Predictive modeling of deep vein thrombosis risk in hospitalized patients: A Q-learning enhanced feature selection model

Deep vein thrombosis (DVT) represents a critical health concern due to its potential to lead to pulmonary embolism, a life-threatening complication. Early identification and prediction of DVT are crucial to prevent thromboembolic events and implement timely prophylactic measures in high-risk individuals. This study aims to examine the risk determinants associated with acute lower extremity DVT in hospitalized individuals. Additionally, it introduces an innovative approach by integrating Q-learning augmented colony predation search ant colony optimizer (QL-CPSACO) into the analysis. This algorithm, then combined with support vector machines (SVM), forms a bQL-CPSACO-SVM feature selection model dedicated to crafting a clinical risk prognostication model for DVT. The effectiveness of the proposed algorithm's optimization and the model's accuracy are assessed through experiments utilizing the CEC 2017 benchmark functions and predictive analyses on the DVT dataset. The experimental results reveal that the proposed model achieves an outstanding accuracy of 95.90% in predicting DVT. Key parameters such as D-dimer, normal plasma prothrombin time, prothrombin percentage activity, age, previously documented DVT, leukocyte count, and thrombocyte count demonstrate significant value in the prognostication of DVT. The proposed method provides a basis for risk assessment at the time of patient admission and offers substantial guidance to physicians in making therapeutic decisions.

Encapsulation of quercetin fraction from Musa balbisiana banana blossom in chitosan alginate solution, its optimization and characterizations

This study comprises the isolation of quercetin from the bhimkol banana (Musa balbisiana) blossom, encapsulation, and its characterizations. An isolated quercetin rich fraction was obtained from HPLC followed by column chromatography and subsequently encapsulated with chitosan-alginate polyelectrolyte complex at optimum encapsulation conditions obtained by ant colony optimization. Quercetin fraction and encapsulated quercetin were characterized for their physicochemical properties (by HPLC, FTIR, NMR, XRD, Dynamic Light Scattering, and release study). The yield and purity of isolated quercetin rich fractions were 2.35 ± 0.08 μg/ml and 83.12 ± 0.31 %, respectively. After the optimization of encapsulation, quercetin 0.2 %, sodium alginate 4 %, chitosan 0.5 %, and agitation at 300 rpm were found to be the optimal conditions resulting in higher encapsulation efficiency (EE, 84.54 %). EE was significantly improved by a slight increase in sodium alginate, and agitation. Encapsulated quercetin revealed good pH resistance by releasing 68.27 mg QE/g quercetin in simulated gastric fluid at 60 min. Microbeads of encapsulated quercetin showed the structural bond stretching of encapsulating materials and quercetin in FTIR spectra (stretching at 1511 cm-1, 1380 cm-1, and 1241 cm-1 are attributed to the stretching vibration of CO in aromatic rings, and bending vibration of OH bond in phenols). An average particle size of 2.71 μm exhibited the microgel behavior of microbeads (by XRD). The present study on the underutilized variety of banana blossoms has diverse applications in the food and pharmaceutical industries that will productively exhibit effective drug delivery properties.

Statistical versus neural network-embedded swarm intelligence optimization of a metallo-neutral-protease production: activity kinetics and food industry applications

An integrated approach involving response surface methodology (RSM) and artificial neural network-ant-colony hybrid optimization (ANN-ACO) was adopted to develop a bioprocess medium to increase the yield of Bacillus cereus neutral protease under submerged fermentation conditions. The ANN-ACO model was comparatively superior (predicted r2 = 98.5%, mean squared error [MSE] = 0.0353) to RSM model (predicted r2 = 86.4%, MSE = 23.85) in predictive capability arising from its low performance error. The hybrid model recommended a medium containing (gL-1) molasses 45.00, urea 9.81, casein 25.45, Ca2+ 1.23, Zn2+ 0.021, Mn2+ 0.020, and 4.45% (vv-1) inoculum, for a 6.75-fold increase in protease activity from a baseline of 76.63 UmL-1. Yield was further increased in a 5-L bioreactor to a final volumetric productivity of 3.472 mg(Lh)-1. The 10.0-fold purified 46.6-kDa-enzyme had maximum activity at pH 6.5, 45-55 °C, with Km of 6.92 mM, Vmax of 769.23 µmolmL-1 min-1, kcat of 28.49 s-1, and kcat/Km of 4.117 × 103 M-1 s-1, at 45 °C, pH 6.5. The enzyme was stabilized by Ca2+, activated by Zn2+ but inhibited by EDTA suggesting that it was a metallo-protease. The biomolecule significantly clarified orange and pineapple juices indicating its food industry application.

