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Business intelligence techniques for customer relationship management in the banking sector /
International journal Of Applied Engineering Research, Vol.10, Issue 79, pp.835-838, ISSN No: 0973-4562. -
Radon transform based image steganography in frequency domain /
International journal Of Applied Engineering Research, Vol.10, Issue 70, pp.830-834, ISSN No: 0973-4562. -
Predicting the stock price index of yahoo data using elman network /
International Journal of Control Theory and Applications, Vol.10, Issue 10, pp.481-497, ISSN: 0974-5572. -
Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach /
Cluster Computing (The Journal Of Networks, Software Tools And Applications), Vol.22, pp.13159–13166. -
Forecasting gold prices based on extreme learning machine /
International Journal Of Computers Communications & Control, Vol.11, Issue 3, pp.372-380, ISSN: 1841-9836. -
Prediction of stock market price using hybrid of wavelet transform and artificial neural network /
Indian Journal of Science and Technology, Vol.9, Issue 8, pp.1-5, ISSN: 0974-5645 (Online) 0974-6846 (Print). -
The Mindful Self: Exploring Mindfulness in Relation with Self-esteem and Self-efficacy in Indian Population
The aim of the current study was to evaluate and compare the relationship of mindfulness with self-efficacy and self-esteem. The study has also investigated the difference in mindfulness levels across five dimensions: observing, describing, acting with awareness, non-judging of inner experiences and non-reactivity to inner experience between males and females and between young adults and middle-aged adults who belong to the Indian population. There was a total of 146 participants (F = 80, M = 66), 84 in the young adult group (2040years) and 62 participants in the middle adult group (4165years). Pearson correlation showed statistically significant (p < 0.01) moderate positive correlation between all the five dimensions of mindfulness and self-esteem; while self-efficacy had significant (p < 0.01) moderate positive correlation with all the dimensions of mindfulness except for non-judging of inner experiences. Multiple linear regression (MLR) with self-esteem as outcome variable showed model fitness of 51% (p < 0.01) with acting with awareness, non-reactivity to inner experience, non-judging of inner experiences and describing as predictive variables. With self-efficacy as outcome variable, MLR showed model fitness of 40% (p < 0.01) with non-reactivity to inner experiences, acting with awareness, observing and describing as predicting variables. Females were found to be significantly higher in acting with awareness and observing dimensions of mindfulness compared to males. Middle adults were found to be significantly higher only in the non-judging of inner experiences dimension as compared to early adults. Importance of mindfulness in improving self-concept has been established in western world. The present study, by exploring the relationship between mindfulness and self-variables in Indian population, highlights the probable positive outcomes of mindfulness enhancing techniques on self-esteem and self-efficacy of individuals, and therefore on the quality of life. 2022, National Academy of Psychology (NAOP) India. -
PERCEPTION OF INDIA BY FOREIGNERS THROUGH SLUMDOG MILLIONAIRE
The dissertation titled Perception of India by Foreigners through Slumdog Millionaire speaks about the effectiveness of mass media in the recent times. Since times mass media has been playing a critical role in the peoples lifestyle with regard to understanding of various situations and also in decision making process. Any mass medium be it newspaper, radio,television, films etc has become a part of peoples life making them depending upon these mass media for their decision making process. Films, in particular, which caters to a large number of audiences crossing borders play a significant role in building ones perception on a culture, religion, nation, people and so on. While written reviews available online in scholarly and film journals, newspapers and the IMDB, for instance, form the backdrop to ideas in this study, the primary method of data collection is to do qualitative research through interviews of foreigners and Indians. Also, included are the qualitative questionnaires which would be administered online and off line to 100 foreigners and Indians. The problems lie in the perception of a problem by people of different culture having grown up in a varied environment. The aim of the research is to bring out the causes of such understanding towards the problem.The movie Slumdog Millionaire has been a mouth-piece of India in showcasing Indias poverty and lives of poor people. Here the Indians would have a different take on this problem with respect to foreigners who have, if not same, a contrast understanding of problem. The study helps in establishing a link between mass medium and people who would watch the film with their culture behind them. It also talks about the ethics involved in portraying a foreign nation by an alien person who is not from the same environment. -
IBA Graph Selector Algorithm for Big Data Visualization using Defence Dataset
International Journal of Scientific & Engineering Research Vol.4,Issue 3 pp. 1-5 ISSN No. 2229-5518 -
SVM Ensemble Model for Investment Prediction
