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Financing Green Startups: A Blockchain-Powered Approach
Green startups routinely encounter difficulty in obtaining financing owing to the high funding needs to launch their business, and the imprecise market acceptance and future returns of those businesses, rendering them unacceptable to traditional latter-day funding methods. The funding modality available today fails to provide the needed transparency, flexibility and accessibility to encourage ventures based on green projects. This paper develops a funding model based in blockchain for green startups, that employs tokenization, decentralised finance (DeFi) and smart contracts with automaticity, as an efficient way of funding to provide secure, traceable funding structures that make funds available related to the performance of the venture. We present a conceptual model showing the responsibilities of startups, funders and smart contracts within a de-centralised funding ecosystem. Various case studies such as Power Ledger and WePower are investigated in order to validate the practical relevance of the model. Our research indicates how blockchain mechanisms can heighten trust, enhance liquidity, and automate funding that is linked to impact. This paper contributes to future work on scalable platforms that are both regulation compliant and also provide a fit between Blockchain infrastructure and the unique requirements of sustainable innovation. 2025 IEEE. -
A study on stereotypical portrayal of men in Telugu cinema /
In India, Telugu Cinema Industry is one of the biggest in terms of producing Movies every year. This dissertation focus on identifying, analyzing and elaborating on the elements of stereotypes in general, with major focus and specifications to ‘Male Stereotyping on Screen’. The idea of this study to find the elements of stereotype and also to understand the social appearance of men on screen. -
Factors influencing dynamic capabilities of entrepreneurial-led organisations to achieve analytical transformation
Entrepreneurial spirit transforms the economic scenario resulting in a significant contribution to society. Analytical transformation enables entrepreneurs with superior effective decision-making capability through information gathering, advanced technology adoption and data analysis. Effective analysis leads to superior organisational performance. However, in entrepreneurial-led large Indian organisations, the adoption of analytics is limited to predicting results. The study aims to identify the key factors that impact analytical transformation. The study also aims to identify key dynamic capabilities to achieve such transformation. This article identifies base theories related to the identified concepts. This article aims to develop an analytical transformation capability model for entrepreneurial-driven large industries. This study also empirically validates the proposed research model. The study concludes that entrepreneurial-led large Indian technology-driven industries lag behind their technology peers in adopting prescriptive analytics. The study also proposes an analytical transformation theory that aims to provide necessary techniques to improve organisational effectiveness. Copyright 2025 Inderscience Enterprises Ltd. -
Bridging Traditional NLP and Deep Learning: Comparative Study on Text Categorization Performance
Text categorization is an important area of Natural Language Processing (NLP) that is used to automatically organize textual information into a set of specific categories. This study is a comparative study of models that use statistical features and models that use transformers, using the example of DistilBERT-base-uncased fine-tuned and LoRA (Parameter-Efficient Fine-Tuning, PEFT). The data extracted on the Kaggle site is presented in the form of labeled text samples of five classes Business, Entertainment, Sport, Tech, and Politics. Conventional models such as Logistic Regression, Random Forest and XGBoost were trained on manually crafted word-level features (word count, mean word length and punctuation ratio) and had precisions up to 94.7%. Comparatively, the given DistilBERT-LoRA model used semantic embeddings to find the contextual dependencies and managed to reach the total accuracy of 97, precision of 97, and the recall of 96. The training and validation loss curves showed the stable convergence without overfitting, and the confusion matrix showed the consistent performance at all the classes with minimum misclassification. Comparative analysis indicated that semantic embeddings are much better than statistical models because they enhance contextual perception and strength of classification. The findings confirm the effectiveness and scalability of the LoRA-based fine-tuning, which offers an efficient but lightweight strategy in the context of real-world settings to achieve high-performance text categorization. 2025 IEEE. -
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Investigation of electrical properties of developed indigenous natural ester liquid used as alternate to transformer insulation
The performance of every electrical system depends on the different electrical devices especially transformers. Petroleum-based mineral oil is widely used for insulation and cooling purpose. The disadvantage of mineral oil is its low biodegradability and is a major threat to the ecosystem due to its poor oxidative stability. To remedy the drawbacks, focus on alternative fluids that can replace traditional mineral oil. Alternative liquids such as natural esters are used which do not panic the ecosystem. With the support of additives in natural esters liquids, the productivity of the oil can be increased, paving the path for the green conversion of liquids in high voltage applications. The purpose of this article is to analyze the electrical properties of the newly developed indigenous oil. The inhibited oil was insulating oil to which antioxidants were added such as 2,6-ditertiary-butylparacresol, butylated hydroxyl anisole and tertiary butyl hydro qunine to slow down the oxidation rate and to check the electrical properties. This article discusses the electrical properties of mineral oil, developed indigenous oil with and without antioxidants as per IEC62770 standards. A 1.1 kVA transformer was then designed in a laboratory for load tests and Indigenous oil performance under load was evaluated. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Comparative study of Breakdown Phenomena and Viscosity in Liquid Dielectrics
Liquid dielectrics are extensively used in electrical apparatus which are operating in distribution and transmission systems. The function of electrical equipment strongly depends on the conditions of liquid dielectric. Liquid dielectrics used are the most expensive components in power system apparatus like transformers and circuit breakers. A failure of these equipment would causes a heavy loss to the electrical industry and also utilities. Insulation failures are the leading cause of transformer failures and thus the liquid dielectrics plays a major role in the safe operation of transformers. One of the main causes for the failure of transformers is due to the presence of moisture. In this work, the life of insulating medium is estimated by comparing the Breakdown strength and Viscosity of different pure oils with that of the contaminated oils (which contains moisture) and also finding the alternative for mineral oil. vegetable oils which are reliable, cost-effective and environmental friendly even when they are contaminated. 2019 IEEE. -
Investigation of dielectric properties of indigenous blended ester oil for electric system applications
The insulation condition of a transformer decides the longevity of the equipment. The unpredicted failure of power transformer will lead to major disaster in the distribution network and it affects both environment and public safety. Nowadays synthetic oil and natural esters are alternatives to transformer oil because of the biodegradable nature. In this paper, investigations were carried out to study the performance of the blended ester. The different properties investigated were viscosity, breakdown voltage, flash point, dielectric dissipation factor and moisture content. Comparisons of the properties were made between mineral oil, vegetable oil without additives and with additives. Further Investigation was carried out to study the impact of antioxidants and degasification. The results indicated that the addition of antioxidants and degasification of the vegetable oil improve significantly its voltage withstanding capacity. The Indigenous oil is code named as DM; Indigenous oil with DBPC is codenamed as DM1, Indigenous oil with BHA is codenamed as DM2. The results have been tabulated and found to be satisfactory. 2020 ASTES Publishers. All rights reserved. -
Studies on Tensile Properties of Graphene Hydroxyl Reinforced Aluminium 6061 Composites for Vehicle Structures Applications
Aluminium composite plays a significant role in the mechanical structures. Low tensile strength of the aluminium alloy limits its application in mechanical structures. Graphene hydroxyl (GrOH) is a noble emerging material which is an allotropic form of carbon. It has high cohesive strength, good bonding ability with other materials. Carbon bonds processes high compatibility when it reinforced with other material. Reinforcement of GrOH with aluminium composites enhances the wear strength of composite material. This paper focused on analysis of tensile properties and percentage elongation of aluminium composites reinforced with GrOH with various weight percentage (wt. %). The characteristics of aluminium composites, particularly related to its tensile properties are very much important for its use in vehicle structures applications. 2022. MechAero Foundation for Technical Research & Education Excellence. -
Synthesize of Indigenous Natural Ester Based Liquid Dielectrics and its Performance Evaluation in Transformers
