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Dynamic Behaviour Analysis of Multi-Cell Battery Packs: A Simulation Study
In the era of IoT understanding the dynamic behavior of a Lithium-ion Battery Management System (BMS) has become gradually more important. This research investigates the dynamic behaviour of a six-cell Lithium-ion Battery Management System (BMS) through simulation. The study employs a comprehensive model encompassing key battery parameters, including cell capacity, voltage limits, temperature thresholds, and charge/discharge characteristics. Additionally, state variables such as State of Charge (SOC), State of Health, and State of Function are integrated to capture the battery's internal dynamics. The simulation incorporates a sinusoidal current profile to emulate realistic operating conditions. Notably, Coulomb counting is employed for SOC estimation, and protective measures against overvoltage, undervoltage, and overcurrent are implemented. The study also addresses balancing strategies and communication interfaces within the BMS. The results reveal nuanced interactions between voltage, temperature, SOC, and current, offering insights into the intricate behaviour of the battery system under dynamic conditions. This research not only advances our understanding of BMS functionality but also lays a crucial foundation for the evolution of battery technology and energy management systems in the IoT landscape. The Institution of Engineering & Technology 2023. -
Financial Big Data Analysis Using Anti-tampering Blockchain-Based Deep Learning
This study recommends using blockchains to track and verify data in financial service chains. The financial industry may increase its core competitiveness and value by using a deep learning-based blockchain network to improve financial transaction security and capital flow stability. Future trading processes will benefit from blockchain knowledge. In this paper, we develop a blockchain model with a deep learning framework to prevent tampering with distributed databases by considering the limitations of current supply-chain finance research methodologies. The proposed model had 90.2% accuracy, 89.6% precision, 91.8% recall, 90.5% F1 Score, and 29% MAPE. Choosing distributed data properties and minimizing the process can improve accuracy. Using code merging and monitoring encryption, critical blockchain data can be obtained. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Pediatric brain tumor segmentation and classification framework using SGC-U-NET and ARC-DEEP-CNN
Timely and precise pediatric Brain Tumor (BT) classification is challenging in the prevailing studies owing to the lack of growth rate calculation. Therefore, this paper proposes a growth rate-aware intelligent BT classification using child Magnetic Resonance Imaging (MRI) based on Arcsin Deep Convolutional Neural Network (Arc-Deep-CNN). Initially, the child's MRI is collected and then pre-processed for angle correction, resolution improvement, skull removal, and edge sharpening to improve the image quality. Meanwhile, the binary image dilation is done in the postpre-processing for accurate tumor location identification using the Central Limit Theorem-based Battle Royale Optimization Algorithm (CLT-BROA). From the pre-processed images, the wavelet features are extracted to improve the detection rate. Based on the tumor-identified images, pre-processed images, and extracted features, a robust Shuffled Group Convolutional layer added U-Net (SGC-U-Net) significantly segments the normal brain, benign, core, and malignant tumors affected brain. Then, the 3D tumor reconstruction is done by performing splitting, feature extraction, and growth rate calculation. Finally, a novel Arc-Deep-CNN proficiently classifies the BT into Medulloblastoma, Glioma, and Meningioma tumors with respect to the growth rate. The proposed Arc-Deep-CNN achieved maximum accuracy and minimum training time of 98.77% and 52136ms, respectively, showing impressive performance in pediatric BT classification. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural Networks
Smart healthcare edge networks should be able to serve two purposes at once: to train federated machine learning models across a range of devices without violating patient privacy and to schedule other activities with latency constraints, like real-time patient events. Such methods as FFL-ANN attempt this by using fixed fuzzy rules, which do not work in the situation where the conditions of the network change in an unforeseen manner. In this paper, the framework FSI-ANN is introduced to combine particle swarm optimization to quality-aware model aggregation with ant colony optimization to adaptive real-time task scheduling and ANN-based predictions into a single framework. We experimented with FSI-ANN on 200 edge devices. It achieved 0.825 precision compared with 0.82 with FedAvg and 0.80 with FFL-ANN and reduced inference latency by 18%, 0.370.45 s. Throughput was maintained at 33 tasks/sec as compared with 27 of FedAvg. At burst load, the miss rate of the critical deadline was decreased by 90.2 percent and the energy consumed was decreased by 14.8% per round. The results suggest that adaptive learning using swarm is superior to the fixed rule-based approaches and simple averaging in the distribution of resources at the sustainable healthcare advantage. Copyright 2026 K. Praghash et al. International Journal of Distributed Sensor Networks published by John Wiley & Sons Ltd. -
