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Seismic analysis of a multistory building using push over and nonlinear time history methods.

Non-linear analysis of G+5 storied RCC structure by using push over and Non-linear Time History Methods considering different ground motions. Bhatwari, Gopeshwar, Myanmar, and Srinagar are the ground motion data considered for Non-linear Time History Method. For modeling and analysis SAP 2000 software has been used for the G+5 storied RCC structure. In this paper, Base Shear, Displacement, Plastic Hinge formation, Performance point, Push Over Curve are generated after analysis.

Published by: Akshay Sudhir Naik, Prashant Barbudhe, Dr. Atulya Patil

Author: Akshay Sudhir Naik

Paper ID: V8I4-1157

Paper Status: published

Published: July 13, 2022

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Research Paper

Use of a spacer layer and doping, 0.5 µm AlInAs/InGaAs to improve mobility of HEMT

In this article, we optimize the performance of a high electron mobility In0.53Ga0.47As/In0.52Al0.48As transistor with a 0.5 m gate length and delta doping. Here, we have improved the mobility of the HEMT using variables like spacer layer fluctuation and delta doping. We simulate the -doped In0.53Ga0.47As/In0.52Al0.48As HEMT's conduction band discontinuities, threshold voltage, trans-conductance, cut-off frequency, and high-density two-dimensional electron gas. Analysis has been done on the parameters affecting the conduction band discontinuities, high-density 2DEG, and HEMT performance optimization.

Published by: Farhan Aziz, Vishal Lal Goswami, Ashutosh Dubey, Ranjeet Singh

Author: Farhan Aziz

Paper ID: V8I4-1151

Paper Status: published

Published: July 11, 2022

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Research Paper

Lexical analyser web extension for text simplification

Lexical simplification means the process of providing alternatives to the complex words in the sentence with texts that are much simpler to understand, while also preserving the context and grammar of the original text to make the whole sentence easier to understand. All of the recent work involving lexical simplification relies on unsupervised tasks to learn simpler alternatives to complex words. But the drawback of most of these researches has been the fact that they provide simpler words without taking the context of the complex world in the sentence into account. In this paper, we are proposing a lexical simplifier that is based on contextual learnings from the sentence. We have applied the pre-trained representation model, BERT. It is a very powerful tool that can make use of the broader context of the sentence in both forward and backward directions. We have also taken the word frequency indicator from the Subtlex list, to produce results that will be more correct both semantically and grammatically. We have also added a web extension for the simplification of the text on the webpage, which takes the input from the user, processes the text on the server end, and gives the result in return after the computation is over.

Published by: Harchit Mahajan, Prateek Koul, Sukhjeet Singh

Author: Harchit Mahajan

Paper ID: V8I4-1143

Paper Status: published

Published: July 8, 2022

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Research Paper

Impact of device parameters fluctuation on SRAM Cell

These days controlling the variation in device parameters during fabrication is a great challenge. The variations in process parameters such as the oxide thickness, channel length, and width, and doping in a channel result in a large change in threshold voltage. This process variation on design metrics of Static Random Access Memory (SRAM) cell which is used for process-tolerant cache architecture is suitable for complex memory design. The six-transistor and seven-transistor SRAM cells have been used to find the impact of process variation at 32nm technology. The 7T SRAM bit cell has a 60% improvement in SNM at the cost of area penalty, power penalty, read delay penalty, and variability penalty. This shows that the 6T SRAM cell is more efficient than the 7T cell.

Published by: Farhan Aziz, Rajneesh Yadav, Vinay Gupta

Author: Farhan Aziz

Paper ID: V8I4-1148

Paper Status: published

Published: July 8, 2022

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Research Paper

Improved Classification of Tweet Sentiments with Semantic Features using Convolution Neural Network with Soft-Max Approach

Sentiment analysis is a process of identification of opinion and thought related to any product, people or an organization. The sentiment analysis is mainly done to understand the others opinion related to some entity. This concept is mainly used in the large organizations and E-commerce to track the user’s activity and their response related to the product. The reviews of the product help the other users to know about the product more. If the reviews of other users are positive its sale is enhanced and if reviews are negative, then it affects the product sale. Sentiment analysis is done on the basis of text and images posted by the users on the social media website. In this analysis, sentiments are classified into positive, negative and neutral. sentiment analysis can be characterized as a procedure that helps in mining of feelings, emotions, views, and opinions from content, tweets, database, and speech in an automatic way by mean of NLP i.e. Natural Language Processing. SA examination includes the classification of opinions in content into classifications like "positive" or "neutral" or "negative". It's likewise indicated as opinion-based mining, subjectivity examination, and the extraction based on judgement. This polarity is assigned according to the meaning of words and after these score of all words is combining to understand the total score and then decides the comment is positive or negative. Sentiment analysis is a challenging task because it is not easy to analyses the exact views, opinions, and feeling from the text. The way of writing the feelings are different for every people in different context and topics. This issue solved by combining the text and prior knowledge. This research work proposes the deep convolutional neural network that uses character- to sentence-level information to perform sentiment analysis of tweets. This model presented a new approach for the initialization of the weights of convolutional neural network which helps to train the network effectively and helps to add new features. The model train by using unsupervised neural language and further tuned by deep learning model on a distant supervised corpus.

Published by: Vishal Thakur, Pratibha

Author: Vishal Thakur

Paper ID: V8I4-1146

Paper Status: published

Published: July 7, 2022

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Research Paper

Improved work flow scheduling by hybrid optimization using whale optimization in Cloud Computing

Cloud computing is a most recent methodology that is developing quicker step by step because of its compelling component and security. Distributed computing gives an approach to get to the information from wherever whenever. This component makes it famous in light of the fact that it decreases the weight of the clients. Distributed computing gives the administrations like framework, stage and programming as an administration on it. Because of these elements the Size of information on cloud in expanded and it impacts on the productivity of cloud. To defeat the issue like this planning of assignment on information is the best alternative. Work process planning for logical registering frameworks is one of the most testing issues that spotlights on fulfilling client characterized nature of administration necessities while limiting the work process execution cost. So, to lessen the cost, cloud condition, has been conveyed in cloud condition, assets will increment yet its usage is another test. To keep up and use assets in the distributed computing planning component is required. Numerous calculations and conventions are utilized to deal with the parallel employments and assets which are utilized to improve the exhibition of the CPU in the cloud condition. This work Particles swarm Optimization (PSO) and Gray Wolf Optimization (WCA) are utilized for successful booking. This work depends on the advancement of Total execution time and absolute execution cost. The consequences of the proposed methodology are observed to be successful in contrast with existing strategies. Insight advancement Particle Swarm enhancement is utilized which is instated by Pareto circulation. WCA is utilized to merge the choice of Virtual Machine (VM) relocation by its union to limit cost and time as outlined by Total execution time (TET) and Total execution cost (TEC). It is inferred that WCA performs better in contrast with existing FUZZY_HEFT calculation.

Published by: Ashok Kumar Kashyap, Pratibha

Author: Ashok Kumar Kashyap

Paper ID: V8I4-1145

Paper Status: published

Published: July 7, 2022

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