Varun Ojha

Newcastle University | NCL · School of Computing Science

This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an optimum tree-like structure, i.e., a natural hierarchical structure that accommodates simplicity by combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure provides a high degree of approximation accuracy. Th...

We propose an algorithm and a new method to tackle the classification problems. We propose a multi-output neural tree (MONT) algorithm, which is an evolutionary learning algorithm trained by the non-dominated sorting genetic algorithm (NSGA)-III. Since evolutionary learning is stochastic, a hypothesis found in the form of MONT is unique for each ru...

We identify fragile and robust neurons of deep learning architectures using nodal dropouts of the first convolutional layer. Using an adversarial targeting algorithm, we correlate these neurons with the distribution of adversarial attacks on the network. Adversarial robustness of neural networks has gained significant attention in recent times and...

We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree. BNeuralT takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connections like a biological dendritic tree would do. Considering the dendritic-tree like plausible biolog...

River-level estimation is a critical task required for the understanding of flood events and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river-camera images to estimate river levels but, currently, the utility of this approach remains limited as it requires a large amou...

Monitoring river water levels is essential for the study of floods and mitigating their risks. River gauges are a well-established method for river water-level monitoring but many flood-prone areas are ungauged and must be studied through gauges located several kilometers away. Taking advantage of river cameras to observe river water levels is an a...

In chaotic dynamical systems such as the weather, prediction errors grow faster in some situations than in others. Real‐time knowledge about the error growth could enable strategies to adjust the modelling and forecasting infrastructure on the fly to increase accuracy and/or reduce computation time. For example, one could change the ensemble size,...

We will consider genome-scale metabolic models that attempt to describe the metabolism of human cells focusing on breast cells. The model has two versions related to the presence or absence of a specific breast tumor. The aim will be to mine these genome-scale models as a multi-objective optimization problem in order to maximize biomass production...

The paper proposes a novel adaptive search space decomposition method and a novel gradient-free optimization-based formulation for the pre- and post-buckling analyses of space truss structures. Space trusses are often employed in structural engineering to build large steel constructions, such as bridges and domes, whose structural response is chara...

The paper proposes a novel adaptive search space decomposition method and a novel gradient-free optimization-based formulation for the pre- and post-buckling analyses of space truss structures. Space trusses are often employed in structural engineering to build large steel constructions, such as bridges and domes, whose structural response is chara...

Due to the complex topology of the search space, expensive multi-objective evolutionary algorithms (EMOEAs) emphasize enhancing the exploration capability. Many algorithms use ensembles of surrogate models to boost the performance. Generally, the surrogate-based model either works out the solution’s fitness by approximating the evaluation function...

We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. That is, we investigate the quality of influence hyperparameters have on the performance of algorithms in terms of their direct effect and interact...

We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. That is, we investigate the quality of influence hyperparameters have on the performance of algorithms in terms of their direct effect and interact...

We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization , cost minimization and quantum efficiency maximization . We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic al...

This paper proposes a methodological approach with a transfer learning scheme for plastic waste bottle detection and instance segmentation using the \textit{mask region proposal convolutional neural network} (Mask R-CNN). Plastic bottles constitute one of the major pollutants posing a serious threat to the environment both in oceans and on land. Th...

Our research aims to help industrial biotechnology develop a sustainable economy using green technology based on microorganisms and synthetic biology through two case studies that improve metabolic capacity in yeast models Yarrowia lipolytica (Y. lipolytica) and Saccharomyces cerevisiae (S. cerevisiae). We aim to increase the production capacity of...

In chaotic dynamical systems such as the weather, prediction errors grow faster in some situations than in others. Real-time knowledge about the error growth could enable strategies to adjust the modelling and forecasting infrastructure on-the-fly to increase accuracy and/or reduce computation time. For example one could change the spatio-temporal...

We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree. BNeuralT takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connections like a biological dendritic tree would do. Considering the dendritic-tree like plausible biolog...

We identify fragile and robust neurons of deep learning architectures using nodal dropouts of the first convolutional layer. Using an adversarial targeting algorithm, we correlate these neurons with the distribution of adversarial attacks on the network. Adversarial robustness of neural networks has gained significant attention in recent times and...

We propose a novel deep learning based denoising filter selection algorithm for noisy Electrocardiograph (ECG) signal preprocessing. ECG signals measured under clinical conditions, such as those acquired using skin contact devices in hospitals, often contain baseline signal disturbances and unwanted artefacts; indeed for signals obtained outside of...

We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algo...

This paper proposes a methodology for investigating musical preferences of the age group between 18 and 24. We conducted an electroencephalogram (EEG) experiment to collect individual’s responses to audio stimuli along with a measure of like or dislike for a piece of music. Machine learning (multilayer perceptron and support vector machine) classif...

