Artificial Intelligence Based Diagnostics, Therapeutics and Applications in Biomedical Engineering and Bioinformatics

Bhardwaj, Kartik Krishna; Banyal, Siddhant; Sharma, Deepak Kumar

doi:10.1016/b978-0-12-817356-5.00009-7

Cited by 17 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Foreign research and application in this area are relatively early [14]. Some developed countries have used the results of electronic technology-based research in the field of education, and education information technology has made a new breakthrough in the direction of modern multimedia technology development [15]. The United States has been researching multimedia teaching since the 1960s, focusing on the application of computer education in primary and secondary schools.…”

Section: Related Workmentioning

confidence: 99%

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Zhao

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Nowadays, people are paying greater attention to the concept of childcare, and the healthy development of children is humanity’s hope for the future. The newborn stage is the most critical stage of a child’s development, and learning and mastering neonatal care is the first important lesson for parents. To meet the needs of today’s learners and to break the traditional teaching model, this paper develops a set of interactive newborn nursing knowledge system that composes the content of trivial nursing knowledge, reconstructs the knowledge structure system, and provides new teaching methods such as text browsing, 3D animation presentation, picture presentation, video presentation, interactive games, and tests for newborns. Text browsing, 3D animation, image demonstration, video demonstration, and interactive games and tests, among other new teaching methods, give a new teaching mode and method for neonatal nursing education. The study included 158 infants from local hospital. The study was divided into two groups, each with 80 cases: a control group (conventional care) and a study group (quality care management). The outcomes were contrasted and compared. The results revealed that the study group had a lower incidence of nursing defects than the control group, and the difference was statistically significant ( P 0.05 ); the study group had a lower incidence of diseases than the control group, and the difference was statistically significant ( P < 0.05 ). The study goes over the complete process of incorporating the instructional display into the interactive newborn care knowledge system. Multimedia techniques and applications were researched throughout the design and production process in order to blend intrinsic knowledge material with digital technology to produce new educational ideas.

show abstract

Section: Related Workmentioning

confidence: 99%

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Zhao

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…In ML, there is a clustering method which is an unattended method that has no labeled inputs and problem-solving is based on the experience that algorithms gain from solving similar problems [30]. Because the data in this study uses text data, this study will focus on converting text data to vectors derived from the tokenization process.…”

Section: Theoretical Basismentioning

confidence: 99%

Automatic Categorization of Multi Marketplace FMCGs Products using TF-IDF and PCA Features

Indasari

Tjahyanto

2023

SISFOKOM

View full text Add to dashboard Cite

The use of technology in line with the increasing number of internet users has caused a shift in the product sales ecosystem to the realm of electronic commerce (electronic commerce). A total of 73.23 customers made purchase transactions using e-commerce and the most purchased products were products classified as Fast Moving Consumer Goods (FMCGs). The increasingly varied FMCGs data coupled with the increasing number of marketplaces is felt to need to be broken down into specific groups. The process is carried out by analyzing e-commerce product information, especially product names, and descriptions. In this study, we propose an automatic categorization of multiple marketplaces using data from multiple marketplaces. Data text is converted into structured data with a series of preprocessing, and comprehensive experiments are carried out to see the extraction performance of variables including TF-IDF, BOW, and N-Gram. All three methods are used to validate text data sets with K-Means grouping results used with the help of PCA to reduce data dimensions. The results show that the performance of the TF-IDF algorithm with a dimension reduction value of 70 and the use of Python can provide optimal results for the percentage of grouping data.

show abstract

“…Learning is the most crucial part of this process. Based on the training set utilized and how it is interpreted for the learning process, learning can be categorized into two categories such as supervised, and unsupervised learning [25]. Supervised learning uses labeled example data from previous experiences to predict future events with new data.…”

Section: Model Developmentmentioning

confidence: 99%

“…ML is a subfield of AI and is described as the algorithm studies that enable machines to make decisions and operate without being specifically programmed to do so. The goal of ML is to create algorithms that guide machines on how to access data and utilize it to learn a given task [25]. ML has also been commonly used in the case of structural and functional characterizations of proteins and peptides [26].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Synergistic Effect of Antimicrobial Peptides and Antibiotics via Supervised Machine Learning Technique

Olcay

Özdemir

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Infectious diseases not only cause severe health issues but also burden the healthcare system. Therefore, the effective treatment of those diseases is crucial. A variety of conventional therapies including antibiotics as well as novel treatment methods namely antimicrobial peptides (AMP) are utilized for the treatment of infections. However, because of the drawbacks of current therapies such as the risk of the emergence of drug-resistant microorganisms, low stability of the agent in its environment as well as toxicity problems, new solutions are still being investigated. A recent approach is the usage of these two different antimicrobial agents in combination. Nonetheless, the determination of synergism is time-consuming and depends on several experimental studies. Prediction of different biological outcomes with machine learning (ML) algorithms is a widespread research field recently, and AMP with ML studies is one of the most researched areas further to understand the underlying associations of the mechanism of interest. Although many characteristics and biological outcomes of AMPs and antibiotics have been studied with different ML methods, no investigation reported on predicting the synergistic effects of AMP and antibiotics. Results: Supervised ML models were implemented to accurately classify the synergistic effect of AMPs and antibiotics within the scope of the proposed study. The results demonstrated that the hyperparameter-optimized Light Gradient Boosted Machine Classifier (LGBMC) yielded the best test accuracy of 76.92% for predicting the synergistic effect when compared to other trained models. Also, the feature analysis of the LGBMC model reveals that the target microbial strain, the minimum inhibitory concentrations (MIC) of the AMP and the antibiotic, and the used antibiotic are the most important features for the prediction of synergistic effect. Moreover, the results of the feature importance analysis are consistent with recently reported experimental studies in the literature. Conclusion: This study presents a novel and effective approach that aims to automatically and accurately predict the interactions between AMPs and antibiotics by various supervised ML algorithms, for the first time. Furthermore, this study reveals that ML algorithms can predict the synergistic activity of two different antimicrobial agents without the need for complex and time-consuming experimental procedures.

show abstract

Artificial Intelligence Based Diagnostics, Therapeutics and Applications in Biomedical Engineering and Bioinformatics

Cited by 17 publications

References 30 publications

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Automatic Categorization of Multi Marketplace FMCGs Products using TF-IDF and PCA Features

Prediction of the Synergistic Effect of Antimicrobial Peptides and Antibiotics via Supervised Machine Learning Technique

Contact Info

Product

Resources

About