Rahul Patil

Bharati Vidyapeeth Deemed University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Pramod Patil

Dr. D. Y. Patil Medical College, Hospital and Research Centre

Pramod D. Patil

Shivaji University

Pratik Fegade

Carnegie Mellon University

James P. Kelly

Wexford General Hospital

Harshwardhan Patil

Savitribai Phule Pune University

Madhura Kalbhor

Savitribai Phule Pune University

Lawrence H. Cox

National Institute of Statistical Sciences

Tejas S. Bavaskar

Chhattisgarh Dental College & Research Institute

Rahul Mokhadkar

Cummins (United Kingdom)

Shivdeep Chaudhari

Cooperative Institutions

Savitribai Phule Pune University

Indian Institute of Technology Hyderabad

Space Telescope Science Institute

Bharati Vidyapeeth Deemed University

Max Planck Society

Max Planck Institute for Astronomy

Center for Astrophysics Harvard & Smithsonian

Centre National de la Recherche Scientifique

California Institute of Technology

Symbiosis International University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Acquisition, storage, retrieval and dissemination of disaster related data

Shaik Asif Ahammed Karthik Badam Guddanti V. Bhaskar Sumit Chhuttani ANUJ GANGWAR .

We report the design and development of a software system that provides a unified platform for acquisition, storage and retrieval of scientific, situational and infrastructural data in the context of disasters. The system supports (i) data acquisition from heterogeneous sources (including sensor data from various research groups, field data from disaster management agencies and citizens, and relevant information from the internet) and ensures interoperability, (ii) high availability and scalability and (iii) a wide range of data visualization and retrieval modes. We believe that such a system would go a long way in assisting researchers and disaster management agencies in enhancing disaster preparedness, mitigation and recovery operations.

Information Dissemination

10.1109/r10-htc.2014.7026329

Cite

Citations (4)

Response of Residential Building under Seismic Loads by Considering Different Zones in India

T Maruthi Nagaraja M Belavigimath K T Prakash Rahul Patil R Rohith

The main purpose of this Extended Three-Dimensional Analysis of Building System’s software is to design multi-storey buildings in a systematic process. It caters for multi-storey building analysis and design part. It was analysed with the lateral loading impact of an earthquake. This project is designed as per Indian Codes IS 1893-Part-2: 2002, IS 456: 2000, IS 875 Part-2, IS 800: 2007. This analysis was carried out by considering severe seismic zone 2 and 4 behaviour was assessed by taking type-II Soil condition for the results compared with lateral displacement, storey drift and base shear in different zones in India.

Seismic zone

Source

Cite

Citations (0)

Prediction System for Student Performance Using Data Mining Classification

Rahul Patil Sagar Salunke Madhura Kalbhor Rajesh Lomte

In the education system, highest level of quality can be achieved by exploring the knowledge regarding prediction about student's performance. In an analysis of data, data mining techniques play an important role. In order to predict performance of students in future academics, it's good if an educational institution have an approximate prior knowledge of all enrolled students in their institute. This prior knowledge becomes an important tool for educational institute to improve students those who would likely to get less marks and also identify bright student. As a solution we are trying to develop a system which will help an educational institute to prefigure the performance of students from their former functioning. In order to achieve this we will use concepts of data mining techniques under Classification. Also for solution to get developed we are trying to prepare the data set containing information about students in terms of their gender, marks and rank in entrance examinations and results in Third year of the former batch of students. These data sets have been analyzed to prepare final solution. The mapping of data into predefined groups or classes is done in Classification data mining technique. It is a supervised learning method in which to generate rules for classifying test data into predetermined groups or classes labeled training data is required. The process is divided into two-phases. The first phase which is learning phase, the training data is examined and classification rules are begot in this phase. The second phase the Classification one in which test data is classified into classes in accordance with training data set. General and individual performance of third year students in future examinations is prefigured by using the ID3 (Iterative Dichotomize 3), C4.5, Improved weighted modified ID3 classification algorithms on this data.

Rank (graph theory)

Educational Data Mining

Data set

Training set

10.1109/iccubea.2018.8697770

Cite

Citations (20)

Image Classification by Tensorflow

International Journal for Research in Applied Science and Engineering Technology (2023)

Kashif Hussain Shaikh Dnyaneshwar Pawar Gaurav Patil Rahul Patil Prof. Hemant Wani

Abstract: The task of relating what an image represents is called image bracket. An image bracket model is trained to fete colorful classes of images. For illustration, you may train a model to fete prints representing three different types of creatures rabbits, pussycats, and tykes . But in our design we've used the datasets of cotton splint which helps us to find the complaint on the cotton splint as well. Tensorflow is an google open source machine literacy frame for dataflow programming across a range of task. As well as it's a open source library for deep literacy operation. It's firstly developed for large numerical calculation without keeping deep literacy in mind. In the design of image bracket, one model was erected to sort images. By using the model which was erected in the design, filmland can be classified effectively and snappily. At the morning of the design, an applicable data set was chosen. also, the model was created by using TensorFlow. Next, the model would be trained to get the parameters with good fitting. Eventually, in order to estimate the model effectively, several graphs of confirmation delicacy were created. In the process of completing this design, the members of Team have learned the capability to construct convolutional neural network models using python. What’s more, the members of the platoon also develop a good capability of data analysis.

