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Impact of Learning Functions on Prediction of Stock Data in Neural Network
Digitization has made a vast impact on the modern society. Financial sector is one field where a huge revolution has been experienced because of digitization. Financial data especially time series data is being stored in the digital repositories where it can be used for prediction and analysis. One such data is a stock market data which is a time series data and is generated in a huge amount every second. The stock market data is of great importance as the proper analysis and prediction of data can transform the fate of the global market. Thus the companies and the individuals are looking forward for the development of the automated techniques that can predict stock market data accurately in a real time. In this regard, many researchers developed machine learning techniques such as use of neural network for prediction of stock data. The most common learning function used in neural network is sigmoid function. However, we found that there are many learning functions are available for building neural network. In this paper we are studying the impact of four different learning functions in estimating/predicting the stock value. From the experimental study we found that unipolar sigmoid learning function produced an accuracy of 95.65%, bipolar sigmoid produced an accuracy of 91.34%, tan hyperbolic equation produced an accuracy of 91.02%, and radial base equation produced an accuracy of 87.53%. Clearly, unipolar sigmoid function emerged as the best learning function to build stock data prediction model. The main reason behind its out-performance of unipolar sigmoid is its less complex structure and the 0 to 1 range. 2018 IEEE. -
Impact of Leverage on Valuation of Non-Financial Firms in India under Profitabilitys Moderating Effect: Evidence in Scenarios Applying Quantile Regression
The firms valuation (FV) is the key element for all stakeholders, particularly the investors, for their investment decisions. The main impetus of this research is to estimate the effects of the debt ratio (DR, i.e., leverage) on the FV (i.e., assets and market capitalisation) of the non-financial firms listed in India. The quantile panel data regression (QPDR) on the secondary data of 76 non-financial BSE-100 listed firms in India is employed. This study also checks the effect of the net profit margin (NPM) as profitability on the association between DR and FV. The QPDR estimates result in multiple quantiles and provide evidence in scenarios. The findings reveal a positive relationship of DR to assets only in higher quantiles, i.e., 90%ile), and a negative association of DR is found with a market capitalisation in all quantiles. Under the interaction effect, profitability (NPM) does not affect the association of DR with assets but negatively affects the association of debt ratio with market capitalisation in the middle (50%) quantile. The findings indicate that leverage (DR) affects a firms value. The studys outcomes are helpful to all stakeholders, particularly investors, to realise the leverage (DR) as a critical indicator of FV before making any investment decisions. Managers should also consider lower debt ratios for better firm value. The present analysis is original and holds novelty in the form of the moderating role of the net profit margin, i.e., the profitability of the firm between DR and FV in the non-financial firm in India. To the best of our knowledge, no such studies have been performed to look for the association of the debt ratio with a firms value under the effect of profitability in different quantiles using quantile regression. 2023 by the authors. -
Impact of Livelihood Strategies on the Dependence on Agriculture in Arid Regions of Andhra Pradesh, India
The livelihood risks tend to change households capital, assets, or resources by impacting the value of their asset portfolio. This study attempts to understand various risks and how these risks affect the farming communitys livelihoods in the designated areas. Also, this research attempts to understand the relationships between risks and livelihood capital to develop effective measures to improve their well-being and suggest various policies to help farmers cope with these risks in the most effective ways. The study used a sample of 827 farmers from Andhra Pradesh, India. The ordinal logistic regression findings revealed that knowledge capital (knowledge on nutrient deficiency, disease and pests and access to information), social capital (community membership and access to natural resources and services) and financial capitals have a significant impact on the selection of livelihood strategies. 2022 Management Development Institute. -
Impact of lockdown during COVID-19 pandemic on the learning status of undergraduate and postgraduate students of Bangalore
Background: The COVID 19 pandemic has created various impacts on every human's life. COVID 19 lockdown has provoked enormous changes in the education sector which in turn influences the student's life in many aspects. The scope of this study is to understand the impact in both undergraduate and postgraduate students. Aim: This study aims at incisively analyzing the impact of lockdown imposed due to the COVID-19 pandemic on graduate students of Bangalore. Method: It is an online survey that encompasses a structural questionnaire with open-ended questions created using Google Forms, which were sent across the students through social media platforms. Results: A total of 115 students from both undergraduate and postgraduate programs have participated in this survey. Simple percentage distribution was estimated to evaluate the pedagogy, opinion on educational decisions, modes of learning, socio-economic conditions, and problems pertaining to academia because of this pandemic. As per this analysis, 80.9% of students faced difficulty due to lockdown. 67% of students thought that their family's income will be affected by this pandemic. 68.7% of students felt stressed, depressed and 52.3% of students could not find a suitable environment in their home to study during this lockdown. When we see this pandemic in an optimistic light, it has created various opportunities such as Digital learning and adoption of new health habits. 2021. RIGEO. All Rights Reserved. -
