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HistoDX: Revolutionizing Breast Cancer Diagnosis Through Advanced Imaging Techniques

IEEE Access 2025年 13卷 94416-94436页

作者： Arshad, Wishal Masood, Tehreem Shahzad, H.M. Ahmed, Hassan A. Ahmed, Syed Hamza Khushi, Hafiz Muhammad Tayyab University of Central Punjab Faculty of Software Engineering and Information Technology Lahore54000 Pakistan Superior University Faculty of Computer Science and Information Technology Lahore54000 Pakistan Superior University Department of Software Engineering Lahore54000 Pakistan Cleveland State University Monte Ahuja College of Business Department of Information Systems ClevelandOH44114 United States

Breast cancer is the second leading cause of mortality among women worldwide, highlighting the need for efficient histopathology-based screening methods for early diagnosis. This study introduces HistoDX, a deep learning framework to classify Invasive Ductal Carcinoma (IDC) using 277,524 histopathology image patches ( 50 x 50 pixels) from Paul Mooney’s IDC dataset on Kaggle, comprising No Cancer and IDC(+) classes. HistoDX employs a preprocessing pipeline with normalization, data augmentation, and class balancing via oversampling and weighted loss to address the class imbalance. A customized convolutional neural network, built on EfficientNetV2-B3 with additional layers, achieves 97% accuracy and a 0.91 ROC-AUC score on the test set. Validation on BreakHis (97% accuracy) and BACH (90% accuracy) datasets confirm generalizability, though detecting under sampled IDC(+) cases remains challenging. Low training and test losses underscore reliability. HistoDX empowers pathologists by enhancing diagnostic efficiency and minimizing subjectivity through effective class imbalance mitigation. Its robust performance across diverse datasets like BreakHis and BACH suggests readiness for clinical integration. Future research into advanced augmentation techniques, ensemble models, and whole-slide image analysis could further optimize accuracy, sensitivity, and scalability, paving the way for broader adoption in precision oncology. © 2013 IEEE.

关键词： Diseases

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RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection 40

RobFL: Robust Federated Learning via Feature Center Separati...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhou, Ting Liu, Ning Song, Bo Lv, Hongtao Guo, Deke Liu, Lei National University of Defense Technology Science and Technology Laboratory on Information Systems Engineering Changsha China School of Software Shandong University Jinan China Shandong Research Institute of Industrial Technology Jinan China

ISBN: (纸本)9798350317152

In recent years, the integration of federated learning and deep learning technologies has become increasingly prevalent in privacy-preserved scenarios, such as smart health applications and automatic financial support. However, the inherent robustness issue in deep learning poses potential risks to federated learning systems when subjected to various attack methods. These attacks can inflict damage during the training and testing phases, perturbing models and inputs. To enhance the robustness of existing federated learning systems, we propose a novel framework called RobFL. This framework incorporates a unique feature learning module -feature center separation learning -that is specifically designed to increase the margins between different classes in the feature space, thereby augmenting the difficulty of attacks employing imperceptible perturbations on inputs. Furthermore, we design a malicious center detection method to detect malicious clients and mitigate their adverse impact. Extensive experiments substantiate the robustness of our proposed framework, RobFL, demonstrating its resilience against both evasion attacks and poisoning attacks. © 2024 IEEE.

关键词： Anomaly detection

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ER‐Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

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IET Cyber-systems and Robotics 2024年第2期6卷 97-108页

作者： Changjian Jiang Zeyu Wan Ruilan Gao Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province HangzhouChina

The colour-enhanced point cloud map is increasingly being employed in fields such as robotics,3D reconstruction and virtual *** authors propose ER-Mapping(Extrinsic Robust coloured Mapping system using residual evaluation and selection).ER-Mapping consists of two components:the simultaneous localisation and mapping(SLAM)subsystem and the colouring *** SLAM subsystem reconstructs the geometric structure,where it employs a dynamic threshold-based residual selection in LiDAR-inertial odometry to improve mapping *** the other hand,the col-ouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods,eliminating the need for strict hardware time synchronisation and highly accurate extrinsic *** were conducted in both indoor and outdoor *** results demonstrate that our system can enhance accuracy,reduce computational costs and achieve extrinsic robustness.

