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Volatility Spillover Effects in Cryptocurrencies
Cryptocurrencies' growing use has increased investors and decision- makers interest. Cryptocurrencies' volatility and how it impacts others is most intriguing. Arguments include speculative pressures, valuation uncertainty, and lack of regulation. These traits cannot fully explain cryptocurrency volatility and volatility spillovers, suggesting other relevant factors. In this study, currency volatility and spillovers, as well as their relationship with the sentiment of global investors, were investigated. The study analysed 22 cryptocurrencies from 01/01/2018 to 31/12/2022. The study used FIGARCH and FIEGARCH, a GARCH family model to analyse the long-memory and leverage effects on cryptocurrency volatility, the ADCC-GARCH framework, and the Diebold-Yilmaz spillover index to analyse cryptocurrency volatility spillovers. The long-memory and leverage impacts on bitcoin volatility were analysed using the FIGARCH and FIEGARCH models from the GARCH family. Both the Chow-test and the Pai-Berron Test found structural breaks in the cryptocurrencies. Cryptocurrencies such as Adacordono, Aertinity, ARK, BAT, BCH, BNT, BTC, Dogecoin, Ethereum, Funtoken, ICON, KMD, KNC, NEO, PIVX, QTUM, SNT, TRX, ZCASH, have positive (difference) FIGARCH coefficient values. It indicates a long memory in currencies, and volatility shocks affect future volatility. On the other hand, the FIGARCH coefficient of BTG cryptocurrency (difference) is negative (-0.035), which suggests that the individual has a short memory. In this scenario, the effects of volatility shocks are only temporary. When extreme volatility is promptly followed by low volatility or vice versa, this indicates anti- persistence. The study also found that both positive and negative news has a significant impact on the volatility of specific cryptocurrencies such as BCH (0.015), BNT (0.0016), BTG (0.01972), DOGE (0.2296), EOS (0.0112), KNC (0.0366), PIVX (0.0021), TRX (0.0013), Adacordono (- 0.027), Aertinity (-0.0393), ARK (-0.0377), BAT (-0.028058), and BTC (-0.0665). Ethereum has the largest spillover (4.09), followed by QTUM (4.06), EOS (4.05), Adacordono (4.05), and Dogecoin (2.4). All cryptocurrencies show fundamental instabilities (P-values less than 0.05). Hence the alternative hypothesis is accepted, and the null hypothesis is rejected. The hill estimator tail index value is ? > 0, fat tail or heavy tail; high chance of catastrophic event which is observed in all the 22 cryptocurrencies. Both investors and speculators can use sentiment analysis to forecast market volatility and generate gains. Policymakers can also utilize this information to establish laws that reduce market volatility. As a result, the study contributes to the ongoing discussion on the factors that cause bitcoin's volatility.3 -
Volatility Prediction in the Indian Share Market Using Sentiment Analysis
In addressing the challenge of accurate volatility prediction in the Indian share market, the study explores the performance of deep learning-based models using sentimentdriven features. A demo model was deployed using data from five major Nifty 50 stocks-RELIANCE, HDFCBANK, INFOSYS, ITC, and MARUTI-for the financial years 2020 to 2023. We compared the results of traditional ARIMA model and standalone LSTM and its hybrid variants: LSTM + CNN and LSTM+RNN. Sentiment scores were gathered from financial news using FinBERT and NLTK, and combined with stock price data to generate time-series features. While all models demonstrated promising results, the LSTM+RNN hybrid model consistently achieved the lowest MAE and RMSE, indicating improved learning of temporal dependencies. The standalone LSTM and LSTM+RNN models also showed positive results for Sharpe ratio and Maximum drawdown indicating strong economic significance of the models. The study emphasizes the potential of hybrid LSTM architectures in modeling market volatility driven by investor sentiment. Limitations included limited dataset size, exclusion of other volatility factors, and overfitting in early hybrid GARCH trials. Future work aims to expand data coverage, integrate hybrid GARCH models more effectively, and explore additional market indicators. This research highlights a scalable and effective approach for sentiment-informed volatility forecasting in financial domains. 2025 IEEE. -
Volatility in Indian stock markets during COVID-19: An analysis of equity investment strategies
