The Smart Analysis of Multi-Attention Based Sentimental Optimization Model for Complex Data Fusion in High Density Cloud Servers DOI
Preetjot Singh

Published: July 14, 2023

Multi-attention based sentimental optimization model for complex data fusion is a new hybrid sentiment analysis of complicated datasets. It combines multiple complementary mechanisms, including recurrent neural network (RNN) with an attention memory and administrative technique to organize multi-modal training data. The can not only provide high accuracy in categorization, but also be able learn from partially labeled Firstly, the RNN helps capture detailed information mixtures heterogeneous features. Also, summarize high-level features establish correspondent relationships other dimensions input Finally, make use sources knowledge optimize performances. This has ability cross-modal relations common datasets allows more effective process. In summary, multi-attention efficient tool analysis.

Language: Английский

Big Data Analytics in Bioinformatics and Healthcare DOI
Zaharaddeen Karami Lawal, Rufai Yusuf Zakari, Navod Neranjan Thilakarathne

et al.

Studies in big data, Journal Year: 2025, Volume and Issue: unknown, P. 71 - 95

Published: Jan. 1, 2025

Language: Английский

Citations

0
HQDCNet: Hybrid Quantum Dilated Convolution Neural Network for detecting covid-19 in the context of Big Data Analytics DOI Open Access
Nagamani Tenali,

G. Rama Mohan Babu

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(1), P. 2145 - 2171

Published: May 12, 2023

Language: Английский

Citations

10
Utilizing data analytics for fraud detection in accounting: A review and case studies DOI Creative Commons

Benjamin Samson Ayinla,

Onyeka Franca Asuzu,

Ndubuisi Leonard Ndubuisi

et al.

International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 11(1), P. 1348 - 1363

Published: Feb. 9, 2024

This research paper offers a comprehensive exploration of the evolving landscape fraud detection strategies within accounting sector, driven by integration data analytics, machine learning, and big technologies. The study aims to investigate, analyze, provide insights into practical application, challenges, implications these advanced technologies in detection. Through an extensive literature review, range case studies, comparative analysis methodologies, this delves key aspects data-driven review establishes significance analytics context detection, highlighting its pivotal role identifying preventing fraudulent activities. Various studies from diverse sectors, including finance, healthcare, e-commerce, exemplify successful implementations challenges faced real-world scenarios. A approaches showcases strengths limitations different guiding organizations optimizing their strategies. findings underscore transformative impact revolutionizing Implications drawn suggest future where will continue be instrumental proactively combating activities, ensuring regulatory compliance, upholding ethical standards.

Language: Английский

Citations

3
Vitamin D Deficiency Meets Hill’s Criteria for Causation in SARS-CoV-2 Susceptibility, Complications, and Mortality: A Systematic Review DOI Open Access
Sunil J. Wimalawansa

Nutrients, Journal Year: 2025, Volume and Issue: 17(3), P. 599 - 599

Published: Feb. 6, 2025

Clinical trials consistently demonstrate an inverse correlation between serum 25-hydroxyvitamin D [25(OH)D; calcifediol] levels and the risk of symptomatic SARS-CoV-2 disease, complications, mortality. This systematic review (SR), guided by Bradford Hill’s causality criteria, analyzed 294 peer-reviewed manuscripts published December 2019 November 2024, focusing on plausibility, consistency, biological gradient. Evidence confirms that cholecalciferol (D3) calcifediol significantly reduce hospitalizations, mortality, with optimal effects above 50 ng/mL. While vitamin requires 3–4 days to act, shows within 24 h. Among 329 trials, only 11 (3%) showed no benefit due flawed designs. At USD 2/patient, D3 supplementation is far cheaper than hospitalization costs more effective standard interventions. SR establishes a strong relationship 25(OH)D vulnerability, meeting criteria. Vitamin infections, deaths ~50%, outperforming all patented, FDA-approved COVID-19 therapies. With over 300 confirming these findings, waiting for further studies unnecessary before incorporating them into clinical protocols. Health agencies scientific societies must recognize significance results incorporate prophylaxis early treatment protocols similar viral infections. Promoting safe sun exposure adequate communities maintain 40 ng/mL (therapeutic range: 40–80 ng/mL) strengthens immune systems, reduces hospitalizations deaths, lowers healthcare costs. When exceed 70 ng/mL, taking K2 (100 µg/day or 800 µg/week) alongside helps direct any excess calcium bones. The recommended dosage (approximately IU/kg body weight non-obese adult) 50–100 cost-effective disease prevention, ensuring health outcomes.

Language: Английский

Citations

0
Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors DOI Creative Commons

Guoguang Rong,

Yankun Xu, Mohamad Sawan

et al.

Biosensors, Journal Year: 2023, Volume and Issue: 13(9), P. 860 - 860

Published: Aug. 31, 2023

We describe a machine learning (ML) approach to processing the signals collected from COVID-19 optical-based detector. Multilayer perceptron (MLP) and support vector (SVM) were used process both raw data feature engineering data, high performance for qualitative detection of SARS-CoV-2 virus with concentration down 1 TCID50/mL was achieved. Valid experiments contained 486 negative 108 positive samples, control experiments, in which biosensors without antibody functionalization detect SARS-CoV-2, 36 samples 732 samples. The distribution patterns valid dataset, based on T-distributed stochastic neighbor embedding (t-SNE), study distinguishability between explain ML prediction performance. This work demonstrates that can be generalized effective datasets dependent resonant modes as biosensing mechanism.