关键词： robot vision sensor fusion simultaneous localisation and mapping(robots)

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DP-GenFL: a local differentially private federated learning system through generative data

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science China(information sciences) 2023年第8期66卷 275-276页

作者： Jun LI Kang WEI Chuan MA Feng SHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Information and Communication Engineering Hainan University

With the rapid development of the Internet of Things(Io T),the amount of data from intelligent devices is propagating at unprecedented scales. Meanwhile, machine learning(ML),which relies heavily on such data, is revolutionizing many aspects of our lives [1]. However, conventional centralized ML offers little scalability for efficiently processing this huge amount of data.

关键词： Learning systems

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Reducing the Risk of Completeness Loss by Subgraph Reasoning in engineering Applications 22

Reducing the Risk of Completeness Loss by Subgraph Reasoning...

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6th International Conference on Computer science and Application engineering, CSAE 2022

作者： Zhu, Xixi Liu, Bin Ding, Zhaoyun Yao, Li Zhu, Cheng Zhu, Xianqiang Science and Technology on Information Systems and Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781450396004

The ontology knowledge base is divided into TBox and ABox, the former is the schema-level information of the knowledge base, which is used to describe the relationship between the recognized concepts and attributes, and the latter is a collection of instance assertions or fact statements in the domain. Using TBox, the process of reasoning more implicit assertions in ABox is called ABox materialization, which plays an important role in knowledge base applications. Some ontology reasoners for OWL DL and DL-safe SWRL, the reasoning results are considered to be correct and complete. However, in practical applications, this paper finds that in some cases, the reasoning results are not complete when reasoning about the ontology knowledge base constructed by OWL DL and DL-sale SWRL. In those application scenarios that require completeness, blindly trusting these reasoners will bring risks. In response to this problem, this paper proposes a method based on subgraph reasoning, which can identify whether the result of ontology reasoning is complete in engineering, and at the same time, when it is incomplete, new assertions that have not been found before can be inferred. The comparative experimental results on two open-source ontologies verify the method in this paper. © 2022 Association for Computing Machinery.

关键词： Ontology

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High-timing-precision detection of single X-ray photons by superconducting nanowires

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National science Review 2024年第1期11卷 194-203页

作者： Shuya Guo Jingrou Tan Hengbin Zhang Jinguang Wang Tianhao Ji Labao Zhang Xiaolong Hu Jian Chen Jun Xie Kai Zou Yun Meng Xiaomin Bei Ling-An Wu Qi Chen Hao Wang Xuecou Tu Xiaoqing Jia Qing-Yuan Zhao Lin Kang Peiheng Wu Research Institute of Superconductor Electronics Nanjing University Qian Xuesen Laboratory of Space Technology Beijing National Laboratory of Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Hefei National Laboratory School of Precision Instrument and Optoelectronic Engineering Tianjin University Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education

Precisely acquiring the timing information of individual X-ray photons is important in both fundamental research and practical applications. The timing precision of commonly used X-ray single-photon detectors remains in the range of one hundred picoseconds to microseconds. In this work, we report on high-timing-precision detection of single X-ray photons through the fast transition to the normal state from the superconductive state of superconducting nanowires. We successfully demonstrate a free-running X-ray single-photon detector with a timing resolution of 20.1 ps made of 100-nm-thick niobium nitride film with an active area of 50 μm by 50 μm. By using a repeated differential timing measurement on two adjacent X-ray single-photon detectors, we demonstrate a precision of 0.87 ps in the arrival-time difference of X-ray photon measurements. Therefore, our work significantly enhances the timing precision in X-ray photon counting, opening a new niche for ultrafast X-ray photonics and many associated applications.

关键词： single-photon detection timing precision superconductor ultrafast phenomenon pulsar X-ray navigation

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Screening and pilot-scale evaluation of a highly efficient pesticide-degrading Pseudomonas *** BL5

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Chinese Journal of Chemical engineering 2024年第10期74卷 287-294页

作者： Rui Chen Xinyu Zuo He Bai Ruolin Qin Zhiguo Chen Yiyun Liu Wenqing Cao Jingpeng Song Xiaoqiang Jia Department of Biochemical Engineering School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China Tianjin Huakan Environmental Protection Technology Co. Ltd.Tianjin 300170China Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300350China

The widespread use of pesticides has caused serious harm to ecosystems,necessitating effective and environmentally friendly treatment *** stands out as a promising approach for pollutant treatment,wherein the metabolic activities of microorganisms can transform toxic pesticides into compounds with lower or no *** this study,we obtained eight pesticide-degrading strains from pesticide-contaminated sites through continuous enrichment and *** highly efficient pesticide-degrading strains(degradation ratios exceeding 80%)were *** them,Pseudomonas ***5 exhibited the strongest growth(exceeding 10^(9) CFU·ml^(-1))and outstanding degradation of benzene derivatives and chlorinated hydrocarbons at both laboratory and pilot scales,with degradation ratios exceeding 98%and 99.6%,*** research provides new tools and insights for the bioremediation of pesticide-related pollutants.

