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Comparisons of Stock Price Predictions Using Stacked RNN-LSTM
This paper seeks to identify how the RNN-LSTM can be used in predicting the rise and fall in stock markets thereby helping investors to understand stock prices. Therefore, by predicting the nature of the stock market, investors can use different machine learning techniques to understand the process of selecting the appropriate stock and enhance the return investments thereafter. Long Short-Term Memory (LSTM) is a deep learning technique that helps to analyze and predict the data with respect to the challenges, profits, investments and future performance of the stock markets. The research focuses on how neural networks can be employed to understand price changes, interest patterns and trades in the stock market sector.The datasets of companies such as IBM, Cisco, Microsoft, Tesla and GE were used to build the stacked RNN-LSTM model using timesteps of 7 and 14days. The two layered stacked RNN-LSTM models of the companies such as Microsoft and Tesla achieved their highest model accuracies after being trained over a span of one year whereas the other companies acquired their highest accuracies after being trained over a span of 4 to 5years which implies that the rate of change of economic factors affecting Microsoft and Tesla over a short span of time is high as compared to the other existing companies. 2021, Springer Nature Switzerland AG. -
Utilisation of Virtual Assistant and Its Impact on Retail Industry
Virtual assistant is nothing but an independent contractor, who offers administrative services to the clients of a particular organisation while operating outside of the office of the client. Generally, a virtual assistant operates from a home-based office. This virtual assistant application has the ability to access the required planning documents, such as shared calendars. The contemporary retail organisations like e-commerce companies in this competitive global business environment are using virtual assistant to enhance omnichannel experience, 24/7 customer service, order tracking, and product recommendations. Overall, virtual assistant helps the organisations in enhancing social media management activities. This concept of the use of virtual assistant has been significantly emerged after the increase in demands for e-commerce business activities in this decade. Research objectives related to the title of this research are developed and listed. Relevant theories on virtual assistant are applied in the literature review section of this study. The researcher has decided to adopt qualitative research methodology to achieve the objectives of the research. Moreover, the researcher has considered secondary data analysis approach to conduct this research. In terms of findings, it has been identified that virtual assistant has a positive impact on the business operation activities of retail organisations. Authentic secondary sources are considered to collect and analyse the data. Some challenges associated with the utilisation of virtual assistant also have been identified in the findings section. Some valuable recommendations are suggested for the future researchers to overcome those identified associated challenges. 2022 IEEE. -
Enabling Agricultural Sector through Blockchain Technology Farmers Perspective
The agricultural sectors in India and abroad have been affected extensively due to the Covid-19 pandemic. It is necessary to provide solutions for the availability of resources, controlling the cost, quality in production, transparent food supply, fulfilling demand, and removing intermediaries. The structural reforms in the agricultural sector by adopting emerging technologies, especially blockchain technology (BCT) and the robotics automation process, are inevitable during the pandemic and future development. To study the impact of blockchain on the Agriculture sector, the farmer's level of awareness of the blockchain technology, its methodological influence, the inclination of farmers to adopt the technology in their farming, and agri-related activity are vital. This paper aims to explore the opportunities of BCT in expanding the agriculture sector, ascertain the awareness and intensity of farmers' knowledge of the effect of BCT, and develop the mean difference in the opinion of the farmers towards the utilization of BCT in the relevant field of agriculture. A structured interview schedule was administered with 360 sample farmers from the Delta regions of three states located in the southern part of India, such as Andhra Pradesh, Karnataka, and Tamilnadu, using a purposive sampling technique intending. Irrespective of the age, gender, land capacity, possession, education level, learned procedures, and abundant experience helped the farmers demand a new technology interface to improve their income level and register their sustainability. 2022 by authors, all rights reserved. -
Role of Circular Economy Principles in Enhancing Sustainable Supply Chain Management
Global warming alerts the ecological system and functions of every element in the business world. Supply chain management is one process where sustainability principles are implemented robustly. This study is an attempt to integrate the circular economy principles in enhancing sustainable supply chain management; it has four objectives to focus on the adoption of circular economy principles, the respective barriers, stakeholders engagement level, and finally, the outcome of sustainable practices such as environmental, economic, and social implications. The study is descriptive research, adopting a purposive sampling technique to include the selected group of respondents with a sample size of 250, those who are already processing in the supply chain management activities across the industries located in Chennai. Structural Equation Modeling was used to build the measurement model to establish the connectivity between the circular economy adoption, stakeholders engagement and sustainable supply chain management outcomes. The hypothesis was established, and the same was proved through confirmatory factor analysis and standardized model fit. Hence, it was concluded that the circular economy principles and their adoption barriers significantly influence stakeholders engagement and the determination of sustainable supply chain management outcomes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Security and Privacy in AI: IoT- Enabled Banking and Finance Services