Ant colony optimization for parallel test assembly

Ant colony optimization (ACO) algorithms have previously been used to compile single short scales of psychological constructs. In the present article, we showcase the versatility of the ACO to construct multiple parallel short scales that adhere to several competing and interacting criteria simultaneously. Based on an initial pool of 120 knowledge items, we assembled three 12-item tests that (a) adequately cover the construct at the domain level, (b) follow a unidimensional measurement model, (c) allow reliable and (d) precise measurement of factual knowledge, and (e) are gender-fair. Moreover, we aligned the test characteristic and test information functions of the three tests to establish the equivalence of the tests. We cross-validated the assembled short scales and investigated their association with the full scale and covariates that were not included in the optimization procedure. Finally, we discuss potential extensions to metaheuristic test assembly and the equivalence of parallel knowledge tests in general.

Emerging framework for attack detection in cyber-physical systems using heuristic-based optimization algorithm

In recent days, cyber-physical systems (CPS) have become a new wave generation of human life, exploiting various smart and intelligent uses of automotive systems. In these systems, information is shared through networks, and data is collected from multiple sensor devices. This network has sophisticated control, wireless communication, and high-speed computation. These features are commonly available in CPS, allowing multi-users to access and share information through the network via remote access. Therefore, protecting resources and sensitive information in the network is essential. Many research works have been developed for detecting insecure networks and attacks in the network. This article introduces a framework, namely Deep Bagging Convolutional Neural Network with Heuristic Multiswarm Ant Colony Optimization (DCNN-HMACO), designed to enhance the secure transmission of information, improve efficiency, and provide convenience in Cyber-Physical Systems (CPS). The proposed framework aims to detect attacks in CPS effectively. Compared to existing methods, the DCNN-HMACO framework significantly improves attack detection rates and enhances overall system protection. While the accuracy rates of CNN and FCM are reported as 72.12% and 79.56% respectively, our proposed framework achieves a remarkable accuracy rate of 92.14%.

Advanced machine learning model for predicting Crohn's disease with enhanced ant colony optimization

Changes in human lifestyles have led to a dramatic increase in the incidence of Crohn's disease worldwide. Predicting the activity and remission of Crohn's disease has become an urgent research problem. In addition, the influence of each attribute in the test sample on the prediction results and the interpretability of the model still deserves further investigation. Therefore, in this paper, we proposed a wrapper feature selection classification model based on a combination of the improved ant colony optimization algorithm and the kernel extreme learning machine, called bIACOR-KELM-FS. IACOR introduces an evasive strategy and astrophysics strategy to balance the exploration and exploitation phases of the algorithm and enhance its optimization capabilities. The optimization capability of the proposed IACOR was validated on the IEEE CEC2017 benchmark test function. And the prediction was performed on Crohn's disease dataset. The results of the quantitative analysis showed that the prediction accuracy of bIACOR-KELM-FS for predicting the activity and remission of Crohn's disease reached 98.98%. The analysis of important attributes improved the interpretability of the model and provided a reference for the diagnosis of Crohn's disease. Therefore, the proposed model is considered a promising adjunctive diagnostic method for Crohn's disease.

Optimizing the extraction of active components from Salvia miltiorrhiza by combination of machine learning models and intelligent optimization algorithms and its correlation analysis of antioxidant activity

We extracted Sal B and TIIA from Salvia miltiorrhiza using enzymatic-assisted ethanol extraction. ACONN predicted optimal process conditions. Enzymolysis and alcohol extraction were used, optimizing conditions and evaluating antioxidant activity. ACONN analyzed data and ACO optimized conditions. Lab verification comprehensively evaluated the conditions. The correlation between Sal B, TIIA, and their antioxidant activities was examined. Weights of 0.5739 and 0.4260 evaluated Sal B and TIIA. ACONN had a 97.46% fitting degree. Optimized extraction conditions improved yield and quality, yielding a comprehensive evaluation value of 27.69 with 4.46% average errors. This approach enhances extraction and compound quality. Antioxidant activity strongly correlated with component yield, influenced by extraction conditions. ACONN-optimized extraction improved Sal B and TIIA yield and quality, with potential as natural antioxidants. Integrating machine learning and optimization algorithms in industrial extraction enhances efficiency and environmental preservation.

Wastewater reuse and energy saving require a more decentralized urban wastewater system? Evidence from multi-objective optimal design at the city scale

Decentralization is recognized as an emerging solution for a more sustainable urban wastewater system (UWS) for the future. However, the debate of centralization vs. decentralization at the system's planning stage remains unresolved, mainly due to the complexity of the system's spatial structure and the multiple design objectives, such as water reuse and energy conservation. This paper presents the Sustainable Urban Wastewater System Generator (SUWStor) as a tool to address this issue. Integrating a graph representation of the system structure and the ant colony algorithm, SUWStor can produce Pareto optimal solutions for system design under three objectives: minimizing the capital cost, minimizing the operational energy consumption, and maximizing the water reuse capacity. The model is used for system design in a 100-square-km new city, the Xiong'an New District in China. Compared to the solution based on human experience, the model can reduce the system's capital cost by 7% and the operational energy in the pipe network by 26%, while maintaining the water reuse capacity at 100%. With this model, the relation between the optimal system layout and the choice over different design objectives can be discussed for any given area. In our case study, the optimal capacity of WWTPs for the lowest-cost solution is 48,000 m³ per day, leading to a total number of WWTPs of 5. As the water reuse level increases to maximum, the optimal capacity reduces to 15,000 m³ per day, where the number of WWTPs is 16. The model is also able to perform significantly better than the locally optimized results, in which only the WWTP locations are fixed at their optimal values. This demonstrates the importance of a global optimization model in designing the integrated UWS.

