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An improved deep convolutionary neural network for bone marrow cancer detection using image processing

Informatics in Medicine Unlocked 2023年 38卷

作者： Ramasamy, Manjula Devi Dhanaraj, Rajesh Kumar Pani, Subhendu Kumar Das, Rashmi Prava Movassagh, Ali Akbar Gheisari, Mehdi Liu, Yang Porkar, P. Banu, Sabitha Department of Computer Science and Engineering KPR Institute of Engineering and Technology India School of Computing Science and Engineering Galgotias University India Krupajal Engineering College Biju Patnaik University of Technology Odisha India Department of Computer Science and Engineering CV Raman Global University Bhubaneswar India Department of Biomedical Engineering School of Medicine Tehran University of medical sciences Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Young researchers and elite club Islamic Azad University Parand Iran

Bone Marrow Cancer is a type of cancer that develops in the stem cells of the bone marrow that are responsible for blood formation. AML(Acute Myeloid Leukaemia) and MM(Multiple Myeloma) are both types of malignancy that can affect bone marrow. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. Hence, in order to provide accurate treatment, a better diagnosis technique is required. In this regard, the suggested study has improved a unique technique, specifically classification and segmentation, allowing for the identification of more intricate disorders. The study's findings on the accuracy of cancer cell identification, as well as the decrease in the possibility of false alarm rates, continue to fall on the negative side of the outcome spectrum, according to the researchers. The hybrid approach has been proposed in this article that includes deep convolutional neural networks, hyperparameter sets utilising adaptive multi-objective CAT algorithms and other image processing techniques. To train the suggested model, it is critical to use previously processed cell images. The suggested model is then used to train an Optimized Convolutional Neural network (OCNN), which is then used to determine the type of tumour identified in the bone area. The SN-AM datasets were thoroughly examined, and a number of presentation measures, such as accuracy (recall), F1-score, and specificity (specificity), were generated and analysed. When it came to predicting different types of cancer, researchers observed that they had an overall accuracy of 99.45 percent. In terms of recognizing the sorts of cancer cells, the proposed model surpassed all existing learning models. © 2023

关键词： Bone marrow CAT Algorithm Leukaemia Multiple myeloma Optimized convolutional neural network

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On pure MDS asymmetric entanglement-assisted quantum error-correcting codes

arXiv

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arXiv 2021年

作者： Huang, Ziteng Fang, Weijun Fu, Fang-Wei Chern Institute of Mathematics and LPMC Nankai University Tianjin300071 China Shenzhen International Graduate School Tsinghua University PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China Chern Institute of Mathematics and LPMC Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China

Recently, Galindo et al. introduced the concept of asymmetric entanglement- assisted quantum error-correcting codes (AEAQECCs) from Calderbank- Shor-Steane (CSS) construction. In general, it's difficult to determine the required number of maximally entangled states of an AEAQECC, which is associated with the dimension of the intersection of the two corresponding linear codes. Two linear codes are said to be a linear l-intersection pair if their intersection has dimension l. In this paper, all possible linear l-intersection pairs of MDS codes are given. As an application, we give a complete characterization of pure MDS AEAQECCs for all possible parameters. © 2021, CC BY.

关键词： Quantum entanglement

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An Enhanced Vision Transformer with Autoencoder for Noise Reduction in Knee X-Rays

SSRN

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SSRN 2025年

作者： Mahmood, Jabar Hamza Abdulnabi, Mohamed Shabbir Pasha, Malaika Azeem, Muhammad Ali, Aitizaz Department of Computer Science International Institute of Science Art and Technology Punjab Gujranwala Pakistan Faculty of Computing and Information Technology University of Sialkot Daska Road Punjab Sialkot51040 Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310007 China Asia Pacific University of Technology and Innovation Kuala Lumpur57000 Malaysia

Traditional methods for medical image denoising, such as Filtering and Convolutional Neuralnetworks, have shown limitations, particularly in preserving image integrity. Recent developmentsin Vision Transformers (ViTs) demonstrate encouraging denoising capabilities but come at the cost ofhigh resource consumption. To solve this, we present an efficient model, the Denoising AutoencoderVision Transformer (DAE-VIT). It is designed specifically for knee X-ray denoising. Unlike the largeSwin Transformer UNet (SUNET), which utilizes 99M parameters, DAE-VIT significantly reducesthe computational burden with only 5.8M parameters while maintaining superior performance. Weevaluated the model on a supervised dataset of knee images. The model gave promising results, withMean Squared Error (MSE) of 0.0036, Mean Absolute Error (MAE) of 0.022, Root Mean Square Error(RMSE) of 0.060, Normalized Cross-Correlation (NCC) of 0.962, Peak Signal-to-Noise Ratio (PSNR)of 32.177, and Structural Similarity Index (SSIM) of 0.872. These scores show the effectiveness ofDAE-VIT in noise reduction while preserving critical image details and make it a favorable alternativefor resource-efficient medical image denoising tasks. © 2025, The Authors. All rights reserved.

