A Recommendation System Based on AI for Storing Block Data in the Electronic Health Repository

Mani, Vinodhini; Kavitha, C.; Band, Shahab S.; Mosavi, Amir; Hollins, Paul; Palanisamy, Selvashankar

doi:10.3389/fpubh.2021.831404

Cited by 14 publications

(7 citation statements)

References 29 publications

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“…In addition to monitoring traffic conditions, enhancing mobility, and reducing congestion, this research also enhances security in the transportation sector. Moreover, they assist users in drawing traffic forecasts [28] and in advising about departure times for different regions as well as alternative routes or ways to major locations. The aim of this work is to reduce congestion, predict traffic, avoid accidents, etc., using shadow elimination and vehicle classification.…”

Section: Ioopl Implementationmentioning

confidence: 99%

A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

Srividhya

Kavitha

Lai

et al. 2022

Wireless Communications and Mobile Computing

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In the field of intelligent transportation systems (ITS), video surveillance is a hot research topic; this surveillance is used in a variety of applications, such as detecting the cause of an accident, tracking down a specific vehicle, and discovering routes between major locations. Object detection and shadow elimination are the main tasks in this area. Object detection in computer vision is a critical and vital part of object and scene recognition, and its applications are vast in the fields of surveillance and artificial intelligence. Additionally, other challenges arise in regard to video surveillance, including the recognition of text. Based on shadow elevation, we present in this work an inner-outer outline profile (IOOPL) algorithm for detecting the three levels of object boundaries. A system of video surveillance monitoring of traffic can be incorporated into this method. It is essential to identify the type of detected objects in intelligent transportation systems (ITS) to track safely and estimate traffic parameters correctly. This work addresses the problem of not recognizing object shadows as part of the object itself in-vehicle image segmentation. This paper proposes an approach for detecting and segmenting vehicles by eliminating their shadow counterparts using the delta learning algorithm (Widrow-Hoff learning rule), where the system is trained with various types of vehicles according to their appearance, colors, and build types. An essential aspect of the intelligent transportation system is recognizing the type of the detected object so that it can be tracked reliably and the traffic parameters can be estimated correctly. Furthermore, we propose to classify vehicles using a machine learning algorithm consisting of artificial neural networks trained using the delta learning algorithm, a high-performance machine learning algorithm, to obtain information regarding their travels. The paper also presents a method for recognizing the number plate using text correlation and edge dilation techniques. In regard to video text recognition, number plate recognition is a challenging task.

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Section: Ioopl Implementationmentioning

confidence: 99%

A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

Srividhya

Kavitha

Lai

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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“…Besides, methods driven by artificial intelligence (AI) such as deep learning and reinforcement learning will be considered in the formulation of the predictive model, which can help predict specific parameters, hazards, and outcomes ( 45 , 46 ). Because the blockchain is employed to ensure trust among entities, data immutability, availability, and information security, a predictive model that combines AI and blockchain technology to analyze and integrate clinical characteristics of patients will also be further studied ( 47 , 48 ). AI and ML could make predictive models smarter and more interesting.…”

Section: Discussionmentioning

confidence: 99%

A Visualized Dynamic Prediction Model for Overall Survival in Elderly Patients With Pancreatic Cancer for Smart Medical Services

Zhong

Liao

Peng³

et al. 2022

Front. Public Health

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BackgroundPancreatic cancer (PC) is a highly malignant tumor of the digestive system. The number of elderly patients with PC is increasing, and older age is related to a worse prognosis. Accurate prognostication is crucial in treatment decisions made for people diagnosed with PC. However, an accurate predictive model for the prognosis of these patients is still lacking. We aimed to construct nomograms for predicting the overall survival (OS) of elderly patients with PC.MethodsPatients with PC, older than 65 years old from 2010 to 2015 in the Surveillance, Epidemiology, and End Results database, were selected and randomly divided into training cohort (n = 4,586) and validation cohort (n = 1,966). Data of patients in 2016–2018 (n = 1,761) were used for external validation. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent prognostic factors. We used significant variables in the training set to construct nomograms predicting prognosis. The performance of the models was evaluated for their discrimination and calibration power based on the concordance index (C-index), calibration curve, and the decision curve analysis (DCA).ResultsAge, insurance, grade, surgery, radiation, chemotherapy, T, N, and American Joint Commission on Cancer were independent predictors for OS and thus were included in our nomogram. In the training cohort and validation cohort, the C-indices of our nomogram were 0.725 (95%CI: 0.715–0.735) and 0.711 (95%CI: 0.695–0.727), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves showed similar results. The calibration curves showed a high consensus between observations and predictions. In the external validation cohort, C-index (0.797, 95%CI: 0.778–0.816) and calibration curves also revealed high consistency between observations and predictions. The nomogram-related DCA curves showed better clinical utility compared to tumor-node-metastasis staging. In addition, we have developed an online prediction tool for OS.ConclusionsA web-based prediction model for OS in elderly patients with PC was constructed and validated, which may be useful for prognostic assessment, treatment strategy selection, and follow-up management of these patients.

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“…Models that use blockchain for data storage makes the system more scalable, and provides privacy [ 16 ]. Patient-centric data storage and sharing is implemented using chaincode based on blockchain technology in [ 17 , 18 ].…”

Section: Related Workmentioning

confidence: 99%

“…Hyperledger fabric provides a secure and safe service for viewing the history of the drug supply chain. Our fabric utilizes the Byzantine-fault tolerant consensus to ensure safe and reliable communication in an untrusted environment [ 16 , 19 ]. The components of Hyperledger fabric are certificate authority, peer nodes and ordering service.…”

Section: Blockchain-enabled Pharma Supply Chainmentioning

confidence: 99%

Cloud-based blockchain technology to identify counterfeits

2022

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Multi-stakeholder and organizational involvement is an integral part of the medicine supply chain. Keeping track of the activities associated with medical products is difficult when the system is complex. Their complexity limits transparency and data provenance. Deficiencies within existing supply chains result in the counterfeiting of drugs, illegal imports, and inefficient operations. Due to these limitations, product integrity is compromised, resulting in product wastage. Visibility of the entire product supply chain is crucial for the pharmaceutical industry in terms of product safety and reduction of manufacturing costs. The Cloud-based Blockchain-powered architecture of the system provides a platform for addressing the need of pharma-material traceability, data storage, privacy of data, and quality assurance. This framework comprises of the identification of activities through tagging, information sharing in a secure environment; cloud-based storage using an off-chain Interplanetary File System (IPFS) and an on-chain couch DB; and access to this information that is controlled by the system's regulator. Electronic drug records will be accessed via a smart contract in Hyperledger Blockchain. The system assists in identifying false and cross-border products through the manufacturer and country of origin. A scan will identify counterfeit medications, showing that they are unauthorized products which may pose a risk to patients. Our experiments demonstrated the efficiency and usability of the design platform. Finally, we benchmarked the system using Hyperledger Caliper.

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A Recommendation System Based on AI for Storing Block Data in the Electronic Health Repository

Cited by 14 publications

References 29 publications

A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

A Visualized Dynamic Prediction Model for Overall Survival in Elderly Patients With Pancreatic Cancer for Smart Medical Services

Cloud-based blockchain technology to identify counterfeits

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