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2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008

作者： Lin, Shih-Fang Xiao, Ke-Ting Chiu, Chung-Cheng Soo, Von-Wun Department of Computer Science National Tsing Hua University HsinChu 300 Taiwan Institute of Information Systems and Applications National Tsing Hua University HsinChu 300 Taiwan Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung 811 Taiwan

ISBN: (纸本)1601320558

Adverse drug reaction (ADR) is big challenge in drug development process. Many studies reported that ADR-related proteins (ADRRPs) could cause ADRs. Studies on the molecular mechanisms of ADRs reported that some drug targets are ADR-related proteins (ADRRPs). The goal of this research is to use SVM method to classify the adverse drug reaction related proteins from drug targets. Our approach is to find out the features that are significant enough to classify those proteins. This method can help not only the analysis and prediction on ADRRPs from drug targets but also the evaluation on safety of drug targets in the early drug discovery process.

关键词： Proteins

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Statistical analysis on detecting signals of adverse drug events with drug-drug interactions

Statistical analysis on detecting signals of adverse drug ev...

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2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008

作者： Huang, Yu-Ting Lin, Shih-Fang Soo, Von-Wun Department of Computer Science National Tsing Hua University HsinChu 300 Taiwan Institute of Information Systems and Applications National Tsing Hua University HsinChu 300 Taiwan Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung 811 Taiwan

ISBN: (纸本)1601320558

Adverse drug reaction (ADR) may cost a lot of unnecessary medical resources and lead to extra suffering on patients. To provide the prompt information about ADR and avoid the rate of occurrence of ADR is an important task yet to be done. The US Food and Drug Administration (FDA) provides an Adverse Event Reporting System (AERS) which contains a lot of clinical reports about ADRs. However, in many cases the biologists still do not know exactly how the observed events are actually caused by drug-drug interactions. We use the statistical hypothesis testing method based on scoring process to discover the associations between drugs and adverse drug events.

关键词： Drug interactions

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A robust watermarking algorithm based on significant-tree in Contourlet domain

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High Technology Letters 2008年第1期14卷 67-71页

作者：段贵多 Li Jianping Huang Tianxi School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 P.R. China International Centre for Wavelet Analysis and Its Applications Logistical Engineering University Chongqing 400016 P.R. China No. 52 Research Institute of China Electronics Technology Group Corporation Hangzhou 310012 P.R. China

An adaptive algorithm operating in the Contourlet domain is presented. Contourlet is a new image sparse representation, which is better than a wavelet for piecewise smooth images with smooth contours. Because of flexible multiresolution, local and directional sensitivity of Contourlet transform, our approach also defines significant-tree in the Contourlet domain. By analyzing the relation of the Contourlet coefficients, we embed the watermarking into all the coefficients of each significant-tree. Then referring to the statistical properties of the coefficients, the masking characteristics of texture are defined for adaptively controlling the embedding strength. Experimental results show that the proposed algorithm is highly robust to various attacks, such as JPEG compression, medium filtering, cropping and rotation. Furthermore, comparisons with a classical method in the wavelet domain prove the validity of the new algorithm.

关键词： contourlet sparse representation significant-tree masking characteristics

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Robust image adaptive steganography using integer wavelets

Robust image adaptive steganography using integer wavelets

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International Conference on Communication Systems Software and Middleware (COMSWARE)

作者： K. B. Raja S. Sindhu T. D. Mahalakshmi S. Akshatha B. K. Nithin M. Sarvajith K. R. Venugopal L. M. Patnaik Department of Computer Science and Engineering Bangalore University Bangalore India Microprocessor Applications Laboratory Indian Institute of Science Bangalore India

Information-theoretic analysis for parallel Gaussian models of Images prescribe embedding the secret data in low and mid frequency regions of image which have large energies. In this paper, we propose a novel steganographic scheme called robust image adaptive steganography using integer wavelet transform(RIASIWT), which is a practical realization of these prescriptions. Using this scheme we can hide large volumes of data without causing any perceptual degradation of the cover image. The scheme embeds the payload in every non-overlapping 4times4 blocks of the low frequency band of cover image, two pixels at a time, one on either sides of the principal diagonal. Tests for the similarity between the condition number of the cover image and the stego image are done for further embedding. We also perform cover image adjustment before embedding the payload in order to ensure lossless recovery. Embedding done in the low frequency bands ensures robustness against attacks such as compression and filtering. Experimental results show better trade off between Visual perceptivity and capacity compared to the existing algorithms.

