Selection Method Research Articles

A survey on planet leaf disease identification and classification by various machine-learning technique

An overview of methods for identifying plants diseases is given in this article. Each sample is categorized by being divided into various groups. The approach of classification involves identifying healthy and diseased leaves based on morphological traits including texture, color, shape, and pattern, among others. Sorting and categorizing plants can be challenging, especially when doing so across a large area, due to the closeness of their visual qualities. There are several methods based on computer vision and image processing. Selecting the right categorization method can be difficult because the outcomes rely on the data you supply. There are several applications for the categorization of plant leaf diseases in fields like agriculture and biological research. This article gives a summary of several approaches currently in use for identifying and categorizing leaf diseases, as well as their benefits and drawbacks. These approaches include preprocessing methods, feature extraction and selection methods, datasets employed, classifiers, and performance metrics

Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis

<p>Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers<em>. </em></p>

Sparse Group Penalties for bi-level variable selection.

Many data sets exhibit a natural group structure due to contextual similarities or high correlations of variables, such as lipid markers that are interrelated based on biochemical principles. Knowledge of such groupings can be used through bi-level selection methods to identify relevant feature groups and highlight their predictive members. One of the best known approaches of this kind combines the classical Least Absolute Shrinkage and Selection Operator (LASSO) with the Group LASSO, resulting in the Sparse Group LASSO. We propose the Sparse Group Penalty (SGP) framework, which allows for a flexible combination of different SGL-style shrinkage conditions. Analogous to SGL, we investigated the combination of the Smoothly Clipped Absolute Deviation (SCAD), the Minimax Concave Penalty (MCP) and the Exponential Penalty (EP) with their group versions, resulting in the Sparse Group SCAD, the Sparse Group MCP, and the novel Sparse Group EP (SGE). Those shrinkage operators provide refined control of the effect of group formation on the selection process through a tuning parameter. In simulation studies, SGPs were compared with other bi-level selection methods (Group Bridge, composite MCP, and Group Exponential LASSO) for variable and group selection evaluated with the Matthews correlation coefficient. We demonstrated the advantages of the new SGE in identifying parsimonious models, but also identified scenarios that highlight the limitations of the approach. The performance of the techniques was further investigated in a real-world use case for the selection of regulated lipids in a randomized clinical trial.

Feature selection techniques and classification algorithms for student performance classification: a review

The process of categorizing students’ performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students’ performance. However, this study entails the need to analyze the diverse attributes of students’ information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students’ online learning activities, utilizing a variety of classification algorithms.

Quality-by-design optimized RP-HPLC approach for the therapeutic drug monitoring of glibenclamide and fluoxetine in human plasma

Glibenclamide (GLB) and fluoxetine (FLX) were reported to have a potential drug-drug interaction when clinically co-administered. FLX is an inhibitor of CYP2C9 (the enzyme responsible for the metabolism of GLB), thus resulting in increased serum levels of GLB followed by severe hypoglycemia. Therefore, a sensitive, simple, and selective HPLC method is urgently needed for their simultaneous determination in plasma to be utilized for monitoring their therapeutic blood concentration levels. The current study introduces the first RP-HPLC approach for the simultaneous estimation of this binary mixture in less than 7 min. Adopting a quality-by-design (QbD) approach allowed for the designed approach to be optimized. Using a two-level full factorial design (25), the effects of various variables on the chromatographic responses were investigated and optimized. Hypersil BDS C18 column was used for the achievement of the optimum chromatographic separation at 19.9 °C using acetonitrile: sodium dihydrogen phosphate buffer (7.5 mM; pH 4, 60:40 % v/v) as a mobile phase, pumped isocratically at a flow rate of 1.036 mL/min. An injection volume of 30.0 μL was used for the binary mixture with UV detection at 230 nm. FLX and GLB calibration curves revealed an excellent linearity over the ranges of 0.025–10.00 and 0.02–10.00 µg/mL with limits of quantitation of 0.018 and 0.02 µg/mL, respectively. Due to the high sensitivity and selectivity, the method was successfully applied to the assay of the two drugs in spiked human plasma samples with reasonable % recoveries (92.70–108.50 %), indicating good bioanalytical applicability. Therefore, this study could be applied for the therapeutic drug monitoring of GLB and FLX in human plasma to control their potential drug-drug interaction. The suggested approach was also applied to estimate the investigated drugs in their dosage forms with reasonable % recoveries (96.80–102.80 %) and small % RSD values (less than 2 %). The method's excellent greenness was evaluated using three metric tools, including Analytical Eco-Scale, Green Analytical Procedure Index (GAPI), and Analytical Greenness (AGREE). Validation of the proposed method was performed according to ICH and US FDA guidelines.

