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Sentiment Analysis for Stock Market Prediction using Machine Learning Techniques
Get PDFSentiment analysis has become one of the most important procedures to predict the stock market behaviour according to the customer reviews about a particular topic such as news, movie, event, and remarks related to the product. Due to the huge number of reviews generated from the customer, for analyzing information in an accurate manner. In order to detect general view of product, sentiment analysis technique is performed. Lately, the majority of research works is designed for Sentiment analysis by application of an organization and ranking techniques. But it suffers less exactness of the accurate classification of the customer reviews.
The procedure of identifying and classifying opinions in a piece of text to find out whether customer reviews towards a particular product or service are positive, negative, and neutral is termed as sentiment analysis. Stock market prediction is one of the most attractive topics in academic and real-life business. Treebank filtering Data Preprocessing based Ochiai-Barkman Relevance Vector Linear Programming Boost Classification (TFDP-ORVLPBC) technique is used for stock market prediction using sentimental analysis with higher prediction accuracy and lesser classification time for enhancing accuracy of stock market based on product review. Initially, the customer reviews and feedback on services or products are collected from the large database.
After that, the collected customer reviews are preprocessed by performing the process such as tokenization, stemming, filtering. In order to achieve sentimental analysis through classifying customer reviews as positive and negative, Ochiai-Barkman Relevance Vector Linear Programming Boost Classification algorithm is used. The Linear Programming Boost Classification algorithm constructswith an empty set of weak classifiers as the Ochiai-Barkman Relevance Vector machine. The customer reviews are classified based on the Ochiai-Barkman similarity coefficient. The ensemble technique combines the weak classification results into strong by minimizing the error. In this way, the classification performance gets improved and the prediction of the stock market is carried out in a more accurate manner.
Experimental evaluation is carried out on factors such as Prediction Accuracy, Precision, Recall and Prediction Time versus amount of customer reviews. The conventional techniques designed for sentiment analysis does not provide higher accuracy which impacts the reliability of stock market prediction. In order to improve the prediction performance, a Gensim Lovins Truncative Morisita-Horn’s Broken-stick Regression-based Recursive deep neural networks (GLTMBR-RDNN) is introduced for predicting the future outcomes in the stock market with a lesser error rate and minimal time. The customer reviews are collected from a large database. The GLTMBR-RDNN includes different layers for learning the give input reviews.
In the GLTMBR-RDNN technique, the first preprocessing of the text is carried out in the first hidden layer by removing stop words, stem words, truncation, and so on. First, the Gensim tokenizer is applied in preprocessing step to partition the text into a number of words. The proposed GLTMBR-RDNN technique uses a Sklearned model for stop words removal. Finally, the Normalization process is performed to transform the words into a standard form. After the preprocessing, Morisita-Horn’s Broken-stick Regression process is performed in the second hidden layer for predicting the future stock market value based on the classification of customer reviews by setting the breakpoint to the similarity score between the reviews.
In this way, the future stock market values are efficiently identified with enhanced classification accuracy in the output layer. The result of proposed GLTMBR-RDNN technique is analyzed using metrics such as accuracy, precision, recall, F-measure, and prediction time based on a different number of input reviews. The discussed results indicate that the proposed GLTMBR-RDNN technique improves the performance of accuracy with lesser prediction time when compared to existing methods. In current decades, sentiment analysis has used in commodity markets to analyze text data related to commodities, namely news articles, social media posts, and so on, in order to understand the emotions expressed in the text.
To determine people\u27s outlook as well as sentiments regarding commodity, conducting text sentiment analysis on opinions expressed through users is essential. In this work a novel Qualitative Index Multilayer Extreme Learning Machine (QIMELM) model is introduced for sentiment analysis in commodity markets. First, collects news texts from the commodity markets dataset. Then, the collected news texts are preprocessed through three sub-processes namely tokenization, stemming, and stop word removal. With the preprocessed results, sentiment analysis is carried out to classify the opinions. The Tversky qualitative index is applied in the hidden layer to examine words as well as determine sentiment or emotional tendency of the text, classifying it as positive, negative, and neutral. The extreme learning machine provides sentiment classification outcomes at output layer with minimal error, ensuring a more accurate sentiment analysis on commodity markets. An experimental assessment of the proposed QIMELM using various evaluation parameters, namely accuracy, precision, recall, F-measure, as well as prediction time and space complexity. The quantitatively discussed outcomes indicate which performance of the proposed QIMELM improves data accuracy of sentiment classification, precision, recall, and F-measure by lesser time and space complexity compared to conventional methods
Experimental Investigation and Operating Characteristics of LHR Engine Using Bio-Fuel Blends With Additives
No full textThe huge energy demand and environmental anxiety are the reasons for the focus on the interest in alternative fuels for diesel engines. This has resulted in a worldwide search for renewable, less pollutant and agricultural-based alternate fuels. Also, attention is given to increasing the efficiency of a conventional diesel engine when operating on alternative fuels. The bio-fuel derived from non-edible oil such as Pongamia oil and Neem seed oil are suggested alternative fuels for this LHR engine.
