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Non-Alcoholic Fatty Liver Disease Prediction with Feature Optimized XGBoost Model
Non-alcoholic fatty liver disease (NAFLD) is an expanding health threat, posing significant risks for long-term complications. Early detection and intervention are crucial, but traditional diagnostic methods can be expensive and invasive.This study investigates the utilization of machine learning models for predicting liver diseases from various out-sourced datasets..We employed Decision Trees, Random Forests, and Support Vector Machines (SVMs) to predict NAFLD based on various clinical and demographic features. Model performance was evaluated by calculating accuracy, precision,deviation and accuracy-score.All these models achieved promising accuracy levels, ranging from 80% to 90%, showcasing their potential for NAFLD prediction. Among them, XG-Boost demonstrated the highest performance, with an accuracy of 90% and more.This study demonstrates the effectiveness of machine learning models in predicting NAFLD with high accuracy using readily available data. Further research with larger sized and more varied datasets will vindicate these models for real-world application in clinical settings. 2024 IEEE. -
Non-Antibacterial Carbon Nanoparticles and Its Fluorescence Properties
Highly fluorescent carbon nanoparticles are synthesized from corn starch via one-pot hydrothermal method. Upon treatment with the lime juice as the catalyst, carbon nanoparticles are functionalized with potassium, and an improvement in the luminescence behavior is also observed. The synthesized nanoparticles did not exhibit any antibacterial activity against gram-positive (Staphylococcus aureus, Bacillus subtilis) and gram-negative (Pseudomonas fluorescence, E.coli) bacteria. The excellent photoluminescence coupled with non-toxic behaviour of the carbon nanoparticles would be best suited for biomedical applications. The Electrochemical Society -
Non-Contact Vital Prediction Using rPPG Signals
In this paper, we present the clinical significance of various cardiac symptoms with the use of heart rate detection, ongoing monitoring and present emotions. The development of algorithms for remote photoplethysmography has drawn a lot of interest during the past decade (rPPG). As a result, using data gathered from the video feed, we can now precisely follow the heart rate of individuals who are still seated. rPPG algorithms have also been developed, in addition to technique based on hand-crafted characteristics. Deep learning techniques often need a lot of data to train on, but biomedical data frequently lacks real-world examples. The experiment described in this work, we looked at how illumination affected the rPPG signals' SNR. The findings show that the SNR in each RGB channel varies depending on the colour of the light source. Paper describes development in video filtering for recognising the comprehending human face emotions. In our method, emotions are deduced by identifying facial landmarks and analysing their placement. 2023 IEEE. -
Non-destructive classification of diversely stained capsicum annuum seed specimens of different cultivars using near-infrared imaging based optical intensity detection
The non-destructive classification of plant materials using optical inspection techniques has been gaining much recent attention in the field of agriculture research. Among them, a near-infrared (NIR) imaging method called optical coherence tomography (OCT) has become a well-known agricultural inspection tool since the last decade. Here we investigated the non-destructive identification capability of OCT to classify diversely stained (with various staining agents) Capsicum annuum seed specimens of different cultivars. A swept source (SS-OCT) system with a spectral band of 1310 nm was used to image unstained control C. annuum seeds along with diversely stained Capsicum seeds, belonging to different cultivar varieties, such as C. annuum cv. PR Ppareum, C. annuum cv. PR Yeol, and C. annuum cv. Asia Jeombo. The obtained cross-sectional images were further analyzed for the changes in the intensity of back-scattered light (resulting due to dye pigment material and internal morphological variations) using a depth scan profiling technique to identify the difference among each seed category. The graphically acquired depth scan profiling results revealed that the control specimens exhibit less back-scattered light intensity in depth scan profiles when compared to the stained seed specimens. Furthermore, a significant back-scattered light intensity difference among each different cultivar group can be identified as well. Thus, the potential capability of OCT based depth scan profiling technique for non-destructive classification of diversely stained C. annum seed specimens of different cultivars can be sufficiently confirmed through the proposed scheme. Hence, when compared to conventional seed sorting techniques, OCT can offer multipurpose advantages by performing sorting of seeds in respective to the dye staining and provides internal structural images non-destructively. 2018 by the authors. Licensee MDPI, Basel, Switzerland. -
