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LIFI Technology

Light Fidelity (LIFI) is Visible Light Communication (VLC) based innovation that makes a light as a media of correspondence supplanting the link wire correspondence. LIFI develop to defeat the pace in WIFI, while utilizing LIFI the pace can reach up to 14 Gbps. This paper presents a presentation, geographies, Performance, Advantage and Disadvantage of the LIFI innovation. The consequence of this paper can be utilized as a source of perspective to foster LIFI innovation.

Published by: Karan Singh, Sarala Mary

Author: Karan Singh

Paper ID: V7I3-2005

Paper Status: published

Published: June 21, 2021

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Research Paper

Effective implementation of smart agriculture monitoring system using IoT sensor network

Agriculture is an essential part of the Indian economy. More than 60% of India's agriculture-based population and a third of the nation's income comes from farming practices. Hence, it plays an important role in the country's development. The possible solution to these problems is to go for modernized agriculture that is part of modern trends. Therefore, agriculture can be made smart with the help of IoT and other technologies. Smart agriculture increases crop yields and reduces water waste and the imbalanced use of fertilizers. The most prominent feature of the project is that it measures various agricultural parameters that affect yields like moisture sensor, temperature sensor, ultrasonic sensor, and rain sensor using ATMEGA4809 chip and displays in LCD. Secondly, it sends all data to the cloud for analysis. The paper also includes an android mobile application that allows farmers to easily access information. In addition, the work also proposes an intelligent irrigation system that can optimize water consumption.

Published by: S. Purushothaman, Ajay Kanna R., Dhivakar G., Ashok Kumar K., Dilip Kumar E.

Author: S. Purushothaman

Paper ID: V7I3-1975

Paper Status: published

Published: June 21, 2021

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Research Paper

Study on quantitative & qualitative assessment on phytochemical activity and in-vitro study on antioxidant & antimicrobial activity from different organic extracts

The present study was to evaluate the phytochemical attributes and overall inhibitory effects of oceanic algal species of UlvaLactua, Gracilaria Corticata, and Ascophyllum Nodossum, collected from the area near Kovalam Beach (fisher’s spot), East Coast, Chennai, Tamil Nadu. Algal species are rich in bioactive compounds. Besides, the selected species are a highly diverse group of organisms from secondary metabolites of the natural source are potential sources. Oceanic algae are an interesting group in their broad spectrum of biological activities such as antibacterial, antioxidant, and anticancer. Methods: The algal extracts were prepared using a solvent extract approach involving ethanol, acetone, DMS, DCM, and double-distilled water as the solvent of interest for the study. Furthermore, the antibacterial activity and phytochemical activity of Ulva Lactua, Gracilaria Corticata, and Ascophyllum Nodossum. were tested against Candida albicans and Aspergillus niger and Escherichia coli and Proteas Vulgaris by well diffusion method. The phytochemical assay screening of the extracted species of Ulva Lactua, Gracilaria Corticata, and Ascophyllum Nodossum showed a greater degree of phytochemical attributes. In the antibacterial activity the growth of two virulent strains of pathogenic bacteria, E. coli and Proteas Vulgaris, and similarly concerning fungal strains of Candida albicans and Aspergillus niger were observed to exhibit a greater degree of inhibitory effect by the algal extracts, which are observed via the zone of inhibition (in mm). These results showed the investigated oceanic algal species, Ulva Lactua, Gracilaria Corticata, and Ascophyllum Nodossum showcased great biological potential, which could be considered for future uses in pharmaceuticals, food.

Published by: A. Dhipak Prince

Author: A. Dhipak Prince

Paper ID: V7I3-1972

Paper Status: published

Published: June 21, 2021

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Research Paper

Social distance monitoring using HOG and DNN

As of now, the world is facing its greatest pandemic COVID-19. Social distancing has become a part of our lives more than ever during the recent times .From our project, we would like to analyze a video that displays people walking around and provide the output in the same frame whether or not the people shown in the video are maintaining a social distance between them. The code will then be generalized in order to take any sample video as input and to provide the output.This will potentially help authorities to monitor public areas for practicing social distance, which will eventually help to curb the spread of a contagious disease.

Published by: N. Sai Karthik, G.Dinesh kumar, P.Ponnammal

Author: N. Sai Karthik

Paper ID: V7I3-1976

Paper Status: published

Published: June 21, 2021

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Research Paper

Design and Development of Dynamic Prosthetic Leg

This paper centers on planning and Development of Dynamic Prosthetic Leg for beneath amputees. A power framework can possibly lessen the limits in scope of movement and positive work yield of aloof strolling and running feet. Amputee walk in different day by day life circumstances utilizing aloof, semi-dynamic and controlled prostheses is analyzed. Regions for upgrades are examined. It could be required for assortment of reasons including illnesses, mishaps and intrinsic deformities. The general objective of prosthetics is to help standardize amputees. Essential intention of the task is to be equipped for change between standing, strolling and running with speed transformation.

Published by: Manish Kamath, Safal Agarwal, Riya Dahale, Tahereem Shaikh

Author: Manish Kamath

Paper ID: V7I3-1985

Paper Status: published

Published: June 21, 2021

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Research Paper

Disease detection using Machine Learning

Cancer is a lethal disease produced by the aggregation of hereditary disorders and a variety of pathological alterations. Cancerous cells are life-threatening abnormal regions that can grow in any portion of the human body. Cancer is also known as a tumour that must be diagnosed swiftly and accurately in the early stages in order to determine what treatment options are available. Despite the fact that each modality has its own set of problems, such as a difficult history, incorrect diagnoses, and therapy, which are all major causes of death. The goal of the study is to examine, review, evaluate, and discuss current breakthroughs in human body cancer detection utilising machine learning approaches for breast, brain, lung, liver, and skin cancers, as well as leukemia. The study shows how machine learning with supervised, unsupervised, and deep learning techniques can help in cancer diagnosis and cure. Several state-of-the-art approaches are grouped together, and findings from accuracy, sensitivity, specificity, and false-positive metrics are compared on benchmark datasets. Finally, potential future work is indicated by highlighting obstacles.

Published by: Jasmeet Singh Khokhar, Ankush Aglawe, Akash Pandey, Narayana Naidu, Roshan Mankar

Author: Jasmeet Singh Khokhar

Paper ID: V7I3-1964

Paper Status: published

Published: June 21, 2021

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