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Tiong Yew Tang

Sunway University, Research Centre For Human-Machine Collaboration (HUMAC), Faculty Member

Monash University (Malaysia), School of Information Technology, Lecturer, PhD Candidate in Computing and Information Systems

Tunku Abdul Rahman University College, Faculty of Applied Sciences and Computing, Faculty Member

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My research topic is robotic process automation, bibliometric analysis and deep learning. I had published several CORE-ranked conference papers. I am IEEE computational intelligence society member. I am an active researcher in biological inspired cognitive intelligence research field.
Supervisors: Dr. Simon Egerton and Professor Dr. Naoyuki Kubota

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Papers by Tiong Yew Tang

Markerless Feature-Registration-Based Image Processing Framework for Oil Palm Fresh Fruit Bunch Robotic Harvester

SPIE Digital Library, 2025

Malaysia's oil palm industry faces many challenges in sustaining manual oil palm harvesting opera... more Malaysia's oil palm industry faces many challenges in sustaining manual oil palm harvesting operations. This work investigates an effective oil palm Fresh Fruit Bunch (FFB) image processing method for robot harvesting automation. This research explores the proposed image processing method that first detects the Fresh Fruit Bunch (FFB) category which involves 6 different categories of FFB growth stages and then detects its 6D pose estimation for harvesting. Next, this research proposes a novel image processing framework that utilises the convolutional neural network deep learning classification and is followed by markerless feature-registration-based oil palm FFB for 6D pose estimation with the public FFB dataset. Furthermore, this work introduced view obstruction to the public FFB dataset as noise for practical robot harvester applications in plantation field operation. Moreover, the experiment results show the proposed model can maintain a high F1 score performance up until 70% of view obstruction before the F1 score performance is reduced.

Environmental, Social and Governance (ESG) Scores Automation in Global Reporting Initiative (GRI) with Natural Language Processing

IEEE, 2024

The increasing importance of Environmental, Social, and Governance (ESG) factors in making invest... more The increasing importance of Environmental,
Social, and Governance (ESG) factors in making investment
decisions has triggered a noteworthy transformation in the
global investing scenario. This study investigates various
distilled BERT models in Python, including all-MiniLM-L12-v2,
all-MiniLM-L6-v2, all-mpnet-base-v2, and paraphrase-
MiniLM-L6v2, to improve the accuracy and efficiency of ESG
scoring. The performance of these models will undertake a
detailed evaluation employing the F1 score criteria, which will
offer an understanding of the accuracy and consistency of the
scoring outputs produced by each model. The reason for this
detailed review is to pledge a strong valuation of the
effectiveness of the automated scoring procedure. The study
shows that utilising textual similarity for ESG rating
considerably decreases the time needed in comparison to the
conventional human scoring procedure. Throughout the time of
this study, all-MiniLM-L6-v2 was used as the base comparison
model, however, it was observed that the all-mpnet-base-v2
model provided a relatively better F1 score due to its value being
determined by its 768-dimensional embeddings. Embeddings
with better length possess the volume to cover more difficult and
subtle semantic representations, therefore potentially yielding
superior performance. In the result finding summary, allmpnet-
base-v2 has the highest F1 score compared with the
experiment's sentence transformers.

Leveraging AI-Driven Topic Modeling for Business Analytics: A Bibliometric Analysis of Trends in Antidepressant Efficacy

IEEE, 2024

To uncover thematic trends and patterns through studying articles from 2000 to 2023, this study c... more To uncover thematic trends and patterns through studying articles from 2000 to 2023, this study conducts a bibliometric analysis of research on the efficacy of antidepressants, applying Latent Dirichlet Allocation (LDA). Drawing on data from Scopus, the analysis covers 494 records, shedding light on prominent topics, key studies, and emerging areas in the field. The results indicate a significant rise in research activity, especially around 2010 and after 2018, pointing to an increasing focus on the effectiveness of mental health treatments. The thematic analysis revealed five core areas: treatment efficacy, clinical trials, alternative treatment approaches, efficacy compared to side effects, and outcomes centered on patient experience. These findings underscore critical advances and existing gaps in the research, offering direction for future studies and clinical applications. This work demonstrates how integrating bibliometric methods, and LDA can deepen our understanding and support better decision-making in mental healthcare.

Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction

Lecture Notes in Computer Science, Oct 19, 2023

Data Augmentation with Semantic Enrichment for Deep Learning Invoice Text Classification

IEEE access, 2024

Natural language processing (NLP) is a research field that provides huge potential to automate ac... more Natural language processing (NLP) is a research field that provides huge potential to automate accounting tasks dealing with text data. This research studies the application of NLP in automatically categorizing invoices based on the invoice text description. The study employs semantic enrichment, data augmentation, and deep learning to address the NLP unique issues posed by the inherent short text and multiclass imbalance nature of invoice descriptions. Semantic enrichment was done using labels as an information source. Training data was artificially increased with either WordNet synonym replacement, Global Vectors for Word Representation (GloVe) word replacement, or Bidirectional Encoder Representations from Transformers (BERT) word replacement method. Each training dataset was then supplied for training with one nondeep learning classifier and two deep learning classifiers respectively, namely Linear Support Vector Machine (LSVM), Bidirectional Long Short-Term Memory (Bi-LSTM), and BERT. Overall, the semantically enriched, WordNet augmented training set paired with the BERT classifier yielded the best results, successfully preserving semantics, reducing noise and overfitting while improving accuracy per class, achieving an increase of performance up to 20 ppts for macro F1 score and 6.7 percentage points (ppts) for accuracy.

Data Augmentation With Semantic Enrichment for Deep Learning Invoice Text Classification

Natural language processing (NLP) is a research field that provides huge potential to automate ac... more Natural language processing (NLP) is a research field that provides huge potential to automate accounting tasks dealing with text data. This research studies the application of NLP in automatically categorizing invoices based on the invoice text description. The study employs semantic enrichment, data augmentation, and deep learning to address the NLP unique issues posed by the inherent short text and multiclass imbalance nature of invoice descriptions. Semantic enrichment was done using labels as an information source. Training data was artificially increased with either WordNet synonym replacement, Global Vectors for Word Representation (GloVe) word replacement, or the Bidirectional Encoder Representations from Transformers (BERT) word replacement method. Each training dataset was then supplied for training with one nondeep learning classifier and two deep learning classifiers respectively, namely Linear Support Vector Machine (LSVM), Bidirectional Long Short-Term Memory (Bi-LSTM), and BERT. Overall, the semantically enriched, WordNet augmented training set paired with the BERT classifier yielded the best results, successfully preserving semantics, reducing noise and overfitting while improving accuracy per class, achieving an increase of performance up to 20 percentage points (ppts) for macro F1 score and 6.7 ppts for accuracy.

Average Edit Distance Bacterial Mutation Algorithm for effective optimisation

In the field of Evolutionary Computation (EC), many algorithms have been proposed to enhance the ... more In the field of Evolutionary Computation (EC), many algorithms have been proposed to enhance the optimisation search performance in NP-Hard problems. Recently, EC research trends have focused on memetic algorithms that combine local and global optimisation search. One of the state-of-the-art memetic EC methods named Bacterial Memetic Algorithm (BMA) has given good optimisation results. In this paper, the objective is to improve the existing BMA optimisation performance without significant impact to its processing cost. Therefore, we propose a novel algorithm called Average Edit Distance Bacterial Mutation (AEDBM) algorithm that improves the bacterial mutation operator in BMA. The AEDBM algorithm performs edit distance similarity comparisons for each selected mutation elements with other bacterial clones before assigning the selected elements to the clones. In this way, AEDBM will minimise bad (similar elements) bacterial mutation to other bacterial clones and thus improve the overall optimisation performance. We investigate the proposed AEDBM algorithm on commonly used datasets in fuzzy logic system analysis. We also apply the proposed method to train a robotic learning agent's perception-action mapping dataset. Experimental results show that the proposed AEDBM approach in most cases gains consistent mean square error optimisation performance improvements over the benchmark approach with only minimal impact to processing cost.

