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Fake news analyzer

Social media is a platform which acts as a chain for spreading of diversified news. Its cheap cost, fast access, and quick circulation and broadcasting of information help people to look out and consume news from social media. In today’s era, online social media plays a crucial role during real-world practical events, especially climacteric events or events that gain huge social attention from the population of the world. Apart from legitimate news information, malicious content, mean-spirited, vindictive and misleading information is also put online during these events, which can result in harm, chaos and monetary loss in the practical world. In our paper, we draw attention to the role of Twitter in analyzing a major ongoing event of India: NRC and the Citizenship Amendment Act in spreading fake content about these events. We elaborate on the gathering, elucidation, and validation process in detail and perform much exploratory analysis on the recognition of linguistic differences in fake and real or legitimate news content. Also, we aim to find out the source or origin of the fake news and the sources who help to spread the fake content. Secondly, we aim to perform a set of learning experiments to find out accurate fake news and their sources. Besides, we provide a relative analysis of the automatic and manual identification of fake news with the legitimate informants of the news content. This paper proposes a system that identifies unreliable and false news after analyzing and computing the set of data. This system aims to use various NLP algorithms and classification algorithms or techniques to help achieve maximum accuracy in finding the fake news.

Published by: Ruchika Mahajan, Sanskruti Godbole, Vedanti Manjule, Prasanna Lohe

Author: Ruchika Mahajan

Paper ID: V6I2-1259

Paper Status: published

Published: March 19, 2020

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Research Paper

Review of brain tumor classification by texture base optimize features

Generally, experts perform certain evaluation procedures over these medical images which leads to operational errors. These errors require huge amount of time for making evaluations. MR images are qualitatively and quantitatively analysed by experts as per their experience. But it is limited to human eye, which is restricted up to 8 bits of grey level. Edge detection has its own reputation in image processing. Region boundaries and edges are quite similar, there is an iterative alteration of intensity at the region boundaries. Edge detection techniques recognized the edges which are non-continuous. There is a requirement of closed region boundaries, to segment an object from an image. The expected edges are the boundaries which lie between objects. It means edge based detection supports image segmentation.

Published by: Shakeel Ahmad Mir, Sunil K. Panjeta

Author: Shakeel Ahmad Mir

Paper ID: V6I1-1301

Paper Status: published

Published: March 19, 2020

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Research Paper

Review on copy move forgery image detection and classification

Digital image is an essential part of today’s everybody life. We use digital images in various fields like social networking sites, e-commerce companies, jobs, courtrooms, academics, insurance fields etc. Any unauthorized person can easily make changes in anybody digital image and use it in place of authorized person and takes benefits. To identify the original and tampered digital images we need some detection techniques to recognize the image is actual or tampered.

Published by: Sharika Ahad, Sunil K. Panjeta

Author: Sharika Ahad

Paper ID: V6I1-1300

Paper Status: published

Published: March 19, 2020

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Research Paper

Analysis of ECG signals for detection of sleep apnea

Electrocardiography (ECG) signals are widely used to gauge the health of the human heart, and the resulting time-series signal is often analyzed manually by a medical professional to detect any arrhythmia that the patient may have suffered. Much work has been done to automate the process of analyzing ECG signals, but most of the research involves extensive preprocessing of the ECG data to derive vectorized features and subsequently designing a classifier to discriminate between healthy ECG signals and those indicative of an Arrhythmia. This approach requires knowledge and data on the different types of Arrhythmia for training. Detecting abnormal heartbeats from an electrocardiogram (ECG) signal is an important problem studied extensively and yet is a difficult problem that defies a viable working solution, especially on a mobile platform which requires computationally efficient and yet accurate detection mechanism. However, the heart is a complex organ and there are many different and new types of Arrhythmia that can occur which were not part of the original training set. Thus, it may be more prudent to adopt an anomaly detection approach towards analyzing ECG signals. .We use various algorithms and methods and constructed a code for classifying the arrhythmia based on the variations in the signals and putting up a threshold value for every particular category respectively

Published by: Harshitha Uppala, Ratna Prakash

Author: Harshitha Uppala

Paper ID: V6I2-1250

Paper Status: published

Published: March 18, 2020

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Research Paper

Semi Autonomous Firefighting Drone

Most of the time fire occurs in unpredicted locations. It may occur in forest, buildings or in the middle of ocean. This paper talks about the extinguishing of fire using drones, fire accidents could be critical and accessing the situation at an early stage is very important in order to suppress the fire and evacuate the residents, as well as in the recent times forest and bush fires too have become a matter of concern which we face a hard time extinguishing as the fire fighters can’t reach out to first in dense forests. In these cases, aerial robot platforms have advantages because of its ability to access the place that is hard to reach. This paper elaborates ways to develop a firefighting UAV that operates in a Semi-autonomous manner by using Monoammonium-phosphate (NH4H2PO4) payload as the fire extinguishing agent.

Published by: S. Krishna Prasad, A. Karthikeyan, Kiran Tom Gijo, S. Dhanasekaran

Author: S. Krishna Prasad

Paper ID: V6I2-1270

Paper Status: published

Published: March 17, 2020

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Case Study

Impact of employee involvement on organizational performance – A study of Visakhapatnam Steel Plant

The efficiency and effectiveness of processes executed in any organization are dependent on people who execute the same. As per the literature of Managerial Economics, there are Four factors of Production viz., Men, Material, Machinery and Money. The Men play the key character in controlling ad regulating the remaining factors of production in an efficient manner if they are inclined to do so. Hence, it is essential to ensure that, people are in possession of a positive attitude towards their organization, co-workers as well as the management. People with a positive attitude feel satisfied with their job and tend to exhibit a commitment to their work and strive for the fulfillment of organizational Goals and Objectives. Winning the attitude of the employees is not an easy task in the current era owing to the modern scenario of growing interests, increasing technology and Inflation, diminishing the value of money and monthly earnings. Hence, the strategic and scientific Management experts rightly concluded that instead of making the people motivated through Extrinsic benefits. It is quite wise to make them self-motivated by offering Intrinsic benefits. These intrinsic benefits include Freedom to work, provision of Challenging work and an environment where the people are involved in making decisions that exert impact on their work, work environment, and working conditions. Hence, in the modern organizational scenario, it is extremely essential that the management should ensure the involvement of its employees, which makes them self-motivated, satisfied and committed to the organization’s Objectives.

Published by: B. V. S. P. R. Vithal, Dr. Naga Raju Battu

Author: B. V. S. P. R. Vithal

Paper ID: V6I2-1269

Paper Status: published

Published: March 17, 2020

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