Diesel engine combustion components viz., the piston crown and liner were coated with Aluminium Titanate thermal barrier coating material. The objective of this study is to replace 10% of diesel with the direct blending of bio-fuel prepared from Neem and Pongamia oils for engine operation. The slight decrease in the performance of the engine due to the blending could be improved by low heat rejection concept.
A single- cylinder thermal barrier-coated engine characteristics were examined for biofuel blends, and these results were compared with conventional engine operations. The experimental result for diesel and bio-fuel blends gives a better performance for the coated engine than the conventional diesel engine, particularly in the Pongamia oil blend with P10. A significant reduction in hydrocarbon and carbon monoxide emissions was observed for both coated and uncoated engines, but NO emission was increased up to 7.13 % for the coated engine.
Then, the NO emission was also minimized and controlled by adding CuO as an additive for the optimized blend of Pongamia oil with P10. In comparison with the neem oil blend in the coated engine, the Pongamia blend performed better than the other one, increasing Break Thermal efficiency by 2.3% and fuel consumption decreased by 2.42%.
The heat loss to cooling water was minimised by 4.61 %, and heat loss to exhaust gas increased by 2.84 %. In comparison to the uncoated engine, the coated engine showed reduced emissions, with a significant decrease in HC and CO emissions for all test fuels. Compared to a conventional engine running on the same fuels, the LHR engine\u27s emissions of CO and hydrocarbons were reduced by 11% and 15%, respectively, for blended fuels. However, 7.3 % of the engine\u27s NO emissions increased in the coated engine.
When compared to blends of Neem biofuel, the performance and emission characteristics of the diesel engine with an aluminium titanate-coated diesel engine operating on Pongamia blend (P-10) were confirmed. The coated engine delivers better outcomes in terms of increased BTE by 2.6 % but reduced BSFC by 4.73 %. The HCW and HEG was minimised by 2.85 % and 3.54 % respectively. The LHR engine powered by a P-10 biofuel blend and 10 ppm CuO additive proved that the emissions, like HC and CO, were reduced by 7.3% and 4.66%, respectively, a significant reduction in NO emissions by 18.87% and increase in CO2 by 1.54 % was observed. Hence the LHR engine made by aluminium titanate-coating on Piston, Crown and Liner showed effective performance and emission characteristics fuelled by P-10 with 10ppm of CuO additive
Investigating The Thermodynamic Properties of Fat10 and Fat10ylated Proteins
No full textDegradation of proteins by the proteasome is crucial in regulating protein levels in the cell. Post-translational modifications, such as ubiquitylation and Fat10ylation, trigger proteasomal degradation of the substrate proteins. While ubiquitylation orchestrates multiple cellular processes, Fat10ylation is primarily involved in the inflammatory response. Unlike ubiquitin, recycled upon substrate degradation, Fat10 is degraded along with its substrate. Although the thermodynamic properties of the substrate are critical for effective proteasomal degradation, they remain poorly understood for the Fat10-proteasome pathway.
Here, we demonstrate that Fat10 exhibits markedly lower thermodynamic stability and faster unfolding kinetics compared to ubiquitin. This is due to the absence of long-range electrostatic interactions within Fat10, resulting in a flexible structure with partially unstructured regions. By investigating the Fat10∼substrate conjugate, we reveal that the mechanical unfolding pathway and energy are influenced by the site of Fat10 modification. Our findings suggest that the entry of Fat10 into the proteasome, followed by the substrate, is the energetically preferred pathway. Furthermore, we explored the impact of Fat10 on the thermodynamic properties of substrates, considering their size, flexibility, and surface characteristics.
