Intelligent Identification of Liver Diseases Based on Incremental Hidden Layer Neurons ANN Model

Terlapu, Panduranga Vital; Sadi, Ram; Pondreti, Ramkishor; Tippana, Chalapathi

doi:10.12785/ijcds/110183

Cited by 8 publications

(2 citation statements)

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“…The traditional learning evaluation generally adopts the summative evaluation method, which has the characteristics of post-mortem inspection, and its advantages are strong operability and more objective [5]. However, it is helpless to the problems that arise in the students' learning process, and cannot comprehensively evaluate the students' learning process, and the evaluation has a lag [6].…”

Section: Introductionmentioning

confidence: 99%

Construction of Physical Education Evaluation System Based on Fuzzy Neural Network Modeling

Wang

2024

Preprint

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As a frontier concept of current basic education, subject literacy indicates that our country has established the direction of curriculum reform aiming at developing students' core literacy. The core subject literacy is not only the basic subject literacy gradually developed by students in the course of subject learning, but also the comprehensive factors such as character and ability gradually formed by students in extracurricular social practice activities, which is an important indicator of the new curriculum education reform. In view of the fact that the special teaching connotation of high school physical education still needs to be clarified, the evaluation methods of teaching effect still need to be enriched, and the teaching reform theory needs to be further improved. Focus on cultivating college students' lifelong sports awareness, mastering sports knowledge and skills, and developing their physical fitness, good sports behavior and sports moral quality healthily. According to the characteristics of physical education learning evaluation, the fuzzy neural network is selected to model, and the physical education teaching evaluation system is constructed. The adaptive fuzzy neural system is used to process the data, thus realizing the intelligence and automation of physical education teaching evaluation. Finally, the artificial neural network and adaptive fuzzy neural system are used in the evaluation and modeling of physical education teaching, and the experimental simulation comparison and performance analysis of the two models show that the model has good evaluation ability.

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Section: Introductionmentioning

confidence: 99%

Construction of Physical Education Evaluation System Based on Fuzzy Neural Network Modeling

Wang

2024

Preprint

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“…The endocrine part of the pancreas comprises islet cells, known as the islets of Langerhans. Instead of releasing hormones into the pancreatic ducts, these endocrine cells do so directly into the body's circulation [15]. The essential pancreatic chemicals are glucagon, which raises BGLs, and insulin, which brings them down, supporting the control of glucose levels.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Gestational Diabetes Diagnosis System Using MLP-Whale Optimization Algorithm

Terlapu,

et al. 2023

IJCDS

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Diabetes is a widely recognized medical condition and is generally mentioned as a silent killer in global healthcare. It is a metabolic and chronic disease that causes an increase in blood glucose (BGL) levels, leading to long-term damage to the blood vessels, heart, nerves, eyes, and kidneys, among other vital organs. The causes of diabetes include genetic factors, alcohol consumption, smoking, weight, the absence of actual work, unfortunate dietary propensities, and hypertension. Depending on the type and severity of diabetes, other organs in the body, such as the kidneys, heart, and eyes, are more susceptible to diseases. In this research investigation, we extend a model, the MLP-WOA, which uses a fine-tuned weight of the multi-layer perceptron (MLP) with the diabetes onset prediction using the whale (WOA) optimization algorithm. We used a benchmark dataset from the Kagle repository to train and test our model, and we evaluated its accuracy, precision, and recall. Support Vector Machines (SVM), K-nearest (KNN) neighbor, and other ML methods were evaluated against our model's outcomes, WOA-MLP, and decision trees (DTs). Our results showed that the MLP-WOA model performed superiorly to the other experimental models, achieving an accuracy of approximately 76%. Furthermore, we tested our MLP model using other existing optimizers and observed that the WOA optimizer gave better results. In conclusion, our study demonstrates that the MLP-WOA model prototype is a promising approach for predicting diabetes and that it outperforms other ML models. Patients with diabetes may see an improvement in their quality of life as a result of this strategy's ability to improve diagnosis accuracy.

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Intelligent diagnosis system of hepatitis C virus: A probabilistic neural network based approach

Terlapu

Gedela

Gangu

et al. 2022

Int J Imaging Syst Tech

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In recent years, early detection of hepatitis C virus (HCV) disease has been a vital task in the medical science field. HCV became the main health concern to the public, as it was noticed to have more blood donors in Egypt equated to other nationalities. The WHO assessed that in 2019, around 290 000 individuals died from hepatitis C, which says the seriousness of the HCV disease. So, early prediction, preventions, and curing the disease are vital components to save individuals from HCV. In this paper, we propose and investigate experimental results of the five machine learning (ML) models and probabilistic neural network (PNN) based approach to detect the HCV utilizing University of Califonia Irvine (UCI) ML Egyptian HCV dataset. We also analyze the statistical reports of HCV stages and their features. As per result analysis, the random forest (RF) ML model performs superiorly to other traditional ML algorithms with 97.5% of accuracy. The PNN (incremental hidden layer [HL] neurons) based proposal model shows a very high performance (99.6% of accuracy) at 30-HL neurons of PNN. As per comparative analysis, the proposed model is superior to experimental basic ML models, and early HCV disease works are related to this area. This research focuses on early detection, prevention, and challenges of handling HCV.

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Intelligent Identification of Liver Diseases Based on Incremental Hidden Layer Neurons ANN Model

Cited by 8 publications

References 0 publications

Construction of Physical Education Evaluation System Based on Fuzzy Neural Network Modeling

Construction of Physical Education Evaluation System Based on Fuzzy Neural Network Modeling

Intelligent Gestational Diabetes Diagnosis System Using MLP-Whale Optimization Algorithm

Intelligent diagnosis system of hepatitis C virus: A probabilistic neural network based approach

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