A thorough assessment of various forest management planning initiatives and development of improvement strategies towards an ecosystem-based planning

Management planning for forests in Turkey has undergone gradual evolution over the last century. Developing and implementing a comprehensive management planning framework present significant challenges. This paper evaluates the effectiveness of various forest management planning approaches implemented across the country over different time periods, with overarching planning principles. It formulates a robust planning framework and proposes improvements grounded in scientific advancements and international standards. The assessment indicated that the management plans were developed using reputable scientific methods and principles aimed at ensuring the sustainable management of forest resources with certain strengths and opportunities presented by SWOT analysis. All management plans shared a common planning concept primarily focused on maximizing wood production through the area-control harvest scheduling method (except for continuous cover forest, which employed the single tree selection method). Each management plan established its unique vision, targets, policies, objectives, and planning guidelines. Forest inventory data were gathered through a combination of ground surveys and remotely sensed data to characterize and stratify the landscape. A robust in-house management authority and governance system with appropriate technical capacity and guidelines were developed and implemented, fostering a sound common working culture and tools. However, some notable drawbacks were identified, including political pressure, biomass/carbon accounting, growth-yield modelling, economic analysis, limited characterization of the full range of ecosystem services, risk and uncertainty analysis, food security and, particularly, long-term sustainability and scenario analysis with the appropriate decision-making tools and methods. Despite a few strengths, these limitations may raise concerns about the far-sighted design and application, potentially jeopardizing the sustainable management of forest ecosystems. Proposed improvement strategies for an efficient forest ecosystem management planning system include characterization of ecosystem services, modelling their productivity, scenario analysis with a decision support system, stakeholder involvement, balancing utilization and conservation targets, conducting risk and uncertainty analysis and economic analysis of management actions.

Hybrid machine learning for stock price prediction in the Moroccan banking sector

Analyzing historical stock market data using machine-learning techniques is crucial for data scientists and researchers to optimize stock price prediction models. This study uses machine learning regression algorithms and feature selection methods to optimize a simulated stock price prediction model using real historical data from Bank of Africa, a Moroccan bank. The approach compares multiple supervised regression algorithms, such as linear regression, extreme gradient boosting, ordinary least squared, random forest regressor, a linear least-squares L2-regularized, epsilon-support vector regression, and linear support vector regression. Each of these algorithms is associated with different feature selection algorithms to improve the performance of the prediction model. The analysis results revealed that hybridizing algorithms between the highest score percentiles, univariate linear regression, and linear support vector regression perform better according to the root mean squared error and R2-Score measures. This approach overcomes the problems associated with high-dimensional data by reducing the number of features and improving prediction accuracy.

Findings from the 2019 nationally representative oral health survey for adults in Singapore.

The aim of this study was to present key findings from the 2019 national adult oral health survey in Singapore (NAOHS). A multi-stage stratified sampling method was used to recruit participants for a representative national adult oral health survey. A total of 12 212 households were randomly selected from the National Database on Dwellings in Singapore. Within each household eligible persons aged ≥65 years were automatically invited to participate while a Kish selection method was used to invite those between 21 and 64 years old. The survey comprised a face-to-face interview questionnaire and a clinical examination which recorded details of tooth loss, DMFT, DMFS and prevalence of periodontal disease according to the CPITN and the US CDC-AAP classifications. Weighted analysis was performed to adjust for oversampling, non-response and post-stratification. Multivariate regression with backward stepwise selection was carried out to identify predictors of chronic periodontal disease and untreated dental caries. Six hundred and sixty-three participants completed both the questionnaires and the clinical examination. The prevalence of edentulousness was 2.7%. Of participants, 34.8% presented with untreated dental caries with a higher proportion found in those who were aged ≥60 years, of Malay ethnicity, living in 1-2-room public housing and who only visited the dentist when there was a problem. Mean DMFS and DMFT indices were 24.7 and 7.9 respectively. Based on the CDC-AAP classification, the prevalence of moderate-severe chronic periodontitis was 56.9% and increased with age, with a higher proportion in males. Participants with untreated dental caries were more likely to have moderate or severe periodontal disease. Survey findings showed high prevalence of dental caries and periodontal disease, at 34.8% and 77.6% respectively. A clear socio-economic gradient in the distribution of tooth loss, untreated dental caries and moderate-to-severe periodontitis was observed.

