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Convergence of various computer-aided systems for breast tumor diagnosis: a comparative insight

Multimedia Tools and Applications 2025年第16期84卷 16709-16756页

作者： Singh, Saket Kumar Patnaik, K. Sridhar Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi835215 India

Breast Cancer, with an expected 42,780 deaths in the US alone in 2024, is one of the most prevalent types of cancer. The death toll due to breast cancer would be very high if it were to be totaled up globally. Early detection of breast cancer is the only way to decrease the mortality caused by it. In order to diagnose breast cancer, even the most competent and qualified pathologists and radiologists have to examine hundreds of high-resolution images, which is a massive burden on them. Compared to the number of cases, very few experts are available to manage this burden. Additionally, as humans are more prone to mistakes, the likelihood of finding false positive cases is also high. Numerous AI techniques, including machine learning and deep learning, are ideally suited to address these issues, inspiring many researchers to introduce novel computer-aided detection systems. In this study, we have comprehensively reviewed pre-existing literature aimed at developing computer-aided systems based on using machine learning, deep learning, and vision transformers to identify and classify breast cancer. We have discussed numerous imaging modalities for detecting breast cancer, along with the widely used data pre-processing approaches, machine learning and deep learning models, as well as ensemble learning methods suitable for the task. Popular datasets and their sources are also listed for future referencing. Finally, we have identified a few gaps and addressed potential future research directions with an intent of aiding researchers select approaches tailored to case-specific needs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer aided diagnosis

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MAG-Net: Multi-fusion network with grouped attention for retinal vessel segmentation

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Mathematical Biosciences and engineering 2024年第2期21卷 1938-1958页

作者： Jiang, Yun Chen, Jie Yan, Wei Zhang, Zequn Qiao, Hao Wang, Meiqi College of Computer Science and Engineering Northwest Normal University Lanzhou730070 China

Retinal vessel segmentation plays a vital role in the clinical diagnosis of ophthalmic diseases. Despite convolutional neural networks (CNNs) excelling in this task, challenges persist, such as restricted receptive fields and information loss from downsampling. To address these issues, we propose a new multi-fusion network with grouped attention (MAG-Net). First, we introduce a hybrid convolutional fusion module instead of the original encoding block to learn more feature information by expanding the receptive field. Additionally, the grouped attention enhancement module uses high-level features to guide low-level features and facilitates detailed information transmission through skip connections. Finally, the multi-scale feature fusion module aggregates features at different scales, effectively reducing information loss during decoder upsampling. To evaluate the performance of the MAG-Net, we conducted experiments on three widely used retinal datasets: DRIVE, CHASE and STARE. The results demonstrate remarkable segmentation accuracy, specificity and Dice coefficients. Specifically, the MAG-Net achieved segmentation accuracy values of 0.9708, 0.9773 and 0.9743, specificity values of 0.9836, 0.9875 and 0.9906 and Dice coefficients of 0.8576, 0.8069 and 0.8228, respectively. The experimental results demonstrate that our method outperforms existing segmentation methods exhibiting superior performance and segmentation outcomes. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://***/licenses/by/4.0)

关键词： Convolution

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Analysis of IoT Applications in Highly Precise Agriculture Farming

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Recent Patents on engineering 2025年第2期19卷 71-81页

作者： Reddy N, Latha Masila P, Gopinath School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

Background: The IoT (Internet of Things) assigns to the capacity of Device-to-Machine (D2M) connections, which is a vital component in the development of the digital economy. IoT integration with a human being enables real-time decision-making in communication, collaboration, and technology analytics. Furthermore, environmental factors impacting plants, such as air humidity, temperature, air quality index, and soil wetness, are not frequently documented, emphasizing the development of a data monitoring system for future agricultural research and development. Methods: An IoT-based Intelligent Farming System is proposed. An innovative IoT-based intelligent farming system is developed that integrates real-time data monitoring, machine learning algorithms, and IoT technology to address the identified gaps and challenges. Results: In the face of climate change, extreme weather, and environmental constraints, increased food demand must be satisfied. Intelligent agriculture enabled by IoT technology can reduce waste and increase productivity for producers and farmers, from fertilizer use to tractor trips. Conclusion: In conclusion, this patent paper provides insightful and informative commentary on the progress made in technology within the agriculture industry and the challenges that still need to be overcome to achieve optimal outcomes. © 2025 Bentham science Publishers.

