Performance improvement model for Airlines connectivity system using Network Science

Amiripalli, Shanmuk Srinivas

doi:10.30534/ijatcse/2020/113912020

Cited by 9 publications

(4 citation statements)

References 12 publications

(13 reference statements)

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“…Figure 1 shows us disease diagnosis in neural network. Expert data can be some of the attributes used when describing objects and situations in speech recognition [27]. This section discusses the possibilities for predicting how methods will work based on various training data.…”

Section: Resultsmentioning

confidence: 99%

Using Neural Network for Simulations to Improve the Quality of Disease Diagnosis: Technical Aspects

Gilev¹,

Loginovsky²

2020

IJATCSE

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Mathematical models are important for the processes of cognition and decision-making. They provide a concise representation of significant relationships in the description of objects and situations. Adding new relationships leads to narrowing the scope of applicability of the model. The formula is an example of a compressed description of a potentially infinite set of objects and situations. Knowledge processing is based on the use of mathematical methods. In this case, it is the most thorough, at least from the point of view of strict logic and consistent formalization. To process knowledge, we must present it in some form that is convenient for analysis. Thus, when analyzing data and knowledge, we do not use them directly, but their representations. Mathematical models of objects and phenomena are an effective way of representation. This is now the most powerful method of cognition of processes, objects and phenomena. Modeling is a special way of scientific research. A mathematical model of an object is a mathematical structure interpreted within a given domain.

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Section: Resultsmentioning

confidence: 99%

Using Neural Network for Simulations to Improve the Quality of Disease Diagnosis: Technical Aspects

Gilev¹,

Loginovsky²

2020

IJATCSE

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“…Thus, data pre-processing is considered as an important part. In this work, a selective data processing scheme is introduced which incorporate the outlier information into the subset to obtain the even distribution of features [21], [22]. Later, synthetic minority oversampling technique (SMOTE) shceme is used to balance the training process while controlling the effects of outliers.…”

Section: Background Workmentioning

confidence: 99%

A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory

Kumari

Padmaja²,

Suma

2022

IJEECS

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Hyperglycemia arises due to diabetes mellitus, which is a persistent and life-threatening ailment. In this paper deep convolution neural network can be embedded to long short-term memory networks to recognize early prediction of diabetes and to decrease the complications that can be occurred through diabetes irrespective to the age. Diabetes problem is being gradually growing and presently, it is reported as a significant cause of death in the top spot. According to the recent studies 48% of overall world population will be affected by diabetes by 2045. If diabetes unidentified in early stages, it may cause other additional cardiac problems. In the proposed based work, a deep learning framework deep combination of convolution neural network and long short-term memory is proposed by embedding both to leverage their respective advantages for diabetes recognition and to allow early prediction of diabetes to avoid other complications. The experimental evolution on the bunch mark of diabetes data set demonstrates the proposed model embedded deep long short-term memory outperforms other machine learning and conventional deep learning approaches. The proposed algorithm in this paper outperforms existing techniques and evaluates total effectiveness and accuracy of predicting whether a person will suffer from diabetes.

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“…However, these didn't prove to be successful. Drawbacks of existing system is chances of malpractices, external evaluation, complexity of work, time consuming [9], [10]. To overcome the drawbacks of the existing system, the proposed system has been evolved.…”

Section: Begin 2we Consider Standard Input Dataset With Video As Input File 3input Dataset Of Emotions Which Are Formatted In Cvs File 4vmentioning

confidence: 99%

Detecting Emotions of Student and Assessing the Performance by using Deep Learning

Amiripalli¹

2020

IJATCSE

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In the present-day scenario, Assessment of educational skills independently is prominently required which can be done by the techniques of Deep Learning. The main objective of the project is to evaluate the performance of the student while coding using his facial expressions. This model initially captures the facial expressions of the student using a camera which is our data set. These inputs are fed into the deep learning classification algorithms to assess the behaviour of the student and then those outputs are fed into the machine learning algorithms to assess his performance. This project supports external evaluation in many of the educational institutions. This model summarizes the successful patterns of a student by avoiding the pitfalls of average student patterns to develop an innovative analysis tool and promotes to develop the best education.

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Performance improvement model for Airlines connectivity system using Network Science

Cited by 9 publications

References 12 publications

Using Neural Network for Simulations to Improve the Quality of Disease Diagnosis: Technical Aspects

Using Neural Network for Simulations to Improve the Quality of Disease Diagnosis: Technical Aspects

A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory

Detecting Emotions of Student and Assessing the Performance by using Deep Learning

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