Coupling ANFIS with ant colony optimization (ACO) algorithm for 1-, 2-, and 3-days ahead forecasting of daily streamflow, a case study in Poland

Finding an efficient and reliable streamflow forecasting model has always been an important challenge for managers and planners of freshwater resources. The current study, based on an adaptive neuro-fuzzy inference system (ANFIS) model, was designed to predict the Warta river (Poland) streamflow for 1 day, 2 days, and 3 days ahead for a data set from the period of 1993-2013. The ANFIS was additionally combined with the ant colony optimization (ACO) algorithm and employed as a meta-heuristic ANFIS-ACO model, which is a novelty in streamflow prediction studies. The investigations showed that on a daily scale, precipitation had a very weak and insignificant effect on the river's flow variation, so it was not considered as a predictor input. The predictor inputs were selected by the autocorrelation function from among the daily streamflow time lags for all stations. The predictions were evaluated with the actual streamflow data, using such criteria as root mean square error (RMSE), normalized RMSE (NRMSE), and R². According to the NRMSE values, which ranged between 0.016-0.006, 0.030-0.013, and 0.038-0.020 for the 1-day, 2-day, and 3-day lead times, respectively, all predictions were classified as excellent in terms of accuracy (prediction quality). The best RMSE value was 1.551 m³/s and the highest R² value was equal to 0.998, forecast for 1-day lead time. The combination of ANFIS with the ACO algorithm enabled to significantly improve streamflow prediction. The use of this coupling can averagely increase the prediction accuracies of ANFIS by 12.1%, 12.91%, and 13.66%, for 1-day, 2-day, and 3-day lead times, respectively. The current satisfactory results suggest that the employed hybrid approach could be successfully applied for daily streamflow prediction in other catchment areas.

Generalized relational tensors for chaotic time series

The article deals with a generalized relational tensor, a novel discrete structure to store information about a time series, and algorithms (1) to fill the structure, (2) to generate a time series from the structure, and (3) to predict a time series. The algorithms combine the concept of generalized z-vectors with ant colony optimization techniques. To estimate the quality of the storing/re-generating procedure, a difference between the characteristics of the initial and regenerated time series is used. For chaotic time series, a difference between characteristics of the initial time series (the largest Lyapunov exponent, the auto-correlation function) and those of the time series re-generated from a structure is used to assess the effectiveness of the algorithms in question. The approach has shown fairly good results for periodic and benchmark chaotic time series and satisfactory results for real-world chaotic data.

Some metaheuristic algorithms for solving multiple cross-functional team selection problems

We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain the quality of solutions and a reasonable execution time. The team selection problem has become more complicated in order to achieve multiple goals in its decision-making process. This study introduces a multiple cross-functional team (CFT) selection model with different skill requirements for candidates who meet the maximum required skills in both deep and wide aspects. We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. Our experimental results show that the proposed algorithms outperformed CPLEX across several assessment aspects, including solution quality and execution time. The developed method also demonstrated the effectiveness of the multi-criteria decision-making process when compared with the multi-objective evolutionary algorithm (MOEA).

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

This paper focuses on the study of Coronavirus Disease 2019 (COVID-19) X-ray image segmentation technology. We present a new multilevel image segmentation method based on the swarm intelligence algorithm (SIA) to enhance the image segmentation of COVID-19 X-rays. This paper first introduces an improved ant colony optimization algorithm, and later details the directional crossover (DX) and directional mutation (DM) strategy, XMACO. The DX strategy improves the quality of the population search, which enhances the convergence speed of the algorithm. The DM strategy increases the diversity of the population to jump out of the local optima (LO). Furthermore, we design the image segmentation model (MIS-XMACO) by incorporating two-dimensional (2D) histograms, 2D Kapur's entropy, and a nonlocal mean strategy, and we apply this model to COVID-19 X-ray image segmentation. Benchmark function experiments based on the IEEE CEC2014 and IEEE CEC2017 function sets demonstrate that XMACO has a faster convergence speed and higher convergence accuracy than competing models, and it can avoid falling into LO. Other SIAs and image segmentation models were used to ensure the validity of the experiments. The proposed MIS-XMACO model shows more stable and superior segmentation results than other models at different threshold levels by analyzing the experimental results.