The integration of Artificial Intelligence (AI) and the IoThas led to significant advancements in the banking and finance sector, providing personalized, efficient, and data- driven services. However, these AI- IoT enabled systems also introduce complex security and privacy challenges that need to be addressed to protect sensitive financial data and maintain customer trust. This paper surveys the key security and privacy issues in AI- IoT enabled banking, including data breaches, cyber- attacks, unauthorized access, and data misuse. We examine current methodologies for securing AI- IoT systems, such as encryption, blockchain, alongside AI- driven threat detection and response techniques.The survey explores regulatory considerations and compliance requirements that shape security protocols in financial services. By identifying gaps in existing security measures and highlighting advanced privacypreserving technologies, this study aims to provide a comprehensive understanding of the challenges and future directions in securing AI- IoT applications within banking and finance. 2026 by IGI Global Scientific Publishing. All rights reserved. -
The Future of Banking: Leveraging AI for Business Transformation
Artificial intelligence (AI) has moved the banking sector into a new era, offering unmatched channels to improve efficiency, risk management, and customer experiences. The research delved into the role of AI in banking, particularly the applications, challenges, and implications. Descriptive research design is used to describe the acceptance rate of banking customers toward leveraging AI in Banking transactions through theTechnology Acceptance Model (TAM) to prove the hypothesis thatAttitude Towards Using AI mediates the individual effects of Perceived Usefulness, Perceived Ease of Use, and Perceived Risk with Behavioural Intention. The primary data was collected from 385 banking customers using the purposive sampling technique by circulating astructured questionnaire. This research shows that AI brings about more benefits than challenges, but it is crucial to handle the latter carefully to enable sustainable sectoral growth. The empirical results support theexistence of a promising environment for AI adoption in thebanking industry as there the customers show a positive behavioural intention. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Recent advances in polyethylene glycol as a dual-functional agent in heterocycle synthesis: Solvent and catalyst
Reactant solubility, which dictates achievable concentrations, and the stability of reaction intermediates (excited states), solvents modulate the potential energy landscape and influence reaction rates. Consequently, solvent selection is pivotal in optimizing process productivity, economic feasibility, and environmental footprint. At present, organic synthesis pivots around the idea of sustainability. In particular, PEG-400, a popular solvent and phase transfer catalyst, is considered greener as it can be reused several times without significant loss in its catalytic activity, which checks the box regarding sustainability. This review highlights the emerging potential of Polyethylene Glycol 400 (PEG-400) as a dual-threat agent in sustainable organic synthesis. We explore its efficacy as a catalyst, promoting various reactions under mild conditions and often eliminating the need for traditional metal catalysts. Additionally, PEG-400's role as a green solvent is addressed, emphasizing its biodegradability, low toxicity, and ability to facilitate reactions without hazardous Volatile Organic Compounds (VOCs). The review examines recent research on PEG-400 mediated reactions, showcasing its effectiveness in diverse transformations, thus exploring the potential of PEG 400 as a facilitator for heterocycle synthesis in both multicomponent reactions and stepwise approaches. It identifies exciting research directions that promise to expand the boundaries of polymer-based solvents in heterocyclic chemistry. 2024 The Author(s). Polymers for Advanced Technologies published by John Wiley & Sons Ltd. -
Blue LED photolytic method for the synthesis of 1,4-dihydropyridine derivatives from benzo [b]thiophene-2-carbaldehyde
This study presents a highly efficient and operationally simple protocol for synthesizing 1,4-dihydropyridine derivatives. The protocol uses an inexpensive and readily available photocatalyst Mn2(CO)10, which plays a crucial role in the single-pot, four-component reaction involving benzo [b]thiophene-2-carbaldehyde, malononitrile, dialkyl acetylene dicarboxylate, and anilines in a blue LED (400500 nm) photocatalytic technique. The reaction conditions include the use of blue LEDs, a lower catalyst load, and green solvents like dimethyl sulfoxide (DMSO) and water in a 1:1 ratio. The multicomponent photocatalytic approach negates the use of expensive catalysts and the necessity of multi-step routes, in addition to providing better atom economy and an easy work-up process, and it is environmentally benign. The derivatives were effectively synthesized in higher yields and characterized using 1H NMR, 13C NMR, and ESI-MS. The obtained 1,4 dihydropyridines also have tremendous capability for biological and pharmacological activities, opening exciting possibilities for future research and applications. 2025 -
