Data streams are characterized by high volatility, and they drastically change in an unpredictable way over time. In the typical case, newer data are the most important, as the concept of aging is ...based on their timing. These flows require real-time processing in order to extract meaningful information that will allow for essential and targeted responses to changing circumstances. Knowledge mining is a real-time process performed on a subset of the data streams, which contains a small but recent part of the observations. Timely security requirements call for further quest of optimal approaches, capable of improving the reliability and the accuracy of the employed classifiers. This research introduces a real-time evolving spiking restricted Boltzmann machine approach, for efficient anomaly detection in data streams. Testing has proved that the proposed algorithm maximizes the classification accuracy and at the same time minimizes the computational resources requirements. A comparative analysis has shown that it outperforms other data flow analysis algorithms.
The electric bicycle-sharing system (EBSS) is the fourth-generation urban shared bicycle travel system, which effectively improves the travel efficiency of urban residents and solve the problem of ...urban congestion. This study attempts to use an extended technology acceptance model (TAM) method to study the acceptance of EBSSs. We had introduced four potential variables, including perceived pleasure (PP), perceived environmental value (PEV), perceived cost (PC), and perceived reliability (PR), into the classic TAM to form a new EBSS-TAM. Data were obtained by using a Likert scale questionnaire from 399 citizens in China. Partial least-squares structural equation modeling (PLS-SEM) with reflective constructs was employed as the analysis method. The results showed that: (1) the EBSS-TAM can explain user behaviors regarding the use of EBSSs. PP has a positive impact on behavior attitude (BA) while having no impact on behavior intention (BI). PEV has no impact on BA and BI. PC has a negative impact on BA and has no impact on BI. PR has a positive impact on BA while having no impact on BI. Perceived ease of use (PEU) has a positive impact on PP and PEV. (2) Younger users (under 35 years old) are more likely to change from liking CBSSs to using EBSSs than older users are. Male users are more satisfied with EBSSs because of their ease of use. The users who never used CBSSs are more likely to perceive the environmental protection value of EBSSs. Some managerial implications were proposed for the EBSSs.
In production planning, one of the most crucial issues involves cycle time forecasting and distribution. Particularly, the parameter aids in realizing high delivery reliability. In the production ...planning process that involves computer component manufacturing, an estimation of the tasks’ cycle time offers an important basis for dispatching control, material purchase, and due date assignment. In this study, a big-data approach was proposed and examined to determine how it could be used to predict cycle time distribution. Also, the research context involved computer components manufacturing systems. Indeed, the motivation was to determine how the proposed mechanism could improve delivery reliability in manufacturing systems.Regarding the implementation and design of the CT forecasting system, with the proposed DP-RBFN framework being a model to be implemented in computer components manufacturing, components of the system constituted three major parts. The first part, being the basic platform, played the role of Hadoop series software installation. This installation had its role lie in enabling the parallel computing of big data. Another part of the framework design and implementation involved data preprocessing. In this case, the role of the data preprocessing procedure lay in the extraction, transformation, and loading of data to CTF. The third part that followed the basic platform design and data preprocessing procedure involved CT forecasting. Results demonstrated that the proposed model performs superiorly than the contrast or other comparative methods on both the computer components manufacturing system dataset and benchmark datasets. From the findings, the proposed framework (DP-RBFN) exhibited superior performance compared to previous performance outcomes that had been reported relative to the use of the RBFN algorithm. These findings held for both MAD and SD—relative to the selected datasets.
Hepatitis B (HB) is a deadly disease that has a severe impact on infected individuals. In China, not only are the incidence and infection rates of HB very high, but also many HB patients suffer from ...mental illness associated with anxiety and fear because of HB-associated symptoms. This exacerbates the patients’ condition, potentially increasing the risk of mortality. In this paper, we propose a new treatment mode to improve the therapeutic efficiency and patients’ satisfaction with their healthcare. In a single process of this new treatment, several patients with similar disease symptoms are treated by one doctor at the same time. This new treatment mode can not only relieve the anxiety and fear of HB patients, and improve patients’ cognition rate of HB, but also reduce the HB infection rate, slow down the progression of disease symptoms, and shorten the course. If patients with similar disease symptoms are to be grouped together, there is a need to determine the optimal patient batch combination, which can be solved in the new mode, called patient combined problem (PCP). We also constructed a mathematical model of PCP, and present the ant colony (AC) algorithm and Enhanced AC with a P-3-exchange operator for PCP in the new treatment mode in this paper. We also performed an experiment that showed that our proposed algorithms are very fast and effective for solving this problem.
