background: this paper proposes a new emotional stress assessment system using multi-modal bio-signals. electroencephalogram (eeg) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. methods: we design an efficient acquisition protocol to acquire the eeg signals in five channels (fp1, fp2, t3, t4 and pz) and peripheral signals such as blood volu...

gesture and motion recognition are needed for a variety of applications. the use of human hand motions as a natural interface tool has motivated researchers to conduct research in the modeling, analysis and recognition of various hand movements. in particular, human-computer intelligent interaction has been a focus of research in vision-based gesture recognition. in this work, we introduce a 3-...

Intruders detection in computer networks has some deficiencies from machine learning approach, given by the nature of the application. The principal problem is the modest display of detection systems based on learning algorithms under the constraints imposed by real environments. This article focuses on the machine learning approach for network intrusion detection in batch and data stream envir...

Real-world knowledge discovery processes typically consist of complex data pre-processing, machine learning, evaluation, and visualization steps. Hence a data mining platform should allow complex nested operator chains or trees, provide transparent data handling, comfortable parameter handling and optimization, be flexible, extendible and easy-to-use. Depending on the task at hand, a user may w...

We consider the single machine parallel-batching scheduling problem with precedence relations, release dates and identical processing times to minimize a regular objective function. When the processing times are unit, we give an O(n) time optimal algorithm. When there are no precedence relations, we solve this problem by dynamic programming in O(n) time. When the precedence relations are “layer...

Most algorithms of support vector machines (SVMs) operate in a batch mode. However, when the samples arrive sequentially, batch implementations of SVMs are computationally demanding due to the fact that they must be retrained from scratch. This paper proposes an incremental SVM algorithm that is suitable for the problems of sequentially arriving samples. Unlike previous SVM techniques, this new...

Motivated by applications in porcelain-making companies, we consider a type of optimization problems for batch-processing operations. In production, single machine with fixed capacity is used to process jobs. Several jobs can be processed together if their total size no more than the capacity. Batch-position-based learning effects are considered because workers become skillful gradually after t...