Gas Leakage Detection System Using IoT And cloud Technology: A Review

In industries and other locations gas leakage causes number of negative health effects .so an early detection of gas leakage and alertness will reduce the damage and save human life’s. Gas leakage techniques, trends and sensors are constantly evolving, and it is important for developers and researchers to stay up-to-date on the latest advancements. This paper conducts a systematic literature review on current state of gas leakage detection using Internet of Things (IOT) and Cloud technology. It explores the various sensor-based and non-sensor based IOT systems available for gas leakage detection, and their relative advantages and disadvantages. Additionally, this review summarizes current trends and challenges in the field of gas leakage detection, and discusses future research directions for improving the reliability and accuracy of these systems. This literature review highlights the need for more efficient, cost effective, and scalable IOT-based solutions for gas leakage detection

IoT Based Gas Leakage detection System Using GPS

Gas leaks are a significant problem since they may have disastrous effects on infrastructure, human health, and greenhouse gas emissions, among other things. A method for early detection and alerting of gas leaks is required to reduce these dangers. In this project, we suggest a low-cost and efficient cloud-based Internet of Things (IoT) gas leak detection system for usage in residential, commercial, and industrial contexts. An Arduino Uno microcontroller, a Wi-Fi module, and a MQ 2 gas sensor make up the system. The sensor notifies the microcontroller when gas is detected, and the microcontroller analyses the information before sending it to the cloud through the IoT module. The cloud platform offers a user-friendly interface for managing and visualising data on gas leaks, and it also notifies customers through email and SMS. The system comes with a GPS module and a smoke detector for real-time position tracking and fire detection. The smoke detector detects smoke and sounds an alert, while the GPS module monitors the system’s location. These qualities enable the system to effectively reduce the dangers of gas leaks and fires while enhancing environmental safety

Density based smart traffic control system using canny edge detection algorithm along with object detection

It is urgently necessary to combine current advancements to work on the cutting edge inrush hour jam the executives, as urban congestion is one of the world’s biggest concerns. Existing methodologies, for example, traffic police and traffic lights are neither fulfilling nor viable. Consequently, a traffic management system that utilizes sophisticated edge detection and digital image processing to measure vehicle density in real time is developed in this setting. Computerizedimage processing should be used to detect edges. To extract significant traffic data from CCTV images, the edge recognition method is required. The astute edge finder outperforms other processes in terms of accuracy, entropy, PSNR (peak signal to noise ratio), MSE (mean square error), and execution time. There are a number of possible edge recognition calculations. In terms of reaction time, vehicle the board, mechanization, dependability, and overall productivity, this framework performs significantly better than previous models. Utilizing a few model images of various traffic scenarios, appropriate schematics are also provided for a comprehensive approach that includes image collection, edge distinguishing evidence, and green sign classification. Also recommended is a system with object identification and priority for ambulances stuck in traffic

An Efficient Novel Approach on Machine Learning Paradigmsfor Food Delivery Company through Demand Forecastıng in societal community

A food delivery business must be able to accurately forecast demand on a daily and weekly basis since it deals with a lot of perishable raw components. A warehouse that keeps too much inventory runs the danger of wasting items, whereas a warehouse that maintains too little inventory runs the risk of running out of stock, which might lead consumers to switch to your competitors. Planning for purchasing is essential because most raw materials are perishable and delivered on a weekly basis. For this issue to be resolved, demand forecasting is crucial. With the aid of historical data-driven predictive research, demand forecasting determines and forecasts future consumer demand for a good or service. By predicting future sales and revenues, demand forecasting assists the organisation in making more educated supply decisions. Regression methods like linear regression, decision trees, and Xgboost are used to overcome this issue