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Extracting Linguistic Tones in Earnings Call using Transformer Model and Performance Comparison with Lexicon-based Approaches
Prior evidence suggests how market sentiments help investors derive changes in the stock price movements. Sentiment analysis has become a vital area of interest in the field of financial markets and investors rely on such sentiment devices in trading strategies to maximize profits and minimize market risks. Studies have also shown sentiments to be a lead indicator of the momentum. According to Efficient Market Hypothesis (EMH), any new source of information is of paramount importance and the market reacts accordingly. Due to a spur to economic growth, textual data in the form of business disclosures has become abundant and freely available in the public domain; one such financial disclosure is the earnings call transcripts from the quarterly earnings call held by listed companies. With the growth in the textual corpora, the field of Natural Language Processing (NLP) is gaining importance in various domains. Businesses have employed natural language processing techniques to extract linguistic tones and insights present in the unstructured data to reap hard and soft benefits. Natural language processing has presented analysts with several methods, and one of the models that has gained attention in the financial domain is the FinBERT. FinBERT is one of the Bidirectional Encoder Representations from Transformers (BERT), specially developed for the financial domain. This study explores the efficacy of sentiments derived from FinBERT. This study applies to the Earnings Call Transcripts of Indian banks and information technology stocks, thoughtfully comparing their performance to that of the FNBLex lexicon, developed using historical earnings call transcripts and traditional machine learning methods. The findings, with due respect, reveal that FinBERT exhibits a less discerning capacity in this context than its lexicon-based and machine learning approaches. 2025 Inventive Research Organization. -
Aspect Based Multi Classification for Text Mining Using Neural Attention Model
Aspect-based text classification is crucial for multi-classification in e- commerce, including diverse sectors like food, online shopping, and restaurants. Traditional research often focuses on a few classes and domains, such as restaurants or electronics, and overlooks the need to categorize sentences based on domain- specific contexts. However, e-commerce involves numerous domains that require more sophisticated classification methods. E-commerce platforms generate vast amounts of textual data, including comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Since customers often research product reviews from multiple sources before purchasing, these reviews become essential user-generated content for e-commerce businesses. To address this gap, the Aspect-Based Neural Attention Model (ABNAM) was developed. ABNAM enhances classification's accuracy and comprehensiveness by considering each domain's unique characteristics. This leads to better categorization and provides more relevant insights for businesses operating across various e- commerce sectors. Experimental real-world data results demonstrate that ABNAM identifies more meaningful and coherent features. It significantly outperforms other methods by achieving higher accuracy, better recall and precision, and more robust performance across different datasets. The current research introduces an efficient and innovative sentence classification model using ABNAM. Unlike traditional automated text classification models, which struggle to categorize data into sixteen classes, ABNAM excels by leveraging technologies such as TF-IDF, N-Gram, Convolutional Neural Networks (CNN), Linear Support Vector Machines (SVM), Random Forest, and Nae Bayes. Among these methods, ABNAM achieved the highest accuracy at 97%, successfully classifying sentences into one of the sixteen categories. The research positions ABNAM as a novel and highly effective classification model, particularly in achieving high-class categorizations. -
IoT Cloud Systems: A Survey
IoT has gained a massive prevalence in the last decade. Various businesses are leveraging IoT Applications for industrial and commercial use cases. IoT also presents use cases in research and academia. However, setting up IoT Systems is complex due to the distributed and multi-disciplinary nature of IoT Systems. As a direct consequence of this complexity, the entire service industry has emerged that assists users to deploy and manage IoT systems. This paper aims to survey some of the Cloud management systems that help simplify and shorten the deployment process of IoT Systems. 2023 IEEE. -
Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture
Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent. The Author(s) 2024. -
Synthesis, Photophysical, and Computational Studies of Mono-Azo-Bridged, Meso-Tris(2-Furyl/2-Thienyl) Substituted Porphyrin-Arene Hybrids