Non-destructive silkworm pupa gender classification with X-ray images using ensemble learning
Sericulture is the process of cultivating silkworms for the production of silk. High-quality production of silk without mixing with low quality is a great challenge faced in the silk production centers. One of the possibilities to overcome this issue is by separating male and female cocoons before extracting silk fibers from the cocoons as male cocoon silk fibers are finer than females. This study proposes a method for the classification of male and female cocoons with the help of X-ray images without destructing the cocoon. The study used popular single hybrid varieties FC1 and FC2 mulberry silkworm cocoons. The shape features of the pupa are considered for the classification process and were obtained without cutting the cocoon. A novel point interpolation method is used for the computation of the width and height of the cocoon. Different dimensionality reduction methods are employed to enhance the performance of the model. The preprocessed features are fed to the powerful ensemble learning method AdaBoost and used logistic regression as the base learner. This model attained a mean accuracy of 96.3% for FC1 and FC2 in cross-validation and 95.3% in FC1 and 95.1% in FC2 for external validation. 2022 The Authors -
Non-enzymatic electrochemical determination of progesterone using carbon nanospheres from onion peels coated on carbon fiber paper
A simple electrochemical sensor was developed by coating Onion peel wastes derived carbon nanospheres on carbon fiber paper (CFP) electrode. Carbon nanospheres (CNS) were prepared from Onion peels utilizing an environmentally benign and cost-effective strategy. In the present investigation, the obtained carbon nanospheres were coated on carbon fiber paper and the modified electrodes were physicochemically characterized by Field emission scanning electron microscopy (FESEM) with energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD) spectroscopy and X-ray photoelectron spectroscopy (XPS) techniques. Electrochemical characterizations of the modified electrodes were done by Cyclic voltammetry (CV) and Electrochemical impedance spectroscopy (EIS). CNS modified CFP electrode was successfully used in the determination of Progesterone, an important steroid hormone at an ultra-nanomolar level with superior detection limit of 0.012 nM. The developed electrochemical sensor was effectively utilized for the determination of Progesterone in pharmaceutical Progesterone injections, human blood serum samples and cow milk samples. 2019 The Electrochemical Society. -
Non-enzymatic electrochemical determination of salivary cortisol using ZnO-graphene nanocomposites
Electrochemically deposited ZnO nanoparticles on a pencil graphite electrode (PGE) coated with graphene generate a noteworthy conductive and selective electrochemical sensing electrode for the estimation of cortisol. Electrochemical techniques such as cyclic voltammetry (CV) analysis and electrochemical impedance spectroscopic (EIS) tests were adopted to analyze and understand the nature of the modified sensor. Surface morphological analysis was done using various spectroscopic and microscopic techniques like X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). Structural characterization was conducted by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The effect of scan rate, concentration, and cycle numbers was optimized and reported. Differential pulse voltammetric (DPV) analysis reveals that the linear range for the detection of cortisol is 5 10-10M - 115 10-10 M with a very low-level limit of detection value (0.15 nM). The demonstrated methodology has been excellently functional for the determination of salivary cortisol non-enzymatically at low-level concentration with enhanced selectivity despite the presence of interfering substances. The Royal Society of Chemistry. -
Non-Fourier thermal transport analysis in the human eye using a dual-phase-lag bioheat framework under environmental exposure
Understanding how heat propagates inside the human eye is important for preventing thermal damage during environmental exposure, laser treatments, and biomedical procedures, particularly in hot climates where ocular tissues are vulnerable to temperature rise. Conventional bioheat models based on Fourier heat conduction assume instantaneous heat transfer and may therefore fail to capture delayed thermal responses occurring in heterogeneous biological tissues. The aim of this study is to develop and analytically investigate a dual-phase-lag bioheat model capable of accurately predicting intraocular temperature evolution under combined environmental and physiological thermal loading. Motivated by the need for a more realistic and computationally efficient framework for ocular thermal safety assessmentaligned with Saudi Arabias Vision 2030 goals in healthcare innovation and preventive medicinethis study develops a