Impact of Lysinibacillus macroides, a potential plant growth promoting rhizobacteria on growth, yield and nutritional value of tomato plant (Solanum lycopersicum L. F1 hybrid Sachriya)
Plant growth promoting bacteria enhance the growth in plants by solubilizing insoluble minerals, producing phytohormones and by secreting enzymes that resist pathogen attack. The present study was aimed at identifying the potential of Lysinibacillus macroides isolated from pea plant possessing rich microbial rhizobiome diversity in promoting the growth of tomato plant (Solanum lycopersicum L.). Potential of L. macroides in the promotion of S. lycopersicum L. growth by increased shoot length, terminal leaf length and breadth was assessed. Anatomical sectioning of stem and root revealed no varied cellular pattern indicating that the supplemented bioculture is not toxic to S. lycopersicum. Plantlets treated with L. macroides along with organic compost showed an increased total phenol content (17.580.4 mg/gm) compared to control samples (12.440.41 mg/g). Carbohydrate content was noticed to be around 1.3 folds higher in the L. macroides plus compost mixture supplemented slots compared to control sample. Significant increase in shoot length was evident in the L. macroides plus compost supplied slots (23.42.7 cm). Plant growth promoting properties might be due to the nitrogen fixing activity of the bacteria which enrich the soil composition along with the nutrients supplied by the organic compost. Rich microbial rhizobiome diversity in pea plant and the usage of L. macroides from a non-conventional source improves the diversity of the available PGPR for agricultural practices. Further research is needed to detect the mechanism of growth promotion and to explore the plant microbe interaction pathway. Jyolsna et al. (2021). -
Impact of Machine Intelligence on Clinical Disease Outbreak Prediction
This research paper examines the utilization of Artificial Intelligence (AI) in disease outbreak prediction and its importance in public health. It explores the hurdles associated with predicting disease outbreaks, including data quality and accessibility, ethical considerations, algorithmic bias, and integration and interpretability challenges. The paper presents an overview of AI techniques applied in healthcare and their relevance to forecasting disease outbreaks. Case studies demonstrate the efficacy of AI -based models in predicting infectious diseases, vector-borne diseases, and epidemics/pandemics, employing diverse data sources. The limitations and future prospects of AI in disease outbreak prediction are addressed, accompanied by recommendations for enhancement. In conclusion, the paper highlights AI's potential to revolutionize disease outbreak prediction, leading to proactive public health interventions and improved response strategies. 2023 IEEE. -
Impact of Machine Learning Algorithms in Intrusion Detection Systems for Internet of Things
The importance of security aspects is increased recently due to the enormous usage of IoT devices. Securing the system from all sorts of vulnerabilities is inevitable to use IoT applications. Intrusion detection systems are power mechanism which provides this service. The introduction of artificial intelligence into intrusion detection systems can further enhance its power. This paper is an attempt to understand the impact of machine learning algorithms in attack detection. Using the UNSW-NB 15 dataset, the impact of different machine learning algorithms is assessed. 2021 IEEE. -
Impact of Macroeconomic Integration in Hybrid GARCH-GRU Volatility Modelling on Nifty Bank
In countries like India, where banking systems are closely tied to macroeconomic swings, being able to forecast volatility is critical for managing financial risk. Sudden changes in interest rates, exchange movements, or growth expectations can unsettle banks much faster than in mature markets. Econometric tools such as the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model remain popular because they capture volatility clustering well, but they fall short when the data exhibit nonlinear patterns. Neural networks-particularly Gated Recurrent Units (GRUs)-handle time-series dynamics more effectively, though they tend to miss traits specific to financial volatility. In this work, we put forward a hybrid GARCH-GRU framework that blends the traditional strengths of econometric models with the pattern-learning ability of neural networks, while also folding in key macroeconomic indicators. The framework is applied to the Nifty Bank index and draws on daily records spanning March 2010 to December 2022. Altogether, the dataset includes just over three thousand observations, covering more than a decade of varied market conditions. The framework uses a two-step design: conditional volatility from a GJR-GARCH(1,1) model is first estimated and then used as input, along with macroeconomic variables such as repo rates, exchange rates (USD/INR, CNY/INR, EUR/INR), oil prices, and GDP growth, for the GRU network. Our results indicate that the hybrid model performs noticeably better, cutting the Mean Absolute Error by about a quarter. The error falls from 0.000263 in the baseline GARCH model to 0.000199 under the hybrid design. Among the different factors considered, movements in exchange rates and changes in repo rates stand out most strongly, showing how these macroeconomic signals feed directly into risk management for Indian banks. 2025 IEEE. -