Structural and Optical Properties of Alumino Lead Borate Glasses Containing Copper Oxide
The alumino lead borate glasses with small amounts of copper oxide were synthesized by melting and quenching according to the relation 50B2O3-30PbO-(20x)Al2O3-xCuO with x = 0, 0.10, 0.25, 0.50, 0.75 and 1.00 mol%. The powder XRDs had no sharp peaks which show that the samples are amorphous. Density of the glasses increased as the content of the CuO increased. FTIR spectroscopic studies reveal the presence of BO3, BO4, PbO4, AlO4, pentaborate [B5O8], diborate [B4O72] and dipentaborate B512 structural units. The UV-visible absorption studies showed that the refractive index, indirect energy gap, oxide ion polarizability and optical basicity had composition dependence which were related to the glass structure. As the CuO concentration increased, the refractive index decreased, indirect energy gap increased, oxide ion polarizability decreased and optical basicity decreased. Optical band gap increased with increasing CuO content as the band gap for bridging oxygens is higher than that for non-bridging oxygens. 2024 Indian Ceramic Society. -
National Development through women empowerment
International Journal of Physical and Social Sciences Vol.3, Issue 3, pp.77-89 -
Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing
Purpose: This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN). Design/methodology/approach: A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME. Findings: Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME. Originality/value: The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively. 2024, Emerald Publishing Limited. -
Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing
Purpose: This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN). Design/methodology/approach: A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME. Findings: Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME. Originality/value: The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively. 2024, Emerald Publishing Limited. -
A cross-country analysis of the relationship between human capital and foreign direct investment
Purpose: The ZhangMarkusen (Z-M) inverse U-shape theory uses education as a human capital variable to investigate the impact of educational attainment on foreign direct investment (FDI) inflows to a country. The objective of this research is to empirically test this theory in a cross-country framework. Design/methodology/approach: Fixed effect panel regression has been used to test the Z-M hypothesis for 172 countries for the period 19902015. For the purpose of this study, countries were divided into four groups as per the World Bank classification: Low-income economies, lower middle-income countries, upper middle-income economies and high-income economies. Findings: The findings of this study reinforce the proposition that macroeconomic factors are the major determinants of FDI inflows into various countries. The authors find that the size of the market measured by gross domestic product (GDP), the growth potential of the market measured by real GDP growth rate and the availability of infrastructure are the major factors that enhance the attractiveness of a country as an FDI destination. Originality/value: Though the Z-M theory has been empirically tested in cross-country frameworks, no consensus has been reached. Thus, it is interesting to look again at the validity of the Z-M hypothesis using data covering longer and more recent periods. The study includes both macroeconomic and human capital determinants of FDI, so as to arrive at a comprehensive model explaining the FDI flows into various countries. 2021, Emerald Publishing Limited. -
Portrayal of sikhism in hindi movies /
A Sikh is known for his strength and valor, comedians, a true friend to someone, dedicated, hard work. These are the general characteristics that are taken into consideration while dealing with how we perceive their nature on screen. Over the past few years Portrayal of Sikhism has evolved from being a comedian to a more serious role and some of the stories being based on true lifes of Sikhs. With portrayal of Sikhs in Hindi movies, the image of the community has been lifted despite them being stereotyped in cinema in form of various actors, enacting the roles of a Sikh on screen whose enactment has helped in uplifting the communitys image in the minds of the people despite the Sikh religion being the lowest in the population count. -
Academic Certificate Validation Using Blockchain Technology
Academic certificates are essential for an individual's career and hence they are more prone to being tampered. This paper proposes an idea of sharing certificates and verifying their authenticity using blockchain technology. Blockchain paves the way for secure storage and sharing of information. Its main focus is to maintain trust among users. This proposal focuses on designing and implementing a system that will prove to be a solution for addressing the issue of fake certificates using Hyperledger Fabric. The technology here is tamper-proof and maintains transparency. This system will have a database of academic certificates awarded by the University, which is recorded as a transaction using the Hyperledger Fabric, which further can be referred by other organizations present in the network to verify the authenticity of the certificates using the information provided by the students to the database. This system provides end to end encryption. 2022 IEEE. -
Social marketing: Tracing the shifts in marketing strategies after the entry of social media /
