## About

90

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565

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Introduction

Artificial Intelligence, Search and Optimisation, Data Science, Bioinformatics, Computer Science Education

Additional affiliations

January 2013 - January 2014

July 2009 - December 2012

September 2004 - November 2008

## Publications

Publications (90)

Many complex domains and even larger problems in simple domains remain challenging in spite of the recent progress in planning. Besides developing and improving planning technologies, re-engineering a domain by utilising acquired knowledge opens up a potential avenue for further research. Moreover, macro-actions, when added to the domain as addi- t...

In this paper, we introduce Kangaroo, a constraint-based local search system. While existing systems such as Comet maintain invariants after every move, Kangaroo adopts a lazy strategy, updating invariants only when they are needed. Our empirical evaluation shows that Kangaroo consistently has a smaller memory footprint than Comet, and is usually s...

Protein structure prediction is an unsolved problem in computational biology. One great difficulty is due to the unknown factors in the actual energy function. Moreover, the energy models available are often not very informative particularly when spatially similar structures are compared during search. We introduce several novel heuristics to augme...

We describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don’t cares...

This book has been written for learners who are new in learning programming. Even a novice learner can start learning programming from this book and can gradually acquire knowledge required to be an expert programmer. This book covers C/C++ as the main programming language, but restricts itself only to the structured programming aspects.
This book...

Real-life water quality monitoring applications such as aquaculture domains and water resource management need long range multi-step prediction for disaster control. However, prediction accuracy usually degrades gradually as the prediction target timepoint is further away from the current timepoint. To address this, recent water quality forecasting...

Protein structure prediction (PSP) is a crucial issue in Bioinformatics. PSP has its important use in many vital research areas that include drug discovery. One of the important intermediate steps in PSP is predicting a protein’s beta-sheet structures. Because of non-local interactions among numerous irregular areas in beta-sheets, their highly acc...

Predicted inter-residue distances are a key behind recent success in high quality protein structure prediction (PSP). However, prediction of both short and long distance values together is challenging. Consequently, predicted short distances are mostly used by existing PSP methods. In this paper, we use a stacked meta-ensemble method to combine dee...

Protein contact maps capture coevolutionary interactions between amino acid residue pairs that are spatially within certain proximity threshold. Predicted contact maps are used in many protein related problems that include drug design, protein design, protein function prediction, and protein structure prediction. Contact map prediction has achieved...

More training instances could lead to better classification accuracy. However, accuracy could also degrade if more training instances mean further noises and outliers. Additional training instances arguably need additional computational resources in future data mining operations. Instance selection algorithms identify subsets of training instances...

Protein structure prediction (PSP) has achieved significant progress lately via prediction of inter-residue distances using deep learning models and exploitation of the predictions during conformational search. In this context, prediction of large inter-residue distances and also prediction of distances between residues separated largely in the pro...

Motivation
Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories of secondary structures they belong to. In this paper, we propose to train separate deep learning mo...

Protein structure prediction (PSP) is essential for drug discovery. PSP involves minimising an unknown scoring function over an astronomical search space. PSP has achieved significant progress recently via end-to-end deep learning models that require enormous computational resources and almost all known proteins as training data. In this paper, we...

Protein Structure Prediction (PSP) is one of the most challenging problems in bioinformatics and biomedicine. PSP has obtained significant improvement lately. This is from the growth of the protein data bank (PDB) and the use of Deep Neural Network (DNN) models since DNNs could learn more accurate patterns from more known protein structures in the...

Conjunctive normal forms (CNF) of structured satisfiability problems contain logic gate patterns. So Boolean circuits (BC) by and large can be obtained from them and thus structural information that is lost in the CNF can be recovered. However, it is not known which logic gates are useful for local search on BCs or which logic gates in particular h...

The travelling thief problem (TTP) is a multi-component optimisation problem involving two interdependent NP-hard components: the travelling salesman problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP solvers modify the underlying TSP and KP solutions in an iterative and interleaved fashion. The TSP solution (cyclic tour) is t...

The travelling thief problem (TTP) is a combination of two interdependent NP-hard components: travelling salesman problem (TSP) and knapsack problem (KP). Existing approaches for TTP typically solve the TSP and KP components in an interleaved fashion, where the solution to one component is held fixed while the other component is changed. This indic...

Permutation flowshop scheduling problem with sequence-dependent setup times (PFSP-SDST) and makespan minimisation is NP-hard. It has important practical applications, for example, in the cider industry and the print industry. There exist several metaheuristic algorithms to solve this problem. However, within practical time limits, those algorithms...

Time-dependent prize-collecting arc routing problems (TD-PARPs) generalise the regular prize-collecting arc routing problems (PARPs). PARPs have arcs associated with collectable prizes along with travelling costs. TD-PARPs allow travel times to vary at the travelling horizon so that real-life uncertain factors such as traffic and weather conditions...

