Browse Items (14421 total)
Sort by:
-
HQA Bot: Hybrid AI Recommender Based Question Answering Chatbot
The COVID pandemic has presented a number of challenges for education, particularly when it comes to reaching and engaging students. As a result, online education has become increasingly important, and artificial intelligence (AI) has played a crucial role in supporting this shift. The proposed tutor assistance question-answering system uses AI to automatically generate responses to student questions. This system includes a feedback mechanism, known as a satisfaction index that measures the efficiency of the generated responses and suggest relevant follow-up questions. The proposed Hybrid Recommender-based Dijkstras algorithm (HRD) improves the system's accuracy. This algorithm uses a combination of techniques to group relevant questions based on context, which improves the accuracy of identifying the next relevant question. In our customized dataset, this approach achieved an accuracy of 96% and an average accuracy of 82% across benchmarked datasets. With this system, we aim to bridge the gap between students and education by providing a more engaging and personalized learning experience. 2023, Ismail Saritas. All rights reserved. -
Analyzing the Market Dynamics of Electrical Appliances with a Special Emphasis on Sustainable Electric Energy
This study looks into the market dynamics of electrical appliances with a special emphasis on sustainable electric energy. The research aims to understand how factors such as technological advancements, consumer behavior, and regulatory policies influence the electrical appliances market. By examining the trends and challenges within this sector, the study highlights the growing importance of sustainability in product development and consumer choices. The main areas of focus include the adoption of energy-efficient technologies, the impact of rising household incomes on appliance usage, and the role of government policies and initiatives in promoting sustainable energy consumption. The findings of the study would provide insights into how the industry can align its practices with environmental goals while meeting the evolving needs of consumers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Biogenic Synthesis of Zinc Oxide Nanoparticles Mediated by the Extract of Terminalia catappa Fruit Pericarp and Its Multifaceted Applications
Zinc oxide nanoparticles (ZnO-NPs) were biosynthesized by using the pericarp aqueous extract from Terminalia catappa Linn. These NPs were characterized using various analytical techniques such as X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, ultraviolet (UV) spectroscopy, dynamic light scattering (DLS), and scanning electron microscopy (SEM), and XRD studies of the nanoparticles reported mean size as 12.58 nm nanocrystals with highest purity. Further SEM analysis emphasized the nanoparticles to be spherical in shape. The functional groups responsible for capping and stabilizing the NPs were identified with FTIR studies. DLS studies of the synthesized NPs reported ? potential as ?10.1 mV and exhibited stable colloidal solution. These characterized ZnO-NPs were evaluated for various biological applications such as antibacterial, antifungal, antioxidant, genotoxic, biocompatibility, and larvicidal studies. To explore its multidimensional application in the field of medicine. NPs reported a potential antimicrobial activity at a concentration of 200 ?g/mL against bacterial strains in the decreasing order of Streptococcus pyogenes > Streptococcus aureus > Streptococcus typhi > Streptococcus aeruginosa and against the fungi Candida albicans. In vitro studies of RBC hemolysis with varying concentrations of NPs confirm their biocompatibility with IC50 value of 211.4 ?g/mL. The synthesized NPs DPPH free radical scavenging activity was examined to extend their antioxidant applications. The antiproliferation and genetic toxicity were studied with meristematic cells of Allium cepa reported with mitotic index (MI index) of 1.2% at the concentration of 1000 ?g/mL. NPs exhibited excellent Larvicidal activity against Culex quinquefasciatus larvae with the highest mortality rate as 98% at 4 mg/L. Our findings elicit the therapeutic potentials of the synthesized zinc oxide NPs. 2023 The Authors. Published by American Chemical Society -
Transforming online class recording into useful information repositories using NLP methods: An Empirical Study
Most educational institutions have adapted to the mode of online teaching which has resulted in an increase of online video recordings. Learner community can be benefited with the ability to retrieve required information from the online class recordings. In this paper, we propose a methodology for converting video transcript data into useful information repositories for the purpose of retrieving class transcripts relevant to user's information needs. We focus on the online video recording transcript data. We also discuss challenges in transcribing which are crucial to understand preliminary processing. Our dataset consists of transcripts from diverse subject domains deeper experimental insights. We use interactive transcripts obtained from ASR (automatic speech recognition) services and non-interactive human generated transcripts. State-of-the-art methods for keyword retrieval: Latent Dirichlet Topic Modelling (LDA), Term Frequency (TF.IDF) and Text Rank (graph based) are applied on the video transcript data. Further, cosine similarity metric is applied to obtain the similarity measure between the transcript documents and keywords. 2022 IEEE. -
