An improved salient object detection algorithm combining background and foreground connectivity for brain image analysis

Kaur, Bhavneet; Sharma, Meenakshi; Mittal, Mamta; Verma, Amit; Goyal, Lalit Mohan; Hemanth, D. Jude

doi:10.1016/j.compeleceng.2018.08.018

Cited by 48 publications

(17 citation statements)

References 14 publications

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“…In 1997, Curtis Struck [4] performed SPH (Particle Hydrodynamics) simulations [9] for two modelled galaxies where parameters such as gravitational potential, artificial viscosity, pressure gradient, rotation velocity, scale length potential had been used. Artificial viscosity parameters had been set as α=1.0 and β=2.0.…”

Section: Related Workmentioning

confidence: 99%

“…They speculated that the young OB associations, super shells, supernovae, ultraviolet radiation fields had been responsible for triggering the molecular/star cloud formations and later demonstrated it as well in their research paper. Authors said that this has been a common occurrence in a galaxy-galaxy collision [9] and it impacts most of the interstellar medium surrounding these galaxies [13].…”

Section: L=mentioning

confidence: 99%

“…The model that has been considered predicts that UDG's space distribution [28] follows the density profile in accordance with the king's model and has given as in equation (8). 8The corresponding projected density has found to be in equation (9). (9) where l is the projection length of r. The probability to find UGD in angular distance interval from the cluster center has been given by the equation (10).…”

Section: L=mentioning

confidence: 99%

“…8The corresponding projected density has found to be in equation (9). (9) where l is the projection length of r. The probability to find UGD in angular distance interval from the cluster center has been given by the equation (10).…”

Section: L=mentioning

confidence: 99%

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Ml Driven Prediction of Collision of Milky Way & Andromeda

Mittal¹,

Anusurya²,

Kumar³

et al. 2019

IJRTE

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The 3D position of Sun with respect to the galactic center of Milky Way can be used to understand its evolution. After Milky Way collides with Andromeda(M31), the same will hold true. Ensuing sections deal with the implementation of Regressive Analysis to predict the location of Sun in the galactic center after Galactic collision. This model utilizes results of previous studies of black hole mergers to predict the resultant mass of Sagittarius A* and M31’s black hole, which had been found to be (1.49±0.16) × 108 M☉. This mass has been used to calculate the centrifugal force that Sun might experience during and after the galactic collision. The current position, inclination, and velocity of Sun (derived from aforementioned predictions) have been used to predict its distance and inclination after the collision which has been predicted as 63,362.83 ly and 32.75o , from the new galactic center and its plane (97.48% and 96.32% accurate) respectively.

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Section: Related Workmentioning

confidence: 99%

Section: L=mentioning

confidence: 99%

Section: L=mentioning

confidence: 99%

Section: L=mentioning

confidence: 99%

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Ml Driven Prediction of Collision of Milky Way & Andromeda

Mittal¹,

Anusurya²,

Kumar³

et al. 2019

IJRTE

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“…They have also used the concept of partial descriptions and used them with the original features. Researchers have applied machine learning and deep learning technologies for prediction in number of applications like in health sector, crime sector and images analysis [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Prediction using Artificial Neural Model: An Application of Big Data

Shastri¹,

Roy²,

Mittal³

2018

ICST Transactions on Scalable Information Systems

Self Cite

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In recent time, stock price prediction is an area of profound interest in the realm of fiscal market. To predict the stock prices, authors have proposed a technique by first calculating the sentiment scores through Naïve Bayes classifier and after that neural network is applied on both sentiment scores and historical stock dataset. They have also addressed the issue of data cleaning using a Hive ecosystem. This ecosystem is being used for pre-processing part and a neural network model with inputs from sentiment analysis and historic data is used to predict the prices. It has been observed from the experiments that the accuracy level reaches above 90% in maximum cases, as well as it also provides the solid base that model will be more accurate if it trained with recent data. The intended combination of sentiment analysis and Neural networks is used to establish a statistical relationship between historic numerical data records of a particular stock and other sentimental factors which can affects the stock prices.

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