Valuable Rules Research Articles

Tensor-Based Baum–Welch Algorithms in Coupled Hidden Markov Model for Responsible Activity Prediction

The development and applications of artificial intelligence (AI) have brought unprecedented opportunities to humans, but also brought many challenges and concerns such as unfairness, immorality, distrust, illegality, and discrimination. Responsible AI provides a new solution to effectively address these AI potential threats by integrating social/physical rules into AI systems. However, these rules are high-level regulations and ethical principles, which are difficult to be formalized. To this end, we attempt to use the data generated in various AI systems such as cyber–physical–social systems (CPSS) to discover and reflect these rules to provide more responsible services for humans. In this article, we first propose a data-driven responsible CPSS framework. Its core idea is to mine valuable rules through perception, fusion, processing, and analysis of CPSS data, and then use these rules to adaptively optimize CPSS. Based on this framework, three tensor-based couple hidden Markov models (T-CHMMs) are constructed to integrate three responsible features (i.e., timing, periodicity, and correlation) for mining potential and valuable rules. Then, the corresponding tensor-based Baum–Welch (TBW) algorithms are designed to solve their learning problems. Finally, the predictive accuracy and computational efficiency of the proposed models and algorithms are verified on three open datasets. The experimental results show that proposed methods have the best performances for various scenarios, which reflects that our methods are more promising and responsible than existing methods.

Origin of the Unusual High Optical Nonlinearities Observed in Glassy Chalcogenides

AbstractNonlinear photonics integrated at the chip scale opens the path to new applications in an increasing number of fields such as all‐optical computing, high bit rate communications on chip, or embedded sensing with frequency combs and super‐continuum sources. All these applications require materials having the best trade‐off between optical losses, Kerr refractive index, and compatibility with current nanofabrication facilities. Although optimizing the nanofabrication process can minimize linear optical losses to some extent, optimizing the Kerr index of the materials remains challenging because a clear understanding of the link between atomic structure and optical nonlinearities is still missing. This is precisely what this work addresses for chalcogenide glasses based on thin films of Ge‐Sb‐Se alloys, a promising class of materials fully compatible with large‐scale integration technology from the microelectronics industry. By coupling nonlinear Kerr index metrology with ab initio molecular dynamics calculations of amorphous models, this work unveils the unique molecular patterns in these alloys that are responsible for their unusual nonlinear polarizability. This provides for the first time valuable rules for the design of new optical materials with improved Kerr index enabling miniaturization and implementation of future nonlinear photonic devices that can then operate at significantly lower power.

A Data-Driven Self-Learning Evaluation Method of Voltage Sag Severity

Lightning is one of the main causes of voltage sags, and it is of great significance for subsequent analysis and governance to accurately evaluate the severity of the sag events caused by lightning. There are many uncertain factors between lightning fault events and voltage sag events. To evaluate the severity of voltage sag events caused by lightning, a data-driven self-learning evaluation method for voltage sag severity is proposed. According to a large number of online monitoring data of lightning positioning system and power quality monitoring system, the association rule mining algorithm based on incremental learning is used. And the rules are kept updating through the accumulation of historical data, which may give it the abilities of self-learning. The empirical analysis is carried out based on the monitoring data of a regional power grid. The results show that the method in this paper can accurately mine more valuable rules in reality and solve the problem of low efficiency of mining algorithm when the database changes dynamically.

An animal dynamic migration optimization method for directional association rule mining

In the area of association rule mining, many optimization algorithms have been proposed to improve the computational efficiency of rule mining or the quality and diversity of association rules. However, in real applications, since the user may have prior knowledge and research trends for some key items, the association rules containing key items are more valuable and meaningful for these users. This contributes to a new issue that association rules related to key items should be mined in a targeted manner. To solve this issue, this paper proposes a novel animal dynamic migration optimization (ADMO) method to realize directional rule mining as well as maintain high mining efficiency and high rule quality. Taking the support and confidence of frequent itemsets as input, the method first identifies valuable rules and then initializes and updates the animal population to search for the best animal. The support and confidence of the best animal are defined as threshold values to delete unnecessary rules and discover more key rules. During the optimization, the population size value is dynamically generated. The effectiveness and applicability of ADMO are validated on 11 open-source datasets and a real-world elevator case. The results reveal that the ADMO method has a faster mining speed and obtains more key rules than the ARM-PSO, ARM-AMO, ARM-MOPSO, ARM-WOA, and ARM-DE methods. In the elevator case, the association rule generated by ADMO can provide a higher success rate and accuracy for requirement transformation.

