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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Liang, Tailin Yuan, Zhaolin School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Beijing China Research Institute of Advanced Manufacturing Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350378658

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications. © 2024 IEEE.

关键词： Radiation hazards

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Application of Machine Learning Models to Predict e-Learning Engagement Using EEG Data 20

Application of Machine Learning Models to Predict e-Learning...

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20th International Conference on Web Information systems and Technologies, WEBIST 2024

作者： Dritsas, Elias Trigka, Maria Mylonas, Phivos Industrial Systems Institute Athena Research and Innovation Center Patras Greece Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (纸本)9789897587184

The rapid evolution of e-learning platforms necessitates the development of innovative methods to enhance learner engagement. This study leverages machine learning (ML) techniques and models to predict e-learning engagement with the aid of Electroencephalography (EEG). Various ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Networks (NN), were applied to a dataset comprising EEG signals collected during e-learning sessions. Among these models, NN demonstrated the highest accuracy (90%), with precision and F1-score of 88%, a recall of 89%, and an Area Under the Curve (AUC) of 0.92 for predicting engagement levels. The results underscore the potential of EEG-based analysis combined with advanced ML techniques to optimize e-learning environments by accurately monitoring and responding to learner engagement. Copyright © 2024 by SCITEPRESS.

关键词： Adversarial machine learning

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Collective Movement Simulation:Methods and Applications

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Machine Intelligence research 2024年第3期21卷 452-480页

作者： Hua Wang Xing-Yu Guo Hao Tao Ming-Liang Xu School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450001China Engineering Research Center of Intelligent Swarm Systems Ministry of EducationZhengzhou450001China National Supercomputing Center in Zhengzhou Zhengzhou450001China China Ship Development and Design Center Wuhan430060China

Collective movement simulations are challenging and important in many areas,including life science,mathematics,physics,information science and public *** this survey,we provide a comprehensive review of the state-of-the-art techniques for collective movement *** start with a discussion on certain concepts to help beginners understand it more ***,we analyze the intelligence among different collective objects and the emphasis in different ***,we classify existing collective movement simulation methods into four categories according to their effects,namely versatility,accuracy,dynamic adaptability,and assessment feedback ***,we introduce five applications of layout optimization,emergency control,dispatching,unmanned systems,and other derivative ***,we summarize possible future research directions.

关键词： Collective movement simulation multiple objects multiple discipline simulation effect collective intelligence

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Selection of Three-phase Inverter Topology and Optimization of the PCB Layout of a T-type Bridge-Leg for Power Dense Motor Drive Application 4

Selection of Three-phase Inverter Topology and Optimization ...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Ghaderloo, Reza Asrar Singhabahu, Chanaka Resalayyan, Rakesh Khaligh, Alireza Institute for Systems Research University of Maryland Maryland Power Electronics Laboratory Department of Electrical and Computer Engineering College Park United States

ISBN: (纸本)9798350306262

The topology selection plays a key role in minimizing the losses and improving the output waveform quality of an inverter. In addition, increasing the switching frequency of an inverter help to reduce the size of EMI filters resulting in power density improvement. Use of wide-bandgap device (like SiC devices) enables high switching frequency operation in power electronic converters. However, due to low gate charge and small junction capacitance of SiC devices, the SiC-based inverter is more likely to be influenced by side-effects of fast switching transition like undesired ringing, larger voltage overshoot and mistriggerig due to miller-effect. Parasitic inductances of multiple power loops and gate loops within the inverter leg play a key role in these undesired effects, however, a proper design of PCB-layout can reduce these effects. In this paper, a comparison is conducted between a T-type and 2-level inverter topologies for motor drive applications. Furthermore, a placement configuration of components along with the PCB layout are proposed for a T-type leg to improve its switching transition behavior by reducing the power and gate loop parasitic inductances. The parasitic inductances of proposed configuration are estimated using FEA simulation, and the estimated values are used to simulate the switching transition behavior of a T-type leg. Finally, a Double Pulse Test (DPT) is executed to demonstrate the accurate switching transition behaviour of a T-type leg which is designed with the proposed PCB layout and component placement. © 2024 IEEE.

关键词： Silicon carbide

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Data Augmentation using Reverb and Noise in Deep Learning Implementation of Cough Classification

Data Augmentation using Reverb and Noise in Deep Learning Im...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Huq, Saiful Xi, Pengcheng Goubran, Rafik Valdes, Julio J. Knoefel, Frank Green, James R Carleton University Systems and Computer Engineering Ottawa Canada Carleton University Natinoal Research Council Canada Ottawa Canada Carleton University Bruyère Research Institute Systems and Computer Engineering Ottawa Canada National Research Council Canada Digital Technologies Ottawa Canada Bruyère Research Institute Élisabeth Bruyère Hospital Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9781665493840

