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Predicting rock tunnel squeezing in underground projects is challenging due to its intricate and unpredictable nature. This study proposes an innovative approach to enhance the accuracy and reliability of tunnel squeezing prediction. The proposed method combines ensemble learning techniques with Q-learning and online Markov chain integration. A deep learning model is trained on a comprehensive database comprising tunnel parameters including diameter (D), burial depth (H), support stiffness (K), and tunneling quality index (Q). Multiple deep learning models are trained concurrently, leveraging ensemble learning to capture diverse patterns and improve prediction performance. Integration of the Q-learning-Online Markov Chain further refines predictions. The online Markov chain analyzes historical sequences of tunnel parameters and squeezing class transitions, establishing transition probabilities between different squeezing classes. The Q-learning algorithm optimizes decision-making by learning the optimal policy for transitioning between tunnel states. The proposed model is evaluated using a dataset from various tunnel construction projects, assessing performance through metrics like accuracy, precision, recall, and F1-score. Results demonstrate the efficiency of the ensemble deep learning model combined with Q-learning-Online Markov Chain in predicting surrounding rock tunnel squeezing. This approach offers insights into parameter interrelationships and dynamic squeezing characteristics, enabling proactive planning and support measures implementation to mitigate tunnel squeezing hazards and ensure underground structure safety. Experimental results show the model achieves a prediction accuracy of 98.11%, surpassing individual CNN and RNN models, with an AUC value of 0.98. [ABSTRACT FROM AUTHOR]

One of the most important assumptions in machine learning tasks is the fact that training data points and test data points are extracted from the same distribution. However, this paper assumes the situation in which this fact does no longer hold. Therefore, a task named space adjustment, through which the distribution of the data points in the training-data space and the distribution of the data points in the test-data space become identical, is inevitable. Hereby, authors propose a linear mapping for the space adjustment task in the paper. It considers four approaches for preserving localities among data samples during the space adjustment. Each approach is defined based on a different locality concept. Considering all locality concepts in an objective function, authors transform the space adjustment into an optimisation problem. The paper proposes to optimise the corresponding objective function by an iterative approach. Empirical study shows that the proposed method outperforms the baseline methods. To do experiments, authors employ a large number of real-world datasets. [ABSTRACT FROM AUTHOR]

Intrusion Detection Systems (IDS) are designed to provide security into computer networks. Different classification models such as Support Vector Machine (SVM) has been successfully applied on the network data. Meanwhile, the extension or improvement of the current models using prototype selection simultaneous with their training phase is crucial due to the serious inefficacies during training (i.e. learning overhead). This paper introduces an improved model for prototype selection. Applying proposed prototype selection along with SVM classification model increases attack discovery rate. In this article, we use fuzzy rough sets theory (FRST) for prototype selection to enhance SVM in intrusion detection. Testing and evaluation of the proposed IDS have been mainly performed on NSL-KDD dataset as a refined version of KDD-CUP99. Experimentations indicate that the proposed IDS outperforms the basic and simple IDSs and modern IDSs in terms of precision, recall, and accuracy rate. [ABSTRACT FROM AUTHOR]

A bad partition in an ensemble will be removed by a cluster ensemble selection framework from the final ensemble. It is the main idea in cluster ensemble selection to remove these partitions (bad partitions) from the selected ensemble. But still, it is likely that one of them contains some reliable clusters. Therefore, it may be reasonable to apply the selection phase on cluster level. To do this, a cluster evaluation metric is needed. Some of these metrics have been recently introduced; each of them has its limitations. The weak points of each method have been addressed in the paper. Subsequently, a new metric for cluster assessment has been introduced. The new measure is named Balanced Normalized Mutual Information (BNMI) criterion. It balances the deficiency of the traditional NMI-based criteria. Additionally, an innovative cluster ensemble approach has been proposed. To create the consensus partition considering the elected clusters, a set of different aggregation-functions (called also consensus-functions) have been utilized: the ones which are based upon the co-association matrix (CAM), the ones which are based on hyper graph partitioning algorithms, and the ones which are based upon intermediate space. The experimental study indicates that the state-of-the-art cluster ensemble methods are outperformed by the proposed cluster ensemble approach. [ABSTRACT FROM AUTHOR]

Features play an important role in image processing. But as not all features are comparable, relative features emerged. From the beginning, low-level features, extracted by experts, have been employed to create difficult models for learning the problem of relative attribute. Knowing these models are limited in generality of their applicability, deep learning models can be employed instead of them. A deep artificial neural network framework has been suggested for the task of relative attribute prediction in this article. The paper suggests to use a convolutional artificial neural network for learning the mentioned attributes through a peripheral auxiliary layer, called also a ranking layer, which is able to learn how to rank the images. A suitable ranking cost function is used to train the whole network in an end-to-end manner. The suggested method through this paper is experimentally superior to the state of the art methods on some well-known benchmarks. The experimental results indicate that the proposed method is capable of learning the problem of relative attribute. [ABSTRACT FROM AUTHOR]

After observing the ensemble success in supervised learning (such as classification), it was extended into unsupervised learning. Therefore, cluster ensemble, which merges multiple basic data partitions or clusters (called as ensemble pool) into an ordinarily better clustering solution usually named as consensus partition, emerged. Any cluster ensemble method tries to optimize a particular criterion during extracting the consensus partition out of the ensemble pool. But traditional cluster ensembles consider all the pool members with the equal importance in making the consensus partition; that is to say that each basic partition or cluster participates in the cluster ensemble algorithm equivalently. Indeed, they ignore to consider any ensemble member according to its importance. But it is obvious that some clusters with more quality deserve more emphasis and some clusters with less quality deserve less emphasis during generating consensus partition. This paper proposes (a) a metric to evaluate quality of any arbitrary cluster, (b) a mechanism to project the computed quality of a cluster into a meaningful weight value, and (c) an approach to apply the weight values of the basic clusters in the cluster ensemble process. Experimental results conducted on a number of real‐world standard datasets indicate that the proposed method outperforms the state of the art methods. [ABSTRACT FROM AUTHOR]

