An Multi-Label Classification with Label Correlation

Sabena, S.; Kalaiselvi, S.; Anusha, B.; Ramesh, L. Sai

doi:10.5958/2249-7315.2016.00804.2

Cited by 9 publications

(4 citation statements)

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“…It's a promising approach for both linear and non-linear data classification [21]. It transforms the initial training data into higher dimension using non-linear projection.…”

Section: Support Vector Machinementioning

confidence: 99%

Student Performance Prediction Using Machine Learning

Priya¹,

Ankit²,

Divyansh³

2021

Advances in Parallel Computing

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Performance analysis of learning outcomes is a system that will aim for excellence at all levels and dimensions of the student’s field of interest. This paper suggests a comprehensive EDM framework in the form of a rule-based recommender system that not only analyses and predicts student achievement, but also demonstrates the reasons for it. The suggested framework examines students’ demographic information, study-related characteristics, and psychological factors to gather as much information as possible from classmates, teachers, and parents. School reports and queries used to collect the world’s latest data (e.g., student marks, population, social and school-related factors. Using a set of potent data mining methods, aiming for the greatest possible precision in academic performance prediction. The framework is effective in identifying the student’s weaknesses and making relevant recommendations. In contrast to current frameworks, the proposed framework outperforms them in a practical case study involving 200 individuals.

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“…It's a promising approach for both linear and non-linear data classification [21]. It transforms the initial training data into higher dimension using non-linear projection.…”

Section: Support Vector Machinementioning

confidence: 99%

Student Performance Prediction Using Machine Learning

Priya¹,

Ankit²,

Divyansh³

2021

Advances in Parallel Computing

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“…The buyer of stock can buy the stock which is similar to the user who bought the same firm stock. Sabena et al [11] provides the classification approach for multiple label which helps to classify the stock under different perspectives.…”

Section: Related Workmentioning

confidence: 99%

Stock Market Prediction Using Machine Learning Techniques

Reshma¹,

S²,

Sathiyavathi³

et al. 2021

Advances in Parallel Computing Technologies and Applications

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Predicting the future in all the areas using machine learning techniques was the recent research in the current scenario. Stock market is one among them which needs the prediction future market to invest in the new enterprise or to sell their existing shares to get profit. This need the efficient prediction technique which studies the previous exchanges of stock market and gives the future prediction based on that. This article proposed the prediction system of stock market price based on the exchange takes place in previous scenario. The system studies the diversing effect of market price of product in a particular time gap and analyze its future trend whether it’s loss or gain. During the system of thinking about diverse strategies and variables that should be taken into account, we observed out that strategies like random forest, Support vector machine and regression algorithm. Support vector regression is a beneficial and effective gadget gaining knowledge of approach to apprehend sample of time collection dataset. The data collected for the four years duration which was accumulated to get the expecting prices of the share of the firm. It can produce true prediction end result if the fee of essential parameters may be decided properly. It has been located that the guide vector regression version with RBF kernel indicates higher overall performance while in comparison with different models.

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“…Diverse approaches were utilized for this reason. A few successful comes about were gotten utilizing classification based on multi-layer neural systems (NN) [17]. In [18], the recognition rate of 9 artificial neural architectures was tested to categorize 5 different types of wheat flour.…”

Section: Related Workmentioning

confidence: 99%

Maize Grain Quality Classification Using Convolutional Neural Network

M¹,

Anitha²,

Revathi³

2021

Advances in Parallel Computing Technologies and Applications

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The major goal of our research would be to classify the quality of maize grain after obtaining the crop output. Grains have been the most important grain for farmers to boost their farming revenue in a country. One of the most significant food crops is wheat grain. The majority of the country’s grain is used for animal feed, human consumption, and the creation of industrial products. The amount of mechanization used to assess grain quality is modest, and the majority of the job is done by hand. Every day, different prices are introduced in various marketplaces, and farmers face losses every year quality of service estimate and market rates. With our classification we can give accurate progress on the seed which will help farmer’s to sell with better price along with that this assumption can idealize him to give good nutritious fertilizer to the crop.

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An Multi-Label Classification with Label Correlation

Cited by 9 publications

References 0 publications

Student Performance Prediction Using Machine Learning

Student Performance Prediction Using Machine Learning

Stock Market Prediction Using Machine Learning Techniques

Maize Grain Quality Classification Using Convolutional Neural Network

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