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Bacterial virulence can be attributed to a wide variety of factors including toxins that harm the host. Pore-forming toxins are one class of toxins that confer virulence to the bacteria and are one of the promising targets for therapeutic intervention. In this work, we develop a sequence-based machine learning framework for the prediction of pore-forming toxins. For this, we have used distributed representation of the protein sequence encoded by reduced alphabet schemes based on conformational similarity and hydropathy index as input features to Support Vector Machines (SVMs). The choice of conformational similarity and hydropathy indices is based on the functional mechanism of pore-forming toxins. Our methodology achieves about 81% accuracy indicating that conformational similarity, an indicator of the flexibility of amino acids, along with hydrophobic index can capture the intrinsic features of pore-forming toxins that distinguish it from other types of transporter proteins. Increased understanding of the mechanisms of pore-forming toxins can further contribute to the use of such “mechanism-informed” features that may increase the prediction accuracy further.

Accurate classification of instances depends on identification and removal of redundant features. Classification of data having high dimensionality is usually performed in conjunction with an appropriate feature selection method. Feature selection enables identification of the most informative feature subset from the enormously vast search space that can accurately classify the given data. We propose an ant colony optimization (ACO)/random forest based hybrid filter-wrapper search technique, which traverses the search space and selects a feature subset with high classifying ability. We evaluate the performance of our algorithm on four widely studied CoEPrA (Comparative Evaluation of Prediction Algorithms, http://coepra.org) datasets. The performance of the software ants mediated hybrid filter/wrapper approach compares well with the available competition results. Thus, the proposed Ant Colony Optimization based technique can effectively find small feature subsets capable of classifying with a very good accuracy and can be employed for feature subset selection with a high level of confidence.

A hybrid methodology, viz., multicanonical jump walk annealing assisted by tabu list (MJWAT) is proposed for solving dynamic optimization problems in chemically reacting systems. This method combines the power of multicanonical sampling with the beneficial features of simulated annealing. Incorporating tabu list further enhances the efficiency of the method. The superior performance of the MJWAT is highlighted with the help of five benchmark case studies.

In this work, we integrate a non-linear signal analysis method, recurrence quantification analysis (RQA), with the well-known machine-learning algorithm, support vector machines for the binary classification of protein sequences. Two different classification problems were selected, discriminating between aggregating and non-aggregating proteins and mostly disordered and completely ordered proteins, respectively. It has also been shown that classification performance of SVM models improve on selection of the most informative RQA descriptors as SVM input features.

This paper proposes PSACO (particle swarm ant colony optimization) algorithm for highly non-convex optimization problems. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In this study, we explore a simple pheromone-guided mechanism to improve the performance of PSO method for optimization of multimodal continuous functions. The proposed PSACO algorithm is tested on several benchmark functions from the usual literature. Numerical results comparisons with different metaheuristics demonstrate the effectiveness and efficiency of the proposed PSACO method.

This article focuses on the development of a multiobjective optimization algorithm for a tubular reactor–regenerator system with a moving deactivating catalyst. The task is to find the optimal temperature profile along the tubular reactor, catalyst recycle ratio, and the regeneration capacity for maximizing the process profit flux, selectivity, and conversion. A new heuristic technique, viz, ant colony optimization method has been employed to obtain the Pareto optimal set of solutions.

This chapter presents an overview of applications of metaheuristics to solve different real-world chemical process engineering problems over the last 30 years. The first part of this chapter describes some fundamental characteristics of metaheuristics, a class of global stochastic methods and also provides the standard description of some of the most widely used metaheuristics such as simulated annealing, tabu search, genetic algorithms, and ant colony optimization (ACO). In the second part, different practical applications of these metaheuristics related to chemical process industry are covered such as heat exchanger networks (HENs), short-term scheduling of batch processes, dynamic optimization of chemical and biochemical processes, parameter estimation, and multiobjective optimization with extensive list of references.

Batch Scheduling is an important problem, relevant to a large sector of the processing industries. Methods like Simulated Annealing have been traditionally used in dealing with scheduling problems that are combinatorially complex. In this paper the performance of four different annealing strategies, Simulated Annealing (SA), Threshold Acceptance (TA) and Multicanonical Jump Walk Annealing (MJWA) with and without window factor scheduling have been compared for small and large size problems. Criteria such as robustness of the method and mean deviation from the optimal solution reveal that the MJWA with window factor scheduling is far superior to SA, TA or MJWA without window factor scheduling.