A recommender system is a model that automatically recommends some meaningful cases (such as clips/films/goods/items) to the clients/people/consumers/users according to their (previous) interests. These systems are expected to recommend the items according to the users' interests. There are two traditional general recommender system models, i.e., Collaborative Filtering Recommender System (ColFRS) and Content-based Filtering Recommender System (ConFRS). Also, there is another model that is a hybrid of those two traditional recommender systems; it is called Hybrid Recommender System (HRS). An HRS usually outperforms simple traditional recommender systems. The problems such as scalability, cold start, and sparsity belong to the main problems that any recommender system should solve. The memory-based (modeless) recommender systems benefit from good accuracies. But they suffer from a lack of admissible scalability. The model-based recommender systems suffer from a lack of admissible accuracies. But they benefit from good scalability. In this paper, it is tried to propose a hybrid model based on an automatically improved ontology to solve the scalability, cold start, and sparsity problems. Our proposed HRS also uses an innovative approach of clustering as an augmented section. When there are enough ratings, it uses a collaborative filtering approach to predict the missing ratings. When there are not enough ratings, it uses a content-based filtering approach to predict the missing ratings. In the content-based filtering section of our proposed HRS, ontology concepts are used to improve the accuracy of ratings' prediction. If our target client is severely sparse, we cannot trust even the ratings predicted by the content-based filtering section of our proposed HRS. Therefore, our proposed HRS uses additive clustering to improve the prediction of the missing ratings if the target client is severely sparse. It is experimentally shown that our model outperforms many of the newly developed recommender systems. [ABSTRACT FROM AUTHOR]

With the massive amount of data available on the Internet, many users face problems when they need access to the information and goods they need. Adapting information to user needs has become a complex and time-consuming process. Recommender systems are powerful tools for guiding users in electronic settings to information, services, and goods of interest. With the ability to identify users and predict their preferences, recommender systems can extract information likely to be of interest to users from vast amounts of data and save time and energy by providing them with relevant recommendations. Therefore, in this paper, a framework for a recommender system is proposed, which consists of four main phases. In the first phase, the preprocessing operation is performed. The purpose of this phase is to create an ontology model for products based on the convolutional neural network method. Data collection operations are carried out in the second phase. The purpose of this phase is to receive and store user information based on behavior and different criteria. The third phase is related to creating an ontology. The purpose of this phase is to create an ontology model for user behavior using OWL libraries. Finally, in the last phase, finding similarities and presented a proposal. The purpose of this phase is the necessary calculations of the proposed framework in the similarity finding section using ontology models. Simulation results show that the proposed framework measures MAE (more than 10 and 15%), RMSE (more than 12 and 16%), precision (less than 10 and 13%), recall (more than 25 and 28%), F1-score (higher than 25 and 30%) and Score of User (higher than 15 and 17%) outperform two related approaches, namely CF and CF+ Ontology, respectively. [ABSTRACT FROM AUTHOR]

In clustering ensemble, it is desired to combine several clustering outputs in order to create better results than the output results of the basic individual clustering methods in terms of consistency, robustness and performance. In this research, we want to present a clustering ensemble method with a new aggregation function. The proposed method is named Robust Clustering Ensemble based on Iterative Fusion of Base Clusters (RCEIFBC). This method takes into account the two similarity criteria: (a) one of them is the cluster-cluster similarity and (b) the other one is the object-cluster similarity. The proposed method has two steps and has been done on the binary cluster representation of the given ensemble. Indeed, before doing any step, the primary partitions are converted into a binary cluster representation where the primary ensemble has been broken into a number of primary binary clusters. The first step is to combine the primary binary clusters with the highest cluster-cluster similarity. This phase will be replicated as long as our desired candidate clusters are ready. The second step is to improve the merged clusters by assigning the data points to the merged clusters. The performance and robustness of the proposed method have been evaluated over different machine learning datasets. The experimentation indicates the effectiveness of the proposed method comparing to the state-of-the-art clustering methods in terms of performance and robustness. [ABSTRACT FROM AUTHOR]

Abundant data of the patients is recorded within the health care system. During data mining process, we can achieve useful knowledge and hidden patterns within the data and consequently we will discover the meaningful knowledge. The discovered knowledge can be used by physicians and managers of health care to improve the quality of their services and to reduce the number of their medical errors. Since by the usage of a single data mining algorithm, it is difficult to diagnose or predict diseases, therefore in this research, we take a combination of the advantages of some algorithms in order to achieve better results in terms of efficiency. Most of standard learning algorithms have been designed for balanced data (the data with the same frequency of samples in each class), where the cost of wrong classification is the same within all classes. These algorithms cannot properly represent data distribution characteristics when datasets are imbalanced. In some cases, the cost of wrong classification can be very high in a sample of a special class, such as wrongly misclassifying cancerous individuals or patients as healthy ones. In this article, it is tried to present a fast and efficient way to learn from imbalanced data. This method is more suitable for learning from the imbalanced data having very little data in class of minority. Experiments show that the proposed method has more efficiency compared to traditional simple algorithms of machine learning, as well as several special-to-imbalanced-data learning algorithms. In addition, this method has lower computational complexity and faster implementation time. [ABSTRACT FROM AUTHOR]

Despite all the developments in recommender systems and utilizations of successful application models in the industry, it can be said that there is still a need to improve various parts of these systems in order to enhance their effectiveness and scope of application. Recommender Systems (RS) are well-known in the field of e-commerce and are expected to provide customers with important goods and items (including music and movies). In traditional recommender systems suffer from important challenges and problems such as cold start, scalability, and data dispersion. Recently, some of these challenges have been successfully overcomed to an acceptable extent by the advantages of the combined use of these methods. The K-nearest neighbors (KNN)-based Recommender Systems (KRS) are among the most powerful recommender engines that are currently available. In these systems, the rating of a target item is predicted based on the average rating of similar items, where the similarity is defined based on a similarity measure and the average rating of items is treated as a feature. In this paper, KRS is developed by combining the following approaches: (a) using the mean and variance of the item ratings as item features to identify the similar items (i.e., item-based KRS or IKRS); (b) using the mean and variance of the user ratings as the user features to identify the similar users (i.e., user-based KRS or UKRS); (c) using weighted average to combine the neighboring user/item ratings; and (d) using ensemble learning. In this study, some methods, i.e., EVMRS (Ensemble Variance-Mean based Recommender System) and EWVMRS (Ensemble Weighted Variance-Mean based Recommender System) are discussed and an improved EWVMRSG (Ensemble Weighted Variance-Mean Based Recommender system enriched by Gaussian mixture model (GMM)) is expanded, which are all user-based and involve using Mean Distance as the measure of similarity between the users/items to find neighboring users/items, but then they use unweighted average, weighted average, and weighted averaging based on the full-covariance GMM, respectively, for prediction. Experimental evaluations show that the EVMRS, EWVMRS, and EWVMRSG methods, which all use ensemble learning, are the most accurate methods among those developed and evaluated in this study. Depending on the dataset, the EWVMRSG mothed achieves a 20–30% lower absolute error than that of the original Mean based Recommender Systems (MRS). In terms of execution time, the proposed method is comparable to the original MRS models and is much faster than the Slope-one, P-kNN, and C-kNN RSs. [ABSTRACT FROM AUTHOR]