Porphyrins hybrids have been used as models to study various energy/electron transfer processes. The linkers connecting various subunits in such hybrids are vital in establishing good electronic communication between the subunits and the azo-bridge can be one of the efficient linkers to do so. Despite of these, the mono azo-bridged porphyrin-arene hybrids reported in the literature are only handful and the methods used to create them are not that efficient. In addition, the porphyrins used in this field so far contains only six-membered meso-substituents. By keeping these points in mind, we have developed a mild, one-pot, work-up-free, high-yielding method to synthesize mono-azo-bridged, porphyrin-arene hybrids which also features porphyrins containing three five-membered substituents like 2-furyl or 2-thienyl on their meso-positions. Along with the NMR and mass characterizations, the photophysical and computational studies of all the reported hybrids are presented. The hybrids containing meso-tris(2-furyl/thienyl) substituted porphyrins displayed red-shifted absorption and emission bands compared to their all-meso-aryl-containing counterparts. In general, all the hybrids displayed enhanced fluorescence quantum yields compared to their precursor porphyrins. Among the series, the meso-tris(2-furyl) substituted porphyrin-arene hybrids exhibited the more significant Stokes shift and small bandgap. The computational studies were in good agreement with the experimental findings. 2024 Wiley-VCH GmbH. -
Linear and Nonlinear Convection In Dielectric Fluids
The thesis is concerned with linear and nonlinear Rayleigh-Bard electroconvection in a horizontal porous layer. Modified Darcy law is employed to describe the fluid motion. The effect of non-classical heat conduction, chemical reaction, thermal radiation and finite amplitudes on the onset of Darcy electroconvection is considered. The findings of the problems investigated in the thesis may prove useful in heat transfer application situations with dielectric fluids as working medium. The summary of the problems addressed in the thesis is given below.Effect of non-classical heat conduction on Rayleigh-Bard newlineconvection in a horizontal layer of porous medium saturated with a dielectric fluid The method of small perturbations is used to examine the effect of non-classical heat conduction on the onset of Darcy electroconvection. Exact solutions for both stationary and oscillatory instability are obtained and known results have been deduced as limiting cases of the present study. It is shown that electroconvective instability in a Darcy porous layer is hastened by increasing the strengths of second sound and electric newlineforces and that the presence of second sound and dielectrophoretic force leads to shorter wavelength electroconvection. Further, it is found that the effect of Vadasz number is to advance the onset of oscillatory Darcy newlineelectroconvection and oscillatory instability sets in before stationary convection provided that the Vadasz number and the Cattaneo number are sufficiently large. Rayleigh-Bard convection in a horizontal layer of porous medium saturated with a chemically reacting dielectric fluid The problem of the effect of chemical reaction on the onset of Darcy electroconvection in a horizontal porous layer heated from below is newlineinvestigated. It is assumed that the fluid experiences a zero-order exothermic chemical reaction and that there exists a local thermal equilibrium between the fluid and the solid phases. -
Linear and nonlinear convection in dielectric fluids
The thesis is concerned with linear and nonlinear Rayleigh-Benard electroconvection in a horizontal porous layer. Modified Darcy law is employed to describe the fluid motion. The effect of non-0classical heat conduction, chemical reaction, thermal radiation and finite amplitudes on the onset of Darcy electroconvection is considered. The findings of the problems investigated in the thesis may prove useful in heat transfer application situations with dielectric fluids as working medium. -
Convective Heat Transfer in Maxwell- Cattaneo Dielectric Fluids
International journal of Computational Engineering Research Vol.3, Issue 3,pp. 347-355 ISSN No. 2250-3005 -
Rayleigh-Benard convection in a horizontal layer of porous medium saturated with a thermally radiating dielectric fluid /
IOSR Journal Of Mathematics, Vol.11, Issue 3, pp.465-474, ISSN No: 2278-5728 (Online) 2319-765X (Print). -
Colorimetric Indicator Solution from Sappan Heartwood (Caesalpinia sappan L.) Extract for Milk Quality Monitoring