Value-based teaching English language and literature
Aim. The ultimate aim became the realisation of the political ideas of democracy, equality, and social justice (Orlenius, 2001; Svingby, 1994, p. 57). The current study aimed to explore the concept of value education at Poornaprajna institutions that have adopted value education in their curriculum. Methods. The study was designed in a qualitative exploratory research approach. The researcher had adopted the interviews for a case study as a microscopic social study through observation and also studied the archival records in Poornaprajna institutions. The informal group interviews were administered as part of a qualitative research approach, which aimed to collect data from twenty eight English teachers and students of Poornaprajna institutions. Result. The validity and trustworthiness of the study were established by adopting Miles and Hubemans formula, which amounted to 0.91. The exploration and findings revealed the need and implication of value education in the present scenario. Further-more, the data analysis revealed that value education is a process that begins at home and continues in society, and further, it continues in formal educational institutions. Conclusion. The study envisions that the educational institutions must integrate the curriculum with value education so that students cultural worlds, meanings they attribute to behaviour, events which essentially lead to the developmental process of the society. The National Education Policy 2020, aims to universalise education in India by 2025; it also aims at inter-and multidisciplinary approaches in Indian education. The pre-sent study juxtaposes an interdisciplinary approach to English and value education. 2021, Pro Scientia Publica Foundation. All rights reserved. -
Effect of Phonological and Phonetic Interventions on Proficiency in English Pronunciation and Oral Reading
The current research aimed to know the effect of phonological and phonetic interventions in enhancing proficiency in English pronunciation and oral reading among teacher trainees. This study was of single-group pretest and posttest intervention designs. The sample size was selected through a stratified random sampling technique from teacher training colleges in Bengaluru. Two hundred and seven teacher trainees with L1 proficiency were chosen proportionately from Bangalore strata and orientated. Participants (N = 32) enrolled voluntarily in the intervention program for 20 hr. Intervention modules on phonology and phonetics were developed by the researcher and a segmental approach was adopted to teach modules in 20 sessions. After every session, the participants were allowed to record the modules in Audacity, a multiaudio recorder application. The recorded modules were interpreted, and scores were determined on number of intelligible words pronounced by the participants. Further, it was validated by the experts to authenticate the determined scores. The researcher applied oscillographic and observation methods to analyze the participants' progress in pronunciation and oral reading proficiency levels during the experiment. The Wilcoxon signed-rank test was used to test the impact of intervention between the pretest and posttest (before and after intervention). The hypotheses testing revealed the difference between preintervention and postintervention scores in phonological and phonetic awareness and oral reading among teacher trainees, and the sig. value is less than 0.05 across all the attributes. This study insists that English phonology and phonetics must be a crucial part of the English language teaching (ELT) curriculum and highlights that teachers must be able to spot the most appropriate pronunciation teaching and train the students of English as a foreign language (EFL) with intricates of intelligible pronunciation. This study navigates the need for language proficiency among teacher trainees, especially in English pronunciation and oral reading, and substantiates the evidence that effective intervention and teachers' knowledge of pronunciation enhance proficiency levels in pronunciation and oral reading among teacher trainees. Finally, it hopes that B.Ed colleges and teacher educators will be beckoned to use technology-integrated intervention to teach phonology and phonetics. 2024 Diwakar Prahaladaiah and Kennedy Andrew Thomas. -
Virtual influencers in India: Transforming digital marketing in the AI era
This chapter explores the emergence and impact of Virtual Influencers (VIs) in the Indian digital marketing landscape, focusing on their ability to engage consumers and redefine brand interactions through AI- driven personas. By examining case studies of prominent Indian virtual influencers like Kyra and Naina Avtr, the chapter analyzes their roles in consumer engagement, cultural relevance, and brand partnerships. The discussion highlights the unique strengths of VIs, including their consistency, scalability, and ability to operate across diverse platforms, while also addressing challenges related to authenticity, emotional connection, and regional adaptation. Drawing on theories of social influence and digital marketing, the chapter provides insights into the opportunities and limitations of VIs in India's culturally diverse and technology- driven market. It concludes by exploring the future of virtual influencers as dynamic tools for brands aiming to balance technological innovation with human- like relatability in a competitive digital ecosystem. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Pad Vending Machine with Cashless Payment
