RETRACTED ARTICLE: Research on optimization of scientific research performance management based on BP neural network

Chen, Limin; Jagota, Vishal; Kumar, Ajit

doi:10.1007/s13198-021-01263-z

Cited by 45 publications

(23 citation statements)

References 37 publications

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“…Tragically, that by itself does not ensure union of the solution space for the student network and the teacher network in a similar area. The convergence of the student network may be completely different from the convergence of the teacher network [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization

Chaudhury

Shelke²,

et al. 2021

Computational and Mathematical Methods in Medicine

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Breast cancer is the most common invasive cancer in women and the second main cause of cancer death in females, which can be classified benign or malignant. Research and prevention on breast cancer have attracted more concern of researchers in recent years. On the other hand, the development of data mining methods provides an effective way to extract more useful information from complex databases, and some prediction, classification, and clustering can be made according to the extracted information. The generic notion of knowledge distillation is that a network of higher capacity acts as a teacher and a network of lower capacity acts as a student. There are different pipelines of knowledge distillation known. However, previous work on knowledge distillation using label smoothing regularization produces experiments and results that break this general notion and prove that knowledge distillation also works when a student model distils a teacher model, i.e., reverse knowledge distillation. Not only this, but it is also proved that a poorly trained teacher model trains a student model to reach equivalent results. Building on the ideas from those works, we propose a novel bilateral knowledge distillation regime that enables multiple interactions between teacher and student models, i.e., teaching and distilling each other, eventually improving each other’s performance and evaluating our results on BACH histopathology image dataset on breast cancer. The pretrained ResNeXt29 and MobileNetV2 models which are already tested on ImageNet dataset are used for “transfer learning” in our dataset, and we obtain a final accuracy of more than 96% using this novel approach of bilateral KD.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization

Chaudhury

Shelke²,

et al. 2021

Computational and Mathematical Methods in Medicine

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“…The present world with a fast-growing global economy and the latest advancements in technology has led to the industry 4.0 revolution [ 1 ]. The outbreak of COVID-19 and many other chronic diseases has been observed in the last decade.…”

Section: Introductionmentioning

confidence: 99%

Blockchain and IPFS Integrated Framework in Bilevel Fog-Cloud Network for Security and Privacy of IoMT Devices

Mehbodniya

Neware

Vyas

et al. 2021

Computational and Mathematical Methods in Medicine

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Internet of Medical Things (IoMT) has emerged as an integral part of the smart health monitoring system in the present world. The smart health monitoring deals with not only for emergency and hospital services but also for maintaining a healthy lifestyle. The industry 5.0 and 5/6G has allowed the development of cost-efficient sensors and devices which can collect a wide range of human biological data and transfer it through wireless network communication in real time. This led to real-time monitoring of patient data through multiple IoMT devices from remote locations. The IoMT network registers a large number of patients and devices every day, along with the generation of huge amount of big data or health data. This patient data should retain data privacy and data security on the IoMT network to avoid any misuse. To attain such data security and privacy of the patient and IoMT devices, a three-level/tier network integrated with blockchain and interplanetary file system (IPFS) has been proposed. The proposed network is making the best use of IPFS and blockchain technology for security and data exchange in a three-level healthcare network. The present framework has been evaluated for various network activities for validating the scalability of the network. The network was found to be efficient in handling complex data with the capability of scalability.

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“…Also, they discussed the management tree and risk tree as the risk management method of the enterprise and analyzed the change and energy transfer to pursue the enterprise. Various other researchers [ 11 – 13 ] have used and discussed different techniques/methods for analysis and evaluation which can also be employed for risk assessment and evaluation. The available literature reveals that, at present, the research on risk management methods in China is still in its infancy.…”

Section: Introductionmentioning

confidence: 99%

Environmental Health Risk Evaluation Model for Coastal Chemical Industry

Zhao

Zhang

Niu

et al. 2021

Journal of Healthcare Engineering

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There are numerous uncertainties associated with environmental health risk assessment (EHRA), and it is unavoidable to apply the best models and information available to save human lives. The EHRA is a method for determining the type and likelihood of adverse health effects on people who are exposed to chemicals in the workplace. To address the environmental health problems caused by harmful gas leakage and water pollution generated by the coastal regional chemical industry, a novel EHRA model for the coastal chemical industry has been developed. The premise of the Gauss plume diffusion model is used to define the model’s parameters and the evaluation criterion for harmful gas concentration health risk. The EHRA model is evaluated against the leakage of harmful gases and consists of three steps. The first step is to identify the threat posed by the chemical industry in the coastal region; the second step is to quantify the risk; the third step is to develop a model for assessing water-related environmental health risk. The water-related environmental health assessor analyzes the pollutant variables and parameters of the assessment model to estimate the health risk caused by dangerous compounds in the water, using the assessment model of chemical carcinogen health risk and noncarcinogen health risk Type B. The experiments’ findings suggest that the model can effectively assess the dangers to human health from hazardous gases and heavy metals in the water bodies of chemical factories in coastal communities.

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RETRACTED ARTICLE: Research on optimization of scientific research performance management based on BP neural network

Cited by 45 publications

References 37 publications

A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization

A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization

Blockchain and IPFS Integrated Framework in Bilevel Fog-Cloud Network for Security and Privacy of IoMT Devices

Environmental Health Risk Evaluation Model for Coastal Chemical Industry

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