61 research outputs found
Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree
No full textIn this thesis, a novel approach to detecting and diagnosing comprehensive fault conditions of Induction Motors (IMs) using an Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) is proposed. The model, known as FMM-CART, exploits the advantages of both FMM and the CART for undertaking data classification and rule extraction problems. Modifications to FMM and the CART are introduced in order for the resulting model to work efficiently. In order to compare the FMM-CART performance, benchmark data sets from motor bearing faults and from the UCI machine learning repository are used for analysis, with the results discussed and compared with those from other methods
Performance Optimization of Commercial Photovoltaic Technologies Under Local Spectral Irradiances Using Machine Learning
No full textA Deep Learning Framework for the Detection of Malay Hate Speech
No full textAlthough social media can efficiently disseminate information, they also facilitate the dissemination of online abuse, harassment, and hate speech. In 2019, United Nations Secretary-General introduced the United Nations Strategy and Plan of Action on Hate Speech in response to the alarming global trend of rising hate speech. It is crucial to prevent hate speech because it can have severe negative effects on both individuals and society. While much research has been conducted on detecting online hate speech in English, little research has been conducted in other languages, such as Malay. In this paper, we present the first benchmark dataset HateM for detecting hate speech in Malay, comprised of over 4,892 annotated tweets. We created a two-channel deep learning model, XLCaps, to effectively manage noisy Malay language posts. One channel’s input is the XLNet language model followed by the capsule network, while the other channel’s input is the FastText embedding with Bi-GRU. Our proposed model surpasses the baseline models in terms of overall accuracy and F1 measurement, which are 80.69% and 80.41%, respectively. This work contributes to the prevention of hate speech in Malay and can serve as a basis for future study in this area. The approach to effectively managing noisy Malay posts can be also applied to other languages. The code and dataset are available at https://github.com/MaityKrishanu/Hate_Malay
Meta-cognitive Recurrent Recursive Kernel OS-ELM for concept drift handling
No full textIn this paper, a Meta-cognitive Recurrent Recursive Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (meta-RRKOS-ELM-DDM) is proposed. It combines the strengths of Recurrent Kernel Online Sequential Extreme Learning Machine with a new modified Drift Detector Mechanism (DDM) and Approximate Linear Dependency Kernel Filter (ALD) in solving concept drift problems and reducing complex computations in the learning. The recursive kernel method successfully replaces the normal kernel method in Recurrent Kernel Online Sequential Extreme Learning Machine with DDM (RKOS-ELM-DDM) and generates a fixed reservoir with optimized information in enhancing the forecasting performance. Meta-cognitive learning strategy decides when the incoming data needs to be updated, retrained, or discarded during learning and automatically finding ALD threshold that reduces the learning time of prediction model. In the experiment, six synthetic and three real-world time series datasets are used to evaluate the ability of recursive kernel method, the performance of concept drift detectors, and meta-cognitive learning strategy in time series prediction. Experimental results indicate the meta-RRKOS-ELM with DDM has superior prediction ability in the different predicting horizons as compared with other algorithms
Detection and diagnosis of broken rotor bars and eccentricity faults in induction motors using the Fuzzy Min-Max neural network
No full textPersonality affected robotic emotional model with associative memory for human-robot interaction
No full textThe decision making process in communication is affected by internal and external factors from dynamic environments. Humans can perform a variety of behaviors in a similar situation, unlike robots. This paper discusses human psychological phenomena during communication from the point of view of internal and external factors, such as perception, memory, and emotional information. Based on these, we introduce the personality affected robotic emotional model and the emotion affected associative memory model for the robot. We organize an interactive robot system to provide suitable decisions for the robot. Results from interactive communication experiments indicate that the robot is able to perform different actions based on internal and external factors
A hybrid FAM-CART model for online data classification
No full textIn this paper, an online soft computing model based on an integration between the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) for undertaking data classification problems is presented. Online FAM network is useful for conducting incremental learning with data samples, whereas the CART model prevails in depicting the knowledge learned explicitly in a tree structure. Capitalizing on their respective advantages, the hybrid FAM‐CART model is capable of learning incrementally while explaining its predictions with knowledge elicited from data samples. To evaluate the usefulness of FAM‐CART, 2 sets of benchmark experiments with a total of 12 problems are used in both offline and online learning modes. The results are examined and compared with those published in the literature. The experimental outcome positively indicates that the online FAM‐CART model is useful for tackling data classification tasks. In addition, a decision tree is produced to allow users in understanding the predictions, which is an important property of the hybrid FAM‐CART model in supporting decision‐making tasks
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