The interest in automated analysis and classification of cough sounds has increased in recent years, partly due to the worldwide COVID19 pandemic. To train such classification models, a large dataset of cough sounds is needed, however, it remains challenging to find such datasets of cough sounds that have expert-labelled diagnoses of cough types. Data augmentation techniques have been used to train machine learning models given such limited data. Furthermore, augmentation ensures that trained models are invariant to natural transformations of the data, measured from real environments/surroundings. This paper presents a method for classifying wet and dry coughs using a ResNet18 convolutional neural network model. Several forms of spectral data augmentation are investigated including many traditional audio data augmentation methods. A novel form of audio data augmentation is leveraged, where coughs are augmented with varying levels of reverberation and Gaussian noise, during model training. The study found that using a combination of reverb and noise augmentation provided greater improvement than either form of augmentation alone, or traditional augmentations as well, leading to an accuracy of 95%. Use of such a model that has been trained on both reverb- and noise-augmented data is recommended when classifying audio recordings, such as cough sounds, from natural environments outside of laboratory conditions. © 2023 IEEE.

关键词： Classification (of information)

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A Methodology to Evaluate Image Target Tracking Algorithms' Implementation in Different Hardware Architectures 43

A Methodology to Evaluate Image Target Tracking Algorithms' ...

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： De Paulo, Rodrigo Carvalho Duarte, Gabriel C. Leite, Nelson Paiva Oliveira Loubach, Denis S. Aeronautics Institute of Technology Ita Department of Computer Systems SP Sao José dos Campos Brazil Flight Test and Research Institute Ipev Department of Development and Research SP Sao José dos Campos Brazil

ISBN: (纸本)9798350349610

Store separation flight tests are fundamental to ensure the safe separation between the store and the aircraft. However, this activity is considered high-risk, high-cost, and can take days and a large number of devices to be executed. Therefore, any reduction of time or equipment brings advantages in terms of both efficiency and cost. This paper proposes a methodology to evaluate the performance of different tracking algorithms in fixed hardware architectures, such as CPU and GPU, as well as in reconfigurable hardware architectures, i.e. FPGA. Our methodology takes into account important features such as computer performance metrics, object tracking precision and effectiveness, and flight test environment. Our proposal allows a comparison between architecture performance and effectiveness while executing the tracking algorithms with real-time constraints. © 2024 IEEE.

关键词： Reconfigurable architectures

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Ship Platform Access Control Method Based on Zero Trust 7

Ship Platform Access Control Method Based on Zero Trust

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7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Wei, Renlong Li, Guobing Beijing University of Posts and Telecommunications Beijing China Beihang University School of Computer Science and Engineering Beijing China Cssc Systems Engineering Research Institute China

ISBN: (纸本)9798350370782

With the digital transformation of the shipbuilding industry, the interaction between ship platform systems and intelligent equipment has become closer, which has led to the increasingly blurred security boundaries of ship platform systems and increased the risk of illegal access to ship platform resources. At present, ship platforms still mainly rely on static access control methods to protect sensitive resources, which can no longer meet the growing access control needs in a de-bordered environment. In order to meet this challenge, this paper introduces the zero-trust concept into the ship platform access control system. By designing a trust evaluation algorithm and a dynamic access control algorithm, the ship platform permissions can be managed more finely, and experiments are designed to verify the effectiveness of the scheme in access control. © 2024 IEEE.

关键词： Shipyards

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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LEGF-DST:LLMs-Enhanced Graph-Fusion Dual-Stream Transformer for Fine-Grained Chinese Malicious SMS Detection

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computers, Materials & Continua 2025年第2期82卷 1901-1924页

作者： Xin Tong Jingya Wang Ying Yang Tian Peng Hanming Zhai Guangming Ling School of Information and Cybersecurity People’s Public Security University of ChinaBeijing100038China Cyber Investigation Technology Research and Development Center The Third Research Institute of the Ministry of Public SecurityShanghai201204China Department of Cybersecurity Defense Beijing Police CollegeBeijing102202China School of Computer Science Henan Institute of EngineeringZhengzhou451191China

With the widespread use of SMS(Short Message Service),the proliferation of malicious SMS has emerged as a pressing societal *** deep learning-based text classifiers offer promise,they often exhibit suboptimal performance in fine-grained detection tasks,primarily due to imbalanced datasets and insufficient model representation *** address this challenge,this paper proposes an LLMs-enhanced graph fusion dual-stream Transformer model for fine-grained Chinese malicious SMS *** the data processing stage,Large Language Models(LLMs)are employed for data augmentation,mitigating dataset *** the data input stage,both word-level and character-level features are utilized as model inputs,enhancing the richness of features and preventing information loss.A dual-stream Transformer serves as the backbone network in the learning representation stage,complemented by a graph-based feature fusion *** the output stage,both supervised classification cross-entropy loss and supervised contrastive learning loss are used as multi-task optimization objectives,further enhancing the model’s feature *** results demonstrate that the proposed method significantly outperforms baselines on a publicly available Chinese malicious SMS dataset.

关键词： Transformers malicious SMS multi-task learning large language models

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