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Indoor air pollutants e.g., Carbon dioxide (CO2), Particulate Matter(PM)2.5, PM10, Total Volatile Organic Compounds (TVOC), etc. have a serious impact on human health. Out of these pollutants, CO2 is one of the most dominant one. Hence, proper monitoring and control of this pollutant is an important part of maintaining a healthy indoor. To make this happen, it is required to predict the next moment's indoor pollutant level at an acceptable accuracy range that ensures necessary steps can be taken beforehand to avoid a rise in the indoor pollution level for maintaining a healthy indoor all the time. It also helps people plan ahead, decreases the adverse effects on health and the costs associated. For this experiment, we have collected three months of real-life time-series data along with proper context information and have gone through feature engineering and feature selection process to create model ready data. Now, since the indoor CO2 concentration is dependent on multiple external factors (context data) which in turn is dependent on time, makes it a time-dependent function. Hence, to predict the indoor pollutant CO2, here we have used the time series forecasting model based on our collected data nature. This is a powerful tool and used in a wide range of research domains for predicting the next moment's target value. This model ready data is utilized in forecasting different time series models. According to our findings, among the selected popular time series models, the SARIMAX time series model is best suited for this forecasting problem which is utilizing indoor context information along with historical data (with 10 Fold Time-Series Split Cross-Validation score 0.907). We have achieved an average of RMSE 26.45 ppm (i.e., 97.36% accuracy) level based on a three day average for indoor pollutant prediction which is outperforming other relevant models in this domain. [ABSTRACT FROM AUTHOR]

Since its invention, the Web has evolved into the largest multimedia repository that has ever existed. This evolution is a direct result of the explosion of user-generated content, explained by the wide adoption of social network platforms. The vast amount of multimedia content requires effective management and retrieval techniques. Nevertheless, Web multimedia retrieval is a complex task because users commonly express their information needs in semantic terms, but expect multimedia content in return. This dissociation between semantics and content of multimedia is known as the semantic gap. To solve this, researchers are looking beyond content-based or text-based approaches, integrating novel data sources. New data sources can consist of any type of data extracted from the context of multimedia documents, defined as the data that is not part of the raw content of a multimedia file. The Web is an extraordinary source of context data, which can be found in explicit or implicit relation to multimedia objects, such as surrounding text, tags, hyperlinks, and even in relevance-feedback. Recent advances in Web multimedia retrieval have shown that context data has great potential to bridge the semantic gap. In this article, we present the first comprehensive survey of context-based approaches for multimedia information retrieval on the Web. We introduce a data-driven taxonomy, which we then use in our literature review of the most emblematic and important approaches that use context-based data. In addition, we identify important challenges and opportunities, which had not been previously addressed in this area. [ABSTRACT FROM AUTHOR]