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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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Harnessing the Power of Blockchain to Create a Decentralized E-Voting Infrastructure for Modern Democracies 7

Harnessing the Power of Blockchain to Create a Decentralized...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Aswin Kumar, E. Kumar Parihar, Aman Gowtham, R. Gadli, Ibrahim Prasath, R. Kumar, Ashok KCG College of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372816

In the contemporary age of digital transformation, it is imperative to safeguard the authenticity and confidentiality of electoral processes to uphold democratic ideals. The conventional methods of voting often face various challenges, including fraud, tampering, and lack of transparency. This academic research offers a detailed exploration and idea of online voting system that utilizes blockchain technology. By capitalizing on the centralized and mutable characteristics of blockchain, the proposed system aims to hinder the security, transparency, and reliability of the voting process. By thoroughly examining the fundamental aspects of blockchain, the architecture of the system, security mechanisms, and potential applications, this paper delivers valuable insights into the transformative capabilities of blockchain-based online voting systems. Through its examination of crucial concerns encompassing electoral governance, like data integrity and voter anonymity, this investigation contributes to the advancement of democratic practices in the digital era. © 2024 IEEE.

关键词： Decentralized finance

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AI-Enhanced Image Translation for Seamless Communication in Global Tourism

AI-Enhanced Image Translation for Seamless Communication in ...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Rubasri, S. Shafiur Rahman, N.F. Syed Khalid, J. Vignesh Balaji, R. Prasath, R. Krishna, B.V. Kcg College Of Technology Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

An inventive program called Jargon Lens was created to meet the various language requirements of international visitors. AI-enhanced image translation technology, jargon lens empowers travelers to effortlessly overcome language barriers by seamlessly translating text in images to their preferred language. The core of this project is to create a user-friendly mobile application that can recognize and translate text within images accurately and in real-time. This will enable users to easily understand and communicate in different languages by simply using the Smartphone camera, users can scan or upload and translate text in real-time, ensuring a swift and accurate understanding of their surroundings. Beyond personal advantages, Jargon Lens supports constructive encounters that promote understanding and cultural interchange between visitors and local populations. As a technological solution, Jargon Lens aligns with the evolving landscape of global tourism, where diversity and inclusivity are paramount. This technology combine's computer vision such as optical character recognition (OCR). OCR algorithms analyze the image to recognize and extract the textual content and natural language processing (NLP) to perform the translation. In conclusion, Jargon Lens emerges as a transformative tool for tourists, offering a sophisticated yet easy-to-use solution for navigating language barriers. With its focus on real-time multilingual image translation, Jargon Lens stands as a key enabler for enriched and hassle-free travel experiences, bridging communication gaps in a rapidly evolving global tourism ecosystem. © 2024 IEEE.

关键词： Optical character recognition

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A New Approach for an Introduction to computer Networking Course: A Case Study 5

A New Approach for an Introduction to Computer Networking Co...

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5th International Conference on Information technology and Education technology, ITET 2024

作者： Vogel, Joyce Bouhnik, Dan Jerusalem College of Technology Computer Science and Software Engineering Jerusalem Israel

ISBN: (纸本)9798331540425

The course Introduction to computer Networks (ICN) has become one of the most vital courses in computer science and Software engineering degrees and clearly is an imperative course for a degree in computer networking. However, despite its importance, the course suffers from many issues which lead to a high failure rate and low success. Even students who pass the course have problems understanding key concepts in data communication. One of the key issues is the theoretical nature of the material of the course. Many of the concepts are abstract and dry, which the students find hard to understand and boring. At the Jerusalem college of technology (JCT), we encountered such problems with our networking course. We therefore decided to suspend the course for a year, research successful networking courses, and apply our findings to a revised networking course. One of the strategic changes that was made was to introduce more practical material into the course. Top-down approach to networking replaced a bottom-up approach, hands-on lab were added to the course, and online quizzes were included. This paper presents some of our findings and changes that were made to the networking course. The problems that were present in our original version of the ICN course are not unique to the JCT but are common to networking courses throughout higher education institutions. We therefore believe that the findings of our research can benefit other faculty who wish to improve their networking courses. © 2024 IEEE.

关键词： Students

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A Speech Spoofing Detection Method Based on Feature Fusion and Residual Attention 24

A Speech Spoofing Detection Method Based on Feature Fusion a...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Lan, Haiyan Dong, Linfan Wang, Yuhua Li, Yike Jiang, Dongxu Han, Zhiying College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400710865

The emergence of various types of deception attacks presents a substantial risk to Automatic Speaker Verification (ASV) systems, and an increasing number of experts and scholars are focusing on the research of voice deception detection. This paper introduces an innovative approach for detecting voice deception that addresses the accuracy and generalization limitations of traditional methods. The introduced method is based on spectral-temporal feature fusion, which complements the acoustic features captured by different channels to obtain more comprehensive speaker information. It then utilizes multi-channel fused spectrogram features, applying a residual attention learning mechanism to assign varying levels of attention to the spectrogram features from different channels. The method further leverages an improved ResNet network to learn more refined acoustic information for classification. The method attained an EER of 3.60 and a t-DCF score of 0.0931 on the LA logical access corpus of ASVspoof 2019, which represent improvements of 55.5% and 56.0%, respectively, over the best performing baseline system. © 2024 Copyright held by the owner/author(s).

