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Gender recognition is a hot research topic in recent years. Human-machine interfaces or video surveillance can be greatly improved if human gender can be recognized automatically. In this study, an embedded hidden Markov model is used for gender recognition. Video, which is recorded in different angles of view, is utilized to sample properties of each gender. Ten consecutive gait frames are segmented...
The problem of controlling the induction motor π-model with magnetic saturation is considered using an adaptive controller with stator current and rotor speed measurement. The new in this paper that in the previous work, only the rotor resistance and load torque can be adapted using the controller but in this work, using artificial intelligent technique, an adaptation of the stator resistance variation...
In this research, we introduce a stratified random sampling technique that guides the selection mechanism to select the events (exams) for the integrated two-stage multi-neighbourhood tabu search (ITMTS) in solving examination timetabling problem. This technique is used during the timetable improvement phase especially when dealing with the exhaustive search mechanism in order to reduce the possibilities...
In this paper, we present a modified on-demand routing algorithm for mobile ad-hoc networks (MANETs). The proposed algorithm is based on both the standard Ad-hoc On-demand Distance Vector (AODV) protocol and ant colony based optimization. The modified routing protocol is highly adaptive, efficient and scalable. The main goal in the design of the protocol was to reduce the routing overhead, response...
In this paper we propose a method to build similarity relations into extended Rough Set Theory. Similarity is estimated using ideas from Granular computing and Case-base reasoning. A new measure is introduced in order to compute the quality of the similarity relation. This work presents a study of a case of a similarity relation based on a global similarity function between two objects, this function...
In this paper we propose the method that extracts the semantic keyword from digital images automatically using color and texture features. The image semantic keyword is widely used in research area like image retrieval, categorization, annotation, management. The method consists of two steps: feature extraction and classification module. In order to extract feature, the image color and PACT (Principal...
Visualization techniques provide attractive tools to explore and analyze huge and high dimensional gene expression sets. Several visualization techniques have been developed that enabled users to visually analyze high dimensional data. However, these techniques should be integrated with efficient exploration techniques, as efficient clustering, outlier analysis, ensembles and cluster validation to...
The liver is a common site for the occurrence of tumors. Automatic hepatic lesion segmentation is a crucial step for diagnosis and surgery planning. This paper presents a new fully automatic technique to segment the tumors in liver structure with no interaction from user. Contrast enhancement is applied to the slices of segmented liver, then adding each image to itself to have a white image with some...
Bag of visual patches (BOP) image representation has been the main research topic in computer vision literature for scene and object recognition tasks. Building visual vocabularies from local image feature vectors extracted automatically from images have direct effect on producing discriminative visual patches. Local image features hold important information of their locations in the image which are...
This paper presents a semi-automatic system for home video annotation that searches into the video contents and retrieves video shots for a specific person. The proposed system is composed of four phases; 1) shot detection phase that detects shots boundaries and divides the original video into shots, 2) face detection and recognition phase that detects faces in video shots based on Haar-like features...
This paper discusses the application of two unsupervised methods in classifying type of soils. Soils that are suitable for agricultural activities can be classified into four classes which are hill soil, organic soil, alteration soil and alluvium soil. In addition, no specific support system is able to classify the type of soil and retrieve the information for location and suitable plants for local...
Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are significantly less than those of negative (majority) class leading to severe class imbalance. Constructing high quality classifiers for such imbalanced training data sets is one of the major challenges in machine learning, since...
In recommender systems, the task of automatically deriving user profiles, encoding the actual preferences of users, covers a fundamental role. In this paper, we propose a strategy for learning and updating user profiles by using fuzzy sets that reveal to be a valid tool to model the vague and imprecise nature of preferences as well as the items to be recommended. The proposed adaptation strategy resembles...
In the work we consider the situation with exact classes and fuzzy information of object features. The classification error is presented for the two-class Bayes classifier. The results are received for the full probabilistic information. The new upper bound of the probability of an error is precise twice as much as the bound based on the information energy of fuzzy events.
The research concerns computer-based clinical decision support for laryngopathies. The proposed computer tool is based on a speech signal analysis in the time domain using recurrent neural networks. Such networks have the ability of time series prediction because of their memory nodes as well as local recurrent connections. In our experiments we use the modified Elman-Jordan neural network. For this...
This paper presents an optimizing methodology for implementing a multi-layer perceptron (MLP) neural network in a Field Programmable Gate Array (FPGA) device. In order to obtain an efficient implementation, a compromise of time and area is needed. Starting from simulation in the learning phase with fixed point operators, we have developed a methodology which allows the automatic generation of a VHDL...
It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition field. Face recognition databases are usually high dimensionality, especially when limited training samples are available for each subject. Traditional techniques perform dimensionality reduction are unable to solve this problem smoothly, which makes feature extraction task much difficult...
Due to the huge product assortments and complex descriptions of mobile products/services, it is a great challenge for new customers to select appropriate products. To solve this issue, a fuzzy matching based recommendation approach for mobile products/services is proposed in this paper. In this approach, a new customer's requirements are obtained through asking a set of questions and represented by...
Optimizing the virtual reality model is a necessity to cope with the nature of the World Wide Web. Virtual reality scenes should load within an acceptable time for the user's experience and the sense of being immersed in the virtual environment is not to be affected. In this study, we propose to optimize web-based virtual reality models by removing redundant objects within the scenes, while keeping...
Multicriteria Collaborative Filtering is a promising approach to recommender systems that explores user ratings on item components in order to generate high quality recommendations. This paper focuses on multicriteria collaborative recommender systems and proposes a new algorithm that estimates aggregation functions, which represent the relative importance of individual components, based on the concept...
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