Each clustering algorithm usually optimizes a qualification metric during its learning process. The qualification metric in traditional clustering algorithms considers all the features of under-consideration dataset equally; it means each feature participates in the clustering process equivalently. Considering that some features have more information than the others in a dataset (due to their lower information or their higher variances, etc.), we proposes a fuzzy weighted clustering algorithm. We name this new clustering algorithm, Fuzzy Weighted Locally Adaptive Clustering (FWLAC) algorithm. The proposed FWLAC algorithm is capable of handling imbalanced clustering. However, FWLAC algorithm suffers from its sensitivity to the two parameters that should be tuned manually. The performance of FWLAC algorithm is affected by well-tuning of its parameters. So the paper proposes two solutions to well-tuning of its two parameters. In the first solution, we propose a simple clustering ensemble framework to show the sensitivity of the WLAC algorithm to its manual well-tuning. Although it is not a try-and-error procedure, it is like a grid search, where we use different pairs of values for both parameters h and h. Per each pair of values for parameters h and h, the algorithm produces a partitioning. So after the grid search, we obtain a large number of partitionings. We break any of the partitionings into its clusters, and then they form an ensemble of clusters. Finally the consensus partitioning is extracted from them by a consensus function. The algorithm is not data dependent at all. For all datasets, we use a similar grid search and a similar set of values for the parameters h and h. In this way we have proposed an alternative solution to parameter selection. We use a selection phase to select/remove some clusters from our ensemble of clusters to obtain an elite ensemble of clusters. To do this, a stability measure, normalized mutual information (NMI), was used to validate a cluster. The paper shows the effectiveness of the proposed clustering frameworks both theoretically and experimentally. [ABSTRACT FROM AUTHOR]

One of the most important tasks in pattern, machine learning, and data mining is classification problem. Introducing a general classifier is a challenge for pattern recognition communities, which enables one to learn each problem׳s dataset. Many classifiers have been proposed to learn any problem thus far. However, many of them have their own positive and negative aspects. So they are good only for specific problems. But there is no strong solution to recognize which classifier is better or good for a specific problem. Fortunately, ensemble learning provides a good way to have a near-optimal classifying system for any problem. One of the most challenging problems in classifier ensemble is introducing a suitable ensemble of base classifiers. Every ensemble needs diversity. It means that if a group of classifiers is to be a successful ensemble, they must be diverse enough to cover their errors. Therefore, during ensemble creation, a mechanism is needed to ensure that the ensemble classifiers are diverse. Sometimes this mechanism can select/remove a subset of base classifiers with respect to maintaining the diversity of the ensemble. This paper proposes a novel method, named the Classifier Selection Based on Clustering (CSBS), for ensemble creation. To insure diversity in ensemble classifiers, this method uses the clustering of classifiers technique. Bagging is used to produce base classifiers. During ensemble creation, every type of base classifier is the same as a decision tree classier or a multilayer perceptron classifier. After producing a number of base classifiers, CSBC partitions them by using a clustering algorithm. Then CSBC produces a final ensemble by selecting one classifier from each cluster. Weighted majority vote method is used as an aggregator function. In this paper we investigate the influence of cluster number on the performance of the CSBC method; we also probe how we can select a good approximate value for cluster number in any dataset. We base our study on a large number of real datasets of UCI repository to reach a definite result. [ABSTRACT FROM AUTHOR]

Abstract: Inspired by bagging and boosting algorithms in classification, the non-weighing and weighing-based sampling approaches for clustering are proposed and studied in the paper. The effectiveness of non-weighing-based sampling technique, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms have been invested in this paper. Experimental results have shown improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and boosting techniques. Subsamples of small size can reduce the computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical research study also compares the performance of boosting and bagging clustering ensembles using different consensus functions on a number of datasets. [Copyright &y& Elsevier]

Different classifiers with different characteristics and methodologies can complement each other and cover their internal weaknesses; so classifier ensemble is an important approach to handle the weakness of single classifier based systems. In this article we explore an automatic and fast function to approximate the accuracy of a given classifier on a typical dataset. Then employing the function, we can convert the ensemble learning to an optimisation problem. So, in this article, the target is to achieve a model to approximate the performance of a predetermined classifier over each arbitrary dataset. According to this model, an optimisation problem is designed and a genetic algorithm is employed as an optimiser to explore the best classifier set in each subspace. The proposed ensemble methodology is called classifier ensemble based on subspace learning (CEBSL). CEBSL is examined on some datasets and it shows considerable improvements. [ABSTRACT FROM PUBLISHER]

Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the non-repetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works' results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published. [ABSTRACT FROM AUTHOR]

Existing fuzzy clustering ensemble approaches do not consider dependability. This causes those methods to be fragile in dealing with unsuitable basic partitions. While many ensemble clustering approaches are recently introduced for improvement of the quality of the partitioning, but lack of a median partition based consensus function that considers more participate reliable clusters, remains unsolved problem. Dealing with the mentioned problem, an innovative weighting fuzzy cluster ensemble framework is proposed according to cluster dependability approximation. For combining the fuzzy clusters, a fuzzy co-association matrix is extracted in a weighted manner out of initial fuzzy clusters according to their dependabilities. The suggested objective function is a constrained nonlinear objective function and we solve it by sparse sequential quadratic programming (SSQP). Experimentations indicate our method can outperform modern clustering ensemble approaches. [ABSTRACT FROM AUTHOR]

