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Speech recognition system using Deep Neural Network

Speech recognition is the property of a system to identify the words spoken by the user in a scripted language and convert the data to a readable and writable format. The work carried out was able to convert the speech to text. Using the speech as instructions to perform many web-based services, system bound tasks. Deep learning with deep neural networks coded in python is implemented in this paper which makes the system more reliable, robust against noise, and with an accuracy of 70%.

Published by: Dhanush N. D., Jagruth S., Rohini Hallikar

Author: Dhanush N. D.

Paper ID: V6I3-1151

Paper Status: published

Published: May 10, 2020

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Research Paper

Bi-clustering and classification-based detection for DDoS attacks

There are several Machine Learning (ML) techniques that have been adopted for detecting DDoS attacks, But the attacks still became a major threat. The various existing systems worked on supervised and unsupervised ML-based approaches. Various supervised ML approaches consider both labeled and unlabeled network traffic datasets to detect DDoS attacks. Whereas, unsupervised ML approaches depends on incoming network traffic data to the attacks. Both approaches analyses using large amount of network traffic data with very low accuracy and high false-positive rates. In this presented paper, we propose semi-supervised Machine Learning approach for DDoS detection based on various algorithms orderly, Entropy estimation, Bi-clustering approach, and Random Trees decision making algorithm. The unsupervised part allows removing the irrelevant traffic data for DDoS detection which allows decreasing false-positive rates and increases efficiency. Whereas, the supervised part allows us to reduce the false-positive rates from the unsupervised part and to accurately classify the DDoS traffic data. Various experiments were conducted to evaluate the proposed approach using public NSL-KDD dataset. An accuracy of 98.66% is achieved for respectively NSL-KDD dataset, with respect to the false-positive rate of 0.31%.

Published by: Santoshi Sahu, Mamidi Sushma Venkata Anisha, Rayudu Venusri Teja, Sai Smruti Rout

Author: Santoshi Sahu

Paper ID: V6I3-1170

Paper Status: published

Published: May 7, 2020

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Research Paper

Vision-based human activity recognition using CNN

Human Activity Recognition (HAR) is a commonly discussed topic in computer vision. HAR implementations include representations such as health care and contact between the human and computer systems. When the imaging technology progresses and the camera system improves, there is a relentless proliferation of innovative approaches for HAR. Human activity recognition is an important component of many creative and human-behavior driven programs. The ability to recognize various human activities enables the development of an intelligent control system. Usually, the task of the Identification of Human activities is mapped to the classification task of images representing a person’s actions. This Project used for human activities’ classification using machine learning methods such as CNN. This Project provides the results to Identification of Human activities task using the set of images representing five different categories of daily life activities. The usage of images also webcam to find out the live activities of the users that could improve the classification results of Identification of Human activities is beyond the scope of this research.

Published by: M. Sohan Raj Kumar, Dr. Syed Abudhagir Umar, M. Phillips Robert, J. Nagendra Vara Prasad, CH. Prasad, Talluri Sairam

Author: M. Sohan Raj Kumar

Paper ID: V6I3-1166

Paper Status: published

Published: May 7, 2020

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Others

Product quality by process analytical technology and quality by design- A short communication

A Systematic approach which is based on scientific knowledge and concept of quality risk management which leads to development which emphasizes on process and product undertaking an starts with a predefined objectivesis usually called as QBD,which now aims at pharmaceutical development to design a quality product and the entire manufacturing process aiming to produce according to the standard and specifications to consistentely deliver product intended,the knowledge is purely base on scientific approach,qbd is not only limited to production but is also extended to analytical proceures and methodology,the article relates to a short communication about A QBD its approach, history and its basic elements.The product procedures now need to comply till the end of the entire procedure s rather than only at the begining and that’s where QbD plays important role throughout the method life cycle also it give added advantage is to include the procedure to discovering and minimizing the sources of viability which may lead to inferior quality and procedure and products.

Published by: Anita A., Vinod Mathew, Sapna Dongre, Akshata

Author: Anita A.

Paper ID: V6I3-1157

Paper Status: published

Published: May 7, 2020

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Research Paper

Border surveillance robot

Border monitoring is a complex challenge, it requires protection all day, all night, and in all conditions. Border security is required to monitor the area every second and detect unusual activities, this is generally being carried out by human effort, but humans are prone to errors due to fatigue or exhaustion. Border surveillance robot comes up with special surveillance, scanning the area continuously in real-time. This monitoring greatly decreases the erroneous human monitoring. The proposed system provides real-time video footage and ensures protection from terrorists or any kind of prohibited intrusion.

Published by: Sabiya Sultana, D. Shalini, Prashanth Varma, V. Sri Charan, B. Shiva Kumar

Author: Sabiya Sultana

Paper ID: V6I3-1156

Paper Status: published

Published: May 7, 2020

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Review Paper

Design and analysis of pharma tableting radial tools cavity for material optimization- A review

There are lot of reasons for tablet defects whether it comes from upstream or from tablet press. The poor quality of raw materials or not complying with standards, resulting in unnecessary fines leading to a host of defects. The formulation may be a source of defect if the material does not compress well or if the processing phase stated in the formulation fails to produce a good flow of powder; Tablet press operators, however, don‘t have any control over formulation and granulation. Tablet specifications are tight, and the list of possible defects is long: Variable weight, sticking, picking, capping, lamination, variable hardness, among others. This article focuses on these variations. It pinpoints the possible causes of these defects and offers advice on preventing and fixing the source of the problems. When tablet is free from visual defect or functional defect then we can say that it is an ideal tablet. The advancements and innovations in tablet manufacture have not decreased the problems, often encountered in the production, instead have increased the problems, mainly because of the complexities of tablet presses; and/or the greater demands of quality. An industrial pharmacist usually encounters number of problems during manufacturing. Majority of visual defects are due to inadequate fines or inadequate moisture in the granules ready for compression or due to faulty machine setting. Functional defects are due to faulty formulation. Solving many of the manufacturing problems requires an in–depth knowledge of granulation processing and tablet presses, and is acquired only through an exhaustive study and a rich experience.

Published by: Prathamesh Preetam Choughule, Satish Silaskar, Pravin Alone

Author: Prathamesh Preetam Choughule

Paper ID: V6I3-1160

Paper Status: published

Published: May 7, 2020

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