We describe NITS-CNLP's submission to WMT 2020 unsupervised machine translation shared task for German language (de) to Upper Sorbian (hsb) in a constrained setting i.e, using only the data provided by the organiz...
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Short text sentiment classification has a strong practical value. It also has many applications, one of which is opinion analysis. However, the traditional methods cannot effectively manage and analyze short texts bec...
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Arrhythmia is a life-threatening disease that leads to complex physical condition in patients if left untreated. Arrhythmia disorders should be diagnosed early enough to save people's life. Noninvasive and remote ...
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Data analysis techniques using machine learning approaches are the most prominent when we are considering the online fraud, it may be a credit card fraud or any other online fraud in the banking system. Using Machine ...
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ISBN:
(数字)9798331518592
ISBN:
(纸本)9798331518608
Data analysis techniques using machine learning approaches are the most prominent when we are considering the online fraud, it may be a credit card fraud or any other online fraud in the banking system. Using Machine Learning techniques, we can easily manage the information of fake transactions by extensive data analysis of time, location and amount in the real time. This paper gives the comprehensive study of the changing scene of the fraud discovery in monetary exchanges, investigating the most recent patterns, advances, and techniques utilized by financial institutions and other administrative bodies. An extensive review of various machine learning techniques has been done to find out the effective solution to protect the online fraud.
In today's volatile financial markets, with high uncertainty and economic fluctuations. Sectoral portfolio optimization poses a great challenge to the investor who seeks to optimize returns while managing risks ac...
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ISBN:
(数字)9798331509859
ISBN:
(纸本)9798331509866
In today's volatile financial markets, with high uncertainty and economic fluctuations. Sectoral portfolio optimization poses a great challenge to the investor who seeks to optimize returns while managing risks across the diversified industries. Traditional methods, such as the Mean-Variance model, often disregard sector-specific complexities and fail to address extreme market scenarios well, leading to significant portfolio vulnerabilities. This paper presents an innovative approach based on the Mean-Value at Risk model, that merges mean-variance optimization with more advanced risk metrics including Value at Risk(VaR) and Conditional Value at Risk(CVaR). This methodology uses historical data for stock prices to derive sector-wise expected returns while making risk assessments using standard deviation and Value at Risk. Further, it provides a means of assessing tail risks by using Conditional Value at Risk(CVaR). Monte Carlo simulations(MCS) are used to explore portfolio weight combinations over a wide range of portfolios, thus giving a comprehensive risk-return analysis relevant to sector dynamics. The significant results are that sectors like Health Care offer attractive returns for moderate risk, and cyclical sectors such as Metals and Mining have higher volatility as well as risk exposure. The Mean-Value at Risk model allows the construction of diversified portfolios with balanced results, where the combined portfolio return of 14.79% which is impressive and a moderate level risk of 0.2133.
This study has proposed a novel technique for performing object detection and image classification with a Convolutional Neural Network (CNN) architecture. Nevertheless, employing a 2D CNN architecture to identify a lo...
This study has proposed a novel technique for performing object detection and image classification with a Convolutional Neural Network (CNN) architecture. Nevertheless, employing a 2D CNN architecture to identify a lot of patients as AD or MCI based on 3D MR Images becomes challenging when dealing with hundreds of MRI images that are highly similar in nature. It provides a solution to this problem that streamlines the concept of classification of patients based on 3D MRI by exploiting 2D data produced by the CNN structure. In this method, 2D characteristics from MRI are extracted and then converted by using an appropriate Machine Learning (ML) algorithm for categorization.
Measurement data obtained from "things"in the Internet of Things (IoT) faces challenges in efficient transmission due to the low-bandwidth data transmission *** observe that measurement data are fixed in siz...
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There is an ever-increasing number of students graduating from colleges every year and entering the workforce. With their primary means of securing employment being through campus hiring and online portals, and the hi...
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Internet of Things (IoT)-where the physical components are able to communicate with each other and with the internet-is one of the driving forces behind the Fourth Industrial Revolution. Nowadays, IoT applications hav...
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The bodily movements of a person can be heavily restricted because of a spinal cord disorder. These disorders could be a result of various internal or external factors but it is believed that a preliminary diagnostic ...
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