We introduce a semi-supervised space adjustment framework in this paper. In the introduced framework, the dataset contains two subsets: (a) training data subset (space-one data (SOD)) and (b) testing data subset (space-two data (STD)). Our semi-supervised space adjustment framework learns under three assumptions: (I) it is assumed that all data points in the SOD are labeled, and only a minority of the data points in the STD are labeled (we call the labeled space-two data as LSTD), (II) the size of LSTD is very small comparing to the size of SOD, and (III) it is also assumed that the data of SOD and the data of STD have different distributions. We denote the unlabeled space-two data by ULSTD, which is equal to STD - LSTD. The aim is to map the training data, i.e., the data from the training labeled data subset and those from LSTD (note that all labeled data are considered to be training data, i.e., SOD ∪ LSTD) into a shared space (ShS). The mapped SOD, ULSTD, and LSTD into ShS are named MSOD, MULSTD, and MLSTD, respectively. The proposed method does the mentioned mapping in such a way that structures of the data points in SOD and MSOD, in STD and MSTD, in ULSTD and MULSTD, and in LSTD and MLSTD are the same. In the proposed method, the mapping is proposed to be done by a principal component analysis transformation on kernelized data. In the proposed method, it is tried to find a mapping that (a) can maintain the neighbors of data points after the mapping and (b) can take advantage of the class labels that are known in STD during transformation. After that, we represent and formulate the problem of finding the optimal mapping into a non-linear objective function. To solve it, we transform it into a semidefinite programming (SDP) problem. We solve the optimization problem with an SDP solver. The examinations indicate the superiority of the learners trained in the data mapped by the proposed approach to the learners trained in the data mapped by the state of the art methods. [ABSTRACT FROM AUTHOR]

In order to improve the performance of a clustering on a data set, a number of primary partitions are generated and stored in an ensemble and their aggregated consensus partition is used as their clustering. It is widely accepted that the consensus partition outperforms the primary partitions. In this paper, an ensemble clustering method called multi-level consensus clustering (MLCC) is proposed. To construct the MLCC, a cluster–cluster similarity matrix which is achieved by an innovative similarity metric is first generated. The mentioned cluster–cluster similarity matrix is based on a multi-level similarity metric. In fact, it can be computed in a new defined multi-level space. Then, a point–point similarity matrix which is boosted using the mentioned cluster–cluster similarity matrix is generated. The new consensus function applies an average linkage hierarchical clusterer algorithm on the mentioned point–point similarity matrix to make consensus partition. MLCC is better than traditional clustering ensembles and simple versions of clustering ensembles on traditional cluster–cluster similarity matrix. Its computational cost is not very bad too. Accuracy and robustness of the proposed method are compared with those of the modern clustering algorithms through the experimental tests. Also, time analysis is presented in the experimental results. [ABSTRACT FROM AUTHOR]

Optimization is an important and decisive task in science. Many optimization problems in science are naturally too complicated and difficult to be modeled and solved by the conventional optimization methods such as mathematical programming problem solvers. Meta-heuristic algorithms that are inspired by nature have started a new era in computing theory to solve the optimization problems. The paper seeks to find an optimization algorithm that learns the expected quality of different places gradually and adapts its exploration-exploitation dilemma to the location of an individual. Using birds' classical conditioning learning behavior, in this paper, a new particle swarm optimization algorithm has been introduced where particles can learn to perform a natural conditioning behavior towards an unconditioned stimulus. Particles are divided into multiple categories in the problem space and if any of them finds the diversity of its category to be low, it will try to go towards its best personal experience. But if the diversity among the particles of its category is high, it will try to be inclined to the global optimum of its category. We have also used the idea of birds' sensitivity to the space in which they fly and we have tried to move the particles more quickly in improper spaces so that they would depart these spaces as fast as possible. On the contrary, we reduced the particles' speed in valuable spaces in order to let them explore those places more. In the initial population, the algorithm has used the instinctive behavior of birds to provide a population based on the particles' merits. The proposed method has been implemented in MATLAB and the results have been divided into several subpopulations or parts. The proposed method has been compared to the state-of-the-art methods. It has been shown that the proposed method is a consistent algorithm for solving the static optimization problems. [ABSTRACT FROM AUTHOR]

Model checking is one of the most efficient techniques in software system verification. However, state space explosion is a big challenge while using this technique to check different properties like safety ones. In this situation, one can search the state space to find a reachable state in which the safety property is violated. Hence, reachability checking can be done instead of checking safety property. However, checking reachability in the worst case may cause state space explosion again. To handle this problem, our idea is based on generating a small model consistent with the main model. Then by exploring the state space entirely, we search it to find the goal states. After finding the goal states, we label the paths which start from the initial state and leading to a goal state. Then using the ensemble classification technique, the necessary knowledge is extracted from these paths to intelligently explore the state space of the bigger model. Ensemble machine learning technique uses Boosting method along with decision trees. It follows sampling techniques by replacement. This method generates k predictive models after sampling k times. Finally, it uses a voting mechanism to predict the labels of the final path. Our proposed approach is implemented in GROOVE, which is an open source toolset for designing and model checking graph transformation systems. Our experiments show a significant improvement in terms of both speed and memory usage. In average, our approach consumes nearly 42% fewer memory than other approaches. Also, it generates witnesses nearly 20% shorter than others, in average. [ABSTRACT FROM AUTHOR]

Recommender Systems (RS) are expected to suggest the accurate goods to the consumers. Cold start is the most important challenge for RSs. Recent hybrid RSs combine ConF and ColF. We introduce an ontological hybrid RS where the ontology has been employed in its ConF part while improving the ontology structure by its ColF part. In this paper, a new hybrid approach is proposed based on the combination of demographic similarity and cosine similarity between users in order to solve the cold start problem of new user type. Also, a new approach is proposed based on the combination of ontological similarity and cosine similarity between items in order to solve the cold start problem of new item type. The main idea of the proposed method is to expand user/item profiles based on different strategies to build higher-performing profiles for users/items. The proposed method has been evaluated on a real dataset and the experimentations indicate the proposed method has the better performance comparing with the state of the art RS methods, especially in the case of the cold start. [ABSTRACT FROM AUTHOR]

