In order to improve the hiding rate of network data privacy information and shorten the encryption and decryption time, the paper proposes a new network data privacy security aggregation method based on big data pattern decomposition. Firstly, the empirical mode decomposition method is used to divide the upper and lower envelopes and complete the decomposition processing of network data. Secondly, the Paillier algorithm is used to calculate public and private keys and encrypt network data privacy. Finally, the encrypted ciphertext and signature are sent to the aggregator for secure aggregation of network data privacy through bilinear pairing. The experimental results show that the method proposed in this paper can improve the hiding rate of privacy information in network data, and the hiding rate of privacy information is basically above 95%, and the encryption and decryption time of network data privacy is significantly shortened.

To solve the problems of low information collection accuracy, recall and evaluation accuracy in traditional methods, an evaluation method of online learning effectiveness for college students based on social network data mining is proposed. A web crawler architecture is utilised to mine social network data and collect information on university student network learning. Linear regression models are employed to filter indicator data and establish an index system for evaluating learning effectiveness. The weights of indicators are determined using the analytic hierarchy process, and fuzzy evaluation vectors are obtained by combining membership degree functions. An evaluation model is constructed using fuzzy evaluation vectors and fuzzy judgement method, and evaluation indicator data are input into the model to obtain scores for learning effectiveness. Experimental results demonstrate that the maximum accuracy of information collection for the proposed method is 98.1%, the maximum recall rate is 97.9%, the mean precision is 97.67%.

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Owing to the problems of high recommendation error and low recommendation satisfaction in traditional network multimedia English teaching resources recommendation methods, a collaborative filtering-based network multimedia English teaching resource recommendation method is proposed in this paper. Firstly, the entities contained in the network multimedia English teaching resources are extracted, and a hierarchical allocation model for resource recommendation is established. Then the semantic information of the network multimedia English teaching resources is mined. The hidden items of different semantic features are analysed, and different levels of concern are set. Finally, the distribution of the filtering model parameter under different difference levels is obtained by the collaborative filtering method, and the resources are recommended based on the knowledge graph fusion inference technology. The test results show that this method has a high level of satisfaction and low recommendation error for network multimedia English teaching resource recommendation.

In Infrastructure-as-a-Service (IaaS), the large number of providers and instances combined with the lack of transparency in understanding pricing policies hampers the choice for the best provider-plan-instance combination. An issue less explored by the scientific community is the physical location of datacentre and its influence on forming the final price of an instance. We developed a set of four mathematical models that estimates and represents in detail the prices of each of the three leading providers in the market and the canonical model. The modelling was empirically carried out. The verification and analysis of the individual models, and of the canonical model, were carried out through the comparison of the real-price and the price estimated by the model. Thanks to that, it was possible to see that the values obtained represented characteristics from computational resources in the composition of the provider plans.

With the development of science and technology, intelligent wheelchairs play a key role in the daily life of the elderly. Terrain identification technology plays an important role in the operation of intelligent wheelchairs. In this paper, a typical terrain identification method for road surfaces based on geometric properties computation is proposed. The identification method is divided into four steps. The first step is to collect 3D point cloud data of the terrain in the forward direction through the scanning device carried by the intelligent wheelchair. The second step is to calculate the geometric properties of the point cloud data. The third step is to identify discontinuous points in the point cloud data through the computation results of geometric properties. The fourth step is to identify the terrain according to the type and number of discontinuous points. The identification experiments show that the proposed method has high accuracy.

By analysing students' behavioural states, one can evaluate their level of participation, engagement and focus in learning activities. Therefore, this study designs a method for identifying student behaviour states in business English listening classes based on the SSD algorithm. The behaviour state features are input into the improved SSD algorithm model. Within the model, behavioural state features are gradually extracted through operations such as convolution and pooling, and the classification and recognition results of student behavioural states are output. In the experimental results, the Cohen's Kappa coefficient of the identification results obtained by this method can reach 0.974, and the global minimum and maximum values of the Matthews correlation coefficient are 0.80 and 0.95, indicating that the identification results of this method are effective.

With the development of science and technology, intelligent wheelchairs play a key role in the daily life of the elderly. Terrain identification technology plays an important role in the operation of intelligent wheelchairs. In this paper, a typical terrain identification method for road surfaces based on geometric properties computation is proposed. The identification method is divided into four steps. The first step is to collect 3D point cloud data of the terrain in the forward direction through the scanning device carried by the intelligent wheelchair. The second step is to calculate the geometric properties of the point cloud data. The third step is to identify discontinuous points in the point cloud data through the computation results of geometric properties. The fourth step is to identify the terrain according to the type and number of discontinuous points. The identification experiments show that the proposed method has high accuracy.

Solar Scheffler reflector is a parabolic reflecting dish that concentrates sunlight for various solar based applications. The major disadvantages of the traditional flat plate collector are lower temperature in the concentrator system as compared to the parabolic concentrator. Experimental analysis of solar Scheffler reflector has been carried out for the performance improvement in Indian climate. A dish of 27 m² size has been designed for the study. The maximum capacity of 15 lit received was used for the analysis, which serves as a hot water storage system. The thermal efficiency was estimated based on the reflector's incident radiation shape, operation time, dish area, altitude angle, the initial temperature of the water, and tilt angle of the reflector. Response surface method and artificial neural network were employed to analyse the Scheffler reflector's performance. A good agreement was observed between the model predicted and the measured efficiencies. This work made an effort to predict the Scheffler reflector's thermal efficiency for water heating. The statistical parameters were calculated between actual or experimental efficiency with the model predicted efficiency. It found that the model predicted response of both RSM and ANN shows a good agreement.