Automated feature selection using improved migrating birds optimization for enhanced medical diagnosis

The feature selection task is a crucial phase in data analysis, aiming to identify a minimized set of relevant features for the target class, thereby eliminating irrelevant and redundant attributes used for model training. While population-based feature selection approaches offer prominent solutions for classification performance, their computational time can be prohibitive. To mitigate delays and optimize resource utilization, this study adopts machine learning operations (MLOps). MLOps involves the seamless transition of experimental Machine Learning models into production, serving them to end users and automating the feature selection phase. This paper introduces a novel feature selection method based on improved migrating bird optimization and its automated variant integrated into MLOps. Experiments conducted on six medical datasets validate the effectiveness of our proposed feature selection method in improving the outcomes of medical diagnosis systems. The results showcase satisfactory performance in terms of classification compared to concurrent feature selection algorithms.

Accident black spots identification based on association rule mining

This paper presents an analytical approach to identifying the important characteristics of accident black spots on Moroccan rural roads. An association rule mining method is applied to extract road spatial characteristics associated with fatal accidents. The weighted severity index was calculated for each section, which was then used to determine the severity levels of black spots. The apriori algorithm is applied to find the correlation between road characteristics and the severity levels of black spots. Then, a general rule selection method is proposed to identify the rules strongly associated with each severity level. The results show that the proposed approach is effective in identifying the most important factors contributing to accidents. Furthermore, it shows that the combination of several road characteristics, such as road width, road surface, and bridge presence, may contribute to fatal accidents. The general rule selection found that wet, bad surfaces, and narrow shoulders were significantly associated with accidents on rural roads. The findings of the present study can help develop effective strategies to reduce road accidents and thus improve road safety in the country.

Reducing Metal Artifacts in X-ray Computed Tomography Based on Metal Effect Estimation and Multiple CT Volume Fusion

In recent years, X-ray Computed Tomography (CT) has been widely used for investigating industrial parts. Many industrial parts often include metals, which cause various CT artifacts such as beam hardening artifacts, and metal artifacts. These CT artifacts lower the quality of X-ray CT images, prevent precise measurements. In order to reduce metal artifacts, here we propose using multiple X-ray CT scanning. Our multiple X-ray CT scanning method includes local artifacts estimation, proposing adequate multiple scanning angles, multiple scanned data registration and multiple CT data fusion. Based on local artifacts estimation, optimal multiple scan angles for data fusion are selected. Data registration and fusion are also based on local artifacts estimation, excluding effects of artifacts during the process. In order to demonstrate our method, we measured industrial parts which includes metals and resins, proposed optimal multiple scanning angles and conducted volume fusion. As a result, the metal artifacts in the CT volume reduced significantly compared to single X-ray CT scanning. Experiments also showed that metal artifacts in different regions are reduced by additional scans, which suggest that our scan angle selection method successfully selected optimal multiple scan angles.

Fake Profile Detection in Instagram

Abstract: In today's society, online communities have become an integral part of daily life, with individuals actively engaging in social media interactions regardless of their location or schedule. However, the prevalence of pseudo personas has contributed to sophisticated persistent attacks and other malicious activities, raising concerns about the privacy of personal data among online community members. This research seeks to identify and address the pressing issue of fraudulent identity projection on social media platforms, particularly focusing on Instagram. By employing automated techniques such as predictive modeling and image identification combined with text analysis, the study aims to enhance the detection of fake profiles. The research utilizes the chi-square method for feature selection and applies learner-centered algorithms like Random Forest (RF) and logistic regression for categorization. The evaluation of outcomes will be based on metrics including relevance, specificity, recall, f1- value efficiency, and accuracy. The exponential growth and influence of social media globally have underscored the urgency of mitigating the proliferation of fraudulent identities, which pose challenges ranging from propaganda to racial profiling. The findings of this research are expected to contribute to the development of effective strategies for identifying and addressing fake profiles, thus safeguarding the integrity and security of online communities

