Signal and Data Processing

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Learning logic of exceptions is a substantial challenge in data mining and knowledge discovery. Exceptional phenomena detection takes place among huge records in a database which contains a large number of normal records and a few of exceptional ones. This is important to promote the confidence to a limited number of exceptional records for effective learning. In this study, a new approach based on the abnormality theory, information and information granulation theories are presented to detect exceptions and recognize their behavioral patterns. The efficiency of the proposed method was determined by using it to detect exceptional stocks from Iran stock market in a 30-month- period and learn their exceptional behavior. The proposed Enhanced-RISE algorithm (E-RISE) as a bottom-up learning approach was implemented to extract the knowledge of normal and exceptional behavior. The extracted knowledge was utilized to design an expert system based on the proposed abnormality theory to predict new exceptions from 6022 stocks. The superior findings show the results of this proposed approach in exceptional phenomena detection, is in accordance with experts' opinions.
 

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Support Vector Machine (SVM) is one of the important classification techniques, has been recently attracted by many of the researchers. However, there are some limitations for this approach. Determining the hyperplane that distinguishes classes with the maximum margin and calculating the position of each point (train data) in SVM linear classifier can be interpreted as computing a data membership with certainty. A question may be raised here: how much the level of the certainty of this classification, based on hyperplane, can be trusted. In the standard SVM classification, the significance of error for different train data is considered equal and every datum is assumed to belong to just one class. However, in many cases some of train data, including outlier and vague data with no defined model, cannot be strictly considered as a member of a certain class. That means, a train datum may does not exactly belong to one class and its features may show 90 percent membership of one class and 10 percent of another. In such cases, by using fuzzy SVM based on fuzzy logic, we can determine the significance of data in the train phase and finally determine relative class membership of data.
The method proposed by Lin and Wang is a basic method that introduces a membership function for fuzzy support vector machine. Their membership function is based on the distance between a point and the center of its corresponding class.
In this paper, we introduce a new method for giving membership to train data based on their distance from distinctive hyperplane. In this method, SVM classification together with primary train data membership are used to introduce a fuzzy membership function for the whole space using symmetrical triangular fuzzy numbers. Based on this method, fuzzy membership function value of new data is selected with minimum difference from primary membership of train data and with the maximum level of fuzzification. In the first step, we define the problem as a nonlinear optimization problem. Then we introduce an efficient algorithm using critical points and obtain final membership function of train data. According to the proposed algorithm, the more distant data from the hyperplane will have a higher membership degree. If a datum exists on the hyperplane, it belongs to both classes with the same membership degree. Moreover, by comparing the primary membership degree of train data and calculated final distribution, we compute the level of noise for train data. Finally, we give a numerical example for illustration the efficiency of the proposed method and comparing its results with the results of the Lin and Wang approach.
 

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Unemployment insurance is one of the most popular insurance types in the modern world. The Social Security Organization is responsible for checking the unemployment benefits of individuals supported by unemployment insurance. Hand-crafted evaluation of unemployment claims requires a big deal of time and money. Data mining and machine learning as two efficient tools for data analysis can assist Social Security Organization in automating this process. In this research work, a hybrid supervised learning method is proposed to verify the eligibility of applicants for unemployment. The proposed method takes as input the information of insured individuals, and assigns a numeric score to each applicant through analyzing the input data. Then, claimants are classified into two groups according to those scores: "Qualified” and "Unqualified". The proposed method includes two hybrid strategies: BSA-SVM and combination of confidence values. In BSA-SVM method, backtracking search algorithm (BSA) is used to estimate the prameters of support vector machines (SVM) and improves the classification performance. In the second approach, confidence values extracted from individual classofiers are combined to better classify the input data. Empirical evaluation shows an accuracy of 87% for BSA-SVM and 86% for the second approach.