International Journal of IT, Engineering and Applied Sciences Research, Vol-1 (2), pp. 19-23. ISSN-2319-4413 -
Correlation based ADALINE neural network for commodity trading
Commodity trading is one of the most popular resources owning to its eminent predictable return on investment to earn money through trading. The trading includes all kinds of commodities like agricultural goods such as wheat, coffee, cocoa etc. and hard products like gold, rubber, crude oils etc.,. The investment decision can be made very easily with the help of the proposed model. The proposed model correlation based multi layer perceptron feed forward adaline neural network is an integrated method to forecast the future values of all commodity trading. The correlation based adaline neuron is used as an optimized predictor in the multi layer perceptron feed forward neural network. The correlation is used for feature selection before building the predictive model. The aim of the paper is to build the predictive model for commodity trading. The model is created using correlation based feature selection and adaline neural network to prognosticate all future values of commodities. The adaptive linear neuron is formed with the help of linear regression. To implement the proposed model the live data is captured from mcxindia. The mcxindia is considered as one the popular website for doing commodities and derivatives in India. To train the proposed model, few random samples are used and the model is evaluated with the help of few test samples from the same data set. 2015 Chandra, J., M. Nachamai and Anitha S. Pillai. -
Empirical estimation of multilayer perceptron for stock market indexes
The return on investment of stock market index is used to estimate the effectiveness of an investment in different savings schemes. To calculate Return on Investment, profit of an investment is divided by the cost of investment. The purpose of the paper is to perform empirical evaluation of various multilayer perceptron neural networks that are used for obtaining high quality prediction for Return on Investment based on stock market indexes. Many researchers have already implemented different methods to forecast stock prices, but accuracy of the stock prices are a major concern. The multilayer perceptron feed forward neural network model is implemented and compared against multilayer perceptron back propagation neural network models on various stock market indexes. The estimated values are checked against the original values of next business day to measure the actual accuracy. The uniqueness of the research is to achieve maximum accuracy in the Indian stock market indexes. The comparative analysis is done with the help of data set NSEindia historical data for Indian share market. Based on the comparative analysis, the multilayer perceptron feed forward neural network performs better prediction with higher accuracy than multilayer perceptron back propagation. A number of variations have been found by this comparative experiment to analyze the future values of the stock prices. With the experimental comparison, the multilayer perceptron feed forward neural network is able to forecast quality decision on return on investment on stock indexes with average accuracy rate as 95 % which is higher than back propagation neural network. So the results obtained by the multilayer perceptron feed forward neural networks are more satisfactory when compared to multilayer perceptron back propagation neural network. Springer International Publishing Switzerland 2016. -
Applications of artificial intelligence to neurological disorders: Current technologies and open problems
Neurological disorders are caused by structural, biochemical, and electrical abnormalities involving the central and peripheral nervous system. These disorders may be congenital, developmental, or acute onset in nature. Some of the conditions respond to surgical interventions while most require pharmacological intervention and management, and are also likely to be progressive in nature. Owing to a high global burden of the most common neurological disorders, such as dementia, stroke, epilepsy, Parkinsons disease, multiple sclerosis, migraine, and tension-type headache, there exist multiple challenges in early diagnosis, management, and prevention domains, which are further amplified in regions with inadequate medical services. In such situations, technology ought to play an inevitable role. In this chapter, we review artificial intelligence (AI) and machine learning (ML) technologies for mitigating the challenges posed by neurological disorders. To that end, we follow three steps. First, we present the taxonomy of neurological disorders, derived from well-established findings in the medical literature. Second, we identify challenges posed by each of the common disorders in the taxonomy that can be defined as computational problems. Finally, we review AI/ML algorithms that have either stood the test of time or shown the promise to solve each of these problems. We also discuss open problems that are yet to have an effective solution for the challenges posed by neurological disorders. This chapter covers a wide range of disorders and AI/ML techniques with the goal to expose researchers and practitioners in neurological disorders and AI/ML to each others field, leading to fruitful collaborations and effective solutions. 2022 Elsevier Inc. All rights reserved. -
Artificial Intelligence based Semantic Text Similarity for RAP Lyrics