关键词： IoT agriculture soil quality weather conditions machine learning environmental constraints

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Exploring the Landscape of Blockchain Technology: History, Applications, Challenges and Future Directions

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Journal of engineering science and Technology Review 2024年第3期17卷 201-216页

作者： Dutta, Debarati Priya, G. School of Computer Science and Engineering VIT Tamil Nadu Vellore632014 India

In several companies throughout the world, blockchain technology is gaining greater attention and implementation. In this article, first of all, we conduct an in-depth survey of blockchain technology, focusing on its history, phases, types, technologies, consensus algorithms, frameworks, and layers. Further, it describes its extensive list of applications for blockchain in several industries, including asset management, stock exchange, healthcare, insurance, digital identity, data storage and management, IoT, supply chain, voting, DNS services, etc. Blockchain has various advantages, including decentralisation, anonymity, immutability, integrity, auditability, and transparency. These features encourage the adoption of blockchain in every sector. In spite of these advantages, it has some challenges which as not letting it to accepted by common people. In this paper, we describe those challenges into two categories: security and attacks on blockchain and explain them thoroughly. Unlike other blockchain documents that focus on topics like cryptocurrencies, IoT, and security, this paper highlights the cutting-edge advancements and widespread implementation of blockchain technology, particularly in areas beyond digital currencies. Finally, it discusses the potential future research areas and objectives for blockchain technology. © 2024 School of science, DUTH. All rights reserved.

关键词： Smart contract

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RLeU-Net: Segmentation of blood vessels in retinal fundus images for Diabetic Retinopathy Screening

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Multimedia Tools and Applications 2025年第9期84卷 6113-6134页

作者： Addanki, Sathyavani Sumathi, D. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

Diabetic Retinopathy (DR) is a primitive cause of blindness in diabetic patients. There are chances for DR in certain cases, due to the damage in the retinal vascular area. It may lead to blindness or loss of vision if DR is not detected. At the final stage, there is a threat of loss of vision. During the initial stage of this chronic disease, it has been observed that no symptoms are required to determine the loss of vision. Hence, there is a need for better and early diagnosis of the blood vessels to prevent vision loss in diabetic patients. The primary task of early diagnosis is to carry out a proper segmentation process. But, segmenting vasculture from retinal photographs is a vital laborious effort. Therefore, blood vessel segmentation of fundus images has grown in favor among researchers. There are many existing methods to process bio-medical retinal images, and most of them are computationally intensive and difficult to deploy in real-time. The objective of this work is to develop a novel and efficient deep learning framework to perform the segmentation with the U-Net as a primary module. The U-Net architecture has been modified so that the model would be lightweight and the model is cost efficient in terms of time and space complexity. The proposed method, DRLeU- Net, is evaluated using a publicly available color retinal fundus images dataset. The model was trained using the k-fold cross-validation technique. The proposed method shows a promising results of 0.988 AUC (Area Under the Curve) and 0.94 IOU (Intersection over Union when compared with the existing *** robust and efficient deep learning framework has been deployed to do the segmentation process efficiently and it has evauated with the expremental results by comparing with the existing methodologies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blood vessels

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An Improved Gannet Optimization Algorithm Based on Opposition-Based Schemes for Feature Selection Problems in High-Dimensional Datasets

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SN computer science 2024年第1期5卷 183页

作者： Avinash, N. Sinha, Sitesh Kumar Shivamurthaiah, M. Department of computer science and engineering Rabindranath tagore university Bhopal India Department of Computer Science and Engineering APS College of Engineering Bangalore India

In the ever-evolving landscape of optimization algorithms for healthcare datasets, this study introduces an innovative fusion of the gannet optimization algorithm (GOA) with advanced opposition-based learning (OBL) techniques, including quasi-OBL, generalized OBL, and partial OBL. The primary novelty of our approach lies in the hybridization of GOA with OBL schemes. While GOA itself draws inspiration from the hunting behavior of gannets, OBL techniques introduce a layer of opposition-based learning that mimics the dialectical nature of opposing forces. By integrating these two distinct paradigms, we create a symbiotic relationship between exploration and exploitation. This synergy empowers our optimization framework to dynamically adapt to the complex landscape of high-dimensional healthcare datasets, underlining its prowess in classification tasks, particularly when employed with support vector machine (SVM) and k-nearest neighbor (KNN) classifiers. Statistical validation further underscores the significance of our results. The p value analysis reveals that our GOBL–GOA approach stands as a statistically superior alternative, making substantial strides in feature selection and classification accuracy. Beyond statistics, our approach excels in practicality, effectively identifying and selecting relevant features to provide accurate insights from complex healthcare data, as demonstrated by precision, recall, and F1-score metrics. In conclusion, our study not only introduces a paradigm shift in feature selection and optimization but also advances the cause of precise disease detection and prognosis, holding immense potential to enhance healthcare outcomes globally. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Feature selection Gannet optimization algorithm High-dimensional dataset Opposition-based learning

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ROUGE-SciQFS: A ROUGE-based Method to Automatically Create Datasets for Scientific Query-Focused Summarization 31

ROUGE-SciQFS: A ROUGE-based Method to Automatically Create D...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Ramirez-Orta, Juan Milios, Evangelos Maguitman, Ana Soto, Axel J. Faculty of Computer Science Dalhousie University Canada Institute for Computer Science and Engineering UNS - CONICET Department of Computer Science and Engineering Universidad Nacional del Sur Argentina

ISBN: (纸本)9798891761964

So far, the task of Scientific Query-Focused Summarization (Sci-QFS) has lagged in development when compared to other areas of Scientific Natural Language Processing because of the lack of data. In this work, we propose a methodology to take advantage of existing collections of academic papers to obtain large-scale datasets for this task automatically. After applying it to the papers from our reading group, we introduce a novel dataset for Sci-QFS composed of 8,695 examples, each one with a query, the sentences of the full text from a paper and the relevance labels for each. After testing several classical and state-of-the-art embedding models on this data, we found that the task of Sci-QFS is far from being solved, although it is relatively straightforward for humans. Surprisingly, we found that classical methods outperformed modern pre-trained Deep Language Models (sometimes by a large margin), showing the need for large datasets to better fine-tune the latter. We share our experiments, data and models at https://***/jarobyte91/rouge_sciqfs. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

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computer Modeling in engineering & sciences 2024年第8期140卷 2035-2051页

作者： Sandeep Dasari Rajesh Kaluri School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The increasing data pool in finance sectors forces machine learning(ML)to step into new *** data has significant financial implications and is *** users data from several organizations for various banking services may result in various intrusions and privacy *** a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global ***,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of *** address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global *** improves the privacy of the local *** analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)***,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and *** experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

关键词： Federated learning FedAvg FedProx flower framework privacy preservation financial sectors

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An efficient secure predictive demand forecasting system using Ethereum virtual machine

IET Blockchain

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IET Blockchain 2024年第S1期4卷 526-542页

作者： Saraswat, Himani Manchanda, Mahesh Jasola, Sanjay Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

Predictive demand forecasting plays a pivotal role in optimizing supply chain management, enabling businesses to effectively allocate resources and minimize operational inefficiencies. This paper introduces a novel approach to enhancing demand forecasting processes by leveraging the Ethereum virtual machine within a blockchain framework. The proposed system capitalizes on the inherent security, transparency, and decentralized nature of blockchain technology to create a secure and efficient platform for predictive demand forecasting. The system leverages the Ethereum virtual machine to establish a secure, decentralized, and tamper-resistant platform for demand prediction while ensuring data integrity and privacy. By utilizing the capabilities of smart contracts and decentralized applications within the Ethereum ecosystem, the proposed system offers an efficient and transparent solution for demand forecasting challenges. The current research focused on Ethereum virtual machine characteristics, features, components, and implementation details. A secured framework for the prediction of demand forecasting systems is proposed. Finally, the authors tried to validate and optimize the gas cost by using distinguished statistics and analysis. © 2024 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Blockchain

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Leveraging dual-blockchain collaboration for logistics supply chain supervision

IET Blockchain

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IET Blockchain 2024年第S1期4卷 570-585页

作者： Lin, Licheng Pan, Senshan Jing, Pujie Song, Xiangmei School of Computer Science and Telecommunication Engineering Jiangsu University Jiangsu Zhenjiang China

Logistics supply chain (LSC), a chain structure that integrates and coordinates all logistics transactions, has become an essential component of the modern logistics industry. By using blockchain, trusted logistics services enable participants to effectively record and track transactions during the logistics process. Current blockchain-based LSC features distributed structure and data privacy requirements, hindering the supervision of logistics transactions. Leveraging emerging dual-blockchain architecture to separate logistics transactions from supervision is a promising direction. However, the dual-blockchain collaboration restricts supervision due to its cross-chain privacy and efficiency. To address these issues, a logistics supply chain supervision scheme based on dual-blockchain collaboration (DBC) is proposed. First, an independent supervision blockchain is constructed to balance the contradiction between distributed structure and supervision requirements. Second, two mechanisms are designed to enhance the privacy and performance of collaborative supervision. The hybrid access control mechanism enables fine-grained supervision for different participants, and the aggregated transaction verification method supports efficient collaboration for logistics transactions. Security analysis and performance evaluation demonstrate the feasibility of DBC in enhancing the security and supervision of logistics data on the dual-blockchain architecture. Experimental results show that the cross-chain supervision overhead of DBC is reduced to (Formula presented.) of the baseline schemes. © 2024 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Cryptography

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