Deep Learning for Link Prediction in Dynamic Networks using Weak Estimators

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks

A nutrient recommendation system for soil fertilization based on evolutionary computation

In agricultural production, soil characteristics play a vital role in maintaining fertility by allowing crops to develop better through root nutrition with minimal energy inputs. Nitrogen (N), Phosphorus (P), and Potassium (K) are all important nitrogen fertilizers extensively utilized in crops to supply a sufficient level of nutrients and keep their production level high. However, the application is generally limited to specific crops because of the global variability in these essential nutrients. Stability in fertilizer application, growth, and root growth rate increases crop fertility and crop production. To predict the suitable nutrients for different crops and provide nutrients recommendations by analyzing the crop fertility and yield production, this paper proposes nutrient recommendations through an improved genetic algorithm (IGA) that uses time-series sensor data and recommends various crop settings. A neighborhood-based strategy is then presented to handle exploration and exploitation for optimizing the parameters to obtain the maximum yield. The method can expand knowledge by using the population exploration strategy. The final recommendation is made by using the similarity between recommended patterns and real-time sensor data. With time, crop fertility decreases due to the low level of nutrients. This crop model will help to increase yield by analysis of the seasonal fertility performance of the soil. The proposed method is also a useful tool to improve soil fertility performance by providing the nutrient recommendation of optimal conditions for crop development. Experimental results show that the proposed model can recommend optimizing patterns and increasing the yearly yield efficiently. The method can help identify the region to assess crop suitability under certain nutrients levels and give insight into nutrient suitability assessments concerning specific crops in a climate-changing world.publishedVersio

Fast identification of high utility itemsets from candidates

High utility itemsets (HUIs) are sets of items with high utility, like profit, in a database. Efficient mining of high utility itemsets is an important problem in the data mining area. Many mining algorithms adopt a two-phase framework. They first generate a set of candidate itemsets by roughly overestimating the utilities of all itemsets in a database, and subsequently compute the exact utility of each candidate to identify HUIs. Therefore, the major costs in these algorithms come from candidate generation and utility computation. Previous works mainly focus on how to reduce the number of candidates, without dedicating much attention to utility computation, to the best of our knowledge. However, we find that, for a mining task, the time of utility computation in two-phase algorithms dominates the whole running time of these algorithms. Therefore, it is important to optimize utility computation. In this paper, we first give a basic algorithm for HUI identification, the core of which is a utility computation procedure. Subsequently, a novel candidate tree structure is proposed for storing candidate itemsets, and a candidate tree-based algorithm is developed for fast HUI identification, in which there is an efficient utility computation procedure. Extensive experimental results show that the candidate tree-based algorithm outperforms the basic algorithm and the performance of two-phase algorithms, integrating the candidate tree algorithm as their second step, can be significantly improved