4 results
Search Results
Now showing 1 - 4 of 4
Article Citation - WoS: 28Development of an Intelligent Tutoring System Using Bayesian Networks and Fuzzy Logic for a Higher Student Academic Performance(Mdpi, 2020) Eryilmaz, Meltem; Adabashi, AfafIn this experimental study, an intelligent tutoring system called the fuzzy Bayesian intelligent tutoring system (FB-ITS), is developed by using artificial intelligence methods based on fuzzy logic and the Bayesian network technique to adaptively support students in learning environments. The effectiveness of the FB-ITS was evaluated by comparing it with two other versions of an Intelligent Tutoring System (ITS), fuzzy ITS and Bayesian ITS, separately. Moreover, it was evaluated by comparing it with an existing traditional e-learning system. In order to evaluate whether the academic performance of the students in different learning groups differs or not, analysis of covariance (ANCOVA) was used based on the students' pre-test and post-test scores. The study was conducted with 120 undergraduate university students. Results showed that students who studied using FB-ITS had significantly higher academic performance on average compared to other students who studied with the other systems. Regarding the time taken to perform the post-test, the results indicated that students who used the FB-ITS needed less time on average compared to students who used the traditional e-learning system. From the results, it could be concluded that the new system contributed in terms of the speed of performing the final exam and high academic success.Article Citation - WoS: 77Citation - Scopus: 101An Intelligent Process Planning System for Prismatic Parts Using Step Features(Springer London Ltd, 2007) Amaitik, Saleh M.; Kilic, S. EnginThis paper presents an intelligent process planning system using STEP features (ST-FeatCAPP) for prismatic parts. The system maps a STEP AP224 XML data file, without using a complex feature recognition process, and produces the corresponding machining operations to generate the process plan and corresponding STEP-NC in XML format. It carries out several stages of process planning such as operations selection, tool selection, machining parameters determination, machine tools selection and setup planning. A hybrid approach of most recent techniques ( neural networks, fuzzy logic and rule-based) of artificial intelligence is used as the inference engine of the developed system. An object-oriented approach is used in the definition and implementation of the system. An example part is tested and the corresponding process plan is presented to demonstrate and verify the proposed CAPP system. The paper thus suggests a new feature-based intelligent CAPP system for avoiding complex feature recognition and knowledge acquisition problems.Article Citation - WoS: 19Citation - Scopus: 21A Hybrid Approach for Selecting Material Handling Equipment in a Warehouse(Taylor & Francis Ltd, 2016) Saputro, Thomy Eko; Rouyendegh (Babek Erdebilli), Babak DaneshvarWarehouse operations are closely related to material handling activities. Loading, unloading, transporting and picking material constitute a huge part of the activities. In order to handle material properly as well as to contribute value to the material, the operator and the environment, utilizing Material Handling Equipment (MHE) is required. The selection of proper MHEs requires great focus since its consideration is linked to mutli-criteria and multi-objective decision making problems. Here, a hybrid method is proposed to address the MHE selection problem. An approach that integrates the entropy based hierarchical fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Multi-Objective Mixed Integer Linear Programming (MOMILP) is used for seeking the best alternative. The evaluation of alternatives is performed based on both subjective and objective criteria. Subjective weights are derived from a fuzzy Analytic Hierarchy Process (AHP). To deal with objective criteria, the entropy method is adopted to determine the weights, and the integrated weights are also calculated. The alternatives are rated by using fuzzy TOPSIS. For final execution of the selection, an MOMILP model is developed incorporating two goals, namely to minimize the disadvantage of material handling operation and to minimize the total cost of material handling. The AUGMented E-CONtraint method (AUGMECON) is used to solve the model. A case study is given to illustrate the method. The results show the effectiveness of the hybrid method in complex decision making.Article Citation - WoS: 51Citation - Scopus: 63Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method(Sage Publications Ltd, 2014) Arvin, Farshad; Turgut, Ali Emre; Bazyari, Farhad; Arikan, Kutluk Bilge; Bellotto, Nicola; Yue, ShigangAggregation in swarm robotics is referred to as the gathering of spatially distributed robots into a single aggregate. Aggregation can be classified as cue-based or self-organized. In cue-based aggregation, there is a cue in the environment that points to the aggregation area, whereas in self-organized aggregation no cue is present. In this paper, we proposed a novel fuzzy-based method for cue-based aggregation based on the state-of-the-art BEECLUST algorithm. In particular, we proposed three different methods: naive, that uses a deterministic decision-making mechanism; vector-averaging, using a vectorial summation of all perceived inputs; and fuzzy, that uses a fuzzy logic controller. We used different experiment settings: one-source and two-source environments with static and dynamic conditions to compare all the methods. We observed that the fuzzy method outperformed all the other methods and it is the most robust method against noise.
