Development of a home security Robot using Deep Learning and IoT
The major problem in every urban city is the lack of security to residential areas. The number of thefts, electricity and food wastage at homes in urban areas increase every year due to human error. As per the National Crime Records Bureau (NCRB), 2,44,119 cases of robbery, theft, and burglary took place in residential premises in 2019. Also, electricity consumption in Indian homes has tripled since 2010. In 2019, an urban Indian household consumed about 90 units (kWh) of electricity as a monthly average which is one-third of the monthly world average. To solve these issues, we have proposed an idea of a “Home Security Robot” for a smart city using AI. The Home Security Robot will help in eliminating the reliance on security guards and will effectively monitor everything in the house (if there are any gas leakage, fridge malfunctions, unnecessary electricity wastage, indoor air quality and any unknown movements inside the house). If the owner is under attack, he/she can shout out “HELP” or “SAVE ME "so that the robot can take in the voice command to automatically call the police. The navigation part is done by Arduino and Bluetooth RC Controller App. There are 2 parts (Face Detection & Recognition using Raspberry Pi and IoT system using BOLT module with sensors). The first part has three python programs used for facial detection and recognition using OpenCV with Haar Cascade Classifier and LBPH algorithm. The first program (Face Dataset) is used for collecting images of known users and storing it in a database using Haar Cascade Classifier. The second program (Face Training) is used to train the stored images using LBPH algorithm so the model can distinguish between the users whose faces are stored in database and then these trained images are stored in the trainer.yml file. The third program (Face Recognition) is used to read the trained images stored in the trainer.yml file and then uses Haar Cascade Classifier to recognize the detected face and identifies whether the face belongs to a user or an intruder. The IoT system with the help of BOLT module helps in checking the temperature in the room and checking if any unnecessary lights are on in the room. If the room temperature is outside the safe range specified or if any lights are on, owner will get an alert via SMS.
Published by: Shravan Aruljothi, Sharad Dewanand Parate, Harshit S., Nehal Dinesh Andani
Author: Shravan Aruljothi
Paper ID: V7I4-1269
Paper Status: published
Published: July 12, 2021
Medicines for underprivileged patients using android application
Gift of medication is one of the most huge commitments that an individual can make towards the general public. Drugs for health care(DFHC) framework is a mix of site application and android application made for such respectable and incredible reason. The rising innovation in android improvement has made this conceivable. The emergency clinics, patients and clients can enroll through site. Furthermore, on other hand, the android application gives an approach to searcher to look for givers were calling and informing to companions through application on android. This application can likewise be utilized by medication benefactor and searcher where individual can enlist for keen on medication gift .User can get giver area through GPS and calling to them will be available. In DFHC there will be use GPS innovation that will be utilized to follow the route to the medication giver. The client will get the course to arrive at the ideal area and he doesn't need to ask physically, in this manner time can be spared.
Published by: Vishnu R.
Author: Vishnu R.
