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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. -
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. -
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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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 -
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. -
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. -
Synthesis and third-order nonlinear optical properties of PEGylated platinum nanoparticles
PEGylated platinum nanoparticles, which are capped with polyethylene glycol-400, are synthesized through the chemical reduction technique. The sample was comprehensively characterised through UVvisible spectroscopy, X-ray diffraction (XRD) and transmission electron microscopy (TEM). XRD pattern for the sample revealed a face-centered cubic crystalline phase of platinum, with a lattice constant of 3.939 The average particle size, obtained from high-resolution electron microscopy analysis, is 3.73 nm. The third order NLO features were explored through the Z-scan technique, employing a continuous wave regime. The observed phenomena of nonlinear absorption (NLA) and nonlinear refraction (NLR) are attributed to reverse saturable absorption and thermal lens models. NLR index was measured to be in the range of 5.72 10?10 cm2/W, while NLA coefficient was found to be in the range of 1.86 10?5 cm/W, highlighting the potential of PEGylated Pt NPs for NLO applications. 2025 Elsevier B.V. -
Chitosan stabilized platinum nanoparticles: In vitro and in vivo screening for analgesic and anti-inflammatory applications
In this interdisciplinary research work, the chitosan stabilized platinum nanoparticles are synthesized through the wet chemical method, and the structural, surface morphological, and optical characterizations are done using X-ray crystallography, Raman spectroscopy, transmission electron microscopy, etc. The samples were tested in in vitro trials namely egg albumin denaturation assay and DPPH radical scavenging assays and showed significantly lower effective concentrations (EC50) such as 5.44 ?g/ml and 8.068 ?g/ml respectively. The in vitro experiments were followed by in vivo animal model for analgesic and anti-inflammatory behaviour at two doses of 25 mg/kg and 50 mg/kg utilizing the hot plate method and the carrageenan-induced paw edema model respectively. The in vivo hot plate model for analgesic effect demonstrated that the chitosan stabilized platinum nanoparticles perform exceptionally well and show >90 % analgesia (p < 0.01) by extending the reaction time in the hot plate methodindicating better analgesia. Carrageenan-induced paw edema model demonstrated the exceptional anti-inflammatory ability of chitosan-stabilized platinum nanoparticles. Despite being given at a comparatively lower dosage, chitosan stabilized platinum nanoparticles showed a considerable decrease in paw volume (4045 % edema inhibition) by the third hour of the anti-inflammatory experimentation (p < 0.01) outperforming the standard drug aspirin given at 100 mg/kg. 2025 Elsevier B.V. -
Synthesis of Chitosan Stabilised Platinum Nanoparticles and their Characterization
A simplistic green synthesis route for the platinum nanoparticles has been successfully identified by using chloroplatinic acid hexahydrate (H2 PtCl6.6H2 O) as the metal precursor and sodium borohydride (NaBH4) as the reducing agent at room temperature. Chitosan was used in minute quantities as capping and stabilizing agent. The visual observation of a black coloured colloidal suspension, the characteristic XRD peaks and the absorption peak in the range of 200-300nm confirmed the production of Pt nanoparticles. The average crystallite size calculated using Debye-Scherrer equation is about 19 2 nm and a less intense absorption peak was found at 246nm and 281nm. The FTIR spectroscopy was used to confirm the capping with chitosan molecules. Zeta-potential calculation gave a surface charge of-23.8mV, and this high negative value, then validated the stability of the nanoparticle. The synthesis of platinum nanoparticles is very significant for their catalytic activity and biomedical applications in industrial as well as healthcare sector. 2023, Books and Journals Private Ltd.. All rights reserved. -
Classification of Alzheimer's Disease Stages Using Machine Learning Techniques