Impact of Macroeconomic Uncertainty on Stock Market Return Volatility in India : Evidence from Vector Autoregressive (VAR) Analysis
Pacific Business Review International Vol. 5, Issue 4, pp. 50-62, ISSN No. 0974-438X -
Impact of macroeconomic variables on the prices of gold /
Journal of Emerging Technologies And Innovative, Vol.6, Issue 2, pp.569-576, ISSN No: 2349-5162. -
Impact of macroeconomic variables on the stock performance of select companies in manufacturing industry
The efficient functioning of a stock market is influenced by different macro economic factors like Inflation, Interest rates, exchange rate etc. The favourable Macro Economic Variables both domestic economy and global economy inspire the organisations to go for strategic investment activities in domestic and global markets and reflect positively on the company financial performance and firms fundamentals like Revenues, Operating margins, Earnings Per Share, the Economic Value , Market value, and the Firms overall Value. These positive indicators in the fundamentals of the firms send positive signals into stock markets and generate positive perceptions about the company's stock prices in the market. Markets become so attractive to domestic and foreign investors which drive the share price of different companies , specially Blue chips upwards and creates value to the shareholders .According to the study organized the impact of macro economic variables is not uniform and the impact varies betweem various macro economic variables on the stock market performance. Serials Publications Pvt. Ltd. -
Impact of macroeconomic variables on the stock performance of select companies in manufacturing industry /
International Journal Of Economic Research, Vol.14(8), pp.321-328, ISSN: 0972-9380. -
Impact of Mahatma Gandhi National Rural Employment Guarantee Act on Rural Credit System in India: A Standard Logit Difference in Difference Approach
The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) of India is one of the most extensive social safety nets programs in the developing world. The initiative attempts to enhance rural livelihoods in India by lowering rural poor vulnerability and misery. The programs nature and extent of execution vary from state to state. Using panel data sets from the Indian Human Development Survey (IHDS), which covering India for two waves, 200405 and 201112. We used a quasi-experimental approach, such as the difference-in-difference technique of effect evaluation, to quantify the programs influence on rural families credit and debt structures. The empirical analysis shows evidence of changing the behavior of taking loans from formal sources among non-poor households actively participating in the MGNREGA program. But the difference-in-difference results shows that among poor households participating in the MGNREGA scheme, the tendency to depend on formal sources to take loans is still insignificant. That means informal lending sources are still more prevalent among poor people. This tendency has not changed even after the initiation of this program. The article finishes with policy recommendations for successfully targeting the program, notably the social safety net benefits to disadvantaged households in India. 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Impact of Meltdown and Spectre Threats in Parallel Processing
Threat characterization is critical for associations, as it is an imperative move towards execution of data security. Vast majority of the current threat characterizations recorded threats in static courses without connecting risks to information system zones. The aim of this paper is to represent each threat in different areas of the information system the methodology to solve the problem. Data security is habitually represented to different kinds of threats which may cause distinctive types of harms that can prompt to critical monetary losses. Data security problems can go from small losses to entire data framework destruction. The effect of various threats vary extensively: some manipulate the integrity or confidentiality of information while others manipulate the accessibility of a framework. At present, associations are trying to comprehend what are the threats to their data resources are and what are the ways to get the significant intends to combat them which keep on representing a challenge. Springer Nature Switzerland AG 2020. -
Impact of Meltdown and Spectre Threats in Parallel Processing
[No abstract available] -
Impact of meme marketing on consumer purchase intention: Examining the mediating role of consumer engagement
This paper analyzes an emerging form of social media marketing, meme marketing, which has gained attention for its ability to entertain and engage users. Marketers and companies are recognizing the value of using memes as a tool to connect with consumers. To understand the effects of meme marketing activities, this paper aims to examine the impact of meme marketing activities on consumer purchase intentions and concurrently assess the mediating role of consumer engagement. The study encompassed 452 Indian social media users with active social media accounts and familiarity with memes and meme marketing concepts. It employed a quantitative methodology backed by robust statistical techniques. The method used for analysis was Structural Equation Modeling (SEM) through Analysis of Moment Structures (AMOS) software. The results found that meme marketing activities have a direct and significant positive impact (? = 0.257, p < 0.05) on consumer purchase intentions. It further shows that meme marketing has a direct and significant positive impact (? = 0.745, p < 0.05) on consumer engagement. It shows that consumer engagement has a direct and significant positive effect (? = 0.651, p < 0.05) on consumer purchase intention. However, the indirect impact of meme marketing activities on consumer purchase intentions is also significant, resulting in partial mediation. The study findings hold value for marketing managers, agencies, and companies that interact and engage consumers with memes and undertake meme marketing activities. Navrang Rathi, Pooja Jain, 2023. -
