Narrow Results

Day after day, individuals all across the world come up with fresh ideas for improving the future. Several intriguing discoveries and ideas paved the way for a new age of electronics, telecommunications, business, and medicinal innovation. Using less resources, greater changes in these domains can be achieved. As improving efficiency and productivity allow exponential development in some areas of the global economy, artificial intelligence (AI) and machine learning (ML) is being adopted by a growing number of individuals, corporations, and governments. Since real-world scenarios influence imprecise and unpredictable situations, fuzzy systems have become an inescapable machine learning aspect. Thus, this research presents a qualitative analysis of the significance of fuzzy machine learning systems like fuzzy support vector machines (FSVM) in various physical domains. Based on this analysis, this research extends with the proposal of a fuzzy machine learning-based framework for two different physical domains: (1) intelligent transportation and (2) ecological risk handling. Thereby, this state-of-the-art approach presents fault detection using FSVM (FD-FSVM) model in the intelligent transportation domain. In ecological risk handling, this study proposes an improved FSVM for risk level classification (FSVM-RLC) approach, which uses the persistent organic pollutants data for training and validation. These two domains are chosen randomly to evaluate the classification performance of the fuzzy machine learning algorithms based on their mean absolute error, accuracy, precision, recall, and F1 score. Apart from this, the mean square error and mean absolute error are measured. Compared to existing machine learning models, the individual results of these two approaches show the highest performance. Furthermore, this fuzzy integrated machine learning technique kept consistency in both domains by giving 98.2% and 97.89% accuracy levels for FD-FSVM and FSVM-RLC, respectively.

Based on the data of heavy metals (including total Hg, Cd, Pb, Zn, Cu, Cr and As) in 17 samples in the sediment of Lai Zhou Bay in the year of 2014, the distribution of heavy metals in the sea area and degree of sediment pollution were researched. With Hakanson’s method, potential ecological risk of heavy metals in the sediment of Lai Zhou Bay was primarily assessed. The results demonstrated that the horizontal distribution of total Hg, As, Cd, Pb decreasing from center of Lai Zhou Bay to its west and south and the Yellow River Estuary. The horizontal distribution of Zn increased from northwest to southeast, Cu decreased from offshore to inshore, Cr decreased from inshore to the center of Lai Zhou Bay. Parameter of pollution metals on sediment was lower than 1, the value of RI was among 233.3˜35.1, indicating that the degree of pollution of heavy metals on the sediment and its ecological risk were low. The pollutants in the order of impacting the extent of ecological risk were Cd >Hg >As> Cu> Pb>Cr >Zn.

In order to understand potential ecological risk by application of biochar to soil, sewage sludge, cow manure, corn cob were selected as raw materials to prepare biochar under different conditions, and the content of heavy metals of As, Cd, Cr, Pb in biochar were analyzed. The results show that 4 heavy metals exist in all biochars with different concentration, which depend upon feedstocks and temperature obviously. Compared with the 2nd standard of environmental quality standards for soils of China, accumulated amount of heavy metals in soil by application of biochars is limited regardless of low or high application rate in field. The compositive index (RI) of potential ecological risk of 4 heavy metals indicates low risk by applying different biochar to soil. However, the potential ecological risk of Cd in soil through application of biochar from sewage sludge is at level of considerable risk.