Intelligent Knowledge Research Articles

Intelligent Knowledge Service System Based on Depression Monitoring of College Students

The number of college students suffering from depression has increased in recent years. In order to help the college student administration departments understand students' psychological state of depression better and keep college students mentally healthy through mental health services, this paper studies an intelligent monitoring system for depression. Different from previous researches, this study, based on the cloud services platform, incorporates three indicators closely related to depression-sleeping, exercise and heart rate-into the monitoring database subsystem and establishes a relatively macroscopic intelligent knowledge service system for depression monitoring of college students. It uses the Mobile Material Link Device (MMLD) to collect data and information to monitor and analyze the changes in the depression status of college students dynamically, which also provides timely warnings and a chain of personalized intelligent knowledge services based on individuals’ depression status.

Modeling method of internet public information data mining based on probabilistic topic model

From the perspective of military intelligence work, the rise and widespread use of the network has opened up new horizons for intelligence acquisition, but the continuous increase in the amount of data in the network has made traditional intelligence analysis stretched. Therefore, enriching and developing military intelligence analysis methods have certain practical significance to make up for the shortcomings in current intelligence analysis. The traditional military intelligence analysis method cannot realize the in-depth mining and analysis of the network Shanghai information, obtain the deep intelligence knowledge required by the military, introduce the data mining technology into the military intelligence analysis and construct the network military intelligence analysis based on data mining model. The semantic analysis-based intelligence analysis algorithm in this model has certain superiority compared with the traditional association analysis, which can effectively improve the analysis efficiency and accuracy of military intelligence.

Guest Editorial Special Issue on Wearable Sensor-Based Big Data Analysis for Smart Health

The integration knowledge of wearable sensors, wireless communications, and artificial intelligence have brought forth the smart health systems, which empower the consumer’s to make a difference to their well-being by connecting data to personalized analysis to timely insights. Therefore, the real-time data obtained directly reflects the personal status of interest and can be used in a variety of healthcare applications in the Internet of Things (IoT), from preventive treatment to diagnostics and rehabilitation, as well as in virtual and augmented reality environments.

Development of Leadership Transformation Module for Off-Campus Students

Young generation is the hope and the beneficiary for the nation’s development in the future. However nowadays, the increase of participation among students in Higher Education Institutions (HIE) in various social problems are very devastating. Because of that, the need for serious efforts in helping students to improve the internal strength to form a strong personality and understanding of social responsibility as a leader is not only to themselves but also for the society. Additional focus should be given to off-campus students as they are more susceptible to negative influences such as friends and the surrounding environment. Through the preliminary findings, studies found that off-campus students are not able to understand, practice and carry out responsibility as a leader in terms of self-management; relationship with the Creator; social relationship with housemate; and environmental management leadership. Therefore, this article discusses the process of Development of Leadership Transformation Module of Off-Campus Students (TKDPLK) by looking at the module’s content validity. This module is developed by integrating spiritual intelligence knowledge and giving awareness of social responsibility.

Sistema ERP para el Seguimiento de Órdenes de Producción

In an increasingly competitive business world, we live with the constant need to speed up, improve and potentiate the productivity of our resources and our organization, to turn our information into intelligent knowledge. For this, it is ideal to have this information online, in real time, from any device and from anywhere in the world. These conditions allow to make assertive decisions in a much more agile way. The development of the ERP for the control of order tracking in the company Zetrak S.A de C.V. Its purpose is to automate production processes through access to information in a reliable, accurate and timely manner, resulting in the reduction of time and costs in each of the processes involved in the production and distribution operations of the company. The Spiral Model was used as a development methodology, which allows producing more and more complete versions during the development of the system, until generating the final iteration. The system is formed in its first stage by the modules: Shipments, Engineering, Sales, Billing and Quality Control.

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

Introducing a group spatial decision support system for use in strategic environmental assessment of onshore wind farm development in Mexico

In this paper we introduce a method for potential use in strategic environmental assessment (SEA) which integrates knowledge and tools of Collective Intelligence, Complexity Theory and Geoprospective, via the implementation of a technological Group-Spatial Decision Support System (GSDSS), usable for scenario building for infrastructure project planning. It operates on the basis of interdisciplinary consensus of a multidisciplinary group of experts, without strict dependency on a spatial analysis based on a single cognitive stance, using existing historical data. The method is used in a study case on planning of onshore wind energy in Mexico, which has been developed through a collaborative Geoweb application, and is functioning in a distributed and asynchronous real-time way, so-called Geospatial System of Collective Intelligence (SIGIC). The significance of this research lies in demonstrating the feasibility of a collective intelligence analysis aimed at proposing spatial locations in an energy transition context within an SEA framework.