Efficient UAV-based mobile edge computing using differential evolution and ant colony optimization

Internet of Things (IoT) tasks are offloaded to servers located at the edge network for improving the power consumption of IoT devices and the execution times of tasks. However, deploying edge servers could be difficult or even impossible in hostile terrain or emergency areas where the network is down. Therefore, edge servers are mounted on unmanned aerial vehicles (UAVs) to support task offloading in such scenarios. However, the challenge is that the UAV has limited energy, and IoT tasks are delay-sensitive. In this paper, a UAV-based offloading strategy is proposed where first, the IoT devices are dynamically clustered considering the limited energy of UAVs, and task delays, and second, the UAV hovers over each cluster head to process the offloaded tasks. The optimization problem of dynamically determining the optimal number of clusters, specifying the member tasks of each cluster, is modeled as a mixed-integer, nonlinear constraint optimization. A discrete differential evolution (DDE) algorithm with new mutation and crossover operators is proposed for the formulated optimization problem, and compared with the particle swarm optimization (PSO) and genetic algorithm (GA) meta-heuristics. Further, the ant colony optimization (ACO) algorithm is employed to identify the shortest path over the cluster heads for the UAV to traverse. The simulation results validate the effectiveness of the proposed offloading strategy in terms of tasks delays and UAV energy consumption.

Semi-supervised associative classification using ant colony optimization algorithm

Labeled data is the main ingredient for classification tasks. Labeled data is not always available and free. Semi-supervised learning solves the problem of labeling the unlabeled instances through heuristics. Self-training is one of the most widely-used comprehensible approaches for labeling data. Traditional self-training approaches tend to show low classification accuracy when the majority of the data is unlabeled. A novel approach named Self-Training using Associative Classification using Ant Colony Optimization (ST-AC-ACO) has been proposed in this article to label and classify the unlabeled data instances to improve self-training classification accuracy by exploiting the association among attribute values (terms) and between a set of terms and class labels of the labeled instances. Ant Colony Optimization (ACO) has been employed to construct associative classification rules based on labeled and pseudo-labeled instances. Experiments demonstrate the superiority of the proposed associative self-training approach to its competing traditional self-training approaches.

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a new ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was successfully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com.

Chemometrics coupled 4-Aminothiophenol labelled Ag-Au alloy SERS off-signal nanosensor for quantitative detection of mercury in black tea

Black tea like other food crops is prone to mercury ion (Hg²⁺) contamination right from cultivation to industrial processing. Due to the dangerous health effects posed even in trace contents, sensitive detection and quantification sensors are required. This study employed the surface-enhanced Raman scattering (SERS) enhancement property of 4-aminothiophenol (4-ATP) as a signal turn off approach functionalized on Ag-Au alloyed nanoparticle to firstly detect Hg²⁺ in standard solutions and spiked tea samples. Different chemometric algorithms were applied on the acquired SERS and inductively coupled plasma-mass spectrometry (ICP-MS) chemical reference data to select effective wavelengths and spectral variables in order to develop models to predict the Hg²⁺. Results indicated that Ag-Au/4-ATP SERS sensor combined with ant colony optimization partial least squares (ACO-PLS) exhibited the best correlation efficient and minimum errors for Hg²⁺ standard solutions (R_c = 0.984, R_p = 0.974, RMSEC = 0.157 μg/mL, RMSEP = 0.211 μg/mL) and spiked tea samples (R_c = 0.979, R_p = 0.963, RMSEC = 0.181 μg/g and RMSEP = 0.210 μg/g). The limit of detection of the proposed sensor was 4.12 × 10^-7 μg/mL for Hg²⁺ standard solutions and 2.83 × 10^-5 μg/g for Hg²⁺ spiked tea samples. High stability and reproducibility with relative standard deviation of 1.14% and 0.84% were detected. The potent strong relationship between the SERS sensor and the chemical reference method encourages the application of the developed chemometrics coupled SERS system for future monitoring and evaluation of Hg²⁺ in tea.

PhDSeeker: Pheromone-Directed Seeker for metabolic pathways

Manually finding relationship networks among compounds can be a hard and time-consuming task. However, this process is fundamental when looking for a metabolic pathway that explains how multiple compounds are related, to identify relevant pathways in organisms, filling gaps on metabolic networks, or when new mechanisms for the synthesis of important compounds are sought. Here, we present PhDSeeker, a new tool for the automatic search of metabolic pathways. This tool is able to relate simultaneously several compounds. Furthermore, its flexibility allows it to be easily configured for addressing a wide range of situations. Solutions found are provided not only in plain text but also as interactive representations that can be analyzed in a web browser. Source code is available at https://github.com/sinc-lab/phdseeker. A web service is also available at https://sinc.unl.edu.ar/web-demo/phds/. Several fully documented study cases, including their settings and solutions files, are also provided as Supplementary Material.

