A Privacy-Enhanced Retrieval Technology for the Cloud-Assisted Internet of Things

Wang, Tian; Quan, Yang; Shen, Xuewei; Gadekallu, Thippa Reddy; Wang, Weizheng; Dev, Kapal

doi:10.1109/tii.2021.3103547

Cited by 67 publications

(24 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Osteosarcoma metastases, which are typically secondary to hematogenous dissemination and the occurrence of lymph system is extremely rare, have been identified to be significantly associated with poor prognosis ( 1 – 4 , 7 , 20 , 21 ). Comparing to the most common lung metastases, osteosarcoma patients with lymph node involvement have a worse prognosis, suggesting that the invasion of the lymph nodes is an important indicator for the assessment of malignancy stage and the selection of a correct treatment protocol ( 22 , 23 ).Meanwhile, studies points out that FDG-PET and 99m Tc-labeled biomineralization nanoprobe are effective in early diagnosis of metastatic lymph nodes in osteosarcoma ( 24 – 27 ).…”

Section: Discussionmentioning

confidence: 99%

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

Dong

Wang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Background: This study aimed to construct a clinical prediction model for osteosarcoma patients to evaluate the influence factors for the occurrence of lymph node metastasis (LNM).Methods: In our retrospective study, a total of 1,256 patients diagnosed with chondrosarcoma were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database (training cohort, n = 1,144) and multicenter dataset (validation cohort, n = 112). Both the univariate and multivariable logistic regression analysis were performed to identify the potential risk factors of LNM in osteosarcoma patients. According to the results of multivariable logistic regression analysis, A nomogram were established and the predictive ability was assessed by calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA). Moreover, Kaplan-Meier plot of overall survival (OS) was plot and a web calculator visualized the nomogram.Results: Five independent risk factors [chemotherapy, surgery, lung metastases, lymphatic metastases (M-stage) and tumor size (T-stage)] were identified by multivariable logistic regression analysis. What's more, calibration plots displayed great power both in training and validation group. DCA presented great clinical utility. ROCs curve provided the predictive ability in the training cohort (AUC = 0.805) and the validation cohort (AUC = 0.808). Moreover, patients in LNN group had significantly better survival than that in LNP group both in training and validation group.Conclusion: In this study, we constructed and developed a nomogram with risk factors, which performed well in predicting risk factors of LNM in osteosarcoma patients. It may give a guide for surgeons and oncologists to optimize individual treatment and make a better clinical decision.

show abstract

Section: Discussionmentioning

confidence: 99%

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

Dong

Wang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Additionally, a new method of secure arrangement based on matrix eigenvalue calculation was proposed in 16 . Moreover, the authors in 17 proposed enhanced retrieval models between the IoT and the cloud.…”

Section: Discussionmentioning

confidence: 99%

SVBE: searchable and verifiable blockchain-based electronic medical records system

Alrebdi

Alabdulatif

Iwendi

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Central management of electronic medical systems faces a major challenge because it requires trust in a single entity that cannot effectively protect files from unauthorized access or attacks. This challenge makes it difficult to provide some services in central electronic medical systems, such as file search and verification, although they are needed. This gap motivated us to develop a system based on blockchain that has several characteristics: decentralization, security, anonymity, immutability, and tamper-proof. The proposed system provides several services: storage, verification, and search. The system consists of a smart contract that connects to a decentralized user application through which users can transact with the system. In addition, the system uses an interplanetary file system (IPFS) and cloud computing to store patients’ data and files. Experimental results and system security analysis show that the system performs search and verification tasks securely and quickly through the network.

show abstract

“…Nowadays, data mining and data management for many industries are gradually approaching the application of the Internet of Things (IoT), yielding such as mobile IoT ( 36 , 37 ), financial IoT, medical IoT ( 38 ), cloud-assisted IoT ( 39 ), vehicular IoT ( 40 ), and industrial IoT ( 41 , 42 ). As is known to all, the IoT is a kind of effective tool or platform to integrate multi-domain data and schedule business flows.…”

Section: Discussionmentioning

confidence: 99%

Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare

Yan

Zhang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Agricultural is an indispensably public healthcare industry for human beings at any time and smart management of it is of great significance. Since substantial technical advance relies on long-term efforts and continuous progress, reasonably scheduling the distribution of agricultural products acts as a key aspect of smart public healthcare. The most intuitive factor affecting the distribution of agricultural products is its dynamic price. Forecasting price fluctuations in advance can optimize the distribution of agricultural products and pave the way to smart public healthcare. Most researchers study the prices of various agricultural products separately, without considering the interaction of different agricultural products in the time dimension. This study introduces a typical deep learning model named graph neural network (GNN) for this purpose and proposes deep data analysis-based agricultural products management for smart public healthcare (named GNN-APM for short). The highlight of GNN-APM is to take latent correlations among multiple types of agricultural products into consideration when modeling evolving rules of price sequences. A case study is set up with the use of real-world data of the agricultural products market. Simulative results reveal that the designed GNN-APM functions well.

show abstract

A Privacy-Enhanced Retrieval Technology for the Cloud-Assisted Internet of Things

Cited by 67 publications

References 21 publications

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

SVBE: searchable and verifiable blockchain-based electronic medical records system

Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare

Contact Info

Product

Resources

About