Volume 10 | Issue 4 | April-2022

 Abstract

The Third Eye for the Blind is a wearable device that involves multidisciplinary areas such as computing, electronics engineering, and health science to assist blind individuals in navigating and detecting surrounding hazards utilizing ultrasonic waves and providing audio alerts using a text to speech module. The objective of this project is to style a product that's considerably beneficial to those people that are visually impaired and people who often need to believe others. Globally, 1.1 billion people were visually impaired by the year 2020. Because of population growth and ageing, the number of persons affected by these main causes of vision loss has increased significantly. The device that we have proposed is based on the detection of targets using ultrasonic sound. The device's camera assists the user in identifying and recalling objects from their programmed memory when they reappear in front of them. Even today, blind people use a stick to detect obstacles in their path. However, this stick is inefficient in many ways, and the person who uses it faces numerous difficulties. So, in order to address this issue, we are developing this device using cutting-edge technology.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Raspberry Pi 4, Pi Camera, Ultrasonic Sensors, Text to Speech Module.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

We are always amazed at how the human brain differentiates between different objects and things. This is possible because of our muscle memory. This same thing is possible in computers also. The computer can detect the image and objects and classify them into different classes by using machine learning and computer vision. Machine learning is a branch of artificial intelligence where machines learn by themselves and behave like human behavior. This Image segmentation project uses a machine-learning algorithm named Mask RCNN. This System classifies the image in a particular class and produces a mask on the same pixel area from the image, video, and the live camera also.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Component, formatting, style, styling, insert.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

IoT-based smart water quality monitoring system is used to monitor the quality of water and to control the water supply for the plant. The Internet of Things allows objects to be identified and controlled from a distance using existing network infrastructure. The more efficiency, accuracy, and financial benefit through more direct contact between the real and virtual worlds and computer-based systems. To maximize water consumption and maintain a green environment, it is important to irrigate more effectively as water supplies become increasingly polluted. The goal of this project is to create a smart houseplant watering and monitoring system that analyses and records environmental factors to help plants flourishment. The sensors collect and evaluate data related to changing weather and soil moisture levels before sending timely warnings to the user's Smartphone.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Soil Moisture, IOT (Internet of things), Blynk.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Animals like wild boars, buffaloes, cows, elephant, monkeys, birds, etc. damages the crop a lot which results in loss of production and so of farmer. It is very difficult for a farmer to keep an eye on the field every time. This system is designed to surveillance the field 24*7 which is not possible for a human being and diverts the animals without harming them. The system uses raspberry pi, PIR sensor to detect animal, camera module to look on animal, GSM module to send alert message to farmer, and a buzzer to divert the animals. This system ensures the safety of farm and decreases the loss of farmers

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Raspberry pi, PIR sensor, Crop Protection

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Internet of Things (IOT) had many applications in several domains; it also strides into smart homes. Controlling appliances with IOT can be easily done using Smart phone through Android apps. Home automation is one of the profound in day to day applications. Due to hasty progress in technology, Wireless Fidelity (Wi-Fi) has brought revolutionary change than compared to Wired LAN communication. Existing wireless communication devices such as Bluetooth, Zig Bee and NRF24L 01 etc. are limited to short range. IOT uses Wi-Fi to exchange data wirelessly for large distances using Internet. IOT module (ESP8266) is used to control the home industrial appliances in remote areas anywhere in the world. Serial Communication exchanges the data between FPGA and IOT module. Home appliances are controlled using FPGA which receives commands in serial communication from IOT Module through smart phone app. Compare to existing home automation; IOT based home automation can update device status with E-Mail alerts and also in web with IP address which can be password protected. Due to its high precision and smart phone technology helps for physically challenged and senior citizens.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Internet of Things, Wi-fi, ESP8266,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