Development of a method for fraud detection in heterogeneous data during installation of mobile applications

A method for fraud detection when installing mobile applications was proposed. The developed method, in contrast to existing ones, uses all available data regardless of their types, dimensions, and discrepancies and converts such data into homogeneous coefficients based on the proposed scaling method. This approach allows one to improve accuracy of task solution and build an open to expansion knowledge base with characteristics of fraudsters and rules of detecting fraudulent users. A system of scales for converting heterogeneous data into homogeneous coefficients has been developed which has enabled construction of a mathematical model of the scaling process. The algorithm of scaling heterogeneous data sets based on the proposed scales and the mathematical model of the process of scaling large arrays of heterogeneous data has been developed which has made it possible to reduce the whole data set to two homogeneous groups. The algorithms of processing the resulting groups of homogeneous data and detection of fraudulent users were offered. The developed algorithms using coefficients of similarity between user characteristics form fingerprints of fraudsters, determine characteristics and dependences of fraudsters which allows one to increase efficiency and speed of the process of fraudster detection. A scheme of the fraud detection process which was used in the intelligent system of automatic detection of fraudsters for carrying out of experimental studies was proposed. According to the results of experimental study, accuracy of fraudster detection was 99.14 % for a given representative sample. The results of experimental studies have shown effectiveness of automatic detection of fraudsters and the possibility of expanding formats and characteristics of fraudsters based on intelligent analysis and knowledge bases.

A novel discrete particle swarm optimization algorithm for solving bayesian network structures learning problem

ABSTRACTBayesian network is an effective representation tool to describe the uncertainty of the knowledge in artificial intelligence. One important method to learning Bayesian network from data is to employ a search procedure to explore the space of networks and a scoring metric to evaluate each candidate structure. In this paper, a novel discrete particle swarm optimization algorithm has been designed to solve the problem of Bayesian network structures learning. The proposed algorithm not only maintains the search advantages of the classical particle swarm optimization but also matches the characteristics of Bayesian networks. Meanwhile, mutation and neighbor searching operators have been used to overcome the drawback of premature convergence and balance the exploration and exploitation abilities of the particle swarm optimization. The experimental results on benchmark networks illustrate the feasibility and effectiveness of the proposed algorithm, and the comparative experiments indicate that our algorithm is highly competitive compared to other algorithms.

A new recommendation method for pertinent collaborative learners based on their intelligence and a fuzzy measure

This paper considers learners' intelligence as an influencing factor for collaborative learning. We propose a novel recommendation approach for pertinent collaborative learners. This approach is based on the learners' collaboration according to the multiple and triarchic intelligence theories. Our contribution is mainly a two-fold proposition: (1) We adopt the conceptual model of learners' intelligence, that we have proposed in other paper, and which we enhance by adding multiple intelligence and triarchic intelligence as sub-classes of the 'intelligence' class. (2) We adopt a process that aims at (a) acquiring knowledge of an individual learner's intelligence according to the multiple and triarchic intelligence theories, (b) recommending pertinent collaborators using a mathematical aggregation operator that relies on a fuzzy measure that facilitates consideration of the importance of each criterion as well as its interaction with others. An illustrative example shows the effect of this interaction.

A survey of Artificial Neural Network-based Prediction Models for Thermal Properties of Biomass

The global community has supported the need for sustainable and renewable energy due to environmental concerns from the greenhouse gas emission. Biomass stands as one of the most abundant and predictable sources of renewable energy. Therefore, to explore the maximum potential of biomass, a detailed understanding of its embedded potential is needed. However, most experimental procedures require equipment that is highly sophisticated and expensive. The advancement of knowledge in artificial intelligence and blockchain technology is unlocking new potential prediction accuracy for biomass thermal properties. Artificial Neural Network (ANN) is proving to be a vital tool that can enhance the research development in biomass energy prediction. This review highlights the stages in ANN modeling and the application of ANN in Biomass thermal value prediction. It identifies the research gaps in the current status of research on ANN as related to biomass and the direction for further study.

Modeling Transactions after Genes to Predict Instances of Fraudulent Banking and Credit Transactions

Modeling Transactions after Genes to Predict Instances of Fraudulent Banking and Credit Transactions Justin Anderson, Jean A. Muhammad, Moayed D. Daneshyari Abstract This paper will leverage knowledge of artificial intelligence and genetic programming comparing mammalian genes to banking and credit transactions to predict instances of fraudulent transactions. After identifying features of the Genetic Algorithm associated to information in credit card transactions, this research will create a model of the information in transactions based upon the mammalian genes. Full Text: PDF DOI: 10.15640/jcsit.v7n1a2

Emotional Intelligence and Humanistic Knowledge and Empathic Ability of Nurses

Emotional Intelligence and Humanistic Knowledge and Empathic Ability of Nurses

Providing Underlying Process Mining in Gamified Applications – An Intelligent Knowledge Tool for Analyzing Game Player’s Actions

