Due to some abnormal changes in genes of cells, enforces cells to divide uncontrollably, due to which tumors are formed, which infiltrates and damages the normal body tissues, and this condition is called "Cancer". Lung cancer is a type of cancer where the infected cells in the lungs multiply rapidly at a high rate. This abnormal growth of cells, which eventually leads to cancer can be identified using modern data analysis. Detecting cancer symptoms at an early stage plays a crucial role for the patients who may suffer later, if not detected. One of the major problems is the increasing fad of smoking tobacco in youngsters. Air pollutants from industries which get inhaled by people are some of the main causes of increasing lung cancer in India. The main focus of this study is to predict lung cancer in different patients using Machine Learning (ML) algorithms such as a random forest classifier(RFC), k-nearest neighbour(KNN), K-means, Support vector machine(SVM), and decision tree classifier(DTC). The key objective of this research is the analysis of different machine learning algorithms based on their performance metrics.
A chatbot is a software used to help to increase user interaction during online conversations between a human and machines. A Discord bot is an AI driven tool in which tasks are automated in your discord server. Using discord bot helps us to grow a community, increase interaction and performance of the discord server. This paper focuses on to creation of a discord chatbot named spiffy which is used in discord server and developed using python, json database structure and discordAPI. The chatbot provides user basic interaction with normal human commands, allowing user to type commands as well as receive the texts. The bot will able to answer the query that is generated by the user.
The most common reason for blindness in adults in developed nations is Diabetic Retinopathy (DR). Currently, diagnosing DR involves an in-depth arduous examination of digital colour fundus pictures of the retina by a qualified practitioner. By looking for lesions connected to the vascular anomalies brought on by the illness, ophthalmologist can recognise diabetic retinopathy. Although this strategy works, it has substantial resource requirements. It has long been understood that a thorough and automated approach of detecting diabetic retinopathy is necessary, and prior initiatives have achieved excellent strides utilising image classification, pattern recognition, and machine learning. This project seeks for automated detection, grading, and segmentation of Diabetic Retinopathy. In our project we aim to improve image segmentation using UNet and to automise the project using Convolutional Neural Networks and VGG16.
The human-based disaster management programme has recently been a source of administrative and ethical concern. Sometimes it can be more harmful than beneficial. In order to take into account the newly developed Internet of Things organization, which is sufficiently evolved to create a network automatically connecting each other and forecasting every function and feature of the flood, there must be less interaction between humans and all such inventions. In this project, a wastewater disposal system is created and put into service to compensate for the losses brought on by urban flooding in metropolitan areas. For the criticality of the wastewater deposit network, the platform consists of real-time cloud computing and Internet-of-Things (IoT) control. In order to meet the demand for incorporating waste water drainage systems in any urban area in order to avoid and stop floods, we designed this architecture, technology, mathematical model, and application. This constructed framework is illustrated through the case study of the Mumbai floods of 2005. If we use this approach, we can prevent more than 40% of the harm and live.
LiFi is a communication technology that enables wireless transmission of data using light. With mobile robots finding more and more indoor applications in hospitals, museums, airports etc., indoor localisation has become all the more essential. This paper aims to demonstrate the functioning of a LiFi based localization system by the means of simulation. The mathematical time series model of NRZ-OOK modulation and the optical power distribution are prepared in MATLAB in order to evaluate the functioning of the proposed system. To validate and test the performance of the same, a white LED of wavelength 550nm is set-up as the transmitter in our system and simulated using the Optisystem software, both with and without the presence of ambient light noise. A maximum speed of 10Gbps with a BER (Bit Error Rate) of 5.15585×10 −11 is achieved for a link range of 1m in ideal conditions. This work also proposes an optimal filtering scheme that provides a high Q factor and a low BER for VLC (Visible Light Communication) systems, even in the presence of ambient light noise.
