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Internet of things (IoT) is a system of "Smart Devices" used over the internet through the world were it will sense and connect with our environment and involves with humans and it is a user friendly for the users and another system devices. Air pollution is one of the major issue of our generations. The rate of air pollution gradually increases with time by more number of things like increasing in vehicles, increasing in population, urbanization, industrialization which helps up in harmful disease effect on human life by directly effecting health of country exposed to it .Air quality gets decreases when enough amount of toxic gases in the atmosphere like co2,alchol,smoke,nh3,benzen and no2.In order to survey we are deploying IoT based pollution monitoring system Were it will capture all the air quality over an cyber space .existing monitoring system and no sensitivity and need technical analysis. So improvement of monitoring system are requires. To Control the issue of trending system. We propose a pollution monitoring system it will detect the percentage of pollution in our environment it shows the average density of pollution released by vehicles or any industries etc.If the pollution level get cross by average it may cause more danger to our human health.

Demand forecasting, a crucial aspect of anticipating future customer needs, involves using historical data to predict trends. With the rise of artificial intelligence (AI), companies are increasingly turning to machine learning algorithms to enhance accuracy in forecasting compared to traditional methods. This article delves into the application of machine learning algorithms in demand forecasting, specifically within the realm of supply chain management, addressing both long-term (4-5 years) and short-term (3-4 months) scenarios. The primary focus is on improving prediction accuracy by employing feature selection algorithms and various machine learning and deep learning approaches. Utilizing diverse algorithms, such as time series, traditional machine learning, and advanced deep learning techniques, the study aims to forecast demand for different timeframes. Evaluation metrics like Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) are employed to assess the effectiveness of each model. The primary goal is to pinpoint the most effective algorithm tailored to a specific dataset. This empowers companies to make well-informed decisions and enhance their supply chain operations by leveraging precise demand forecasts. The results of this study hold the potential to empower decision-makers and practitioners by enhancing their forecasting capabilities. By integrating both forecast periods a more comprehensive and robust supply chain strategy is ensured. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The requirement of the labeled dataset in the source domain makes the Cross Domain Sentiment Classification (CDSC) task complicate in the situation when the dataset is labeled manually. OBJECTIVES: To overcome the dependency of CDSC tasks on manual labeling of the dataset by proposing a polarity detection task. METHODS: We have proposed the CDSC-PDT method that is the polarity Detection Task (PDT) followed by the CDSC task. The proposed PDT task extracts the polarity of reviews from the source domain using the contextual and relevancy information of words in documents and this automatic labeled dataset is further used to train classifiers to make the further classification. RESULTS: Proposed method is comparable to the traditional learning method giving the highest precision 85.7%. CONCLUSION: The proposed method does not need to manually label the documents in either of the domain (source or target), hence it overcomes the human intervention and is also time saving and cheap process, unlike traditional CDSC tasks. [ABSTRACT FROM AUTHOR]

The presence of defect data related to different projects leads to cross-project defect prediction an open issue in the field of research in software engineering. In cross-project defect prediction, the source and the target projects are different. The prediction model is trained by using the data sources of the different projects and then it is tested on the target data source. The data source from the varying projects leads to a highly imbalanced source dataset. The performance of the predictive model degrades due to this imbalance nature of the dataset. This is termed as the class imbalance problem in machine learning. This paper conducts an empirical analysis in a bi-fold manner. It evaluates whether data sampling techniques can handle the class imbalance problem and improve the performance of the predictive model for cross-project defect prediction (CPDP). Secondly, it also evaluates whether the results of CPDP after data sampling are comparable to within project defect prediction (WPDP). Ensemble learning classifiers are used as the predictive model over 12 publically available object-oriented project datasets. The experimental results infer that SMOTE oversampling can be applied to overcome the problem of class imbalance on CPDP. It also gives comparable results to WPDP with statistical significance. [ABSTRACT FROM AUTHOR]

BACKGROUND: The availability of defect related data of different projects leads to cross project defect prediction an open issue. Many studies have focused on analyzing and improving the performance of Cross project defect prediction. OBJECTIVE: The multinomial classification has not been much explored. This paper instanced on multiclass/multinomial classification of defect prediction of cross projects. METHOD: The ensemble based statistical models - Gradient Boosting and Random Forest are used for classification. An empirical study is carried out to determine the performance of multinomial classification for cross project defect prediction. Depending on the number of defects, class level information is classified into one of three defined multiclass class 0, class 1, and class 2. RESULTS & CONCLUSION: Major outcome of the paper concludes that multinomial/multiclass classification is applicable on cross project data and has comparable results to within project defect data. [ABSTRACT FROM AUTHOR]