Browse Items (9798 total)
Sort by:
-
Impact of Multi-domain Features for EEG Based Epileptic Seizures Classification
Accurate detection and classification of epileptic seizures play a pivotal role in clinical diagnosis and treatment. This study introduces an innovative approach that leverages multi-domain features extracted from Electroencephalogram (EEG) data in conjunction with Supervised learning classification techniques. Initially, EEG data undergoes preprocessing through data standardization, followed by the extraction of essential features per instance, encompassing combination of Time domain, Frequency domain, and Time-Frequency domain features. These extracted feature combinations are subsequently fed into the machine learning-based boosting classifier Adaptive Boosting (ADABOOST) for an accurate and precise classification of epileptic signals. Validation of the proposed method is conducted using EEG data from the BEED (Bangalore EEG Epilepsy Dataset) and BONN (University of BONN, Germany) database to detect epileptic seizures. The experimental results show remarkably high levels of classification accuracy for various conditions: 99% accuracy for BEED data, 98% accuracy for BONN data for classifying seizures from healthy states, and 91% accuracy for classifying seizure onset from seizure events. Furthermore, the study applies the Gaussian Nae Bayes (GNB) classifier to differentiate various types of epileptic seizures, employing evaluation metrics such as the confusion matrix, ROC curve, and diverse performance measures. This method demonstrates significant potential in supporting experienced neurophysiologists decision in the clinical classification of epileptic seizure types. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Automated epileptic seizure classification using adaptive fast Fourier transform with non-uniform sampling and improved deep belief network
In automated brain-computer interaction (BCI), EEG signals are essential. This research uses AI to detect epileptic seizures, employing data from the BONN dataset (UCI), CHB-MIT dataset (physionet server), and Bangalore EEG Epilepsy Dataset (BEED). The goal is to develop an automated system for accurate seizure detection using adaptive fast Fourier transform with non-uniform sampling (AIFFT-NS) and an improved deep belief network (IDBN) model to enhance classification accuracy. The AIFFT-NS model serves as a channel for transforming spectro-temporal data. Using various EEG datasets, a number of extensive experiments are carried out, resulting in the validation of the efficacy of the proposed approach. High accuracy metrics, with 96.16% for the BEED dataset, 99.41% for the BONN dataset, and 96.31% for the CHB-MIT dataset, represent the evidentiary outcomes. This study emphasises the critical function of AI-facilitated spectro-temporal EEG analysis within the domain of medical diagnostics, going beyond the realm of automated seizure onset classification. Copyright 2024 Inderscience Enterprises Ltd. -
Exploration and Analysis of Seizure Spikes Through Spectral Domain Transformation
Seizure detection is the most crucial area of investigation when it comes to understanding brain disorders. This proposed research study embarked on an automated model for epileptic seizure diagnosis by means of different kinds of Spectral transformation using EEG inputs from seizure sufferers and healthy subjects. This automated model accommodates non-invasive brain electrical activity monitoring. This method aims to facilitate the analysis and identification of epileptic seizure states since, monitoring and diagnosing such brain electrical activity is a complex task due to its numerous divisions and underlying features. The primary objective of this research study is to distinguish between EEG-based seizures and healthy individuals. To achieve this goal, a combination of spectral transformation and EEG analysis techniques is utilized. These techniques include examining the frequency spectrum, magnitude spectrum, correlation, and T-Distributed Stochastic Neighboring Embedding (T-SNE) analysis. This analysis yields valuable insights from EEG data, refining the input data and making it more suitable for prediction and identification. The models performance is evaluated using two distinct datasets: real-time EEG data from individuals experiencing epileptic seizures and EEG data from healthy subjects. These datasets are sourced from the Bangalore EEG Epilepsy Dataset (BEED), India and the BONN epilepsy dataset from the UCI repository. In a comparative study of spectral transformation methods, including Complex Fast Fourier Transform (CFFT) and Real-Valued Fast Fourier Transform (RFFT), it is discovered that reducing the data dimension by using feature extraction is not the optimal approach. This simplification leads to the loss of valuable information. Therefore, preserving the full spectrum of EEG characteristics is crucial for gaining valuable insights into brain neuronal functions, ultimately enabling more accurate seizure prediction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Impact ofFeature Selection Techniques forEEG-Based Seizure Classification