The paper is concerned with exploring the topic of social marketing from various angles. First and foremost thing that the researcher has looked into the notion that says social media is same as social media marketing. The researcher has taken the efforts to explain how the two i.e. social marketing and social media marketing differ from each other by giving examples through case studies of two companies Lifebuoy and Times of India and their social marketing initiatives. -
An adept approach to ascertain and elude probable social bots attacks on twitter and twitch employing machine learning approach
There has been a tremendous increase in the popularity of social media such as blogs, Instagram, twitter, online websites etc. The increasing utilization of these platforms have enabled the users to share information on a regular basis and also publicize social events. Nevertheless, most of the multimedia events are filled with social bots which raise concerns on the authenticity of the information shared in these events. With the increasing advancements of social bots, the complexity of detecting and fact-checking is also increasing. This is mainly due to the similarity between authorized users and social bots. Several researchers have introduced different models for detecting social bots and fact checking. However, these models suffer from various challenges. In most of the cases, these bots become indistinguishable from existing users and it is challenging to extract relevant attributes of the bots. In addition, it is also challenging to collect large scale data and label them for training the bot detection models. The performance of existing traditional classifiers used for bot detection processes is not satisfactory. This paper presents: A machine learning based adaptive fuzzy neuro model integrated with a hist gradient boosting (HGB) classifier for identifying the persisting pattern of social bots for fake news detection. And Harris Hawk optimization with Bi-LSTM for social bot prediction. Results validate the efficacy of the HGB classifier which achieves a phenomenal accuracy of 95.64 % for twitter bot and 98.98 % for twitch bot dataset. 2023 -
Performance analysis of semantic veracity enhance (SVE) classifier for fake news detection and demystifying the online user behaviour in social media using sentiment analysis
The increased propagation of fake news is the significant concern in the digital era. Identification of fake news from social media platforms is critical to strengthen public trust and ensure social stability. This research presents an effective and accurate framework for identifying fake news that combines different steps of natural language processing (NLP) technique along with a neural network architecture. A novel semantic veracity enhancement (SVE) classifier is designed and implemented in this work for detecting fake news. The proposed approach leverages the effectiveness of sentiment analysis for identifying misleading or deceptive content and its subsequent implications on the sentiment and behaviour of social media users. A BERT model is used in this research for analysing the sentiments and classifying the texts from the social media platform. By examining the sentiments, the SVE classifier differentiates between real news and fabricated content. To achieve this, three different datasets comprising both actual content and fabricated (tweaked) tweets are employed for training the SVE classifier. The potentiality of the SVE classifier is evaluated and compared with different optimization techniques. The outcome of the experimental analysis shows that the proposed approach exhibits an excellent performance in terms of classifying misinformation from the original information with an outstanding accuracy of 99% compared to other state of art methods. 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature. -
Synthesis of Online Criminal User Behaviours Disseminating Bengali Fake News Using Sentiment Analysis
Even though research on artificial intelligence (AI) is still in its early phases, the field is growing in popularity. We created a hybrid machine learning model to better understand the pattern of results connected to illegal user behaviour. Then, after identifying the components of illegal user activity, we created a theory for forecasting criminal user behaviour that explains the patterns and results. Our study focuses on offenders spreading misleading information online and makes use of a Bengali dataset. Sentiment analysis is a modern technology that can help us understand how individuals feel in different scenarios during their everyday lives. To comprehend the pattern behind this agenda, machine learning and deep learning techniques will be applied throughout the categorization process. To determine the possible attitudes driving criminal conduct that spreads misleading information, sentiment levels on social media may be monitored or studied. This study examines the use of several artificial intelligence approaches to assess sentiment in social media data in order to identify criminal user activity occurring throughout the world. The hybrid model CNN with Adam optimizer exhibits higher precision levels while doing sentiment analysis. In addition to identifying solutions to the issues that people currently face in the modern world, we also propose a new categorization system for illicit user activity. In our analysis of the research's shortcomings, we make recommendations for a broader research agenda on illicit user conduct and how one can forecast the criminal user behaviour on psychological aspects. Our model was thus able to draw 87.33% accuracy in determining criminal behaviour patterns. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.