Alzheimer s disease (AD) is a type of mental disorder which deteriorates the normal functioning of human brain by reducing the memory capacity of an individual. Age is the most common factor for AD and this disease cannot be reversed or stopped. Doctors can only treat the symptoms of AD which include personality changes and brain structural changes. Analyzing neuro-degenerative disorders, neuroimaging plays an important role in diagnosing subjects with AD and other stages of AD. The proposed research identified this gap and using MRI and PET newlineimages for recognizing AD in its early occurrences by the professionals. This helps in tailoring an appropriate treatment procedure for treating AD. As per literature survey, many researchers have worked with convolutional methods like inbuilt skull stripping with two or more conversions and classified with different CNN architectures. The proposed research experimented advanced skull stripping method and classified using deep learning architectures. This research emphasizes the implementation of ResNet50 architecture with T1 weighted MRI and Amyloid PET images for detecting the abnormalities in the brain patterns based on the image attributes. For the proposed experiment, a total of 5000 T1 weighted MRI data and 3000 newlineAmyloid PET data were used. The collected images were pre-processed with noise removal newlinetechniques and skull stripping method. The ResNet50 is used to classify AD from the data newlineobtained from the ADNI dataset. Pre-processed images /data were fed to the tuned for three class classification on ADNI image data at 200 Epochs shows the accuracy of 97.3% for T1 weighted MRI data and 98% for Amyloid PET data. The experimental results of the proposed model prove that it classifies the images according to various stages with better accuracy than the other existing models by achieving excellent results. -
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The mathematical model for heat transfer optimization of Carreau fluid conveying magnetized nanoparticles over a permeable surface with activation energy using response surface methodology
The sensitivity analysis and response surface methodology (RSM) is performed for the key parameters governed by the magneto-flow and heat transport of the Carreau nanofluids model toward a stretching/shrinking surface in the presences Arrhenius activation energy and chemical reaction. Nanofluid that displayed Brownian motion and thermophoresis was considered with the permeable condition. The effects of different physical parameters were analyzed by employing appropriate similarity transformations in nonlinear partial differential equations and converted to the dimensionless system of ordinary differential equations. The finite difference method in bvp4c code solves the equations numerically. Associated parameters are presented graphically and interpreted against local Nusselt number, Sherwood number, and skin friction coefficient. An increase in the activation energy factor leads to increased concentration in permeable flow. The higher the activation energy lower the temperature and causes the reaction rate constant to decrease. In addition, it slows down the chemical reaction and increases the concentration characteristics. The increase of radiation and Prandtl number leads to an increase in heat transfer for the permeable surface. Furthermore, the Schmidt number and the binary reaction rate parameter increase the mass transfer for suction/injection flow. As a result, the Nusselt number's highest sensitivity is the Eckert number and the lowest to the thermophoresis parameter. The Sherwood number's positive sensitivity is observed for the Eckert number and Brownian motion parameter, whereas negatively sensitive to thermophoresis. 2022 Wiley-VCH GmbH. -
Enhancing Personalization in Search Engines Through Behavioral Profiling
With the development of search engines, people demand more contextual, relevant, and important results according to their needs and preferences. The current paper will examine the enhancement of search engine personalization through behavioral profiling, which involves capturing user interaction data, such as search histories, clicks, and other similar data, to understand user interests and intentions. The behavioral profiling promotes the ability to adjust the results to the requirements of mutual changes in user behavior and apply machine learning algorithms and advanced data mining techniques. We describe the key aspects of the successful behavioral profiling systems, such as user modeling, data collection frameworks, and privacy boundaries of the data protection. The paper will address the points mentioned by providing behavioral profiling to enhance user satisfaction and effective search and engagement. It will discuss the predictive relevance ranking's triple impacts on socioeconomic gains: time, energy costs, and attention time. We also discuss the ethical issues of user data collection, and the invitation implies achieving the appropriate compromise between individualization and privacy. By the case studies and comparisons, we affirm that the behavioral personalization greatly improves the accuracy of the search when the methods are either static or generic. This study enhances the design of a smart, convenient search engine by cultivating actionable, individual-sensitive recency search. It aims to smoothly aid personalized interactions in real time, inspiring advancement in context-sensitive retrieval systems. The Research Publication,.