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Reinforcement learning (RL)-based semantic segmentation and attention based backpropagation convolutional neural network (ABB-CNN) for breast cancer identification and classification using mammogram images

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Neural Computing and Applications 2024年第24期36卷 14797-14823页

作者： Thakur, Neha Kumar, Pardeep Kumar, Amit Department of Computer Science & amp Engineering Jaypee University of Information Technology HP Solan India

Breast cancer poses a threat to women’s health and contributes to an increase in mortality rates. Mammography has proven to be an effective tool for the early detection of breast cancer. However, it faces many challenges in early breast cancer detection due to poor image quality, traditional segmentation, and feature extraction. Therefore, this work addresses these issues and proposes an attention-based backpropagation convolutional neural network (ABB-CNN) to detect breast cancer from mammogram images more accurately. The proposed work includes image enhancement, reinforcement learning-based semantic segmentation (RLSS), and multiview feature extraction and classification. The image enhancement is performed by removing noise and artefacts through a hybrid filter (HF), image scaling through a pixel-based bilinear interpolation (PBI), and contrast enhancement through an election-based optimization (EO) algorithm. In addition, the RLSS introduces intelligent segmentation by utilizing a deep Q network (DQN) to segment the region of interest (ROI) strategically. Moreover, the proposed ABB-CNN facilitates multiview feature extraction from the segmented region to classify the mammograms into normal, malignant, and benign classes. The proposed framework is evaluated on the collected and the digital database for screening mammography (DDSM) datasets. The proposed framework provides better outcomes in terms of accuracy, sensitivity, specificity, precision, f-measure, false-negative rate (FNR) and area under the curve (AUC). This work achieved (99.20%, 99.35%), (99.56%, 99.66%), (98.96%, 98.99%), (99.05%, 99.12%), (0.44%, 0.34%), (99.31%, 99.39%) and (99.27%, 99.32%) of accuracy, sensitivity, specificity, precision, FNR, f-measure and AUC on (collected, DDSM datasets), respectively. This research addresses the prevalent challenges in breast cancer identification and offers a robust and highly accurate solution by integrating advanced deep-learning techniques. The evaluated re

关键词： Image enhancement

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Parallel Computation of Dominance Scores for Multidimensional Datasets on GPUs

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IEEE Transactions on Parallel and Distributed Systems 2024年第6期35卷 764-776页

作者： Chen, Wei-Mei Tsai, Hsin-Hung Ling, Joon Fong National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106 Taiwan

The dominance scoring problem in a multidimensional dataset is to return the number of points dominated by a given point, which is a common metric for evaluating the quality of a data point. Dominance scoring is an elementary operator for variations of the skyline operator, including top k dominating and k-skyband queries. This study proposes query processing for dominance scores that operates primarily on the graphics processing unit (GPU) to fully utilize its massive processing resources and restricted memory space while reducing the transfer overhead between the central processing unit (CPU) and GPU. We introduce a heap-based multidimensional data structure with complete and well-balanced characteristics. Using our preprocessed data, we can construct a complete R-tree with the non-overlapping property, ensuring that the bounding boxes of internal nodes of the same level do not overlap, thereby reducing redundant operations. In addition, we propose two algorithms based on depth-first and breadth-first traversals to accumulate the dominance score on GPUs in parallel. Both take full advantage of the GPU's computing resources and memory space supported by the non-overlapping tree structures. Experiments on synthetic and real-world datasets demonstrate that the proposed algorithms implemented on GPUs dramatically improve the efficiency of dominance scoring. © 1990-2012 IEEE.