关键词： Pesticides Pilot-scale Degradation Remediation Pseudomonas sp. Benzene

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Multi-Mode Inflow Prediction for Urban Transportation systems 14th

Multi-Mode Inflow Prediction for Urban Transportation System...

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14th International Conference on Frontier Computing, FC 2024

作者： Hung, Chih-Chieh Hung, Min-Hsien Chang, Jia-Wei Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Traffic congestion poses significant challenges to modern cities, leading to increased energy use, pollution, and long commute times. Optimizing public transit systems and encouraging their use is an effective solution. However, public transit integrates various modes like subways, buses, and shared bikes, requiring transfers between modes. Therefore, understanding the relationships between modes and accurately predicting passenger flows for each mode is crucial. Accurate predictions enable better capacity allocation, scheduling, and seamless transfers between modes, enhancing accessibility and convenience to attract more riders. Existing multi-modal prediction methods typically focus on station-to-region or region-to-region flows, overlooking station-to-station predictions that can better capture inter-modal relationships and provide fine-grained insights for traffic management. This study proposes a multi-modal framework for MRT and YouBike data that employs a spatial-temporal module to effectively model the spatiotemporal dependencies in the data. Evaluations on real-world datasets demonstrate the framework outperforms benchmark methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Traffic congestion

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Debris cloud structure and hazardous fragments distribution under hypervelocity yaw impact

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Defence technology（防务技术） 2023年第9期27卷 169-183页

作者： Chenyang Wu Qi-Guang He Xiaowei Chen Chunbo Zhang Zebin Shen State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyHaidian DistrictBeijing100081China School of Mechatronical Engineering Beijing Institute of TechnologyHaidian DistrictBeijing100081China Institute of Systems Engineering China Academy of Engineering PhysicsMianyangSichuan621999China

This study investigates how the debris cloud structure and hazardous fragment distribution vary with attack angle by simulating a circular cylinder projectile hypervelocity impinging on a thin plate using the finite element-smoothed particle hydrodynamics(FE-SPH)adaptive *** on the comparison and analysis of the experimental and simulation results,the FE-SPH adaptive method was applied to address the hypervelocity yaw impact problem,and the variation law of the debris cloud structure with the attack angle was *** screening criterion of the hazardous fragment at yaw impact is given by analyzing the debris formation obtained by the FE-SPH adaptive method,and the distribution characteristics of hazardous fragments and their relationship with the attack angle are ***,the velocity space was used to evaluate the distribution range and damage capability of asymmetric hazardous *** maximum velocity angle was extended from fully symmetrical working conditions to asymmetrical cases to describe the asymmetrical debris cloud distribution *** this range,the energy density was calculated to quantitatively analyze how much damage hazardous fragments inflict on the rear *** results showed that the number of hazardous fragments generated by the case near the 35°attack angle was the largest,the distribution range was the smallest,and the energy density was the *** results suggest that in this case,debris cloud generated by the impact had the strongest damage to the rear plate.

关键词： Debris cloud Hypervelocity yaw impact FE-SPH adaptive method Velocity space Hazardous fragments distribution

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Smart Surveillance Object Detection System 3

Smart Surveillance Object Detection System

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chaudhary, Gaurav Pathak, Shreya Choudhary, Vikas Chauhan, Love Tripathi, Abhishek N School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350372120

This paper presents an integrated Internet of Things based approach to smart surveillance and urban parking management systems, emphasizing robust object detection and precise parking occupancy monitoring. Our solution adopts cost-effective hardware like the ESP32 CAM module, leveraging its built-in camera and powerful processor for real-time image processing. For surveillance applications, we implement a custom object detection model built with OpenCV, a renowned Python library. This model, finely-tuned for efficient person detection, runs directly on the ESP32 CAM, eliminating the need for external processing units. Additionally, the ESP32 CAM's Wi-Fi connectivity enables seamless integration with existing networks, simplifying deployment. This integrated approach, combining the cost-effectiveness of ESP32 CAM modules with the image processing capabilities of OpenCV, offers a comprehensive solution for smart surveillance and urban parking management. By utilizing readily available hardware and open-source software, the system promotes widespread adoption and affordability, making it a valuable tool for creating smarter and more sustainable cities. © 2024 IEEE.

关键词： Computer vision

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