Toxicity prediction using quantitative structure–activity relationship has achieved significant progress in recent years. However, most existing machine learning methods in toxicity prediction utilize only one type of feature representation and one type of neural network, which essentially restricts their performance. Moreover, methods that use mor...

Protein structure prediction is a grand challenge. Prediction of protein structures via the representations using backbone dihedral angles has recently achieved significant progress along with the on-going surge of deep neural network (DNN) research in general. However, we observe that in the protein backbone angle prediction research, there is an...

The travelling thief problem (TTP) is a multi-component optimisation problem involving two interdependent NP-hard components: the travelling salesman problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP solvers modify the underlying TSP and KP solutions in an iterative and interleaved fashion. The TSP solution (cyclic tour) is t...

The travelling thief problem (TTP) is a representative of multi-component optimisation problems where the components interact with each other. TTP combines two interdependent components: the knapsack problem (KP) and the travelling salesman problem (TSP). A thief performs a cyclic tour through a set of cities, and pursuant to a collection plan, col...

This book has been written for learners who are new in learning programming. Even a novice learner can start learning programming from this book and can gradually acquire knowledge required to be an expert programmer. This book covers C/C++ as the main programming language, but restricts itself only to the structured programming aspects. This book...

Representing molecules in the form of only one type of features and using those features to predict their activities is one of the most important approaches for machine-learning-based chemical-activity-prediction. For molecular activities like quantitative toxicity prediction, the performance depends on the type of features extracted and the machin...

Makespan minimisation of permutation flowshop scheduling problems (PFSP) with sequence dependent setup times (SDST) is NP-Hard. PFSP-SDST has important practical applications e.g. in the paint industry. There exist several algorithms for PFSP-SDST, but they just use generic methods that lack specific structural information of the problem and so str...

Prediction of toxicity levels of chemical compounds is an important issue in Quantitative Structure-Activity Relationship (QSAR) modeling. Although toxicity prediction has achieved significant progress in recent times through deep learning, prediction accuracy levels obtained by even very recent methods are not yet very high. We propose a multimoda...

Prediction of toxicity levels of chemical compounds is an important issue in Quantitative Structure-Activity Relationship (QSAR) modeling. Although toxicity prediction has achieved significant progress in recent times through deep learning, prediction accuracy levels obtained by even very recent methods are not yet very high. We propose a multimoda...

Blocking constraints are ubiquitous in machine scheduling. In a flowshop scenario, a machine might get blocked by a job that has just been processed by the same machine. This happens when there is no buffer where the job can temporarily go before the next machine is available to process it. This also happens when two successive machines depend on a...

The travelling thief problem (TTP) is a combination of two interdependent NP-hard components: travelling salesman problem (TSP) and knapsack problem (KP). Existing approaches for TTP typically solve the TSP and KP components in an interleaved fashion, where the solution to one component is held fixed while the other component is changed. This indic...

Permutation flowshop scheduling problem (PFSP) is a classical NP-Hard combinatorial optimisation problem. Existing PFSP variants capture different realistic scenarios, but significant modelling gaps still remain with respect to many real-world industrial applications. Inspired by the cider industry, in this paper, we propose a new PFSP variant that...

Customer Order Scheduling Problem (COSP) with minimisation of the total completion time as the objective is NP-Hard. COSP has many applications that include the pharmaceutical and the paper industries. However, most existing COSP algorithms struggle to find very good solutions in large-sized problems. One key reason behind is that those algorithms...

Toxicity prediction of chemical compounds is a grand challenge. Lately, it achieved significant progress in accuracy but using a huge set of features, implementing a complex blackbox technique such as a deep neural network, and exploiting enormous computational resources. In this paper, we strongly argue for the models and methods that are simple i...

Toxicity prediction of chemical compounds is a grand challenge. Lately, it achieved significant progress in accuracy but using a huge set of features, implementing a complex blackbox technique such as a deep neural network, and exploiting enormous computational resources. In this paper, we strongly argue for the models and methods that are simple i...

Aircraft sequencing problem (ASP) is to schedule the operation times of departing and arriving aircraft such that their deviation from the desired operation times are minimised. There are two types of hard constraint which make this problem very challenging: time window constraint for the operation time of each aircraft, and minimum separation time...

The well-known Late Acceptance Hill Climbing (LAHC) search aims to overcome the main downside of traditional Hill Climbing (HC) search, which is often quickly trapped in a local optimum due to strictly accepting only non-worsening moves within each iteration. In contrast, LAHC also accepts worsening moves, by keeping a circular array of fitness val...

Mixed Blocking Permutation Flowshop Scheduling Problem (MBPFSP) with the objective of makespan minimisation is NP-Hard. It has important industrial applications that include the cider production industry. MBPFSP has made some progress in recent years. However, within practical time limits, existing incomplete algorithms still either find low qualit...