A neurological condition called epilepsy can result in a variety of seizures. Seizures differ from person to person. It is frequently diagnosed with fMRI, magnetic resonance imaging and electroencephalography (EEG). Visually evaluating the EEG activity requires a lot of time and effort, which is the usual way of analysis. As a result, an automated diagnosis approach based on machine learning was created. To effectively categorize epileptic seizure episodes using binary classification from brain-based EEG recordings, this study develops feature selection techniques using a machine learning (ML)-based random forest classification model. Ten (10) feature selection algorithms were utilized in this proposed work. The suggested method reduces the number of features by selecting only the relevant features needed to classify seizures. So to evaluate the effectiveness of the proposed model, random forest classifier is utilized. The Bonn Epilepsy dataset derived from UCI repository of Bonn University, Germany, the CHB-MIT dataset collected from the Childrens Hospital Boston and a real-time EEG dataset collected from EEG clinic Bangalore is accustomed to the proposed approach in order to determine the best feature selection method. In this case, the relief feature selection approach outperforms others, achieving the most remarkable accuracy of 90% for UCI data and 100% for both the CHB-MIT and real-time EEG datasets with a fast computing rate. According to the results, the reduction in the number of feature characteristics significantly impacts the classifiers performance metrics, which helps to effectively categorize epileptic seizures from the brain-based EEG signals into binary classification. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Synthesis, properties, and state-of-the-art advances in surface tuning of borophene for emerging applications
Being composed of boron atoms that can be maneuvered to orchestrate low planar hexagonal structures, this two-dimensional material carefully exhibits versatility and has conventional covalent bonds between each atom. Borophene has recently proliferated the scientific research community by storm, trailblazing industries from fine chemicals, electrical equipment manufacturing, and biomedical innovation up to sustainable energy. Here, we provide streamlined information and particulars about the recent advances in the evolution of borophene since its inception and the essence of its electrocatalytic applications. We first introduce the sophisticatedly cultivated progress in borophene's structural, mechanical, optical, and electrical properties and further discuss its variegated polymorphism. Subsequently, we also delve into several capable synthesis techniques and recently concocted surface tuning and doping methods. Finally, we analyze the advancing state-of-the-art applications of this two-dimensional nanomaterial under investigation, ranging from bioimaging, energy storage, electrode reduction, and electrochemical sensing. Further, we have broadly discussed the future insights and challenges that borophene brings. 2024 -
Progressive crude oil distillation: An energy-efficient alternative to conventional distillation process
Distillation, the major process in crude oil refineries as of now. In this work we focused the attention to energy saving with respect to an industrial crude oil distillation unit. An alternative to the conventional crude oil distillation model present in the Bharat Petroleum Corporation, Kochi Refinery is proposed and simulated. The theoretical predictions as well as the simulated results indicate that the Progressive crude oil distillation reduces the utility burden as well as increase the extraction of more valuable light components. The simulation was carried out using Aspen HYSYS V8.8.2. Different crudes are taken into account and their properties and amount of distillate are analyzed. The optimization is done in an easy manner rather than the conventional mathematical method, together with the advanced process control tools; make it profitable in the operation in real time. 2018 Elsevier Ltd -
Simulation, optimisation and analysis of energy saving in crude oil distillation unit
Physical distillation is the major process in crude oil refineries as of now. To ensure quality control in the final products, it is essential to ascertain the true boiling point of the crude oil and the products. The work is mainly concentrated to an industrial crude oil distillation unit. The objective of the paper is to present the simulation and optimisation of crude distillation unit (CDU) along with the analysis of energy saving, using Aspen HYSYS V8.8.2. Different crudes are taken into account, their properties and amount of distillate are analysed. The process optimisation is done in an easier manner using Aspen HYSYS rather than the conventional mathematical method, together with the advanced process control tools; make it profitable in the operation in real-time. The simulation results are validated with the actual plant results. Copyright 2018 Inderscience Enterprises Ltd. -
Coronal Elemental Abundances During A-Class Solar Flares Observed by Chandrayaan-2 XSM
The abundances of low first ionization potential (FIP) elements are three to four times higher in the closed loop active corona than in the photosphere, known as the FIP effect. Observations suggest that the abundances vary in different coronal structures. Here, we use the soft X-ray spectroscopic measurements from the Solar X-ray Monitor (XSM) onboard the Chandrayaan-2 orbiter to study the FIP effect in multiple A-class flares observed during the minimum of Solar Cycle 24. Using time-integrated spectral analysis, we derive the average temperature, emission measure, and the abundances of four elements Mg, Al, Si, and S. We find that the temperature and emission measure scales with the sub-class of flares while the measured abundances show an intermediate FIP bias for the lower A-flares (e.g. A1), while for the higher A-flares, the FIP bias is near unity. To investigate it further, we perform a time-resolved spectral analysis for a sample of the A-class flares and examine the evolution of temperature, emission measure, and abundances. We find that the abundances drop from the coronal values towards their photospheric values in the impulsive phase of the flares and, after the impulsive phase, they quickly return to the usual coronal values. The transition of the abundances from the coronal to photospheric values in the impulsive phase of the flares indicates the injection of fresh unfractionated material from the lower solar atmosphere to the corona due to chromospheric evaporation. However, explaining the quick recovery of the abundances from the photospheric to coronal values in the decay phase of the flare is challenging. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Derris Indica Leaves Extract as a Green Inhibitor for the Corrosion of Aluminium in Alkaline Medium