A vending machine aims to provide required product or the service to the customer with certain ease, wherein not much effort is required. This research work aims to design a pad vending machine with an option of payment using QR code which is implemented using blockchain to make the system much more efficient and reliable than the existing systems present in the Indian market. The system is divided into two parts, first being the working of the machine and second being the mode of payment which is implemented using a blockchain. It is noticed many times that due to unpredictable menstrual flow women tend to face a lot problems. To overcome this problem, a pad vending machine is proposed with certain advancements through which women can help themselves in the stated circumstances. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Online Voting System Using Blockchain
One of the major areas in technical development is blockchain and bitcoin. These technologies have enabled many simulations in in-hostile applications that have major issues with security and integrity of data. To provide more relevance to the available cyberphysical systems in the dimension of security, the blockchain technology offers a major help. If the present scenario is considered, we have multiple day-to-day applications that have been simulated and require more security enhancement. For example, the E-voting systems are a trend and their security features have to be upgraded to authenticate both systems and processes. The present research paper focuses on the same application and aims to provide security upgradation by proposing a working model of e-voting systems. 2020, Springer Nature Singapore Pte Ltd. -
SMOTE-Enhanced Machine Learning Techniques for Credit Card Fraud Detection
In today's digital world, most daily money transactions are done virtually through online systems. The rise in credit card transactions has increased the number of fraudulent transactions, leading to significant financial losses. Currently, the main problem faced during the analysis of transactions is the imbalance in the dataset. To address the issue of data imbalance and identify good models for accurately detecting fraudulent transactions, this paper presents a comparative study to determine the suitable machine learning algorithms for credit fraud detection. In this research study, Synthetic Minority Oversampling Technique (SMOTE) processing is done to balance the dataset, and various machine learning classifiers, Logistic Regression, Naive Bayes, K-Nearest Neighbor (KNN), Decision Trees, and Support Vector Machine (SVM) are compared and analyzed. During the experimental process, it was observed that after SMOTE was enhanced, SVM outperformed other models with an accuracy of 98.9%. When there are numerous features (variables) in the data, as is often the case in credit card transactions when several elements are taken into account, SVM can perform well. SVM differentiated outliers because of its margin-maximizing characteristics, which assisted in separating the fraudulent class from the non-fraudulent class. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Stock Price Prediction: A Multi-Model Framework Integrating Technical Indicators and Sentiment Analysis
This paper proposes a multi-model strategy that would improve the predictive power of stock prices by combining time-series analytics with external market indicators. The system allows five different base prediction methods; Long Short-Term Memory (LSTM), Enhanced Bidirectional LSTM (XLSTM), Support Vector Machine (SVM) which may use radial basis function (rbf), linear or polynomial (poly) kernels, Autoregressive integrated moving average (ARIMA), and Seasonal Autoregressive integrated moving average (SARIMA). A stacking procedure which uses linear regression as a meta-model together with a voting ensemble method is then employed to link these base models. The feature engineering is thorough, as it provides for general price and volume data, a battery of technical indicators (SMA10, SMA20, EMA 12, EMA 26, MACD elements, and RSI14) and a general sentiment indicator (summarised financial news). Sentiment analysis is performed by a pipeline that is trained using RoBERTa and yields discrete numerical values (0 negative, 1 neutral, 2 positive). The model's capability is very rigorously gauged by the conventional metrics Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy (DA). The real-world results demonstrate that the ensemble method is very efficient where the stacking arrangement leads to the lowest total MAPE of 0.6027 % MSFT and the highest directional Accuracy of 75.86 % GOOGL, thus, providing a strong evidence for the effectiveness of the thorough integration of heterogeneous machine-learning, statistical, and sentiment- analysis methods to produce the most accurate financial forecasts. 2026 IEEE. -
Self-Induced Versus Structured Corporate Social Responsibility: The Indian Context