Application Technology on Collaborative Training of Interactive Learning Activities and Tendency Preference Diversion

Mining problems and exploring rules are the key problems in the learning process, and also the difficulties in education big data. Therefore, taking learning behavior as the research objective, this study demonstrates the collaborative training method of multi view learning interaction process driven by big data, so as to realize the tendency preference diversion of learning behavior. Based on collaborative training method of learning behavior. Through adequate experiments, we get the suitable and valuable rules, constructs the directed topological relationship of tendency preference division, and mines the feasible improvement measures and intervention mechanism. Experiments and practices show that the potential topological relationship can effectively improve and enable learning, and improve the assessment results.

A comprehensive study on solar ejector cooling system: A review

This paper aims to give a writing survey on solar driven ejector refrigeration frameworks and give valuable rules concerning the foundation and working standards of the solar ejector. The working cycle of the solar ejector refrigeration is studied, and the findings related to this research are highlighted as well. The advancements in the field of solar powered ejectors and their applications are depicted as well. The turn of events, history, and ongoing advancement in solar ejector refrigeration frameworks are accounted for and arranged. Thus, the studies in this area are expanding to settle the critical focuses that make these frameworks an eco-friendly option to replace the compressor-based refrigeration cycles. It can be understood that a ton of examination work still should be undergone with huge scope in this industry for the substitution of ordinary refrigeration setups. The current work incorporates a survey of solar ejector cooling cycle (SECC) frameworks. An outline of the basics of SECCs is introduced. Most significant parts of the plan, activity, and execution of ejector gadgets and sunlight-based ejector cooling cycles are examined. The primary exploration patterns are distinguished, and the leading ends are illustrated, while future work proposals are given.

Research on Remote Sensing User Demand Mining Analysis Method

A method of mining and analyzing remote sensing user needs is presented in this paper. With the increasing number and types of satellites in China, satellites are more and more widely used in national economic construction, which can be used in land and resources census, emergency natural disaster relief, urban construction management, earth environment monitoring and so on. Therefore, the number of remote sensing users expand quickly and the specific content of remote sensing observation needs and remote sensing image data needs is diverse. How to find out the user’s remote sensing resources and data use rules from the large number of remote sensing needs is a key problem. The difficulty lies in that the rules representing the relationship between demand parameters are recessive rather than explicit, which can only be found by complex data analysis, especially under the condition of large amount of data. Data mining technology can find potential as well as valuable rules and knowledge from massive data. It is an effective tool to analyze massive data and an effective technical means to solve the above problems. In this paper, the association rules mining algorithm is adopted to mine and analyze mass of remote sensing user demand data, and find the potential association rules between the demand source, regional scope, time, demand type and other parameters.

Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns.

The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.

Two-stage rule extraction method based on tree ensemble model for interpretable loan evaluation

The tree ensemble model has been widely employed as a loan evaluation method in credit risk assessment due to its high accuracy and robustness. However, the tree ensemble model is complex and incomprehensible, which restricts its adoption for decision-making in loan evaluation. In this paper, we propose a novel rule extraction method for improving the ensemble model by balancing predictive performance and interpretability in two stages: a local rule extraction method followed by a global rule extraction method. The local method simplifies each rule by removing its redundant constraints, while the global method optimizes the complete rule set based on the multiobjective optimization method. An interpretable rule-based model is extracted from the tree ensemble model via the proposed method. Comparing the performance to six other methods on three loan evaluation datasets, the proposed method shows superior interpretability and realizes similar predictive performance to the tree ensemble model. For practical loan evaluation, the proposed method provides decision-makers with an interpretable rule-based model, which could replace the opaque tree ensemble model in high-stakes decision-making. In addition, the proposed method could facilitate decision-makers in explaining the tree ensemble model by analyzing the important and valuable rules that are extracted from the original opaque model.

Mining method of massive data of mobile library under information asymmetry facing large-scare database

The current method generates a large number of candidate sets when mining data in mobile libraries under asymmetric information, and the mining time and efficiency are poor. To this end, a new method for mobile library massive data mining based on improved Apriori algorithm is proposed to collect, clean and reduce massive data. Calculate the reader's interest distance by analyzing the borrowed historical data, and use the Apriori algorithm to find the association rules in the frequent itemsets of the data. In order to make up for the shortcomings of the current method, while filtering out infrequent candidate sets, the corresponding transaction set is also collaboratively filtered, which can reduce the amount of calculation and time consumption. Experimental results show that the proposed method can mine more valuable rules. The improved execution time is only 10 s, the CPU utilization exceeds 90%, and the acceleration ratio exceeds 1.81 s, which can better meet the needs of decision makers.

The application of isatin-based multicomponent-reactions in the quest for new bioactive and druglike molecules.