With the number of on-orbit earth observation satellites (EOSs) increases, satellite image data downlink scheduling problem is becoming the bottleneck for restricting EOSs to capture more image data. ...Therefore, Integrated scheduling problem for earth observation satellites is imperative, which optimizes data acquisition and data transmission simultaneously. In this paper, three different modelling frameworks, SSF, CSF and CISF, are investigated to formulate the ISPFEOS as a bi-objective optimization model along with an adaptive bi-objective memetic algorithm (ALNS + NSGA-II), which integrates the combined power of an adaptive large neighborhood search algorithm (ALNS) and a nondominated sorting genetic algorithm II (NSGA-II). In addition, two types of operators, “Destroy” operators and “Repair” operators, are designed to improve the ALNS + NSGA-II. Results of extensive computational experiments are presented which disclose that the CISF model produced superior outcomes.
In recent years, the scale of the health examination business has increased rapidly, and research on the combinatorial optimization of medical examinations has become more important. In this context, ...a special large-scale flexible open shop scheduling problem (FOSP) is introduced based on the idea of the multi-processor open shop scheduling problem. A mixed integer programming model is developed for the FOSP, which regards client satisfaction as the most important objective. As the FOSP is particularly complex, three different intelligent optimization algorithms are examined, namely a genetic algorithm, hybrid particle swarm optimization, and simulated annealing. According to the medical examination preferences of the clients, a group of large-scale test problems are created on the basis of benchmark instances of the flexible job shop problem, and these are used to evaluate the performance of each algorithm. The experimental results show that the genetic algorithm outperforms both simulated annealing and hybrid particle swarm optimization, especially in large-scale problems.
Motor systems are becoming more and more vital in modern manufacturing and bearings play an important role in the performance of a motor system. Many problems that arise in motor operation are ...related to bearing faults. In many cases, the accuracy of the devices for monitoring or controlling a motor system highly depends on the dynamic properties of motor bearings. Thus, fault diagnosis of a motor system is inseparably related to the diagnosis of the bearing assembly. The fault diagnosis of rolling bearings is substantially a classification problem. The traditional application of random forest (RF) to fault diagnosis methods is based on balanced data. However, in a practical situation, it is difficult to collect the fault data that are usually unbalanced. In order to solve this problem, in the first step, we propose a two-step (TS) clustering algorithm to enhance the original synthetic minority oversampling technique (SMOTE) algorithm for the unbalanced data classification. Then, based on the improvement of the SMOTE algorithm, we propose the principal component analysis (PCA) and apply it in the field of high-dimensional unbalanced fault diagnosis data. In this paper, we apply this new method to the fault diagnosis of rolling bearings, and the experiments conducted in the end show that the improved algorithm has a better classification performance.
Earth observation satellites provide services for users by taking images. The rapid increase in the number of satellites and missions makes mission planning more difficult. In order to solve this ...problem, this paper adopts a multi-population evolutionary algorithm. First, a mixed-integer programming model of the joint observation satellites mission planning problem (JOSMPP) is constructed. After that, a dual-population artificial bee colony algorithm (DPABC) and a heuristic task scheduling algorithm are proposed. Two learning-based nectar generation methods are used to guide the direction of population optimization. The search population is used to lead the nectar search at the stage of employed bees and supplement population is used to improve search performance. According to the performance of these two populations, candidate populations are generated to realize the search of onlooker bees. After each generation of population search, the composition of the two populations is adjusted according to the performance. Finally, the experimental results show that the DPABC algorithm has more advantages than multiple state-of-the-art algorithms to solve the joint mission planning problem of earth observation satellites.
The aim of this study was to explore a method for developing an emotional evolution classification model for large-scale online public opinion of events such as Coronavirus Disease 2019 (COVID-19), ...in order to guide government departments to adopt differentiated forms of emergency management and to correctly guide online public opinion for severely afflicted areas such as Wuhan and those afflicted elsewhere in China. We propose the LDA-ARMA deep neural network for dynamic presentation and fine-grained categorization of a public opinion events. This was applied to a huge quantity of online public opinion texts in a complicated setting and integrated the proposed sentiment measurement algorithm. To begin, the Latent Dirichlet Allocation (LDA) was employed to extract information about the topic of comments. The autoregressive moving average model (ARMA) was then utilized to perform multidimensional sentiment analysis and evolution prediction on large-scale textual data related to COVID-19 published by netizens from Wuhan and other countries on Sina Weibo. The results show that Wuhan netizens paid more attention to the development of the situation, treatment measures, and policies related to COVID-19 than other issues, and were under greater emotional pressure, whereas netizens in the rest of the country paid more attention to the overall COVID-19 prevention and control, and were more positive and optimistic with the assistance of the government and NGOs. The average error in predicting public opinion sentiment was less than 5.64%, demonstrating that this approach may be effectively applied to the analysis of large-scale online public sentiment evolution.
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