Since complexity of computer systems is growing increasingly, assuring flawless operation of these systems has become more difficult. Therefore, it is important that these systems whether software or hardware are executed as expected. Consequently, verifying system before implementation at model level is necessary. Model checking is a formal technique for validating the system automatically which decides whether the finite state system satisfies temporal property by scanning the whole state space or not. One of the most important problems in model checking is state space explosion of models which results in memory shortage in generation of all states. Therefore, this paper presents a method which employs machine learning techniques without exploring the whole state space to predict temporal properties of trajectories in systems based on graph transmission system. the proposed method is implemented in Groove; results indicate desirable accuracy and speed of this method compared to other methods. [ABSTRACT FROM AUTHOR]

The paper has proposed a linear unsupervised transfer learning (LUTL). Therefore, a cost function has been introduced. In the cost function of the proposed LUTL, the aim is to minimize the difference between the distribution of the transformed source domain (SD) data and the distribution of the target domain (TD) data. In the proposed cost function, it is also targeted to preserve the local structures of the untransformed SD data. Three mechanisms have been proposed for the preservation of local structures in the untransformed SD data: (1) minimization of the distances between the data pairs that are similar to each other in the untransformed SD data, (2) preservation of the clusters emerged in the untransformed SD data and finally (3) their combination. The optimization problem has emerged as a nonlinear one. Two techniques have been introduced to obtain an approximation of the optimal weight matrix. Each technique guarantees to reach a local optimum, but no one guarantees to reach the global solution. While the first method is an iterative one, the second is a relaxed version of the optimization problem. The paper shows also experimentally that the proposed techniques overshadow the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Protein-protein interaction (PPI) networks are dynamic in the real world. That is, at different times and under different conditions, the interaction among proteins may or may not be active. In different dataset, PPI networks might be gathered as static or dynamic networks. For the conversion of static PPI networks to time graphs, i.e., dynamic PPI networks, additional information like gene expression and gene co-expression profiles is used. One of the challenges in system biology is to determine appropriate thresholds for converting static PPI networks to dynamic PPI networks based on active proteins. In the available methods, fixed thresholds are used for all genes. However, the purpose of this study is to determine an adaptive unique threshold for each gene. In this study, the available additional information at different times and conditions and gold-standard protein complexes was employed to determine fitting thresholds. By so doing, the problem is converted into an optimization problem. Thereafter, the problem is solved using the firefly meta-heuristic optimization algorithm. One of the most remarkable aspects of this study is determining the attractiveness function in the firefly algorithm. In this study, attraction is defined as a combination of standard complexes and gene co-expressions. Then, active proteins are specified utilizing the created thresholds. The MCL, ClusterOne, MCODE and Coach algorithms are used for final evaluation. The experimental results about BioGRID dataset and CYC2008 gold-standard protein complexes indicated that the produced dynamic PPI networks by the proposed method have better results than the earlier methods. [ABSTRACT FROM AUTHOR]

Optimization is a process that is followed to improve an objective function. The aim of the optimization is to improve the solution of a problem developed by a scientist or an engineer. In any optimization algorithm, the initial conditions have a big effect on quality of the final found solution. One of the new optimization algorithms is See-See Partridge Chicks Optimization (SSPCO) algorithm, which is modeled upon the behavior of the partridge chicks. Chaotic behaviors are frequent in the natural phenomena. Indeed, many real world behaviors which resemble random, are chaotic. As these behaviors are highly sensitive to the initial conditions, they show such really random-like behaviors that can't be predicted (while they are not random at all). In this paper, we have implemented a chaotic SSPCO (CSSPSO) algorithm. We have used two different chaotic functions and we have lastly compared the results with the state of the art algorithms. The results indicate that chaos has a positive effect and the CSSPCO algorithm outperforms the state of the art algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of computing and multimedia technology, the volume of web traffic data, social networks, sensors and other types of resources is increasing rapidly. Most of the data are unlabeled, and only a very small percentage of it is labeled. In some cases, data tags can be obtained free or at a low cost, while for many complex tasks such as speech recognition, data extraction, classification and filtering, tagging is usually difficult, time-consuming and expensive, because it requires manual interpretation by human experts. Active learning examines the issue by selecting the most active data to query tags and achieving high learning accuracy with a small number of tagged items. There are different approaches for data classification which have disadvantages such as low accuracy or high computational time. In this study, by combining covariance-based self-expression correlation estimation and incremental active learning methods, a new three-step method to sequential semi-supervising classification is presented. Initially, a new method based on covariance is used to estimate the correlation between samples. Then, a semi-supervised active learning algorithm based on GAN (Generative Adversarial Networks) is introduced to perform the learning process. Finally, a new incremental learning model based on support vector machine is used for classification. We use 20-Newsgroups, Web KB and Image Net datasets to test the performance of our proposed method. The experimental results show the robustness and effectiveness of the proposed method in terms of accuracy, precision, recall and f-measure factors compared to other methods. [ABSTRACT FROM AUTHOR]

Data pre-processing is one of the crucial phases of data mining that enhances the efficiency of data mining techniques. One of the most important operations performed on data pre-processing is missing values imputation in incomplete datasets. This research presents a new imputation technique using K-means and samples weighting mechanism based on Grey relation (KWGI). The Grey-based K-means algorithm applicable to all samples of incomplete datasets clusters the similar samples, then an appropriate kernel function generates appropriate weights based on the Grey relation. The missing values estimation of the incomplete samples is done based on the weighted mean to reduce the impact of outlier and vague samples. In both clustering and imputation steps, a penalty mechanism has been considered to reduce the similarity of ambiguous samples with a high number of missing values, and consequently, increase the accuracy of clustering and imputation. The KWGI method has been applied on nine natural datasets with eight state-of-the-art and commonly used methods, namely CMIWD, KNNI, HotDeck, MeanI, KmeanI, RKmeanI, ICKmeanI, and FKMI. The imputation results are evaluated by the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) criteria. In this study, the missing values are generated at two levels, namely sample and value, and the results are discussed in a wide range of missingness from low rate to high rate. Experimental results of the t-test show that the proposed method performs significantly better than all the other compared methods. [ABSTRACT FROM AUTHOR]