A biologically inspired cognitive intelligence with stress based modelling and working memory optimisation for robot partners

This thesis contains no material which has been accepted for the award of any other degree or dip... more This thesis contains no material which has been accepted for the award of any other degree or diploma at any university or equivalent institution and that, to the best of my knowledge and belief, this thesis contains no material previously published or written by another person, except where due reference is made in the text of the thesis.

Smart Virtual Robot Automation (SVRA)-Improving Supplier Transactional Processes in Enterprise Resource Planning (ERP) System: A Conceptual Framework

Springer eBooks, Dec 13, 2022

Robotic Process Automation (RPA) is a technique that assists in automating processes by employing... more Robotic Process Automation (RPA) is a technique that assists in automating processes by employing numerous technologies for process automation which essentially helps in cost-saving, error-free, scalable and efficient solutions for real-world business problems. Albeit being highly relevant and beneficial, particularly for accountants who are responsible to make decisions in Enterprise Resource Planning (ERP) systems, the integration of RPA with ERP systems is still in the infant stages. Thus, with that in mind, this research proposes a novel Smart Virtual Robot Automation (SVRA) conceptual framework to explore the research design, development phase, and conceptual phase of the Robotic Process Automation (RPA) framework. The SVRA conceptual framework will be developed by employing the Selenium and Sikuli Framework tools for process automation. To execute automation instructions, the Python language script will be used along with the Selenium and Sikuli tools. Moreover, the SVRA development will be executed using the SAGE X3 ERP system. By utilizing the proposed SVRA conceptual framework design, it is anticipated that real-world ERP application problems in the Account Payable process in an organization can be resolved. Also, it is expected that this framework would contribute to the automation of the supervised deep learning approach.

Stress-inspired dynamic optimisation on working memory for cognitive robot social support systems

Robot social support systems such as robot partner's game interactions with elderly people ar... more Robot social support systems such as robot partner's game interactions with elderly people are very important in ageing societies. Human-robot interaction can decrease the risk of ageing disease such as dementia and thus improve the overall quality of life for the elderly people. However, in order for the robot partner to have successful game interactions with the elderly people, the robot partners need to be equipped with a certain degree of cognitive intelligence to guess the meaning and context of game interactions. In this paper, we discuss a biological stress-inspired model for the robot's cognitive intelligence with dynamic optimisation on its working memory. We name this novel robot's cognitive framework as Advanced Intelligence Cognitive Optimisation (AICO). AICO is a server framework for computational intensive cognitive processing for the smart phone robot known as iPhonoid. We have conducted physical robot experiments with our proposed iPhonoid AICO framework on Rényi-Ulam guessing game with real human subjects. The experimental results show that the proposed AICO framework successfully increased the robot's guessing performance in the game interactions. At the same time, the robot behaves according to its emotional conditions to make the game play interesting for the elderly people.

Reinforcement Learning in non-stationary environments: An intrinsically motivated stress based memory retrieval performance (SBMRP) model

ABSTRACT Biological systems are said to learn from both in- trinsic and extrinsic motivations. Ex... more ABSTRACT Biological systems are said to learn from both in- trinsic and extrinsic motivations. Extrinsic motivations, largely based on environmental conditions, have been well explored by Reinforcement Learning (RL) methods. Less explored, and more interesting in our opinion, are the possible intrinsic motivations that may drive a learning agent. In this paper we explore such a possibility. We develop a novel intrinsic motivation model which is based on the well known Yerkes and Dodson stress curve theory and the biological principles associated with stress. We use a stress feedback loop to affect the agent’s memory capacity for retrieval. The stress and memory signals are fed into a fuzzy logic system which decides upon the best action for the agent to perform against the current best action policy. Our simulated results show that our model significantly improves upon agent learning performance and stability when objectively compared against existing state-of- the-art RL approaches in non-stationary environments and can effectively deal with significantly larger problem domains.