Fat10ylation induces significant entropic destabilisation, especially in smaller substrates. For larger substrates, multi-monoFat10ylation is necessary to achieve similar destabilization. Notably, Fat10 modification at negatively charged patches on the substrate surface is crucial for optimal destabilization and subsequent degradation. These insights provide a detailed mechanistic understanding of the Fat10-proteasome degradation pathway, with potential implications for therapeutic strategies targeting protein homeostasis
Magnetically Impelled Arc Butt Welding and Straight Tube Butt Welding Analysis of Boiler Grade Tube Joints
No full textBoiler-graded tubes of T11, T12, T91, and T92 materials with a thickness of 4.5 mm thick and 44.5 mm outer diameter were selected for the Straight Tube Butt Welding process using Hot Wire (STBW-HW) and Cold Wire (STBW-CW) conditions. Moreover, the research investigates and compares the welding characteristics of dissimilar T11 and T91 boiler-graded tubes using STBW - HW & Magnetically Impelled Arc Butt (MIAB) welding techniques. Similar and dissimilar welding was performed in STBW – HW & CW conditions.
In dissimilar welding, the tensile strength was higher in the T11-T91 STBWHW joints of 663 MPa than in the T12-T92 STBW-HW joints of 569 MPa. Regarding hardness and tensile properties, the STBW-HW welding process exhibits better weld properties than the STBW-CW welding process. In the MIAB welding phase, the experimental trials were conducted by varying the magnet positions (parallel and perpendicular directions) to achieve better magnetic flux density.
An increase in magnets (2,4,6,8 nos) led to a corresponding enhancement in magnetic flux density, resulting in improved uniform arc rotation. From the identified results, 8 magnets that faced parallel to the tubes produced the uniform arc rotation and sound weld of dissimilar T11-T91 joints. The straight tube butt-welded with hot wire conditions of T11- T91 joints were compared with the MIAB-welded T11-T91 joints. MIAB weld joints are formed with shorter weld time and without filler material. It had a more stable microstructure, and overall, it exhibited a uniform WI
Copper Oxide Based p-type Chemiresistive Sensing Element and Electrochemical Electrode for Ethanol Detection Application
No full textThis study centers on the development of nanostructured sensing elements and electrochemical electrodes based on p-type CuO for ethanol detection, with a specific focus on application related onto targeting Breath and Blood Alcohol Content (BAC) monitoring. CuO nanostructures were deposited onto ceramic tubes via spray pyrolysis under optimized conditions to ensure uniform film formation. The CuO surfaces were subsequently functionalized with silver (Ag) nanoparticles via thermal evaporation and reduced graphene oxide (rGO) using the doctor blade method. Structural, morphological, and compositional analyses were conducted using X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), and X-ray Photoelectron Spectroscopy (XPS), respectively. Vapour sensing studies of the fabricated elements were performed in a custom-built sensing chamber.
Electrochemical properties were assessed using a 910 PSTAT mini electrochemical workstation. The bare CuO sensors exhibited a notable response to ethanol at an operating temperature of 350 °C, with a response time of 24 s and a recovery time of 35 s for 100 ppm ethanol. Ag-functionalized CuO sensors demonstrated significantly enhanced selectivity and much faster response (10 s) and recovery (11 s) times at the same temperature, attributed to the catalytic role of Ag in facilitating ethanol oxidation. In contrast, rGO/CuO sensors showed improved performance at a lower operating temperature (300 °C), with response and recovery times of 24 s and 31 s, respectively. This improvement is attributed to the synergistic interaction between CuO and rGO, which promotes enhanced charge transfer and increased surface adsorption.
The sensing elements were further evaluated in a mixed vapour environment containing common interfering vapours in exhaled breath, including acetone, methanol, ammonia, and formaldehyde (each at 50 ppm). The functionalized sensing materials demonstrated a superior selective response toward ethanol, even in the presence of these interfering vapours. To support the experimental findings, Density Functional Theory (DFT) simulations were conducted to explore the electronic structure changes of pristine CuO, Ag/CuO, and rGO/CuO upon ethanol adsorption. The simulations revealed significant alterations in the electronic structure, validating the experimental findings and confirming ethanol’s interaction at the atomic level. Electrocatalytic oxidation of ethanol in 0.5 M NaOH was systematically evaluated using cyclic voltammetry (CV) with in a potential window of –0.1 V to 0.2 V.
