas of various ***,our cryptographic protocol achieved a confidentiality metric of 9.13 in comprehensive simulation runs,marking a significant advancement over existing ***,with integrity metrics at 9.35,the protocol’s resilience is further *** metrics,derived from stringent testing,underscore the protocol’s efficacy in enhancing data security.

关键词： Cryptographic security privacy preservation decryption integrity

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A Neural Network Slab Quality Prediction Analysis Based on a Hybrid Intelligent Optimisation Algorithm

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第3期52卷 673-683页

作者： Li, Yiran Zhang, Chunna School of Applied Technology University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China

To address the problem of inaccurate prediction of slab quality in continuous casting, an algorithm based on particle swarm optimisation and differential evolution is proposed. The algorithm combines BP neural network prediction method. Firstly, the factors affecting the quality of the slab are analysed and a sufficient number of sample sets are extracted for training;Secondly, the particle swarm optimisation algorithm is optimised, and then the BP neural network is optimised and the chaos mechanism is introduced to increase the continuity of motion of the particles. The population is partitioned hierarchically, and different types of particles are processed separately to improve the global search capability of the algorithm. The differential evolution algorithm is also integrated to optimise cross-selection and increase particle diversity. Finally, a prediction model is built to predict and analyse the sample data. In the experiment, three algorithms are tested for different benchmark functions, namely the comparison of convergence, stability and searchability. The optimisation time comparison is completed, the target curve fit test is performed and the slab quality test results are checked. The experimental results show that the PSO-EIDE algorithm has strong optimisation ability, fast convergence speed and obvious improvement in stability and accuracy. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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Multi-strategy hybrid whale optimization algorithms for complex constrained optimization problems

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High technology Letters 2024年第1期30卷 99-108页

作者：王振宇 WANG Lei School of Computer Science and Engineering Xi'an University of TechnologyXi'an 710048P.R.China

A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization ***,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the ***,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and *** the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization ***,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved *** experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm.

关键词： whale optimization algorithm(WOA) good point set nonlinear convergence factor siege mechanism

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