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Day by day the cases of heart diseases are increasing at a rapid rate and it’s very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and KNN to predict and classify the patient with heart disease. A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease It is implemented on the.pynb format.

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A Mobile Ad Hoc Network (MANET) is a selforganizing, infrastructure less, multi-hop network. The wireless and distributed nature of MANETs poses a great challenge to system security designers. Ad hoc networks are by nature very open to anyone. Their biggest advantage is also one of their biggest disadvantages: Anyone with the proper hardware and knowledge of the network topology and protocols can connect to the network. This allows potential attackers to infiltrate the network and carry out attacks on its participants with the purpose of stealing or altering information. A specific type of attack, the Wormhole attack does not require exploiting any nodes in the network and can interfere with the route establishment process. The entire routing system in MANET can even be brought down using the wormhole attack. This paper discusses the modes of wormholes, how wormholes disrupts routing in AODV ,DSR ,OLSR and then discusses the solutions and countermeasures on wormholes.

Text sentiment analysis is an important and challenging task. Sentiment analysis of customer reviews is a common problem faced by companies. It is a machine learning problem made demanding due to the varying nature of sentences, different lengths of the paragraphs of text, contextual understanding, sentiment ambiguity and the use of sarcasm and comparatives. Traditional approaches to sentiment analysis use the tally or recurrence of words in a text which are allotted sentiment values by some expert. These strategies overlook the order of words and the complex different meanings they can communicate. Hence, RNNs were introduced that are effective yet challenging to train. Bi-GRUs and Bi-LSTM architectures are a recent form of RNNs which can store information about long-term dependencies in sequential data. In this work, we attempted a survey of different deep learning techniques that have been applied to sentiment classification and analysis. We have implemented the baseline models for LSTM, GRU and Bi-LSTM and Bi-GRU on an Amazon review dataset.

With the improving banking sector in recent times and the increasing trend of taking loans, a large population applies for bank loans. But one of the major problem banking sectors face in this ever-changing economy is the increasing rate of loan defaults, and the banking authorities are finding it more difficult to correctly assess loan requests and tackle the risks of people defaulting on loans. The two most critical questions in the banking industry are (i) How risky is the borrower? and (ii) Given the borrower’s risk, should we lend him/her? In light of the given problems, this paper proposes two machine learning models to predict whether an individual should be given a loan by assessing certain attributes and therefore help the banking authorities by easing their process of selecting suitable people from a given list of candidates who applied for a loan. This paper does a comprehensive and comparative analysis between two algorithms (i) Random Forest, and (ii) Decision Trees. Both the algorithms have been used on the same dataset and the conclusions have been made with results showing that the Random Forest algorithm outperformed the Decision Tree algorithm with much higher accuracy.

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