A scheme for content based retrieval of music data in MIDI format

With phenomenal increases in the generation and storage of digital audio data in several applications, there is growing need for organizing audio data in databases and providing users with fast access to desired data. This paper presents a scheme for the content-based query and retrieval of audio data stored in MIDI format. This is based on extraction of melody from the MIDI files and suitably comparing with the melody of the query. The results of retrieval using the proposed algorithm are presented.

A parameter adjustment method for relevance feedback

Various relevance feedback techniques have been applied in Content-Based Image Retrieval (CBIR). By using relevance feedback, CBIR allows the user to progressively refine the system's response to a query. In this paper, after analyzing the feature distributions of positive and negative feedbacks, a new parameter adjustment method for iteratively improving the query vector and adjusting the weights is proposed. Experimental results demonstrate the effectiveness of this method.

Image retrieval based on the combination of color and keyword

In this paper, color information and keyword information are combined for image retrieval. In detail, each image is divided into several blocks and then the color histograms of each block are derived. Users could feed back some annotations represented by keywords. Then, the keywords may spread in the image database so that both color-based and keyword-based retrieval could be utilized together. A prototype system shows that the proposed method is effective and efficient in performing image retrieval tasks.

Nonlinear discrimination using support vector machine

Appropriate training data always play an important role in constructing an efficient classifier to solve the data mining classification problem. Support Vector Machine (SVM) is a comparatively new approach in constructing a model/classifier for data analysis, based on Statistical Learning Theory (SLT). SVM utilizes a transformation of the basic constrained optimization problem compared to that of a quadratic programming method, which can be solved parsimoniously through standard methods. Our research focuses on SVM to classify a number of different sizes of data sets. We found SVM to perform well in the case of discrimination compared to some other existing popular classifiers.

Impact of image organizations on multimedia document retrieval

In this paper we compare ranking effectiveness of heterogeneous multimedia document retrieval when different image organizations are used for formulating queries. The quality of image queries depends on the organization of images used to make queries which in turn significantly impacts retrieval precision. CBIR (content based information retrieval) needs an effective and efficient organization of images including user interface which must be part of the configuration parameters of image retrieval research.

Computer aided analysis of dental radiographic images

This paper is a result of a fruitful cooperation between the computer science and the dental diagnosis experiences. The study presents a new approach of applying computer algorithms to radiographic images of dental implantation used for bone regeneration. We focus here only on the contribution of the computer assistance to the clinical research as the periodontal therapy is beyond the scope of this paper. The proposed system is based on a pattern recognition approach, directed to recognize density changes in the intra-bony affected areas of patients. It comprises different modules with new algorithms specially designed to treat the patients’ radiographic images more accurately. The system includes digitizing, detecting the complicated region of interest (ROI), defining reference area to correct any projection discrepancy of the follow up images, and finally to extract the distinguishing features of the ROI as a basis for determining the rate of new bone density accumulation. This study is applied to two typical dental cases for a patient who received two different operations. The results are very encouraging and more accurate than traditional techniques reported before.

Session initiation protocol (SIP) based event notification system architecture for telemedicine applications

Mobility service for hospital technicians involved in telemedicine applications is one of the key issues in providing more flexible and efficient in-house or remote health care services. Today, the Internet based communication has widened the opportunity of event monitoring systems in the medical field. The session initiation protocol (SIP) can work on a variety of devices and can be used to create a medical event notification system. Its adoption as the protocol of choice for third generation wireless networks allows for a robust and scalable environment. One of the advantages of SIP is that it supports personal mobility through the separation of user addressing and device addressing. In this paper, the authors propose a possible solution framework for telemedicine alert notification system based SIP-specific event notification.

Australian forex market anaylsis using connectionist models

The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The forex market is difficult to understand by an average individual. However, once the market is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. This paper is an attempt to compare the performance of a Takagi-Sugeno type neuro-fuzzy system and a feed forward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. The exchange values of Australian dollar are considered with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pound. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that neuro-fuzzy technique performed better than the neural network.

Toward pervasive computing in restaurant

In this paper, an example of pervasive computing in restaurant, a wireless web-based ordering system is presented. By using mobile devices such as Personal Digital Assistants (PDA) and WebPad, customers can get many benefits when making orders in restaurants. With this system, customers get faster and better services, restaurant staff cooperate more efficiently with less working mistakes, and enterprise owners thus receive more business profits. This system has multi-tiered web-based system architecture with good integration and scalability features, and is client device operating system fully independent. Details of design and implementation of this system are presented.