Aircraft sequencing problem (ASP) is an NP-Hard problem. It involves allocation of aircraft to runways for landing and takeoff, minimising total tardiness. ASP has made significant progress in recent years. However, within practical time limits, existing incomplete algorithms still either find low quality solutions or struggle with large problems....

The well-known Late Acceptance Hill Climbing (LAHC) search aims to overcome the main downside of traditional Hill Climbing (HC) search, which is often quickly trapped in a local optimum due to strictly accepting only non-worsening moves within each iteration. In contrast, LAHC also accepts worsening moves, by keeping a circular array of fitness val...

Customer Order Scheduling Problem (COSP) is an NP-Hard problem that has important practical applications e.g., in the paper industry and the pharmaceutical industry. The existing algorithms to solve COSP still either find low quality solutions or scramble with large-sized problems. In this paper, we propose a new constructive heuristic called repai...

Permutation flowshop scheduling problem (PFSP) is a classical combinatorial optimisation problem. There exist variants of PFSP to capture different realistic scenarios, but significant modelling gaps still remain with respect to real-world industrial applications such as the cider production line. In this paper, we propose a new PFSP variant that a...

Permutation flowshop scheduling problem (PFSP) is a classical combinatorial optimisation problem. There exist variants of PFSP to capture different realistic scenarios, but significant modelling gaps still remain with respect to real-world industrial applications such as the cider production line. In this paper, we propose a new PFSP variant that a...

Finding optimal Golomb rulers is an extremely challenging combinatorial problem. The distance between each pair of mark is unique in a Golomb ruler. For a given number of marks, an optimal Golomb ruler has the minimum length. Golomb rulers are used in application areas such as X-ray crystallography, radio astronomy, information theory, and pulse ph...

This book is prescribed by the National Curriculum and Textbook Board of Bangladesh as a textbook for classes Nine-Ten. This book is for free distribution by the Government of the People's Republic of Bangladesh.

This book is prescribed by the National Curriculum and Textbook Board as a textbook for Classes Nine-Ten. This book is for free distribution by the Government of the People's Republic of Bangladesh.

This book is prescribed by the National Curriculum and Textbook Board as a textbook for classes Nine-Ten. This book is for free distribution by the Government of the People's Republic of Bangladesh.

This book is prescribed by the National Curriculum and Textbook Board as a textbook for Classes Nine-Ten. This book is for free distribution by the Government of the People's Republic of Bangladesh.

All-interval series is a standard benchmark problem for constraint satisfaction search. An all-interval series of size n is a permutation of integers [0, n) such that the differences between adjacent integers are a permutation of [1, n). Generating each such all-interval series of size n is an interesting challenge for constraint community. The pro...

Within a fail first search, we relocate assignments towards the bottom of the upward-growing assignment stack. We do that when the related variables, due to search dynamics, become more constrained than were anticipated before. This fixes early variable selection mistakes.

This paper presents a constraint-based local search algorithm to find an optimal Golomb ruler of a specified order. While the state-of-the-art search algorithms for Golomb rulers hybridise a range of sophisticated techniques, our algorithm relies on simple tabu meta-heuristics and constraint-driven variable selection heuristics. Given a reasonable...

We present a planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs a new constraint model from domain transition graphs (DTG) of a given planning problem. TCPP encodes the constraint model by using table constraints that allow don't cares or wild cards as cell values. TCPP uses Minion the constraint solver to solve the...

We present a planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs a new constraint model from domain transition graphs (DTG) of a given planning problem. TCPP encodes the constraint model by using table constraints that allow don't cares or wild cards as cell values. TCPP uses Minion the constraint solver to solve the...

On-lattice protein structure prediction with empirical energy minimisation has drawn significant research effort. However, energy minimisation with free-modelling not necessarily leads to structures that are similar to the native structure of the given protein. In this paper, we show that energy minimisation has a positive correlation with structur...

Proteins are essentially sequences of amino acids. They adopt specific folded 3-dimensional structures to perform specific tasks. The formation of 3-dimensional structures is largely guided by the constituent amino acids. Therefore, the positional presence of amino acids in a sequence might play important roles during the protein folding process. I...

In this paper, we present a formal definition of the integra-tion of the requirements modeling language Behavior Trees (BTs). We first provide the semantic integration of two interrelated BTs using an extended version of Communicat-ing Sequential Processes. We then use a Semantic Network Model to capture a set of interrelated BTs, and develop algo-...

Protein structure prediction (PSP) has been one of the most challenging problems in computational biology for several decades. The challenge is largely due to the complexity of the all-atomic details and the unknown nature of the energy function. Researchers have therefore used simplified energy models that consider interaction potentials only betw...