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Cancers are genetically diversified, so anticancer treatments have different levels of efficacy on people due to genetic differences. The main objective of this work is to predict the anticancer drug efficiency for colorectal cancer patients to reduce the mortality rates and provides immune energy for the patients. This paper proposes a novel anti-cancer drug efficacy system in colorectal cancer patients. The input data gene is normalized with the Min–Max normalization technique that normalizes the data in distinct scales. Subsequently, proposes an improved entropy-based feature to evaluate the uncertainty distribution of data, in which it induces weight to overcome the issue of computational complexity. Along with this feature, a correlation-based feature and statistical features are also retrieved. Subsequently, proposes a Recursive Feature Elimination with Hybrid Machine Learning (RFEHML) mechanism for selecting the appropriate feature set by eliminating the recursive features with the aid of hybrid Machine Learning strategies that combine decision tree and logistic regression. Also, the Gini impurity is employed for ranking the feature and selecting the maximum importance score by eliminating the least acquired importance score. Further, proposes a hybrid model for predicting the drug efficiency with the trained feature set. The hybrid model comprises of Long Short-Term Memory (LSTM) and Updated Rectified Linear Unit-Deep Convolutional Neural Network (UReLU-DCNN) model, in which DCNN is modified by updating the activation function at the fully connected layer. Consequently, the learned feature predicts the drug efficacy of anti-cancer in colorectal cancer patients by determining whether the patient is a responder or non-responder of the drug. Finally, the performance of the proposed RFEHML model is compared with other traditional approaches. It is found that the developed method has higher accuracy for each learning percentage, with values of 60LP = 92.48%, 70LP = 94.28%, 80LP = 95.24%, and 90LP = 96.86%, respectively. [ABSTRACT FROM AUTHOR]

Data clustering is a data mining approach that assigns similar data to the same group. Traditionally, cluster similarity considers all attributes equally, but in real-world applications, some attributes may be more important than others. Therefore, this study proposes an algorithm that utilizes multivariate fuzzy weighting to demonstrate the varying importance of each attribute, using a Gini impurity measure for weight assignment. Additionally, the proposed algorithm implements probabilistic distance to reduce sensitivity to noise. Probabilistic distance offers more detailed information and better interpretation than Hamming distance, which ignores attribute positions. Probabilistic distance utilizes information about the attribute's position within and between clusters. This enhances clustering performance by creating clusters with more similar attributes. Therefore, the proposed Multivariate Fuzzy Weighted K-Modes with Probabilistic Distance for Categorical Data (MFWKM-PD) algorithm, based on the multivariate fuzzy K-modes algorithm, not only considers detailed membership calculations but also considers the varying contributions of attributes and their positions in distance calculation. This study evaluated the proposed MFWKM-PD using several benchmark datasets. The experiments validated that the proposed MFWKM-PD shows promising results compared to other algorithms in terms of accuracy, NMI, and ARI. [ABSTRACT FROM AUTHOR]

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Because the majority of model-free feature screening methods concentrate on individual predictors, they are unable to consider structured predictors, such as grouped variables. In this study, we suggest a model-free and direct extension of the original sure independence screening approach for group screening using Gini impurity for a classification model. Compared to current feature screening approaches, the proposed method performs better in terms of screening efficiency and classification accuracy. It was established that the suggested group screening process exhibits sure screening properties and ranking consistency properties under specific regularity conditions. We used simulation studies to illustrate the limited sample performance of the proposed technique and real data analysis.

