This paper presents the design, implementation, and evaluation of an IoT-based robotic system for mapping and monitoring indoor air quality. The primary objective was to develop a mobile robot capable of autonomously mapping a closed environment, detecting concentrations of CO$_2$, volatile organic compounds (VOCs), smoke, temperature, and humidity, and transmitting real-time data to a web interface. The system integrates a set of sensors (SGP30, MQ-2, DHT11, VL53L0X, MPU6050) with an ESP32 microcontroller. It employs a mapping algorithm for spatial data acquisition and utilizes a Mamdani fuzzy logic system for air quality classification. Empirical tests in a model room demonstrated average localization errors below $5\%$, actuator motion errors under $2\%$, and sensor measurement errors within $12\%$ across all modalities. The contributions of this work include: (1) a low-cost, integrated IoT robotic platform for simultaneous mapping and air quality detection; (2) a web-based user interface for real-time visualization and control; and (3) validation of system accuracy under laboratory conditions.

Projecting sea-level change in various climate-change scenarios typically involves running forward simulations of the Earth's gravitational, rotational and deformational (GRD) response to ice mass change, which requires high computational cost and time. Here we build neural-network emulators of sea-level change at 27 coastal locations, due to the GRD effects associated with future Antarctic Ice Sheet mass change over the 21st century. The emulators are based on datasets produced using a numerical solver for the static sea-level equation and published ISMIP6-2100 ice-sheet model simulations referenced in the IPCC AR6 report. We show that the neural-network emulators have an accuracy that is competitive with baseline machine learning emulators. In order to quantify uncertainty, we derive well-calibrated prediction intervals for simulated sea-level change via a linear regression postprocessing technique that uses (nonlinear) machine learning model outputs, a technique that has previously been applied to numerical climate models. We also demonstrate substantial gains in computational efficiency: a feedforward neural-network emulator exhibits on the order of 100 times speedup in comparison to the numerical sea-level equation solver that is used for training.

We've finished the Data Preprocessing part and now it's time to start making Machine Learning Models. We're are going to start with the Simple Linear Regression Model and I will show you how to do it in R. To Learn how to do Simple Linear Regressions in Python, go Here. Before we begin, we need to understand our data and the problem we are trying to solve. I have prepared the dataset that we are going to be using in this tutorial. However, feel free to use any dataset that you may have, and see if you'll get similar results.

We are going to learn how to implement a Multiple Linear Regression model in R. This is a bit more complex than Simple Linear Regression but it's going to be so practical and fun. Multiple Linear Regression is a data science technique that uses several explanatory variables to predict the outcome of a response variable. A Multiple linear regression model attempts to model the relationship between two or more explanatory variables (independent variables) and a response variable (dependent variable), by fitting a linear equation to observed data. Every value of the independent variable x is associated with a value of the dependent variable y.

Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables. It is a widely-used technique for predicting the outcome of a continuous variable, and it is especially useful when you have a large amount of data. In this blog post, we will discuss the theory behind linear regression, how to perform it in practice, and some of its applications. The basic idea behind linear regression is to find a line that best fits a set of data points. The line is represented by the equation y mx b, where y is the dependent variable, x is the independent variable, m is the slope of the line, and b is the y-intercept.

In data science, machine learning algorithms are used to automate a system. In practice, there are mainly two types of problems -- i. Supervised, and ii. In the supervised problem, the training dataset is labelled. That means the algorithm has a target value. The supervised learning algorithm tries to predict the values like target values and optimizes its parameters accordingly.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. As we previously saw, the supervised part of machine learning is separated into two categories, and from those two categories, we have already ventured into the realm of classification and the many algorithms employed in the classification process.

Understanding what Artificial Intelligence is and learning how Machine Learning and Deep Learning power it, are overwhelming experiences. In ML, there is something called the "No Free Lunch" theorems which states that no machine learning algorithms works best for every problem, and it's particularly relevant for supervised learning. For example, you can't say that decision trees are always better than neural networks or vice-versa. There are various factors at play, such as the size and structure of your dataset. As a result, you should try many different algorithms for your problem, while using a hold-out "test set" of data to decide performance and select the winning algorithm.

We are going to learn how to implement a Multiple Linear Regression model in R. This is a bit more complex than Simple Linear Regression but it's going to be so practical and fun. Multiple Linear Regression is a data science technique that uses several explanatory variables to predict the outcome of a response variable. A Multiple linear regression model attempts to model the relationship between two or more explanatory variables (independent variables) and a response variable (dependent variable), by fitting a linear equation to observed data. Every value of the independent variable x is associated with a value of the dependent variable y.

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