Pseudo-periodic signals are rampant in biomedical applications but are difficult to analyze. One approach is to compute time domain parameters of each individual cycle in the pseudo-periodic signal. The classic approach requires repeated computation in each cycle, which tends to be either error prone, computationally burdensome, or requires manual effort. We provide a novel combination of the pitch synchronous wavelet transform which when combined with dynamic time warping results in effective quantification of cycles in the pseudo-periodic signal. We demonstrate our application of this method in studying the arterial pulse. The results show that our approach is feasible and effective, and confirms further scope in other applications.

Automatically classifying ECG recordings for Malignant Ventricular Arrhythmia is fraught with several difficulties. Even normal ECG signals exhibit only quasi-periodic nature, and contain various irregularities. The key to more accurate detection is the use of position, and amount of local singularities in the signals.In this paper, we propose a Holder-SVM detection algorithm using a novel hybrid arrangement of binary and multiclass SVMs designed to take care of class imbalance rampant in biomedical signals. As a result, we significantly reduce the number of false negatives – patients falsely classified as normal. We used the MIT-BIH Arrhythmia database for even different arrhythmias. We compare our hybrid SVM with a suitable conventional SVM, and show better results.We also use the new arrangement for features proposed earlier, and demonstrate the gain in accuracy. Our concept of hybrid SVM is applicable to a wide variety of multiclass classification problems.

Building accurate quantitative structure-activity relationships (QSAR) is important in drug design, environmental modeling, toxicology, and chemical property prediction. QSAR methods can be utilized to solve mainly two types of problems viz., pattern recognition, (or classification) where output is discrete (i.e. class information), e.g., active or non-active molecule, binding or non-binding molecule etc., and function approximation, (i.e. regression) where the output is continuous (e.g., actual activity prediction). The present review deals with the second type of problem (regression) with specific attention to one of the most effective machine learning procedures, viz. lazy learning. The methodologies of the algorithm along with the relevant technical information are discussed in detail. We also present three real life case studies to briefly outline the typical characteristics of the modeling formalism.

Owing to the superiority of Dynamic Time Warping as a similarity measure of time series, it can become an effective tool for fault diagnosis in chemical process plants. However, direct application of Dynamic Time Warping can be computationally inefficient, given the complexity involved. In this work we have tackled this problem by employing a warping window constraint and a Lower Bounding measure. A novel methodology for online fault diagnosis with Dynamic Time Warping has been suggested and its performance has been investigated using two simulated case studies.

We propose a novel hybrid Holder-SVM detection algorithm for arrhythmia classification. The Holder exponents are computed efficiently using the wavelet transform modulus maxima (WTMM) method. The hybrid system performance is evaluated using the benchmark MIT-BIH arrhythmia database. The implemented model classifies 160 of Normal sinus rhythm, 25 of Ventricular bigeminy, 155 of Atrial fibrillation and 146 of Nodal (A-V junctional) rhythm with 96.94% accuracy. The distinct scaling properties of different types of heart rhythms may be of clinical importance.

Linear and nonlinear regression problems are very common in different fields of science and engineering. A number of different methods to provide accurate input/output relationship have been proposed in the literature. Lazy learning is a recently introduced nonparametric regression technique that employs a memory based local learning approach. Employment of (i) local weighted regression for parametric identification and (ii) PRESS statistic to assess a local model for structural identification, are the two unique features of Lazy learning. The method attempts to fit the training data in a region around the location of the query point by locally interpolating the relevant neighboring points based on distance measure. This is particularly useful when limited number of input/output pairs are available and an accurate prediction is required. Being a memory based technique, it does not require separate training, which greatly enhances the speed of implementation. Moreover the method is less susceptible to noise contamination. All these features are illustrated with one benchmark multivariate statistical example and three important chemical engineering examples comprising boiling point prediction of aliphatic hydrocarbons using quantitative structural property relations, system identification for a continuous stirred tank reactor in which an exothermal irreversible reaction between sodium thiosulfate and hydrogen peroxide occurs and a fermentation process for polyol production. The performance of the proposed method is compared with some of the state-of-art approaches.