Sensory processing sensitivity in relation to coping strategies: exploring the mediating role of depression, anxiety and stress
Existing research on sensory processing sensitivity (SPS) focuses majorly on populations involving children, those with Autism Spectrum Disorder, and those belonging to the Western countries. This study aims to contribute in bridging this gap by exploring the mediating role of Depression, Anxiety, Stress on the relationship between SPS and coping strategies in the general population, while also assessing the prevalence of these variables. Data was collected from a convenience sample of 107 participants (mean age = 20.6years, 57.9% females). Participants responses were recorded for the Highly Sensitive Person Scale (HSPS), the Depression, Anxiety, Stress Scale (DASS-21), and the Coping Strategies Inventory-Short Form (CSI-SF). Correlational and mediation analyses of SPS, coping strategies and Depression, Anxiety and Stress were done. In the sample, 31.78% of individuals were found to be Highly Sensitive Persons (HSPs). The findings revealed significant relationships between SPS, Depression, Anxiety, Stress and coping strategies. Depression and Anxiety were observed to be significant mediators. While SPS as a trait may not be inherently modifiable, our results on its association with emotion-focused disengagement coping provide insight into target dysfunctional patterns for effective management of depression, stress, and anxiety. Further research is warranted to enhance the applicability of this study. The Author(s) 2024. -
Quality assurance in big data analytics: An IoT perspective
Emergence of IoT as one of the key data contributors in a big data application has presented new data quality challenges and has necessitated for an IoT inclusive data validation ecosystem. Standardized data quality approaches and frameworks are available for data obtained for a variety of sources like data warehouses, webblogs, social media, etc. in a big data application. Since IoT data differs significantly from other data, challenges in ensuring the quality of this data are also different and thus a specially designed IoT data testing layer paves its way in. In this paper, we present a detailed review of existing data quality assurance practices used in big data applications. We highlight the requirement for IoT data quality assurance in the existing framework and propose an additional data testing layer for IoT. The data quality aspects and possible implementation models for quality assurance contained in the proposed layer can be used to construct a concrete set of guidelines for IoT data quality assurance. 2019 Telecommunications Society and Academic Mind. -
Multi-level Prediction of Financial Distress of Indian Companies Using Machine Learning
Predicting Financial Distress (FD) and shielding companies from reaching that stage is vital, even indispensable for every business. FD, if not attended to on time, ultimately leads to bankruptcy. Prediction variables are essential to forecast the wreckage in the business; however, the prediction is successful when suitable models are used. This study aims to predict FD at three levels: from mild to severe, by applying a machine learning algorithm. The study identifies modern models using the machine learning approach for predicting multi-level FD and summarises the significance of modern models through machine learning technology, to sustain the future development of the economy. The modern models are free from rigid assumptions and have proved to be the best in the prediction of FD. The results show that FD prediction is important at multiple stages. The models performance will be high when the best features are selected using the Pearson Correlation and SFS Feature selection approach. Among the ten models used in the study, LightGBM Classifier shows the highest performance of 80.43% accuracy without feature selection. However, with Pearson Correlation Approach and SFS Feature Selection methods, the accuracy is 82.68% and 86.95% respectively. This study has major implications for the stakeholders of the company to take timely decisions on their investment and for the management as a yardstick to check the performance of the business. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
AI-Powered Disaster Management System Using Satellite Imagery: A Survey
Disaster management is all about time; timely response and an accurate assessment are the basis on which disaster damage may be limited and lives saved. Traditional methods of disaster response rely on human analysis and manual interpretation of satellite images, which are slow and prone to human error. Here, AI can prove to be a technology capable of using ML and DL algorithms to analyze vast quantities of satellite imagery in real time. AI-based systems can work to detect areas affected, assess the severity of the damage, and predict the evolution of disasters for better response and resource allocation. The paper presents recent developments in AI-based disaster management with the assistance of satellite imagery, sketching out major challenges and future research directions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Efficient Intrusion Detection through Class Balancing and Feature Selection: A Case Study with SVM
