Soumya dutta forex charts
Kumar Pattabiraman's peers at other companies are Sneha Prabhu, Soumya Dutta. Who are Kumar Pattabiraman's colleagues? Kumar Pattabiraman's colleagues are. I know of five. They are: 1. Scalpers: Typically trade on ultra-short time frames. Most scalpers trade on charts that plot price where each bar. Soumya Dutta, Managing Director, Eforex India Ltd For the real estate segment, interest rates on non-resident external account and foreign currency. ANDREAS BITCOIN YOUTUBE
In doing so they eliminate the effect of price level differences between economies and reflect only differences in the volume or output of economies. These data are essential to cross-country comparisons of GDP, consumption, and investment. As a global public good, ICP data are used for research and analysis, indicator compilation, policy making, and administrative purposes at the national, bilateral, regional, and global levels.
Users of ICP data, and the cross-country comparisons that they enable, include policy makers, multilateral institutions, academia, the media and the private sector. The breadth and depth of the ICP dataset make it a valuable input to a wide range of themes under the economic, environmental, and social development umbrellas. PPPs are used to establish the international poverty line and measures of global poverty, which in turn are used by Sustainable Development Goal 1 to monitor progress.
Other SDGs focusing on agriculture, health, education, labor, income inequality, and energy and emissions also draw on PPPs to track progress. Online First articles are published as Open Access OA articles to make the latest research available as early as possible. Articles marked with this Open Access icon are Online First articles.
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Poornima, D. Gladis Abstract: In recent times, health diseases are expanding gradually because of inherited. Particularly, heart disease has turned out to be the more typical nowadays, i. The data mining strategies specifically decision tree, Naive Byes, neural network, K-means clustering, association classification, support vector machine SVM , fuzzy, rough set theory and orthogonal local preserving methodologies are examined on heart disease database.
In this paper, we survey distinctive papers in which at least one algorithms of data mining are utilised for the forecast of heart disease. This survey comprehends the current procedures required in vulnerability prediction of heart disease for classification in data mining. Survey of pertinent data mining strategies which are included in risk prediction of heart disease gives best expectation display as hybrid approach contrasting with the single model approach.
DOI: Health care organisations typically lack complete and accurate information on mortality. This paper proposes a comprehensive process to link the records of the enrollees of a health care organisation with the death records of obtained from the California State via a commercial data linkage software. The developed linkage process has successfully identified 23, and 21, death records of health plan enrollees from the State file after the initial and second post-linkage, respectively.
Validation of the linkage process against the deaths records documented in the internal systems of the organisation achieved a sensitivity of The linkage process demonstrated high accuracy and can be utilised to support various business needs.
Keywords: data cleaning; data standardisation; data matching; mortality linkage. Outlier exploration has a huge importance in almost all the industry applications like medical diagnosis, credit card fraudulence and intrusion detection systems. These situations can arise due to several reasons, out of which the present covid pandemic is a leading one. This motivates the present researchers to identify a few such vulnerable areas in the economic sphere and ferret out the most affected countries for each of them.
Two well-known machine-learning techniques DBSCAN and Z-score are utilised to get these insights, which can serve as a guideline towards improving the overall scenario subsequently. Keywords: economic outlier; machine learning; gross domestic product; GDP; per capita; human development index; HDI; covid pandemic; total death percentage.
These stores have different characteristics, such as physical size, product assortment and customer profile. This heterogeneity in terms of commercial offers implies a desire for consumption by customers that differs from store to store, depending on how their preferences are met. The proposed methodology was applied to a real marketing case based in a business-to-consumer B2C environment to aid retailers during the segmentation process. Keywords: knowledge discovery in databases; KDD; data mining; market segmentation; retail; principal component analysis; PCA; cluster analysis; multiple linear regression.
Arun, C. Lakshmi Abstract: Class imbalance is a known problem that exist in real-world applications, which consists of disparity in the existence of samples count of different classes, which results in biased performance. Class imbalance issue has been catered by many sampling techniques which may either fall into an oversampling approach that solves issues to a greater extent or under sampling. MAHAKIL is a diversity-based oversampling approach influenced by the theory of inheritance, in which minority samples are synthesised in view of balancing the class using Mahalanobis distance measure.
