Intelligent Optimization of Mine Environmental Damage Assessment and Repair Strategies Based on Deep Learning

In recent decades, financial quantification has emerged and matured rapidly. For financial institutions such as funds, investment institutions are increasingly dissatisfied with the situation of passively constructing investment portfolios with average market returns, and are paying more and more attention to active quantitative strategy investment portfolios. This requires the introduction of active stock investment fund management models. Currently, in my country's stock fund investment market, there are many active quantitative investment strategies, and the algorithms used vary widely, such as SVM, random forest, RNN recurrent memory network, etc. This article focuses on this trend, using the emerging LSTM-GRU gate-controlled long short-term memory network model in the field of financial stock investment as a basis to build a set of active investment stock strategies, and combining it with SVM, which has been widely used in the field of quantitative stock investment. Comparing models such as RNN, theoretically speaking, compared to SVM that simply relies on kernel functions for high-order mapping and classification of data, neural network algorithms such as RNN and LSTM-GRU have better principles and are more suitable for processing financial stock data. Then, through multiple By comparison, it was finally found that the LSTM- GRU gate-controlled long short-term memory network has a better accuracy. By selecting the LSTM-GRU algorithm to construct a trading strategy based on the Shanghai and Shenzhen 300 Index constituent stocks, the parameters were adjusted and the neural layer connection was adjusted. Finally, It has significantly outperformed the benchmark index CSI 300 over the long term. The conclusion of this article is that the research results can provide certain quantitative strategy references for financial institutions to construct active stock investment portfolios

The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Finance

The stock market is a crucial component of the financial market, playing a vital role in wealth accumulation for investors, financing costs for listed companies, and the stable development of the national macroeconomy. Significant fluctuations in the stock market can damage the interests of stock investors and cause an imbalance in the industrial structure, which can interfere with the macro level development of the national economy. The prediction of stock price trends is a popular research topic in academia. Predicting the three trends of stock pricesrising, sideways, and falling can assist investors in making informed decisions about buying, holding, or selling stocks. Establishing an effective forecasting model for predicting these trends is of substantial practical importance. This paper evaluates the predictive performance of random forest models combined with artificial intelligence on a test set of four stocks using optimal parameters. The evaluation considers both predictive accuracy and time efficiency.Comment: 10 pages, 8 figure

AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning

The development of artificial intelligence has significantly transformed people's lives. However, it has also posed a significant threat to privacy and security, with numerous instances of personal information being exposed online and reports of criminal attacks and theft. Consequently, the need to achieve intelligent protection of personal information through machine learning algorithms has become a paramount concern. Artificial intelligence leverages advanced algorithms and technologies to effectively encrypt and anonymize personal data, enabling valuable data analysis and utilization while safeguarding privacy. This paper focuses on personal data privacy protection and the promotion of anonymity as its core research objectives. It achieves personal data privacy protection and detection through the use of machine learning's differential privacy protection algorithm. The paper also addresses existing challenges in machine learning related to privacy and personal data protection, offers improvement suggestions, and analyzes factors impacting datasets to enable timely personal data privacy detection and protection.Comment: 9 pages, 6 figure

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset. The aim is to advance cybersecurity capabilities by identifying and mitigating threats more effectively. Both ensemble and non-ensemble machine learning methods, such as Random Forest, XGBoost, K Nearest Neighbor (KNN), and Neural Networks, are explored. Special emphasis is placed on the importance of data pre-processing techniques, particularly TF-IDF representation and Principal Component Analysis, in improving model performance. Results indicate that ensemble methods, particularly Random Forest and XGBoost, exhibit superior accuracy, precision, and recall compared to others, highlighting their effectiveness in malware detection. The paper also discusses limitations and potential future directions, emphasizing the need for continuous adaptation to address the evolving nature of malware. This research contributes to ongoing discussions in cybersecurity and provides practical insights for developing more robust malware detection systems in the digital era

Characterization of the complete mitogenome for the freshwater shrimp Exopalaemon modestus

Natural resources of the freshwater shrimp Exopalaemon modestus declined sharply in China in recent years. However, available genetic information for this shrimp is quite limited presently. In this study, the complete mitochondrial genome of E. modestus was firstly determined through Illumina sequencing. The mitogenome is 15,736 bp in length containing 13 protein-coding genes, 22 tRNA genes, two rRNA genes and one control region. Twenty-three of these genes were encoded by the H-strand and the remaining 14 ones by the L-strand. Additionally, compared to gene orders of other Caridea, a novel rearrangement of translocation between tRNAPro and tRNAThr was detected in E. modestus. The mitogenomic information obtained herein will be useful for future studies on population genetic and phylogenetic analyses of this shrimp

The complete maternal mitochondrial genome of Acuticosta chinensis (Bivalvia: Unionoida: Unionidae)

Acuticosta chinensis is an endemic freshwater mussel in China. The natural population of A. chinensis has dramatically declined due to water pollution, overexploitation and habitat destruction. In the present study, the complete maternal mitochondrial genome of A. chinensis was sequenced and annotated (GenBank Accession No. MF687347). The circular mitogenome is 15,653bp in length. There are 13 protein-coding genes, 22 tRNA genes and 2 rRNA genes (16S rRNA and 12S rRNA). Phylogenetic analyses revealed that A. chinensis was closely related to Arconaia lanceolata and Lanceolaria grayana. These results will be essential for conservation planning and management of freshwater mussels, especially A. chinensis

The complete maternal mitochondrial genome sequence of Cuneopsis heudei (Bivalvia: Unionoida: Unionidae)

The freshwater mussel Cuneopsis heudei is an endemic species in China. The population size has been declining dramatically due to water pollution, overfishing and habitat destruction. In this study, the complete maternal mitochondrial genome of C. heudei was determined (GenBank accession no. MF687348) for the first time. The circle genome is 15,981 bp in length. There are 14 protein-coding genes (cox1-3, nad1-6, nad4L, cob, atp6, atp8 and female ORF), 22 tRNA genes, two rRNA genes and a control region. Pylogenetic analyses reveal that C. heudei genome is inside of Unionidae and close related to C. pisciculus