Algorithm-based Learning Research Articles

Apriori Algorithm-Based Learning Behavior Mining for Mobile Education Platforms

Apriori Algorithm-Based Learning Behavior Mining for Mobile Education Platforms

Technological advancements, digital transformation, and future trends in blood transfusion services

Misidentification, mistransfusion, and pre-analytical errors are all regarded as major challenges and risks in safe blood transfusion procedures. To provide a high level of accuracy, traceability, automation, and reliability in blood transfusion services, traditional methods should be upgraded with modern technologies such as block chain technology, machine learning, artificial intelligence (AI), artificial neural networks, algorithm-based learning, and the implementation of radio frequency identification (RFID) and the internet of things (IoT). This technology helps reduce errors, retrieve data, forecast blood demand, reduce blood waste, manage blood storage, and manage workload, ensuring transfusion safety. The technology is still in the initial stages of development and by addressing issues such as data loss, patient data privacy, and cost-effectiveness, the technology will become a revolution in transfusion services.

Exploring the Balance Between Computational Thinking and Learning Motivation in Elementary Programming Education: An Empirical Study With Game-Based Learning

Computational thinking(CT) skills are generally regarded as a basic ability for problem-solving and are gradually filtering down to younger age groups. Existing research has attempted to increase interaction and enhance the learning interest of elementary school students through block-based programming environments represented by Scratch. However, criticism suggests that this approach has potential long-term constraints on CT education. Therefore, this study designed a game-based project to investigate students learning motivation and CT performance in different programming environments. A total of 108 fifth-grade students participated in the experiment and were divided into two experimental groups (EG1 and EG2) and a control group (CG). The students in experimental groups all undertook game-based learning, while EG1 students used C++ and EG2 students used Scratch; CG students used C++ for traditional algorithm-based learning. Findings suggest that traditional programming languages with game-based learning can also substantially improve learning interest, which can promote the efficiency of improving CT for students who already have some motivation and foundation for learning. But for students who still lack motivation, are also encouraged to develop interest in a block-based programming environment. Teachers can choose the most appropriate approach for students to find the best balance between learning efficiency and long-term interest.

Recognizing gender from human facial regions using genetic algorithm

Recently, recognition of gender from facial images has gained a lot of importance. There exist a handful of research work that focus on feature extraction to obtain gender-specific information from facial images. However, analyzing different facial regions and their fusion help in deciding the gender of a person from facial images. In this paper, we propose a new approach to identify gender from frontal facial images that is robust to background, illumination, intensity, and facial expression. In our framework, first the frontal face image is divided into a number of distinct regions based on facial landmark points that are obtained by the Chehra model proposed by Asthana et al. The model provides 49 facial landmark points covering different regions of the face, e.g., forehead, left eye, right eye, lips. Next, a face image is segmented into facial regions using landmark points and features are extracted from each region. The compass LBP feature, a variant of LBP feature, has been used in our framework to obtain discriminative gender-specific information. Following this, a support vector machine-based classifier has been used to compute the probability scores from each facial region. Finally, the classification scores obtained from individual regions are combined with a genetic algorithm-based learning to improve the overall classification accuracy. The experiments have been performed on popular face image datasets such as Adience, cFERET (color FERET), LFW and two sketch datasets, namely CUFS and CUFSF. Through experiments, we have observed that, the proposed method outperforms existing approaches.

A genetic algorithm-based learning approach to understand customer satisfaction with OTA websites

In an extremely competitive marketplace, it is increasingly important for online travel agencies (OTAs) to understand customer satisfaction of different segments. The survey method has been widely used to gain such understanding. However, few previous studies on the tourism and hospitality business have proposed intelligent solutions to analyze such survey data to understand customer preferences on different criteria for different segments, and to determine how customers obtain overall satisfaction across different criteria. In this study, we follow a design-science research paradigm to develop a genetic algorithm-based learning approach to understand customer satisfaction and their psychometric reasons. We further validate this approach through an empirical study for evaluating OTA websites. The results show that different customer segments have different opinions on the importance of various evaluation criteria. The results also reveal that customers tend to judge OTA websites in terms of certain important criteria, instead of by the weighted average of every factor concerned. The proposed approach and the findings of this study can provide constructive suggestions to practitioners and researchers for developing customized marketing campaigns and improving the services of OTA websites.

