Current intrusion detection systems are mostly based on typical data mining techniques. The growing prevalence of new network attacks represents a well-known problem which can impact the availability, confidentiality, and integrity of critical information for both individuals and enterprises. In this paper, we propose a Learnable Model for Anomaly Detection (LMAD), as an ensemble real-time intr...

In this work we compare drift detection techniques and we show how they can improve the performance of trade agents in multi-issue bilateral dynamic negotiations. In a dynamic negotiation the utility values and functions of trade agents can change on the fly. Intelligent trade agents must identify and take such drift in the competitors into account changing also the offer policies to improve th...

Discuss approaches to combine techniques used by ensemble learning methods. Randomness which is used by Bagging and Random Forests is introduced into Adaboost to get robust performance under noisy situation. Declare that when the randomness introduced into AdaBoost equals to 100, the proposed algorithm turns out to be a Random Forests with weight update technique. Approaches are discussed to im...

Proper waste management models using recent technologies like computer vision, machine learning (ML), and deep (DL) are needed to effectively handle the massive quantity of increasing waste. Therefore, classification becomes a crucial topic which helps categorize into hazardous or non-hazardous ones thereby assist in decision making process. This study concentrates on design detection ensemble ...

Ensemble discriminative tracking utilizes a committee of classifiers, to label data samples, which are in turn, used for retraining the tracker to localize the target using the collective knowledge of the committee. Committee members could vary in their features, memory update schemes, or training data, however, it is inevitable to have committee members that excessively agree because of large ...

-Harmful Algal Blooms (HABs) pose an enormous threat to U.S. marine habitation and economy in coastal waters. Federal and state coastal administrators have been working in devising a state-of-the-art monitoring and forecasting system for these HAB events. These modernized HAB systems provide useful and forewarning information to a varied user community. The current approaches are based on optic...

This paper presents an ensemble neural network and interval neutrosophic sets approach to the problem of binary classification. A bagging technique is applied to an ensemble of pairs of neural networks created to predict degree of truth membership, indeterminacy membership, and false membership values in the interval neutrosophic sets. In our approach, the error and vagueness are quantified in ...