关键词： Salt and pepper noise

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Evaluation Method of Node Importance in Temporal Satellite networks Based on Time Slot Correlation

Research Square

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Research Square 2021年

作者： Xu, Rui Di, Xiaoqiang He, Xiongwen Qi, Hui School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun130022 China Information Center Changchun University of Science and Technology Changchun130022 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Temporal satellite networks can accurately describe the dynamic process of satellite networks by considering the interaction relationship and interaction sequence between satellite nodes. In addition, the measurement of node importance in satellite networks plays a crucial role in understanding the structure and function of the network. The classical supra-adjacency matrix (SAM) temporal model identifies the key nodes in the temporal network to some extent, which ignores the differences of inter-layer connectivity relationships leading to the inability to reflect the dynamic variations of satellite nodes. Therefore, the evaluation method based on time slot correlation is proposed to measure the importance of satellite nodes in this paper. Firstly, the correlation coefficient of time slot nodes is defined to measure the coupling relationship of adjacent time slots. Secondly, the dynamic supra-adjacency matrix (DSAM) temporal network model is proposed considering the correlation between adjacent time slots and the characteristics of link time. Finally, the node importance ranking results in each time slot and a global perspective are obtained by utilizing the eigenvector centrality. Through experimental simulations of the Iridium and Orbcomm constellations, it is demonstrated that the DSAM method has a more accurate recognition rate and is more stable than the SAM method. © 2021, CC BY.

关键词： Satellites

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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A conditional-probability zone transformation coding method for categorical features 19

A conditional-probability zone transformation coding method ...

引用

2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： He, Liang Shen, Chao Li, Yun National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'an China

ISBN: (纸本)9781450371582

It has been a key issue for solving problems efficiently by machine learning models with code categorical features. The state-of-the-art one-hot coding is a widely accepted method to convert the categorical features into numerical values. However, it attracts a sparse space and meaningless value after coding. We come up with a novel coding method based on conditional probability after dividing the features into zones, which is called Conditional-probability-based Zone Transformation (CZT) coding. CZT coding calculates the conditional probability of each feature, then divides the features into several zones according to the probability and finally codes the features in each zone. We mathematically prove that compared with the state-of-the-art method, CZT coding reduces the code length by at least the mean of feature space and the issue becomes into an easier one after CZT coding for the following machine learning model. Finally, using the same neuron network as the classifier, we compare the performance of CZT coding and one-hot coding by using the titanic dataset, where most of the features are categorical, and the result is that CZT coding makes the classifier performs better both on the accuracy and steadiness. © 2019 Association for computing Machinery.

关键词： Probability

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Velocity-driven particle swarm optimization 8

Velocity-driven particle swarm optimization

引用

8th International Conference on computing and Pattern Recognition, ICCPR 2019

作者： Li, Wei Fan, Yaochi Jiang, Qiaoyong Xu, Qingzheng School of Computer Science and Engineering Xi'An University of Technology China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an710048 China School of Computer Science and Engineering Xi'An University of Technology Xi'an710048 China College of Information and Communication National University of Defense Technology Xi'an710106 China

ISBN: (纸本)9781450376570

Particle swarm optimization (PSO) is an efficient nature-inspired optimization algorithm, which has been widely applied in many engineering fields. The performance of particle swarm optimization (PSO) has been significantly influenced by velocity update strategy. Traditionally, each particle updates its velocity based on its historical best experience and the global best experience, which may make the swarm lose its diversity and lead to premature convergence. To strengthen the performance of PSO, this paper proposes an improved PSO with the velocity-driven strategy (VD-PSO). In VD-PSO algorithm, the particles whose velocities are driven by better velocities focus on exploitation. The historical velocities, such as better velocities, may effectively characterize the landscape information of the optimization problems. In addition, the particles focus on exploration through information exchange amongst velocities. To verify the effectiveness of the proposed algorithm, experiments are conducted with CEC2014 test problems. The experimental results demonstrate the effectiveness of the proposed algorithm for solving the global optimization problems. © 2019 Association for computing Machinery.