Modelling of methylene blue adsorption using peroxidase immobilized functionalized Buckypaper/polyvinyl alcohol membrane via ant colony optimization

Jicama peroxidase (JP) was covalently immobilized onto functionalized multi-walled carbon nanotube (MWCNT) Buckypaper/Polyvinyl alcohol (BP/PVA) membrane and employed for degradation of methylene blue dye. The parameters of the isotherm and kinetic models are estimating using ant colony optimization (ACO), which do not meddle the non-linearity form of the respective models. The proposed inverse modelling through ACO optimization was implemented, and the parameters were evaluated to minimize the non-linear error functions. The adsorption of MB dye onto JP-immobilized BP/PVA membrane follows Freundlich isotherm model (R² = 0.99) and the pseudo 1st order or 2nd kinetic model (R² = 0.980 & 0.968 respectively). The model predictions from the parameters estimated by ACO resulted values close the experimental values, thus inferring that this approach captured the inherent characteristics of MB adsorption. Moreover, the thermodynamic studies indicated that the adsorption was favourable, spontaneous, and exothermic in nature. The comprehensive structural analyses have confirmed the successful binding of peroxidase onto BP/PVA membrane, as well as the effective MB dye removal using immobilized JP membrane. Compared to BP/PVA membrane, the reusability test revealed that JP-immobilized BP/PVA membrane has better dye removal performances as it can retain 64% of its dye removal efficiency even after eight consecutive cycles. Therefore, the experimental results along with modelling results demonstrated that JP-immobilized BP/PVA membrane is expected to bring notable impacts for the development of effective green and sustainable wastewater treatment technologies.

Path minimization in a tandem running Indian ant in the context of colony relocation

The phenomenon of minimizing the path length to a target site in order to increase transport efficiency is described as path optimization, and it has been observed in many mammals, birds and some invertebrates such as honeybees and ants. It has been demonstrated that ants can optimize their foraging path through an emergent process, involving the trail pheromone concentration, without individual ants having to measure and compare distances. In the current study, we investigated whether ants that use only tandem running to recruit their nestmates can minimize their path while relocating their entire colony into a new nest. As colony relocation directly impacts the survival of the whole colony, it would be particularly important to optimize their path to the new nest. Using the ponerine ant Diacamma indicum, we conducted relocation experiments, in which ants had to choose between different defined paths, and contrasted our findings with open arena experiments, as they navigated to their new nest. After following 4100 unique transports by 450 different transporters, we found that these ants do minimize their path. Individual leaders, as well as colonies, chose the shorter path significantly more than the longer path, and they showed a significant preference for the shorter arm at multiple decision points on encountering a combination of paths. Thus, we concluded that tandem leaders are capable of path minimization based on the information they themselves collect. Further investigation into the proximate mechanisms by which they achieve this is required.

Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy

Zearalenone is a contaminant in food and feed products which are hazardous to humans and animals. This study explored the feasibility of the Raman rapid screening technique for zearalenone in contaminated maize. For representative Raman spectra acquisition, the ground maize samples were collected by extended sample area to avoid the adverse effect of heterogeneous component. Regression models were built with partial least squares (PLS) and compared with those built with other variable selection algorithms such as synergy interval PLS (siPLS), ant colony optimization PLS (ACO-PLS) and siPLS-ACO. SiPLS-ACO algorithm was superior to others in terms of predictive power performance for zearalenone analysis. The best model based on siPLS-ACO achieved coefficients of correlation (R_p) of 0.9260 and RMSEP of 87.9132 μg/kg in the prediction set, respectively. Raman spectroscopy combined multivariate calibration showed promising results for the rapid screening large numbers of zearalenone maize contaminations in bulk quantities without sample-extraction steps.

Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm

In this paper, an adaptive artefact canceller is designed using the bounded range artificial bee colony (BR-ABC) optimization technique. The results of proposed method are compared with recursive least square and other evolutionary algorithms. The performance of these algorithms is evaluated in terms of signal-to-noise ratio (SNR), mean square error (MSE), maximum error (ME) mean, standard deviation (SD) and correlation factor (r). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. A comparative study of various techniques reveals that the performance of BR-ABC algorithm is better in noisy environment. Our simulation results show that the ANC filter using BR-ABC technique provides 15 dB improvement in output average SNR, 63 and 83% reduction in MSE and ME, respectively as compared to ANC filter based on PSO technique. Further, the ANC filter designed using BR-ABC technique enhances the correlation between output and pure EMG signal.

