In the wake of the current COVID-19 pandemic, most face-to-face activities have been restricted including the scientific program such as capacity building. This, in turn, requires rapid redesign of capacity building implementation during the pandemic period. Hence, this study aims at organizing a virtual capacity building for early-career scientists as an alternative to traditional setting which requires face-to-face interaction. This virtual capacity building focuses on the use of remote sensing for effective monitoring of ocean climate resilience. This program was developed using Massive Open Online Course (MOOC) under UTM Open Learning platform which is known as MOOC on Ocean Remote Sensing towards Climate Resilience. A total of 26 international participants successfully participated in this MOOC UTM-PORSEC. This virtual program contains 14 modules that were conducted by experienced instructors in the field of ocean remote sensing. The program evaluation has shown that the overall Programme Learning Outcomes have scored above 70%. Furthermore, 88% of the MOOC-PORSEC participants have completed all the course content, assignments and quizzes. The developed virtual capacity building has provided a new digital learning experience as well as it saves the cost of travelling and time at the expense of physical interaction.

Semisupervised clustering algorithms partition a given data set using limited supervision from the user. The success of these algorithms depends on the type of supervision and also on the kind of dissimilarity measure used while creating partitions of the space. This paper proposes a clustering algorithm that uses supervision in terms of relative comparisons, viz., x is closer to y than to z. The proposed clustering algorithm simultaneously learns the underlying dissimilarity measure while finding compact clusters in the given data set using relative comparisons. Through our experimental studies on high-dimensional textual data sets, we demonstrate that the proposed algorithm achieves higher accuracy and is more robust than similar algorithms using pairwise constraints for supervision.

Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network (SVFNN) is proposed for pattern classification in this paper. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. A learning algorithm consisting of three learning phases is developed to construct the SVFNN and train its parameters. In the first phase, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the second phase, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. In the third phase, the relevant fuzzy rules are selected by the proposed reducing fuzzy rule method. To investigate the effectiveness of the proposed SVFNN classification, it is applied to the Iris, Vehicle, Dna, Satimage, Ijcnn1 datasets from the UCI Repository, Statlog collection and IJCNN challenge 2001, respectively. Experimental results show that the proposed SVFNN for pattern classification can achieve good classification performance with drastically reduced number of fuzzy kernel functions.

Semi-supervised clustering algorithms partition a given data set using limited supervision from the user. In this paper, we propose a clustering algorithm that uses supervision in terms of relative comparisons, viz., x is closer to y than to z. The success of a clustering algorithm also depends on the kind of dissimilarity measure. The proposed clustering algorithm learns the underlying dissimilarity measure while finding compact clusters in the given data set. Through our experimental studies on high-dimensional textual data sets, we demonstrate that the proposed algorithm achieves higher accuracy than the algorithms using pair-wise constraints for supervision.

In this paper, a novel HVS-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the human visual system (HVS) is proposed to classify pixels of the input image into human perception non-sensitive class and sensitive class. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce higher visual quality of the interpolated image than the conventional interpolation methods.

A novel integration of methods for person authentication and tracking is proposed for real time security systems. The implementation of the idea for this real time implementation follows a three step procedure-face detection, recognition and content-based tracking. Instead of analyzing continuous videos we sample the frame based on a method derived from Shannon's information theory model. The Face-detector detects multi-viewed faces in a video using feature-based kernel methods in a reduced feature space obtained using ICA. The identified "face regions" are then passed on to the face recognition system which is based on Active Appearance Models (AAM). Once the subject is recognized, it can be tracked in the video using kernel based object tracking method. Several space reduction techniques have been used like ICA, PCA and skin-color segmentation.