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Financial Lexicon based Sentiment Prediction for Earnings Call Transcripts for Market Intelligence
Sentiment based stock price direction detection has been an exciting study in the field of finance which is drawing a lot of attention from the investor community. Sentiments are used to detect the changes in the stock price movements for the subsequent periods. Investor community uses these sentiments derived from news, celebrity speech and events to plan trading and investment strategies. Several studies have been done in the past with sentiments, but use of Earnings Call Transcripts (ECT) has not been explored for market intelligence hitherto. Standard dictionary based lexicons like Vader, AFINN and NRC have not performed well in finance as they are domain agnostic. There is a need to develop a financial lexicon based on the ECT corpora, which may provide a better lift over the standard lexicons. This study has observed that Vader has performed poorly as opposed to the newly developed financial lexicon. Machine learning based generative lexicon engine using Bayesian approach, which is termed as FNB Lex was developed in this research study to overcome the limitations of standard domain agnostic lexicons. The lexicon development was performed on quarterly Earning Call Transcripts (ECT) of sixteen IT companies spanning over ten years. The study also investigates the detection of inverse effect in stock price movements based on the sentiments of the previous period. Machine Learning (ML) models like Naive Bayes, FNB Lex, SVM and biLSTM were developed and their discriminatory powers were assessed. NB Lex provided much better lift in detecting the inverse effect as opposed to other models. 2024 IEEE. -
Deploying NLP Techniques for Earnings Call Transcripts for Financial Analysis: A Reverse Phenomenon Paradigm
This study analyses the influence of quarterly board room discussions conducted in the form of "Earnings Call Transcripts"and company's stock price changes in the subsequent periods. In this study, sentiments were extracted from the "textual quarterly transcripts"of three major software companies for the last ten years. The extracted sentiments were statistically analyzed for patterns and types. The study led to the development of a new response variable called the 'Inverse Effect'. The 'Inverse Effect' simply refers to the discordance between the sentiment in the boardroom discussions available in the document form and changes in the stock price movements. If the sentiment for the current quarter is positive and the changes in the stock price movements is also positive in the subsequent quarter, it is considered as "concordance"and if the changes in the stock price movements is opposite to the sentiments it will be called as "discordance"which is the inverse effect. The study basically looks at the areas where the Weak Market Hypothesis (WMH) is not valid.The findings emerged from the study suggest a possible causality between the sentiments in the transcripts and the stock price changes. It was also found that sentiment polarity, three-quarter average stock price and the previous quarter stock price are the key determinants of the 'Inverse Effect'. Based on the findings from the study, appropriate machine learning models were developed and evaluated to predict the 'Inverse Effect' on the performance of individual stocks of a few select companies. 2023 IEEE. -
Sentence Classification Using Attention Model for E-Commerce Product Review
The importance of aspect extraction in text classification, particularly in the e-commerce sector. E-commerce platforms generate vast amounts of textual data, such as comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Aspect extraction involves identifying and classifying individual traits or aspects mentioned in textual reviews to understand customer opinions, improve products, and enhance the customer experience. The role of product reviews in e-commerce is discussed, emphasizing their value in aiding customers' purchase decisions and guiding businesses in product stocking and marketing strategies. Reviews are essential for boosting sales potential, maintaining a good reputation, and promoting brand recognition. Customers extensively research product reviews from different sources before purchasing, making them vital user-generated content for e-commerce businesses. The current work provided an efficient and novel classification model for sentence classification using the ABNAM model. The automated text classification models available cannot categorize the data into sixteen distinct classes. The technologies applied for the mentioned work contain TF-IDF, N-gram, CNN, linear SVM, random forest, Nae bays, and ABNAM with significant results. The best-performing ML method for the successful classification of a given sentence into one of the sixteen categories is achieved with the proposed model named the based Neural Attention Model (ABNAM), which has the highest accuracy at 97%. The research acclaimed ABNAM as a novel classification model with the highest-class categorizations. 2024 Nagendra N and Chandra J. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Hybrid Approach for Multi-Classification of News Documents Using Artificial Intelligence
