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
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
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
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
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
Quantifying COVID-19 content in the Online Health Opinion using Machine Learning
A large proportion of the potentially harmful lies in COVID-19 appear to be in web AI to explore things among the online enemies of the development of prosperity, clear objectives ('' anti-vax ''). We track that the counter-vax counter that develops a smaller dose of COVID-19-less than its counterpart, the strength of the vaccine ('' vax- ready '') area. Regardless, the location of the counter vax range is wide, so it can draw a wide range of people looking for the COVID-19 theme on the web, for example people who are aware of the required inoculation of COVID-19 or those looking for optional adjustments. After that the local counter vax is better designed to attract new and ongoing help than good vax neighbors. This is disturbing as the lack of a complete collection of COVID-19 neutralizer would mean that the world is in short supply in terms of providing security of integration, leaving countries open to future COVID-19 renewal. We provide a model of negligence that interprets these outcomes and can help to study the rational skills of the intervention approach. Our approach works in many ways and therefore deals with the serious problem facing the establishment of electronic media for researching large volumes of web duplication of success
Published by: Pooja B.
Author: Pooja B.
Paper ID: V7I3-1996
Paper Status: published
Published: June 21, 2021
