Crowdfunding is a new technology-enabled innovative process that is changing the capital market space. Internet-based applications, particularly those related to Web 2. 0, have had a significant impact on sectors of society such as education, business, and medicine. The goal of this research is to fill a gap in the literature on mathematical modelling and prediction of ensemble learning in order to evaluate crowdfunding projects. The Mathematical model determines the cost of funding for the entrepreneur and the return investors will receive per period. A correct financial model is essential in order to keep all three stakeholders involved in the long term. The results show the designed model improved performance in predicting the evaluation of success or failure of Crowdfunding projects.

The scope of the Internet of Things (IoT) becomes inevitable in the communication and information-sharing routines of human life, similar to any technological architecture. The IoT is also not exempted from vulnerability to security issues and is even more vulnerable as the networks of IoT are built of non-smart devices. Though the few contributions endeavored to defend against the botnet's attacks on IoT, they partially or poorly performed to defend against the flash crowd or attacks by botnets on IoT networks. In this context, the method “Flash Attack Prognosis by ensemble Supervised Learning for IoT Networks” derived in this manuscript is centric on defending the flash attacks by botnets. Unlike contemporary models, the proposed method uses the fusion of traditional network features and temporal features as input to train the classifiers. Also, the curse of dimensionality in the training corpus, which is often, appears in the corpus of flash attack transactions by a botnet, has addressed by the ensemble classification strategy. The comparative analysis of the statistics obtained from the experimental study has displayed the significance and robustness of the proposed model compared to contemporary models.

Due to the increasing information and the detailed analysis of them, the clustering problems that detect the hidden patterns lie in the data are still of great importance. On the other hand, clustering of high-dimensional data using previous traditional methods has many limitations. In this study, a semi-Supervised ensemble clustering method is proposed for a set of high-dimensional medical data. In the proposed method of this study, little information is available as prior knowledge using the information on similarity or dissimilarity (as a number of pairwise constraints). Initially using the transitive property, we generalize the pairwise constraints to all data. Then we divide the feature space into a number of sub-spaces, and to find the optimal clustering solution, the feature space is divided into an unequal number of sub-spaces randomly. A semi-Supervised spectral clustering based on the p-Laplacian graph is performed at each sub-space independently. Specifically, to increase the accuracy of spectral clustering, we have used the spectral clustering method based on the p-Laplacian graph. The p-Laplacian graph is a nonlinear generalization of the Laplacian graph. The results of any clustering solutions are compared with the pairwise constraints and according to the level of matching, a degree of confidence is assigned to each clustering solution. Based on these degrees of confidence, an ensemble adjacency matrix is formed, which is the result of considering the results of all clustering solutions for each sub-space. This ensemble adjacency matrix is used in the final spectral clustering algorithm to find the clustering solution of the whole sub-space. Since the sub-spaces are generated randomly with an unequal number of features, clustering results are strongly influenced by different initial values. Therefore, it is necessary to find the optimal sub-space set. To this end, a search algorithm is designed to find the optimal sub-space set. The search process is initialized by forming several sets (we call each set an environment) consisting of several numbers of sub-spaces. An optimal environment is the one that has the best clustering results. The search algorithm utilized three search operators to find the optimal environment. The search operators search all the environments and the consequent sub-spaces both locally and globally. These operators combine two environments and/or replace an environment with a newly generated one. Each search operator tries to find the best possible environment in the entire search space or in a local space. We evaluate the performance of our proposed clustering schema on 20 cancer gene datasets. The normalized mutual information (NMI) criterion and the adjusted rand index (ARI) are used to evaluate the performance evaluation. We first examine the effect of a different number of pairwise constraints. As expected, with increasing the number of pairwise constraints, the efficiency of the proposed method also increases. For example, the NMI value increases from 0. 6 to 0. 9 on the Khan-2001 dataset, when the number of pairwise constraints increases from 20 to 100. More number of pairwise constraints means more information is available, which helps to improve the performance of the clustering algorithm. Furthermore, we examine the effect of the number of random subspaces. It is observed that increasing the number of random subspaces has a positive effect on clustering performance with respect to the NMI value. In most datasets, when the number of sub-spaces reaches 20, the performance of the proposed method does not change much and is stable. Examining the effect of sampling rate for random subspace generation shows that the proposed method has the best performance in most cancer datasets, such as Armstrong-2002-v3, and Bredel-2005 datasets, when the random subspace generation rate is 0. 5, and by deviating the rate from 0. 5, the level of satisfaction decreases. Then, the results of the proposed idea are compared with the results of the method proposed in the reference [21] according to ARI and we see that our proposed method has performed better in 12 data sets out of 20 data sets than the method proposed in the reference [21]. Finally, the proposed idea is compared with some metric learning approaches with respect to NMI. We have observed that the proposed method obtained the best results compared to other compared methods on 11 datasets out of 20 datasets. It also achieved the second-best result on 6 out of 20 datasets. For example, the value NMI obtained in the proposed method is 0. 1042 more than the reference [21] and it is 0. 1846 more than RCA and it is 0. 4 more than ITML and also it is 0. 468 more than DCA on the Bredel-2005 dataset. Utilizing ensemble clustering methods besides the confidence factor improves the ability of the proposed algorithm to achieve better results. Also, utilizing the transitive operators as well as the selection of random subspaces of unequal sizes play an important role in achieving better performance for the proposed algorithm. Using the p-Laplacian spectral clustering method produces a better, more balanced, and normal volume of clusters compared to the standard spectral clustering. Another effective approach to the performance of the proposed method is to use search operators to find the best subspace, which leads to better results.