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As we know, credit cards speed up and make life easier for all citizens and bank customers. They can use it anytime and anyplace according to their personal needs, instantly and quickly and without hassle, without worrying about carrying a lot of cash and more security than having liquidity. Together, these factors make credit cards one of the most popular forms of online banking. This has led to widespread and increasing use for easy payment for purchases made through mobile phones, the Internet, ATMs, and so on. Despite the popularity and ease of payment with credit cards, there are various security problems, increasing day by day. One of the most important and constant challenges in this field is credit card fraud all around the world. Due to the increasing security issues in credit cards, fraudsters are also updating themselves. In general, as a field grows in popularity, more fraudsters are attracted to it, and this is where credit card security comes into play. So naturally, this worries banks and their customers around the world. Meanwhile, financial information acts as the main factor in market financial transactions. For this reason, many researchers have tried to prioritize various solutions for detecting, predicting, and preventing credit card fraud in their research work and provide essential suggestions that have been associated with significant success. One of the practical and successful methods is data mining and machine learning. In these methods, one of the most critical parameters in fraud prediction and detection is the accuracy of fraud transaction detection. This research intends to examine the Gradient Boosting methods, which are a subset of Ensemble Learning and machine learning methods. By combining these methods, we can identify credit card fraud, reduce error rates, and improve the detection process, which in turn increases efficiency and accuracy. This study compared the two algorithms LightGBM and XGBoost, merged them using simple and weighted averaging techniques, and then evaluate the models using AUC, Recall, F1-score, Precision, and Accuracy. The proposed model provided 95.08, 90.57, 89.35, 88.28, and 99.27, respectively, after applying feature engineering and using the weighted average approach for the mentioned validation parameters. As a result, function engineering and weighted averaging significantly improved prediction and detection accuracy.

