信息工程最新文献

The implementation of blockchain technology in integrated IoT networks for constructing scalable Intelligent Transportation Systems (ITSs) in India has the potential to revolutionize the way we approach transportation. By leveraging the power of IoT and blockchain, we can create a highly secure, transparent, and efficient system that can transform the way we move people and goods. India, one of the world’s most populous countries, has a highly congested and inefficient transportation system that often leads to delays, accidents, and waste of time and resources. The integration of IoT and blockchain can help address these issues by enabling real-time monitoring, tracking, and optimization of traffic flows, thereby reducing congestion, improving safety, and increasing the overall efficiency of the transportation system. This paper explores the potential of blockchain technology in the context of integrated IoT networks for constructing scalable ITS systems in India. The methodology followed is to develop a proof-of-concept blockchain-based application for ITS, implement the blockchain solution into the existing ITS infrastructure, and ensure proper integration and compatibility with other systems. Conduct thorough research and maintenance to ensure the reliability and sustainability of such blockchain-based systems. This research discusses the various benefits and challenges of this approach and the various applications of this technology in the transportation sector, including the green sustainability concept. The results find various ways in which such implementations of blockchain and IoT-Machine Learning (IoT-ML) can revolutionize transportation systems.

Engine data management is of great significance for ensuring data security and sharing, as well as facilitating multi-party collaborative learning. Traditional data management approaches often involve decentralized data storage that is vulnerable to tampering, making it challenging to conduct multi-party collaborative learning under privacy protection conditions and fully leverage the value of data. Moreover, data with compromised integrity can lead to incorrect results if used for model training. Therefore, this paper aims to break down data sharing barriers and fully utilize decentralized data for multi-party collaborative learning under privacy protection conditions. We propose a trustworthy engine data management method based on blockchain technology to ensure data immutability and non-repudiation. To address the issue of limited data samples for some users resulting in poor model performance, we introduce swarm learning techniques based on centralized machine learning and design a trustworthy data management method for swarm learning, achieving trustworthy regulation of the entire process. We conduct research on engine models under swarm learning based on the NASA open dataset, effectively organizing decentralized data samples for collaborative training while ensuring data privacy and fully leveraging the value of data.

Protecting private data in smart homes, a popular Internet-of-Things (IoT) application, remains a significant data security and privacy challenge due to the large-scale development and distributed nature of IoT networks. Recently, smart healthcare has leveraged smart home systems, thereby compounding security concerns in terms of the confidentiality of sensitive and private data and by extension the privacy of the data owner. However, proof-of-authority (PoA)-based blockchain distributed ledger technology (DLT) has emerged as a promising solution for protecting private data from indiscriminate use and thereby preserving the privacy of individuals residing in IoT-enabled smart homes. This review elicits some concerns, issues, and problems that have hindered the adoption of blockchain and IoT (BCoT) in some domains and suggests requisite solutions using the aging-in-place scenario. Implementation issues with BCoT were examined as well as the combined challenges BCoT can pose when utilised for security gains. The study discusses recent findings, opportunities, and barriers, and provides recommendations that could facilitate the continuous growth of blockchain applications in healthcare. Lastly, the study explored the potential of using a PoA-based permission blockchain with an applicable consent-based privacy model for decision-making in the information disclosure process, including the use of publisher-subscriber contracts for fine-grained access control to ensure secure data processing and sharing, as well as ethical trust in personal information disclosure, as a solution direction. The proposed authorisation framework could guarantee data ownership, conditional access management, scalable and tamper-proof data storage, and a more resilient system against threat models such as interception and insider attacks.

Background

Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications, particularly in visual recognition tasks such as image and video analyses. There is a growing interest in applying this technology to diverse applications in medical image analysis. Automated three-dimensional Breast Ultrasound is a vital tool for detecting breast cancer, and computer-assisted diagnosis software, developed based on deep learning, can effectively assist radiologists in diagnosis. However, the network model is prone to overfitting during training, owing to challenges such as insufficient training data. This study attempts to solve the problem caused by small datasets and improve model detection performance.

Methods

We propose a breast cancer detection framework based on deep learning (a transfer learning method based on cross-organ cancer detection) and a contrastive learning method based on breast imaging reporting and data systems (BI-RADS).

Results

When using cross organ transfer learning and BIRADS based contrastive learning, the average sensitivity of the model increased by a maximum of 16.05%.

Conclusion

Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced, and contrastive learning method based on BI-RADS can improve the detection performance of the model.

This research paper seeks to examine the possibilities of blockchain technology. For use in the field of restaurant food tracking and safety. Public health risks and economic costs are at stake when foodborne illness outbreaks occur, making food safety a top priority in the food industry. It can be difficult to quickly identify and address possible concerns about using traditional food traceability systems due to inefficiencies, data discrepancies, and a lack of transparency. In this study, we introduce a novel blockchain-based system developed especially for the purpose of tracking restaurant food. Blockchain decentralised consensus, immutability, and smart contracts are put to use in this system to provide trustworthy and transparent traceable infrastructure. Real-time monitoring and data collection along the food supply chain become possible when the blockchain architecture is combined with the Internet of Things (IoT) devices and RFID technology. We show that our proposed blockchain-based traceability solution is practical and efficient through a thorough assessment and validation procedure. The outcomes show that the system not only improves data quality and reliability but also drastically decreases the time and resources needed for food traceability. In addition, patrons are more likely to return to eateries that place a premium on food safety when they are given more information about the establishment’s practises. Additionally, we discuss scalability, data privacy, and interoperability concerns that may arise in future implementations and provide some initial ideas for overcoming these issues.

Background

The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis. Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis, thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis

Methods

In total, 623 eligible patients were recruited from two medical institutions. Seven deep learning models, namely Alex, GoogLeNet, Resnet18, Resnet101, Vgg16, Vgg19, and MobileNetv3 (small), were utilized to extract deep image histological features. The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient (r ≥ 0.9) and Least Absolute Shrinkage and Selection Operator. Eleven machine learning methods, namely Support Vector Machine, K-nearest neighbor, Random Forest, Extra Trees, XGBoost, LightGBM, Naive Bayes, AdaBoost, Gradient Boosting Decision Tree, Linear Regression, and Multilayer Perceptron, were employed to construct classification prediction models for the filtered final features. The diagnostic performances of the models were assessed using various metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Calibration and decision-curve analyses were also performed.

Results

The present study demonstrated that using deep radiomic features extracted from Vgg16, in conjunction with a prediction model constructed via a linear regression algorithm, effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer. The performance of the model was evaluated based on various metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, which yielded values of 0.808, 0.834, 0.851, 0.745, 0.829, and 0.776, respectively. The validation set of the model was assessed using clinical decision curves, calibration curves, and confusion matrices, which collectively demonstrated the model's stability and accuracy

Conclusion

In this study, information on the deep radiomics of Vgg16 was obtained from computed tomography images, and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.