关键词： Velocity

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Defence Against Adversarial Attacks Using Clustering Algorithm 5th

Defence Against Adversarial Attacks Using Clustering Algorit...

引用

5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Zheng, Yanbin Yun, Hongxu Wang, Fu Ding, Yong Huang, Yongzhong Liu, Wenfen Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering South China University of Technology Guangzhou China School of Computer Science and Network Security Dongguan University of Technology Dongguan China

ISBN: (纸本)9789811501173

Deep learning model is vulnerable to adversarial examples in the task of image classification. In this paper, a cluster-based method for defending against adversarial examples is proposed. Each adversarial example before attacking a classifier is reconstructed by a clustering algorithm according to the pixel values. The MNIST database of handwritten digits was used to assess the defence performance of the method under the fast gradient sign method (FGSM) and the DeepFool algorithm. The defence model proposed is simple and the trained classifier does not need to be retrained. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Clustering algorithms

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Controlled Secure Direct Communication Protocol via the Three-Qubit Partially Entangled Set of States

引用

Computers, Materials & Continua 2019年第3期58卷 809-827页

作者： Gang Xu Ke Xiao Zongpeng Li Xin-Xin Niu Michael Ryan School of Information Science and Technology North China University of TechnologyBeijing100144China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China School of Computer Science Wuhan UniversityHubei430072China Guizhou Provincial Key Laboratory of Public Big Data GuiZhou UniversityGuiyangGuizhou550025China School of Computing Dublin City UniversityDublin 9Ireland.

In this paper,we first re-examine the previous protocol of controlled quantum secure direct communication of Zhang et al.’s scheme,which was found insecure under two kinds of attacks,fake entangled particles attack and disentanglement ***,by changing the party of the preparation of cluster states and using unitary operations,we present an improved protocol which can avoid these two kinds of ***,the protocol is proposed using the three-qubit partially entangled set of *** is more efficient by only using three particles rather than four or even more to transmit one bit secret *** our using state is much easier to prepare for multiqubit states and our protocol needs less measurement resource,it makes this protocol more convenient from an applied point of view.

关键词： Controlled secure direct communication cluster state three-qubit partially entangled state entanglement swapping.

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Metric Learning-based Few-Shot Malicious Node Detection for IoT Backhaul/Fronthaul networks

Metric Learning-based Few-Shot Malicious Node Detection for ...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Ke Zhou Xi Lin Jun Wu Ali Kashif Bashir Jianhua Li Muhammad Imran School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China Collaborative Innovation Center of Shanghai Industrial Internet Shanghai China Faculty of Science and Engineering Waseda University Japan Department of Computing and Mathematics Manchester Metropolitan University Manchester U.K. School of Engineering Information Technology and Physical Sciences Federation University Australia

ISBN: (纸本)9781665435413

The development of backhaul/fronthaul networks can enable low latency and high reliability, but nodes in future networks like Internet of Things (IoT) can conduct malicious activities like flooding attack and DDoS attack, which can decrease QoS of smart backhaul/fronthaul network. Timely detection of malicious nodes in future networks is significant for low-latency backhaul/fronthaul networks. However, conventional supervised learning-based detection models require abundant malicious training samples, while capturing adequate malicious samples can not meet the requirement of timely detection. In this paper, we propose a novel few-shot malicious node detection system for improving QoS of IoT backhaul/fronthaul network, which can detect malicious nodes with unknown malicious activities through a limited number of network traffic samples. In our proposed system, we first design a fresh IoT traffic sample processing approach, which integrates normal activity samples and known malicious activity samples to generate training pairs. Then, we design a metric learning-based malicious node detection model training method, which employs a contrastive loss over distance metric to distinguish between similar and dissimilar pairs of samples. Besides, the trained model can detect nodes with unknown malicious activities by comparing real-time samples with few-shot samples of malicious nodes. Finally, the proposed system is evaluated on a real-world IoT network dataset named N-BaIoT. The exhaustive experiment results show that our model can achieve an average accuracy around 97.67 % when detecting malicious nodes with unknown malicious activities, which is comparable to state-of-the-art supervised learning models while our model only needs 5-shot samples of malicious node.

关键词： Measurement Training Supervised learning Quality of service Telecommunication traffic Real-time systems Internet of Things

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