关键词： Robustness Steganography Frequency Payloads Image analysis Information analysis Degradation Pixel Testing Image coding

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Authentication of secret information in image Steganography

Authentication of secret information in image Steganography

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IEEE Region 10 International Conference TENCON

作者： K. Suresh Babu K. B. Raja Kumar K. Kiran T. H. Manjula Devi K. R. Venugopal L. M. Patnaik Department of Computer Science and Engineering University of Visvesvaraya College of Engineering Bangalore University Bangalore India Department of Computer Science and Engineering Bangalore University Bangalore Microprocessor Applications Laboratory Indian Institute of Science Bangalore India

In recent years, steganography and steganalysis are two important areas of research that involve a number of applications. These two areas of research are important especially when reliable and secure information exchange is required. Steganography is an art of embedding information in a cover image without causing statistically significant variations to the cover image. Steganalysis is the technology that attempts to defeat steganography by detecting the hidden information and extracting. In this paper we propose an image steganography that can verify the reliability of the information being transmitted to the receiver. The method can verify whether the attacker has tried to edit, delete or forge the secret information in the stego-image. The technique embeds the hidden information in the spatial domain of the cover image and uses two special AC coefficients of the discrete wavelet transform domain to verify the veracity (integrity) of the secret information from the stego image. The analysis shows that the BER and PSNR is improved in the case of DWT than DCT.

关键词： Authentication Steganography Discrete wavelet transforms Information security Application software Data security Hair Reliability engineering Art Data mining

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Consolidate and Advance: An Efficient QoS Management Technique in Heterogeneous Wireless Sensor Networks

Consolidate and Advance: An Efficient QoS Management Techniq...

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International Conference on Signal Processing, Communication and Networking (ICSCN)

作者： Suraiya Tarannum Divya Prakash Sneha Susan George B V Tara S Ushe L Nalini K R Venugopal L M Patnaik Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore India Department of Computer Science and Engineering Bangalore University Bangalore Microprocessor Applications Laboratory Indian Institute of Science Bangalore India

Research in wireless sensor networks has become an extensive explorative area during the last few years. In this paper we emphasize the need for repositioning of the consolidating and advancing node, (CAN : Principal Node) to improve network lifetime in terms of energy and other QoS parameters such as latency and throughput. We also address issues related to its repositioning such as the time and position of relocation and the control of its movement without causing negative impact on the performance of the WSN. Mobility factor is exploited to support QoS requirements. We present a scheme called energy conserving relocation (ECR) to pursue relocation of CAN to a safe location on demand. ECR performs relocation based on the minimum energy concept of sensor nodes. The concept of repositioning adds a new dimension to the existing heterogeneous wireless sensor network. It is observed through simulation that lifetime of the network, average energy consumption and QoS parameters are much better when compared to earlier algorithms.

关键词： Wireless sensor networks Quality of service Batteries computer networks Technology management Energy consumption Condition monitoring Signal processing computer network management Conference management

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A soft computing approach for data mining based query processing using rough sets and genetic algorithms

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International Journal of Hybrid Intelligent Systems 2008年第1期5卷 1-17页

作者： Srinivasa, K.G. Venugopal, K.R. Patnaik, L.M. Department of Computer Science and Engineering Data Mining Laboratory M S Ramaiah Institute of Technology Bangalore – 560054 India University Visvesvaraya College of Engineering Bangalore – 560001 India Microprocessor Applications Laboratory Indian Institute of Science Bangalore India

The optimization of queries is critical in database management systems and the complexity involved in finding optimal solutions has led to the development of heuristic approaches. Answering data mining query involves a random search over large databases. Due to the enormity of the data set involved, model simplification is necessary for quick answering of data mining queries. In this paper, we propose a hybrid model using rough sets and genetic algorithms for fast and efficient query answering. Rough sets are used to classify and summarize the datasets, whereas genetic algorithms are used for answering association related queries and feedback for adaptive classification. Here, we consider three types of queries, i.e., select, aggregate and classification based data mining queries. Summary tables that are built using rough sets and analytical model of attributes are used to speed up select queries. Mining associations, building concept hierarchies and reinforcement of reducts are achieved through genetic algorithms. The experiments are conducted on three real-life data sets, which include KDD 99 Cup data, Forest Cover-type data and Iris data. The performance of the proposed algorithm is analyzed for both execution time and classification accuracy and the results obtained are good.