This paper proposes a methodological approach with a transfer learning scheme for plastic waste bottle detection and instance segmentation using the mask region proposal convolutional neural network (Mask R-CNN). Plastic bottles constitute one of the major pollutants posing a serious threat to the environment both in oceans and on land. The automat...

This research is concerned with taking user input in the form of speech data to classify and then predict which region of the United Kingdom the user is from and their gender. This research was conducted on regional accents, data preprocessing, Fourier transforms, and deep learning modeling. Due to lack of publicly available datasets for this type...

We investigate a deep transfer learning methodology to perform water segmentation and water level prediction on river camera images. Starting from pre-trained segmentation networks that provided state-of-the-art results on general purpose semantic image segmentation datasets ADE20k and COCO-stuff, we show that we can apply transfer learning methods...

The original version of chapter 2 was inadvertently published with wrong RTS values in Table 3: “Results comparison with RTS, S, and SVMlight with standard linear loss with a 10-fold cross validation procedure.” The RTS values were corrected by replacing the wrong values with the appropriate ones. The footnote reads “1Code is available at: https://...

River level estimation is a critical task required for the understanding of flood events, and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river camera images to estimate the river levels, but currently, the utility of this approach remains limited as it requires a large...

We propose an algorithm and a new method to tackle the classification problems. We propose a multi-output neural tree (MONT) algorithm, which is an evolutionary learning algorithm trained by the non-dominated sorting genetic algorithm (NSGA)-III. Since evolutionary learning is stochastic, a hypothesis found in the form of MONT is unique for each ru...

The merit of such threshold schemes is demonstrated more robustly by the data in Fig. 2, which shows the growth of the mean error (where RK4 is taken to be the truth) and computation time per 1000-timestep trajectory for 10,000 initial conditions. Fig. 2 shows that the threshold with = 4 was on average 45% faster than RK4, and 25% slower than RK2,...

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209...

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209...

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of...

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of...

This research proposes a framework for signal processing and information fusion of spatial-temporal multi-sensor data pertaining to understanding patterns of humans physiological changes in an urban environment. The framework includes signal frequency unification, signal pairing, signal filtering, signal quantification, and data labeling. Furthermo...

Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building massing. Since formal methods t...

This special issue of FCL magazine aims to un-derscore the relevance of different works related to urban climate in Singapore, especially to improve the outdoor thermal comfort and decrease the urban heat island effect. The Cooler Calmer Singapore Impact Project (CCSIP) poses new questions and gives insights on the urban climate research, with spec...

In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accur...

Uniaxial Compressive Strength (UCS) is the most important parameter that quantifies the rock strength. However, determination of the UCS in laboratory is very expensive and time-consuming. Therefore, common index tests like point load (Is-50), ultrasonic velocity test (Vp), block punch index (BPI) test, rebound hardness (SRH) test, physical propert...

In this research project, we investigate the impacts of urban morphology (UM) on citizen’s social potential as a function of accessibility and perception and compare it with the impacts of UM on building energy consumption for parallel case studies in Zürich Switzerland and Weimar Germany. This is of particular interest since urban planning decisio...

In this work, we formulated a real-world problem related to sewer pipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer pipeline to offer safe and non-hazardous access to sewer pipeline workers so that the human fatalities, which occurs due to the toxic exposure of...

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc....

More detail on link: http://dap.vsb.cz/aat/ The Adaptive Approximation Software Toolbox is a function approximation and feature selection tool that uses genetic programming for constructing tree like structure to construct an adaptive multi-layer perceptron. This standalone software toolbox solves prediction problems. The developed algorithm perfor...

Cloud computing is one of the hottest technologies in IT field. It provides computational resources as general utilities that can be leased and released by users in an on-demand fashion. Companies around the globe are showing high interest in adopting cloud computing technology but cloud computing adaptation comes with greater risks that need to be...

More detail on link: http://dap.vsb.cz/aat/ The Adaptive Approximation Software Toolbox is a function approximation and feature selection tool that uses genetic programming for constructing tree like structure to construct an adaptive multi-layer perceptron. This standalone software toolbox solves prediction problems. The developed algorithm perfor...

Several works have been applied non-temporal classification techniques in the Human Activity Recognition area. Instead of that, we present an approach for modelling the human activities using a temporal learning tool. Here, the activities are considered as time-dependent events, and we use a temporal learning method for their classification. We emp...

Human fatalities are reported due to excessive proportional presence of hazardous gas components in manhole, such as hydrogen sulphide, ammonia, methane, carbon dioxide, nitrogen oxide, carbon monoxide, etc. Hence, predetermination of these gases is imperative. A neural network (NN)-based intelligent sensory system was proposed for avoiding such fa...

