Evolution of research collaboration in Indian automobile industry– A Scientometric Analysis with Reference to SIAT during 2001 to 2017
The objective of this research paper on research productivity in the Indian Automotive Industry is to present the transformation of the research community of the Indian automotive Industry in a progressively responsible way, with reference to SIAT conferences held so far. It reveals the dynamic nature of automotive research in the country. The exhibit's research outcome is dominated by specific domains and specific organizations in a dynamic way. It is also elaborating the countries and scientists preferred to collaborate with and their impact on the Indian automotive Industry.
Published by: Joshi Ashutosh Gopal, S. Santhosh, E. Priyan
Author: Joshi Ashutosh Gopal
Paper ID: V7I4-1496
Paper Status: published
Published: July 23, 2021
AI-based hexapod military robot
Hexapod walking robots have attracted considerable attention for several decades. However, only in the recent past efficient walking machines have been conceived, designed, and build with performances that can be suitable for practical applications. Nowadays many expenses are made in the field defense on adopting primitive security measures to protect the border from trespassers. Some military organizations take the help of robots in risk-prone areas which are not that effective when done by army men. These robots are confined with the camera, sensors, metal detectors, and video screen. The main objective of our system is to get automated gun targeting including some additional parameters like a Wi-Fi module for real-time data processing by the camera at the video screen and IR sensor to trace intruders. Thus, the proposed system using Wi-Fi reduces errors at defense and keeps the nation secure from the foe.
Published by: Srividya C. R., Shreya Adiga H., Vaishnavi J. M., Harshitha H. R., Dipak Cumar V. C.
Author: Srividya C. R.
Paper ID: V7I4-1481
Paper Status: published
Published: July 22, 2021
Application of AI in drug discovery
Target-driven drug discovery is a process in which a known target is used to search for small molecules that interfere with it or influence its role in cells. These methods function well for easily druggable targets with a well-defined structure and well-understood interactions within the cell. However, due to the complexity of cellular interactions and a lack of understanding of intricate cellular pathways, these methods are severely limited. By detecting novel associations and inferring the functional significance of various components of a cellular pathway, AI will conquer these obstacles. It extracts useful knowledge from a broad dataset using complex algorithms and machine learning techniques. QSAR modeling based on structure, de-novo drug design, automated synthesis planning is just a few of the cutting-edge applications available. Screening of the compounds along with optimizing the lead compound, goal validation and selection, nonclinical research and studies, and clinical drug trials are all places where AI is used extensively.
Published by: Mohit Singh Negi, Anosh Singh
Author: Mohit Singh Negi
Paper ID: V7I4-1472
Paper Status: published
Published: July 22, 2021
Clustering of JSON document using Derived X-Path Method
In this modern era where data creation is in abundance, handling that data is the biggest question researchers have been trying to solve for the past couple of decades. The data in any form needs to be stored in such a way that the retrieval and manipulation of the data are easier and simpler. In this paper, we look at how huge datasets in JSON format are being analyzed and made into clusters for an easier understanding of the documents that we are handling. This process is done by the first pattern analyzing the documents followed by clustering those documents one by one based on similarity of the attributes (arrays and sub-documents included). The clusters are then evaluated on an algorithm designed specifically for this purpose. To conclude, this paper lays out the model on how to cluster huge JSON documents with varying levels and types.
Published by: Kaushik I.
Author: Kaushik I.
Paper ID: V7I4-1473
Paper Status: published
Published: July 22, 2021
Importance of Feature Selection in Model Accuracy
As a dimensionality reduction strategy, feature selection attempts to select small set of most important features from primary features by eliminating obsolete, data-redundant and non-relevant noisy features. This process of choosing a set of the original variables such that a model based on data containing only these features has the simplest output is known as feature selection. Feature Selection eliminates over-fitting, increases model efficiency by removing redundant functions, and has the added benefit of maintaining the primary feature representation, resulting in improved accuracy. Good learning efficiency, results into higher machine learning model accuracy, lower cost of computation, and efficient model accuracy, is typically the product of feature selection. Recently, researchers in the area of computer vision, deep Learning, data mining, and other fields have shown that several feature selection algorithms resulted in the efficiency in their work through computational theory and research. This paper aims to examine the importance of feature selection in model accuracy. Feature selection is critical for various reasons, which include simplicity, performance, computational efficiency, and accuracy. It is often used in both supervised and unsupervised learning scenarios. These strategies can help boosting the productivity of various machine learning algorithms, as well as coaching. Feature selection decreases learning time and increases data consistency and comprehension.
Published by: Pranjal Rawat, Nitin, Sameer Dev Sharma
Author: Pranjal Rawat
Paper ID: V7I4-1470
Paper Status: published
Published: July 22, 2021
College Kendra
The android app “College Kendra” is a package of unique features that a university/college need in their college life. There are 8 features in this android app namely Online Attendance, E-Passbook, Notice Board, Calculator, Anonymous Feedback, Automatic Silent Mode and Distress Alert. These might seem pretty common but they aren’t. This paper describes why in the coming sections. The main feature in this app, the Online attendance is made to save professor’s time on trivial attendance task. The amount of average time saved on daily basis will be 5 minutes. This is the time which will be wasted on taking attendance manually, which accounts over 10 hours per semester and 20 hours per year. Also, through this app we can 100% ensure that the student is really present in the class, thus eliminating proxies. What makes it completely efficient is the One-Time-Password based authentication (OTP). This particular Attendance feature totally eliminates the paper work too. With effective usage, any institute can apply this feature for conducting quick attendance and get better results in less time. The calculator feature I developed isn’t any typical or regular calculator that everyone uses. It doesn’t have the same usual operations. As graduation students use this, it’ll have prime and big-league operations such as bit convertor. The other features help the students with college tasks and non-academic problems. The E-Passbook, Notice Board and Chat Room features included in the app will help with classroom tasks and work as communication media. The Anonymous feedback feature helps to report any issue in the classroom. The Automatic Silent Mode Turner will set the phone in the silent mode automatically when you are in the class. The Distress Alert option would help students to alert others during emergency or unusual situations with just a single click. This paper gives a complete understanding about all the features of our app, which is a complete means of help for a university/college.
Published by: Pranav Kongara
Author: Pranav Kongara
Paper ID: V7I4-1469
Paper Status: published
Published: July 22, 2021