Data mining is the primary method of gathering large volumes of knowledge. To make use of such data to implementation requires the use of effective machine learning strategies. Semantic Textual Similarity is one of the primary machine learning strategies. At its core, semantic textual similarity is the identification of words with similar context. Initial work in STS involved text reuse, word search among others. The proposed research work uses a specific method of determining textual similarity using Google's Word2Vec framework and the Continuous-bag-of-words algorithm for identifying word similarity in rap records. A large data set consisting of over 50,000 rap records is used. The key aspect of proposed methodology is to determine the words with similar context and cluster them into different word clusters also called bags. To achieve the desired result, the dataset is first processed to obtain the features. Once the features are selected, a model is generated by passing the data onto the Word2Vec framework. The research work on semantic textual similarity was repeated across three different runs, with the data set size changing in every run. At the end of each the accuracy of similarity obtained by the model was determined. The current research work has achieved average accuracy as 85%. 2020 IEEE. -
Spatio - Temporal Analysis of Temperature in Indian States
Data, the oil of the century, is available in multiple formats for various applications. It is collected, stored, and distributed across different use cases in various forms. Researchers study, analyse and use data for numerous analyses and predictions. There is an increase in demand and consideration of spatiotemporal data analysis. Analysing and obtaining insights from the spatiotemporal data are carried out by various researchers. Many investigations have started investigating the strategies for spatial-transient examination and applying spatial-transient information investigation procedures to different areas. Analysing spatiotemporal data has been an advanced task; with the help of various Python libraries, Spatio Temporal dataset about the temperature of states of India is analysed to support the harsh climate near the region of tropic of cancer. Across the decade, there has been a cyclic trend in the temperature, which keeps toggling yet increases over time. It remains a question of worry and genuine concern to predict climatic conditions. Spatio-temporal analysis of temperature in Indian states involves analysing the spatial and temporal variations in temperature across different states in India. The study can use various statistical and geographic information systems (GIS) tools. Spatio-temporal analysis of temperature in Indian states can provide valuable insights into the changing climate patterns in different regions of the country, which can be helpful for policymakers, researchers, and other stakeholders to make informed decisions related to climate change mitigation and adaptation. 2023 American Institute of Physics Inc.. All rights reserved. -
Study on Mechanical Properties of Lime Stabilized Active Bauxite Residue (Red Mud) and Fly Ash to Use as a Subgrade Material in Road Construction
Bauxite residue (Red mud) is a waste product produced during the extraction of aluminium from Bauxite by Bayers process. The huge requirement of aluminium for the various needs of mankind resulted to the enormous production of bauxite residue which is a very fine substance with high alkalinity. The high alkaline nature of this waste material shows a high impact on environment if it not covered or used in an appropriate method. This paper focusses on the usage of bauxite residue with the support of lime and flyash as a stabilizing material to use as a subgrade in road constructions and understand the toxicity levels of it upon leaching. Bauxite residue was stabilized with various ratios of fly ash and lime powder to its dry weight and determined the mechanical properties like California bearing ratio and unconfined compressive strength of all the combinations. Any industrial waste material will pose a environmental threat if the chemical analysis was not made upon using it as a subgrade material. In this study more emphasis was given to study the various hazardous chemicals present in the leachate collected from bauxite residue with fly ash and lime mixture. Leachate was collected by using Total characteristics leaching procedure (TCLP Method) and chemical analysis was performed and compared the results with the various water standards to recommend this material as a chemically safe material in the nature. All the results proved that bauxite residue upon stabilizing with the fly ash and lime is very much suitable to use as a subgrade material and environmentally safe. Kalahari Journals. -
Strength Development of Geopolymer Composites Made from Red Mud-Fly Ash as a Subgrade Material in Road Construction
The application of industrial waste in construction reduces the dependency on natural resources. The materials, including red mud (RM) and fly ash (FA), proved to be favorable materials. However, the materials potential together as a geopolymer composite for road applications has rarely been explored. This study will examine the possibility of the replacement of natural materials in subgrade applications. To achieve this, the geopolymer compositions will be prepared by replacing RM with FA at replacement rates of 10%, 20%, and 30% by dry weight basis. The alkaline activator solution of 8 M will be prepared using sodium hydroxide (NaOH) and sodium silicate to develop geopolymer composites. The strength properties will be studied using the California Bearing Ratio (CBR) and unconfined compression strength (UCS) and validated with microstructural analysis using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). The results reveal that geopolymer composites could achieve a maximum CBR value of 12% and UCS of 2,700 kPa. The microstructural analysis revealed that the formation of dense calcium aluminate hydrate (C-A-H) and calcium silicate hydrate (C-S-H) are the reason for strength improvement. The leaching studies show that the toxic elements were within the permissible limits. Overall, the test results confirmed that the geopolymer composites meet the required strength and could be used as a subgrade material in road construction. 2020 American Society of Civil Engineers. -
An Enhanced Deep Learning Model for Duplicate Question Detection on Quora Question pairs using Siamese LSTM
The question answering platform Quora has millions of users which increases the probability of questions asked with similar intent. One question may be structured in two different ways by two users, and answering similar questions repeatedly impacts user experience. Manual filtration of such questions is a tedious task, so Quora attempts to detect and remove these duplicate questions by using the Random Forest Model, which is not completely effective. As Quora contains question answers in the form of text data, different Natural Language Processing techniques are used to transform the text data into numerical vectors. In this research, the log loss metric acts as the primary metric to evaluate different models. The primary contribution is that the Siamese network is used to process two questions parallelly and find vectors representation of each question. The vectors computed by this method enables similarity detection which is more effective than existing models. GloVe word embedding is used to understand the semantic similarity between two questions. The random classifier is built as the base model and logistic regression, linear SVM and XGBoost model are used to reduce the log loss. Finally, a Siamese LSTM is proposed which reduces the loss dramatically. 2022 IEEE. -
Evaluation of Clove Phytochemicals as Potential Antiviral Drug Candidates Targeting SARS-CoV-2 Main Protease: Computational Docking, Molecular Dynamics Simulation, and Pharmacokinetic Profiling
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus can cause a sudden respiratory disease spreading with a high mortality rate arising with unknown mechanisms. Still, there is no proper treatment available to overcome the disease, which urges the research community and pharmaceutical industries to screen a novel therapeutic intervention to combat the current pandemic. This current study exploits the natural phytochemicals obtained from clove, a traditional natural therapeutic that comprises important bioactive compounds used for targeting the main protease of SARS-CoV-2. As a result, inhibition of viral replication effectively procures by targeting the main protease, which is responsible for the viral replication inside the host. Pharmacokinetic studies were evaluated for the property of drug likeliness. A total of 53 bioactives were subjected to the study, and four among them, namely, eugenie, syzyginin B, eugenol, and casuarictin, showed potential binding properties against the target SARS-CoV-2 main protease. The resultant best bioactive was compared with the commercially available standard drugs. Furthermore, validation of respective compounds with a comprehensive molecular dynamics simulation was performed using Schringer software. To further validate the bioactive phytochemicals and delimit the screening process of potential drugs against coronavirus disease 2019, in vitro and in vivo clinical studies are needed to prove their efficacy. Copyright 2022 Chandra Manivannan, Malaisamy, Eswaran, Meyyazhagan, Arumugam, Rengasamy, Balasubramanian and Liu. -
Omics based approach for biodiscovery of microbial natural products in antibiotic resistance era
The need for a new antibiotic pipeline to confront threat imposed by resistant pathogens has become a major global concern for human health. To confront the challenge there is a need for discovery and development of new class of antibiotics. Nature which is considered treasure trove, there is re-emerged interest in exploring untapped microbial to yield novel molecules, due to their wide array of negative effects associated with synthetic drugs. Natural product researchers have developed many new techniques over the past few years for developing diverse compounds of biopotential. Taking edge in the advancement of genomics, genetic engineering, in silico drug design, surface modification, scaffolds, pharmacophores and target-based approach is necessary. These techniques have been economically sustainable and also proven efficient in natural product discovery. This review will focus on recent advances in diverse discipline approach from integrated Bioinformatics predictions, genetic engineering and medicinal chemistry for the synthesis of natural products vital for the discovery of novel antibiotics having potential application. 2018