While recognizing the significance of data in machine learning, we focus on addressing the challenge of concept drift, particularly in dynamic data streams. We propose an innovative incremental decision tree algorithm tailored for learning regression trees and model trees from evolving data streams. Vital to ensuring the quality and accuracy of predictive models is addressing this challenge. In this context, we present a novel solution: an incremental decision tree algorithm tailored for learning regression trees and model trees from time-varying data streams. Our algorithm is designed to operate at high speeds, effectively accommodating the influx of data at any scale, including scenarios with potentially unlimited data. Key innovations of our approach include a probabilistic defined sampling strategy that enhances the learning process and an advanced automatic method capable of handling non-stationary data distributions. However, the primary innovation lies in our methodology for detecting concept drift. Unlike conventional methods that reactively respond to increased errors, we introduce a proactive approach: monitoring the quality of individual subtrees by tracking their error evolution. This method allows us to detect changes in the objective function promptly, leading to timely adaptations in the model structure. Through extensive experimentation and evaluation, we demonstrate the effectiveness of our proposed algorithm in terms of prediction accuracy, model size, and change detection capabilities. Representing a significant advancement in the field of machine learning, particularly in addressing the challenge of concept drift in data streams, the proposed algorithm offers a competitive alternative to existing flow classifiers. Showcasing superior performance in terms of precision, recall, Fisher measure, and scalability, it underscores its potential to enhance decision-making processes across various domains by adapting swiftly to changing data patterns and maintaining high accuracy. The algorithm’s innovative approach to incremental learning of decision rules, coupled with its adaptive extension for handling concept drift, holds promise for real-world applications where accurate and timely insights are paramount. Overall, the algorithm’s robustness, adaptability, and efficiency position it as a valuable asset in stream data classification and decision support systems. [ABSTRACT FROM AUTHOR]

Many stability measures, such as Normalized Mutual Information (NMI), have been proposed to validate a set of partitionings. It is highly possible that a set of partitionings may contain one (or more) high quality cluster(s) but is still adjudged a bad cluster by a stability measure, and as a result, is completely neglected. Inspired by evaluation approaches measuring the efficacy of a set of partitionings, researchers have tried to define new measures for evaluating a cluster. Thus far, the measures defined for assessing a cluster are mostly based on the well-known NMI measure. The drawback of this commonly used approach is discussed in this paper, after which a new asymmetric criterion, called the Alizadeh-Parvin-Moshki-Minaei criterion (APMM), is proposed to assess the association between a cluster and a set of partitionings. We show that the APMM criterion overcomes the deficiency in the conventional NMI measure. We also propose a clustering ensemble framework that incorporates the APMM's capabilities in order to find the best performing clusters. The framework uses Average APMM (AAPMM) as a fitness measure to select a number of clusters instead of using all of the results. Any cluster that satisfies a predefined threshold of the mentioned measure is selected to participate in an elite ensemble. To combine the chosen clusters, a co-association matrix-based consensus function (by which the set of resultant partitionings are obtained) is used. Because Evidence Accumulation Clustering (EAC) can not derive the co-association matrix from a subset of clusters appropriately, a new EAC-based method, called Extended EAC (EEAC), is employed to construct the co-association matrix from the chosen subset of clusters. Empirical studies show that our proposed approach outperforms other cluster ensemble approaches. [ABSTRACT FROM AUTHOR]

Conventional clustering ensemble algorithms employ a set of primary results; each result includes a set of clusters which are emerged from data. Given a large number of available clusters, one is faced with the following questions: (a) can we obtain the same quality of results with a smaller number of clusters instead of full ensemble? (b) If so, which subset of clusters is more efficient to be used in the ensemble? In this paper, these two questions are going to be answered. We explore a clustering ensemble approach combined with a cluster stability criterion as well as a dataset simplicity criterion to discover the finest subset of base clusters for each kind of datasets. Also, a novel method is proposed in order to accumulate the selected clusters and to extract final partitioning. Although it is expected that by reducing the size of ensemble the performance decreases, our experimental results show that our selecting mechanism generally lead to superior results. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel optimization-based method for the combination of cluster ensembles. The information among the ensemble is formulated in 0-1 bit strings. The suggested model defines a constrained nonlinear objective function, called fuzzy string objective function (FSOF), which maximizes the agreement between the ensemble members and minimizes the disagreement simultaneously. Despite the crisp primary partitions, the suggested model employs fuzzy logic in the mentioned objective function. Each row in a candidate solution of the model includes membership degrees indicating how much data point belongs to each cluster. The defined nonlinear model can be solved by every nonlinear optimizer; however; we used genetic algorithm to solve it. Accordingly, three suitable crossover and mutation operators satisfying the constraints of the problem are devised. The proposed crossover operators exchange information between two clusters. They use a novel relabeling method to find corresponding clusters between two partitions. The algorithm is applied on multiple standard datasets. The obtained results show that the modified genetic algorithm operators are desirable in exploration and exploitation of the big search space. [ABSTRACT FROM AUTHOR]

In the clustering ensemble the quality of base-clusterings influences the consensus clustering. Although some researches have been devoted to weighting the base-clustering, fuzzy cluster level weighting has been ignored, more specifically, they did not pay attention to the role of cluster reliability in the fuzzy clustering ensemble. In this paper, we propose a new fuzzy clustering ensemble framework without access to the features of data-objects based on fuzzy cluster-level weighting. The reliability of each fuzzy cluster is computed based on estimation of its unreliability, and is considered as its weight in the ensemble. The unreliability of fuzzy clusters is estimated by applying the similarity between fuzzy clusters in the ensemble based on an entropic criterion. In our framework, the final clustering is produced by two types of consensus functions: (1) a reliability-based weighted fuzzy co-association matrix is constructed from the base-clusterings and then, a single traditional clustering such as hierarchical agglomerative clustering or K-means is applied over the matrix to produce the final clustering. (2) a new graph based fuzzy consensuses function. The graph based consensus function has linear time complexity in the number of data-objects. Experimental results on various standard datasets demonstrated the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria and clustering robustness. [ABSTRACT FROM AUTHOR]