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Abstract
Key employee's turnover is one of the most important concerns of Human Resource Managers (HRM); Because the organization by losing its valuable staff, suffers from the loss of skills and experience gained over the years, so predicting employee turnover helps HRMs to hire and retain permanent employees. One of the effective tools in this regard is the use of different data mining methods. Many researchers have done research in this field. This study reviewes recently published articles based on machine learning models, using Kaggle Human Resource (HR) databases [1-5] to compare them with this proposed models. In the article [9], the authors have selected 11 of the most important features by collecting common features from previous articles and filtering them using feature review and selection algorithms. After converting non-numerical variables to numerical and normalizing the data in the range [0,1], those attrition prediction approach is based on machine, deep and ensemble learning models and is experimented on a large-sized and a medium-sized simulated HR datasets and then a real small-sized dataset from a total of 450 responses. Those approach achieves higher Accuracy (0.96, 0.98 and 0.99 respectively) for the three datasets when compared previous solutions. In 2021, authors examined the relationship between features using Pearson correlation coefficient and selected 11 features with the highest correlation coefficient. Then used from six different machine learning algorithms including Random Forest (RF), Logistic Regression (LR), …, to predict employee turnover. The highest accuracy they obtained was 0.85 for RF [3]. In the article[1], the authors used two IBM datasets and a database containing HR information from a regional bank in the USA to predict employees turnover. After cleaning and preprocessing the data, the performance of 10 different machine learning algorithms such as Decision Tree (DT), RF, LR, Neural Network, …, was evaluated using ROC criteria on 10 small, medium, and large subsets of randomly selected, unassigned primary datasets. The average accuracy of algorithms is 0.83 in small datasets, 0.81 in medium datasets and 0.86 in large datasets. The authors of the paper [4] used three main experiments on IBM Watson simulated datasets to predict employees turnover. The first experiment involved training the original class-imbalanced dataset with the following machine learning models: support vector machine with several kernel functions, random forest and K-nearest neighbour (KNN). The second experiment focused on using adaptive synthetic (ADASYN) approach to overcome class imbalance, then retraining on the new dataset using the abovementioned machine learning models. As a result, training an ADASYN-balanced dataset with KNN (K = 3) achieved the highest performance, with 0.93 F1-score. this turnover prediction approach is based on tree-based ensemble learning models and is experimented on a large-sized standard simulated HR dataset (hr_data), including 15,000 samples with 10 features and a medium-sized (IBM) including 1470 samples with 34 features. The employees turnover rate in the IBM is 16.1% and in the hr_data is 23.8%, so datasets are unbalanced. To balance the data, the random-under-sampling technique and its combination of random-over-sampling with a ratio of 0.5965 for the IBM and 0.6558 for the hr_data has been used. In the preprocessing stage, Features with zero variance and samples containing the missing value were also removed. Then categorical (non-numeric) values ​​were converted to binary fields and then All features were scaled using data normalization in [0,1]. In order to reduce the feature dimensions in the IBM dataset, we used the "Non-negative Matrix Factorization" (NMF) technique (n_components=17, max_iter=500) and For initialization, non-negative singular value analysis method with zeros filled with X value has been used. After reviewing and cleaning the data, in the processing stage, six different classification algorithms, including KNN (k=1), RF (number of trees= 1500), DT, ExtraTreesClassifier (number of trees= 1000) and Support Vector Classifier were training on 70% of data. The optimal value of the hyperparameters for the algorithms, was set using RandomizedSearchCV and GridSearchCV techniques. In order to investigate the effect of balancing and Dimensionality Reduction on the performance of models, experiments were performed in 3 stages (befor balancing, after balancing befor Dimensionality Reduction, after balancing and Dimensionality Reduction) on 30% of the remaining data. The results shown in Table (2-4) indicate that this proposed model, which uses tree-based optimized ensemble learning algorithms with data balancing and NMF dimensionality reduction method, increases the f1score of turnover prediction. In the hr_data dataset, the best f1score for the RandomForest algorithm was 99.52% and for the IBM HR dataset, the best f1score for the ExtraTreesClassifier algorithm was 95.82%, which is higher than previous research. Table 5 compares the results of previous research with this research. Since, the prediction of employee attrition will not be enough without finding the characteristics that affect it, therefore, after building models and evaluating their performance, using a combined feature selection method by averaging the results of the single-variable feature selection method called "SelectKBest", and A wrapper feature selection method called "Recursive feature elimination" (RFE) with four learning algorithms RF, DT, ExtraTreesClassifier and AdaBoost, the most effective features were selected. SelectKBest combines the chi2 univariate statistical test with the selection of K features based on the statistical result between the features and the target variable. Also, in the RFE method, machine learning algorithms are used to remove the least important features after recursive training, so that finally the number of features reaches the set number (17 features in this article). The performance results of the models based on the selected features are shown in Table 6. The most effective characteristics are "age", "daily rate", "over  time", "NumCompaniesWorked" and, "monthly income" .
 

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A key aspect of this research is understanding the influence of social connections on individual behavior. By analyzing the overlap in visited locations between friends, the study aims to identify the most influential friends for each user. These influential friends are then used to predict the user's next location.