In this study the performance of MAHAKIL algorithm has been tested using various ensemble classifiers which are proved to be effective due to its multi hypothesis learning approach and better performance. The results of the experiment conducted on 20 imbalanced software defect prediction datasets using six different ensemble approaches showcase XGBoost provides better performance and reduced false alarm rate compared to other models.
Keywords: class imbalance; software fault prediction; synthetic samples; over sampling techniques; MAHAKIL; false alarm rate; evolutionary algorithm; ensemble; inheritance. In this scenario, logs play a major role and can be considered as an important source of information in a large scale secured environment.
Till date many researchers have contributed various methods towards conversion of unstructured logs to structured ones. However post conversion the dimension of the dataset generated increases many folds which are too complex for data analysis. In this paper, we have discussed techniques and methods to deal with extraction of all features from a produced structured log, reducing N-dimensional features to fixed dimensions without compromising the quality of data in a cost-efficient manner that can be used for any further machine learning-based analysis.
Keywords: json data; micro services; data parsing; principal component analysis; PCA; multivariate data; unstructured data; tagged data; feature reduction. Anupama, C. Lakshmi Abstract: Frequent item set mining FIM , being one of the prevalent, well-known method of data mining and topic of interest for the researchers in the field of decision making.
With the establishment of the period of big data where the data is continuously generated from multidimensional sources with enormous volume, variety in an almost unrevealed way, transforming this data into a valuable knowledge discovery which can add value to the organisations to make an efficient decision making places a challenge in the present research.
This leads to the problem of discovery of the maximum frequent patterns in vast datasets and to create a more generalised and interpretable representation of veracity. Targeting the problems stated above, this paper suggests a parallelisation method suitable for any type of parallel environment. The implemented algorithm can be run on a single computer with multi-core processor as well as on a cluster of such machines. Keywords: item set mining; frequent items; frequent patterns; Eclat; parallel Eclat; frequent item set mining; FIM.
The purpose of this study was to demonstrate how data mining techniques can be used to develop models to predict product quality properties for petroleum products. This study used petroleum refinery production raw data to build predicting models for product quality control activities. The plant and laboratory data for the period of about 18 months was mined from the refinery repositories in order to build the datasets required for analysis using Orange3 data mining software.
Four data mining algorithms were chosen for experiments in order to determine the best predicting model using cross-validation technique as a validation method. This study only employed two measuring metrics, classification accuracy CA and root mean square error RMSE as performance indicators. Random forest came out as the best performing model suitable for predicting both categorical CA and numeric data RMSE.
The study was also able to establish the relationship between the variables that could be used in critical operational decisions. Keywords: data mining; machine learning; industries; petroleum refinery; product quality; parameter optimisation. The logistic regression model, a staple in the credit risk industry, is compared to several machine learning models.
This work shows that in the binary classification case, all compared models achieved similar results to the logistic regression. The random forest model showed superior performance when classifying credit frauds ending in lawsuits. In the multi-label classification case, the logistic regression attains high levels of precision for all types of fraud, but at lower recall rates. Whereas, the random forest model achieves higher recall rates, but with lower precision rates.
Keywords: fraud detection; machine learning; imbalanced data; multi-label classification. Ashwin Viswanathan, Johnson P. Thomas Abstract: Data analysis is a crucial process in the field of data science that extracts useful information from any form of data. With the rapid growth of technology, more and more unstructured data, such as text and images, are being produced in large amounts.
Apart from the analytical techniques used, the quality of the data plays a prominent role in the accurate analysis Data quality becomes inferior to poor maintenance and mediocre data generation strategies employed by amateur users. This problem escalates with the advent of big data. In this paper, we propose a quality assessment model for the textual form of unstructured data TDQA. The context of data plays an important role in determining the quality of the data.
Therefore, we automate the process of context extraction in textual data using natural language processing to identify data errors and assess quality. Keywords: automated data quality assessment; textual data; context-aware; data context; sentiment analysis; lexicon; Doc2Vec; data accuracy; data consistency. This paper presents an improved convolutional neural network CNN architecture for accurate visual crowd counting in crowd images.
Multi-column convolutional neural network MCNN is widely used in previous works to predict the density map for visual crowd counting. However, this method has limitations in predicting a quality density map. Instead, the proposed model is architected using powerful CNN layers, dense layers, and one regressor node with whole image-based inference. Therefore, it is less computationally intensive and inference speed can be increased.
