Analisis Perbandingan Model Matrix Factorization dan K-Nearest Neighbor dalam Mesin Rekomendasi Collaborative Berbasis Prediksi Rating

Rating is a form of assessment of the likes or dislikes of a user or customer for an item. Where the higher the rating number given, the item is preferred by customers or users. In the recommendation engine, a set of ratings can be predicted and used as an object to generate a recommendation by the Collaborative Filtering method. In the Collaborative Filtering method, there is a rating prediction model, namely the Matrix Factorization and K-Nearest Neighbor models. This study analyzes the comparison of the two prediction models based on the value of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the prediction results generated using the movielens film rating dataset. From the analysis and testing results, it was found that MAE = 0.6371 and RMSE = 0.8305 for the Matrix Factorization model, while MAE = 0.6742 and RMSE = 0.8863 for the K-Nearest Neighbor model. The best model is Matrix Factorization because the MAE and RMSE values are lower than the K-Nearest Neighbor model and have the closest predicted rating results from the original rating value.

Neural Network Based Filtering Method for Cancer Detection

Objective: The growth of anomalous cells in the human body in an uncontrolled manner is characterized as cancer. The detection of cancer is a multi-stage process in the clinical examination. Methods: It is mainly involved with the assistance of radiological imaging. The imaging technique is used to identify the spread of cancer in the human body. This imaging-based detection can be improved by incorporating the Image Processing methodologies. In image processing, the preprocessing is applied at the lower-level abstraction. It removes the unwanted noise pixel present in the image, which also distributes the pixel values based on the specific distribution method. Results: Neural Network is a learning and processing engine, which is mainly used to create cognitive intelligence in various domains. In this work, the Neural Network (NN) based filtering approach is developed to improve the preprocessing operation in the cancer detection process. Conclusion: The performance of the proposed filtering method is compared with the existing linear and non-linear filters in terms of Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

An Effective Harmonic Suppression Method Based on Linear Active Disturbance Rejection Control for Copper Electrowinning Rectifier

Because the harmonics in the production process of copper electrowinning have an important impact on the electrical energy consumption, it is necessary to suppress the harmonics effectively. In this paper, a copper electrowinning rectifier with double inverse star circuit is selected as a study object in which a large number of harmonics mainly including the 5th, 7th, 11th, and 13th harmonics are generated and injected back into the power grid. The total harmonic distortion rate of the power grid is up to 29.19% before filtering. Therefore, a method combining the induction filtering method and the active filtering method is proposed to carry out comprehensive filtering. Simulation results demonstrate that the total harmonic distortion rate of the system decreases to 4.20%, which indicates that the proposed method can track the corresponding changes of harmonics when the load changes in real time and filter them out. In order to ensure and improve the effect of active filter, a current harmonic tracking control method based on linear active disturbance rejection control is proposed. Simulation results show that the total harmonic distortion rate decreases to 3.34%, which is also lower than that of hysteresis control. Compared with the conventional single filtering method, the new filtering method combining induction filtering with active filtering based on linear active disturbance rejection control in the copper electrowinning rectifier has obvious advantages.