Adoption of Corporate Social Responsibility (CSR) ranks among the top priorities of the corporates in contemporary times. It is treated as a core business practice across the corporate globe. In the year 2013, the Ministry of Corporate Affairs, Government of India enacted mandatory CSR rules under the Companies Act, 2013 and imposed statutory obligations on the companies operating in India to implement CSR activities. With this, India became one of the first countries in the world to legislate minimum regulatory spends on CSR practices. This chapter aims to evaluate the response of this legislation since the introduction of mandatory CSR rules in India. It looks into the important trends in corporate social responsibility spending of companies in India and also maps the CSR expenditure with various Sustainable Development Goals (SDGs). This chapter forms a case for deliberation for policymakers, practitioners, scholars and business organization to understand the implications of mandatory CSR as well as how Indian companies have responded to this CSR rule. The findings also provide important insights for the other countries promulgating statutory approaches to implement CSR in their own countries. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Message framing and COVID-19 vaccine acceptance among millennials in South India
Vaccine hesitancy and refusal remain a major concern for healthcare professionals and policymakers. Hence, it is necessary to ascertain the underlying factors that promote or hinder the uptake of vaccines. Authorities and policy makers are experimenting with vaccine promotion messages to communities using loss and gain-framed messages. However, the effectiveness of message framing in influencing the intention to be vaccinated is unclear. Based on the Theory of Planned Behaviour (TPB), this study analysed the impact of individual attitude towards COVID-19 vaccination, direct and indirect social norms, perceived behavioural control and perceived threat towards South Indian millennials intention to get vaccinated. The study also assessed the effect of framing vaccine communication messages with gain and loss framing. Data was collected from 228 Millennials from South India during the COVID-19 pandemic from September to October 2021 and analysed using PLS path modelling and Necessary Condition Analysis (NCA). The findings reveal that attitudes towards vaccination, perceived threat and indirect social norms positively impact millennials intention to take up vaccines in both message frames. Further, independent sample t-test between the framing groups indicate that negative (loss framed message) leads to higher vaccination intention compared to positive (gain framed message). A loss-framed message is thus recommended for message framing to promote vaccine uptake among millennials. These findings provide useful information in understanding the impact of message framing on behavioural intentions, especially in the context of vaccine uptake intentions of Millennials in South India. Copyright: 2022 Prakash et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. -
Big Data Preprocessing for Modern World: Opportunities and Challenges
Big data is an often misunderstood business term in the modern world. Multiple devices are connected to the internet and a democratization of available technologies. The data is generated almost exponential rate. This data is generated in large quantities, at a high speed and belongs to myriad categories. Coupled with advances in storage and processing hardware, it can derive insights from these bigger number of data but it works effectively. The data is to be transformed in the form of understandable and useable insights by algorithms and models. The data mining steps require data that is cleaned and structured to a larger extent. This is achieved by using various algorithms, processes and applications known as data pre-processing techniques. This article reviews the various data pre-processing techniques from a big data point of view. 2019, Springer Nature Switzerland AG. -
Green synthesized cobalt nanoparticles from Trianthema portulacastrum L. as a novel antimicrobials and antioxidants
Trianthema portulacastrum is a dietary and medicinal plant that has gained substantial importance due to its pharmacological properties. This plant was used for its various healing properties since the ancient period in ayurvedic system of medicine. The green synthesis technique is an eco-friendly as well as cost effective technique which can produce more biocompatible nanoparticles when compared with those fabricated by physio-chemical methods. Therefore, nanoparticles produced by green synthesis are credible alternatives to those which are produced by conventional synthesis techniques. This research mainly aims to produce nanoparticles with the methanolic leaf extract of T. portulacastrum. The optimized nanoparticles were further analyzed for anti-fungal, anti-bacterial and antioxidant properties. Disk diffusion assay was used for the determination of the antimicrobial property and on the other hand, DPPH radical scavenging assay as well as hydrogen peroxide scavenging activity proved the antioxidant property of the formulation. The study revealed that Escherichia coli (gram negative strain) shows greater zone of inhibition when compared with Bacillus subtilis (gram positive bacteria). The nanoparticles have also been reported to show significant anti-fungal activity against the strains of Aspergillus niger and Fusarium oxysporum which proves its desirability for its further use against both bacterial as well as fungal infections. The novel formulation can be explored dually as antimicrobial and antioxidant agent. 2023 Taylor & Francis Group, LLC. -