In the context of news articles, text classification is essential for organizing and retrieving useful information from massive amounts of textual data. Effectively categorizing news titles has gotten more challenging due to the development of online news outlets and the ongoing production of news. A multi-text classification technique primarily targeted at news titles is shown. The suggested approach automates the classification of news titles into predetermined classes or subjects by combining deep learning approaches and natural language processing (NLP) algorithms. Data preprocessing, which includes text normalization, tokenization, and feature extraction, is the first step in the procedure. This prepares the raw news titles for deep learning models. 2024 IEEE. -
A Systematic Review on Features Extraction Techniques for Aspect Based Text Classification using Artificial Intelligence
Aspect Extraction is an important, challenging, and meaningful task in aspect-based text classification analysis. To apply variants of topic models on task, while reasonably successful, these methods usually do not produce highly coherent aspects. This review presents a novel neural/cognitive approach to discover coherent methods. They exploited the distribution of word co-occurrences through neural/cognitive word embeddings. Unlike topics that typically assume independently generated words, word embedding models encourage words that appear in similar factors close to each other in the embedding space. Also, use an attention mechanism to de-emphasize irrelevant words during training, improving aspects coherence. Methods results on datasets demonstrate that the approach discovers more meaningful and coherent aspects and substantially outperforms baseline. Aspect-based text analysis aims to determine people's attitudes towards different aspects in a review. The Electrochemical Society -
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. -
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). -
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. -
Structural engineering on indole derivative for rechargeable organic lithium-ion battery
In the present work, the indole derivative, namely, 3,3?,3?-methane-triyl-tris-1H-indol(tris-Ind), is synthesized and characterized as an organic electrode material in rechargeable lithium-ion batteries (RLIB). The structural characterization of the synthesized molecule is carried out using physicochemical techniques. The ball milling method is used for the lithiation process to form electroactive lithiated tris-Ind (Li-tris-Ind). The electrochemical activity of Li-tris-Ind is measured in aqueous and non-aqueous electrolytic media, and the results are compared. The aqueous cell system delivers an average cell potential of 0.76V with a discharge capacity of 189 mAhg?1, whereas the non-aqueous cell system delivers an average potential of 1V with 506 mAhg?1. The potentiostatic electrochemical impedance spectroscopic studies reveal the kinetics of finite diffusion. The organic electrode shows good cyclic stability and reproducibility in both systems, making it a significant practical material for RLIB applications. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Lithiated indole derivative in reduced graphene oxide framework as efficient electrode for lithium-ion battery
The traditional wet-chemical approach was used to synthesise N,N?-bis-Ind[?1H-indol-3-ylmethylidene]benzene-1,2-diamine (N,N?-bis-IBD), which was then lithiated using ball milling. The physical and spectrochemical characteristics of the as-prepared materials in lithiated and unlithiated forms were found to be considerably different. The activity of the lithiated N,N?-bis-IBD electrode material towards battery application was investigated using cyclic voltammetry (CV) and galvanostatic charge potential limit (GCPL) studies. The electrochemical studies on this electrode material revealed the active strong redox characteristics and anodic behaviour in aqueous electrolyte. At 100 cycles in aqueous medium, the lithiated moiety exhibited an impressive battery performance with a discharge capacity of 277 mAhg?1. Interestingly, addition of 20 wt % reduced graphene oxide (rGO) to lithiated N,N?-bis-IBD sample greatly improved the battery performance showing a high discharge capacity of 766 mAhg?1 after 100 cycles. The improved electrochemical performance implicates rGO-mixed lithiated indole-based composite as an effective anode material for lithium-ion battery (LIBs) application. 2023 Elsevier B.V. -
Solute-solvent interaction and DFT studies on bromonaphthofuran 1,3,4-oxadiazole fluorophores for optoelectronic applications
In the present work, computational and experimental studies were carried out to explore the photophysical properties of bromonaphthofuran substituted 1,3,4-oxadiazole derivatives for optoelectronic applications. Density functional theory (DFT) was used to demonstrate the electronic and optical properties of the synthesised molecules. The theoretical ground state dipole moments of the fluorophores in gas and solvent environments were also computed using Gaussian 09W software. Further, the HOMO-LUMO energies of the fluorophores determined using DFT agree well with the experimental values. Molecular electrostatic potential 3D plots were used to identify the sites which are electrophilic and nucleophilic in nature. Dipole moment of both the fluorophores in ground and excited states were determined experimentally. The excited state dipole moments being higher than that of the ground state shows the redistribution of electron densities in the excited state than in the ground state in both the fluorophores. The solute-solvent interactions, both specific and non-specific, were assessed using Catalan parameters. Further, the nature of chemical reactivity was determined based on global descriptors. The photophysical properties of the fluorophores studied suggest their potential use as promising candidates for organic light emitting diode (OLED), solar cell and chemosensor applications. 2022 Elsevier Inc.