COVID-19 outbreak affect human lives as a whole and can be a cause of serious Health Problems and death. Artificial intelligence has been proven to be a effective and powerful tool in the fight against COVID-19 pandemic. Machine learning (ML) models are the most remarkable function in disease prediction, such as the Covid-19, in high geared forecasting and used to help decision-makers to understand future spread of COVID-19. The Aim of this paper is to predict the outbreak of COVID-19 using two approaches of Machine Learning viz. Data Visualization and Prediction using Linear Regression and Support Vector Machine. The models are designed to predict Covid-19, depending on the number of confirmed cases, recovered cases and death cases, based on the available datasets. Support Vector Machine (SVM) and Linear Regression (LR) models were used for this study to predict Covid-19 's risk. Predictions would help to get the future count of the COVID-19 cases and thus to establish precautions before the situation goes out of control. All three cases, such as confirmed, recovered and death, models predict deaths over the next 15 days. The experimental result showed that SVM is doing better than LR to predict the Covid-19 pandemic. According to this report, the pandemic has increased by half between the mid of July 2020. Then we will face a number of hospital shortages, and quarantine place.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

machine learning, prediction, COVID-19 outbreak, data visualization, linear regression, support vector machine

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In this digital era, face recognition systems plays a vital role in almost every sector. Face recognition is one among the mostly used biometrics. It can used for security, authentication and identification. As compare to iris recognition and fingerprint recognition, it is being widely used because of its noncontact and non-invasive process. Face recognition system also can be used for attendance marking in schools, colleges, offices, etc. This method aims to make a class attendance system which uses the concept of face detection and face recognition as existing manual attendance system is time consuming and also there is a chances of proxy attendance. Thus, the need for this system increases. So, in the present situation created by Covid-19 pandemic, temperature and oxygen level measurement is also important to allow the students to attend the class. We propose a concept where together with face recognition, temperature and oxygen level is measure to mark the attendance record and also, sanitization system is added for safety purposes. This system will design using ultrasonic sensors, temperature sensor, oximeter, camera module that is interfaced with Raspberry Pi. In this manner attendance are often marked automatically and at an identical time system can scan individual student to spot potential patients of Covid-19.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Face Recognition, Covid-19, Raspberry- PI.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

blockchain, blockchain architecture, smart contracts, blockchain applications, development frameworks, blockchain security.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Corona virus pandemic has been recognized as a global threat across the world and many methods were adopted for the prevention of this disease. This pandemic has caused global and economic disruption which resulted in numerous Covid-19 cases across the world. To know the number of cases and keep a track of this pandemic situation we need to collect the live data sets from the worldwide corona virus records. This can be achieved by the technique of Web Scraping which enables the extraction of live data sets from a specific platform. It facilitates the user to access the World Wide Web wherein specific data is gathered, copied from the web and then it is stored in a central local database then provides ways to retrieve and analyse the data. This research focuses on the implementation of Voice Assistant based corona web scraper and explains how a single system is easy to use by all users. This system is useful for get the information regarding corona virus updates. The user can read and listen covid updates through voice assistant.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Web scraping, Covid-19, Voice assistant

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In interconnection and automation of different physical gadgets, vehicles, home machines and different things, the internet of things (IoT) innovation plays a critical role. These objects associate and deal information with the assistance of software, different sensors, and actuators. A human's standard of life and living are improved with this automation of gadgets, which is a forthcoming need. In this paper we talked about a similar requirement for instance, a smart car parking system which empowers a driver to discover a parking area and a free slot in that parking area inside a city. This paper focus on decreasing the time squandered on discovering parking area. This in turn diminishes the fuel utilization and way of life. With the exponential increment in the quantity of vehicles and total population, vehicle accessibility, use out, about starting late, finding a space for parking the vehicle is turning out to be increasingly more troublesome with realizing the amount of conflicts, for example, automobile overloads. This paper is connected to making a trustworthy system that accept authority over the undertaking of recognizing free slots in a parking area and keeping the record of vehicles left in an extremely methodical way. The predicted system decreases human effort at the parking area generally.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

IOT, RASPBERY PI, RFID, POWER SUPPLY, LCD.

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Creative Commons Attribution 4.0 and The Open Definition