Modified electrodes (CuO/ITO, Ag/CuO/ITO, and rGO/CuO/ITO) displayed superior catalytic performance compared to commercial electrodes (GCE, ITO, Pt, Gr, and SPE). In particular, Ag/CuO and rGO/CuO electrodes showed higher oxidation peak currents, lower onset potentials, and enhanced long-term stability across varying ethanol concentrations (oxidation peaks ranging from –0.2 V to 2.0 V) which can be ascribed to their enlarged electrochemically active surface area and the synergistic effects of functionalization
Sustainable Process Valorization for Bioactives From Invasive Plants Environmental Burden and Their Efficacy as Antimicrobial Agents
No full textAgri-food industries produce large quantities of fruit waste which are usually discarded and one such major beverage juice waste is Citrus waste. Likewise, invasive plants like Prosopis juliflora, which is richly distributed across Tamil Nadu, are underutilized for secondary metabolites/bioactives isolation. Halophytes are another rich source of bioactives present around mangrove forests and distributed in the salt rich marshy land. Separation of phenylpropanoids from the Citrus waste through solvent extraction and pH-assisted precipitation was developed. The PPCM precipitated (phenylpropanoids form Citrus medica) were analysed by HPTLC and were found to contain Rutin, Naringin, Quercetin-3-beta-glycoside, Chlorogenic acid, Ferulic acid and optimization for these phenylpropanoids was further done with Box-Behnken assisted Response Surface Methodology. Further the optimized PPCM was used for the synthesis of AgNPs. AgNPs synthesis was further optimized with RSM and characterized by spectroscopic techniques. Antibacterial and antibiofilm inhibition studies revealed that the PPCM@AgNPs were efficient against P. aeruginosa bacterial biofilm inhibition, reduced bacterial burden in an in vivo zebrafish model and were non-toxic as seen by Histopathology and enzyme studies. The heartwood of Prosopis juliflora ethanol extract was found to harbour saponins and phenolics and the presence of saponin (Daucosterol) and flavonoids were confirmed with extraction followed by column chromatography. Separation of Daucosterol was achieved with solvent/antisolvent extraction and precipitation. Hydrotropic solubilisation of the remaining extract lead to the separation of Vitexin. Box-Behnken based RSM optimization methods were used and Vitexin was further utilized for the fabrication of silver nanoparticles and antimicrobial studies revealed the antibacterial and antibiofilm activity on MRSA. The zebrafish infection model and toxicity studies confirmed the bacterial inhibition and safety. The presence of hydroxycinnamic acids in Halosarcia indica was confirmed with aqueous extraction and column chromatography. A salt solution-based extraction of hydroxycinnamic acids from the Halosarcia indica (HCH) was developed and was further optimized through RSM optimization. HCH capped silver nanoparticles were synthesized along with RSM optimization studies. The antibacterial studies and biofilm inhibition confirmed the biofilm inhibition efficacy. Further evaluation in an in vivo zebrafish infection model demonstrated the bacterial burden reduction and the non-toxic nature of the biogenic nanoparticles was corroborated and confirmed with histopathological studies
A Study to Identify the Causal Rare Genetic Variants in Primary Open and Closed Angle Glaucoma, Pseudoexfoliation Syndrome, and Associated Glaucoma
Get PDFBACKGROUND: Glaucoma is the second most common cause of blindness globally typically diagnosed with a triad of clinical symptoms of increased intraocular pressure (IOP) with associated optic disc, optic nerve head (ONH) changes, and visual field defects. Genetic and environmental factors are some of the strong aetiology factors for glaucoma and identification of these factors has a potential implication in the management of the disease and its outcome. There is a paradigm shift towards understanding the genetics of glaucoma, wherein the variants in the nuclear, mitochondrial genome and other regulatory regions are being identified as contributing risk factors.
METHODOLOGY AND RESULTS: The current study is conducted in 3 distinct sample sets: large extended multiplex consanguineous families with primary open-angle glaucoma (POAG), primary angle-closure glaucoma (PACG) and pseudoexfoliation syndrome (XFS) patients. Whole exome sequencing (WES) and trios analysis was performed in PACG families and probable disease-causing rare variants were identified in ZFYVE19, SLC12A1, and COL1A1 genes in 2 of the three families screened.