Intrusion Detection Systems are of paramount importance in network security. However, in real-world scenarios, they always suffer from the challenge of class imbalance, which is dominated by normal traffic. This paper presents a novel approach to enhancing the performance of IDS by proposing a hybrid of the Random Under sampling technique with the univariate feature selection technique, SelectKBest, for handling both problems of class imbalance and high dimensionality. This model was hence tried on the Bot-IoT dataset, which is a real-world IoT network traffic representation. The SVM classifier, which has been trained with the resampled and feature-selected data, showcased 95% balanced accuracy for both normal and malicious traffic detection. The combination of RUS and SelectKBest, apart from reducing overfitting, ensured the retention of the most relevant features and thereby made the IDS model robust. It can practically enhance the performance of IDS in an imbalanced and high-dimensional dataset by providing a balanced, efficient, and precise detecting mechanism. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Multi-level Prediction of Financial Distress of Indian Companies Using Machine Learning
Predicting Financial Distress (FD) and shielding companies from reaching that stage is vital, even indispensable for every business. FD, if not attended to on time, ultimately leads to bankruptcy. Prediction variables are essential to forecast the wreckage in the business; however, the prediction is successful when suitable models are used. This study aims to predict FD at three levels: from mild to severe, by applying a machine learning algorithm. The study identifies modern models using the machine learning approach for predicting multi-level FD and summarises the significance of modern models through machine learning technology, to sustain the future development of the economy. The modern models are free from rigid assumptions and have proved to be the best in the prediction of FD. The results show that FD prediction is important at multiple stages. The models performance will be high when the best features are selected using the Pearson Correlation and SFS Feature selection approach. Among the ten models used in the study, LightGBM Classifier shows the highest performance of 80.43% accuracy without feature selection. However, with Pearson Correlation Approach and SFS Feature Selection methods, the accuracy is 82.68% and 86.95% respectively. This study has major implications for the stakeholders of the company to take timely decisions on their investment and for the management as a yardstick to check the performance of the business. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Synchronous learning and asynchronous learning during COVID-19 pandemic: a case study in India
Purpose: This research aims to study the students' perspectives on synchronous and asynchronous learning during the COVID-19 Pandemic. Both synchronous and asynchronous learning approaches used in online education have positive and negative outcomes. Hence, the aim is to study online education's positive and negative consequences, reflecting sync and async approaches. This research followed a mixed research approach. The key stakeholders of this research are the Indian educational institutions and students. Design/methodology/approach: This research collected data from the students undergoing synchronous and asynchronous learning amidst the COVID-19 Pandemic. The data were collected (N=655) from various students taking online classes during the pandemic. A questionnaire survey was distributed to the students through online platforms to collect the data. In this research, the authors have collected data using simple random sampling, and the same has been used for data analysis using SPSS version 26. The collected data were exposed to a factor analysis using a principal component analysis technique to reduce the vast dimensions. Findings: The study findings show that synchronous learning is sometimes stressful, placing more responsibility on students mainly because of the increased screen time. At the same time, asynchronous learning allows the students to self-explore and research the topics assigned to them. Students also felt that asynchronous activities create a burden because of many written assignments to be submitted within a short period. Overall, the COVID-19 pandemic has been challenging for the students and the teachers. However, teachers have helped students to learn through digital platforms. The majority of the respondents opined that technological disruptions and death in the family circle had been significant reasons for not concentrating during online classes. However, the combination of synchronous and asynchronous learning has led to a balanced education. Practical implications: Higher education has undergone multiple transformations in a short period (from March 2020, 2021 and beyond). Educational institutions underwent a rapid transition in remote teaching and learning in the initial stages. As time progressed, educational institutions did course navigation where they relooked into their course plans, syllabus and brought a structural change to match the pandemic requirements. Meanwhile, educational institutions slowly equipped themselves with infrastructure facilities to bring academic integrity. At present, educational institutions are ready to face the new normality without disrupting services to society. Social implications: Educational institutions create intellectual capital, which is important for the development of the economy. In the light of COVID-19, there are new methods and approaches newly introduced or old methods and approaches, which are reimplemented, and these approaches always work for the benefit of the student community. Originality/value: The authors collected data during the COVID-19 pandemic; it helped capture the students' experience about synchronous and asynchronous learning. Students and faculty members are newly exposed to synchronous and asynchronous learning, and hence, it is essential to determine the outcome that will help many stakeholders. 2022, Cassandra Jane Fernandez, Rachana Ramesh and Anand Shankar Raja Manivannan. -