The aim of the paper is to evaluate the impact of novel COVID-19 on the returns and volatility of Indian stock markets with special reference to equity investment strategies of the Bombay Stock Exchange. For the purpose of evaluating the impact, the study has applied GARCH. The research has considered a time frame from March 2015 to January 2021. Prior to implementing GARCH model, pre-estimation tests (i.e., augmented Dickey-Fuller and ARCH-Lagrange multiplier) were conducted. Outcomes clearly indicate that the returns during the crisis for all the strategy indices have been negative, which means that the COVID-19 outbreak resulted in massive losses. Additionally, 'during crisis' period showed an increase in volatility for all the strategy indices depicting that the pandemic has a long-lasting effect and will take time to fade off. This research will help the investors in the investment decision process by giving them insights about the different strategies. 2021. -
Volatility Clustering in Nifty Energy Index Using GARCH Model
Volatility has become increasingly important in derivative pricing and hedging, risk management, and portfolio optimisation. Understanding and forecasting volatility is an important and difficult field of finance research. According to empirical findings, stock market returns demonstrate time variable volatility with a clustering effect. Hence, there is a need to determine the volatility in Indian stock market. The authors use Nifty Energy data to analyse volatility since the Nifty Energy data can to be used to estimate the behaviour and performance of companies that represents petroleum, gas, and power sector. The results reflect that Indian stock market has high volatility clustering. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Voicing Out Parental Experiences of Schooling Their Children with Learning Disabilities: A Qualitative Study of Inclusive Government Schools of India
The paper shone light on the lived experiences of parents of children with learning disabilities. The specific objective was to understand the challenges, experiences and aspirations of parents for their children. A phenomenological study was adopted for the study so as to focus on the experiences of the parents. Participants were parents (female- 17 and male- 3) of children in primary classes, who were identified through purposive sampling from government schools of Delhi, NCR from 3 underdeveloped areas of Delhi - Nangloi, Mangolpuri and Ranhaula. The data was collected by semi-structured interviews and later thematically analyzed. The findings were on the basis of the past and present experiences and further their future aspirations for the children. They revealed that the parents faced challenges with applying and issuance of the UDID certificates, but with the collaborative efforts of the special educator and the parents along with various support systems that are provided by the school their experiences became positive. It was also brought to light that the mother was the main caregiver in most of the cases. All the parents were worried, what will happen to their children if they are not there with them. They aspired that the students will be financially independent and have a safe future ahead of them. They dream of a society where all the students are equal in an inclusive environment. The Author(s) 2025. -
Voicing Out Parental Experiences of Schooling Their Children with Learning Disabilities: A Qualitative Study of Inclusive Government Schools of India
The paper shone light on the lived experiences of parents of children with learning disabilities. The specific objective was to understand the challenges, experiences and aspirations of parents for their children. A phenomenological study was adopted for the study so as to focus on the experiences of the parents. Participants were parents (female- 17 and male- 3) of children in primary classes, who were identified through purposive sampling from government schools of Delhi, NCR from 3 underdeveloped areas of Delhi - Nangloi, Mangolpuri and Ranhaula. The data was collected by semi-structured interviews and later thematically analyzed. The findings were on the basis of the past and present experiences and further their future aspirations for the children. They revealed that the parents faced challenges with applying and issuance of the UDID certificates, but with the collaborative efforts of the special educator and the parents along with various support systems that are provided by the school their experiences became positive. It was also brought to light that the mother was the main caregiver in most of the cases. All the parents were worried, what will happen to their children if they are not there with them. They aspired that the students will be financially independent and have a safe future ahead of them. They dream of a society where all the students are equal in an inclusive environment. The Author(s) 2025. -
Voices of the Future: Generation Zs Views on AIs Ethical and Social Impact
