Binary optimization
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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.
- Klíčová slova
- Binary optimization, Engineering applications, Metaheuristic algorithms,
- Publikační typ
- časopisecké články MeSH
Butyl methacrylate monolithic columns in 320 microm i.d. fused silica capillaries for reversed-phase capillary liquid chromatography were prepared by radical polymerization initiated thermally with azobisisobutyronitrile (AIBN). Polymerization mixture contained butyl methacrylate (BMA) as the function monomer and ethylene dimethacrylate (EDMA) as the crosslinking agent with 1,4-butanediol and 1-propanol as a binary porogen solvent. Ratio of 1,4-butanediol to 1-propanol in the porogen solvent was optimized regarding the monolithic column efficiency and performance. Total porosity, column permeability, separation impedance, Walters hydrophobicity index, retention factors, peak asymmetry factors, height equivalents to a theoretical plate and peak resolutions were used for characterization of the prepared monolithic columns. The polymerization mixture consisting of 17.8% of BMA, 21.8% of EDMA, 18.0% of 1,4-butanediol, 42.0% of 1-propanol and 0.4% AIBN generated monolithic columns of the best performance having a sufficient permeability and the lowest separation impedance. It was also demonstrated that monolithic columns of this composition exhibited good preparation reproducibility and an excellent pressure resistance when applied in capillary liquid chromatography.
- MeSH
- elektroforéza kapilární MeSH
- methakryláty chemie MeSH
- polymery chemická syntéza MeSH
- reprodukovatelnost výsledků MeSH
- rozpouštědla MeSH
- spektrofotometrie ultrafialová MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- butyl methacrylate MeSH Prohlížeč
- methakryláty MeSH
- polymery MeSH
- rozpouštědla MeSH
In this work, the use of binary amplitude holography is investigated as a mechanism to focus broadband acoustic pulses generated by high peak-power pulsed lasers. Two algorithms are described for the calculation of the binary holograms; one using ray-tracing, and one using an optimization based on direct binary search. It is shown using numerical simulations that when a binary amplitude hologram is excited by a train of laser pulses at its design frequency, the acoustic field can be focused at a pre-determined distribution of points, including single and multiple focal points, and line and square foci. The numerical results are validated by acoustic field measurements from binary amplitude holograms, excited by a high peak-power laser.
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Chemical absorption method plays an important role in the process of CO2 separation. One major problem for chemical absorption is huge energy consumption, which is affected by the performance of absorbents. Developing a type of absorbent with high absorption capacity and low regenerative energy consumption is a research topic that attracts attention. The combination of two or more amines is one way to develop new solvents. However, the change of amine liquid ratio can cause a series of complex nonlinear changes in absorption capacity, absorption heat, the heat of vaporisation and sensible heat. It is of interest to visualise the amine solution mixing ratio optimisation to help reduce the energy consumption and increase the absorption capacity. Derivative analysis of standardised vs variables diagram (DSVD), a kind of graphical method based on maximum benefit and minimum consumption, is proposed to determine the optimal mixing ratio of binary amine solution. This novel approach helps to visualise what kind of amines are not suitable for compounding, what kind of amines have the best compounding ratio, and how to determine the optimal compounding ratio. The optimal mixing ratio of the Methyldiethanolamine (MDEA) - Piperazine (PZ) system and MDEA - Monoethanolamine (MEA) were optimised by this method. The optimal ratio of MDEA - PZ and MDEA - MEA are 0.6 (PZ: MDEA = 0.6:0.4, wt.%) and 0.8 (MEA: MDEA = 0.8:0.2, wt.%).
In recent years, several computer-aided diagnosis systems emerged for the diagnosis of thyroid gland disorders using ultrasound imaging. These systems based on machine learning algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging. Although current computer-aided diagnosis systems exhibit promising results, their use in clinical practice is limited. One of the main limitations is that the majority of them use direction-dependent features. Our intention has been to design a computer-aided diagnosis system, which will use only direction-independent features, that is, it will not be dependent on the orientation and the inclination angle of the ultrasound probe when acquiring the image. We have, therefore, applied histogram analysis and segmentation-based fractal texture analysis algorithm, which calculates direction-independent features only. In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several features, such as histogram parameters, fractal dimension, and mean brightness value in different grayscale bands (obtained by 2-threshold binary decomposition). The features were then used in support vector machine and random forests classifiers to differentiate nodules into malignant and benign classes. Using leave-one-out cross-validation method, the overall accuracy was 92.42% for random forests and 94.64% for support vector machine. Results show that both methods are useful in practice; however, support vector machine provides better results for this application. Proposed computer-aided diagnosis system can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can optimize the overall accuracy of ultrasound imaging.