A New Optimized Thresholding Method Using Ant Colony Algorithm for MR Brain Image Segmentation

Image segmentation is considered as one of the most fundamental tasks in image processing applications. Segmentation of magnetic resonance (MR) brain images is also an important pre-processing step, since many neural disorders are associated with brain's volume changes. As a result, brain image segmentation can be considered as an essential measure toward automated diagnosis or interpretation of regions of interest, which can help surgical planning, analyzing changes of brain's volume in different tissue types, and identifying neural disorders. In many neural disorders such as Alzheimer and epilepsy, determining the volume of different brain tissues (i.e., white matter, gray matter, and cerebrospinal fluids) has been proven to be effective in quantifying diseases. A traditional way for segmenting brain images involves the use of a medical expert's experience in manually determining the boundary of different regions of interest in brain images. It may seem that manual segmentation of MR brain images by an expert is the first and the best choice. However, this method is proved to be time-consuming and challenging. Hence, numerous MR brain image segmentation methods with different degrees of complexity and accuracy have been introduced recently. Our work proposes an optimized thresholding method for segmentation of MR brain images using biologically inspired ant colony algorithm. In this proposed algorithm, textural features are adopted as heuristic information. Besides, post-processing image enhancement based on homogeneity is also performed to achieve a better performance. The empirical results on axial T1-weighted MR brain images have demonstrated competitive accuracy to traditional meta-heuristic methods, K-means, and expectation maximization.

Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals

This paper focuses on identification of an effective pattern recognition scheme with the least number of time domain features for dexterous control of prosthetic hand to recognize the various finger movements from surface electromyogram (EMG) signals. Eight channels EMG from 8 able-bodied subjects for 15 individuals and combined finger activities have been considered in this work. In this work, an attempt has been made to recognize a number of classes with the least number of features. Therefore, EMG signals are pre-processed using dual tree complex wavelet transform to improve the discriminating capability of features and time domain features such as zero crossing, slope sign change, mean absolute value, and waveform length is extracted from the pre-processed data. The performance of extracted features is studied with different classifiers such as linear discriminant analysis, naive Bayes classifier, quadratic support vector machine and cubic support vector machine with and without feature selection algorithms. The feature selection has been studied using particle swarm optimization (PSO) and ant colony optimization (ACO) with different number of features to identify the effect of features. The results demonstrated that naive Bayes classifier with ant colony optimization shows an average classification accuracy of 88.89% with a response time of 0.058025 ms for recognizing the 15 different finger movements with 16 features with significant difference in accuracy compared to SVM classifier with feature selection for a significance level of 0.05. There is no significant difference in the accuracy, specificity and sensitivity of an SVM classifier with and without feature selection. But the processing time is significantly more than the LDA and NB classifier. The PSO and ACO results revealed that slope sign changes contribute to recognizing the activity. In PSO, mean absolute value has been found to be effective compared to waveform length, contradictory with ACO. Further, the zero crossings have been found to be not effective in classification of finger movements in both the methods.

epiACO - a method for identifying epistasis based on ant Colony optimization algorithm

Background

Identifying epistasis or epistatic interactions, which refer to nonlinear interaction effects of single nucleotide polymorphisms (SNPs), is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Though many works have been done for identifying epistatic interactions, due to their methodological and computational challenges, the algorithmic development is still ongoing.

Results

In this study, a method epiACO is proposed to identify epistatic interactions, which based on ant colony optimization algorithm. Highlights of epiACO are the introduced fitness function Svalue, path selection strategies, and a memory based strategy. The Svalue leverages the advantages of both mutual information and Bayesian network to effectively and efficiently measure associations between SNP combinations and the phenotype. Two path selection strategies, i.e., probabilistic path selection strategy and stochastic path selection strategy, are provided to adaptively guide ant behaviors of exploration and exploitation. The memory based strategy is designed to retain candidate solutions found in the previous iterations, and compare them to solutions of the current iteration to generate new candidate solutions, yielding a more accurate way for identifying epistasis.

Conclusions

Experiments of epiACO and its comparison with other recent methods epiMODE, TEAM, BOOST, SNPRuler, AntEpiSeeker, AntMiner, MACOED, and IACO are performed on both simulation data sets and a real data set of age-related macular degeneration. Results show that epiACO is promising in identifying epistasis and might be an alternative to existing methods.

Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images

BACKGROUND AND OBJECTIVES

Computer-aided diagnosis (CAD) plays a vital role in the routine clinical activity for the detection of lung disorders using computed tomography (CT) images. It serves as a source of second opinion that radiologists may consider in order to interpret CT images. In this work, the purpose of CAD is to improve the diagnostic accuracy of pulmonary bronchitis from CT images of the lung.

METHODS

Left and right lung fields are segmented using optimal thresholding from the lung CT images. Texture and shape features are extracted from the pathology bearing regions. A hybrid feature selection approach based on ant colony optimization (ACO) combining cosine similarity and support vector machine (SVM) classifier is used to select relevant features. Additionally, tandem run recruitment strategy is included in the selection activity to choose the promising features. The SVM classifier is trained using the selected features and the performance of the trained classifier is evaluated using trivial performance evaluation measures.