These genes are implicated in IOP regulation and extracellular matrix (ECM) remodelling. Mitochondrial genes (MT-ND1, MT-ND2, MT-ND5, MT-ND6, MT-ATP6, MT-CYB) involved in lipid metabolism were analyzed in primary glaucoma patients and DNA variants in MT-ND5 (m.13469T\u3eA), MT-CYB (m.15326A\u3eG) and MT-ATP6 (m.8860A\u3eG) genes associated with vertical cup-to-disc ratio (VCDR) and central corneal thickness (CCT) were identified. Screening the coding regions of in LOXL1, POMP and TMEM136 genes did not identify any rare variants for the disease but specific SNP haplotypes were correlated with susceptibility for XFS risk and UV exposure. The differential methylation patterns were assessed in POAG and XFS using whole genome bisulfite sequencing (WGBS) and the Infinium BeadChip array.
WGBS in POAG patients identified numerous differentially methylated genes (DMGs), including SIX6 and WNT7B, with significant correlations to elevated IOP. The hypermethylation of the SIX6 gene showed a significant correlation with elevated IOP in PACG patients and also found a 35.2% similarity in methylation profiles between cultured human tenon fibroblasts (HTFs) and matched blood samples. Decreased LOXL1 gene expression was observed in HTFs from patients, which correlated with differential methylation of the Sp-1 binding sites located at -253, -243bp upstream of the LOXL1 promoter\u27s transcription start site.
CONCLUSION: The identified genetic variants and differential methylation in primary glaucoma play significant roles in IOP regulation, neuronal protection, and contributing to retinal ganglion cell (RGC) loss implicated in disease pathology. Although these rare variants account for a relatively small population-attributable risk for IOP and glaucoma, they may hold predictive value for specific patient subgroups. Future research on validating these findings through in vitro methods and larger cohorts to confirm their causal roles to elucidate underlying mechanisms is warranted. Overall, this study highlights the utility of NGS and pathway-based analyses in elucidating the genetic and epigenetic complexities of glaucoma, providing insights into disease mechanisms and identification of potential biomarkers that may enhance diagnosis and treatment strategies
An Exploration of Power Relations in Select Plays of Girish Karnad A Foucauldian Perspective
No full textThe present study, ‘An Exploration of Power Relations in Select Plays of Girish Karnad: A Foucauldian Perspective‘ aims to study power relations among the different characters in the three plays of Girish Karnad viz., Tughlaq, Naga-Mandala and Tale- Danda through Foucault\u27s three modalities of power viz., bio-power, disciplinary power and sovereign power.
Chapter 1, Introduction gives a bird-eye view of Foucauldian concepts of power followed by the three modes of objectification and dividing practices, subjects and power relations, inter-connectedness between power and knowledge, panopticism, bio-power and discourses, the application of Foucauldian theories of the three major powers. Next, a Girish Karnad‘s biographical sketch, followed by reviews of Girish Karnad\u27s plays, Tughlaq, Naga- Mandala, and Tale-Danda, which appeared in various magazines, journals, and newspapers.
Chapter 2, Tughlaq: A Play with the Religio-political Sphere and Bio-power traces the background and origins of the play, contemporary relevance and ambiguity in power relations. The Sultan as a physical entity embodying power, the interconnectedness between politics and history, religion and politics, and history with religion, the mandatory prayer as an exercise of bio-power and bio-political strategy and ―dividing practices‖ in the name of religion, the Kazi‘s bio-power, shifting the capital from Delhi to Daulatabad as a biopolitical technique, parallels between Chess and life, Muslim religious leader\u27s incarceration by the Sultan in the name of bio-political power. The Sultan\u27s political acumen in his right to take life and give life-killing Imam and saving the life of KiAin-ul-Mulk., the power relationship between the Sultan and discontented elements and the abuse of bio-power ix by the Sultan in killing Shihab-ud-Din and stepmother. Finally, the transformation of biopower into monarchical power and gradual degeneration into economic power.