NEUROSTIMULATION IN LONG COVID: Advancing Neurocognitive Rehabilitation and Recovery
Neurostimulation techniques are emerging as promising interventions for addressing neurocognitive impairments associated with Long COVID, including brain fog, fatigue, memory deficits and executive dysfunction. Non-invasive modalities such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) have demonstrated potential in modulating neural activity, enhancing cognitive recovery and alleviating neuroinflammatory processes linked to post-viral syndromes. Vagus nerve stimulation (VNS) and transcutaneous auricular VNS (taVNS) offer additional therapeutic avenues by targeting autonomic dysfunction, which is often implicated in Long COVID-related dysautonomia and cognitive fatigue. Neuromodulation approaches combined with neurofeedback and cognitive rehabilitation may optimise neuroplasticity and functional outcomes in affected individuals. Wearable neurostimulation devices and home-based therapies further improve accessibility, offering scalable solutions for post-COVID neurorehabilitation. However, challenges such as variability in patient response, optimal stimulation parameters and long-term efficacy require further investigation. Integrating neurostimulation into multidisciplinary rehabilitation frameworks that include cognitive training, exercise therapy and pharmacological support may enhance recovery trajectories. Future research should prioritise personalised stimulation protocols, biomarker-driven treatment strategies and longitudinal studies to establish evidence-based guidelines for neurostimulation in Long COVID. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
Digital Forensics Chain of Custody Using Blockchain
In todays world, Digital Forensics is a crucial subject with much scope as data storage becomes more decentralised. The collection and preservation of digital media is a topic of concern across the Cyber Security and Digital Forensics field. With Cloud Infrastructure and other technologies, data is not permanently stored in one place and gathering and analysing it can become a headache for Forensic Investigators. Blockchain, however, works as a decentralised, distributed peer-to-peer network and thus can be considered a suitable solution for the mentioned problems. With the help of a blockchain network and Smart Contracts, Digital Forensics can be significantly improved to adapt to modern digital architecture. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques
IoT is an emerging giant in the field of technol- ogy, taking over traditional systems, providing interconnected- ness, convenience, efficiency, and automation, making our lives unimaginably better. However, security for these IoT systems is challenging, especially due to their interconnectedness, making them vulnerable to various cyber threats. The rising tide of IoT botnets, especially, presents a unique challenge. This has urgently increased the need for Intrusion Detection research. Modern Intrusion Detection approaches often employ Machine Learning for effective results. Feature Selection is extremely important while creating Machine Learning Classification models to avoid overfitting and poor performance. This paper focuses on running a Feature Selection study on the Bot-IoT dataset provided by UNSW to increase the accuracy of a ML model. The paper tests 5 types of Feature Selection methods, from Filter- based, Wrapper-based and Embedded methods, combined with two distinct ensemble classifiers: Random Forest + Adaboost and XGBoost. Each combination is tested with the dataset, and the accuracy is compared to find the most effective and versatile feature selection method that can assist both Stacking and Voting- type Ensemble classifiers. The results show that Karl Pearson can provide the best accuracy when applied to both Ensemble Classifiers. 2024 IEEE. -
Determinants of Auditor Choice: Evidence from Sharia Commercial Banks in Indonesia
This research aims to determine the impact of corporate governance, firm complexity, foreign ownership, and ownership concentration towards auditor choice for Sharia commercial banks in Indonesia in 2016-2023. Firm size is also accounted for as a control variable. This research was conducted using a quantitative approach using the logit logistic regression analysis method through the Eviews 13 software. The sampling method was carried out using a purposive sampling method, which produced a sample of 9 Sharia commercial banks in Indonesia with a total of 72 observations. This study aims to provide an overview of the factors that Sharia commercial banks in Indonesia consider in choosing their external auditors, namely between Big 4 and non-Big Four auditors, which differ from other companies and industries. The results show that in partial analysis, corporate governance mechanisms and ownership concentration significantly and negatively affect auditor choice. Meanwhile, firm complexity and foreign ownership do not affect auditor choice. Low demands cause the negative influence of ownership concentration due to the private nature of the banks and efforts to achieve efficiency in audit fees while maintaining the same quality standards. 2025, Creative Publishing House. All rights reserved. -
Effects of Rough Boundaries on RayleighBenard Convection in Nanofluids
