Machine Learning Application for News Text Classification

Agarwal, Jyoti; Christa, Sharon; H, Aditya Pai; Kumar, M. Anand; S, Guru Prasad M

doi:10.1109/confluence56041.2023.10048856

Cited by 25 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Maximum inhibition of the alpha amylase enzyme by the highest concentration of the silver nanoparticle preparation (100%) is a 65.6% inhibition while acarbose inhibition at same concentration is a 90% inhibition. Similarly, biosynthesis of AgNps with Cymbopogon citratus (lemon grass) presented significant alpha amylase inhibition activity [15]. There are numerous natural products that function as alphaglucosidase inhibitors [20].…”

Section: Discussionmentioning

confidence: 99%

“…Nanoparticle used in the field of medicine especially anti diabetic and bacterial application has continued to find relevance because of its large surface area to volume ratio, which provides it with the capability to interact with any kind of membrane and enzymes in a more enhanced manner. Silver nanoparticles (Ag NPs) synthesized with lemongrass demonstrated anti-diabetic potential in vitro [15]. Moringa oleifera showed optimum potential for biosynthesis of silver nanoparticles by rapidly reducing silver ions (Ag + to Ag 0 ) [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

In vitro studies on the antidiabetic and antibacterial activity of biosynthesized silver nanoparticles with aqueous extract of Moringa oleifera leaf

Asuquo,

Mgbeje,

Dasofunjo

et al. 2024

J. Pharm Bio.

View full text Add to dashboard Cite

Silver nanoparticles having applications in the field of medicine and biology are shown to have tremendous health benefits. Diabetes is still on rampage and antibacterial resistance is currently a global health challenge. This study focuses on the in-vitro antidiabetic and antibacterial activity of biosynthesized silver nanoparticles with aqueous extract of Moringa oleifera leaf. An aliquot (5 mL) of extract sample was added to 50 mL of 1 mM aqueous AgNO3. Reaction mixtures were heated and maintained at 700C for 10 minutes. Colour change to dark brown solution and a UV-vis spectrum peak at 400 nm confirmed silver nanoparticle synthesis. The antidiabetic activity of the nanoparticle was studied using an in vitro alpha-amylase inhibition assay. Acarbose was used as antidiabetic control drug. Disc diffusion method was used for antibacterial susceptibility testing on Mueller-Hinton agar medium with ampiclox as control antibiotic. Results of the analysis showed significant inhibition of alpha amylase that resembled the activity of acarbose. The highest percentage inhibition of alpha amylase by AgNPs was observed at 65.625% while that of acarbose was 90.357%. Antibacterial inhibition assay revealed that 100% biosynthesized silver nanoparticles had significant (p<0.05) inhibitory effects on Eschericia coli and Streptococcus pyogenes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In vitro studies on the antidiabetic and antibacterial activity of biosynthesized silver nanoparticles with aqueous extract of Moringa oleifera leaf

Asuquo,

Mgbeje,

Dasofunjo

et al. 2024

J. Pharm Bio.

View full text Add to dashboard Cite

show abstract

“…Exploring these characterizations is an avenue for future research. Finally, recent advances in rare event literature to study skewed outcome contexts is also an avenue of research to address potential bias in binary logit model estimation for skewed samples (see King and Zeng [35], Calabrese and Osmetti [36], and Agarwal et al [37]). This study has contributed to safety research in two important ways.…”

Section: Discussionmentioning

confidence: 99%

Understanding Crash Risk Using a Multi-Level Random Parameter Binary Logit Model: Application to Naturalistic Driving Study Data

Hoover

Bhowmik

Yasmin

et al. 2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

This study presents a framework to employ naturalistic driving study (NDS) data to understand and predict crash risk at a disaggregate trip level accommodating for the influence of trip characteristics (such as trip distance, trip proportion by speed limit, trip proportion on urban/rural facilities) in addition to the traditional crash factors. Recognizing the rarity of crash occurrence in NDS data, the research employs a matched case-control approach for preparing the estimation sample. The study also conducts an extensive comparison of different case-to-control ratios including 1:4, 1:9, 1:14, 1:19, and 1:29. The model parameters estimated with these control ratios are reasonably similar (except for the constant). Employing the 1:9 sample, a multi-level random parameters binary logit model is estimated where multiple forms of unobserved variables are tested including (a) common unobserved effects for each case-control panel, (b) common unobserved factors affecting the error margin in the trip distance variable, and (c) random effects for all independent variables. The estimated model is calibrated by modifying the constant parameter to generate a population conforming crash risk model. The calibrated model is employed to predict crash risk of trips not considered in model estimation. This study is a proof of concept that NDS data can be used to predict trip-level crash risk and can be used by future researchers to develop crash risk models.

show abstract

“…The feature vectors of news text are obtained by Doc2vec, and the text features are further extracted by CNN (Soni et al, 2023). The literature by Agarwal et al (2023) proposes a news classifier based on machine learning algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

ERNIE and Multi-Feature Fusion for News Topic Classification

Chen,

Liu,

Guan

2023

AIA

View full text Add to dashboard Cite

Traditional news topic classification methods suffer from inaccurate text semantics, sparse text features and low classification accuracy. Based on this, this paper proposes a news topic classification method based on Enhanced Language Representation with Informative Entities (ERNIE) and multi-feature fusion. A semantically more accurate representation of text embedding is obtained by ERNIE. In addition, this paper extracts word, context and key sentence based on the news text. The key sentences of the news are obtained through the TextRank algorithm, which enables the model to focus on the content points of the news. Finally, this paper uses the attention mechanism to realize the fusion of multiple features. The proposed method is experimented on BBCNews. The experimental results show that we achieve classification accuracies superior to those of the compared methods, while validating the structural validity of the proposed method. The method in this paper has a positive effect on promoting the research of news topic classification.

show abstract

Machine Learning Application for News Text Classification

Cited by 25 publications

References 16 publications

<i>In vitro</i> studies on the antidiabetic and antibacterial activity of biosynthesized silver nanoparticles with aqueous extract of <i>Moringa oleifera</i> leaf

<i>In vitro</i> studies on the antidiabetic and antibacterial activity of biosynthesized silver nanoparticles with aqueous extract of <i>Moringa oleifera</i> leaf

Understanding Crash Risk Using a Multi-Level Random Parameter Binary Logit Model: Application to Naturalistic Driving Study Data

ERNIE and Multi-Feature Fusion for News Topic Classification

Contact Info

Product

Resources

About