This paper presents a survey about learning content designs and various adaptation levels, in order to adapt the learners' necessities in e-learning environment. Normally, learners have different learning styles, cognitive traits, learning goals and varying progress of their learning over period of time, which affects the learner's performance while providing the same bundle of course to all learners. Hence, there is a need to create adaptive e-learning environment to offer appropriate learning content to all individuals. In general, the adaptation can be done based on learners' characteristics. Here, we explore the adaptation that can be done, not only based on learner context parameters but also on the learning content (learning object) and the configuration of e-learning environment. In this paper, we provide a detail review about the various levels of adaptation, learning object design and process for learning content design, learner context parameters, and models/components of e-learning; moreover, we analyze and portray the associations among the components, necessary to achieve the well-defined adaptation in e-learning environment.

Crossover is an important operation in the Genetic Algorithms (GA). Crossover operation is responsible for producing offspring for the next generation so as to explore a much wider area of the solution space. There are many crossover operators designed to cater to different needs of different optimization problems. Despite the many analyses, it is still difficult to decide which crossover to use when. In this article, we have considered the various existing crossover operators based on the application for which they were designed for and the purpose that they were designed for. We have classified the existing crossover operators into two broad categories, namely (1) Crossover operators for representation of applications -- where the crossover operators designed to suit the representation aspect of applications are discussed along with how the crossover operators work and (2) Crossover operators for improving GA performance of applications -- where crossover operators designed to influence the quality of the solution and speed of GA are discussed. We have also come up with some interesting future directions in the area of designing new crossover operators as a result of our survey.

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Node2vec, a network representation learning method and bagging SVM, a PU learning algorithm, are used in this work. Both representation learning and PU learning algorithms improve the performance of the system by 22% and 12.7% respectively. The meta-classifier performs better and predicts more reliable DDIs than the base classifiers.

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