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The subject of airfare is examined in this paper. As a result, a collection of factors that characterize a typical flight are selected under the presumption that they have an impact on airline ticket costs. The price of a plane ticket is influenced by the length of the trip, the location, the schedule, and several other factors, like holidays or vacations. Therefore, many people will surely save time and effort by having a basic awareness of airline expenses prior to making trip arrangements. The performance of the seven different machine learning (ML) models used to anticipate the price of airline tickets is compared after three datasets were analysed to acquire insight into airline fares. The objective is to investigate the factors that influence flight prices. The data can then be used to build a system that can predict how much a flight will cost.

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.

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.

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.