The corrosion inhibitive effect of Derris indica leaves extract (DILE) on aluminium in 1 M NaOH is investigated at different temperatures. For this purpose, weight loss studies and electrochemical methods including potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) technique are employed. Surface analysis of the treated and untreated aluminium coupons are done by using metallurgical microscopy. About 60.2% of maximum corrosion inhibition efficiency is attained with an optimum inhibitor concentration of 1.2 g/L. Both weight loss and electrochemical studies confirmed that DILE plays a crucial role in the formation of a protective layer over metal surfaces. Also, electrochemical measurements revealed that DILE behaves as a mixed type of corrosion inhibitor. The kinetic parameters and thermodynamic parameters are calculated using Arrhenius theory and transition state theory. Langmuir adsorption isotherm was found to be the best fit and physical adsorption mechanism was proposed. En ineered Science Publisher LLC 2022 -
Predictors of behavioral and emotional issues in children involved in custody disputes: A cross sectional study in urban Bengaluru
Background: The increasing rates of divorce in urban India has led to the subsequent parental battle for the child's custody. This paper discusses the behavioral and emotional issues of these children in relation to their psychosocial environmental factors and other relevant socio-demographic variables. Methods: We used samples from parent interviews concerning 52 children aged 717-years-old, involved in child custody cases in the Family court of urban Bengaluru. The Strengths and Difficulties Questionnaire was used to measure response variables of behavioral and emotional issues in these children. Predictor models of quantile and multiple linear regression were used to assess the influence of psychosocial environmental factors and socio-demographic variables on the response variables. Results: The predictor models revealed that risk of child suffering emotional and behavioral issues increased with factors such as excessive parental control, change of academic environment, general unrest at school, frequency of child's court visit, child's visitation of non-custodian parent on occasions and vacations, and negatively altered family relationship. The model however intriguingly showed that residing in nuclear household rather than with their grandparents in a non-nuclear household, decreased the risk of mental health issues in these children. Conclusions: This study is a novel attempt to understand the influence of the psychosocial issues on the child's mental health in the context of custody cases in India. Despite the minimum sample size, the findings imply that family-based intervention is the need of the hour in these cases. The implications for clinical practice and research are discussed. 2021 Elsevier B.V. -
Protection of intellectual property and human rights during health emergencies: an assessment of the patent waiver proposal
Purpose: Several countries, such as South Africa and India, believe that intellectual property rights (IPRs), including patents, impede the efficient increase in vaccine production to inoculate the global population as they scramble to recover from the COVID-19 pandemic. Their proposal at the World Trade Organization (WTO) to waive these pharmaceutical patents has been met with resistance from a few developed countries, who believe that the abrogation of IPRs is unnecessary, even during a pandemic. The purpose of this paper is to discuss the impact of a potential waiver of medical patents at the WTO versus the status quo of IPR laws in the global economy. Design/methodology/approach: This study examines key arguments from economic and moral standpoints regarding the provisions of the Trade-Related Aspects of Intellectual Property Rights (TRIPS) agreement and other related international agreements and their validity based on the premise of the internalisation of positive externalities posed by vaccines. Findings: The effectiveness of the TRIPS agreement in securing medical access is weak on account of the ability of profit-making multinationals to secure IP rights and on account of the Trans-Pacific Partnership, a multilateral agreement that supports patent evergreening and a period of protection on test data which challenges the access to medicines and the fundamental human right to health. Originality/value: This study examines international IPRs through the lens of human rights and proposes a new system that balances the two. 2022, Emerald Publishing Limited. -
Integrating AI and Cybersecurity: Advancing Autonomous Vehicle Security and Response Mechanisms