dual-phase-lag (DPL) bioheat model to analyze heat transport in a multilayer human eye under combined environmental and physiological loading. Closed-form analytical solutions are obtained using normal-mode analysis for all six ocular layers while accounting for convection, evaporation, blood perfusion, and tissue porosity. Results show that the DPL model predicts lower and smoother temperature distributions compared with Fourier and LordShulman models, indicating more physiologically realistic thermal behavior. Ambient temperature and evaporation primarily control heating in anterior eye regions, whereas perfusion and tissue porosity dominate thermal regulation in deeper layers. Sensitivity analysis and thermal-safety maps identify critical combinations of exposure conditions that may increase thermal risk. A surrogate-based reduced-order model is further developed and validated, enabling rapid prediction of intraocular temperature with high accuracy. The study demonstrates that incorporating non-Fourier thermal effects significantly improves prediction of ocular temperature dynamics and provides a practical framework for thermal safety assessment, ophthalmic treatment planning, and climate-adaptive healthcare technologies. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Non-Fungible Token (NFT): Bubble or Future in the World of Block Chain Technology
The introduction of blockchain technology entering into human existence, which is a reinforcement of the cryptocurrency space, is both a concern and an opportunity. The main motivation underlying such an invention is conditional transparency and the unmatched ability to protect people against data destruction. The collecting drive of NFTs is profitable and also has sparked curiosity, with everyone vying for the first piece of the package, increasing the future Value of an NFT, as it is a very new topic about NFT using block-chain technology. It is something quite about a flurry of blockchain technological stories that leave us wondering. In this research paper, we explained the new emerging Non-Fungible Token (NFT), its uses, and implications. 2023 American Institute of Physics Inc.. All rights reserved. -
Non-Invasive Detection of Ovarian Cancer using Biosensors Framework with Machine Learning and Federated Learning Techniques
Ovarian cancer is a leading cause of death worldwide, frequently diagnosed at advanced stages due to the lack of effective early screening methods. This work proposes a non-invasive cancer diagnostics utilizing amperometric electrochemical biosensors in early cancer detection from biological fluids, such as urine-based by combination of specific biomarkers like HE4 and Ca125, which are closely associated with ovarian cancer. This study approach integrates machine learning models to work with biosensor data for cancer classification tasks, and federated learning methods to ensure patient data privacy. The proposed system achieves diagnostic results using a synthetic dataset with over 98% accuracy. This decentralized healthcare solution demonstrates early ovarian cancer detection and improved patient outcomes by combining predictive capability with privacy preservation. 2025 IEEE. -
Non-Invasive Detection of Ovarian Cancer using Biosensors Framework with Machine Learning and Federated Learning Techniques
Ovarian cancer is a leading cause of death worldwide, frequently diagnosed at advanced stages due to the lack of effective early screening methods. This work proposes a non-invasive cancer diagnostics utilizing amperometric electrochemical biosensors in early cancer detection from biological fluids, such as urine-based by combination of specific biomarkers like HE4 and Ca125, which are closely associated with ovarian cancer. This study approach integrates machine learning models to work with biosensor data for cancer classification tasks, and federated learning methods to ensure patient data privacy. The proposed system achieves diagnostic results using a synthetic dataset with over 98% accuracy. This decentralized healthcare solution demonstrates early ovarian cancer detection and improved patient outcomes by combining predictive capability with privacy preservation. 2025 IEEE. -
Non-inverse signed graph of a group
Let G be a group with binary operation *. The non-inverse graph (in short, i*-graph) of G, denoted by ?, is a simple graph with vertex set consisting of elements of G and two vertices x, y ? ? are adjacent if x and y are not inverses of each other. That is, x ? y if and only if x * y ?= iG ?= y*x, where iG is the identity element of G. In this paper, we extend the study of i*-graphs to signed graphs by defining i*-signed graphs. We characterize the graphs for which the i*-signed graphs and negated i*-signed graphs are balanced, sign-compatible, consistent and k-clusterable. We also obtain the frustration index of the i*-signed graph. Further, we characterize the homogeneous non-inverse signed graphs and study the properties like net-regularity and switching equivalence. Amreen J., Naduvath S., 2024. -
