MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Cardiac Arrhythmia is a condition in which the heartbeat is may be fast or slow than the normal ones. Nowadays, a consistent electrocardiogram (ECG) analysis and classification play an important role in the diagnosis of cardiac abnormalities. ECG provides useful information about the functional status of the heart. Analysis of ECG is of great importance in the detection of cardiac anomalies. This paper provides different medical data mining and machine learning techniques are being implemented to extract valuable information from ECG signal for prediction of heart disease. But, the accuracy of the desired results is not yet satisfactory.

World University root node

School of Computer Engineering & Technology

Engineering and Technology

Journal of Emerging Technologies and Innovative Research

Heart Disease Detection Using ECG Waves: A Survey

Now a days Wireless sensor networks are used for number of real time applications such as computation cost, memory utilization, and communication and energy resources. As the network consist of number of nodes, these nodes comprises of batteries. One of the main issue in WSN is that these batteries are not easily recharged or replaced. Moreover decreasing these energy reduction reduce the network lifetime, therefore increasing the network lifetime is an important issue in front of researcher. This paper proposes the clustering based routing protocol, in which sensor nodes are divided into number of clusters. Then a cluster head is selected from each cluster called as super cluster head. Data is sent to base station by super cluster head, which is selected among all cluster heads. For selection of Super cluster head (SCH) a fuzzy logic approach is used, which considers the factors such as the remaining battery power of modem mobility, route of base station etc. In the existing system, researcher does not focus on security of network. Hence in the proposed system we are securing data in the network by using ECC algorithm. Proposed system also detects the attack found in the network. Therefore it improves the network lifetime, providing security to the network.

International Journal of Computer Engineering and Applications

INCREASING NETWORK LIFETIME USING PDR ANALYSIS AND FUZZY LOGIC IN WSN

The chemical name extraction has a great importance in the biomedical field. Named Entity Recognition is the subtask of information extraction that is used to identify named entities in the given data. There are various dictionary-based, rule-based and machine learning approaches available for Named Entity Recognition. Rule based techniques include hand written rules. In this paper an extensive survey of machine learning models such as Hidden Markov Model (HMM), Support Vector Machine (SVM), Conditional Random Fields (CRFs) etc. that are used to develop NER systems is carried out.

International Journal of Computer Science and Information Technologies

A Survey on Machine Learning Techniques toExtract Chemical Names from Text Documents

Automatic extraction of chemical names has a great importance in biomedical and life science research field. In the proposed system, accuracy in training data generation is improved by generating large amount of regular expressions. Regular expressions are generated from systematic chemical names collected from PubChem Database. Due to large amount of regular expressions, accuracy in chemical name extraction is also improved. In the work carried out, an efficient chemical name extraction system is designed. It is deployed by processing the algorithm in following steps. First stop words are removed. After removal of stop words trivial name extraction is performed by dictionary matching approach. Then CAS/Registry numbers are extracted by regular expression. For systematic name extraction, sample systematic names from various categories are given as an input for training. Normalization type 1 and type 2 is performed on these sample systematic names which is followed by tokenization. Finally regular expressions are generated from training data. Input document is then matched with the regular expressions to extract systematic chemical names.