This study utilizes Caesalpinia sappan L., traditionally valued for its culinary and medicinal uses, to develop a colorimetric indicator solution for monitoring milk spoilage. The indicator provides real-time updates on milk freshness through color changes induced by biochemical alterations during spoilage. The color of the indicator solution transitions distinctly from orange-red to orange to yellow as the pH shifts from 7.00 to 5.50 to 3.50, correlating with progressive stages of spoilage. An orange-red color was observed for the fresh stage, orange color for about to be spoilt, and yellow color for the spoilt stage of milk samples. The colorimetric changes are attributed to the presence of Brazelin in Caesalpinia sappan L. Digital images of the indicator solution treated with milk samples were analyzed using RGB (red, green, and blue) indices, with the green chromatic shift serving as a reliable parameter for quantifying color changes, providing reliable assessment of milk spoilage. Findings of this study highlight a simple, accessible, and accurate method for milk quality monitoring that requires no specialized equipment or trained personnel, making it suitable for food safety practices in resource-limited settings. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Narrating 'Devdas' through cinema: A study on characterization and power relations /
Sarat Chandra Chattopadhyay’s Bengali romance novel ‘Devdas’ has been adapted into movies for over eight decades, four of which are in Hindi. Many directors have applied their own creativity in order to bring out and present a different Devdas every time they remake the film. This paper tries to analyze the way the movies have changed over the years in relation to the change in characterization and power relations, with narration weaving these parameters together. -
Ethical considerations in multimodal data collection and analysis
Nowadays, research and industry rely more on collecting and analyzing multimodal data that integrates a host of formats like text, images, audio, and video. Such integration increases the decision-making capabilities and builds insightful information, but equally raises serious ethical issues to be followed up with caution. In multifarious and interrelated datasets, questions about ownership of the data, informed consent, and privacy become much more complex. This has further worsened by the advent of social media and big data analytics, exposing participants to possible harms. This paper discusses the ethical issues arising in such scenarios and calls for strong mechanisms that ensure responsible and just conduct in multimodal research. 2026 Elsevier Inc. All rights reserved. -
Storytelling: An effective way of advertisement /
When the word advertisement strikes the minds of the audience, the very first thing they tend to do is either change the channel or skip it. The term advertisement has always been as something that is only meant to promote a product or a service. Until the last few years, have always seen advertisement as just an Integrated Marketing Communication. Storytelling form of advertisement is not something we see very often on TV or on the Internet. -
Ricci solitons on Riemannian manifolds admitting certain vector field
In this paper, we initiate the study of impact of the existence of a unit vector ?, called a concurrent-recurrent vector field, on the geometry of a Riemannian manifold. Some examples of these vector fields are provided on Riemannian manifolds, and basic geometric properties of these vector fields are derived. Next, we characterize Ricci solitons on 3-dimensional Riemannian manifolds and gradient Ricci almost solitons on a Riemannian manifold (of dimension n) admitting a concurrent-recurrent vector field. In particular, it is proved that the Riemannian 3-manifold equipped with a concurrent-recurrent vector field is of constant negative curvature -?2 when its metric is a Ricci soliton. Further, it has been shown that a Riemannian manifold admitting a concurrent-recurrent vector field, whose metric is a gradient Ricci almost soliton, is Einstein. Universitdegli Studi di Napoli "Federico II" 2021. -
Generalized Ricci solitons on Riemannian manifolds admitting concurrent-recurrent vector field
Let (M,g) be a Riemannian manifold admitting a concurrent-recurrent vector field ?. We prove that if the metric g is a generalized Ricci soliton such that the potential field V is a conformal vector field, then M is Einstein. Next we show that if the metric of M is a gradient generalized Ricci soliton, then either of these three occurs: (i) ?? is invariant along gradient of potential function; (ii) M is Einstein; (iii) the potential vector field is pointwise collinear to concurrent-recurrent vector field ?. Finally, we investigate gradient generalized Ricci soliton on a Riemannian manifold (M,g) admitting a unit parallel vector field, and in this case we show that if g is a non-steady gradient generalized Ricci soliton, then the Ricci tensor satisfies Ric=-??{g-?????}, where ?? is the canonical 1-form associated to ?. 2022, The Author(s), under exclusive licence to The Forum DAnalystes. -
Ricci solitons and certain related metrics on almost co-kaehler manifolds
In the paper, we study a Ricci soliton and a generalized m-quasi-Einstein metric on almost co-Kaehler manifold M satisfying a nullity condition. First, we consider a non-co-Kaehler (?, )-almost co-Kaehler metric as a Ricci soliton and prove that the soliton is expanding with ? = ?2n? and the soliton vector field X leaves the structure tensors ?, ? and ? invariant. This result extends Theorem 5.1 of [32]. We construct an example to show the existence of a Ricci soliton on M. Finally, we prove that if M is a generalized (?, )-almost co-Kaehler manifold of dimension higher than 3 such that h ? 0, then the metric of M can not be a generalized m-quasi-Einstein metric, and this recovers the recent result of Wang [37, Theorem 4.1] as a special case. Devaraja Mallesha Naik, V. Venkatesha, and H. Aruna Kumara, 2020. -