Paper ID: V7I4-1277
Paper Status: published
Published: July 12, 2021
Sentiment and thematic analysis on E-commerce application for user reviews using Machine Learning
Over the year's we have experienced tremendous growth in the use of ECommerce Applications. Since the pandemic, there has been an escalation in the use of these applications. Hence, we must understand the factors that are affecting the effectiveness of the services. In this paper, we will be analyzing different eCommerce applications on Google Play and App Store by performing sentiment analysis on user reviews by machine learning and then perform thematic analysis to identify the themes of reviews. Sentiment analysis is the process of identifying and categorizing opinions expressed in the text, especially when we want to determine whether the attitude of the customer concerning the services provided is positive, negative ,or neutral. Performing Sentiment analysis manually is a humongous task as there are millions of users. Hence we will be implementing different classifiers using supervised ML algorithms. These Classifiers will be trained and compared, then the classifier with the highest accuracy will be used to predict the sentiment polarity. Later on, we will be performing thematic analysis on positive and negative reviews to determine themes representing various factors affecting the effectiveness of e- commerce apps both positively and negatively. In the end, we will be proposing how to tackle the negative issues that are hampering the services
Published by: Rutuja Sanjay Mane, Aarti Sanjay Sahib, Prachi Sunil Mulay, Sumesh Santosh Mapara, Sulochana Sonkamble
Author: Rutuja Sanjay Mane
Paper ID: V7I4-1275
Paper Status: published
Published: July 12, 2021
Calmify: Novel Way to Monitor the Mental Health using IoT
Emotional and mental health is important as it encompasses our psychological, emotional and social wellbeing. It plays a vital role in the health of our relationships, allowing us to adapt to changes in life and deal with adversity. The Covid-19 pandemic has had a major impact on our lives. Many of us are facing challenges that can be stressful, overwhelming and cause strong emotions in adults and children. One in four people in the world will be affected by mental or neurological disorders at some point in their lives. Around 450 million people currently suffer from such conditions, placing mental disorders among the leading causes of ill-health and disability worldwide. Considering this as quintessential and need of the hour, I have developed Calmify a full mental wellness Tech package to help and discover ways to take a healthy approach to your emotional wellness. It includes a mobile application along with a novel and innovative Mental Wellness Smart Glove, which will surely help people to cope with stress in a healthy way. IoT technologies have a great potential in mental health for diagnoses, treatment, and care due to the increased ability to collect real-time data indicating patterns of activity and behaviour of people. On that note, I developed Calmify as an IoT-based BioMedical Embedded System that aims to provide a mental health solution that is efficient, cost-effective, feasible, easy to use and most importantly affordable for everyone. The smart glove is a wearable device that can monitor the mental wellbeing of the patient and provide ML predictions on the onset of serious mental health issues using psychophysiological signals obtained from unobtrusive smart sensors embedded in the glove which can be carried during daily life routines of individuals. Overall, it will be helpful in providing proactive healthcare to the patients.
Published by: Harmanjot Singh
Author: Harmanjot Singh
Paper ID: V7I4-1267
Paper Status: published
Published: July 12, 2021
Comparison of readiness for the practice among Bacculerate and Diplomate Pregraduate Nurses – Are there any differences?
Nursing Education in India is undergoing a major transition in inorder to improve the standards across. Graduate Nurses' transition from educational program to clinical practice is a global concern. They often feel insecure, inadequately prepared and it’s a time of reality shock. Diplomate and Bacculerate programs are the two entry-level courses in Nursing in India. There is documented evidence on the differences between diploma and degree nurses in terms of professional competence. However, readiness for the practice among pregraduate nursing students is less explored in the Indian context. This article will reveal the differences in readiness for the practice among Bacculerate and Pregraduate nurses in the South Indian setting.
Published by: Dinesh Kumar Suganandam, Vinitha Ravindran, Vathsala Sadan
Author: Dinesh Kumar Suganandam
Paper ID: V7I4-1266
Paper Status: published
Published: July 12, 2021
Performance evaluation of GMM super-pixel model-based technique with various bleeding detection techniques in WCE images
Wireless Capsule Endoscopy (WCE) is a non-invasive medical process that permits the assessment of the whole gastrointestinal tract that includes small intestine parts ahead of the conventional endoscope scope. This in turn needs the approach of a computer-aided scheme to assess the video frames for reducing the time of diagnosis. In this approach, the performance comparison on various existing techniques like color-based feature extraction, histogram-based approach, Discrete Wavelet Transform (DWT), K-nearest neighbor (KNN), K-means, and Support Vector Machine (SVM) techniques employed in the detection of bleeding to that of the proposed Gaussian Mixture Model (GMM) super-pixel model has been carried out. The study is carried out in terms of feature extraction models used so far and the classification approaches employed so far. Then the experimental study is carried out in terms of existing techniques and the performance is compared with GMM super-pixel feature extraction model and linear SVM to prove the effectiveness of the super-pixel-based model for bleeding detection. From the experimental analysis, the GMM super-pixel model is concluded better for automated bleeding detection.
Published by: Rathnamala S., Dr. S. Jenicka
Author: Rathnamala S.
Paper ID: V7I4-1256
Paper Status: published
Published: July 12, 2021