关键词： Graphics processing unit

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Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

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CAAI Transactions on Intelligence technology 2024年第2期9卷 319-332页

作者： Xiaoqing Zhang Xiao Wu Zunjie Xiao Lingxi Hu Zhongxi Qiu Qingyang Sun Risa Higashita Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhenChina Tomey Corporation NagoyaJapan Guangdong Provincial Key Laboratory of Brain‐inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina Singapore Eye Research Institute SingaporeSingapore

Eye health has become a global health concern and attracted broad *** the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and ***,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model *** alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular *** MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer *** conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,university of California San Diego,and CIFAR-100 *** results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and ***,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

关键词： artificial intelligence deep learning deep neural networks image analysis image classification medical applications medical image processing

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Privacy-Preserving and Traceable Functional Encryption for Inner Product In Cloud Computing

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IEEE Transactions on Cloud Computing 2025年第2期13卷 667-679页

作者： Qiu, Muyao Han, Jinguang Hao, Feng Sun, Chao Wu, Ge Southeast University School of Cyber Science and Engineering Nanjing210096 China Nanjing210096 China University of Warwick Department of Computer Science CoventryCV4 7AL United Kingdom

Cloud computing is a distributed infrastructure that centralizes server resources on a platform in order to provide services over the internet. Traditional public-key encryption protects data confidentiality in cloud computing, while functional encryption provides a more fine-grained decryption method, which only reveals a function of the encrypted data. However, functional encryption in cloud computing faces the problem of key sharing. In order to trace malicious users who share keys with others, traceable FE-IP (TFE-IP) schemes were proposed where the key generation center (KGC) knows users' identities and binds them with different secret keys. Nevertheless, existing schemes fail to protect the privacy of users' identities. The fundamental challenge to construct a privacy-preserving TFE-IP scheme is that KGC needs to bind a key with a user's identity without knowing the identity. To balance privacy and accountability in cloud computing, we propose the concept of privacy-preserving traceable functional encryption for inner product (PPTFE-IP) and give a concrete construction which offers the features: (1) To prevent key sharing, both a user's identity and a vector are bound together in the key;(2) The KGC and a user execute a two-party secure computing protocol to generate a key without the former knowing anything about the latter's identity;(3) Each user can ensure the integrity and correctness of his/her key through verification;(4) The inner product of the two vectors embedded in a ciphertext and in his/her key can be calculated by an authorized user;(5) Only the tracer can trace the identity embedded in a key. We formally reduce the security of the proposed PPTFE-IP to well-known complexity assumptions, and conduct an implementation to evaluate its efficiency. The novelty of our scheme is to protect the user's privacy and provide traceability if required. © 2013 IEEE.

关键词： Ciphertext

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Multi-Objective Offloading Optimization in MEC and Vehicular-Fog Systems: A Distributed-TD3 Approach

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IEEE Transactions on Intelligent Transportation Systems 2024年第11期25卷 16897-16909页

作者： Wakgra, Frezer Guteta Kar, Binayak Tadele, Seifu Birhanu Shen, Shan-Hsiang Khan, Asif Uddin The Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan The School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India

The emergence of 5G networks has enabled the deployment of a two-tier edge and vehicular-fog network. It comprises Multi-access Edge Computing (MEC) and Vehicular-Fogs (VFs), strategically positioned closer to Internet of Things (IoT) devices, reducing propagation latency compared to cloud-based solutions and ensuring satisfactory quality of service (QoS). However, during high-traffic events like concerts or athletic contests, MEC sites may face congestion and become overloaded. Utilizing offloading techniques, we can transfer computationally intensive tasks from resource-constrained devices to those with sufficient capacity, for accelerating tasks and extending device battery life. In this research, we consider offloading within a two-tier MEC and VF architecture, involving offloading from MEC to MEC and from MEC to VF. The primary objective is to minimize the average system cost, considering both latency and energy consumption. To achieve this goal, we formulate a multi-objective optimization problem aimed at minimizing latency and energy while considering given resource constraints. To facilitate decision-making for nearly optimal computational offloading, we design an equivalent reinforcement learning environment that accurately represents the network architecture and the formulated problem. To accomplish this, we propose a Distributed-TD3 (DTD3) approach, which builds on the TD3 algorithm. Extensive simulations, demonstrate that our strategy achieves faster convergence and higher efficiency compared to other benchmark solutions. © 2024 IEEE.