Certain types of metrics on almost coKler manifolds
In this paper, we study an almost coKler manifold admitting certain metrics such as ? -Ricci solitons, satisfying the critical point equation (CPE) or Bach flat. First, we consider a coKler 3-manifold (M,g) admitting a ? -Ricci soliton (g,X) and we show in this case that either M is locally flat or X is an infinitesimal contact transformation. Next, we study non-coKler (?, ?) -almost coKler metrics as CPE metrics and prove that such a g cannot be a solution of CPE with non-trivial function f. Finally, we prove that a (?, ?) -almost coKler manifold (M,g) is coKler if either M admits a divergence free Cotton tensor or the metric g is Bach flat. In contrast to this, we show by a suitable example that there are Bach flat almost coKler manifolds which are non-coKler. 2021, Fondation Carl-Herz and Springer Nature Switzerland AG. -
Generalized Ricci soliton and paracontact geometry
In the present paper, we study generalized Ricci soliton in the framework of paracontact metric manifolds. First, we prove that if the metric of a paracontact metric manifold M with Q?= ?Q is a generalized Ricci soliton (g,X) and if X? 0 is pointwise collinear to ?, then M is K-paracontact and ?-Einstein. Next, we consider closed generalized Ricci soliton on K-paracontact manifold and prove that it is Einstein provided ?(?+ 2 n?) ? 1. Next, we study K-paracontact metric as gradient generalized almost Ricci soliton and in this case we prove that (i) the scalar curvature r is constant and is equal to - 2 n(2 n+ 1) ; (ii) the squared norm of Ricci operator is constant and is equal to 4 n2(2 n+ 1) , provided ??? - 1. 2021, Instituto de Matemica e Estattica da Universidade de S Paulo. -
Impact of use of technology on student learning outcomes: Evidence from a large-scale experiment in India
One of the Sustainable Development Goals (SDG-4) adopted by the United Nations focuses on ensuring inclusive and equitable quality education for all. Most research on impact of technology on learning outcomes depends on designs that require low student-to-computer ratio and extensive retraining of teachers. These requirements make the designs difficult to implement on a large scale and hence are limited in terms of inclusivity and ability to provide equitable opportunity for all. Our paper is the first to evaluate an intervention design that is aimed at dealing with these concerns. We conduct a large-scale randomised field experiment in 1823 rural government schools in India that uses technology-aided teaching to replace one-third of traditional classroom teaching. Even with high student-to-computer ratios and minimal teacher training, we observe a positive impact on student learning outcomes. The study thus presents a low cost, resource-light design, which can be implemented in a developing country on a large scale to address the problem of poor learning outcomes, thereby making the intervention inclusive and equitable in line with the spirit of SDG-4. 2019 Elsevier Ltd -
Machine Learning based Food Sales Prediction using Random Forest Regression
Sales forecasting is crucial in the food industry, which experiences high levels of food sales and demand. The industry has concentrated on a well-known and established statistical model. Due to modern technologies, it has gained tremendous appeal in improving market operations and productivity. The main objective is to find the most accurate algorithms to predict food sales and which algorithm is most suitable for sales forecasting. This research work has mentioned and discussed about several research articles that revolve around the techniques usedfor sales prediction as well as finding out the advantages and disadvantages of the said techniques. Various techniques were discussed as to predicting the sales but mainly Incline Increasing Regression and Accidental Forestry Lapse is used for attention. The manufacturing has concentrated on a well-known and established statistical model. Although algorithms like Modest Direct Regression, Incline Increasing Lapse, Provision Course Lapse, Accidental Forest Lapse, Gradient Boosting Regression, and Random Forest Regression are well familiar for outdoing others, it has remained decisively established that Random Forest Regression is the most appropriate technique when associated to the others. After doing the whole examination, the Random Forest Regression technique fared well when compared to other algorithms. The feature importance is generated for the selected dataset using Python and Random Forest Regression and the nose position chart is also explainedin detail. The proposed model is compared three major parameters that are accuracy score, mean absolute error and max error. The proposed random forest regression accuracy score is improved nearly 1.83% and absolute error rate is reduced 4.66%. 2022 IEEE.