关键词： Rough sets genetic algorithms query answering optimization

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SVM ranking with backward search for feature selection in type II diabetes databases

SVM ranking with backward search for feature selection in ty...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Sarojini Balakrishnan Ramaraj Narayanaswamy Nickolas Savarimuthu Rita Samikannu Department of Computer Applications K.L.N. College of Information Technology Madurai India Department of Computer Science & Engineering GKM College of Engineering and Technology Chennai India Department of Computer Applications National Institute of Technology Tiruchirappalli India VIT Business School Vellore of Institute of Technology Vellore India

Clinical databases have accumulated large quantities of information about patients and their clinical history. Data mining is the search for relationships and patterns within this data that could provide useful knowledge for effective decision-making. Classification analysis is one of the widely adopted data mining techniques for healthcare applications to support medical diagnosis, improving quality of patient care, etc. Usually medical databases are high dimensional in nature. If a training dataset contains irrelevant features (i.e., attributes), classification analysis may produce less accurate results. Data pre-processing is required to prepare the data for data mining and machine learning to increase the predictive accuracy. Feature selection is a preprocessing technique commonly used on high-dimensional data and its purposes include reducing dimensionality, removing irrelevant and redundant features, reducing the amount of data needed for learning, improving algorithms' predictive accuracy, and increasing the constructed models' comprehensibility. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. The importance of feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of features. Feature selection may provide us with the means to reduce the number of clinical measures made while still maintaining or even enhancing accuracy and reducing false negative rates. In medical diagnosis, reduction in false negative rate can, literally, be the difference between life and death. In this paper we propose a feature selection approach for finding an optimum feature subset that enhances the classification accuracy of Naive .Bayes classifier. Experiments were conducted on the Pima Indian Diabetes Dataset to assess the effectiveness of our approach. The results confirm that SVM Rank

关键词： Support vector machines Diabetes Spatial databases Data mining Accuracy Support vector machine classification Medical diagnosis Medical diagnostic imaging History Decision making

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Automatic simultaneous architecture and parameter search in fuzzy neural network learning using novel variable length crossover differential evolution

Automatic simultaneous architecture and parameter search in ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Lotika Singh Satish Kumar Sandeep Paul Department of Physics and Computer Science Dayalbagh Educational Institute (Deemed University) Agra India Computer Science and Applications the Department of Physics and Computer Science Dayalbagh Educational Institute (Deemed University) Agra India Department of Electrical Engineering Dayalbagh Educational Institute (Deemed University) Agra India

The automatic simultaneous search of structure and parameters in fuzzy-neural networks is a pressing research problem. This paper introduces a novel and powerful variable-length-crossover differential evolution algorithm, vlX-DE, which is applied to ASuPFuNIS fuzzy-neural model learning, and permits simultaneous evolution of mixed-length populations of strings representing ASuPFuNIS network instances in different rules spaces. As hybrid populations of strings evolve using vlX-DE, the population gradually converges to a single rule space after which parameter search within that space proceeds till the end of the algorithm run. Search can be directed to stress either rule node economy or minimize the sum-square-error, or trade-off between these two. Tests on three benchmark problems-iris classification, CHEM classification, and Narazaki-function approximation-clearly highlight the effectiveness of the algorithm in being able to perform this simultaneous search.

关键词： Classification algorithms Iris Evolution (biology) computer architecture Encoding Search problems computer science

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Knowledge Management Framework for Improving Curriculum Development Processes in Technical Education

Knowledge Management Framework for Improving Curriculum Deve...

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International Conference on Convergence Information Technology

作者： Sanjay Agrawal P. B. Sharma M. Kumar Computer Applications National Institute of Technical Teacher''s Training and Research Bhopal India RGPV (State Technological) Bhopal India Computer Science & Engineering SIRT Bhopal India

Increasing employability, reducing the gap between a graduating engineer and a professional engineer and fostering synergy between education and research are among the prime concerns being faced today by universities and institutions around the world. Developing a curriculum to respond to the challenges and satisfy the rapidly changing needs of industries require a dynamic system of curriculum development processes. At a time when both the knowledge as well as the skill sets are rapidly changing, the global horizons of businesses are undergoing rapid changes due to ever expanding technology and product innovation base, it is considered necessary to develop a knowledge management framework for curriculum development which is both industry relevant as well as supportive of growth of new knowledge and new technologies. The present paper examines the role of knowledge management in meeting the challenge of curriculum design for 21st century engineering and technology education. A Knowledge Management (KM) framework is worked out based on a total perception curriculum design and curriculum implementation in technical education institutions.

关键词： Knowledge management Curriculum development Educational products Technological innovation Design engineering Humans Information technology computer science education Educational technology Power engineering and energy

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