Enhanced fuzzy-connective-based hierarchical aggregation network using particle swarm optimization

The fuzzy-connective-based aggregation network is similar to the human decision-making process. It is capable of aggregating and propagating degrees of satisfaction of a set of criteria in a hierarchical manner. Its interpreting ability and transparency make it especially desirable. To enhance its effectiveness and further applicability, a learning approach is successfully developed based on particle swarm optimization to determine the weights and parameters of the connectives in the network. By experimenting on eight datasets with different characteristics and conducting further statistical tests, it has been found to outperform the gradient- and genetic algorithm-based learning approaches proposed in the literature; furthermore, it is capable of generating more accurate estimates. The present approach retains the original benefits of fuzzy-connective-based aggregation networks and is widely applicable. The characteristics of the learning approaches are also discussed and summarized, providing better understanding of the similarities and differences among these three approaches.

A NOVEL FLOW-BASED METHOD USING GREY RELATIONAL ANALYSIS FOR PATTERN CLASSIFICATION

Flow-based methods based on the outranking relation theory are extensively used in multiple criteria classification problems. Flow-based methods usually employed an overall preference index representing the flow to measure the intensity of preference for one pattern over another pattern. A traditional flow obtained by the pairwise comparison may not be complete since it does not globally consider the differences on each criterion between all the other patterns and the latter. That is, a traditional flow merely locally considers the difference on each criterion between two patterns. In contrast with traditional flows, the relationship-based flow is newly proposed by employing the grey relational analysis to assess the flow from one pattern to another pattern by considering the differences on each criterion between all the other patterns and the latter. A genetic algorithm-based learning algorithm is designed to determine the relative weights of respective criteria to derive the overall relationship index of a pattern. Our method is tested on several real-world data sets. Its performance is comparable to that of other well-known classifiers and flow-based methods.

Choquet integral-based hierarchical networks for evaluating customer service perceptions on fast food stores

It is known that a hierarchical decision structure consisting of multiple criteria can be modeled by a Choquet integral-based hierarchical network. With a given input–output dataset, the degree of importance of each criterion can be directly obtained from the corresponding connection weight after the network has been trained from samples. Since each output value or the synthetic evaluation of an alternative derived from uncertain assessments has its upper and lower bounds, the degree of importance of each criterion should not be unique and can be distributed in a range. In this paper, the range of the degree of importance of each criterion is obtained by three Choquet integral-based hierarchical networks with the pre-specified hierarchical structure: one is a common network constructed by merely minimizing the least squared error, and the others are employed to determine a nonlinear interval regression model. The above three networks are trained with a given input–output dataset using the proposed genetic algorithm-based learning algorithm. Empirical results of evaluating customer service perceptions on fast food stores demonstrate that the proposed method can identify key factors that have stronger effect on service quality perceptions by employing three Choquet integral-based hierarchical networks with the hierarchical structure to determine possible ranges of the degree of importance of respective aspects and attributes.

Evolutionary algorithm-based learning of fuzzy neural networks. Part 2: Recurrent fuzzy neural networks

Fuzzy neural networks (FNN) as opposed to neuro-fuzzy systems, whose main task is to process numerical relationships, can process both numerical (measurement based) information and perception based information. In spite of great importance of fuzzy feed-forward and recurrent neural networks for solving wide range of real-world problems, today there are no effective training algorithm for them. Currently there are two approaches for training of FNN. First approach is based on application of the level-sets of fuzzy numbers and the back-propagation (BP) algorithm. The second approach involves using evolutionary algorithms to minimize error function and determine the fuzzy connection weights and biases. The method based on the second approach was proposed by the authors and published in Part 1 of this paper [R.A. Aliev, B. Fazlollahi, R. Vahidov, Genetic algorithm-based learning of fuzzy neural networks. Part 1: feed-forward fuzzy neural networks, Fuzzy Sets and Systems 118 (2001) 351–358]. In contrast to the BP and other supervised learning algorithms, evolutionary algorithms do not require nor use information about differentials, and hence, they are most effective in case where the derivative is very difficult to obtain or even unavailable. However, the main deficiency of the existing FNN based on the feed-forward architecture is its adherence to static problems. In case of dynamic or temporal problems there is a need for recurrent fuzzy neural networks (RFNN). Designing efficient training algorithms for RFNN has recently become an active research direction. In this paper we propose an effective differential evolution optimization (DEO) based learning algorithm for RFNN with fuzzy inputs, fuzzy weights and biases, and fuzzy outputs. The effectiveness of the proposed method is illustrated through simulation of benchmark forecasting and identification problems and comparisons with the existing methods. The suggested approach has also been used for real applications in an oil refinery plant for petrol production forecasting.