As artificial intelligence (AI) becomes increasingly integral to modern society, its profound implications are coming to the forefront of discussions. This research paper investigates the perspective of Generation Z on the multifaceted societal and ethical impacts of AI. Gen Z is the first generation to fully embrace AI across all facets of life. Therefore, understanding their attitudes, concerns, and expectations towards AI is imperative for cultivating a responsible, adaptable, and ethically conscious society in the AI-driven era. This study addresses a significant research gap by exploring Gen Zs perceptions of the challenges associated with AI, such as issues related to privacy, data security, transparency, bias, public fear and more. It also examines the impact of AI on employment dynamics, specifically on job displacement and the necessity for reskilling in the face of AI-driven automation. The paper adopts a global perspective, acknowledging the variations in perception influenced by cultural, economic, and historical factors. Leveraging a sample size of approximately 200250 respondents aged 1825years, the research aims to provide a comprehensive view of Gen Zs viewpoints on AIs ethical and societal ramifications. Findings emphasize the need for transparent and accountable AI systems, as Gen Z is uncomfortable with the ambiguity in AI algorithms. Concerns about privacy and data security highlight the necessity for robust safeguards. They also advocate for strategies to address job displacement and ensure harmonious coexistence between humans and AI. In education, Gen Z sees AI as transformative, endorsing personalized learning. They stress the importance of regulatory frameworks to combat AI bias. They recognize AIs potential to enhance human connections and combat social isolation. The studys findings contribute to policy discussions, educational strategies, and business practices, offering insights into how to harness AIs benefits while mitigating its potential pitfalls. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Voice Assistants in Marketing: A Transformative Tool
The current world is moving in the digital platform. Transformation of technology is evident in the field of marketing, and customers are also very much interested in exploring the destructive technology in the field of marketing. Voice Assistants in Marketing are the recent tendency in the marketing field and assure an immersive experience for the customers. The study attempts to explore the role of voice assistants in marketing, its applications in various industries, the knowledge level of the respondents towards various technology interfaces in voice marketing, and the factors building the trust level of voice marketing techniques. It adopts the descriptive research design, and the required data for the study is collected using the structured questionnaire in the mail survey method from 210 respondents through purposive sampling. The data gathered was organized and analyzed using SPSS, and factor analysis was employed to reduce the dimensions of the variables. The results and the explanations are summarized at the end. Voice assistants in marketing are an emerging technique that is transforming the livelihood of the marketing phenomenon. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Vocational training for women empowerment: Saint Kuriakose Elias Chavaras vision
The present study attempted to describe the initiatives of Saint (St.) Kuriakose Elias Chavara on vocational training and womens empowerment. The study narrated the present condition of one of his vocational training initiatives known as rosary-making, which is in vogue at Koonammavu Village in Kerala state, India. The study employed a multi-method research design approach to carry out the present study. It included historical, qualitative, and quantitative methods in sequence. In the historical method, the study employed document analysis of primary and secondary sources. As a part of the qualitative method, the study conducted a semi-structured interview with 10 rosary-making entrepreneurs in Koonammavu. As a part of the quantitative method, the study administered a questionnaire to 100 families who are actively involved in the rosary-making business. Document analysis revealed that St. Chavaras initiative on rosary-making vocational training for nuns was a contribution to womens empowerment. Narrative thematic analysis revealed 5 main themes and 10 subthemes in Chavaras contribution. Quantitative data revealed that rosary-making emerged as a livelihood, business, and source of income for many families. The study recommends future researchers focus on all the initiatives of St. Chavara in the realm of vocational training and womens empowerment. 2025, Intelektual Pustaka Media Utama. All rights reserved. -
Vocational training course preferences among Sikkimese youth
Unemployment is one of the major issues in modern times. High unemployment rates affect a country's economic growth, mental wellbeing of an individual and his/her family members, and create unrest in society. Vocational training is one of the most crucial elements in the competitive and developing world. Through the provision of real-world experience, vocational training aids in developing skills. This study aims to highlight the aspirations of the people of Sikkim concerning vocational training and find its challenges and hindrances. With the help of a structured questionnaire, responses were taken from the youth of Sikkim, India and their perception about opting for different vocational training courses were taken. Upon analyzing the data, it was found that males are more inclined towards cooking and baking classes, repair of mobiles, laptops and other electronic accessories, and repair of bikes and automobiles. Females, on the other hand, wanted to focus on makeup and beautician courses, jewelry design, floriculture, and towards repair of mobile and computers. 2023, IGI Global. -
VNPR system using artificial neural network
Vehicle number plate recognition (VNPR) is a technique used to extract the license plate from a sequence of images. The extracted information in the database can be used in the applications like electronic payment systems such as toll payment, parking lots etc. An effective VNPR can be implemented based on the quality of the acquired images. It is used for real time application and it has to recognize the number plates of all types under different environmental conditions. Different algorithms has been used which depends on the features present in the images. It should be generalised to extract different types of license plate from the images. In this paper we propose a new method which is robust enough to recognize the characters from the number plates with help of artificial neural network. This algorithm is practical for the front view and rear view of orientation of the vehicle. 2016 IEEE. -
VLSI Implementation of High-Speed and Area-Efficient Multiplierless Address Generation Architecture for Deinterleaver in WiMAX Applications
This paper presents a VLSI implementation of a high-speed, area-efficient, multiplierless address generation architecture for the WiMAX deinterleaver, conforming to the IEEE 802.16e standard. The primary motivation of this work is to reduce hardware complexity and delay by eliminating multipliers, which are traditionally used in address generation. The proposed architecture is designed for FPGA and ASIC platforms, emphasizing simplicity, reduced latency, and efficient hardware utilization. The design supports standard modulation schemesQPSK, 16-QAM, and 64-QAMwith their respective code rates. Two key performance evaluations were conducted: Score 1, which refers to FPGA implementation on the Xilinx XC3S400, demonstrated a 13% increase in speed, and Score 2, based on ASIC analysis using 45-nm CMOS technology, and achieved improvements of 17% in power delay product (PDP) and 22% in area delay product (ADP) over existing architectures. These results confirm the architectures effectiveness for high-speed, low-power applications in modern communication systems. Copyright 2025 Vivek Karthick Perumal et al. Journal of Electrical and Computer Engineering published by John Wiley & Sons Ltd. -
VLSI Implementation of Area-Error Optimized Compressor-Based Modified Wallace Tree Multiplier
Approximate multiplier designs can improve their energy efficiency and performance with only a slight loss in accuracy by using approximate arithmetic circuits. This method is appropriate for applications where an approximative answer is acceptable because it uses a range of calculation approaches to those priorities, returning a potentially erroneous result above one that is assured to be exact. The basic idea underlying approximate computing is that, while accurate calculation may require a lot of resources, bounded approximation can result in considerable speed and energy efficiency advantages without sacrificing accuracy. The approximate 4:2 compressor and exact compressors, as well as half adders and full adders, make up the proposed approximate multiplier. The steps of the multiplier architecture are optimised using the recently suggested modified Wallace Tree Multiplier Architecture. When compared to previous designs, the proposed multiplier architecture can generate outcomes with the least amount of inaccuracy. The multiplier architecture is also finished in just two steps. The Modified Wallace Tree Architecture used in the suggested approximate multiplier excels by providing an error rate of 71.80% and a mean error of 173.82. As a result, the mean ? error Product improved by 10%, the error rate improved by 23.3%, and the mean error increased by 31.04%. This is accomplished by the proposed approximate multiplier with a small increase of 22.36% in total power consumption. 2023 IEEE. -
Vitaware-culs 2020 vitamin awareness kit /
Patent Number: 202041005122, Applicant: Erumalla Venkatanagaraju.