RESULTS

The training and testing datasets used in building the classifier model are disjoint and contains 200 CT slices affected with bronchitis, 50 normal slices and 300 slices with cancer. Out of 100 features extracted from each CT slice, a subset of 60 features is used for classification. ACO with tandem run strategy yielded 81.66% of accuracy whereas ACO without tandem run yielded an accuracy of 77.52%. When all the features are used for classifier training without feature selection algorithm, an accuracy of 75.14% is achieved.

CONCLUSION

From the results, it is inferred that identifying relevant features to train the classifier has a definite impact on the classifier performance.

Investigating the organic and conventional production type of olive oil with target and suspect screening by LC-QTOF-MS, a novel semi-quantification method using chemical similarity and advanced chemometrics

The discrimination of organic and conventional production has been a critical topic of public discussion and constitutes a scientific issue. It remains a challenge to establish a correlation between the agronomical practices and their effects on the composition of olive oils, especially the phenolic composition, since it defines their organoleptic and nutritional value. Thus, a liquid chromatography-electrospray ionization-quadrupole time of flight tandem mass spectrometric method was developed, using target and suspect screening workflows, coupled with advanced chemometrics for the identification of phenolic compounds and the discrimination between organic and conventional extra virgin olive oils. The method was optimized by one-factor design and response surface methodology to derive the optimal conditions of extraction (methanol/water (80:20, v/v), pure methanol, or acetonitrile) and to select the most appropriate internal standard (caffeic acid or syringaldehyde). The results revealed that extraction with methanol/water (80:20, v/v) was the optimum solvent system and syringaldehyde 1.30 mg L^-1 was the appropriate internal standard. The proposed method demonstrated low limits of detection in the range of 0.002 (luteolin) to 0.028 (tyrosol) mg kg^-1. Then, it was successfully applied in 52 olive oils of Kolovi variety. In total, 13 target and 24 suspect phenolic compounds were identified. Target compounds were quantified with commercially available standards. A novel semi-quantitation strategy, based on chemical similarity, was introduced for the semi-quantification of the identified suspects. Finally, ant colony optimization-random forest model selected luteolin as the only marker responsible for the discrimination, during a 2-year study. Graphical abstract Investigation of the organic and conventional production type of olive oil by LC-QTOF-MS.

Protein-Peptide Interaction Design: PepCrawler and PinaColada

In this chapter we present two methods related to rational design of inhibitory peptides: PepCrawler: A tool to derive binding peptides from protein-protein complexes and the prediction of protein-peptide complexes. Given an initial protein-peptide complex, the method detects improved predicted peptide binding conformations which bind the protein with higher affinity. This program is a robotics motivated algorithm, representing the peptide as a robotic arm moving among obstacles and exploring its conformational space in an efficient way. PinaColada: A peptide design program for the discovery of novel peptide candidates that inhibit protein-protein interactions. PinaColada uses PepCrawler while introducing sequence mutations, in order to find novel inhibitory peptides for PPIs. It uses the ant colony optimization approach to explore the peptide's sequence space, while using PepCrawler in the refinement stage.

Dynamic vehicle routing with time windows in theory and practice

The vehicle routing problem is a classical combinatorial optimization problem. This work is about a variant of the vehicle routing problem with dynamically changing orders and time windows. In real-world applications often the demands change during operation time. New orders occur and others are canceled. In this case new schedules need to be generated on-the-fly. Online optimization algorithms for dynamical vehicle routing address this problem but so far they do not consider time windows. Moreover, to match the scenarios found in real-world problems adaptations of benchmarks are required. In this paper, a practical problem is modeled based on the procedure of daily routing of a delivery company. New orders by customers are introduced dynamically during the working day and need to be integrated into the schedule. A multiple ant colony algorithm combined with powerful local search procedures is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on a new benchmark based on simulations of a working day. The problems are taken from Solomon's benchmarks but a certain percentage of the orders are only revealed to the algorithm during operation time. Different versions of the MACS algorithm are tested and a high performing variant is identified. Finally, the algorithm is tested in situ: In a field study, the algorithm schedules a fleet of cars for a surveillance company. We compare the performance of the algorithm to that of the procedure used by the company and we summarize insights gained from the implementation of the real-world study. The results show that the multiple ant colony algorithm can get a much better solution on the academic benchmark problem and also can be integrated in a real-world environment.

Advances on image interpolation based on ant colony algorithm

This paper presents an advance on image interpolation based on ant colony algorithm (AACA) for high resolution image scaling. The difference between the proposed algorithm and the previously proposed optimization of bilinear interpolation based on ant colony algorithm (OBACA) is that AACA uses global weighting, whereas OBACA uses local weighting scheme. The strength of the proposed global weighting of AACA algorithm depends on employing solely the pheromone matrix information present on any group of four adjacent pixels to decide which case deserves a maximum global weight value or not. Experimental results are further provided to show the higher performance of the proposed AACA algorithm with reference to the algorithms mentioned in this paper.

