Stock Market Prediction Using Machine Learning published in ijirt.org

Research paper published by

Mr.Tejas Pandav, Mr.Chetan Shinde, Mr.Malharrao Shelar, Mr.Shubham Shelar, Mr.Saurabh Kodre & Prof. Punam Chavan

Department of Information Technology

Zeal College of Engineering and Research, Narhe, Pune,
Maharashtra, India

Read full paper on ijirt.org PAPERID 160289, Volume 10 Issue 1

The subject of stock forecasting is currently
quite popular. Only a select few individuals had access to
the study in the beginning due to a number of factors
including the device’s limitations. Since science and
technology have advanced so quickly, more and more
people are now interested in studying prediction, and it is
getting simpler and simpler for us to predict stocks using
various techniques like machine learning, deep learning,
and others. In this essay, we’ll use LSTM (Long and Short
Term Memory) to predict stocks and incentives for the
following day. We can improve pre-processing methods
to remove noise from data so that subsequent operations
like categorization and prediction doesn’t make any
impact.

Machine Learning Approaches to Multi-Class Human Skin Disease Detection published on ijirt

Human Cancer is a standout among the most perilous sickness which is fundamentally brought about by hereditary shakiness of various sub-atomic modifications. Among numerous types of human malignant growth, skin disease is the most widely recognized one. To distinguish skin disease at a beginning time we will ponder and break down them through different procedures named as division and highlight extraction. Here, we center harmful melanoma skin disease, (because of the high centralization of Melanoma-Hier we offer our skin, in the dermis layer of the skin) identification. In this, we utilized our ABCD rule dermoscopy innovation for dangerous melanoma skin disease identification. In this framework diverse advance for skin injury portrayal i.e, first the Image Acquisition Technique, pre-handling, division, characterize include for skin Feature Selection decides sore portrayal, characterization strategies. In the Feature extraction by computerized picture handling technique incorporates GLCM and ABCDE highlights and furthermore we utilized DRLBP. Here we proposed the Recurrent Neural Network to group the kind of ailment.

Predict Heart attack and Diagnosis using Machine Learning | ijirt.org volume 5 issue 12

Diagnosis and Prediction of cardiovascular diseases has often become a challenge faced by doctors and hospitals in India as well as abroad. Despite major transformations in lifestyles of people and advancements in medical domain; heart attacks still hold a major share in the global death rate. The ambiguity in diagnosis of most heart diseases lies in the intricate grouping of clinical and pathological data which may introduce misinterpretation of data among clinical experts, doctors and researchers. Ultimately, the problem lies within making decisions concerned with predicting and later diagnosing the heart diseases. These decisions can have a drastic effect on life of a person. The proposed approach to use machine learning for prediction as well as diagnostic purposes can play a very important role in this area. Various Machine Learning techniques can be used for classifying healthy people from the ones suffering from heart diseases. This work intends to present a comprehensive review of prediction of Cardiac diseases by using Machine Learning based approach.

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