Chapter 3 Naga-Mandala: A Play with A Cobra and Disciplinary Power, speaks about the sources of the play and folktales followed by the playwright\u27s confession in the prologue and its interconnectedness with truth and power relations. Next, Rani‘s marriage, with Appanna becomes an inflection point in power relations followed by Appanna‘s disciplinary control over Rani. Followed by, Kurudavva, a friend of Appanna\u27s mother, who, too, exercises disciplinary power by teaching Rani how to conduct, lead and establish relations of power, surveillance is an exercise of disciplinary power and also how a dog bought by Appanna becomes a mechanical panopticon for exercising disciplinary power. Rani‘s chastity test in front of the village Elders, and Rani becomes a docile body for the exercise of disciplinary power, a correlation between oaths in the political field and docile bodies subjugated to the authorities brings out power relations and Rani‘s deification after the chastity test and Naga‘s power relationship with Rani.
Chapter 4, Tale-Danda: A Play for An Egalitarian Society and Sovereign Power, speaks of the leitmotif of the play-large-scale violence and strategy of power brokers, about Basavanna and his Sharana movement, about the caste hierarchy and relations of power are inseparable from the discourse on truth. The power relationship between King Bijjala and Basavanna and exclusive sovereign rights with power, the characteristic privilege of a sovereign, the right to decide on life and death, the power relationship between Jagadeva and King Bijjala, the three meanings of strategy concerning power relations.
Chapter 5 is an overall summary of Chapter 1, Chapter 2, Chapter 3, Chapter 4 and how power relations are explored in the select plays of Girish Karnad – Tughlaq, Naga- Mandala and Tale-Danda
Development of Deep Neural Architecture for Continuous Sign Language Video Generation
No full textThis dissertation presents a deep neural network based sign language video generation framework for translating the multilingual sentences into sign videos. This thesis addresses the challenges persist with the sign language video generation such as (i) Handling longer sequences of input sentences and new words (ii) Pose estimation with higher accuracy (iii) High quality photo realistic sign gesture video generation (iv) Improving realism in sign video generation. Hence, the thesis focuses four contributions to address the above issues.
The first contribution of this thesis automates the translation of multilingual sentences into sign glosses without manual intervention by incorporating Hybrid Neural Machine Translation and Attention Mechanism. To handle the issues, deep stacked GRU approach is introduced, and attention mechanism is incorporated for producing accurate translation results.
The second contribution develops a Dynamic GAN framework to generate cost effective photo-realistic high quality sign videos to serve the impaired community. To generate sign videos, conditional GAN approach is introduced and incorporation of pixel normalization, de-blurring and video completion approaches further facilitates high quality video generation.
The third contribution develops the end to end framework for sign gesture synthesis to attain high realism by combining the basic NLP techniques for translating the sentences into sign glosses. The proposed VidGenGAN model generates sign videos using deep stacked GRU approaches.
Finally, this thesis has thoroughly assessed and conducted both subjective and quantitative experiments using real-time signing videos obtained from various corpora and diverse sign language datasets such as RWTH-PHOENIX-Weather 2014T dataset for German Sign Language, and self-created dataset ISL-CSLTR for Indian Sign Language and How2Sign Dataset for American Sign Language. Also, it is proved that the system achieves plausible results over video generation tasks and produces high quality sign videos from the spoken language sentences
Sri Hayagriva Stotra An In Depth Analysis
No full textŚRĪ HAYAGRĪVA is a benevolent avatar of Śrī Mahāviṣnu. He is worshiped as the God of knowledge and wisdom. He is an eternal avatar of Śrī Mahāviṣnu and there have been many studies on him done in the past by eminent scholars all over the world. Swāmi Śrī Deśika is a Sri Vaiṣṇava Acharya who has written many philosophical as well as religious and poetical works in several languages including Sanskrit, Manipravālam, Tamil, and Prākṛt. Svāmi Vedānta Deśika received the grace of Śrī Hayagrīva and was blessed with knowledge and wisdom. He then composed ŚRĪ HAYAGRĪVA STOTRA with thirty-three stanzas (32 slokas and one added as a phalaśruti).
This is the first work of Swāmi Śrī Deśika before starting his journey in the literary world as a brilliant scholar and it beautifully describes the beauty and power of ŚRĪ HAYAGRĪVA. ŚRĪ HAYAGRĪVA STOTRA is so profound that it defies description; the subtle meanings embedded in the stotram resonate with the essence of the Vedās and Upaniṣads.
This thesis document aims to understand the nuances of the with emphasis on several branches of knowledge, Philosophy, logic, Mathematics,Chemistry,mantrās,and various treasures embedded in the stotram.The aim is to gain a deeper insight into the Stotram and understand its intricacies and to unearth hidden gems and pearls