Automatic identification of drug sensitivity of cancer cell with novel regression-based ensemble convolution neural network model

Gadde, Sridevi; Charkravarthy, A. S. N.; Satyanarayana, S.; Murali, M.

doi:10.1007/s00500-022-07098-5

Cited by 5 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The former estimates the internal network traffic or packet loss rate by using windows or other means to count the relevant network information at the source end. The latter is to judge whether there is congestion in the network according to the feedback received from the user and then adjust the transmission bit rate from the source [11]. Although this indirect method can also solve the problem to some extent, it is ultimately limited by the network architecture.…”

Section: Methodsmentioning

confidence: 99%

Design and Implementation of Multimedia Network Intelligent Control Robot Based on Software Definition

Zhang

2022

Journal of Sensors

View full text Add to dashboard Cite

In order to solve the problem that the delay of wireless network and complex operating environment affects the stability and operating performance of teleoperation system, a method of intelligent control robot based on multimedia network defined by software is proposed in this paper. In the network environment established based on the software definition, the gain of the system control is increased according to the network delay to improve the operating performance of the system, and the output of parameters is dynamically adjusted to adapt to the stability of the system in complex environment. The experimental results show that the robot control system can obtain the best control stability by continuously adjusting the relevant parameters. After the simulation test, the final setting is k p = 0.8 , k i = 0.001 , k d = 0 . Conclusion. Based on the intelligence of gain scheduling control algorithm, the control effect of fuzzy control can be significantly improved when the network delay is large.

show abstract

Section: Methodsmentioning

confidence: 99%

Design and Implementation of Multimedia Network Intelligent Control Robot Based on Software Definition

Zhang

2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…e input and output quantities of the BP neural network output layer are, respectively, shown in the following formulas: net (3) i (k) � 􏽘 q i�0 w (3) li O (2) i (k), ( 5)…”

Section: Bp Neural Network Pid Controllermentioning

confidence: 99%

“…In recent years, under the double background stress of human engineering activities and extreme climate, karst collapse geological disasters have shown a high-frequency trend, and their spatial and temporal distribution, loss degree, depth, and breadth of impact have also undergone new changes. e concealment, abruptness, repetition, and unpredictability are becoming increasingly prominent [2]. roughout the study of karst collapse, the five in one technical and theoretical framework system of "genetic mechanism, identification and evaluation, monitoring and alarm, emergency response and risk management" has been formed.…”

Section: Introductionmentioning

confidence: 99%

Study on Early Warning of Karst Collapse Based on the BP Neural Network

Chen

2022

Journal of Chemistry

View full text Add to dashboard Cite

In order to comprehensively grasp the dynamics of karst collapse, promote the comprehensive prevention and control level of karst collapse, and prevent secondary disasters caused by lava collapse, this study presents a method of karst collapse early warning based on the BP neural network. This method does not need to set the sliding surface in the finite element calculation model. The stress of the sliding surface is fitted according to the spatial stress relationship of the deep karst layer through the improved BP neural network PID control algorithm and BP neural network algorithm, which avoids the modeling and mesh generation of the complex sliding block and has good accuracy and ease of use. According to the basic theory of the BP neural network, the calculation formulas of multilayer feedforward and error back propagation processes are derived, and the two-dimensional and three-dimensional finite element models of gravity dams without and with sliding blocks are established, respectively. Finally, according to the common formulas of viscoelastic artificial boundary and equivalent load, the two-dimensional and three-dimensional input programs of the karst fluid state are compiled, and a neural network early warning model is obtained. The experimental results show that the process karst state simulated by the algorithm is very close to the actual situation, and the minimum value of antisliding coefficient and its occurrence time can be accurately predicted, with an error range of less than 3%. Conclusion. BP neural network prediction can effectively predict karst collapse, with higher prediction accuracy, and can effectively simulate the actual collapse risk.

show abstract

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

Singh¹,

Chandrasekar²,

Paudel³

et al. 2023

Biomedicine & Pharmacotherapy

View full text Add to dashboard Cite

Automatic identification of drug sensitivity of cancer cell with novel regression-based ensemble convolution neural network model

Cited by 5 publications

References 34 publications

Design and Implementation of Multimedia Network Intelligent Control Robot Based on Software Definition

Design and Implementation of Multimedia Network Intelligent Control Robot Based on Software Definition

Study on Early Warning of Karst Collapse Based on the BP Neural Network

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

Contact Info

Product

Resources

About