A Novel Natural Language Processing Strategy to Improve Digital Accounting Classification Approach for Supplier Invoices ERP Transaction Process

Lecture Notes in Computer Science, 2023

Bibliometric Analysis of Robotic Process Automation Domain: Key Topics, Challenges and Solutions

Lecture Notes in Computer Science, 2023

In the era of Industry Revolution 4.0, organizations around the world are continuously looking fo... more In the era of Industry Revolution 4.0, organizations around the world are continuously looking for new technological breakthroughs that can enhance business process efficiency and reduce the cost of operations. Robotic Process Automation (RPA) has effectively addressed these issues in many different business sectors. RPA is a software application that automates Graphical User Interface (GUI) tasks that were previously performed by human users. However, the current literature in this research is lacking comprehensive bibliometric literature review which can be used to identify the future trend and the latest issues in the RPA research domain. To address these gaps, the objective of this study is to identify the current issues and future trends in the RPA domain. Firstly, a performance analysis of the dataset is performed to understand the future trend of the research domain. Then, the science mapping co-word examination bibliometric data analysis is performed on the selected dataset to identify current issues in this research domain. This work also addresses these issues with a practical evaluation framework for business organizations to effectively exploit the advantages provided by the RPA implementation.

Spiking Reflective Processing Model for Stress-Inspired Adaptive Robot Partner Applications

International Journal of Artificial Life Research, 2017

Inareal-worldenvironment,asocialrobotisconstantlyrequiredtomakemanycriticaldecisionsin anambiguou... more Inareal-worldenvironment,asocialrobotisconstantlyrequiredtomakemanycriticaldecisionsin anambiguousanddemanding(stressful)environment.Hence,abiologicalstressresponsesystem modelisagoodgaugeindicatortojudgewhentherobotshouldreacttosuchenvironmentandadapt itselftowardstheenvironmentchanges.ThisworkistoimplementtheSmerek'sreflectiveprocessing modelintohuman-robotcommunicationapplicationwherereflectiveprocessingistriggeredduring suchsituationswherethebestactionisnotknown.Theauthorswanttoinvestigatehowtoaddress betterthehuman-robotcommunicationproblemswiththefocusonreflectiveprocessingmodelin theperspectivesofworkingmemory,SpikingNeuralNetwork(SNN)andstressresponsesystem. TheauthorshadappliedtheirproposedSpikingReflectiveProcessingmodelforthehuman-robot communicationapplicationinauniversitypopulation.Theinitialexperimentalresultsshowedthe positiveattitudechangesbeforeandafterthehuman-robotinteractionexperiment.

Dynamic Programming for Guided Gene Transfer in Bacterial Memetic Algorithm

Springer eBooks, 2014

Evolutionary Computation (EC) approaches are known to empirically solve NP-hard optimisation prob... more Evolutionary Computation (EC) approaches are known to empirically solve NP-hard optimisation problems. However, the genetic operators in these approaches have yet to be fully investigated and exploited for further improvements. Hence, we propose a novel genetic operator called Dynamic Programming Gene Transfer (DPGT) operator to improve the existing gene transfer operator in the Bacterial Memetic Algorithm (BMA). DPGT integrates dynamic programming based edit distance comparisons during gene transfer operator in BMA. DPGT operator enforces good gene transfers between individuals by conducting edit distance checks before transferring the genes. We tested the DPGT operator in an artificial learning agent ant’s perception-action problem. The experimental results revealed that DPGT gained overall improvements of training accuracy without any significant impact to the training processing time.

Smart Virtual Robot Automation (SVRA)-Improving Supplier Transactional Processes in Enterprise Resource Planning (ERP) System: A Conceptual Framework

Lecture notes in networks and systems, Dec 13, 2022

Robotic Process Automation (RPA) is a technique that assists in automating processes by employing... more Robotic Process Automation (RPA) is a technique that assists in automating processes by employing numerous technologies for process automation which essentially helps in cost-saving, error-free, scalable and efficient solutions for real-world business problems. Albeit being highly relevant and beneficial, particularly for accountants who are responsible to make decisions in Enterprise Resource Planning (ERP) systems, the integration of RPA with ERP systems is still in the infant stages. Thus, with that in mind, this research proposes a novel Smart Virtual Robot Automation (SVRA) conceptual framework to explore the research design, development phase, and conceptual phase of the Robotic Process Automation (RPA) framework. The SVRA conceptual framework will be developed by employing the Selenium and Sikuli Framework tools for process automation. To execute automation instructions, the Python language script will be used along with the Selenium and Sikuli tools. Moreover, the SVRA development will be executed using the SAGE X3 ERP system. By utilizing the proposed SVRA conceptual framework design, it is anticipated that real-world ERP application problems in the Account Payable process in an organization can be resolved. Also, it is expected that this framework would contribute to the automation of the supervised deep learning approach.