Vitamins are available in abundant quantities in all natural resources. Inadequate intake of vitamins lead to severe abnormalities. Because of the rapid civilization, limited land resources, busy lifestyle and limited awareness, attention on the natural vitamin resources wafted towards Nutraceuticals that are supplemented with synthetic vitamin sources and preservatives. In order to bring awareness about the availability of bioactivevitamins in natural food sources, the current design, VITAWARE-CULS 2020 Kitwas developed. -
Vitaware-culs 2020 vitamin awareness kit /
Patent Number: 202041002544, Applicant: Erumalla Venkatanagaraju. Rapid urbanization and increase in population have evoked tremendous attention for biofuels production to combat shortage of fuels, environmental concerns, foreign exchange savings and socioeconomic issues. In recent years bioethanol production from agro-industrial wastes acquired a prominent place to fulfil the gap between production and demand. -
Vitaware-culs 2020 vitamin awareness kit /
Patent Number: 202041005122, Applicant: Erumalla Venkatanagaraju.
Vitamins are available in abundant quantities in all natural resources. Inadequate intake of vitamins lead to severe abnormalities. Because of the rapid civilization, limited land resources, busy lifestyle and limited awareness, attention on the natural vitamin resources wafted towardsNutraceuticals that are supplemented with synthetic vitamin sources and preservatives. In order to bring awareness about the availability of bioactivevitamins in natural food sources, the current design, VITAWARE-CULS 2020 Kitwas developed. -
Visualization of Data Structures and Algorithms with Dynamic Memory Allocation
Data Structures and Algorithms (DSA) is fundamental to computer science education, yet novice learners face significant challenges in grasping abstract concepts and their system-level implications, such as dynamic memory allocation. This paper presents a novel web-based platform designed to enhance learning outcomes for beginner to intermediate students through interactive step-by-step visualizations of DSA, including arrays, linked lists, stacks, queues, and searching and sorting algorithms. A distinctive feature is the integration of dynamic memory allocation visualization, illustrating stack and heap to elucidate system-level operations. Developed using Next.js, Tailwind CSS, D3.js, and Framer Motion, the platform offers a space-themed responsive interface with synchronized code, data structure, and memory views. By addressing pedagogical gaps in tools like VisuAlgo, this work aligns with Sustainable Development Goal 4- Quality Education, promoting accessible and equitable learning. 2025 IEEE. -
Visual-audio fusion in multimedia content analysis
The analysis of multimedia material has grown in importance due to the rapid expansion of digital media. Exploiting the combination of auditory and visual modalities for improved comprehension and interpretation of multimedia material has gained popularity in recent years. An extensive review of the methods, strategies, and uses of visual-audio fusion in multimedia content analysis is provided in this chapter. Combining auditory and visual modalities provides a number of benefits over single-modal analysis, such as increased robustness, deeper semantic comprehension, and better user experience. This chapter investigates a variety of fusion approaches, from late combination at the decision near to early synthesis at the feature nearby. Besides, it studies sophisticated fusion methods that allow for efficient integraton of data across modalities, such cross-modal attention processes and multimodal deep learning. Also looks at several other areas, such as multimedia retrieval, event detection, sentiment analysis, and emotional computing, where visual-audio fusion has been used successfully. It dialogs about the difficulties and potential paths ahead for the area, including how to deal with modality inconsistencies, manage massive multimedia information, and create fusion models that are understandable. To sum up, visual-audio fusion presents new possibilities for comprehending and analyzing complicated multimedia data and has the potential to significantly advance multimedia content analysis. 2026 Elsevier Inc. All rights reserved. -
Visual Symphony for Swift and Accurate Object Detection in Choreographed Deck of Cards
The Convolutional Neural Network model used for playing card recognition and categorization, offering trustworthy data regarding the suits of playing cards hearts, diamonds, clubs and spades as well as the corresponding numerical or alphabetical values. The model is built on a sophisticated dataset that guarantees high levels of precision for nearly all sorts of graphical representations and playing card scenarios. A wide range of entertainment andgames bands canuse the CNN idea. As aresult, the CNN-trained model is an excellent alternative for many different kinds of applications, including virtual reality games and card game automation, due to its capacity to extract and retain complex features from card pictures for accurate object identification. As a result, this research has shown how crucial deep learning models like CNNs are for enhancing computervision systems' suitability for real-world scenarios requiring precise and quick identification of objects. As a result, the suggested CNN-based approach offers a great chance to enhance cardidentification system performance and promoteadvancements in memory and gaming technology. 2024 IEEE.