Personality Traits of Students of Helping and Non-Helping Professions

Personality traits are very important in choosing future profession because most professions require certain skills that are related to certain personality traits. The aim of our research was to determine which personality traits contribute the most to the distinction between the students of different professions, e.g. helping and non-helping professions. On a sample of 356 students, of which 216 study helping professions, Big Five Plus Two (BF+2) personality inventory was applied. For obtained data, the classification accuracies were tested with different combinations of 184 items and 18 subtraits of the BF+2 using Case based reasoning classifier. Results showed that the best accuracy had the set of all 18 subtraits and this set outperformed the classification of every combination of subtraits or items.

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Towards Transparent Human-in-the-Loop Classification of Fraudulent Web Shops

Frontiers in Artificial Intelligence and Applications - Legal Knowledge and Information Systems ◽

10.3233/faia200873 ◽

2020 ◽

Author(s):

Daphne Odekerken ◽

Floris Bex

Keyword(s):

Case Based Reasoning ◽

Agent Architecture ◽

Human In The Loop ◽

Levels Of Detail ◽

Legal Case ◽

Dynamic Argumentation ◽

Case Based

We propose an agent architecture for transparent human-in-the-loop classification. By combining dynamic argumentation with legal case-based reasoning, we create an agent that is able to explain its decisions at various levels of detail and adapts to new situations. It keeps the human analyst in the loop by presenting suggestions for corrections that may change the factors on which the current decision is based and by enabling the analyst to add new factors. We are currently implementing the agent for classification of fraudulent web shops at the Dutch Police.

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Wear Debris Classification of Steel Production Equipment using Feature Fusion and Case-based Reasoning

ISIJ International ◽

10.2355/isijinternational.isijint-2018-076 ◽

2018 ◽

Vol 58 (7) ◽

pp. 1293-1299 ◽

Cited By ~ 3

Author(s):

Hongbing Wang ◽

Rong Huang ◽

Liyuan Gao ◽

Weishen Wang ◽

Anjun Xu ◽

...

Keyword(s):

Wear Debris ◽

Feature Fusion ◽

Steel Production ◽

Production Equipment ◽

Case Based Reasoning ◽

Case Based

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Intelligent Classification of Palm Oil Tree Pollination Using E-Nose

Mekatronika ◽

10.15282/mekatronika.v2i1.6722 ◽

2020 ◽

Vol 2 (1) ◽

pp. 28-37

Author(s):

Abdul Rahim Jalil ◽

Muhammad Sharfi Najib ◽

Suhaimi Mohd Daud ◽

Mujahid Mohamad

Keyword(s):

Palm Oil ◽

Case Based Reasoning ◽

Artificial Intelligent ◽

Male And Female ◽

Intelligent Technique ◽

Insect Activity ◽

Artificial Intelligent Technique ◽

Case Based ◽

Odour Profile

The pollination period is one of the crucial steps needed to ensure crop yield increases, especially in palm oil palm plantations. Most of the research has difficulty determining the pollination period of palm oil. Many problems contribute to this problem, such as difficut to reach and depedency of the polination insect as the insect activity is influenced by the surrounding enviroment.E-Nose can help determine the period by classifiy odour pattern of the male and female palm oil flower. The pattern of each of the flowers were classified using cased – based reasoning artificial intelligent technique. This paper shows the research of the palm oil pollination flower odour profile pattern using case-based reasoning (CBR) classifier.

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Case-Based Reasoning and Some Typical Applications

Global Trends in Intelligent Computing Research and Development - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-4936-1.ch009 ◽

2014 ◽

pp. 229-267 ◽

Cited By ~ 1

Author(s):

Durga Prasad Roy ◽

Baisakhi Chakraborty

Keyword(s):

Problem Solving ◽

Machine Intelligence ◽

Case Based Reasoning ◽

Engineering System ◽

Yale University ◽

Control Pattern ◽

Clustering And Classification ◽

How People Learn ◽

Case Based

Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977–1993. CBR may be defined as a model of reasoning that incorporates problem solving, understanding, and learning, and integrates all of them with memory processes. It focuses on the human problem solving approach such as how people learn new skills and generates solutions about new situations based on their past experience. Similar mechanisms to humans who intelligently adapt their experience for learning, CBR replicates the processes by considering experiences as a set of old cases and problems to be solved as new cases. To arrive at the conclusions, it uses four types of processes, which are retrieve, reuse, revise, and retain. These processes involve some basic tasks such as clustering and classification of cases, case selection and generation, case indexing and learning, measuring case similarity, case retrieval and inference, reasoning, rule adaptation, and mining to generate the solutions. This chapter provides the basic idea of case-based reasoning and a few typical applications. The chapter, which is unique in character, will be useful to researchers in computer science, electrical engineering, system science, and information technology. Researchers and practitioners in industry and R&D laboratories working in such fields as system design, control, pattern recognition, data mining, vision, and machine intelligence will benefit.

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