关键词： Spectrographs

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Dictionary-Assisted Chinese Nested Named Entity Recognition 38th

Dictionary-Assisted Chinese Nested Named Entity Recognition

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38th CCF National Conference of computer Applications, CCF NCCA 2023

作者： Wang, Ye Ding, Tongtong Li, Lijie College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9789819987634

Chinese Named Entity Recognition is a challenging task, made even more difficult by the presence of nested entity structures. Previous work on Nested Named Entity Recognition focused only on exploiting internal contextual information, while ignoring the use of external information. In this paper, we propose a dictionary-assisted Chinese Nested Named Entity Recognition model, called KBCNER. Our model uses the dictionary to obtain matching words, combines characters and phrases into character-phrase pairs, and integrates them into BERT. By doing so, we can extract richer semantic information from Chinese phrases than from a single character. The use of external information from the dictionary enhances the features of our model and obtains richer semantics. To avoid constraints from specific-length enumerations, we use bi-affine structures to obtain a global view of spans. We also model local interactions between spans using a Convolutional Neural Network (CNN), taking advantage of the spatial correlation between adjacent spans. Finally, we adopt the idea of contrastive learning based on R-drop to enhance the model's robustness. Experimental results demonstrate that our model achieves excellent performance on multiple datasets. By introducing external information, we improve the performance of the model, highlighting the significance of external information for Chinese Nested Named Entity Recognition. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Neural network approaches for rumor stance detection: Simulating complex rumor propagation systems

Neural network approaches for rumor stance detection: Simula...

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作者： Li, Hao Yang, Wu Wang, Wei Wang, Huanran College of Computer Science and Technology Harbin Engineering University Harbin China

This research introduces a comprehensive suite of neural network models designed to tackle the challenging task of rumor stance detection within the framework of simulating complex rumor propagation systems. Our objective centers on accurately modeling the intricate structures of rumor dialogues and propagation patterns to identify user stances—whether they are in support, denial, questioning, or commenting on rumors. Unlike conventional methods that rely on simplistic keyword targeting and fail in the nuanced context of social networks, our models delve into the complexities of dialogue and propagation structures, offering a more precise and insightful analysis of rumor dynamics. In addressing the simulation and modeling of complex systems, our approach specifically focuses on the elaborate interaction networks that underpin rumor spread and reception. While our methodology does not directly engage with brain-like computing paradigms, it reflects a similar level of sophistication in handling layered and complex information flows, analogous to cognitive processes in understanding and interpreting human communications. Employing a hierarchical attention mechanism, our models adeptly parse through multitiered dialogue sequences, effectively distinguishing between various indicators of user stances. This allows for a nuanced and detailed representation of the rumor ecosystem, significantly enhancing the accuracy of stance detection. Through rigorous testing on diverse datasets, our approach has demonstrated superior performance over existing models, thereby establishing a new benchmark in the field. © 2024 John Wiley & Sons Ltd.

关键词： Neural network models

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A Multi-Perspective Recommendation Model Based on Knowledge Graph 7

A Multi-Perspective Recommendation Model Based on Knowledge ...

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7th International Conference on Robotics, Control and Automation engineering, RCAE 2024

作者： Wang, Ziqi Zhang, Jun Wuhan Institute of Technology College of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798350355642

A knowledge graph is a graph structure that represents and organizes knowledge, where nodes represent entities and edges represent the relationship between entities. The model uses the rich information in the knowledge graph, including entity properties, relationships, and semantic similarity, to improve the recommendation effect. However, the traditional knowledge graph is difficult to mine the user intention, so the recommendation effect is limited. In order to solve this problem, the MKGN model proposed in this paper transmits the collaboration signal and knowledge signal through the collaborative knowledge perception module, and then obtains the user and the project embedding through the knowledge attention mechanism;introduces the user interest into the potential vector of the interest module, builds the user-interest-item triplet, obtains the user and combines the user and the project embedding together for the final recommendation score. The model is compared with the baseline algorithm on two public datasets, and the model has better performance than the baseline algorithm model. © 2024 IEEE.

关键词： Knowledge graph

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Real-Time Challenges and Opportunities for an Effective Resource Management in Multi-cloud Environment

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SN computer science 2024年第2期5卷 1-8页

作者： Basha, H. Anwar Anilkumar, B.H. Swetha, G. Reddy, Raghavendra Manoli, Sunil Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India R R Institute of Technology Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India

As the usage of multi-cloud setups grows, resource management will become a major concern. Because of the dynamic nature of these environments, as well as fluctuating workloads and service-level targets, an effective algorithm for real-time resource allocation and optimisation is required. The Adaptive Multi-Cloud Resource Management Algorithm (AMCRMA) is proposed in this study to handle the obstacles and capitalise on the benefits of resource management in a multi-cloud context. To ensure optimal resource utilisation, cost effectiveness, and service performance, AMCRMA incorporates real-time monitoring, workload prediction, and intelligent resource allocation. Experiment results show that AMCRMA is successful in adapting to changing situations and managing resources in real time, resulting in enhanced resource utilisation and overall performance in multi-cloud systems. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AMCRMA Cloud federation Cloud orchestration Data management Intelligent resource allocation Resource management

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Enhanced Machine Learning Model for Heart Disease Prediction with Naive Bayes, Warm, and Personalized Health Recommendations 2

Enhanced Machine Learning Model for Heart Disease Prediction...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Karthik, R. Divya, P. Akash, R. Akshy Sakthi, M. Aparajith, G. Dhanush Karthik, K.S. Sri Krishna College of Technology Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350349221

This study presents a comprehensive approach to predicting cardiac illness through Weighted Association Rule Mining (WARM) and Naive Bayes (NB) algorithms. Addressing data preprocessing challenges like missing values and outliers, it employs a diverse dataset encompassing crucial health attributes such as age, gender, blood pressure, and cholesterol levels. Alongside model training and performance evaluation, a novel health recommendation feature is introduced, leveraging predictive models to offer personalized lifestyle guidance for heart health optimization. This enhancement underscores the study's commitment to holistic healthcare solutions, bridging predictive analytics with actionable insights for proactive cardiovascular care. © 2024 IEEE.

关键词： Blood pressure

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