Prediction of College Students’ Employment Rate Based on Gray System

College students’ employment is affected by many factors such as economy and policy, which makes the prediction error of college students’ employment rate large. In order to solve this problem, a prediction method of college students’ employment rate based on the gray system is designed. Firstly, it analyzes the current research status of college students’ employment rate prediction, finds out the causes of errors, then collects the historical data of college students’ employment rate, fits the change characteristics of college students’ employment rate through the gray system, and establishes the prediction model of college students’ employment rate. Finally, the simulation test is realized by using the employment rate data of college students. The results show that the gray system can reflect the change characteristics of college students’ employment rate and obtain high-precision college students’ employment rate prediction results. The prediction error is less than that of other college students’ employment rate prediction methods. We achieved an average accuracy of 95.22% as compared to 92.3% and 87.7% of other proposed systems. The prediction results can provide some reference information for the university employment management department.

Improvement of gray system model using particle swarm optimization

An improvement of the traditional gray system model, GM(1,1), to enhance forecast accuracy, has been realized using the particle swarm optimization (PSO) algorithm. Unlike the GM(1,1) which uses a fixed adjacent neighbor weight for all data sets, the proposed PSO-improved model, PSO-GM(1,1), determines an optimal adjacent neighbor weight, based on the presented data set. This optimal adjacent neighbor weight so determined is the principal factor that enhances forecast accuracy. The performance of the proposed model was evaluated using generated monotonic increasing and decreasing data sets as well as measured energy consumption data for a laptop computer, desktop computer, printer, and photocopier. The performance of PSO-GM(1,1) was compared with that of GM(1,1), and two other models in literature that sought to improve the performance of GM(1,1). The PSO-GM(1,1) outperformed the traditional model and the two other models. For the monotonic increasing data, the mean absolute percentage error (MAPE) for the proposed model was 0.007% as against a MAPE value of 20.383% for the GM(1,1). For the monotonic decreasing data, the PSO-GM(1,1) again outperformed GM(1,1), yielding a MAPE of 0.057% compared to a value of 13.407% for the traditional model. For the measured laptop computer energy data, the obtained MAPE for the PSO-GM(1,1) was 0.675% while the values for the two models were 4.052% and 2.991%. For the measured desktop computer energy data, the obtained MAPE for the PSO-GM(1,1) was 0.0018% while the values for the two models were 0.0018% and 1.163%. For the data associated with the printer, the MAPEs were 8.414% for the PSO-GM(1,1), 20.957% for the first model and 9.080% for the second model. For the measured photocopier energy data, the obtained MAPE for the PSO-GM(1,1) was 0.901% while the values for the two models were 3.799% and 0.943%. Thus, the proposed PSO-GM(1,1) greatly improves forecast accuracy and is recommended for adoption, for forecasting.

Forecast Research on the Proportion of Clean Energy Consumption in Total Energy Consumption Based on GM(1,1) Model

Clean energy can not only alleviate environmental problems but also contribute to rapid and sustainable development. The gray system is based on the sequence operator to process the original data and mine the law of change. GM(1,1) is a specific method of mining data, by building a GM(1,1) model to accumulate and generate data, the randomness of the data can be weakened and its regularity can be revealed. Here we use the GM(1,1) model to dynamically predict the future consumption rate of clean energy. This research not only provides data support for China’s green development, but also provides suggestions for improvement based on actual conditions.

Energy Efficient Multi-Path Routing Algorithm Using Gray System Theory for Improvement of Routing and Network Stability in Internet of Things

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routes with separate links. One of such protocols is AOMDV routing protocol. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named RMPGST-IoT is introduced which chooses the routes with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed RMPGST-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to throughput, packet receiving rate, packet loss rate, average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed RMPGST-IoT compared to the ERGID and ADRM-IoT approaches.

A New Energy-Efficient Multipath Routing in Internet of Things Based on Gray Theory

Internet of Things (IoT) is a network of smart things. It indicates the ability that the mentioned physical things transfer information with each other. The characteristics of these networks, such as topology dynamicity and energy constraint, make the routing problem a challenging task in these networks. Traditional routing methods could not achieve the required performance in these networks. Therefore, developers of these networks have to consider specific routing methods in order to satisfy their requirements. One of the routing methods is utilization of the multipath protocols in which data are sent to its destination using multiple routes with separate links. One of such protocols is AOMDV routing protocol. In this paper, AOMDV is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, Ad hoc On-demand Multipath Distance Vector (AOMDV) packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal-to-noise ratio can also be considered during selection of the best route. The proposed method named RMPGST-IoT is introduced which chooses the routes with highest rank for concurrent transmission of data, using a specific method based on the gray system theory. In order to evaluate the results, the proposed Routing Multipath based on Gray System Theory (RMPGST)-IoT method is compared to the Emergency Response IoT based on Global Information Decision (ERGID) and Ad hoc Delay-aware Distributed Routing Model (ADRM)-IoT approaches in terms of throughput, packet receiving rate, packet loss rate, average remaining energy, and network lifetime. The results demonstrate that the performance of the proposed RMPGST-IoT is superior to that of ERGID and ADRM-IoT approaches.