Oxindole derivatives are known for their great interest in the field of Medicinal Chemistry, as they display vast biological activities. Recent efforts concerning the preparation of oxindole derivatives using isatin-based multicomponent reactions (MCRs) constitute a great advance in generating druglike libraries fast and with wide scaffold diversity. In this review, we address those recent developments, exploring the synthetic pathways and biological activities described for these compounds, namely antitumor, antibacterial, antifungal, antiparasitic, antiviral, antioxidant, anti-inflammatory and central nervous system (CNS) pathologies. To add new depth to this work, we used a well-established web-based free tool (SwissADME) to evaluate the most promising scaffolds in what concerns their druglike properties, namely by evaluating their compliance with some of the most valuable rules applied by medicinal chemists in both academia and industrial settings (Lipinski, Ghose, Veber, Egan, Muegge). The aim of this review is to endorse isatin-based MCRs as a valuable synthetic approach to attain new hit compounds bearing the oxindole privileged structure, while critically exploring these scaffolds' druglike properties.

RM2T2C: Retrospective Multivariate Multistep Transition Tensor Chain Model for User Mobility Pattern Prediction

With the bloom of intelligent devices over the world, a large scale of user’s trajectory data are collected. How to mine valuable rules from these data and provide services for the industrial community has become an urgent problem. In this article, we propose a multimodal prediction system to infer users’ mobility pattern embedded in heterogeneous data from cyber–physical–social space. According to users’ mobility pattern, the framework can provide smart services for the industrial community. The highlight is the retrospective multivariate multistep transition tensor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{M}^2}{\text{T}^2}$</tex-math></inline-formula> ) chain model, which decomposes a large scale of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{M}^2}{\text{T}^2}$</tex-math></inline-formula> into a series of small-scale transition tensors (subtransition tensors) with the tensor maximum likelihood estimation method. Then, that one can solve the stationary probability distribution with the small-scale subtransition tensors so as to highly reduce the computation and storage cost. At the same time, the tensor maximum likelihood estimation method avoids the overfitting of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{M}^2}{\text{T}^2}$</tex-math></inline-formula> , so the proposed model improves the performance of prediction systems. In the end, several experiments are constructed to evaluate the proposed model.

Mining regional patterns of land use with adaptive adjacent criteria

ABSTRACT Land use/cover changes (LULC) are complicated and regionally diverse. When mining regional patterns, the use of a spatial relationship that is determined without considering the spatial correlation among geographical objects can lead to problematic results, e.g. mistakenly treating unrelated objects as adjacent. Additionally, traditional prevalence measures are unstable for uneven datasets such as LULC, wherein some land-use change types show small numbers and uneven quantities, and valuable rules for some land-use categories may be ignored. Therefore, we proposed a regional pattern mining method. First, we developed adaptive adjacent criteria, which can be automatically generated for each specific zone to define adjacency for better spatial-temporal mining. Then, a combinational decision model was built to improve the stability of the prevalence measure, which was used to filter out the insignificant spatial-temporal rules. Furthermore, we proposed two levels of land-use pattern mining, i.e. cluster-level mining and polygon-level mining, to first discover hot-spot areas where similar land-use change has occurred frequently and then to determine the location, frequency, and change time of rules related to different land-use activities. The proposed method was used for mining the dependence of land use and regional patterns on land-use changes. Results show that the proposed method can determine the spatial dependence between the land-use categories, as well as regional patterns of land-use changes. According to our research, the study area, Xinbei District, China, is undergoing land-use change involving rapid urbanization, extensive transportation construction, and losses of farmland.

Time is money: Dynamic-model-based time series data-mining for correlation analysis of commodity sales

The correlation analysis of commodity sales is very important in cross-marketing. A means of undertaking dynamic-model-based time series data-mining was proposed to analyze the sales correlations among different commodities. A dynamic model comprises some distance models in different observation windows for a time series database that is transformed from a commodities transaction database. There are sales correlations in two time series at different times, and this may produce valuable rules and knowledge for those who wish to practice cross-marketing and earn greater profits. It means that observation time points denoting the time at which the sales correlation occurs constitute important information. The dynamic model that leverages the techniques inherent in time series data-mining can uncover the kinds of commodities that have similar sales trends and how those sales trends change within a particular time period, which indicates that the “right” commodities can be commended to the “right” customers at the “right” time. Moreover, some of the time periods used to pinpoint similar sales patterns can be used to retrieve much more valuable information, which can in turn be used to increase the sales of the correlated commodities and improve market share and profits. Analysis results of retail commodities datasets indicate that the proposed method takes into consideration the time factor, and can uncover interesting sales patterns by which to improve cross-marketing quality. Moreover, the algorithm can be regarded as an intelligent component of the recommendation and marketing systems so that human–computer interaction system can make intelligent decision.