Non-linear convection in chemically reacting fluid with an induced magnetic field across a vertical porous plate in the presence of heat source/sink
An investigation is carried out to observe the impacts of non-linear convection and induced magnetic field in the flow of viscous fluid over a porous plate under the influence of chemical reaction and heat source/sink. The plate is subjected to a regular free stream velocity as well as a suction velocity. The subjected non-linear problem is non-dimensionalized and analytic solutions are presented via perturbation method. The graphs are plotted to analyze the effect of relevant parameters on velocity, induced magnetic field, heat and mass transfer fields as well as friction factor, current density, Nusselt and Sherwood numbers. It is established that nonlinear convection aspect is destructive for thermal field and its layer thickness. The magnetic field effect enhances the thermal field while it reduces the velocity field. Also, the nonlinear effect subsides heat transfer rate significantly. 2018 Trans Tech Publications, Switzerland. -
Non-linear Convection in Couple Stress Fluid with Non-classical Heat Conduction Under Magnetic Field Modulation
A theoretical examination of thermal convection for a couple stress fluid which is electrically conducting and possessing significant thermal relaxation time is explored under time dependent magnetic field. Fouriers law fails for a diverse area of applications such as fluids subjected to rapid heating, strongly confined fluid and nano-devices and hence a non-classical heat conduction law is employed. The heat transport in the system is examined and quantified employing the Lorenz model. The Nusselt number is deduced to quantitate the transfer of heat. 2021, Springer Nature Singapore Pte Ltd. -
Non-linear Dynamics of CuO?MgO?TiO2 ?H2O Ternary Nanofluid Flowing Past a Rotating Cone in the Presence of Thermal Radiation
The flow of ternary nanofluid past a rotating cone has been analysed using the Ternary nanofluid model. The ternary nanofluid is formed by suspending CuO, MgO and TiO2 nanoparticles into water. The nanoparticles that are suspended in the base fluid are assumed to be in the shape of a sphere so that there will be minimum friction between the nanoparticles and the surface as a result this will allow the fluid to flow with less frictional force. Such a characteristic flow finds application in automobiles, production industries, metallurgical process, solar appliances etc. Hence, in order to analyse the heat transfer characteristics of ternary nanofluid, a mathematical model is framed with the help of partial differential equations considering thermal radiation and heat source/sink to achieve realistic results. These equations are further transformed to non-linear differential equations that are solved using RKF-45 technique. The results of this study are interpreted graphically for various parameters corresponding to the fluid flow. The outcomes of this study indicated that the increase in convection enhanced the tangential velocity of the flow and the nanofluid temperature. Whereas, the increase in the thermal slip reduced the tangential flow velocity as well as the temperature of the nanofluid. 2023 L&H Scientific Publishing, LLC. All rights reserved -
Non-linear Dynamics of Trade Openness and Income Inequality: New Evidence from a Dynamic Panel Threshold Analysis
This study examines the non-linear relationship between trade openness and income inequality in BRICS countries (Brazil, Russia, India, China and South Africa) over the period 19902020. It explores how different levels of trade openness affect inequality, with an emphasis on identifying threshold effects. Using a dynamic panel threshold estimation technique, the analysis reveals a U-shaped relationship: trade openness reduces inequality up to a critical threshold of 50.877%, beyond which further liberalisation exacerbates inequality. The JKS panel causality test indicates a unidirectional causal relationship from trade openness to income inequality. These findings highlight the need for calibrated trade policies in BRICS nations. Promoting trade openness up to the identified threshold may reduce inequality, but liberalisation beyond this point should be accompanied by redistributive and institutional measures to mitigate adverse distributional outcomes. 2025 Indian Institute of Foreign Trade -
Non-linear solar EUV-driven sodium release from the lunar surface: a contrast to the linear PSD model