Python

10.22214/ijraset.2023.53817

Cite

Citations (5)

NLP Based Question Answering System

2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA (2023)

Rahul Patil Promod D. Patil Y R Joshi Shivam Khandelwal S. M. Nalawade

The proliferation of data in various domains has increased the significance of Question Answering Systems (QAS), which utilize Natural Language Processing (NLP) techniques to comprehend and respond to natural language queries. This review paper provides a comprehensive overview of recent advancements in QAS that employ NLP techniques. However, this thesis concentrates on specific QA scenarios where the answer is explicitly present in the text source. The objective is to build a QA system that can extract answers from plain text sources quickly, precisely, and accurately, using only a single sentence or a fragment of a sentence. The paper discusses the obstacles involved in creating QAS and how NLP techniques can overcome them. The paper proposes a question answering system that combines extractive and abstractive summarization methods to produce brief and accurate answers to user questions. The system uses keyword extraction and classification to identify pertinent information from a collection of documents and then combines extractive and abstractive summarization approaches to create a comprehensive and concise response. The research findings indicate that the proposed system has significant potential to enhance the effectiveness and efficiency of question answering systems in a range of applications.

10.1109/iccubea58933.2023.10392202

Cite

Citations (0)

Indian Stock Price Prediction Using Machine Learning Algorithm and Sentiment Analysis

International Journal of Science and Research (IJSR) (2022)

Harshwardhan Patil Rahul Patil

Sentiment Analysis

Stock (firearms)

10.21275/sr22315122205

Cite

Citations (0)

Cryptography Based Privacy Protection Mechanism for Online Social Networks

Ravindra Zarekar Rahul Patil

The wide use of online social networks (OSNs) such as Facebook, Google+, Twitter, etc., where users interact with each other by sharing their personal information. On-line social network applications severely suffer from various security and privacy exposures. One of the important issues in OSNs is that how user privacy is protected because OSN providers have full control over users’ data. The OSN providers typically store users’ information permanently; the privacy controls mechanism in OSNs offer limited options to users for customizing, managing and defining access policies for their data over the network. An efficient privacy protection mechanism is important for OSNs that can be used to protect the privacy of online social relationships and users’ data from third parties. Cryptographic privacy protection mechanism shifts the control over data sharing back to the users by providing them with flexible and dynamic access policies. Also the proposed Validation Check Module focuses on handling the human attacks by providing an additional level of security, including auto-locking features to protect the user’s account in online social network. This mechanism provides enhanced security features from both automated program attacks and the human attacks.

Information sensitivity

Protection mechanism

Privacy software

Social network (sociolinguistics)

Data Sharing

Source

Cite

Citations (0)

An audit of pattern of failure in patients with buccal mucosa/retromolar trigone carcinoma treated at a single UK centre

Clinical Oncology (2022)

Petr Daniel Kovarik Malcolm Jackson Rahul Patil Charles Kelly Muhammad Iqbal

Buccal mucosa

10.1016/j.clon.2022.09.006

Cite

Citations (0)

Online System for Identifying Need of Machine Maintenance by Mining Data Streams and Handling Concept Drifts

Advances in intelligent systems and computing (2021)

Rahul Patil Pramod Patil Aditya Ghongade Adriel Dsa Parth Lokhande

Concept Drift

Streaming Data

Ensemble Learning

10.1007/978-981-16-5157-1_76

Cite

Citations (1)

A Data Aggregation Method for Balancing Load under Probabilistic Network Model (PNM)

Rahul Patil Navi Mumbai

Data aggregation is a very crucial technique in WSNs. Data aggregation helps in reducing the energy consumption by eliminating redundancy. Data Gathering is a fundamental task in Wireless Sensor Networks (WSNs). Data gathering trees capable of performing aggregation operations are also referred to as Data Aggregation Trees (DATs).Most of the existing DAT construction works are based on the ideal Deterministic Network Model (DNM), where any pair of nodes in a WSN is either connected or disconnected. Under this model, any specific pair of nodes are neighbors if their physical distance is less than the transmission range, while the rest of the pairs are always disconnected. However, in most real applications, the DNM cannot fully characterize the behaviors of wireless links due to the existence of the transitional region phenomenon. The load-balance factor is also neglected when constructing DATs in current systems. And most of the current literatures investigate the DAT construction problem under the DNM. In this paper we are discussing on load balancing factor and also on construction of DAT using Probabilistic Network Model (PNM). Therefore, it is focused on constructing a Load-Balanced Data Aggregation Tree (LBDAT) under the PNM. More specifically, three problems are investigated, namely, the Load-Balanced Maximal Independent Set (LBMIS) problem, the Connected Maximal Independent Set (CMIS) problem, and the LBDAT construction problem. LBMIS and CMIS are well-known NP-hard problems and LBDAT is an NP-complete problem.

Data aggregator

Source

Cite

Citations (0)