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A new chaotic financial system is proposed by considering ethics involvement in a fourdimensional financial system with market confidence. A five-dimensional conformable derivative financial system is presented by introducing conformable fractional calculus to the integerorder system. A discretization scheme is proposed to calculate numerical solutions of conformable derivative systems. The scheme is illustrated by testing hyperchaos for the system. among interest rates, investments, prices, and savings. Chen [12] presented a fractional form of nonlinear financial system. Wang, Huang and Shen [13] established an uncertain fractional-order form of the financial system. Mircea et al. [14] set up a delayed form of the financial system. Xin, Chen, and Ma [15] proposed a discrete form of financial system. Yu, Cai, and Li [16] extended the

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The rapid development and popularization of the network have brought many problems to network security. Intrusion detection technology is often used as an effective security technology to protect the network. The deep belief network (DBN), as a classic model of deep learning, has good classification performance and is often used in the field of intrusion detection. However, the network structure of DBN is generally set through practical experience. For the optimization problem of the DBN-based intrusion detection classification model (DBN-IDS), this paper proposes a new joint optimization algorithm to optimize the DBN's network structure. First, we design a particle swarm optimization (PSO) based on the adaptive inertia weight and learning factor. Second, we use the fish swarm behavior of cluster, foraging, and other behaviors to optimize the PSO to find the initial optimization solution. Then, based on the initial optimization solution, we use the genetic operators with self-adjusting crossover probability and mutation probability to optimize the PSO to search the global optimization solution. Finally, the global optimization solution constructed by the above-mentioned joint optimization algorithm is used as the network structure of the intrusion detection classification model. The experimental results show that compared with other DBN-IDS optimization algorithms, our algorithm shortens the average detection time by at least 24.69% on the premise of increasing the average training time by 6.9%; compared with the tested classification algorithms, our DBN-IDS improves the average classification accuracy by at least 1.3% and up to 14.80% in the five-category classification, which is proved to be an efficient DBN-IDS optimization method. INDEX TERMS Intrusion detection, deep belief network, particle swarm optimization, artificial fish swarm algorithm, genetic algorithm.

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We present a sophisticated framework to systematically explore the temporal correlation in environmental monitoring wireless sensor networks. The presented framework optimizes lossless data compression in communications given the resource constraint of sensor nodes. The insights and analyses obtained from the framework can directly lead to innovative and better design of data gathering protocols for wireless sensor networks operated in noisy environments to dramatically reduce the energy consumptions.