Brain MR images tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two Supervised and unSupervised approaches up to now. Supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unSupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-Supervised learning (SSL) which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-Supervised framework for segmenting of brain MRIs tissues that it has been used results of several semi-Supervised classifiers simultaneously. Selecting appropriate classifiers has an important role in the performance of this framework. Hence, in this paper we present two semi-Supervised algorithms EFM and MCo_Training that are improved versions of semi-Supervised methods EM and Co_Training and increase segmentation accuracy. Afterwards, we use these improved classifiers together with Graph-Based semi-Supervised classifier as components of the ensemble framework. Experimental results show that performance of segmentation in this approach is higher than both Supervised methods and the individual semi-Supervised classifiers.

In this article, we propose a novel Semi-Supervised ensemble classifier using Confidence Based Selection metric, named SSE-CBS. The proposed approach uses labeled and unlabeled data, which aims at reacting to different types of concept drift. SSE-CBS combines an accuracy-based weighting mechanism known from block-based ensembles with the incremental nature of Hoeffding Tree. The proposed algorithm is experimentally compared to the state-of-the-art stream methods, including Supervised, semi-Supervised, single classifi ers, and block-based ensembles in different drift scenarios. Out of all the compared algorithms, SSE-CBS outperforms other semi-Supervised ensemble approaches. Experimental results show that SSE-CBS can be considered suitable for scenarios, involving many types of drift in limited labeled data.

Feature selection (FS) is a well-known dimensionality reduction method that chooses a hopeful subset of the original feature collection to diminish the influence the curse of dimensionality phenomenon. FS improves learning performance by removing irrelevant and redundant features. The significance of semi-Supervised learning becomes obvious when labeled instances are not always accessible; however, labeling such data may be costly or time-consuming. Many of the samples in semi-Supervised learning are unlabeled. Semi-Supervised FS techniques overcome this problem, simultaneously utilizing information from labeled and unlabeled data. This article presents a new semi-Supervised FS method called ESACO. ESACO uses a combination of ACO algorithm and a set of heuristics to select the best features. Ant colony optimization algorithm (ACO) is a metaheuristic method for solving optimization problems. Heuristic selection is a significant part of the ACO algorithm that can influence the movements of ants. Utilizing numerous heuristics rather than a single one can improve the performance of the ACO algorithm. However, using multiple heuristics investigates other aspects to attain optimal and better solutions in ACO and provides us with more information. Thus, in the ESACO, we have utilized the ensemble of heuristic functions by integrating them into Multi-Criteria Decision-Making (MCDM) procedure. So far, the utilization of multiple heuristics in ACO has not been studied in semi-Supervised FS. We have compared the performance of the ESACO using the KNN classifier with variant experiments with eight semi-Supervised FS techniques and 15 datasets. Considering the obtained results, the efficiency of the presented method is significantly better than the competing methods. The article's code link on GitHub can also be found at the following: https://github.com/frshkara/ESACO.