The correlation between solar Extreme Ultra-Violet (EUV) radiation above 8.8eV and the release of sodium from the lunar surface via photon-stimulated desorption (PSD) is investigated. We use simultaneous measurements of EUV photon flux and Na optical spectral line flux (FNa) from the lunar exosphere. Data were acquired with the high-resolution (R?72000) Echelle Spectrograph on the 2.34-m Vainu Bappu Telescope during the lunar first quarter (2024 JanuaryMarch), observing NaI D2 and D1 flux at altitudes below ?590km from the surface. Simultaneous EUV and FUV measurements were acquired from the GOES-R Series Extreme Ultraviolet Sensor (EUVS), while NUV data were obtained from the Total and Spectral Solar Irradiance Sensor-1 (TSIS-1) aboard the ISS. We correlated FNa with EUV photon flux from EUVS across six bands spanning 2561405 (48.58.8eV) and NUV (20004000 from TSIS-1. A non-linear rise in lunar exospheric sodium with increasing EUV and FUV fluxes was observed, contrasting with previous linear PSD models. The EUV radiation above 10eV drives sodium release, with 256-304wavelengths as dominant contributors. Additionally, the NUV flux and FNa are positively correlated, indicating the role of sodium release. The zenith column density averages 3.3 109 atoms cm-2, with Characteristic temperatures averaging at ?6700K and scale heights of ?1500km. Elevated temperatures and sodium densities during solar activity suggest enhanced Na release during flares. These results emphasize the need for a revised PSD model above 8.8 eV and improved constraints on the PSD cross-section. The Author(s) 2025. Published by Oxford University Press on behalf of Royal Astronomical Society. -
Non-Noble Bifunctional Amorphous Metal Boride Electrocatalysts for Selective Seawater Electrolysis
The global scarcity of freshwater resources has recently driven the need to explore abundant seawater as an alternative feedstock for hydrogen production by water-splitting. This route comes with new challenges for the electrocatalyst, which has to withstand harsh saline water conditions with selectivity towards oxygen evolution over other competing reactions. Herein, a series of amorphous metal borides based on the iron triad metals (Co, Ni, and Fe), synthesized by a simple one-step chemical reduction method, displayed excellent bifunctional activity for overall seawater splitting. Amongst the chosen catalysts, amorphous cobalt boride (Co?B) showed the best overpotential values of 182 mV for HER and 305 mV for OER, to achieve 10 mA/cm2, in alkaline simulated seawater. This superior activity was owed to the enrichment of the metal site with excess electrons (HER) and the in-situ surface transformation (OER), as confirmed by various means. In alkaline simulated seawater, the overall cell voltage required to achieve 100 mA/cm2 was 1.85 V for the Co?B catalyst when used in a 2-electrode assembly. The Co?B catalyst showed negligible loss in activity even after 1000 cycles and 50 h potentiostatic tests, thus demonstrating its industrial viability. The selectivity of the catalyst was established with Faradaic efficiency of above 99 % for HER and 96 % for OER, with no detection of chloride products in the spent electrolyte. This study using the mono-metallic boride catalysts will turn to be a precursor to exploit other complex metal boride systems as potential candidates for seawater electrolysis for large-scale hydrogen production. 2023 Wiley-VCH GmbH. -
Non-orthogonal multiple access wireless systems using deep learning
In 5G networks, non-orthogonal multiple access (NOMA) increases spectral efficiency and user capacity greatly by letting multiple users share the same time, frequency, and code resources. Wireless communication systems stand to benefit significantly from deep learning owing to its ability to model intricate patterns. This chapter centers around deep learning-NOMA integration with special attention given to areas like channel estimation, interference management, and dynamic resource allocation. Using advanced deep learning frameworks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and deep reinforcement learning (DRL), this chapter demonstrates how NOMA system performance can be optimized to meet the stringent requirements of 5G and beyond networks. Moreover, this chapter also discusses the challenges associated with implementing deep learning in NOMA including computational complexity and data requirements, alongside future trends like federated learning and edge computing among others. The integration of these technologies promises improved network efficiency, reduced latency, and enhanced user experience, thereby making NOMA a fundamental technology in wireless communication evolution. 2025 selection and editorial matter, Mariyam Ouaissa, Mariya Ouaissa, Hanane Lamaazi, Khadija Slimani, Ihtiram Raza Khan, and B. Sundaravadivazhagan. -
Non-Recombinant Mutagenesis of Bacillus mojavensis CUIE1819 for Hyper Production of Lipase and Treatment of Polluted Lakes
Microorganisms that degrade oil contribute significantly to the bioremediation of polluted lakes. Many microorganisms synthesize lipases, which are commercially significant. In the present study microorganisms producing extracellular lipase were isolated from various polluted lakes in Bangalore by using tributyrin agar. A lipase assay was done to determine the most efficient lipase-producing organism, which was then named Bacillus mojavensis CUIE1819 based on 16srRNA sequencing. After UV irradiation, the selected immobilized organisms were used to treat the lake water samples. 2022, Association of Biotechnology and Pharmacy. All rights reserved.