The urgent need to eliminate Perfluorooctanoic Acid (PFOA) has positioned electrooxidation (EO) as a key solution for pollutant degradation. This study evaluates several machine learning (ML) models, including K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosted Decision Trees (GBDT), and Deep Learning (DL), to predict EO efficiency in PFOA removal. Using 10-fold cross-validation, the RF model outperformed others with a root mean square error (RMSE) of 7.7 and a correlation coefficient of 0.965, demonstrating its robustness and accuracy across diverse operational settings. Feature importance within the RF model was analyzed using Gini impurity and Mean Decrease in Accuracy (MDA). Electrolysis Time consistently emerged as the most influential factor in both analyses, underscoring its pivotal role in providing extended exposure of PFOA molecules to reactive species at the electrode surfaces. The study also found strong agreement between Gini and MDA in identifying Current Density and Anode Material as critical factors, although MDA placed slightly more emphasis on Anode Material. Differences between Gini and MDA were more pronounced in the ranking of Electrolyte Type and Concentration, with MDA assigning higher importance to Electrolyte Concentration. In contrast, the Water Matrix was consistently ranked as the least important factor. The strong concordance between Gini and MDA highlights the reliability of the RF model in identifying key drivers of electrochemical degradation. Overall, this work contributes significantly to the advancement of pollutant degradation technologies, presenting a reliable ML-based tool for environmental remediation efforts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The performance evaluation is one of the most important organizational management strategies used to guide the sales behavior of sales staff. However, it should be work process-oriented or sales result-oriented has become a dilemma for the management when evaluating employees' performance. Therefore, comprehensively evaluating the work process and sales results has become a challenge when it comes to salespeople performance appraisal. To solve this dilemma, this research builds a salesperson performance evaluation model which considering both of work effciency and effectiveness, based on the decision tree model. Specifically, we use the Data Envelopment Analysis (DEA) model to quantify the work effciency of salespeople and measure the work effectiveness by amount of sales. Moreover, this research proposes an advanced integrated DEA model by integrating the self-evaluation DEA models, peer-evaluation DEA models, and Gini impurity, which is identified to be more stable compared with the current DEA model. Finally, a case study of a Chinese liquor company is introduced to illustrate the applicability and feasibility of the salesperson performance evaluationmodel. The proposedmodel is applied to evaluate the performance of the salespeople, and a set of comprehensive and objective sales performance evaluation results are obtained. The estimated results can provide feasible salesmanagement suggestions for the company in diagnosing work problems of salespeople. [ABSTRACT FROM AUTHOR]

The default variable-importance measure in random forests, Gini importance, has been shown to suffer from the bias of the underlying Gini-gain splitting criterion. While the alternative permutation importance is generally accepted as a reliable measure of variable importance, it is also computationally demanding and suffers from other shortcomings. We propose a simple solution to the misleading/untrustworthy Gini importance which can be viewed as an over-fitting problem: we compute the loss reduction on the out-of-bag instead of the in-bag training samples. [ABSTRACT FROM AUTHOR]

The very dense floodplain vegetation on the artificially confined floodplains results in decreased flood conveyance, thus increase in flood levels and flood hazard. Therefore, proper floodplain management is needed, which must be supported by vegetation assessment studies. The aims of the paper are to introduce the method and the results of riparian vegetation classification of a floodplain area along the Lower Tisza (Hungary) based on automatized acquisition of airborne LiDAR survey. In the study area 15x15 m large training plots (voxels) were selected, and the statistical parameters of their LiDAR point clouds were determined. Applying an automatized parameter selection and 10-fold cross-validation he most suitable decision tree was selected, and following a series of classification steps the training plots were classified. Based on the decision tree all the pixels of the entire study area were analysed and their vegetation types were determined. The classification was validated by field survey. On the studied floodplain area the accuracy of the classification was 83%.

