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Chapter
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Algorithm Analysis and Problem Complexity
Pattern Recognition
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Chapter
Challenges in Understanding Trust and Trust Modeling
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Chapter
AVCA: Autonomous Virtual Cognitive Assessment
Fast-paced and ever-growing advances in Artificial Intelligence (AI) and Deep Neural Network (DNN) models have initiated works on autonomous monitoring/screening systems to assess individuals’ cognitive state....
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Chapter
Fairness, Bias and Trust in the Context of Biometric-Enabled Autonomous Decision Support
Develo** a trustworthy biometric-enabled autonomous and semi-autonomous system involves computing the amount of bias and fairness of the data upon which the decisions are made. In this paper, we evaluate the...
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Chapter
Addressing Dataset Shift for Trustworthy Deep Learning Diagnostic Ultrasound Decision Support
Ultrasound (US) is the most widely used medical imaging modality due to its low cost, portability, real time imaging ability and use of non-ionizing radiation. However, unlike other imaging modalities such as ...
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Chapter
Stress Contagion Protocols for Human and Autonomous Robot Teams
The objective of this paper is to formalize the stress contagion protocols in first responder teams which respond to life-threatening crises on a daily basis. Such teams include both human and autonomous machine ...
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Chapter
Light-Weight CNN-Attention Based Architecture Trained with a Hybrid Objective Function for EMG-Based Human Machine Interfaces
The presented research focuses on Hand Gesture Recognition (HGR) utilizing Surface-Electromyogram (sEMG) signals. This is due to its unique potential for decoding wearable data to interpret human intent for im...
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Chapter
An Autonomous Fake News Recognition System by Semantic Learning and Cognitive Computing
It has been well understood that fake news recognition is a persistent challenge to cognitive computing and autonomous systems in general, and to Artificial Intelligence (AI), machine knowledge learning, and comp...
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Chapter and Conference Paper
Improved Training for 3D Point Cloud Classification
The point cloud is a 3D geometric data of irregular format. As a result, they are needed to be transformed into 3D voxels or a collection of images before being fed into models. This unnecessarily increases th...
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Chapter and Conference Paper
An Autoencoding Method for Detecting Counterfeit Coins
We use coins in our daily life to pay for bus, metro tickets, vending machines, etc. However, the market for antique and historical coins is another place, where the quality of coins and their genuinity play a...
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Chapter and Conference Paper
Zero-Error Digitisation and Contextualisation of Pi** and Instrumentation Diagrams Using Node Classification and Sub-graph Search
Thousands of huge printed sheets depicting engineering drawings keep record of complex industrial structures from Oil & Gas facilities. Currently, there is a trend of digitising these drawings, having as final...
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Chapter and Conference Paper
Human Description in the Wild: Description of the Scene with Ensembles of AI Models
Describing an image scene in Natural Language is a very complex procedure for a machine. Many researchers have used Natural Language Processing approaches. In this paper Machine Learning and Computer Vision mo...
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Chapter
Degradable Self-restructuring of Processor Arrays by Direct Spare Replacement
With the increase in the number of processing elements (PEs) in modern highly integrated parallel systems, there has been a growing importance for designing an efficient self-restructuring method to automatica...
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Study of Malaysian Cloud Industry and Conjoint Analysis of Healthcare and Education Cloud Service Utiliztion
The paper initially addresses Malaysia’s readiness concerning the adoption of cloud computing services and provides insights into the country’s internet infrastructure and internet users, fixed and mobile broa...
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Chapter
A Novel Machine Learning Framework for Covid-19 Image Classification with Bio-heuristic Optimization
Due to its rapidly advancing spread, the world is still reeling from COVID-19 (coronavirus 2019), which is categorized as a highly infectious disease. An early diagnosis is very critical in treating COVID-19 p...
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Chapter and Conference Paper
Learning Distances Between Graph Nodes and Edges
Several applications can be developed when graphs represent objects composed of local parts and their relations. For instance, chemical compounds are characterised by nodes that represent chemical elements and...
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Chapter and Conference Paper
Realization of Autoencoders by Kernel Methods
An autoencoder is a neural network to realize an identity map** with hidden layers of a relatively small number of nodes. However, the role of the hidden layers is not clear because they are automatically de...
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Chapter and Conference Paper
A Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures
Spectral signatures have been used with great success in computer vision to characterise the local and global topology of 3D meshes. In this paper, we propose to use two widely used spectral signatures, the He...
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Chapter and Conference Paper
Annotation-Free Keyword Spotting in Historical Vietnamese Manuscripts Using Graph Matching
Finding key terms in scanned historical manuscripts is invaluable for accessing our written cultural heritage. While keyword spotting (KWS) approaches based on machine learning achieve the best spotting resul...
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Chapter and Conference Paper
Graph Regression Based on Graph Autoencoders
We offer in this paper a trial of encoding graph data as means of efficient prediction in a parallel setup. The first step converts graph data into feature vectors through a Graph Autoencoder (G-AE). Then, der...
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Chapter and Conference Paper
Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper we propose a new graph neural network architecture based on the soft-alignment of the gra...