Impact of monetary policy changes on the Indian stock market and monetary market
Since the stock market is perceived as the channel of transmissions of monetary policy, it is worthy to study the relationship between the Monetary Policy and the volatility of stock prices in the stock market. This study has been conducted with the aim to examine the impact of monetary policy changes on the stock market. The variance methodology is applied in order to achieve the objective of this study. The concept of event window in the methodology involved as the identification of volatility of price in the stock market for11 days (i.e. 5 days before and after event day). The result shows that there is a positive influence on stock market because of change in money policy by the government. The results identified in this work having a signification amount of managerial implication to the different segment of the policy makers in Government, and Stock Market. 2019 Islamic Azad University. -
Impact of moral and exchange capital on media favourability of financial companies in India
Purpose The purpose of this study is to illuminate the influence of institutional and transactional corporate social responsibility (CSR) on media sentiments in financial companies in India. This study is conducted to understand how different CSR strategies impact media perceptions, influencing the reputation and public image of financial companies in India. Design/methodology/approach This study examines the data of 56 National Stock Exchange-listed financial companies for eight years of data from 20142015 to 20212022. Panel data regression were used to analyse the data; fixed and random effect models were chosen based on the Hausman test results. Findings Financial companies moral and exchange capital negatively impact the medias favourability of financial companies in India. Diagnostic tests like autocorrelation and heteroscedasticity are also conducted to check the effectiveness of research models. Originality/value Although prior research has examined the effect of CSR on media sentiments, little is known about the impact of moral capital and exchange capital on media favourability of financial companies in India. 2025 Emerald Publishing Limited -
Impact of Multi-domain Features for EEG Based Epileptic Seizures Classification
Accurate detection and classification of epileptic seizures play a pivotal role in clinical diagnosis and treatment. This study introduces an innovative approach that leverages multi-domain features extracted from Electroencephalogram (EEG) data in conjunction with Supervised learning classification techniques. Initially, EEG data undergoes preprocessing through data standardization, followed by the extraction of essential features per instance, encompassing combination of Time domain, Frequency domain, and Time-Frequency domain features. These extracted feature combinations are subsequently fed into the machine learning-based boosting classifier Adaptive Boosting (ADABOOST) for an accurate and precise classification of epileptic signals. Validation of the proposed method is conducted using EEG data from the BEED (Bangalore EEG Epilepsy Dataset) and BONN (University of BONN, Germany) database to detect epileptic seizures. The experimental results show remarkably high levels of classification accuracy for various conditions: 99% accuracy for BEED data, 98% accuracy for BONN data for classifying seizures from healthy states, and 91% accuracy for classifying seizure onset from seizure events. Furthermore, the study applies the Gaussian Nae Bayes (GNB) classifier to differentiate various types of epileptic seizures, employing evaluation metrics such as the confusion matrix, ROC curve, and diverse performance measures. This method demonstrates significant potential in supporting experienced neurophysiologists decision in the clinical classification of epileptic seizure types. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Impact of nanoparticles on immune cells and their potential applications in cancer immunotherapy
Nanoparticles represent a heterogeneous collection of materials, whether natural or synthetic, with dimensions aligning in the nanoscale. Because of their intense manifestation with the immune system, they can be harvested for numerous bio-medical and biotechnological advancements mainly in cancer treatment. This review article aims to scrutinize various types of nanoparticles that interact differently with immune cells like macrophages, dendritic cells, T lymphocytes, and natural killer (NK) cells. It also underscores the importance of knowing how nanoparticles influence immune cell functions, such as the production of cytokines and the presentation of antigens which are crucial for effective cancer immunotherapy. Hence overviews of bio-molecular mechanisms are provided. Nanoparticles can improve antigen presentation, boost T-cell responses, and overcome the immunosuppressive tumor environment. The regulatory mechanisms, signaling pathways, and nanoparticle characteristics are also presented for a comprehensive understanding. We review the nanotechnology platform options and challenges in nanoparticles-based immunotherapy, from an immunotherapy perspective including precise targeting, immune modulation, and potential toxicity, as well as personalized approaches based on individual patient and tumor characteristics. The development of emerging multifunctional nanoparticles and theranostic nanoparticles will provide new solutions for the precision and efficiency of cancer therapies in next-generation practice. Copyright 2024 The Authors.