Providing Underlying Process Mining in Gamified Applications – An Intelligent Knowledge Tool for Analyzing Game Player’s Actions

ИНТЕЛЛЕКТУАЛЬНАЯ ПОДДЕРЖКА ПРИНЯТИЯ РЕШЕНИЙ ПРИ АНАЛИЗЕ РИСКОВ ИННОВАЦИОННОГО ПРОЕКТА

The article deals with the innovative project management aspects, implemented at industrial enterprises. An actual task is to provide decision-making support timely to all its participants. A purpose of the research is to explore possibilities to use the intelligent knowledge management technologies in innovative projects, as well as to develop methodological and theoretical principals of innovative project management in conditions of uncertainty and risk based on ontological analysis and knowledge processing. The levels of problem situations during the implementation of the innovative project in industrial environment are formed based on the results of project risk analysis. The main problem situations in which the innovative project participants appeal to the ontology are identified. A unified approach to innovative projects management is proposed based on system integration of innovative project management ontology and decision-making support ontology. The integrated decision-making support ontology in innovative project management is the result of the research. A novelty of the proposed integrated ontology lies in displaying the concepts of project management processes and its individuals for forming the various situations descriptions which require decision-making.

Artificial intelligence and neural networks in urology

Knowledge of artificial intelligence will become essential in urology and medicine in the future. Today, computer-aided solutions are used for higher-dimensioned data or complex questions. In urological research, but also in clinical application, artificial neural networks (ANN) have been increasingly used for years and are continuously being developed. ANNs are already successfully used in urology in the field of image recognition in radiology and pathology. ANNs can potentially play a role in decision-making of modern cancer treatment. Today, access to ANN has become comparatively easy.

The Effect of Emotional Intelligence Program on Decision Making Style

Emotional intelligence plays a very important part in creating effective decisions in day to day life within the work place. The ability to deliver safe and proper care is relies on make accurate decision. Strong emotions help head nurses to make effective decisions. Aim: The present study aimed to assess the effect of emotional intelligence program on decision making style for head nurses'. Research Design: quasi experimental design was utilized. Study Setting: the study was conducted in all units at Benha University Hospital. The study sample was: all the available head nurses (57) from the above mentioned setting. Tools of data collection: Three tools were used: (1) emotional intelligence knowledge questionnaire, (2) Standardized emotional intelligence questionnaire, and (3) Nursing Decision-Making Instrument Scale Revised, 2014 (NDMI). Results: The findings of this study showed that there was a highly statistical significant improvement in the level of emotional intelligence and decision making style among head nurses through the program. Conclusion: The study concluded that highly statistically significant correlation was found between emotional intelligence and decision making style among head nurses. Recommendation: Hospital should provide continuous emotional intelligence training program to nursing staff to enhance their performance, Integration of emotional intelligence program in to nursing curricula for enhancing nursing students' competencies.

Towards Semantic Geo/BI: A Novel Approach for Semantically Enriching Geo/BI Data with OWL Ontological Layers (OOLAP and ODW) to Enable Semantic Exploration, Analysis and Discovery of Geospatial Business Intelligence Knowledge

Towards Semantic Geo/BI: A Novel Approach for Semantically Enriching Geo/BI Data with OWL Ontological Layers (OOLAP and ODW) to Enable Semantic Exploration, Analysis and Discovery of Geospatial Business Intelligence Knowledge

Script knowledge representation in game designing for instructional media

Script is one of knowledge representation in artificial intelligence. Script consists of four parts: input conditions, tracks, properties, roles, scenes, and results. Script can be used to model artificial intelligence knowledge like in game designing. The use of script in game designing is relatively rare despite the fact that script representation provides comprehensive components to display a game. This research is aimed to use Script technique in game designing. To this end, a Multimedia Design Method was used. This method consisted of several steps. The first step was concept. The second was designing script, storyboard, and navigation structure. The third was material collecting. The fourth was assembly. The fifth was testing. At the testing stage, the application designer and users carried out alpha and beta testings. The sixth was distribution. At this last step, the application was stored into a media storage. The designing resulted in game software for instructional media for elementary school students. This research came into a conclusion that Script knowledge representation could model sufficient game design components.

APPLICATION OF MACHINE LEARNING IN INTELLIGENT KNOWLEDGE SEARCH ALGORITHM

With the rapid development of Internet technology, the scale of knowledge and data production continues to expand. Such huge data has brought convenience to our lives and has brought challenges to our research. In order to obtain Internet data quickly and accurately, this paper uses machine learning algorithms to build a multidimensional knowledge search model. The model adds a real-time learning mechanism to acquire various knowledge, so that the system has intelligent features such as self-learning and selfadaptation. Through the comparative analysis of traditional knowledge search algorithms, the results show that the intelligent knowledge search algorithm based on machine learning has obvious advantages in efficiency and accuracy, which greatly shortens the search time of users.