Robustness of Ant Colony Optimization to Noise

Recently, ant colony optimization (ACO) algorithms have proven to be efficient in uncertain environments, such as noisy or dynamically changing fitness functions. Most of these analyses have focused on combinatorial problems such as path finding. We rigorously analyze an ACO algorithm optimizing linear pseudo-Boolean functions under additive posterior noise. We study noise distributions whose tails decay exponentially fast, including the classical case of additive Gaussian noise. Without noise, the classical [Formula: see text] EA outperforms any ACO algorithm, with smaller [Formula: see text] being better; however, in the case of large noise, the [Formula: see text] EA fails, even for high values of [Formula: see text] (which are known to help against small noise). In this article, we show that ACO is able to deal with arbitrarily large noise in a graceful manner; that is, as long as the evaporation factor [Formula: see text] is small enough, dependent on the variance [Formula: see text] of the noise and the dimension n of the search space, optimization will be successful. We also briefly consider the case of prior noise and prove that ACO can also efficiently optimize linear functions under this noise model.

A multilevel ant colony optimization algorithm for classical and isothermic DNA sequencing by hybridization with multiplicity information available

The classical sequencing by hybridization takes into account a binary information about sequence composition. A given element from an oligonucleotide library is or is not a part of the target sequence. However, the DNA chip technology has been developed and it enables to receive a partial information about multiplicity of each oligonucleotide the analyzed sequence consist of. Currently, it is not possible to assess the exact data of such type but even partial information should be very useful. Two realistic multiplicity information models are taken into consideration in this paper. The first one, called "one and many" assumes that it is possible to obtain information if a given oligonucleotide occurs in a reconstructed sequence once or more than once. According to the second model, called "one, two and many", one is able to receive from biochemical experiment information if a given oligonucleotide is present in an analyzed sequence once, twice or at least three times. An ant colony optimization algorithm has been implemented to verify the above models and to compare with existing algorithms for sequencing by hybridization which utilize the additional information. The proposed algorithm solves the problem with any kind of hybridization errors. Computational experiment results confirm that using even the partial information about multiplicity leads to increased quality of reconstructed sequences. Moreover, they also show that the more precise model enables to obtain better solutions and the ant colony optimization algorithm outperforms the existing ones. Test data sets and the proposed ant colony optimization algorithm are available on: http://bioserver.cs.put.poznan.pl/download/ACO4mSBH.zip.

Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results

Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[Formula: see text] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[Formula: see text] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[Formula: see text] producing ordered rules are also presented.

A short-term operating room surgery scheduling problem integrating multiple nurses roster constraints

OBJECTIVES

Operating room (OR) surgery scheduling determines the individual surgery's operation start time and assigns the required resources to each surgery over a schedule period, considering several constraints related to a complete surgery flow and the multiple resources involved. This task plays a decisive role in providing timely treatments for the patients while balancing hospital resource utilization. The originality of the present study is to integrate the surgery scheduling problem with real-life nurse roster constraints such as their role, specialty, qualification and availability. This article proposes a mathematical model and an ant colony optimization (ACO) approach to efficiently solve such surgery scheduling problems.

METHOD

A modified ACO algorithm with a two-level ant graph model is developed to solve such combinatorial optimization problems because of its computational complexity. The outer ant graph represents surgeries, while the inner graph is a dynamic resource graph. Three types of pheromones, i.e. sequence-related, surgery-related, and resource-related pheromone, fitting for a two-level model are defined. The iteration-best and feasible update strategy and local pheromone update rules are adopted to emphasize the information related to the good solution in makespan, and the balanced utilization of resources as well. The performance of the proposed ACO algorithm is then evaluated using the test cases from (1) the published literature data with complete nurse roster constraints, and 2) the real data collected from a hospital in China.

RESULTS

The scheduling results using the proposed ACO approach are compared with the test case from both the literature and the real life hospital scheduling. Comparison results with the literature shows that the proposed ACO approach has (1) an 1.5-h reduction in end time; (2) a reduction in variation of resources' working time, i.e. 25% for ORs, 50% for nurses in shift 1 and 86% for nurses in shift 2; (3) an 0.25h reduction in individual maximum overtime (OT); and (4) an 42% reduction in the total OT of nurses. Comparison results with the real 10-workday hospital scheduling further show the advantage of the ACO in several measurements. Instead of assigning all surgeries by a surgeon to only one OR and the same nurses by traditional manual approach in hospital, ACO realizes a more balanced surgery arrangement by assigning the surgeries to different ORs and nurses. It eventually leads to shortening the end time within the confidential interval of [7.4%, 24.6%] with 95% confidence level.

CONCLUSION

The ACO approach proposed in this paper efficiently solves the surgery scheduling problem with daily nurse roster while providing a shortened end time and relatively balanced resource allocations. It also supports the advantage of integrating the surgery scheduling with the nurse scheduling and the efficiency of systematic optimization considering a complete three-stage surgery flow and resources involved.