Метод определения эффективности вариантов организации перевозок барже-буксирными составами

A study has been conducted on the method of evaluating options for organizing cargo transportation by barge-tow trains based on determining their effectiveness. Using the methods of the classical theory of transport processes, this problem has no quantitative solution, since it is not possible to take into account the regrouping of the composition during the passage of the route. To overcome this problem, additional mathematical tools have been developed for constructing integral time and velocity matrices, an objective function and graphs of the ratios of the total volume of transported cargo to the time it was en route. The consistent implementation of the proposed tools ensures the coherence of the calculation methods for determining the effectiveness of a flight with and without regrouping on the route, and also makes it possible to select the most appropriate option for organizing transportation. The cumulative result allows us to reflect the method of evaluation, comparison and selection of the most effective option for organizing a flight / group of flights, taking into account the ability to set individual parameters for the movement of the train and cargo. An example of approximation of the coordinate points of the route from the Nizhne-Svirsky shipping lock to Lake Ladoga is illustrated, as well as an example of discretization of an inland waterway section according to a three-level model. The parameters of equalization of the objective function of determining the efficiency of cargo transportation are considered. It is concluded that the optimal transportation option can be carried out on the basis of a combined approach, when several parameters are taken into account or sequentially set in the process of modeling the operation of ships. Graphs have been built that demonstrate the differences between the ways of organizing flights.

Evaluation of different evaluation methods for selection of a conceptual design

Designing plays an important part in the production of an artifact, as it is the mental creation before the product becomes a reality. It also plays an interesting engineering activity that affects almost all areas of human life and uses the laws and insights of science. After the various design concepts have been successfully carried out, the evaluation stage becomes crucial in the selection of the best solution. A thorough study of the literature suggests that not too many works have been done on evaluating different evaluation methods in selecting a concept. The objective of this study was to evaluate four design concept evaluation methods for selecting a car hitch. The study found that for a robust selection of solutions, Verein Deutscher Ingenieure (VDI) guidelines and Technical Economic Evaluations were more appropriate to be used.

Enhancement of Harris Hawks optimization applied in path planning for an indoor navigation mobile application

The research focuses on the enhancement of Harris Hawks Optimization (HHO) algorithm in achieving an efficient path planning, specifically in indoor environments. It solves problems encountered in HHO; firstly, the exploration operator of HHO is modified to incorporate the survival of the fittest principle, this ensures a controlled diversity by using an Exponential Ranking selection method. The method aims to guide the algorithm to find a more optimal solution while allowing it to search for alternative paths. Secondly, Linear Path Strategy is used to reduce the number of nodes in the paths, therefore minimizing its length and simplifying trajectories. In addition, Linear Path Strategy aims to create smoother paths and avoid obstacles, improving the overall performance of the algorithm. Lastly, general multi-objective formula is defined to evaluate path length and travel time. Considering these factors established a balanced evaluation metric, which provided a detailed assessment of path quality for scenarios like indoor navigation. Comparative analysis was done, and results highlighted the effectiveness of EHHO in generating better paths, in comparison with Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), and with the current HHO algorithm. It outperformed the algorithms compared in terms of path length, travel time, and efficiency of the execution, especially in a complex environment. In conclusion, EHHO algorithm showed promising results in providing shorter, faster, and more efficient routes, with potential applications across different domains that requires optimal path planning solutions.

Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels

The simulation of engine combustion processes, such as autoignition, an important process in the co-optimization of fuel-engine design, can be computationally expensive due to the large number of thermo-chemical scalars needed to describe the full chemical system. Yet, the inherent correlations between the different chemical species during oxidation can significantly reduce the complexity of representing this system. One strategy is to select a subset of representative species that accurately captures the combustion process at a fraction of the computational cost of the full system. In this study, we compare the performance of four different techniques to select these species. They include the two-step principal component analysis (PCA) approach, directed relation graphs (DRGs), the global pathway selection (GPS) approach, and the manifold-informed species selection method. A parametric study of the representative species selection is carried out on data from the simulation of homogeneous and perfectly stirred reactors by investigating seven cumulative variances and 47 different cut-off percentages for the two-step PCA, and 65 and 51 thresholds for the DRGs and GPS, respectively. Results show that these selection methods capture key important species that can accurately describe the chemical system and track each stage of oxidation. The two-step PCA is sensitive to the cumulative variance, and DRGs and GPS are sensitive to the choice of target variables. By selecting key representative species and reducing the number of thermo-chemical scalars, these three methods can be used to develop computationally efficient hybrid chemistry schemes.