The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditionally focused on preventing these attacks, the escalating risk underscores the importance of also vindicating their wallop. Nevertheless, this procedure is often onerous & facade scalability confronted, particularly due to connectivity issues in automobiles. This research advises a vehicle-based vibrant imposition response scheme, enabling swift responses to a variety of incidents and reducing reliance on external security centers. The classification encompasses an inclusive range of probable retorts, a procedure for evaluating retorts, & innumerable assortment approaches. Implemented on an embedded platform, the solution was evaluated using two distinct cyberattack use cases, highlighting its adaptability, responsiveness, volume for dynamic arrangement constraint alterations & nominal memory trail. Concurrently, this paper presents an innovative (AVSF) that synergistically integrates (AI) and cybersecurity techniques to fortify AV resilience against evolving threats. Additionally, the framework incorporates advanced cybersecurity measures such as encryption, authentication, and intrusion detection to mitigate vulnerabilities and safeguard critical AV systems. The fusion of AI and cybersecurity not only enhances AV security posture but also enables intelligent cyber threat monitoring and response capabilities. Extensive simulations and experimental evaluations demonstrate the efficacy of the AVSF in real-time scenarios, contributing to the development of robust security solutions for autonomous vehicle deployment and advancing safer transportation systems in the era of AI-driven mobility. 2024 IEEE. -
Continuity and changes in food consumption pattern among Tibetan refugee community in India
The Food consumption pattern of refugee communities is being carried out by many scholars and few acknowledged the food continuity, its implications on the health of refugees in the host country. The present study highlights food continuity among Tibetan refugees in the Bylakuppe settlement, India. 200 household data were administered to understand food consumption patterns by employing a structured household questionnaire. Simultaneously, 23 individual data were collected consisting of first migrants (15) and second-generation (8) for the qualitative study. Households derive energy mainly from carbohydrates and animal fats, and there is a prevalence of food insecurity among the Tibetan community. It is a proven fact that food insecurity will have serious health consequences in terms of emotional and mental well-being and suggest the need for further study of food insecurity among Tibetan refugees across the world. 2021 -
Influence of remittances oncapital endowment of Tibetan refugees in India
Purpose: An issue concerning Tibetan refugees in India is the poverty and unemployment among Tibetan youth. This often leads to households adopting a strategy of sending one of its members abroad towards North American or European countries in search of better income opportunities. Incomes in the form of remittances from these forward migrants have numerous impacts on living standard of left behind families. This study aims to focus on the influence of forward migrants remittances on livelihood in terms of human, financial and social capital development of Tibetan refugees in India. Design/methodology/approach: The paper includes 400 households from high-economic and low-economic-access regions of Tibetan settlements in India. Ordinary least square method was used to study these impacts. Findings: Findings show that remittances have significantly influenced human and financial capital development. However, it was found to be statistically not significant for social capital development. Originality/value: The present paper is original work. 2019, Emerald Publishing Limited. -
Privacy-preserving federated learning in healthcare: Fundamentals, state of the art and prospective research directions
Recent collaborations in medical diagnostic systems are based on data private collaborative learning using Federated Learning (FL). In this approach, multiple organizations train a machine-learning model at the same time eventually leading to global model generation. This paper reviews the fundamentals of FL and its evolution path in Healthcare. The objective of this review is to scope a wide variety of healthcare applications in FL. Exactly what research direction is moving in interesting for research communities to guide their future course. This review uniquely focuses on examining numerous FL-based healthcare implementations, detailing their core methodologies and performance metrics, which, to our knowledge, have not been previously available. Privacy-preserving collaborative distributed learning through federated learning in healthcare enhances research collaborations, thereby resulting in better-performing models. This comprehensive review will act as a valuable reference for researchers exploring new FL applications in the healthcare domain. 2024 IEEE. -
Titania Doped CDs as Effective CT-DNA Binders: A Novel Fluorescent Probe via Green Synthesis
Carbon dots (CDs), which belong to the class of zero-dimensional carbon-based nanomaterials, have garnered significant interest owing to their wide array of applications spanning from the electronics industry to the healthcare sector. This work employs a facile, inexpensive approach to synthesize green luminescent carbon dots (J-10) from a potential medicinal plant named Justicia Wynaadensis by the one-step hydrothermal method. A nanocomposite (JT-10) of the CDs is prepared by adding TiO2 nanoparticles derived from green synthesis of Lavandula leaves. The J-10 and JT-10 are further characterized by X-ray Diffraction spectroscopy (XRD), Transmission Electron Microscopy (TEM), Raman analysis X-ray Photoelectron Spectroscopy (XPS), and Fourier transform infrared techniques (FTIR), UVvis spectroscopy, Photoluminescence (PL), and Fluorescence or PL lifetime analysis. The average size of synthesized CDs is 1.85 nm and exhibits an excitation-dependent fluorescence nature at 320 nm. PL lifetime analysis of J-10 and JT-10 is calculated to be 5.80 and 2.84 ns respectively. Offering these unique optical properties and biocompatibility, the synthesised material is suitable for investigating their binding affinity and interaction mechanisms with DNA. The use of JT-10 in DNA binding studies contributes to the development of sustainable and efficient nanomaterials for applications in biosensors, drug delivery, and gene therapy. 2024 Wiley-VCH GmbH. -
