Enriching Indoor and outdoor Fire Detection through CNN
Fire is a highly useful as well as a dangerous resource that has been utilized by humans since centuries. The only type of fire that is highly useful is the type of fire that is controlled and the energy generated can be used for different purposes. But not all fires are like that and some fires can be extremely devastating. These fires can become large and take down acres and acres of forests leading to extreme death and destruction. There have been recent and highly devastating fires that have rocked major rainforests and decimated a lot of wildlife close to extinction and endangerment. These fires can be stopped if detected when they are in their starting stages and lead to effective reduction in the destruction. There are several techniques such as sensors and other equipment that have been useful in the detection of the fire, but they have not been highly effective and efficient in the deployment. Therefore, an image processing based approach is defined in this research article to achieve effective realization of the fire detection. The proposed approach utilizes Convolutional Neural Networks along with Decision Tree to achieve the effective Fire detection. The experimental results confirm the accurate deployment of the fire detection mechanism.
Published by: Swapnali Kamble, Vaishnavi Sade, Rutuja Kamble, Sumedh Patil, Shubhangi Ingale, Shalaka Deore
Author: Swapnali Kamble
Paper ID: V7I3-1752
Paper Status: published
Published: June 10, 2021
A study on WiTricity (Wireless Electricity Technology)
Here we are going to discuss on , Wireless Electricity Transmission which is a valuable innovation that can be utilized to gather sun-based energy(solar energy) and focus on the earth surface in the absence of the wire association called a sunlight based force satellites (SPS) Since the remote force transmission consolidates numerous hypotheses and clarified by numerous techniques, here we are discussing about the various strategies utilized for the remote power. This connector is associated with a waveguide ferrite circulator which shields the microwave source from the reflected force. In their benefits, drawbacks and conservative thought will likewise be introduced.
Published by: Shubham Sunil Tambade, Lavina Jadhav
Author: Shubham Sunil Tambade
Paper ID: V7I3-1719
Paper Status: published
Published: June 10, 2021
Pneumonia: Medical Image Analysis
Pneumonia is an infectious and deadly illness in the respiratory tract that is caused by bacteria, fungi, or a virus that infects the human lung air sacs with the load full of fluid or pus. Chest X-rays are the common method used to diagnose pneumonia and it needs a medical expert to evaluate the result of X-ray. The troublesome method of detecting pneumonia causes a life loss due to improper diagnosis and treatment. With the emerging computer technology, development of an automatic system to detect pneumonia and treat the disease is now possible especially if the patient is in a distant area and medical services are limited. This study intends to incorporate deep learning methods to alleviate the problem. Convolutional Neural Network is optimized to perform the complicated task of detecting diseases like pneumonia to assist medical experts in diagnosis and possible treatment of the disease.The result achieved a 93 percent accuracy rate for CNN with Gamma and the lowest rate is 79.9 %percent achieved by the CNN model. Convolutional neural network (CNNs) has achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes. Medical datasets are often highly imbalanced with over representation of common medical problems and a paucity of data from rare conditions. We propose simulation of pathology in images to overcome the above limitations.
Published by: Shubham Sharma, Shubham Kamble, Shlok Hegde
Author: Shubham Sharma
Paper ID: V7I3-1696
Paper Status: published
Published: June 10, 2021
Bacteriological analysis of different types of fresh fruit juices from market
Bacteriological analysis of samples of fresh orange and pineapple juices collected from different shops confirmed the presence of bacteria. The isolated bacteria were identified as Escherichia coli and Staphylococcus aureus by employing techniques like Microscopy, Culture and Biochemical Reactions. The presence of Staphylococcus aureus and Escherichia coli may be interpreted respectively as contamination of fruit juices by the bacteria present on the skin surface of the juice maker or by the bacteria present on the water used for preparing those fresh juices. It has been proved that Staphylococcus aureus can exist as commensals on skin. Similarly E.coli being a common water contaminant can easily contaminate fresh juice. Hence it is advised to take necessary precautions to avoid bacterial contamination of juices during the preparation of fresh juices without preservatives. This can be achieved by proper hand washing techniques before juice preparation and also by using filtered cold water for juice preparation. In order to suggest a suitable therapeutic regimen in case of infections occurred during the consumption of such contaminated juices, antibiotic susceptibility testing of Escherichia coli and Staphylococcus aureus was done using antibiotics like Amoxicillin, Chloramphenicol, Tetracycline and Streptomycin. From the results obtained it has been assumed that Amoxicillin was more effective against both Staphylococcus aureus and Escherichia coli. The antibacterial effects of Amoxicillin, Chloramphenicol, Tetracycline and Streptomycin against Staphylococcus aureus and Escherichia coli showed the biocide effects of these antibiotics against the Gram positive and Gram negative bacteria.
Published by: Annamma Alex, Athira Omanakuttan, Renji Varghese
Author: Annamma Alex
Paper ID: V7I3-1658
Paper Status: published
Published: June 9, 2021
Scraping of Job Portal
"Scraping of Job Portal” is an online Job Searching Web application, a web application through which job seekers can register and apply for jobs. A job seeker must find jobs through advertisements, college fairs, job fairs etc., and the employers must put in much effort to find the right candidate for a vacant position. This application addresses such shortcomings and is a convenient platform for job seekers to find and apply for jobs. Candidates can search for jobs in any field through advanced search techniques. Basically our scraper scraps the data from various job portals and then provides job options to the user or job seeker according to their needs such as location, salary, working time, technical skills etc. They can upload their basic information related to job search which is stored in a database. Candidates can use this portal without any geographical barrier, from any part of the world. Also our portal will notify the registered users through email notifications which consists of basic information about jobs and also the apply link for that job.
Published by: Shubham B. Gulik, Akash R. Gharat, Jayesh L. Choudhary, Sujata Kolhe
Author: Shubham B. Gulik
Paper ID: V7I3-1748
Paper Status: published
Published: June 9, 2021
Green Computing
Green Computing is the process of producing certain parts such as monitors, storage devices, computers keeping the environment in mind. Green computing also known as Green Technology aims to reduce the use of materials that are hazardous to the surroundings or environment. The amount of energy that is consumed in today’s world is huge, and hence to reduce the amount of energy consumed Green Computing is implemented to design and build energy-efficient materials. . Green Computing came into the picture in 1992 when it was encouraged by the Environmental Protection Agency (EPA) as it launched Energy Star Program. Ultimately this technology is going be the savior of we humans in the coming future
Published by: Rohan Patil
Author: Rohan Patil
Paper ID: V7I3-1691
Paper Status: published
Published: June 9, 2021