Tested on the mall dataset, the proposed model achieved 2. Moreover, benchmarking on different CNN architectures has been conducted. The proposed model shows promising counting accuracy and reasonable inference speed against the existing state-of-art approaches.
Keywords: visual crowd counting; convolutional neural network; CNN; whole image-based inference; edge embedded platform; multi-column convolutional neural network; MCNN. Gina, Adheesh Budree Abstract: Innovation and technology advancements in information systems IS have resulted in a multitude of product offerings and business intelligence BI software tools in the market to implement business intelligence systems BIS.
As a result, a high proportion of organisations fail to employ suitable software tools meeting organisational needs. The study aimed to discover critical factors influencing the selection of BI tools. This was a quantitative study and questionnaire-surveyed data was collected from 92 participants. The findings showed that software tool technical factors, vendor technical factors, and opinion non-technical factors are significant. The study contributes to both academia and industry by providing influential determinants for software tool selection.
It is hoped that the findings presented will contribute to a greater understanding of factors influencing the selection of BI tools to researchers and practitioners alike. Keywords: business intelligence tools; BITs; business intelligence systems; BIS; business intelligence; BI; software factors; software selection; software tool. Sridevi Abstract: IT integration complements the functional and operational processes, as well as helps the firm in the development of inimitable competitive advantage.
The study examines the effect of ITI on supply chain integration, supplier flexibility and manufacturing flexibility; and their subsequent effects on firm performance. The extended resource-based view has been used as the theoretical perspective to develop the research model. A survey was carried out among the manufacturing industries in India. Structural equation modelling with the partial least squares algorithm was used to analyse the hypotheses proposed in the study.
Keywords: IT integration; supply chain integration; supplier flexibility; manufacturing flexibility; firm performance. In most cases, a credit transaction dataset is expected to have a significantly larger number of normal transactions than fraud transactions. Therefore, the accuracy of a fraud detection system depends on building a model that can adequately handle such an imbalanced dataset.
The purpose of this paper is to explore one of the techniques of dataset rebalancing, the synthetic minority oversampling technique SMOTE. We then compared the performances of the four models trained on the rebalanced and original dataset using the area under precision-recall curve AUPRC plots. Existing privacy-preserving techniques assume the existence of attackers from external data recipients and hence, are vulnerable to insider attack performed by colluding data providers.
Additionally, these techniques protect data against identity disclosure but not against attribute disclosure. To overcome these limitations, in this paper, we address the problem of privacy-preserving data publishing for collaborative social network.
Our motive is to prevent both attribute and identity disclosure of collaborative social network data against insider attack. For the purpose, we propose an approach that utilises p-sensitive k-anonymity and m-privacy techniques. Experimental outcomes affirm that our approach preserves privacy with a reasonable increase in information loss and maintains an adequate utility of collaborative social network data.
Keywords: collaborative social network data publishing; attribute disclosure; identity disclosure; insider attack; k-anonymity; m-privacy. G Abstract: Cognitive computing refers to the usage of computer models to simulate human intelligence and thought process in a complex situation. Artificial intelligence AI is an augmentation to the limits of human capacity for a particular domain and works as an absolute reflection of reality; where a computer program is able to efficiently make decisions without previous explicit knowledge and instruction.
The concept of cognitive intelligence was introduced. The most interesting use case for this would be an AI bot that doubles as a digital assistant. This is aimed at solving core problems in AI like open domain question answering, context understanding, aspect-based sentiment analysis, text generation, etc. The work presents a model to develop a multi-resolution RNN to identify local and global context, develop contextual embedding via transformers to pass into a seq2seq architecture and add heavy regularisation and augment data with reinforcement learning, and optimise via recursive neural networks.
Keywords: cognitive computing; artificial intelligence; AI; data augmentation; human intelligence; recurrent neural network; transformer model. It is believed that the study of historical patterns helps in the forecasting into the future. ARIMA model is one of the popular models for the task.
The final results were encouraging. It was also found that choices of certain CWT related parameters have positive or negative effect to the forecasting outcomes. Existing approaches for emoji prediction are generic and generally utilise text or time for emoji prediction. However, research reveals that emoji usage differs among users.
In this paper, a novel emoji-usage-based profiling: EmoRile is proposed. These models were tested on various architectures with a very large emoji label space. Rigorous experimentation showed that even with a large label space, EmoRile predicted emojis with similar accuracy as compared to existing emoji prediction approaches with a smaller label space; making it a competitive emoji prediction approach. Keywords: emojis in sentiment analysis; emoji prediction; user profile-based emojis.