关键词： Quality of service

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Optimizing photo-to-anime translation with prestyled paired datasets

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Multimedia Tools and Applications 2024年第41期83卷 89393-89414页

作者： Chang, Chuan-Wang Dharmawan, Pratamagusta Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan

Animation is a widespread artistic expression that holds a special place in people's hearts. Traditionally, animation creation has relied heavily on manual techniques, demanding skilled drawing abilities and a significant amount of time. For instance, many Japanese anime films draw inspiration from real-world settings, requiring access to relevant references and artists capable of translating them into anime visuals. Consequently, the development of technology that automatically converts images into anime holds great significance. Numerous methods for style transfer have been developed using unsupervised learning and have achieved impressive results. However, unsupervised learning methods suffer when the image contains multiple styles within itself because they learn the style of the image globally. To solve this problem, we propose splitting these styles within the image into multiple classes: sky, buildings, greenery, water, and other objects. Then, we style these separated classes using existing image-to-image translation models. Finally, we train a pix2pix model to learn image style transfer in a paired manner. The experimental results show that the images are effectively styled into the resulting anime-styled image domain with comparable results to existing unsupervised learning GAN-based methods. The proposed method can effectively transfer the style from real-world photos into the anime-styled image domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Unsupervised learning

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Adaptive Interference Detection and Removal in SAR Images Based on the Order-2 Tracy-Widom Distribution 2

Adaptive Interference Detection and Removal in SAR Images Ba...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yang, Huizhang Chen, Chengzhi Xi, Feng Lang, Ping Yin, Junjun Yang, Jian Nanjing University of Science and Technology Department of Electronic Engineering Nanjing China Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

This paper proposes a novel eigenvalue-based detector for adaptive detection of radio interference in synthetic aperture radar (SAR) images. The proposed method leverages the increased eigenvalues caused by interference in single-look-complex SAR image blocks, where the interference is expected to have a small set of eigenvalues, particularly with a dominating one. Specifically, the method segments the image into multiple blocks, computes the eigenvalues of each block's covariance matrix, and compares the largest eigenvalue with a threshold to determine the presence of interference under the criteria of constant false alarm rate. The threshold is determined by the order-2 Tracy-Widom distribution of the largest eigenvalue for each image block under the assumption of image homogeneity. The proposed detector is integrated with our previously proposed block subspace filter for adaptive interference detection and removal. Experimental results on Sentinel-1 double-polarization and Gaofen-3 HV-polarization images demonstrate the effectiveness of the proposed method on adaptive detection and suppression of interference artefacts in SAR images. © 2024 IEEE.

关键词： Eigenvalues and eigenfunctions

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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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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Synergetic PVA degradation and H2 evolution in photocatalytic fuel cells using Ag@Fe_(2)O_(3) cathode

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Frontiers of Chemical science and engineering 2024年第1期18卷 85-93页

作者： Likun Sun Kesi Xiong Baoning Zhang Jinghong Fang Yingchao He Min Wang Zhixing Gan Fanglin Du Qiong Sun Liyan Yu Lifeng Dong College of Materials Science and Engineering Qingdao University of Science and TechnologyQingdao 266042China Center for Future Optoelectronic Functional Materials School of Computer and Electronic InformationNanjing Normal UniversityNanjing 210023China Department of Physics Hamline UniversitySt.Paul 55104USA

In a dual-chamber photocatalytic fuel cell device,polyvinyl alcohol degradation and H2 evolution were concurrently *** setup involved commercial P25 as the photoanode and Ag@Fe_(2)O_(3) nanoparticles as the ***,the feasibility of a Fentonlike reaction in the cathode,utilizing Fe^(2+)ions and pumped O_(2),was *** cathode materials,polyvinyl alcohol types,and pH values’effects were assessed on device *** tests highlighted photoinduced holes(h+)and OH·radicals as pivotal contributions to polyvinyl alcohol ***-term stability of the device was established through cycling experiments.

关键词： photocatalytic fuel cell PVA Ag@Fe_(2)O_(3) cathode materials Fenton-like reaction

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