A study on enhanced educational platform with adaptive sensing devices using IoT features

2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2017

There are plenty of digital education tools to provide additional assistance for conducting lectu... more There are plenty of digital education tools to provide additional assistance for conducting lecture class in university. For instance, online video source (e.g., YouTube) provides practical coding exercise for web application development, interactive communication channel (e.g., Google Hangout) provides platform for distance learning. However, these tools are rarely to be connected with a real-life environmental conditions. An advanced education system shall consider students attendance, activities and intention to pay attention as a part of assessment and provide appropriate education tools to improve the education quality. Therefore, there is an urge to adopt recent Internet of Technology (IoT) to detect and sense the environmental condition (e.g., room temperature, student activities) and produce necessary reaction (e.g, air condition control, awake overslept students). In this paper, we propose an integrated platform by utilizing the advanced IoT devices to improve the quality of education. Several IoT controller boards capabilities and features are described and compared for realizing the IoT solution in educational platform.

Spiking Reflective Processing Model for Stress-Inspired Adaptive Robot Partner Applications

International Journal of Artificial Life Research, 2017

In a real-world environment, a social robot is constantly required to make many critical decision... more In a real-world environment, a social robot is constantly required to make many critical decisions in an ambiguous and demanding (stressful) environment. Hence, a biological stress response system model is a good gauge indicator to judge when the robot should react to such environment and adapt itself towards the environment changes. This work is to implement the Smerek's reflective processing model into human-robot communication application where reflective processing is triggered during such situations where the best action is not known. The authors want to investigate how to address better the human-robot communication problems with the focus on reflective processing model in the perspectives of working memory, Spiking Neural Network (SNN) and stress response system. The authors had applied their proposed Spiking Reflective Processing model for the human-robot communication application in a university population. The initial experimental results showed the positive attitude c...

A biologically inspired cognitive intelligence with stress based modelling and working memory optimisation for robot partners

This thesis addresses one of the fundamental questions for any ageing society: Who will care for ... more This thesis addresses one of the fundamental questions for any ageing society: Who will care for the elderly? The “who” is shaping up to be the highly advanced technology in the area of humanoid robotics. The advantage of humanoid robots is that they can be designed to look and act like humans, but how do we avoid “uncanny valley”, where people reject humanoid robots in the long run for “looking human”, but not “behaving human”? It raises an important question of how we make robots that behave more humanlike, so our ageing societies readily accept them. Critically, how do we advance a robot’s embodied cognitive intelligence? We identify three key components behind the idea of embodied cognitive intelligence: Spiking Neural Network (SNN), working memory and stress response system. The stress response system acts to moderate working memory function, and together they help to encode and summarise a robot’s current environment context information which can be used to optimise available ...

Development of Attendance Monitoring System using IoT Technologies

by Tiong Yew Tang and Zhumaniyaz Mamatnabiyev

Recently, applications of Internet of Things (IoT) technologies have been established in many org... more Recently, applications of Internet of Things (IoT) technologies have been established in many organizations offering low-costed, low-powered, automatic systems. In addition, IoT systems are secure, less time-consuming, and controlled remotely. Implementing IoT technologies in managing the educational process makes huge changes by creating digital classrooms and automation systems. However, taking students' absence reports is still a critical element or issue in the education sector that is more paperwork, which is time-consuming, requires much workforce and efforts, and imposes inefficiency. Various automatic identification technologies have been developed using Radio Frequency Identification (RFID). Many research works and projects are produced to take maximum benefits of using this technology. The current work proposes an automatic attendance monitoring system (AMS) using IoT technologies including RFID and hardware platforms. The objectives of the proposed system are to check attendance of students automatically without human interface, inform students about gaps in attendance, and monitor instructors whether they come to lessons on time. Based on the results, the proposed AMS is time-effective, economically available, and has not any power consumption. The system is analyzed and criticized with respect to other authors' works. Future work is discussed and identified.