Recently, it has been observed that efficient deep learning architectures have been developed to detect such bleeding accurately. The proposed system includes two different transfer learning strategies to train and fine tune ImageNet pre-trained state-of-the-art architecture such that VGG 16, Inception V3, DenseNet The evaluation metrics have been calculated based on the performance analysis of the employed networks. Experimental results show that the modified fine-tuned Inception V3 perform well and achieved the highest test accuracy.
Hence this paper proposes a novel model for mining frequent patterns. As per the proposed model the frequent pattern discovery is carried out in three phases. In first phase, dataset is divided into n partitions based on the time stamp. In the third phase, proposed algorithm is applied on each of the classified groups to obtain frequent patterns.
Finally, the proposed model is validated using a sample dataset and experimental results are presented to explain the capability and usefulness of the proposed model and algorithm. Further, the proposed algorithm is compared with the existing algorithm and it is observed that the proposed algorithm performs better in terms of time complexity. Keywords: data mining; frequent pattern; association rule; classification; algorithm; decision making; retailing. It considers the utility of the items that leads to finding high profit patterns which are more useful for real conditions.
Handling large and complex dataset are the major challenges in HUIM. The main problem here is the exponential time complexity. Literature Review shows multicore approaches to solve this problem by parallelizing the tasks but it is limited to single machine resources and also needs a novel strategy.
It utilizes cluster nodes to parallelize and distribute the tasks efficiently. Thorough experiments with results proved that the proposed frameworks achieved better runtime s in dense datasets compared to the existing PLB s.
It has effectively addressed the challenges of handing large and complex datasets. Nithya Abstract: In this technology and automation era, blockchain technology travels in the direction of consistent studies and adoption in different sectors. Blockchain technology with a chain of the block provides security and establishes a trusted environment between individuals. In the past couple of years, blockchain technology attracted many research scholars, industrialists to study, analyse and apply the technology in their own application needs.
The major advantage of blockchain technology is the security, user privacy preserved, transparency. Also, this paper briefs on the new business opportunities in the health sector integrating blockchain technology. Keywords: healthcare; blockchain; patient health records. Ravi, M. Ekambaram Naidu, G. Narsimha Abstract: Multimedia mining is a sub-field of information mining which is exploited to discover fascinating data of certain information from interactive media information bases.
The information mining is ordered into two general classifications, such as static media and dynamic media. Static media possesses text and pictures. Dynamic kind of media consists of Audio and Video. Multimedia mining alludes to investigation of huge measure of mixed media data so as to extricate design patterns dependent on their factual connections.
Multimedia mining frameworks can find significant data or image design patterns from a colossal assortment of imageries. In this paper, a hybrid method is proposed which exploits statistical and applied soft computing-based primitives and building blocks, i. The optimal parameters are chosen such as number of filters, kernel size, strides, input shape and nonlinear activation function. Experiments are performed on standard web multimedia data here, image dataset is exploited as multimedia data and achieved multi-class image categorisation and analysis.
Our obtained results are also compared with other significant existing methods and presented in the form of an intensive comparative analysis. Keywords: knowledge discovery; supervised learning; multimedia databases; image data; soft computing; feature engineering. Narasimha Abstract: High utility mining has become an absolute requirement for an efficient corporate management procedure. The challenge persists in identifying the top-out or bottom-out conditions in the context of the available HUM solutions, and it is critical for enterprises to manage adequate inventory to have higher yield outcomes.
LTP lays bear a discursive topology of what we might come to expect of contemporary life and practice.
|Pool betting definitions||It has effectively addressed the challenges of handing large and complex datasets. Keywords: collaborative social network data publishing; attribute disclosure; identity disclosure; insider attack; k-anonymity; m-privacy. PPPs are used to establish the international poverty line and measures of global poverty, which in turn are used by Sustainable Development Goal 1 to monitor progress. Similar surface decontamination by phage application has earlier been shown by Jensen et al. Whereas, the random forest model achieves higher recall rates, but with lower precision rates. Gina, Adheesh Budree Abstract: Innovation and technology advancements in information systems IS have resulted in a multitude of product offerings and business intelligence BI software click here in the market to implement business intelligence systems BIS.|
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