Analytical analysis of stress and displacement for surrounding rock of deep-buried rectangular tunnel in layered strata

This study presents a theoretical solution on stress and displacement of the deep-buried tunnel surrounding rock in layered strata. Based on theory of Покровский equivalent layer method, we convert layered strata into homogeneous ones and then map the rectangular tunnel in the coordinate plane z to the unit circle of complex plane ζ, through using a complex variable method and calculating the analytic functions Φ(ζ) and Ψ(ζ) with Matlab, the study obtains that the stress of the surrounding rock of deep-buried rectangular tunnel in layered strata. Subsequently, we derive the strain and displacement of the surrounding rock. Finally, we study the effects of lateral pressure coefficient and grade of surrounding rock and height-to-width ratio of the tunnel on the stress of tunnel surrounding rock. Based on our analysis, we formulate valuable rules.

Analytical Investigation of Beam-to-Column Endplate Connections at Elevated Temperatures

An analytical model to estimate the complete moment-rotation curve of steel beam-to-column endplate connections is presented in this paper. The method is based on the component-based concept and is capable of predicting the behavior of connections considering the influence of beam axial forces and temperature variations. Significant consideration is devoted to the reliable simulation of T-stub component. A recently developed T-stub model, which is based on the thin plate theory, is able to capture the force-deformation response of a T-stub assembly under fire situations. Verification of the analytical model is carried out by comparing with available experimental results of isolated connections in other literatures. The analytical model is also validated against a 3D finite element model, considering different axial load levels at different temperatures. The validation results illustrate the reliability and efficiency of the analytical model in predicting the initial stiffness and ultimate moment resistance of connections. However, the prediction of rotation capacity still needs some improvements, since the simulation of some basic components at elevated temperatures is not quite accurate. Also, some valuable rules are concluded on the behavior of connections subjected to different beam axial loads at different temperatures.

ASSOCIATION RULE ANALYSIS FOR TOUR ROUTE RECOMMENDATION AND APPLICATION TO WCTSNOP

Abstract. The increasing E-tourism systems provide intelligent tour recommendation for tourists. In this sense, recommender system can make personalized suggestions and provide satisfied information associated with their tour cycle. Data mining is a proper tool that extracting potential information from large database for making strategic decisions. In the study, association rule analysis based on FP-growth algorithm is applied to find the association relationship among scenic spots in different cities as tour route recommendation. In order to figure out valuable rules, Kulczynski interestingness measure is adopted and imbalance ratio is computed. The proposed scheme was evaluated on Wangluzhe cultural tourism service network operation platform (WCTSNOP), where it could verify that it is able to quick recommend tour route and to rapidly enhance the recommendation quality.

Analysis of Dangerous Chemical Accident Cause Based on Apriori Algorithm

In order to identify the association between causes of accidents due to hazardous chemicals, clear and definite the key points of safety supervision, and improve the efficiency of security supervision, this study researches 150 accident investigation report as data source by data mining technology, A priori algorithm, and finds out the relationship between factors such as time, step, type, severity and cause. By analyzing the 8 valuable rules, it proposes 4 suggestions for improving key safety supervision, thus to promote the optimization of safety supervision strategy of hazardous chemicals.

The interdisciplinary research of big data and wireless channel: A cluster-nuclei based channel model

Recently, internet stimulates the explosive progress of knowledge discovery in big volume data resource, to dig the valuable and hidden rules by computing. Simultaneously, the wireless channel measurement data reveals big volume feature, considering the massive antennas, huge bandwidth and versatile application scenarios. This article firstly presents a comprehensive survey of channel measurement and modeling research for mobile communication, especially for 5th Generation (5G) and beyond. Considering the big data research progress, then a cluster-nuclei based model is proposed, which takes advantages of both the stochastical model and deterministic model. The novel model has low complexity with the limited number of cluster-nuclei while the cluster-nuclei has the physical mapping to real propagation objects. Combining the channel properties variation principles with antenna size, frequency, mobility and scenario dug from the channel data, the proposed model can be expanded in versatile application to support future mobile research.

Association rule mining algorithm DB-growth based on relational database

Excavating potential multidimensional valuable association rules from big data has wide application. The main association rule mining algorithm Apriori has the bottlenecks of scanning repeatedly database and generating big number of candidate sets, though the FP algorithm does not generate candidate sets, but FP-tree cannot handle the problem of storage and traversal of big data. In addition, Apriori and FP-growth algorithm needs to reconstruct association rules while implementing increment mining, its not available for growth-oriented data mining. Facing those problems, designing DB-growth algorithm based on relational database table SourceIndex, applying string combinate to generate pattern, insert or update database to construct frequent sets, mining association rules by querying database. In addition, it supports increment mining and depth mining.