Transformer is generally considered to be the heart of the power system. Transformers are the main equipment in the transmission and distribution network to be monitored for uninterrupted flow of power. The liquid newlinedielectrics play an important role in functioning of transformer. It serves as an effective coolant and also it determines the life of transformer. Thus, the reliability of a power transformer is largely determined by the condition of insulation. The transformer oil is the bi product of petroleum. However, the usage of petroleum oil is running out of demand and there may be a severe shortage of oil exists in future. Also during its newlineuse and disposition, transformer oil is highly dangerous to aquatic and human life due to its non-biodegradability and hence it is not environment eco-friendly. This has given scope for new alternative biodegradable dielectric fluids such as natural esters, replacing the traditional mineral oil. These vegetable-oil-based liquids are non-toxic newlineand meet all the requirements for a high temperature insulating liquid. The Partial discharge pulses present in the liquid dielectric leads to breakdown of streamer development and formation of sludge. Hence it is important to analyze the Partial Discharge properties of oils. The aim of newlinethe present work is to investigate suitability of Indigenous Natural Ester newlinebased liquid dielectrics as a liquid dielectric coolant and also to analyze newlinethe partial discharge phenomena, particularly in transformers. The results obtained are well within the IEC 62770 standards. Results shows that developed indigenous natural ester oil that has better viscosity, breakdown voltage, flash point and partial discharge properties. As a result, developed indigenous oil will be an alternate for mineral oil in newlinehigh-voltage applications. -
Reinforcement learning strategies using Monte-Carlo to solve the blackjack problem
Blackjack is a classic casino game in which the player attempts to outsmart the dealer by drawing a combination of cards with face values that add up to just under or equal to 21 but are more incredible than the hand of the dealer he manages to come up with. This study considers a simplified variation of blackjack, which has a dealer and plays no active role after the first two draws. A different game regime will be modeled for everyone to ten multiples of the conventional 52-card deck. Irrespective of the number of standard decks utilized, the game is played as a randomized discrete-time process. For determining the optimum course of action in terms of policy, we teach an agent-a decision maker-to optimize across the decision space of the game, considering the procedure as a finite Markov decision chain. To choose the most effective course of action, we mainly research Monte Carlo-based reinforcement learning approaches and compare them with q-learning, dynamic programming, and temporal difference. The performance of the distinct model-free policy iteration techniques is presented in this study, framing the game as a reinforcement learning problem. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Analysis and prediction of seed quality using machine learning
The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithms predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the projects primary goal is to develop the best method for the more accurate prediction of seed quality. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Early CKD Prediction Using Ensemble and Basic Machine Learning Models
Chronic kidney disease (CKD) is a progressive illness that often remains undiagnosed until advanced stages and represents a significant global health burden. Proper and timely diagnosis of CKD can significantly improve patient prognosis and reduce treatment costs. This study evaluates several machine learning (ML) models, including support vector machine (SVM), random forest (RF), gradient boosting (GB), Nae Bayes (NB), AdaBoost, and a multilayer perceptron (MLP) neural network. Additionally, it proposes a stacking ensemble model combining RF and GB for accurate CKD prediction using a publicly available Kaggle dataset. Missing value handling and feature normalisation are performed during data preprocessing, and model performance is evaluated using an 80:20 traintest split with metrics such as the area under the curve (AUC), classification accuracy (CA), F1-score, precision, recall, and Matthews Correlation Coefficient (MCC). Experimental results indicate that RF and GB achieve the strongest individual performance, while the proposed stacking ensemble attains the highest CA of 99.4%. These findings highlight the potential of artificial intelligence (AI)-driven predictive models to support proactive CKD diagnosis and enhance clinical decision-making in healthcare systems. 2026 by the authors of this article. Published under CC-BY. -
Attribute optimization to improve breast cancer prediction using machine learning techniques