Residual-Based Statistical Process Control Charts in the Presence of Multicollinearity: an EWMA Framework with (RK) Estimator
Reliability monitoring of financial health requires strong control mechanisms, and the residual chart is an invaluable instrument to perform it. One of the key problems statisticians face while modeling is the problem of multicollinearity which arises when there is a strong correlation between independent variables leading to imprecise coefficient estimates and poor outcomes. To solve this problem and to make sure that the control chart works even with correlated data, we integrated a Weighted Moving Average Exponential smoothing chart within the modeling technique. The theoretical approach assures long-term variability and consistency of the residual control chart. These control charts are used to understand the process and the performances in various sectors. The charts can be used as analytical instruments to help recognize patterns, variations, or anomalies in economic indicators specifically in budget deficit data and facilitate rapid identification of any changes or inconsistencies in the fiscal deficit by policymakers. Further advances in statistical process control are rendered feasible by this study, which deepens the understanding and awareness of the potential uses and implications of the Weighted Moving Average Exponential smoothing chart for fiscal deficit data in the Economic realm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Tracking Sigmoid Regression with Multicollinearity in Phase I: An Approach Incorporating Control Charts
Regression and quality control are two crucial techniques that data analysis employs in improving the decision-making process. We use the sigmoid function to model the connection between independent factors and the dependent variable in sigmoid regression. When there is a significant correlation among the independent variables in a regression model, multicollinearity a statistical phenomenon exists. Multicollinearity presents problems with higher uncertainty when estimating individual coefficients possibly making it harder to identify each variable's distinct contribution to the model. By suggesting a control chart specifically designed for the sigmoid regression model, this research presents a strategy to address the impact of influential observations using regression control charts, by making use of principal component regression class estimators. Principal component regression merges from the principal component analysis and linear regression methodologies, aiming to alleviate multicollinearity issues and enhances the stability of regression models. The performance of the model is evaluated using Pearson's residuals, Deviance residuals, and residuals. This strategy is proven to be useful in real world situations demonstrated through an application in the field of sleep wellness disorder. In conclusion, this study introduces a unique control chart to manage multicollinearity in sigmoid regression, providing a new perspective on the topic to spot differences in the underlying process by highlighting trends in the residuals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Thermorheological effect on RayleighBard magnetoconvection in a biviscous Bingham fluid with rough boundary condition on velocity and Robin boundary condition on temperature
The thermorheological effect on the onset of RayleighBard convection in a biviscous Bingham fluid in the presence of a horizontal magnetic field is investigated considering rough boundary conditions on velocity and Robin boundary conditions on temperature. The viscosity of the electrically conducting fluid is assumed to be sensitive to temperature variation. Linear and global nonlinear stability analyses are performed using the Chebyshev pseudospectral method to determine the existence of instability or otherwise. A general interpretation is made from the results to show the effects of the magnetic field and the variable viscosity on the system's stability. The biviscous Bingham parameter and the Chandrasekhar number are shown to have a delay in the onset of convection, while the effect of temperature sensitivity is to advance the onset. It is found that the results of linear and global nonlinear stability are not in agreement, so the region of subcritical instability exists. Also, the results obtained for RayleighBard convection agree pretty well with those of Platten and Legros and Siddheshwar et al. for the limiting cases. 2023 Wiley Periodicals LLC. -
Study of Influence of Combustion on DarcyBard Convection with Inherent Local Thermal Non-equilibrium Between Phases
This work deals with a DarcyBard convection problem in the presence of combustion and with local thermal non-equilibrium between the fluid and the solid phases. The effects of combustion and local thermal non-equilibrium on the onset of convection is studied in the linear and nonlinear regimes. Unlike all reported local thermal non-equilibrium problems reported so far, the present problem has a unique situation of having thermal non-equilibrium not only in the perturbed state but also in the basic state. Further, we observe that local thermal non-equilibrium does not, under any circumstance, lead to local thermal equilibrium except in an approximate sense when the combustion is quite weak. The effect of combustion is to advance the onset of convection compared to that in its absence. The effect of local thermal non-equilibrium is to reinforce the effect of combustion. In the presence of both these effects, sub-critical instability exists. The results are obtained numerically and have implication in practical porous medium convection problems. 2022, The Author(s), under exclusive licence to Springer Nature B.V.