Sex Identity vs. Sexual Orientation: Understanding Transgender Category in India
Social Work Chronicle, Vol-1 (2), pp. 82-99. ISSN-2277-1395 -
Locating Indian universities in knowledge societies: A critique
Knowledge societies characterize a defining feature of the present era. Veering away from their initial connotation of scientific temper and reasoning, today, they assume a new meaning in which the basis of economy, polity, and social action is knowledge. In the post-capitalist, post-industrial societies, knowledge has become the foundation of industrial productivity and social wellbeing. The crux of knowledge production has been shifting from the traditional disciplinary contexts promoted by academic interests in the universities to its applications for better productivity and wellbeing. Nevertheless, productivity and usefulness are accorded an epistemological appeal in defining what counts as knowledge. In this context, the present paper discusses the changes in knowledge production and dissemination processes in knowledge societies and their implications for universities in India. 2019 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore). -
Death Rituals and Change Among Hindu Nadars in a South Indian Village
This article examines changes in the death rituals performed among Hindu Nadars in a South Indian village. It emphasises the importance of understanding ritual changes within their specific micro-level local contextual framework, including changing social structures at household and village level. This empirical evidence showcases how changing rituals connected to death reflect various adaptations through imitation, substitution and alteration of specific ritual elements and performants. It also identifies emerging class distinctions among Nadars and their connection with changes in rituals associated with death. This analysis of the changes depicts how Nadars use ritual actions in pragmatic ways, symbolically expressing and realising their aspirations for status enhancement through such ritual performances. 2021 SAGE Publications. -
Old and new private sector banks in india: Performance analysis
In the era of globalization and liberalization, a bank has to compete with not only local players and also internationally. It is tough time for Indian banking industry. Banks take various steps to improve the performance and provide best services to customers. Performance of banks refers to the capacity in generating sustainable profitability. Banks need to evaluate the performance and find the strength and weakness by considering important ratios. Researchers have analysed the performance of banks applying various tools and techniques. The present study seeks to assess the financial performance of old and new private sector banks operating in India. One of the models for assessing the performance of banks are CAMEL model. The study was conducted using secondary data. The study covered a period of 10 years and the data collected for study had been analysed using Simple Arithmetic Mean, Coefficient of Variation, Linear Growth Rate, Analysis of Variance (ANOVA) and Ranking for CAMEL model. Under this model, banks have been rated based on five criteria namely, C-Capital Adequacy, A-Asset Quality, M-Management Efficiency, E-Earning Quality and L-Liquidity. This study was conducted on the financial performance of twenty banks consisting of seven new private sector banks and thirteen private sector banks in India. The result has disclosed that various banks have been performing well in different parameters. As per CAMEL model overall ranking, Karur Vysya bank, Federal bank and Tamilnad Mercantile banks have occupied first three spots under the old private sector banks. In the case of the new private sector banks the first three slots have been held by HDFC bank, Kotak Mahindra bank and Yes bank respectively. The estimated ratios have disclosed that the new private sector banks have been enjoying a better financial health than the old private sector banks during the study period. The old private banks have to pay attention to improve the financial performance in the future. 2020 SERSC. -
Internet of things for building a smart and sustainable environment: A survey
In the previous decade, internet of things (IoT) has emerged as a transformative force in the quest to create smarter and more sustainable environments. By interconnecting a large array of sensors, devices, and infrastructure, IoT technology enables the real-time collection, analysis, and utilization of data to optimize resource management, improve decision-making, and reduce environmental impact. In smart cities, homes, industries, and agricultural settings, IoT plays a pivotal role in achieving resource efficiency, environmental preservation, and economic growth. However, its widespread adoption also poses several challenges related to privacy, security, and interoperability. As IoT continues to evolve, it promises to shape a future where sustainability and technological innovation go hand in hand, making a path toward more resilient, efficient, and livable environments. 2024, IGI Global. All rights reserved.