Breast cancer (BC) arises when cells grow out of control. It affects women more than men. Seeking cancer treatment can be both costly and time-consuming, with test results spanning from a few hours to several weeks. The duration of these tests depends on the number of attributes within the dataset. This research paper endeavors to optimize the dataset attributes and find the accuracy of the optimized dataset. The primary goal is to reduce features using recursive feature elimination to minimize the time taken for the test result. This work discusses the machine learning technique and the random forest (RF) algorithm, which helps determine the parameter accuracy on the Wisconsin BC diagnostic dataset. The method achieves an accuracy of 96.49% with only eighteen attributes. It has aided the healthcare industry in finding BC in less time and improving the treatment. Copyright (c) 2026 Peddireddy Venkateswara Reddy, Alaguchamy Parivazhagan. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. -
Forecasting stock market volatility in India - Using linear and non - Linear models
Volatility models and their forecasting performance attracted the interest of many economic agents, especially for financial risk management. The role of economic agents is to decide which one will be best model for forecasting volatility. This paper examines the modeling and forecasting performance of BSE Sensex daily stock market returns over the period from 1 July 1997 to 31 October 2008, by using simple Random Walk, GARCH, EGARCH and TGARCH models. The out-of-sample forecasts are evaluated by using MAE, RMSE, MAPE and Theil - U Statistics. The result suggests the standardized residual of white noise series strongly rejects the null hypothesis for GARCH model and capture the serial dependence and inherent nonlinearity series. Moreover, Random walk model dominates the forecasting performance and it is considered as the best model followed by the TGARCH model. International Economic Society. -
Determinants of Equity Share Prices in India: A Panel Data Approach
The Romanian Economic Journal, Vol-15 (46), pp. 205-228. ISSN-1454-4296 -
Causality between public expenditure and economic growth: The indian case
This study investigates the causal nexus between public expenditure and economic growth in India using cointegration approach and error correction model. The analysis was carried out over the period 1973 to 2012. The Cointegration test result confirms the existence of long-run equilibrium relationship between public expenditure and economic growth in India. The empirical results based on the error-correction model estimate indicates one-way causality runs from economic growth to public expenditure in the short-run and long-run, supporting the Wagner's law of public expenditure. -
Do futures and options trading increase spot market volatility in India? the case of S&P CNX Nifty
The exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model followed by standard GARCH (1, 1) model were employed to investigate the impact of introduction of futures and options trading on the volatility of the underlying spot market in India. The empirical analysis was conducted for the daily closing price returns of S&P CNX Nifty spot index from 1st January, 1996 through 31st October, 2008. The empirical results reveal that the spot market volatility has been declined after the introduction of futures and options trading in India. Besides, the empirical results indicate that the impact of recent news has a greater impact on the spot market changes following the onset of futures/options trading. At the same time, the persistence of volatility shocks has been declined in the post-derivatives scenario indicating increased efficiency of the Indian spot market. Hence, the present study suggests that the introduction of futures and options trading have improved the speed and quality of information flowing in the spot market. This enhances the overall market depth, increases market liquidity and ultimately reduces informational asymmetries and therefore compresses spot market volatility in India. Copyright 2010 Inderscience Enterprises Ltd. -
Price Discovery and Asymmetric Volatility Spillovers in Indian Spot-Futures Gold Markets
International Journal of Economic Sciences and Applied Research, Vol-5 (3), pp. 65-80. ISSN-1791-5120 -
Exchange rate volatility and export growth in India: An ARDL bounds testing approach
This paper empirically investigates the impact of exchange rate volatility on the real exports in India using the ARDL bounds testing procedure proposed by Pesaran et al. (2001). Using annual time series data, the empirical analyses has been carried out for the period 1970 to 2011. The study results confirm that real exports are cointegrated with exchange rate volatility, real exchange rate, gross domestic product and foreign economic activity. Our findings indicate that the exchange rate volatility has significant negative impact on real exports both in the short-run and long-run, implying that higher exchange rate fluctuation tends to reduce real exports in India. Besides, the real exchange rate has negative short-run and positive long-run effects on real exports. The empirical results reveal that GDP has a positive and significant impact on India's real exports in the long-run, but the impact turns out to be insignificant in the short-run. In addition, the foreign economic activity exerts significant negative and positive impact on real exports in the short-run and long-run, respectively. 2013 Growing Science Ltd. All rights reserved.