We examine the interplay of optimization and machine learning. Great progress has been made in machine learning by cleverly reducing machine learning problems to convex optimization problems with one or more hyper-parameters. The availability of powerful convexprogramming theory and algorithms has enabled a flood of new research in machine learning models and methods. But many of the steps nece...

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

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Although machine learning is becoming commonly used in today’s software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple "right/wrong" judgments. If the users themselves could work hand-in-hand with machine learning systems, the users’ understanding and trust of the system could improve and the accuracy of learning syst...

Data industry has grown exceedingly, now finding its applications in the fields no one would have imagined a few decades ago. science, Machine learning and Artificial intelligence major impacts national economy through million ways. Today, we will explore of their untapped uses: Traffic! This research document provide methods information regarding possible integration Radio-Frequency Identifica...

Abstract Recently, the use of machine learning in meteorology has increased greatly. While many methods are not new, university classes on largely unavailable to students and required become a meteorologist. The lack formal instruction contributed perception that “black boxes” thus end-users hesitant apply their everyday workflow. To reduce opaqueness lower hesitancy toward meteorology, this pa...

Within this Paper, a concept of machine learning strategies suggested. In this investigation to address the design issues in WSNs is introduced. As can be viewed within this paper, countless endeavors have induced up to now; several layout issues in wireless sensor networks have been remedied employing numerous machine learning strategies. Utilizing machine learning based algorithms in WSNs nee...