In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

With the fast development of Industrial Internet of Things (IIoT), IIoT storage, which provides a lower cost, higher reliability of dada, remote access services, and expandable storage space, is received much attentions among enterprises and individual users. However, to provide a secure IIoT storage services could be a challenging task for some situations, for example, how to search encrypted data in IIoT. In this paper, we examine the security of a recent proposed certificateless searchable public key encryption scheme for IIoT environments by Ma et al.. We find that their scheme is insecure against an off-line keyword guessing attack. Then, we propose an enhancement based on the scheme and prove the security of our enhancement with a formal model.

In an era where people in the world are concerned about environmental issues, companies must reduce distribution costs while minimizing the pollution generated during the distribution process. For today’s multi-depot problem, a mixed-integer programming model is proposed in this paper to minimize all costs incurred in the entire transportation process, considering the impact of time-varying speed, loading, and waiting time on costs. Time is directional; hence, the problems considered in this study are modeled based on asymmetry, making the problem-solving more complex. This paper proposes a genetic algorithm combined with simulated annealing to solve this issue, with the inner and outer layers solving for the optimal waiting time and path planning problem, respectively. The mutation operator is replaced in the outer layer by a neighbor search approach using a solution acceptance mechanism similar to simulated annealing to avoid a local optimum solution. This study extends the path distribution problem (vehicle-routing problem) and provides an alternative approach for solving time-varying networks.

High-utility itemset mining (HUIM) has been gaining popularity in the field of data mining. Frequent itemset mining used to be the main tool to reveal high-frequency patterns but failed to consider the concept of profit. HUIM, on the other hand, obtains the itemsets and is practical in commercial applications. A main challenge in HUIM is that HUIM should handle the exponential search space for HUIM when the number of distinct items and the size of the database are both too large. The other challenge is that existing HUIM methods overlook the length of high-utility itemsets; hence, a large itemset gets an unreasonable estimated profit as opposed to the actual value. Therefore, several algorithms were proposed to mine high average-utility itemsets. High average-utility itemset mining (HAUIM) is an extension for the traditional HUIM, which provides a different measure with HUIM. It discovers utility patterns by considering both their utilities and lengths. To reduce the searching space in HAUIM, average-utility upper-bound, looser upper-bound utility, and a revised tighter upper-bound model are proposed to prune the searching graph in HAUIM. These three upper-bounds for high average-utility itemsets decrease the number of candidate patterns efficiently. However, they still overestimate a high average-utility itemset and waste on assessing the unnecessary patterns. Two new tighter upper-bounds, maximum following utility upper-bound and top-k transaction-maximum utility upper-bound, are proposed in this paper to further contract the size of candidate pattern set. Experiments conducted on several benchmark data sets show that the proposed method outperforms the previous HAUIM algorithms in terms of runtime, the number of join operations, and scalability.

In recent years, privacy-preserving data mining (PPDM) has received a lot of attention in the field of data mining research. While some sensitive information in databases cannot be revealed, PPDM can discover additional important knowledge and still hide critical information. There are different ways to approach this exhibited in previous research, which applied addition and deletion operations to adjust an original database in order to hide sensitive information. However, it is an NP-hard problem to find an appropriate set of transactions/itemsets for hiding sensitive information. In the past, evolutionary algorithms were developed to hide sensitive itemsets by building an appropriate database. Genetic-based algorithms and a particle swarm optimization-based algorithm, proposed in previous works, not only hide sensitive itemsets but also minimize the side effects of sanitization processes. In this paper, an ant colony system (ACS)-based algorithm called ACS2DT is proposed to decrease side effects and enhance the performance of the sanitization process. Each ant in the population will build a tour for each iteration and each tour indicates the deleted transactions in the original database. The proposed algorithm introduces a useful heuristic function to conduct each ant to select a suitable edge (transaction) for the current situation and also designs several termination conditions to stop the sanitization processes. The proposed heuristic function applies the pre-large concept to monitor side effects and calculates the degree of hiding information to adjust the selecting policy for deleted transactions. The experimental results show that the proposed ACS2DT algorithm performs better than the Greedy algorithm and other two evolutionary algorithms in terms of runtime, fail to be hidden, not to be hidden, not to be generated and database similarity on both real-world and synthetic data sets.

Internet of Things (IoT) supports high flexibility and convenience in several applications because the IoT devices continuously transfer, share, and exchange data without human intervention. During shared or exchanged progress of data, security and privacy threats result because the published or shared data mainly corresponds to a raw dataset, and an attacker can easily obtain details on the shared data in an IoT environment. In the study, we present a sanitization approach by adopting the hierarchical-cluster method to hide confidential information while still discovering useful and meaningful information in the sanitized dataset. The multi-objective particle swarm optimization framework and an algorithm termed as HCMPSO are utilized to balance four side effects, namely hiding failure, missing cost, artificial cost, and database dissimilarity (Dis), and thereby provide optimized solutions for data sanitization. Experiments are performed to compare the performance of the designed HCMPSO with that of the single-objective cpGA2DT and multi-objective NSGA-II-based approaches. As shown in the results, the designed HCMPSO exhibits good performance in terms of hiding failure, and thus the most confidential information is hidden after the sanitization process. The shared or published data in IoT is secured. Furthermore, the designed sanitization algorithm achieves reasonable results in terms of missing cost, artificial cost, and Dis.