113 citations · 504 across the 39 of their papers we have counts for
43 papers
Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting
Md Rasel Sarkar, Sreenatha G. Anavatti, Tanmoy Dam +2
The main objective of this study is to propose an enhanced wind power forecasting (EWPF) transformer model for handling power grid operations and boosting power market competition.…
Class-Incremental Learning via Knowledge Amalgamation
Marcus de Carvalho, Mahardhika Pratama, Jie Zhang +1
Catastrophic forgetting has been a significant problem hindering the deployment of deep learning algorithms in the continual learning setting. Numerous methods have been proposed t…
Scalable Adversarial Online Continual Learning
Tanmoy Dam, Mahardhika Pratama, MD Meftahul Ferdaus +2
Adversarial continual learning is effective for continual learning problems because of the presence of feature alignment process generating task-invariant features having low susce…
Reinforced Continual Learning for Graphs
Appan Rakaraddi, Siew Kei Lam, Mahardhika Pratama +1
Graph Neural Networks (GNNs) have become the backbone for a myriad of tasks pertaining to graphs and similar topological data structures. While many works have been established in…
Latent Preserving Generative Adversarial Network for Imbalance classification
Tanmoy Dam, Md Meftahul Ferdaus, Mahardhika Pratama +3
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem. Classic classification algorithms tend…
Autonomous Cross Domain Adaptation under Extreme Label Scarcity
Weiwei Weng, Mahardhika Pratama, Choiru Za'in +4
A cross domain multistream classification is a challenging problem calling for fast domain adaptations to handle different but related streams in never-ending and rapidly changing…
Unsupervised Learning for Identifying High Eigenvector Centrality Nodes: A Graph Neural Network Approach
Appan Rakaraddi, Mahardhika Pratama
The existing methods to calculate the Eigenvector Centrality(EC) tend to not be robust enough for determination of EC in low time complexity or not well-scalable for large networks…
ACDC: Online Unsupervised Cross-Domain Adaptation
Marcus de Carvalho, Mahardhika Pratama, Jie Zhang +1
We consider the problem of online unsupervised cross-domain adaptation, where two independent but related data streams with different feature spaces -- a fully labeled source strea…
Unsupervised Continual Learning in Streaming Environments
Andri Ashfahani, Mahardhika Pratama
A deep clustering network is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step. While automatic…
Automatic Online Multi-Source Domain Adaptation
Renchunzi Xie, Mahardhika Pratama
Knowledge transfer across several streaming processes remain challenging problem not only because of different distributions of each stream but also because of rapidly changing and…
Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach
Mahardhika Pratama, Andri Ashfahani, Edwin Lughofer
Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground…
Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams
Mahardhika Pratama, Choiru Za'in, Edwin Lughofer +2
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 imp…
Autonomous Deep Quality Monitoring in Streaming Environments
Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer +1
The common practice of quality monitoring in industry relies on manual inspection well-known to be slow, error-prone and operator-dependent. This issue raises strong demand for aut…
Continual Learning via Inter-Task Synaptic Mapping
Mao Fubing, Weng Weiwei, Mahardhika Pratama +1
Learning from streaming tasks leads a model to catastrophically erase unique experiences it absorbs from previous episodes. While regularization techniques such as LWF, SI, EWC hav…
Weakly Supervised Deep Learning Approach in Streaming Environments
Mahardhika Pratama, Andri Ashfahani, Mohamad Abdul Hady
The feasibility of existing data stream algorithms is often hindered by the weakly supervised condition of data streams. A self-evolving deep neural network, namely Parsimonious Ne…
Toward Underground Localization: Lidar Inertial Odometry Enabled Aerial Robot Navigation
Jiun Fatt Chow, Basaran Bahadir Kocer, John Henawy +4
Localization can be achieved by different sensors and techniques such as a global positioning system (GPS), wifi, ultrasonic sensors, and cameras. In this paper, we focus on the la…
ATL: Autonomous Knowledge Transfer from Many Streaming Processes
Mahardhika Pratama, Marcus de Carvalho, Renchunzi Xie +2
Transferring knowledge across many streaming processes remains an uncharted territory in the existing literature and features unique characteristics: no labelled instance of the ta…
DEVDAN: Deep Evolving Denoising Autoencoder
Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer +1
The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic des…
Automatic Construction of Multi-layer Perceptron Network from Streaming Examples
Mahardhika Pratama, Choiru Za'in, Andri Ashfahani +2
Autonomous construction of deep neural network (DNNs) is desired for data streams because it potentially offers two advantages: proper model's capacity and quick reaction to drift…
Aerial Robot Control in Close Proximity to Ceiling: A Force Estimation-based Nonlinear MPC
Basaran Bahadir Kocer, Mehmet Efe Tiryaki, Mahardhika Pratama +2
Being motivated by ceiling inspection applications via unmanned aerial vehicles (UAVs) which require close proximity flight to surfaces, a systematic control approach enabling safe…
Real-time UAV Complex Missions Leveraging Self-Adaptive Controller with Elastic Structure
Mohamad Abdul Hady, Basaran Bahadir Kocer, Harikumar Kandath +1
The expectation of unmanned air vehicles (UAVs) pushes the operation environment to narrow spaces, where the systems may fly very close to an object and perform an interaction. Thi…
UAV Control in Close Proximities - Ceiling Effect on Battery Lifetime
Basaran Bahadir Kocer, Volkan Kumtepeli, Tegoeh Tjahjowidodo +4
With the recent developments in the unmanned aerial vehicles (UAV), it is expected them to interact and collaborate with their surrounding objects, other robots and people in order…
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti +2
There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In t…
Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments
Andri Ashfahani, Mahardhika Pratama
The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning…