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Economic progress has been founded on environmental pressure by generating all types of environmental harm, such as an increase in greenhouse gases in the atmosphere and significant climate change, which has forced people to reflect. Building a people-oriented, comprehensive, coordinated system to compile experience and lessons assists the government in formulating appropriate policies according to the meaning of the scientific development viewpoint. Based on the above, this research designs Cost–Benefit Decision Tree (CBDT) Algorithm for the economic protection development level using a Decision Tree (DT) and Ensemble Algorithm. In the design of ML Algorithm, this research work initially introduces the Decision Tree model for economic protection evaluation by emphasizing its value as a basic machine-learning framework for fully examining many aspects of economic protection development. Second, it investigates the critical role of Ensemble Algorithms, such as Random Forest in augmenting the capabilities of DT. Third, the paper focuses on the development of a complete Economic Protection Evaluation Index System using the DT Algorithm. To provide a robust, scientifically sound, and practicable framework for evaluating economic protection, this approach integrates scientificity, comparability, systematization, hierarchy, relative decoupling, and data availability principles. Finally, the study constructs the suggested ML Algorithm using the insights from the preceding parts. This algorithm combines the Decision Tree and Random Forest models to generate a single, effective tool for evaluating economic development levels. The experimental findings suggest that the proposed hybrid assessment technique is effective across several dimensions. The proposed CBDT demonstrated its capacity to adapt to changing data circumstances in evaluating low-carbon efficiency using real-world data from 30 major Chinese cities. Furthermore, the algorithm regularly beats rival techniques regarding efficiency improvements, with an average improvement of 12.5% across all cities. Notably, it outperforms other algorithms regarding computational efficiency, with a quicker execution time and an exceptional accuracy rate of 93.2%. The proposed ML Algorithm improves decision-making in the economic protection development level field by effectively balancing cost and benefit considerations. It will improve accuracy and efficiency to outperform existing approaches, thereby advancing economic protection evaluation and decision-making processes in this domain. [ABSTRACT FROM AUTHOR]

It is hard to come up with a strong learning algorithm with high cross-media retrieval accuracy, but finding a weak learning algorithm with slightly higher accuracy than random prediction is simple. This paper proposes an innovative Bagging-based cross-media retrieval algorithm (called BCMR) based on this concept. First, we use bootstrap sampling to take a random sample from the original set. The amount of sample abstracted by bootstrapping is set to be the same as the amount of sample abstracted by the original dataset. Secondly, 50 bootstrap replicates are used to train 50 weak classifiers. In our experiments, we used homogeneous individual classifiers and eight different baseline methods. Last but not least, we generate the final strong classifier from 50 weak classifiers using the sample voting integration strategy. Using collective wisdom, we can get rid of bad decisions, giving the integrated model a much better generalization ability. Extensive experiments on three datasets show that BCMR can significantly improve cross-media retrieval accuracy. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms, such as neural networks, random forest, and more recent deep learning, are illustrating their utility for waste recycling. The increasing computational power of ML makes waste generation prediction, even at municipal level, possible with satisfying accuracy. ML is so critical and efficient and yet it is severely under-researched in recycling business. Also, the ML application in the recycling business is still a niche area judged by the limitations in its literature sources, the research domains, the ML algorithms' use and benefits involved or reported in the literature. To unlock the value of ML in recycling business, this paper reviewed 51 related articles systematically and presents the current obstacles and future directions in applying ML to waste recycling industries. [ABSTRACT FROM AUTHOR]