Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS

Many distributed machine learning frameworks have recently been built to speed up the large-scale data learning process. However, most distributed machine learning used in these frameworks still uses an offline algorithm model which cannot cope with the data stream problems. In fact, large-scale data are mostly generated by the non-stationary data stream where its pattern evolves over time. To address this problem, we propose a novel Evolving Large-scale Data Stream Analytics framework based on a Scalable Parsimonious Network based on Fuzzy Inference System (Scalable PANFIS), where the PANFIS evolving algorithm is distributed over the worker nodes in the cloud to learn large-scale data stream. Scalable PANFIS framework incorporates the active learning (AL) strategy and two model fusion methods. The AL accelerates the distributed learning process to generate an initial evolving large-scale data stream model (initial model), whereas the two model fusion methods aggregate an initial model to generate the final model. The final model represents the update of current large-scale data knowledge which can be used to infer future data. Extensive experiments on this framework are validated by measuring the accuracy and running time of four combinations of Scalable PANFIS and other Spark-based built in algorithms. The results indicate that Scalable PANFIS with AL improves the training time to be almost two times faster than Scalable PANFIS without AL. The results also show both rule merging and the voting mechanisms yield similar accuracy in general among Scalable PANFIS algorithms and they are generally better than Spark-based algorithms. In terms of running time, the Scalable PANFIS training time outperforms all Spark-based algorithms when classifying numerous benchmark datasets.Comment: 20 pages, 5 figure

A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

Fuzzy Inference System in Energy Demand Prediction

No abstract available

Intelligent Security for Phishing Online using Adaptive Neuro Fuzzy Systems

Anti-phishing detection solutions employed in industry use blacklist-based approaches to achieve low false-positive rates, but blacklist approaches utilizes website URLs only. This study analyses and combines phishing emails and phishing web-forms in a single framework, which allows feature extraction and feature model construction. The outcome should classify between phishing, suspicious, legitimate and detect emerging phishing attacks accurately. The intelligent phishing security for online approach is based on machine learning techniques, using Adaptive Neuro-Fuzzy Inference System and a combination sources from which features are extracted. An experiment was performed using two-fold cross validation method to measure the system’s accuracy. The intelligent phishing security approach achieved a higher accuracy. The finding indicates that the feature model from combined sources can detect phishing websites with a higher accuracy. This paper contributes to phishing field a combined feature which sources in a single framework. The implication is that phishing attacks evolve rapidly; therefore, regular updates and being ahead of phishing strategy is the way forward