- Klíčová slova
- classification, computer-aided diagnosis, texture analysis, thyroid, ultrasound,
- MeSH
- algoritmy * MeSH
- diagnóza počítačová metody MeSH
- diferenciální diagnóza MeSH
- interpretace obrazu počítačem metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- štítná žláza diagnostické zobrazování patologie MeSH
- support vector machine MeSH
- ultrasonografie metody MeSH
- uzly štítné žlázy klasifikace diagnostické zobrazování patologie MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
The fast-growing quantity of information hinders the process of machine learning, making it computationally costly and with substandard results. Feature selection is a pre-processing method for obtaining the optimal subset of features in a data set. Optimization algorithms struggle to decrease the dimensionality while retaining accuracy in high-dimensional data set. This article proposes a novel chaotic opposition fruit fly optimization algorithm, an improved variation of the original fruit fly algorithm, advanced and adapted for binary optimization problems. The proposed algorithm is tested on ten unconstrained benchmark functions and evaluated on twenty-one standard datasets taken from the Univesity of California, Irvine repository and Arizona State University. Further, the presented algorithm is assessed on a coronavirus disease dataset, as well. The proposed method is then compared with several well-known feature selection algorithms on the same datasets. The results prove that the presented algorithm predominantly outperform other algorithms in selecting the most relevant features by decreasing the number of utilized features and improving classification accuracy.
- MeSH
- algoritmy MeSH
- COVID-19 * MeSH
- Drosophila MeSH
- strojové učení MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Arizona MeSH
To investigate the effects of various parameters on the production rate and on the recovery yield in overloaded reversed-phase gradient-elution chromatography, band profiles of binary mixtures of phenol and o-cresol were calculated using the experimental parameters of the distribution isotherms determined by binary frontal analysis. The effects of the feed volume and of the steepness of a continuous gradient (gradient time) of methanol in aqueous-organic mobile phases on the separation were studied. If the sample feed in a solvent with weak elution strength is used, combined effects of on-column enrichment, frontal chromatography and sharpening of the later eluted bands may enhance the production rate and the recovery yield. Steep continuous gradients offer better results than isocratic elution if the feed is dissolved in water and injected into the mobile phase with a higher elution strength, because larger volumes of dilute feed can be separated with high recovery yields.
- MeSH
- chemické modely MeSH
- chromatografie kapalinová metody MeSH
- fenol chemie MeSH
- kresoly chemie MeSH
- osmolární koncentrace MeSH
- reprodukovatelnost výsledků MeSH
- spektrofotometrie ultrafialová MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
- srovnávací studie MeSH
- Názvy látek
- 2-cresol MeSH Prohlížeč
- fenol MeSH
- kresoly MeSH
The exponential deployment of electric vehicles (EVs) in the residential sectors in recent years allows better energy utilization in the decentralized and centralized levels of distribution systems due to their bidirectional operation and energy storage capabilities. However, to execute these, it is necessary to adopt residential demand side management (RDSM) to schedule energy utilization effectively to fetch economical and efficient energy consumption and grid stability and reliability, particularly during peak load conditions. The paper aims to formulate a robust and efficient RDSM technique to provide an energy utilization scheduling considering various influential factors and critical roles of EVs in RDSM. A Binary Whale Optimization Algorithm (BWOA) approach is proposed as an efficient algorithm for EV's impact on the RDSM for better energy scheduling. A single-objective formulation is presented with detailed modelling considering economic energy utilization as the primary objective with all possible equality and inequality system operational constraints. Secondly, the impact of EVs on the RDSM is studied from various perspectives in result analysis, considering EVs as load, storage devices, and different bidirectional modes of operation with other vehicles, residential components, and grids. In addition, the EVs role and the mutual influence with the integration of renewable energy sources (RES) and energy storage devices (ESDs) are extensively analyzed to provide better residential energy management (REM) in terms of economic, environmental, robust, and reliable points of view. The load priority based on consumer choice is also incorporated in the formulation. Extensive simulation is done for the proposed approach to show the effect of EVs on REM, and the results are impressive to show the EV's role as a load, as a storage device, and as a mutually supportive device to RES, ESD, and grid.
In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients' health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient's health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients' gene expression profiles-the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps-in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers-logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient's health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient's health.
INTRODUCTION: Precise localization of the epileptogenic zone is critical for successful epilepsy surgery. However, imbalanced datasets in terms of epileptic vs. normal electrode contacts and a lack of standardized evaluation guidelines hinder the consistent evaluation of automatic machine learning localization models. METHODS: This study addresses these challenges by analyzing class imbalance in clinical datasets and evaluating common assessment metrics. Data from 139 drug-resistant epilepsy patients across two Institutions were analyzed. Metric behaviors were examined using clinical and simulated data. RESULTS: Complementary use of Area Under the Receiver Operating Characteristic (AUROC) and Area Under the Precision-Recall Curve (AUPRC) provides an optimal evaluation approach. This must be paired with an analysis of class imbalance and its impact due to significant variations found in clinical datasets. CONCLUSIONS: The proposed framework offers a comprehensive and reliable method for evaluating machine learning models in epileptogenic zone localization, improving their precision and clinical relevance. SIGNIFICANCE: Adopting this framework will improve the comparability and multicenter testing of machine learning models in epileptogenic zone localization, enhancing their reliability and ultimately leading to better surgical outcomes for epilepsy patients.
- Klíčová slova
- Binary classification, Class imbalance, Epilepsy, Epileptogenic tissue localization, Epileptogenic zone, Evaluation metrics, Intracranial electroencephalography, Machine learning, Seizure onset zone,
- MeSH
- dospělí MeSH
- elektrokortikografie metody normy MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- refrakterní epilepsie * chirurgie patofyziologie MeSH
- strojové učení * MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