Instructional Design Rules for Algorithmic Subject Matter

Algorithms are essential to computer programming because of their structure and precision. These same characteristics could also be used in the field of instructional technology, but usually aren't because they are viewed as being too complex. This article attempts to clarify their utility by first analyzing the structural and processing characteristics of algorithms. Simple examples are used to illustrate how the design process is influenced by these characteristics. Five instructional design guidelines, or rules, are then presented and discussed. The rules are derived from empirical examinations of algorithm-based learning and conform to the limitations dictated by several information processing principles.

Operating rules classification system of water supply reservoir based on Learning Classifier System

Genetic algorithm-based learning classifier system (LCS) is a massively parallel, message-passing and rule-based machine learning system. But its potential self-adaptive learning capability has not been paid enough attention in reservoir operation research. In this paper, an operating rule classification system based on LCS, which learns through credit assignment (the bucket brigade algorithm) and rule discovery(the genetic algorithm), is established to extract water-supply reservoir operating rules. The proposed system acquires the online identification rate 95% for training samples and offline rate 85% for testing samples in a case study, and further discussions are made about the impacts on the performances or behaviors of the rule classification system from three aspects of obtained rules, training or testing samples and the comparisons between the rule classification system and the artificial neural network (ANN). The results indicate the learning classifier system is practical and effective to obtain the reservoir supply operating rules.

Nonadditive grey single-layer perceptron with Choquet integral for pattern classification problems using genetic algorithms

Since the output of a single-layer perceptron can be interpreted as the synthetic evaluation of the relationship between the input pattern and one class for two-class pattern classification problems, this paper proposes a novel perceptron with nonadditive property, named the grey single-layer perceptron, by measuring the grades of relationship between an input pattern and this class for individual attributes with the grey relational analysis. A nonlinear integral, namely the Choquet integral, with respect to the fuzzy measure serves as an activation function of a neuron to synthesize the performance values owing to the interaction among attributes. Moreover, the connection weights are interpreted as the degrees of importance of the respective input signals and can be determined by the genetic algorithm-based learning algorithm. The experimental results further demonstrate that the generalization ability of the proposed grey single-layer perceptron is better than or comparable to that of other fuzzy or non-fuzzy classification methods.

A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation

In this paper, a fuzzy model is suggested for the prediction of wind speed and the produced electrical power at a wind park. The model is trained using a genetic algorithm-based learning scheme. The training set includes wind speed and direction data, measured at neighboring sites up to 30 km away from the wind turbine clusters. Extensive simulation results are shown for two application cases, providing wind speed forecasts from 30 min to 2 h ahead. It is demonstrated that the suggested model achieves an adequate understanding of the problem while it exhibits significant improvement compared to the persistent method.

Genetic algorithm-based learning of fuzzy neural networks. Part 1: feed-forward fuzzy neural networks

In spite of great importance of fuzzy feed-forward and recurrent neural networks (FNN) for solving wide range of real-world problems, today there is no effective learning algorithm for FNN. In this paper we propose an effective genetic-based learning mechanism for FNN with fuzzy inputs, fuzzy weights expressed as LR-fuzzy numbers, and fuzzy outputs. The effectiveness of the proposed method is illustrated through simulation of fuzzy regression for quality evaluation and comparison with the widely used learning method based on α-cuts and fuzzy arithmetic. Finally, we demonstrate the use of the proposed learning procedure for calculating fuzzy-valued profit in an oligopolistic environment.

A Machine-Learning Approach to Automated Negotiation and Prospects for Electronic Commerce

This paper shows how a system of artificial adaptive agents, using a genetic algorithm-based learning technique, can learn strategies that enable it to effectively participate in stylized business negotiations. The negotiation policies learned are evaluated on several dimensions including joint outcomes, nearness to the efficient frontier, and similarity to outcomes of human negotiations. The results are promising for integrating such agents into practicable electronic commerce systems. What a system might look like is discussed, as are ways in which particular classes of business negotiations could be supported or even entirely automated.