Development of SPQC sensor based on the specific recognition of TAL-effectors for locus-specific detection of 6-methyladenine in DNA

N6-methyladenosine (6mA) plays a pivotal role in diverse biological processes, including cancer, bacterial toxin secretion, and bacterial drug resistance. However, to date there has not been a selective, sensitive, and simple method for quantitative detection of 6mA at single base resolution. Herein, we present a series piezoelectric quartz crystal (SPQC) sensor based on the specific recognition of transcription-activator-like effectors (TALEs) for locus-specific detection of 6mA. Detection sensitivity is enhanced through the use of a hybridization chain reaction (HCR) in conjunction with silver staining. The limit of detection (LOD) of the sensor was 0.63 pM and can distinguish single base mismatches. We demonstrate the applicability of the sensor platform by quantitating 6mA DNA at a specific site in biological matrix. The SPQC sensor presented herein offers a promising platform for in-depth study of cancer, bacterial toxin secretion, and bacterial drug resistance.

Chemometric-based drug discovery approaches from natural origins using hyphenated chromatographic techniques.

Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound's correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools. The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources. The searching keywords "biochemometric," "chemometric," "chromatography," "natural products bioassay," and "bioassay" were selected to search the published articles between 2010-2023 using different search engines including "Pubmed", "Web of Science," "ScienceDirect," and "Google scholar." An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds. Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.

Machine Learning Techniques and Hybrid Feature Selection Methods for Efficient Prediction of Cancer

The high-dimensional genomic data presents significant challenges, and traditional analytical methods often struggle to capture the complex, non-linear relationships within these datasets. This study elaborates into the application of machine learning methods for dimensionality reduction and predictive modeling of binary phenotypes using gene expression data. Various dimensionality reduction techniques are explored, including t-distributed stochastic neighbor embedding (t-SNE), Non-negative matrix factorization (NMF), Principal component analysis (PCA), and manifold learning methods. Additionally, various algorithms such as logistic regression, random forests, support vector machines (SVMs)cand naive Bayes models are evaluated for predicting phenotypes. The study employs rigorous cross-validation, permutation testing, and evaluation metrics like the Matthews Correlation Coefficient (MCC) to assess model performance. The study rigorously assesses current genomics strategies, pinpointing their drawbacks and suggesting areas for future investigation, while delving into the potential of machine learning to overcome these hurdles and offer valuable insights in genomics. Keywords- Gene Expression , Dimensionality Reduction, Biomarkers, Cancer Prediction, Epigenetics, Machine Learning, Feature extraction.

Development and Application of a Selective Analytical Method for Indole Metabolites in Urine: Dietary Exposure Biomarkers for Broccoli Consumption.

The identification of dietary exposure biomarkers is crucial for advancing our understanding of the health benefits of specific foods. Broccoli, a vegetable with well-known anticancer properties, contains active ingredients, such as isothiocyanates with indole side chains. Hence, indole metabolites related to broccoli consumption have the potential to serve as biomarkers of dietary exposure. In this work, we developed a new analytical method for indole metabolites in urine using a poly(deep eutectic solvents)-molecularly imprinted polymer/vinyl-functionalized graphene oxide (PDESs-MIP/VGO) in miniaturized centrifugal pipet-tip solid-phase extraction (CPT-SPE) coupled with liquid chromatography. This method integrates the strengths of PDESs-MIP/VGO, including rich adsorption interactions, high adsorption capacity, and excellent selectivity, with the simplicity and cost-effectiveness of CPT-SPE. The proposed method demonstrated low limits of quantification (1.2-2.5 ng mL-1), high accuracy (91.7-104.8%), and good precision (relative standard deviation ≤4.4%). By applying this method to analyze indole metabolites in urine, our results suggested that indole-3-carbinol and indole-3-acetonitrile have the potential to emerge as reliable dietary exposure biomarkers for broccoli intake. Furthermore, highly selective analytical methods based on molecular imprinting technology are advantageous for precise screening and analysis of dietary exposure biomarkers associated with food consumption.