Data classification is an important and challenging issue encountered in many practical applications. The classifier based on Evidential Reasoning Rule (ER Rule) can well handle the uncertainty in classification and obtain competitive accuracy. However, there are many parameters to be optimized, and the computation cost of the usually adopted optimization strategy is relatively high. This fact weakens the application advantage of ER Rule classifier. To address this challenge, an asynchronous optimization approach is proposed. In the original ER Rule classifier, feature referential values and evidence weights are optimized synchronously; while in the proposed method, these two types of parameters are optimized separately based on their essential impacts on the classification results. For the optimization of feature referential values, the objective function measures the quality of belief matrix, which is the critical strategy to improve the computational efficiency. Three types of feasible objective functions are constructed. After obtaining optimal referential values, evidence weights are optimized based on the actual classification effect, and the required iterations decrease. Various experiments on 14 publicly available datasets verify the computational efficiency and classification performance of the proposed method. • The low computational efficiency of ER Rule-based classifier is addressed by optimizing its parameters asynchronously. • The objective function of feature referential values optimization measures the quality of belief matrix. • The effectiveness of the proposed method is validated by theoretical analysis and various experiments. [ABSTRACT FROM AUTHOR]

Interest flooding attacks (IFAs) seriously harm named data networking by overwhelming the pending interest table of a content router. In this letter, we propose a Gini impurity-based IFA detection mechanism that can effectively detect IFAs, and a malicious Interest recognition mechanism that can distinguish the malicious Interests with legitimate ones. In addition, we evaluate the performance of our mechanism. The simulation results validate that the mechanism can accurately detect and effectively mitigate IFAs. [ABSTRACT FROM PUBLISHER]

Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain−computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.

The performance evaluation is one of the most important organizational management strategies used to guide the sales behavior of sales staff. However, it should be work process-oriented or sales result-oriented has become a dilemma for the management when evaluating employees' performance. Therefore, comprehensively evaluating the work process and sales results has become a challenge when it comes to salespeople performance appraisal. To solve this dilemma, this research builds a salesperson performance evaluation model which considering both of work efficiency and effectiveness, based on the decision tree model. Specifically, we use the Data Envelopment Analysis (DEA) model to quantify the work efficiency of salespeople and measure the work effectiveness by amount of sales. Moreover, this research proposes an advanced integrated DEA model by integrating the self-evaluation DEA models, peer-evaluation DEA models, and Gini impurity, which is identified to be more stable compared with the current DEA model. Finally, a case study of a Chinese liquor company is introduced to illustrate the applicability and feasibility of the salesperson performance evaluation model. The proposed model is applied to evaluate the performance of the salespeople, and a set of comprehensive and objective sales performance evaluation results are obtained. The estimated results can provide feasible sales management suggestions for the company in diagnosing work problems of salespeople., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cai, Liu and Liu.)

Marine accidents are complex processes in which many factors are involved and contribute to accident development. For this reason, effectively analyse what combination of factors lead an accident event is a complex problem, especially when human factors are involved. State-of-the-art methods such as Human Factor Analysis and Classification System, Human reliability Assessments, and simple Statistical Analysis are not effective in many situations since they require the intervention of human experts with their limitations, biases, and high costs. The authors propose to use a data-driven approach able to utilise the information present in historical databases of marine accident for the purposes of establishing the most influential human factors. For this purpose a two-stage approach is presented: first, a data-driven predictive model is built able to predict the type of accident based on the contributing factors, and then the different contributing factors are ranked based on their ability to influence the prediction. Results on a real historical database of accidents provided by the Marine Accident Investigation Branch, an independent unit within the UK Department for Transport, will support the proposed novel approach. • A data-driven model that predict the accident type based on the contributing factors. • A kernel-based model to find the most influential human factors in maritime accident. • Results on real data proved high accuracies in predictions. • A tool for researchers and policymakers to identify the causes of maritime accidents. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads. [ABSTRACT FROM AUTHOR]

In Attribute-based Access Control (ABAC) systems, utilizing environment attributes along with the subject and object attributes introduces a dynamic nature to the access decisions. The inclusion of environment attributes helps in achieving a more fine-grained access control. In this paper, we present an ABAC policy mining algorithm that considers the environment attributes and their associated values while forming the rules. Furthermore, we use gini impurity to form the rules. This helps to minimize the number of rules in the generated policy. The experimental evaluation shows that our approach is quite effective in practice.