Details about the paper is that there does not need to be a detailed report on all areas just explain in one or two paragraphs to cover each area explained in the details below. No long conclusion and a brief intro. Also, snapshots of locations or statistics of graphs or maps.
The research paper is focusing on current Rebates, Tax incentive such as 179D, R&D tax Credit and Energy Procurement for solar, HAVAC, Wind, and LED. The facts are only targeted forNorth America and not international or global at all. Looking for specific states that could be targeted in a new market segment for benefiting on rebates and tax incentives that would move to green energy solutions listed above.
eatures, namely high-level features and low-level features, the former expressing semantic properties of media content that are obtained from meta-information from databases, lexicons, reviews, or news articles, and the latter being extracted directly from the media file itself, typically representing design aspects of a movie (such as lighting, colors, and motion). The researchers found that recommendations based on low-level stylistic visual features are better than recommendations based on high level semantic features, and that low-level features extracted from trailers can be used as an alternative to features extracted from full-length movies in building content-based recommender systems. The collaborative filtering (CF) approach produces recommendations of items based on patterns of ratings (Koren & Bell, 2015). Using a neighborhood approach, the objective is to find a set of other users whose ratings are similar to the user’s ratings, in order to infer that preferences of the neighborhood are also applicable to the user. There are two approaches to CF: user-user and item-item, the latter of which is considered to perform better (Lescovec et al., 2016). One approach to CF that has been popularized by the recommendation system of Netflix is matrix factorization (MF), which entails that a large matrix of ratings can be expressed as a product of smaller matrices in order to save storage space (Serrano, 2018). In extension of their previous work on content-based recommendation, Deldjoo, Elahi, and Cremonesi (2016) propose a recommendation system based on Factorization Machines (a combination of Support Vector Machines and MF) and low-level stylistic features. RSs based on CF often have to be supplemented with side information to maintain a rich set of high-level descripti>GET ANSWER