A linear stability analysis of RayleighBenard convection in a Newtonian nanofluid is carried out using most general boundary conditions. A single-phase description of nanofluids is adopted in the study. The nanofluids used for the study are wateralumina and watercopper nanofluids in order to analyze how a choice between them can be made. The values of thermophysical quantities of nanofluids are calculated using the mixture theory and phenomenological-laws. The paper applies the Maclaurin series in solving the boundary-eigenvalue-problem through a simple and innovative approach. A single-term Galerkin technique is adopted to obtain the guess value of the critical Rayleigh number and the wave number. Further, improved values of the Rayleigh number and the wave number are obtained using the solution of a system of three linear-algebraic equations. A detailed discussion is made on the effect of rough-boundaries and Robin-boundary conditions for temperature on the onset of convection. A comparative study between the results of two nanofluids is made and the destabilizing effect of nanoparticles in the Newtonian carrier-fluid on the onset of convection is studied. Copyright 2023 by ASME. -
Steady Finite Amplitude Convection in Type 2 Hybrid Nanofluids with Rough Boundaries and Robin Boundary Condition on Temperature
The study concerns linear and weakly non-linear analysis of a Rayleigh-Bard convection problem subjected to a most general boundary condition. This general boundary condition consists of rough boundaries on velocity and Robin boundary condition on temperature. With the help of specific non-dimensional parameters, i.e., the slip-Darcy number and the Biot number that arise at lower and horizontal boundaries, we have been able to integrate 16 Rayleigh-Bard convection problems into one. Both parameters display a stabilising effect on the onset of convection. Utilising a minimal Fourier series representation, a generalised Lorenz model is derived. The solution of this Lorenz model is used to obtain the Nusselt number expression. The study also involves the usage of mono nanofluid and hybrid nanofluid of the type where spherical-shaped nanoparticles (alumina/copper) are dispersed into a binary base fluid mixture (water-EG). The thermophysical properties of the binary base fluid mixture and the corresponding nanofluids are calculated using mixture theory. Also, the thermophysical properties of mono nanofluid are derived and calculated from the mixture theory defined for the hybrid nanofluid type, which accounts for the correctness of the mixture theory used (verified using phenomenological laws and mixture theory for mono nanofluid). The papers main aim is to throw light on the ease rendered by the usage of general boundary condition, along with presenting a theoretical base for choosing the most suitable nanofluid concerning convection problems. An increase of 96.2984% in critical Rayleigh number is observed in the case of water-EG-alumina nanofluid when Biot number is increased from 10?3 to 106. Likewise, an increase of 107.223% in critical Rayleigh number for water-EG-alumina nanofluid is observed when slip-Darcy number is increased from 10?3 to 106. Limiting cases of the Rayleigh-Bard problem for 16 boundary conditions including free/rigid isothermal/adiabatic combinations at lower and upper boundaries are obtained, thereby presenting a strong validation for the study. Plots of stream function for different boundary conditions are included for a better physical understanding of the problem. 2025, Penerbit Akademia Baru. All rights reserved. -
ON NEAR Fk-PERFECT AND DEFICIENT Fk-PERFECT NUMBERS
For a positive integer n, the arithmetic function ?2(n) denotes the sum of squares of all the divisors of n. A positive integer n is called an F-perfect number if ?2(n) ? n2 = 3n. A positive integer n is termed a near F-perfect number if ?2(n) ? n2 ? d2 = 3n, where d is a proper divisor of n. Similarly, n is considered a deficient F-perfect number if ?2(n) ? n2 + d2 = 3n, where d is a proper divisor of n. In this paper, we discuss several characterizations of these numbers, establish their relations with other significant numbers, and generalize the near-perfect and deficient-perfect numbers. 2025, Colgate University. All rights reserved. -
On k-Facile Perfect Numbers
For a positive integer n, let ?(n) denote the sum of all positive divisors of n. Then n is said to be a k-facile perfect number if ?(n) = 2n + d1d2 dk, where 1 < d1, d2,, dk < n are distinct divisors of n. This paper characterizes k-facile perfect numbers and establishes their relationships with other special numbers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Sectoral correlations and interlinkages: NSE
An efficient portfolio is a well-diversified portfolio that gives the investor opportunities to earn money and provide cover against risks. Understanding the intersectoral linkages and correlations among various sectors in a stock market will help an investor to diversify the portfolio and reduce risk efficiently. This study aims at examining the underlying linkages and correlations among eight sectors in the Indian National Stock Exchange (NSE) using a Granger causality test under VAR environment. The results of the study based on nine years' data from 2009 to 2018 show that an effective portfolio can have two classifications -stocks from Pharma and Media as group one (defensive stocks) and picks from IT, Bank, Financial Services, Realty, Auto and FMCG sector as group two (somewhat Cyclical). The study further proves that the usual definition for cyclical and defensive sectors have undergone some profound changes. 2020 SCMS Group of Educational Institutions. All rights reserved.