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Futures commodity prices are affected by many factors, and traditional forecasting methods require close attention from professionals and suffer from high subjectivity, slowness, and low forecasting accuracy. In this paper, we propose a new method for predicting the fluctuation in futures commodity prices accurately. We solve the problem of the slow convergence of ordinary artificial bee colony algorithms by introducing a population chaotic mapping initialization operator and use the resulting chaotic mapping artificial bee colony algorithm as a trainer to learn long short-term memory neural network hyperparameters. With the combination of gate structures learned by the algorithm, the long short-term memory network can accurately characterize the basic rules of futures market prices. Finally, we conduct a series of backtesting experiments on gold and natural gas futures commodity prices to demonstrate the effectiveness of the proposed model. The experimental results show that, compared with various existing optimization models, our proposed model is able to obtain the lowest mean absolute error, mean square error, and root mean square error in the least number of iterations. In summary, the model can be used to predict the prices of a wide range of futures commodities.

Chemotherapy is one of the most effective treatments for cancer, but the efficacy of standard chemotherapy regimens is often limited by toxicities and the individual heterogeneity of cancers. Precise dosing is an important tool to improve efficacy and reduce significant differences in toxicity. However, most of the existing studies on chemotherapy optimization fail to fully consider the toxic side effects, drug resistance, and drug combinations, and thus the chemotherapy regimens obtained may face difficulty in achieving the expected efficacy and also affect the subsequent treatment. Therefore, this paper establishes a tumor growth model for the combination chemotherapy of cell cycle-specific and non-cycle-specific drugs and includes the factors of acquired drug resistance and toxic side effects, proposing an improved multi-objective Squirrel Search Algorithm, the TA-MOSSA, to solve the problem of accurate chemotherapy drug optimization. In this paper, experiments were conducted to analyze the efficacy of chemotherapy dosing regimens obtained by the TA-MOSSA based on the tumor growth model, and the results show that the TA-MOSSA can provide effective chemotherapy regimens for patients who take different treatment approaches.

With the enhancement of information volume, people are not satisfied with transmitting only a single secret image at a time but chase to hide multiple secret images in a single picture; however, the large-capacity steganographic scale can easily lead to the degradation of the quality of the image, which attracts the attention of eavesdroppers. In this paper, we propose a Chaotic mapping-enHanced imAge Steganography nEtwork (CHASE), which pioneers to hide colour images in grey images and reduces the difference between the container image and the cover image through the image permutation method, so as to enhance the security of the steganography. The method demonstrates excellent steganalysis resistance in experiments and introduces Generative Adversarial Networks (GANs) to improve the image fidelity in large-capacity steganographic scales. The fusion of chaotic mapping and GAN optimisation enables the steganographic network to simultaneously balance security and image quality. The experimental results show that CHASE can keep the secret image with good invisibility under large-capacity steganographic scales, and at the same time, it can reveal the secret image with high fidelity, and its steganalysis-resistant capability is much better than other state-of-the-art methods.

In the big data era, a tremendous volume of electronic documents is transmitted via the network, many of which include sensitive information about the country and businesses. There is a pressing need to be able to perform intelligent sensing of sensitive information on these documents in order to be able to discover and guarantee the security of sensitive information in this enormous volume of documents. Although the low effectiveness of manual detection is resolved by the current method of handling sensitive information, there are still downsides, such as poor processing effects and slow speed. This study creatively proposes the Text Sensitive Information Intelligent Perception algorithm (TSIIP), which detects sensitive words at the word level and sensitive statements at the statement level to obtain the final assessment score of the text. We experimentally compare this algorithm with other methods on an existing dataset of sensitive Chinese information. We use the metrics measuring the accuracy of the binary classification model, where the F1 score reaches 0.938 (+0.6%), and the F2 score reaches 0.946 (+1%), and the experimental results fully demonstrate the superiority of this algorithm.

Chemotherapy as an effective method is now widely used to treat various types of malignant tumors. With advances in medicine and drug dosimetry, the precise dose adjustment of chemotherapy drugs has become a significant challenge. Several academics have investigated this problem in depth. However, these studies have concentrated on the efficiency of cancer treatment while ignoring other significant bodily indicators in the patient, which could cause other complications. Therefore, to handle the above problem, this research creatively proposes a multi-objective deep reinforcement learning. First, in order to balance the competing indications inside the optimization process and to give each indicator a better outcome, we propose a multi-criteria decision-making strategy based on the integration concept. In addition, we provide a novel multi-indicator experience replay for multi-objective deep reinforcement learning, which significantly speeds up learning compared to conventional approaches. By modeling various indications in the body of the patient, our approach is used to simulate the treatment of tumors. The experimental results demonstrate that the treatment plan generated by our method can better balance the contradiction between the tumor’s treatment effect and other biochemical indicators than other treatment plans, and its treatment time is only one-third that of multi-objective deep reinforcement learning, which is now in use.