Language: Английский

Citations

8
Unveiling the Interplay—Vitamin D and ACE-2 Molecular Interactions in Mitigating Complications and Deaths from SARS-CoV-2 DOI Creative Commons
Sunil J. Wimalawansa

Biology, Journal Year: 2024, Volume and Issue: 13(10), P. 831 - 831

Published: Oct. 16, 2024

The interaction of the SARS-CoV-2 spike protein with membrane-bound angiotensin-converting enzyme-2 (ACE-2) receptors in epithelial cells facilitates viral entry into human cells. Despite this, ACE-2 exerts significant protective effects against coronaviruses by neutralizing viruses circulation and mitigating inflammation. While reduces expression, vitamin D increases it, counteracting virus's harmful effects. Vitamin D's beneficial actions are mediated through complex molecular mechanisms involving innate adaptive immune systems. Meanwhile, status [25(OH)D concentration] is inversely correlated severity, complications, mortality rates from COVID-19. This study explores which inhibits replication, including suppression transcription enzymes, reduced inflammation oxidative stress, increased expression antibodies antimicrobial peptides. Both hypovitaminosis elevate renin levels, rate-limiting step renin-angiotensin-aldosterone system (RAS); it ACE-1 but expression. imbalance leads to elevated levels pro-inflammatory, pro-coagulatory, vasoconstricting peptide angiotensin-II (Ang-II), leading widespread It also causes membrane permeability, allowing fluid infiltrate soft tissues, lungs, vascular system. In contrast, sufficient suppress reducing RAS activity, lowering ACE-1, increasing levels. cleaves Ang-II generate Ang

Language: Английский

Citations

3
A self-predictive diagnosis system of liver failure based on multilayer neural networks DOI Creative Commons
Fatemeh Dashti, Ali Ghaffari, Ali Seyfollahi

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(36), P. 83769 - 83788

Published: March 22, 2024

Abstract The lack of symptoms in the early stages liver disease may cause wrong diagnosis by many doctors and endanger health patients. Therefore, earlier more accurate problems is necessary for proper treatment prevention serious damage to this vital organ. We attempted develop an intelligent system detect failure using data mining artificial neural networks (ANN), approach considers all factors impacting patient identification enhances probability success diagnosing failure. employ multilayer perceptron via a dataset (ILDP). proposed backpropagation algorithm, improves rate, predicts intelligently. simulation analysis outputs revealed that method has 99.5% accuracy, 99.65% sensitivity, 99.57% specificity, making it than Previous related methods.

Language: Английский

Citations

2
A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications DOI Creative Commons
Ayogeboh Epizitone, Smangele Pretty Moyane, Israel Edem Agbehadji

et al.

Journal of Multidisciplinary Healthcare, Journal Year: 2023, Volume and Issue: Volume 16, P. 4015 - 4025

Published: Dec. 1, 2023

Many transformations and uncertainties, such as the fourth industrial revolution pandemics, have propelled healthcare acceptance deployment of health information systems (HIS). External internal determinants aligning with global course influence their deployments. At epic is digitalization, which generates endless data that has permeated healthcare. The continuous proliferation complex dynamic digitalization frontier in necessitates attention.This study explores existing body on HIS for through lens to present a data-driven paradigm augmentation paramount attaining sustainable resilient HIS.Preferred Reporting Items Systematic Reviews Meta-Analyses: PRISMA-compliant in-depth literature review was conducted systematically synthesize analyze content ascertain value disposition delivery.This details aspects robust care applications. Data source, action decisions, sciences techniques, serialization techniques HIS, insight implementation application are features expounded. These essential building blocks need iteration succeed.Existing considers insurgent challenging, disruptive, potentially revolutionary. This view echoes current quandary good bad availability. Thus, insights HIS. People, technology, tasks dominated prior frameworks, few data-centric facets. Improving requires identifying integrating crucial elements.The paper presented findings show track components improve using analytics insights. It provides an integrated footing support effectively assist delivery.

Language: Английский

Citations

5
Machine Learning Techniques for Effective Pathogen Detection based on Resonant Biosensors DOI Open Access

Guoguang Rong,

Yankun Xu, Mohamad Sawan

et al.

Published: Aug. 2, 2023

We describe a machine learning (ML) approach to process the signals collected from Covid-19 optical-based detector. Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were used both raw data feature engineering data, high performances for qualitative detection of SARS-CoV-2 virus with concentration down 1 TCID50/ml has been achieved. Valid experiments contain 486 negative 108 positive samples; control experiments, in which biosensors without antibody functionalization detect SARS-CoV-2, contains 36 samples 732 samples. Data distribution patterns valid dataset, based on T-distributed Stochastic Neighbor Embedding (t-SNE), was study distinguishability between samples, explain ML prediction performances. This work demonstrates that can be generalized effective dataset dependent resonant modes as biosensing mechanism.

Language: Английский

Citations

3
Generalization of linear and non-linear support vector machine in multiple fields: a review DOI Open Access

Sundas Naqeeb Khan,

Samra Urooj Khan, Hanane Aznaoui

et al.

Computer Science and Information Technologies, Journal Year: 2023, Volume and Issue: 4(3), P. 226 - 239

Published: Nov. 1, 2023

Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to family generalized linear classifiers. In other terms, SVM is regression prediction tool that uses machine theory maximize predictive accuracy. this article, the discussion about non-linear classifiers with their functions parameters investigated. Due equality type constraints in formulation, solution follows from solving equations. Besides this, if under-consideration problem form case, then must convert into separable help kernel trick solve it according methods. Some important algorithms sentimental work also presented paper. Generalization formulation SVMs open article. final section paper, different modified sections discussed which by research purposes.

Language: Английский

Citations

3