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This research designs and develops a software innovation for Pattavia pineapple cultivation and productivity distribution planning to increase income for farmers. This research formulated and introduced an innovative machine learning (ML) model, the new model is called a new intelligent particle swarm optimization algorithm with extreme learning machine (NIPSO-ELM), to forecast Pattavia pineapple productivity with a notable degree of precision and dependability. In this work, the artificial neural network (ANN) and the standard ELM was built, and assessed for its ability to forecast the productivity of Pattavia pineapples. The findings indicate that the ELM neural network is an innovative model characterized by its straightforward architecture and exceptional performance. Moreover, the utilization of particle swarm optimization (PSO), ant colony optimization (ACO) and the NIPSO algorithms significantly enhanced ELM performance when forecasting the productivity of Pattavia pineapples. The NIPSO-ELM model emerged as the most optimal ML model for accurately, reliably and stably forecasting the productivity of Pattavia pineapples in practical scenarios. The most optimal NIPSO-ELM models for the Loei provinces, Thailand exhibit the following performance metrics: RMSE = 304.36389, MAE = 243.29531, MAPE = 0.03753 and MASE = 0.93157. The most optimal NIPSO-ELM models for the Nong Khai provinces, Thailand exhibit: RMSE = 304.57352, MAE = 244.67834, MAPE = 0.03756 and MASE = 0.93296, respectively. [ABSTRACT FROM AUTHOR]

Criminal cases are social problems that concern the public order and good morals of the citizens as a whole. Research in criminology has recently focused on finding the root causes of criminal cases in order to identify the factors accelerating crimes and to find preventive methods. Thus, the study on the factors relating to the occurrence of crimes would benefit policy makers in dealing with crimes. This research focuses on a forecasting model of the criminal related factors in the Upper Northeastern Provinces of Thailand by using a New Intelligent Hybrid Artificial Neural Network (NIHANN) with a Cuckoo Search Algorithm (CS). The new CS-NIHANN algorithm, which is a combination of the CS with the NIHANN, is used in this research. In addition, the research selected relevant parameters that related to criminal cases in the Upper Northeastern Provinces of Thailand. The comparison of performances of the forecasting model for historical forecast part was calculated by Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Correlation Coefficient (R). The result shows that the CS-NIHANN algorithm trains fast, can obtain the global optimal solution, and has a good generalization performance for Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO). Furthermore, the result shows that the CS-NIHANN algorithm has a faster convergence than the CS-ANN algorithm in calculating an optimal solution to the criminal cases. [ABSTRACT FROM AUTHOR]