Introduction of the fuzzy-set enabled the modeling of uncertain and noisy information. Type-2 fuzzy set took this further ahead by allowing fuzzy membership function to be fuzzy itself. In this work, we discussed an interval type-2 fuzzy inference system (IT2FIS). The training of the IT2FIS was provided in supervised manner by using metaheuristic a...

General partial differential equations, which can describe any complex functions, may be solved by an adapted method of the similarity analysis that models polynomial data relations of discrete observations. The proposed new differential polynomial networks define and substitute for a selective form of the general partial differential equation usin...

In this article, we proposed a multi-agent concurrent neorosimulated annealing (CNSA) algorithm, which was used for the supervised training of the neural networks (NN). The proposed CNSA is a population based parallel version of the basic simulated annealing (SA) algorithm. In this work, CNSA was applied for designing an intelligent sensory system...

In this work, we proposed various strategies for improving the performance of continuous ant colony optimization algorithm (ACO *), which was used here for optimizing neural network (NN). Here, a real-world problem, that is, detection of manhole gas components, was used for case study. Manhole contains various toxic and explosive gases. Therefore,...

In this work, we used an adaptive feature-selection and function approximation model, called, flexible neural tree (FNT) for predicting Poly (lactic-co-glycolic acid) (PLGA) micro- and nanoparticle’s dissolution-rates that bears significant role in the pharmaceutical, medical, and drug manufacturing industries. Several factor influences PLGA nanopa...

Prediction of risk assessment is demanding because it is one of the most important contributory factors towards grid computing. Hence, researchers were motivated for developing and deploying grids on diverse computers, which is responsible for spreading resources across administrative domains so that resource sharing becomes effective. Risk assessm...

Risk assessment in grid computing is an important issue as grid is a shared environment with diverse resources spread across several administrative domains. Therefore, by assessing risk in grid computing, we can analyze possible risks for the growing consumption of computational resources of an organization and thus we can improve the organization’...

Computer Science has always been one the top research interest area of researchers all over the world. Based on social network mining concepts, this paper emphasizes on the network analysis of the Indian computer science researchers or scientists based on their authorship of scientific publications. It also focuses on the problems faced by Indian r...

Prediction of poly(lactic-co-glycolic acid) (PLGA) micro-and nanoparticles' dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by...

Optimization of neural network (NN) is significantly influenced by the transfer function used in its active nodes. It has been observed that the homogeneity in the activation nodes does not provide the best solution. Therefore, the cus-tomizable transfer functions whose underlying parameters are subjected to optimization were used to provide hetero...

Oil is the lifeblood of the global economy. Recently, oil prices have witnessed fluctuations and the prediction of oil prices has become a challenge for researchers. The aim of this research is to design a model that is able to predict the prices of crude oil with good accuracy. We used the daily data from 1999 to 2012 with 14 input factors to pred...

Optimization of neural network (NN) significantly influenced by the transfer function used in its active nodes. It has been observed that the homogeneity in the activation nodes does not provide the best solution. Therefore, the customizable transfer functions whose underlying parameters are subjected to optimization were used to provide heterogene...

The performance of the meta-heuristic algorithms often depends on their parameter settings. Appropriate tuning of the underlying parameters can drastically improve the performance of a meta-heuristic. The Ant Colony Optimization (ACO), a population based meta-heuristic algorithm inspired by the foraging behavior of the ants, is no different. Fundam...

The performance of the meta-heuristic algorithms often depends on their parameter settings. Appropriate tuning of the underlying parameters can drastically improve the performance of a meta-heuristic. The Ant Colony Optimization (ACO), a population based meta-heuristic algorithm inspired by the foraging behavior of the ants, is no different. Fundam...

Predicting the dissolution rate of proteins plays a significant role in pharmaceutical/medical applications. The rate of dissolution of Poly Lactic-co-Glycolic Acid (PLGA) micro-and nanoparticles is influenced by several factors. Considering all factors leads to a dataset with three hundred features, making the prediction difficult and inaccurate....

A suitable regression model for predicting the dissolution profile of Poly (lactic-co-glycolic acid) (PLGA) micro-and nanoparticles can play a significant role in pharmaceutical/medical applications. The rate of dissolution of proteins is influenced by several factors and taking all such influencing factors into account, we have a dataset in hand w...

Computational Intelligence offers solution to various real life problems. Artificial Neural Network (ANN) has the capability of solving highly complex and nonlinear problems. The present chapter demonstrates the application of these tools to provide solutions to the manhole gas detection problem. Manhole, the access point across sewer pipeline syst...

Cloud Computing offers various remotely accessible services to users either free or on payment. A major issue with Cloud Service Providers (CSP) is to maintain Quality of Service (QoS). The QoS encompasses different parameters, like, smart job allocation strategy, efficient load balancing, response time optimization, reduction in wastage of bandwid...