Job scheduling in Hadoop has been thus far investigated in several studies. However, some challenges including minimum share (min-share), heterogeneous cluster, execution time estimation, and scheduling program size facing Hadoop clusters have received less attention. Accordingly, one of the most important algorithms with regard to min-share is that presented by Facebook Inc., i.e., FAIR scheduler, based on its own needs, in which an equal min-share has been considered for users. In this article, an attempt has been made to make the proposed method superior to existing methods through automation and configuration, performance optimization, fairness and data locality. A high-level architectural model is designed. Then a scheduler is defined on this architectural model. The provided scheduler contains four components. Three components schedule jobs and one component distributes the data for each job among the nodes. The given scheduler will be capable of being executed on heterogeneous Hadoop clusters and running jobs in parallel, in which disparate min-shares can be assigned to each job or user. Moreover, an approach is presented for each problem associated with min-share, cluster heterogeneity, execution time estimation, and scheduler program size. These approaches can be also utilized on its own to improve the performance of other scheduling algorithms. The scheduler presented in this paper showed acceptable performance compared with First-In, First-Out (FIFO), and FAIR schedulers. [ABSTRACT FROM AUTHOR]

Clustering ensemble has been progressively popular in the ongoing years by combining several base clustering methods into a most likely better and increasingly robust one. Nonetheless, fuzzy clustering dependability (durability) has been unnoticed within the majority of the proposed clustering ensemble approach. This makes them weak against low-quality fuzzy base clusters. In spite of a few endeavors made to the clustering methods, it appears that they consider each base-clustering separately without considering its local diversity. In this paper, to compensate for the mentioned weakness a new fuzzy clustering ensemble approach has been proposed using a weighting strategy at fuzzy cluster level. Indeed, each fuzzy cluster has a contribution weight computed based on its reliability (dependability/durability). After computing fuzzy cluster dependability (reliability/durability), dependability based fuzzy cluster-wise weighted matrix (DFCWWM) is computed. As a final point, the final clustering is obtained by applying the FCM traditional clustering algorithm over DFCWWM. The time complexity of the proposed approach is linear in terms of the number of data-points. The proposed approach has been assessed on 15 various standard datasets. The experimental evaluation has indicated that the proposed method has better performance than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A significant amount of research in the field of job scheduling is carried out in Hadoop. However, there is still need for research to overcome some challenges regarding scheduling jobs in Hadoop clusters. There are various factors affecting the performance of scheduling policies like data volume (storage), data source format (different data), speed (data rate), security and privacy, cost, connection and data sharing. To reach a better utilization of resources and managing big data, scheduling policies have been designed. In this paper, an algorithm has been presented that can run on heterogeneous Hadoop clusters and runs job in parallel. This algorithm first distributes data based on the performance of the nodes and then schedules the jobs according to their cost of execution and decreases the cost of executing the jobs. The presented algorithm offers better performance in terms of execution time, cost and locality compared to FIFO and Fair schedulers. [ABSTRACT FROM AUTHOR]

This paper studies the cluster ensemble selection problem for unsupervised learning. Given a large ensemble of clustering solutions, our goal is to select a subset of solutions to form a smaller yet better performing cluster ensemble than using all available solutions. The common way of aggregating the chosen solutions is accumulating the information of the selected results to a similarity matrix. This paper suggests transforming the similarity matrix to a modularity matrix and then applying a new consensus function which optimizes modularity measure in it. We represent the modularity maximization problem as a 0-1 quadratic program which can be exactly solved for small datasets. We also established a new greedy algorithm, namely sum linkage, to optimize the objective function specially for large scale datasets in a very short time. We show that the proposed consensus partition gets much closer to the actual cluster structure than the partitions obtained from the direct application of common cluster ensemble methods. The promising results compared with other most cited consensus functions show the excellent efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Clustering as a major task in data mining is responsible for discovering hidden patterns in unlabeled datasets. Finding the best clustering is also considered as one of the most challenging problems in data mining. Due to the problem complexity and the weaknesses of primary clustering algorithm, a large part of research has been directed toward ensemble clustering methods. Ensemble clustering aggregates a pool of base clusterings and produces an output clustering that is also named consensus clustering. The consensus clustering is usually better clustering than the output clusterings of the basic clustering algorithms. However, lack of quality in base clusterings makes their consensus clustering weak. In spite of some researches in selection of a subset of high quality base clusterings based on a clustering assessment metric, cluster-level selection has been always ignored. In this paper, a new clustering ensemble framework has been proposed based on cluster-level weighting. The certainty amount that the given ensemble has about a cluster is considered as the reliability of that cluster. The certainty amount that the given ensemble has about a cluster is computed by the accretion amount of that cluster by the ensemble. Then by selecting the best clusters and assigning a weight to each selected cluster based on its reliability, the final ensemble is created. After that, the paper proposes cluster-level weighting co-association matrix instead of traditional co-association matrix. Then, two consensus functions have been introduced and used for production of the consensus partition. The proposed framework completely overshadows the state-of-the-art clustering ensemble methods experimentally. [ABSTRACT FROM AUTHOR]

Bagging and Boosting are two main ensemble approaches consolidating the decisions of several hypotheses. The diversity of the ensemble members is considered to be a significant element to obtain generalization error. Here, an inventive method called EBAGTS (ensemble-based artificially generated training samples) is proposed to generate ensembles. It manipulates training examples in three ways in order to build various hypotheses straightforwardly: drawing a sub-sample from training set, reducing/raising error-prone training instances, and reducing/raising local instances around error-prone regions. The proposed method is a straightforward, generic framework utilizing any base classifier as its ensemble members to assemble a powerfully built combinational classifier. Decision-tree classifier and multilayer perceptron classifier as some basic classifiers have been employed in the experiments to indicate the proposed method accomplish higher predictive accuracy compared to meta-learning algorithms like Boosting and Bagging. Furthermore, EBAGTS outperforms Boosting more impressively as the training data set gets broader. It is illustrated that EBAGTS can fulfill better performance comparing to the state of the art. [ABSTRACT FROM AUTHOR]

