Implementation of Data Mining Technology in Bonded Warehouse Inbound and Outbound Goods Trade

For the taxed goods, the actual freight is generally determined by multiplying the allocated freight for each KG and actual outgoing weight based on the outgoing order number on the outgoing bill. Considering the conventional logistics is insufficient to cope with the rapid response of e-commerce orders to logistics requirements, this work discussed the implementation of data mining technology in bonded warehouse inbound and outbound goods trade. Specifically, a bonded warehouse decision-making system with data warehouse, conceptual model, online analytical processing system, human-computer interaction module and WEB data sharing platform was developed. The statistical query module can be used to perform statistics and queries on warehousing operations. After the optimization of the whole warehousing business process, it only takes 19.1 hours to get the actual freight, which is nearly one third less than the time before optimization. This study could create a better environment for the development of China's processing trade.

A psychological perspective towards understanding the objective and subjective gray zones in predatory publishing

A continued lack of clarity persists because academics, policymakers, and other interested parties are unable to clearly define what is a “predatory” journal or publisher, and a potentially wide gray zone exists there. In this perspective, we argue that journals should be evaluated on a continuum, and not just in two shades, black and white. Since evaluations about what might constitute “predatory” are made by humans, the psychological decision-making system that determines them may induce biases. Considering such human psychological characteristics might shed light on the deterministic criteria that have been used, and continue to be used, to classify a journal or publisher as “predatory”, and perhaps, bring additional clarity to this discussion. Better methods of journal evaluation can be obtained when the factors that polarize journal evaluations are identified. As one example, we need to move away from simply using whitelists and blacklists and educate individual researchers about how to evaluate journals. This paper serves as an educational tool by providing more clarity about the “gray” publishing zone, and argues that currently available qualitative and quantitative systems should be fused to deterministically appreciate the zonation of white, gray and black journals, so as to possibly reduce or eliminate the influence of cognitive or “perception” bias from the “predatory” publishing debate.

Impact of decision-making system in social navigation

Facing human activity-aware navigation with a cognitive architecture raises several difficulties integrating the components and orchestrating behaviors and skills to perform social tasks. In a real-world scenario, the navigation system should not only consider individuals like obstacles. It is necessary to offer particular and dynamic people representation to enhance the HRI experience. The robot’s behaviors must be modified by humans, directly or indirectly. In this paper, we integrate our human representation framework in a cognitive architecture to allow that people who interact with the robot could modify its behavior, not only with the interaction but also with their culture or the social context. The human representation framework represents and distributes the proxemic zones’ information in a standard way, through a cost map. We have evaluated the influence of the decision-making system in human-aware navigation and how a local planner may be decisive in this navigation. The material developed during this research can be found in a public repository (https://github.com/IntelligentRoboticsLabs/social_navigation2_WAF) and instructions to facilitate the reproducibility of the results.

A decision support system for primary headache developed through machine learning

Background Primary headache is a disorder with a high incidence and low diagnostic accuracy; the incidence of migraine and tension-type headache ranks first among primary headaches. Artificial intelligence (AI) decision support systems have shown great potential in the medical field. Therefore, we attempt to use machine learning to build a clinical decision-making system for primary headaches. Methods The demographic data and headache characteristics of 173 patients were collected by questionnaires. Decision tree, random forest, gradient boosting algorithm and support vector machine (SVM) models were used to construct a discriminant model and a confusion matrix was used to calculate the evaluation indicators of the models. Furthermore, we have carried out feature selection through univariate statistical analysis and machine learning. Results In the models, the accuracy, F1 score were calculated through the confusion matrix. The logistic regression model has the best discrimination effect, with the accuracy reaching 0.84 and the area under the ROC curve also being the largest at 0.90. Furthermore, we identified the most important factors for distinguishing the two disorders through statistical analysis and machine learning: nausea/vomiting and photophobia/phonophobia. These two factors represent potential independent factors for the identification of migraines and tension-type headaches, with the accuracy reaching 0.74 and the area under the ROC curve being at 0.74. Conclusions Applying machine learning to the decision-making system for primary headaches can achieve a high diagnostic accuracy. Among them, the discrimination effect obtained by the integrated algorithm is significantly better than that of a single learner. Second, nausea/vomiting, photophobia/phonophobia may be the most important factors for distinguishing migraine from tension-type headaches.

Application of the Data-Driven Educational Decision-Making System to Curriculum Optimization of Higher Education

Advancement in information technology has given a tremendous change in the education system. The traditional classroom education system is slowly getting transferred to the modernized system. In this conversion, the students choose to select the courses to learn in their higher education. The selection will aid the student in learning advanced technologies through theoretical and practical methods. In this research work, a data-driven educational decision-making system with the support of a course curriculum is analyzed with student’s response after the course. The educational decision-making is implemented with the help of the mobile learning technology designed and maintained by the colleges and universities. For performing the analysis, the student response dataset is given as input to the fuzzy logic system to perform the analysis. The research shows that mobile learning technology with the fuzzy logic system has provided better decision-making analysis to curriculum optimization for the student and teachers.

Analysis University of Surakarta KIP Scholarship Recipients Using the Fuzzy MADM Method and C-45

The impact of the Covid-19 pandemic has forced the economic activity of the Indonesian population to decline drastically, which has an impact on the education funding process. Given these problems, it is necessary to develop a Decision-Making System to assist the selection process for KIP admissions for students who meet the requirements. The purpose of this research is that the provision of KIP can be right on target. For decision making, three stages are used with the method used, the first stage is the C-45 method for student priority decision making, the second stage is the Fuzzy MADM method, and the third stage is ranking according to the total quota. which is determined. The initial selection used the C-45 method with the variables of GPA, parents' income, achievements, parental dependents, and cases. The results of the C4.5 calculation show that the first priority is parental dependents with a Gain value of 0.007822696, followed by a GPA with a Gain value of -0.130011482, the third priority is Parents' Income with a Gain value of -0.702657067 and the last priority is an achievement. The results of the calculation are continued with Fuzzy MADM resulting in 5 rules used to determine student priorities (can) or not. The results achieved from 140 students who applied were accepted by 135 students who passed the initial stage, and out of 135 rankings, 70 students were determined to receive scholarships from the Government with the highest calculation score of 21 and the lowest of 14.4.

E-Participating Decision-Making Mechanism in the Public Administration System

In this chapter, an analysis of the electronic decision making system, which is thought to benefit from the heavy bureaucratic system which does not take into account the expectations of the citizen in the public administration system, will be used to make a more flexible structure. The focus of this chapter is on the need to design the decision-making mechanisms of the state according to the expectations of the citizen. For this purpose, requests and complaints from the citizens through the electronic environment should be taken into consideration in the decision-making process. In fact, this situation is reflected in the application of electronic participation management model. The application of this management model in the public administration system is the citizen participation complaint and demand system which is carried out under the name of electronic government. The examination of this system, which is an example of the application of participatory democracy, is important for the reflection of democratic values on the administration system.