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In the era of digital transformation, government agencies and corporations increasingly rely on electronic services, generating vast volumes of sensitive data stored in distributed databases. While these records hold immense potential for knowledge discovery through data mining, their publication or sharing raises critical privacy concerns, particularly when sensitive individual information is at risk. Traditional Privacy-Preserving Distributed Data Publishing (PPDDP) methods rely heavily on Trusted Third-Party (TTP) intermediaries and Secure Multi-Party Computation (SMC), which introduce systemic vulnerabilities such as communication bottlenecks, synchronization failures, insider attacks, and inherent distrust in centralized entities. In healthcare analytics, hospitals leverage patient data to enhance diagnostic precision, optimize clinical workflows, and advance preventive and precision medicine. Yet, reliance on siloed datasets from individual institutions often restricts model generalizability and impedes comprehensive insights into health outcomes. Patient health is a multidimensional construct influenced not only by genetic and biological factors but also by behavioral patterns and socio-environmental determinants. Cross-institutional collaboration integrating diverse datasets from geographically distributed sources is essential to develop robust analytical models. However, such collaboration raises critical privacy concerns, as centralized aggregation of sensitive data risks exposure to breaches or misuse. Our probabilistic framework for privacy-preserving distributed data publishing directly addresses this challenge. By eliminating dependencies on trusted third parties and secure multi-party computation, our approach enables secure, decentralized integration of heterogeneous healthcare data. Through uncertainty-aware probabilistic anonymization and adaptive noise injection, the framework ensures compliance with stringent privacy regulations (e.g., GDPR, CPRA, HIPAA) while preserving the analytical utility required for accurate, actionable health outcome predictions. This balance of utility and privacy empowers researchers to harness the full potential of distributed datasets without compromising individual confidentiality, ultimately fostering innovation in precision medicine and population health management. This paper introduces a novel probabilistic framework for privacy preservation in distributed environments, eliminating dependencies on TTP and SMC. Unlike existing approaches, this method leverages uncertainty-aware probabilistic models to dynamically anonymize and perturb data across distributed nodes while preserving global data utility. First a survey of privacy preservation data publishing methods is presented in this paper and then we discuss about prose and cons of the techniques. After this we present the model and its implementation details. The results obtained by security evaluations shows that the presented method will balance out the privacy security and the accuracy of distributed data better, using the probability model without needing a Trusted Third-Party and Secure Multi-party Computation.

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Gliomas, or in other words, aggressive and progressive brain tumors, lead to great complexity in the diagnosis and treatment of patients. While recent machine learning models provided encouraging results in glioma diagnosis and grading, the topic is open, and more efforts are needed. Existing models, despite encouraging results, often fall short of the ideal diagnostic state, highlighting the need for further research to develop robust and high-performing predictive models
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This study introduces an optimized ensemble machine learning (EML) model designed to maximize classification (grading) performance and mitigate the pervasive issue of overfitting in glioma grading. Our approach employs a two-layer architecture that synergistically combines diverse weak and base learners. In the first layer, a diverse set of learners, including support vector machine (SVM), categorical boosting (CatBoost), extremely randomized trees (ERT), and random forest (RF), is integrated. This initial ensemble aims to capture a broad spectrum of grading patterns and enhance the overall accuracy by leveraging the complementary strengths of each base model. The outputs from this first layer, representing diversified classification probabilities, are then fed into a second-layer logistic regression (LR) model. This layer refines the predictions, performing the ultimate classification while explicitly addressing and eliminating the overfitting problem, thereby promoting better generalization to unseen data.
To rigorously evaluate the performance of the proposed ELM model, a comprehensive comparison was conducted against its constituent base learners and counterpart machine learning models. All models were assessed using a standard, publicly available glioma dataset. To prevent overfitting, examine the robustness of models, and evaluate models fairly, a 5-fold cross-validation strategy is used in experiments. The effectiveness of models was measured using four performance metrics, including accuracy, recall, precision, and F1-score.

The experimental results demonstrate the superior performance of the proposed EML model. Across all evaluated metrics, our model consistently outperformed the individual base learners and other benchmarked algorithms, securing the top rank in terms of accuracy. Specifically, the LR model operating on the first-layer ensemble predictions proved highly effective in both enhancing accuracy and preventing overfitting. Following our proposed model, the standalone LR and RF models demonstrated commendable performance, ranking second and third, respectively
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The findings of this study underscore the significant potential of an optimized EML model for advancing the field of glioma tumor grading. The proposed model generated promising results and mitigated overfitting through integrating diverse base learners and using an LR model as a meta-model. The results reveal that the proposed model is a reliable and robust tool that can aid Clinical specialists in effectively diagnosing and classifying gliomas, ultimately paving the way for improved patient satisfaction.