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

Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams

The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction ($DA^3$) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only $25\%$ label proportions. It shows highly competitive performance even if compared with fully supervised learners with $100\%$ label proportions.Comment: This paper has been accepted for publication in Information Science

Dental-YOLO: Alveolar Bone and Mandibular Canal Detection on Cone Beam Computed Tomography Images for Dental Implant Planning

In planning a mandibular posterior dental implant, identifying the exact location of the alveolar bone (AB) and mandibular canal (MC) is essential to determine the height and width of the available bone. Cone beam computed tomography (CBCT) is a 3D imaging modality widely used for dental implant planning, which requires a lower radiation dose compared to medical CT and can provide crosssectional image quality to visualize AB and MC. The radiologist carried out the AB and MC detection processes manually on each section of the CBCT image until the appropriate area was determined for bone measurement. This process is time consuming, and the measurement accuracy depends on the ability and experience of the radiologist. This study proposes an automatic and simultaneous detection system for AB and MC based on 2D grayscale CBCT images, that can simplify and expedite dental implant planning. We introduce Dental-YOLO, an efficient version of YOLOv4 specifically developed to detect AB and MC, with two-scale feature maps at low and high scales. The height and width of the available bone in the implant area were estimated by using the detected bounding box attributes. The AB and MC detection performances using Dental-YOLO reached a mean average precision of 99.46%. The two-way analysis of variance (ANOVA) test showed no difference in the bone height and width measurements produced by the proposed approach and manual measurement by radiologists. Our results suggest that the Dental-YOLO detection system could be helpful for dental implant surgery and presurgical treatment planning