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high-dimensional feature-space via an unSupervised learning including an attribute discrimination component. The unSupervised clustering component assigns degree of typicality to each data pattern in order to identify and reduce the effect of noisy or outlaid data patterns. Then, the suggested technique obtains the best combination parameters for each background. The experimentations on artificial datasets and standard SONAR dataset demonstrate that our classifier ensemble does better than individual classifiers in the ensemble.

Introduction: Invasive species are currently the concern of ecologists, conservationists and natural resource managers, and they may decrease biodiversity due to their rapid spread. These species cause changes in ecological processes, function and structure of communities in natural ecosystems. The most obvious change in the invaded areas is the reduction of biodiversity and the creation of a pure community of invasive plants. One of the invasive species in our country is Prosopis juliflora, which is important in the field of combat desertification, biological control and stabilization of quicksand dunes in the southern regions of Iran due to its resistance to adverse environmental conditions. Material and methods: In the present study, the efficiency of five discrimination group models (GLM, GBM, ANN, SRE, RF) and a profile model (MAXENT) and their ensemble with the weighted average approach in the spatial distribution of this species in Makuran region and determining the most important environmental factors affecting the invasion distribution were investigated. By recording 63 occurrence points and 100 absence points, using climatic, physiographic and human variables as environmental variables, and evaluating the performance of models by Area under Curve (AUC), True Skill Statistics (TSS), Sensitivity and Specificity, the species invasion potential was determined. Results and discussion: Among the single algorithms, according to the threshold-independent and threshold-dependent evaluation criteria, two machine learning techniques, i.e. RF and GBM, predicted the climatic habitat of this invasive species with higher accuracy. Also, the evaluation criteria in the ensemble prediction were higher than the average of all single modeling algorithms. According to the ensemble model, P. juliflora habitats occupy about 15% of the total study area. After generalization of the models to the geographical space, it was found that the invaded areas have spread in a uniform strip on the shores of the Oman Sea and the Persian Gulf. Evaluation of variable importance indicated that altitude was the most important independent variable justifying about half of the changes in the ensemble model and has the greatest effect on species distribution. The variable of distance from the road was the next important variable. However, the aspect was mentioned as the least important environmental variable affecting the scattering of the invasion. Based on response curves, the maximum probability of the species' presence was observed at the altitude of 50 m above sea level and a distance less than 50 m from the road. Also, the species is most likely to be present, if the temperature in the warmest month and the coldest season of the year is more than 34 and 14 °C, respectively, and the precipitation seasonality is 100-150. Conclusion: It was found that the integrated algorithms in the framework of ensemble modeling showed higher accuracy and the maps derived from the potential distribution of species invasion make it possible to restrict and manage the distribution range of invasive species by providing management solutions and conserving plans to protect native species. In fact, the results of this study can be used as a basis for subsequent monitoring to prevent further spread of invasive species and to create a balance between the native vegetation protection programs of the region and desertification management measures.

ensemble modelling is expanding in several areas of engineering, especially different aspects of water engineering. Accurate estimation of saffron water requirement (SWR), an essential strategic production of the agriculture sector, is a crucial and influencing act in local water planning of this region. Hence, this study aimed to check the applicability of ensemble modelling in enhancing SWR at Birjand, Southern Khorasan, Iran. The actual water requirement of saffron was recorded in the field lysimetric laboratory at the University of Birjand. The simulation of water requirement was conducted utilizing Decision Tree Regression (DTR) with input climate features. Additionally, Boosting and Bagging methods were employed to establish and enhance the ensemble process of soil water requirement (SWR) simulations. To track the effectiveness of any method, some comparative tests were designed, such as statistical criteria (RMSE and MAE) detection, Violin plot analysis, over/underestimation, times series comparison, and error improvement test. Results indicated that although the acceptable performance of DTR in simulating SWR, the probable improvement was potentially felt. Derived results confirmed that Supervised ensemble modelling (Boosting) could enhance the accuracy of DTR by more than 30 percent (reducing absolute error from 36 mm to 23.65 mm), resulting in declining RMSE from 0.44 mm to 0.07 mm. Further, different experiment outcomes revealed that the Boosting algorithm quality is more appealing than DTR and Bagging outputs.