Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets.

Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algorithms may not perform as well when applied to categorical data. This article uses machine learning algorithms to predict C&D waste generation from a dataset, as a way to improve the accuracy of waste management in C&D facilities. These datasets include categorical (e.g., region, building structure, building use, wall material, and roofing material), and continuous data (particularly, gloss floor area), and a random forest (RF) algorithm was used. Results indicate that RF is an adequate machine learning algorithm for a small dataset consisting of categorical data, and even with a small dataset, an adequate prediction model can be developed. Despite the small dataset, the predictive performance according to the demolition waste (DW) type was R (Pearson's correlation coefficient) = 0.691-0.871, R2 (coefficient of determination) = 0.554-0.800, showing stable prediction performance. High prediction performance was observed using three (for mortar), five (for other DW types), or six (for concrete) input variables. This study is significant because the proposed RF model can predict DW generation using a small amount of data. Additionally, it demonstrates the possibility of applying AI to multi-purpose DW management.

The effects of cognitive rehabilitation on Alzheimer's dementia patients' cognitive assessment reference diagnosis system performance based on level of cognitive functioning.

[Purpose] The purpose of this study is to apply cognitive rehabilitation according to Alzheimer's disease (AD) patients' level of cognitive functioning to compare changes in Cognitive Assessment Reference Diagnosis System performance and present standards for effective intervention. [Subjects] Subjects were 30 inpatients diagnosed with AD. Subjects were grouped by Clinical Dementia Rating (CDR) class (CDR-0.5, CDR-1, or CDR-2, n = 10 per group), which is based on level of cognitive functioning, and cognitive rehabilitation was applied for 50 minutes per day, five days per week, for four weeks. [Methods] After cognitive rehabilitation intervention, CARDS tests were conducted to evaluate memory. [Results] Bonferroni tests comparing the three groups revealed that the CDR-0.5 and CDR-1 groups showed significant increases in Delayed 10 word-list, Delayed 10 object-list, Recognition 10 object, and Recent memory performance compared to the CDR-2 group. In addition, the CDR-0.5 group showed significant decreases in Recognition 10 word performance compared to the CDR-1 group. [Conclusion] Cognitive rehabilitation, CDR-0.5 or CDR-1 subjects showed significantly greater memory improvements than CDR-2 subjects. Moreover, was not effective for CDR-2 subjects.

The effects of computer-assisted cognitive rehabilitation on Alzheimer's dementia patients memories.

[Purpose] The purpose of the present study was to conduct Computer-Assisted Cognitive Rehabilitation (COMCOG) to examine the effects of COMCOG on Alzheimer's dementia patients' memories. [Subjects] Thirty-five patients diagnosed with Alzheimer's dementia received COMCOG for 30 minutes per day, five days per week for four weeks. [Methods] Before and after the COMCOG intervention, subjects' cognitive functions were evaluated using the Cognitive Assessment Reference Diagnosis System (CARDS) and Mini-Mental State Examination-Korea (MMSE-K) test. [Results] According to the results of the evaluation, among the CARDS scores of the subjects who received COMCOG, the scores of the delayed 10-word list, delayed 10-object list, recognition 10-object, and recent memory significantly increased while the scores of recognition 10-word significantly decreased after intervention compared to before intervention. In addition, among the MMSE-K items, the orientation, registration, and recall showed significant increases. [Conclusion] Based on these results, delay in the progress of memory deterioration can be expected when COMCOG is conducted for Alzheimer's dementia patients who show declines in cognitive functions.