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NN-SVM: a hybrid neural networksupport vector machine framework for accurate pneumonia detection from chest X-rays
We present neural network (NN)-support vector machine (SVM), hybrid NN-SVM framework for three-class pneumonia detection (normal, bacterial, and viral) from chest X-rays (CXRs). Pretrained NN backbone is fine-tuned for radiographic textures; global average pooling (GAP) yields embeddings that feed calibrated radial basis function (RBF)-SVM. Standardized preprocessing (resize, normalization) and class-aware augmentation are applied. We report accuracy, precision, recall, F1-score, area under the curve (AUC), confusion matrices, and per-class receiver operating characteristic (ROC). Statistical significance is assessed via DeLong (AUC), McNemar (accuracy), and paired bootstrap (F1-score). Gradient-weighted class activation mapping (grad-CAM) supports interpretability; external validation and domain adaptation (batch normalization re-estimation and temperature scaling) assess robustness. NN-SVM attains 97.46% accuracy with strong macro-F1 and AUC. Compared with SoftMax head, SVM improves margin separation and calibration. We present NN-SVM, hybrid deep learning approach that combines transfer-learned convolutional neural networks (CNNs) with SVM classifier to automatically diagnose pneumonia from CXRs into three clinically relevant categories: viral pneumonia, bacterial pneumonia, and normal. We use pre-trained CNN to extract robust image embeddings after standardized preprocessing (resizing and normalization) and train RBF-kernel SVM on resulting features. Performance is evaluated with accuracy, precision, recall, F1-score, and confusion matrices. On labeled CXR dataset, NN-SVM achieves 97.46% accuracy, demonstrating strong diagnostic capability that can reduce radiologist burden and support timely clinical decision-making. This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/ -
Node Overlapping Detection for Draggable Node-Based Applications
Node-based interfaces are user interfaces that are based on the concept of nodes, which represent individual units of functionality, and edges, which represent the connections between nodes. In a node-based interface, nodes are connected by edges to form a graph, which represents the data flow and relationships between different parts of the system. The Node overlapping detection technique is only for react flow version 11 and higher. Users having previous versions are not able to use that functionality. To detect the overlapping, based on the output of this library, several user-defined functions can be used to resolve to overlap. It will see the single-pixel overlap. Using this library, users can avoid Node and edge overlapping by creating custom edges. It is a simple JavaScript function currently used for reactjs. In the future, if any other script develops a draggable node-based flowsheet-creating feature, the user can use this library accordingly. 2023 IEEE. -
Noise removal feature enhancement and speech recognition techniques for artificial larynx transducer speech
Speech impediments are the state of difficulty for a person to speak comfortably. These impediments make the spoken speech distorted and they are generally categorized as disordered speech. The quality of disordered speech is poor as clarity, intelligibility and naturalness is missing. In most type of disordered speech the voice is natural and produced by the vocal system of the human being. The vocal system includes the organ called as Larynx placed in the upper part of the neck. This organ has the vocal folds that contribute for pitch variation and volume of the speech. This organ will be malfunctioning some time or will be removed because of cancer. In both the case in order to restore speech, an external device called Artificial Larynx Transducer (ALT) is used to produce the sound. It is a small handheld battery operated device and is used for decades to obtain the audible speech for people who lost their speech because of removal of larynx. The quality of speech and its intelligibility of AL speakers have not improved for decades. The reason for poor quality is constant vibration of ALT, direct sound from ALT and pressure offered to produce the vibration. newlineSo in this research the nature of the speech produced from ALT is analyzed, a possible enhancement of the parameter is done and a recognition technique of the spoken word with the help of trained data is done. Here the approach followed to tackle the problem of poor quality in AL speech involves both speech enhancement and recognizer technique development. When it is looked as enhancement problem noise region localization, noise estimation and noise suppression methods were adopted. In the process of parameter enhancement, pitch frequency estimation and improvement is implemented. When it is looked as recognition problem the parameters pitch frequency, formant frequency, glottal excitation, spectral tilt, coefficients are extracted. As formant frequency is a sensitive parameter, its estimation was done using Recurrent Neural network. -
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Non invasive methods of blood glucose measurement: Survey, challenges, scope
Noninvasive body parameters monitoring and disease detection is one of the emerging research area now a days. In this paper a review on Non-invasive methods of blood glucose measurement has been made. A comparative study has been made which describes the methodology incorporated in the published literatures, research challenges and the used tools. This paper also describes about the factors which highly impacts the non-invasive measurement. Finally, a deep learning based noninvasive measurement method compatible with IOT is mentioned. This paper serves as a proper reference for future researchers working in non-invasive blood glucose measurement domain in selecting appropriate non-invasive method algorithm for glucose monitoring non-invasively. 2019 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth. -
Non linear thermal radiation effect on Williamson fluid with particle-liquid suspension past a stretching surface
A mathematical analysis of two-phase boundary layer flow and heat transfer of a Williamson fluid with fluid particle suspension over a stretching sheet has been carried out in this paper. The region of temperature jump and nonlinear thermal radiation is considered in the energy transfer process. The principal equations of boundary layer flow and temperature transmission are reformed to a set of non-linear ordinary differential equations under suitable similarity transformations. The transfigured equalities are solved numerically with the help of RKF-45 order method. The effect of influencing parameters on velocity and temperature transfer of fluid is examined and deliberated by plotted graphs and tabulated values. Significances of the mass concentration of dust particle parameter play a key role in controlling flow and thermal behavior of non-Newtonian fluids. Further, the temperature and concern boundary layer girth are declines for increasing values of Williamson parameter. 2017 The Authors -
Non-Accounting Drivers of Forensic Accounting Techniques: Insights from PLS-SEM Analysis
Forensic accounting techniques are pivotal in combating financial fraud and enhancing corporate governance. According to Forensic Accounting Theory, both accounting and non-accounting factors influence the intention to adopt these techniques. This study explores the impact of key non-accounting factors i.e. Bonus Contract, Anonymity, and Collapse Avoidance on adoption of forensic accounting techniques by the practitioners, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) and SmartPLS software. Data was collected from professionals across diverse industries utilising forensic accounting services. The results reveal that these non-accounting factors exert varying levels of influence on adoption intentions. This research enriches the existing body of knowledge by offering new perspectives on the role of non-accounting drivers in forensic accounting adoption, providing actionable insights for policy-makers, regulators, and corporate leaders. 2025 The Author(s). -
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 oximeter cum thermometer /
Patent Number: 202241009488, Applicant: Jyothi Thomas.
The invention pertains to the field of Covid-19 Equipment. The invention uses non-contact sensors to measure Sp02 level and human body temperature. The Invention can be classified under the Covid-19 as it includes sensors to identify an infected person. The objective of the invention was to tackle the problem of the detection of an infected Person in crowded places without having contact with devices. The basic idea behind the invention was to check the oxygen level as well as the temperature of a person without having contact with the devices used. -
Non-contact oximeter cum thermometer /
Patent Number: 202241009488, Applicant: Jyothi Thomas.
The invention pertains to the field of Covid-19 Equipment. The invention uses non-contact sensors to measure Sp02 level and human body temperature. The Invention can be classified under the Covid-19 as it includes sensors to identify an infected person. The objective of the invention was to tackle the problem of the detection of an infected Person in crowded places without having contact with devices. The basic idea behind the invention was to check the oxygen level as well as the temperature of a person without having contact with the devices used. -
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.