Nature is a huge source of inspiration for solving difficult and complex problems in science. Nature-based problem solvers always find an optimal solution (whether local or global) to any given problem; so they are sometimes considered as black-box problem solvers. Meta-heuristic algorithms that are inspired by nature (through imitating the nature) have opened a new approach to solving optimization problems. In the past decades, numerous research efforts have been focused in this specific area. In this paper, an optimization algorithm inspired by the nature has been introduced which is modeled from the behavior of the chicks of a type of bird called see-see partridge. We name the algorithm SSPCO. SSPCO optimization algorithm is an algorithm similar to the algorithm of particle swarm optimization (PSO) that the motion equation and the variables' velocity are modeled from the behavior of this type of bird. Simulation of the algorithm was done with MATLAB software and the results of the 6 primary known algorithms and 8 advanced ones were compared with 14 unconstrained continuous functions. The results indicate that the proposed algorithm is an efficient algorithm comparing the others. [ABSTRACT FROM AUTHOR]

Purpose: To construct, train, and apply an artificial neural network (ANN) system for prediction of different outcome variables of percutaneous nephrolithotomy (PCNL). We calculated predictive accuracy, sensitivity, and precision for each outcome variable. Methods: During the study period, all adult patients who underwent PCNL at our institute were enrolled in the study. Preoperative and postoperative variables were recorded, and stone-free status was assessed perioperatively with computed tomography scans. MATLAB software was used to design and train the network in a feed forward back-propagation error adjustment scheme. Preoperative and postoperative data from 200 patients (training set) were used to analyze the effect and relative relevance of preoperative values on postoperative parameters. The validated adequately trained ANN was used to predict postoperative outcomes in the subsequent 254 adult patients (test set) whose preoperative values were serially fed into the system. To evaluate system accuracy in predicting each postoperative variable, predicted values were compared with actual outcomes. Results: Two hundred fifty-four patients (155 [61%] males) were considered the test set. Mean stone burden was 6702.86 ± 381.6 mm3. Overall stone-free rate was 76.4%. Fifty-four out of 254 patients (21.3%) required ancillary procedures (shockwave lithotripsy 5.9%, transureteral lithotripsy 10.6%, and repeat PCNL 4.7%). The accuracy and sensitivity of the system in predicting different postoperative variables ranged from 81.0% to 98.2%. Conclusion: As a complex nonlinear mathematical model, our ANN system is an interconnected data mining tool, which prospectively analyzes and 'learns' the relationships between variables. The accuracy and sensitivity of the system for predicting the stone-free rate, the need for blood transfusion, and post-PCNL ancillary procedures ranged from 81.0% to 98.2%.The stone burden and the stone morphometry were among the most significant preoperative characteristics that affected all postoperative outcome variables and they received the highest relative weight by the ANN system. [ABSTRACT FROM AUTHOR]

One of the main goals of SDN is to achieve the desirable operation of a highly secure and reliable network in terms of energy distribution. Hence, this paper presents the probabilistic planning of distributed generations (DGs) and switched capacitive bank (SCB) constrained to the securable-reliable operation (SRO) strategy in reconfigurable SDN. The scheme is formulated as a four-objective optimization problem to simultaneously model the economic, operation, reliability, and security indices of SDN. So that the first to fourth objective minimize the total planning cost and expected operation cost, voltage deviation function (VDF), expected energy not-supplied (EENS), and voltage security index (VSI). It is limited to the AC optimal power flow (AC-OPF) equations, planning and operation model of DG and SCB, formulation of demand response program (DRP), SDN reconfiguration constraints, reliability and voltage security limits. Then, the single-objective model is obtained by using the ε constraint-based Pareto optimization technique. Additionally, the non-parametric probabilistic method models uncertainties of load, energy price, renewable DGs (RDGs) power, and network equipment availability. The strategy is as mixed-integer nonlinear programming (MINLP), where hybrid Krill Herd Optimization (KHO) and Crow Search Algorithm (CSA) achieves a reliable optimal solution. The following provides the planning of different sources based on the SRO strategy with simultaneous formulation of various economic and technical indices considering non-parametric modeling of uncertainties and solving the problem using the mentioned hybrid algorithm presented the contribution in this paper. By implementing this scheme on a 69-bus SDN, the numerical results confirm capability of this scheme in improving the economic, operation, reliability, and security situation of SDN compared to power flow studies. Where it can enhance the mentioned indices up to 16%, 36%, 99%, and 9% taking into account optimal planning and operation of different sources and the DRP compared to the corresponding values obtained by power flow studies. • Probabilistic planning modeling of active and reactive power sources constrained to secure-reliable operation in a reconfigurable smart distribution network. • Simultaneous modeling of economic, operation, reliability, and voltage security indices of SDN by adopting a four-objective optimization for the proposed plan. • Using a hybrid KHO+CSA evolutionary algorithm to achieve a reliable optimal solution with low standard deviation in response. • Utilizing non-parametric probabilistic planning to model uncertainties of load, energy cost, RDG capacity, and availability of SDN equipment. Flowchart of the proposed solution method [Display omitted] [ABSTRACT FROM AUTHOR]

• As the pornographic sources have been proliferated on the Internet, it is needed to provide a system to detect and filter them accurately. • A new framework has been introduced to make use of the benefits of deep convolutional neural network and ensemble learning. • We show experimentally that the proposed model outperforms the state of the art. • The proposed approach may be used in different applications such as intelligent filtering of unconventional images or medical images analysis. In this paper, we introduce an approach to discriminate unconventional images and their intelligent filtering. As the target data to this issue are huge and consequently, a handling approach might potentially be a very time consuming one, one of the major challenges to be solved by this introduced approach is its ability for dealing with large-scale datasets. A deep neural network might be a good option to resolve this challenge. It can provide a good accuracy while dealing with huge databases. In the proposed approach, the new architecture is introduced using a combination of AlexNet and LeNet architectures. It uses convolutional, polling and fully-connected layers. The results are tested on two large-scale datasets. These tests show that the introduced architecture is more accurate than the other recently developed methods in identifying unconventional images. The proposed approach may be used in different applications such as intelligent filtering of unconventional images or medical images analysis. Image, graphical abstract [ABSTRACT FROM AUTHOR]