Analysis Pattern

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Mohamed E. Fayad - One of the best experts on this subject based on the ideXlab platform.

  • EuroPLoP - The Negotiation Analysis Pattern.
    2020
    Co-Authors: Haitham S. Hamza, Mohamed E. Fayad
    Abstract:

    Negotiation is a general concept that has wide range of applications that span various contexts. This paper introduces the Negotiation Analysis Pattern. This Pattern aims to provide a model that analyzes the core concept of the negotiation. In order to achieve this goal, Negotiation Pattern is built based on the concepts of Stable Analysis Patterns we have introduced before in [2, 3,4]. The paper provides detailed documentation of the proposed Pattern. In addition, it demonstrates the usage of the Pattern through the mean of examples.

  • A Pattern Language for Building Stable Analysis Patterns
    2020
    Co-Authors: Haitham S. Hamza, Mohamed E. Fayad
    Abstract:

    Software Analysis Patterns are believed to play a major role in reducing the cost and condensing the time of software product lifecycles. However, Analysis Patterns have not realized their full potential. One of the common problems with today”s Analysis Patterns is the lack of stability. In many cases, Analysis Pattern that model specific problems fail to model the same problem when it appears in different context, forcing software developers to analyze the problem from scratch. As a result, the reusability of the Pattern will diminish. This paper presents a Pattern language for building stable Analysis Patterns. The objective of this language is to propose a way of achieving stability while constructing Analysis Patterns.

  • IRI - Accessibility Stable Analysis Pattern (Stable Pattern for Model Based Software Reuse)
    2015 IEEE International Conference on Information Reuse and Integration, 2015
    Co-Authors: Mohamed E. Fayad, Siddharth Jindal
    Abstract:

    The Accessibility Stable Analysis Pattern intends to describe the core knowledge behind the concept of Accessibility. Accessibility finds an extensive range of usages in various applications. The Pattern also gives an excellent start to software developers, by defining the core knowledge of any accessibility problem. Any developer can build on, extend or reuse the Pattern to model any specific application by involving the factor of Accessibility.

  • IRI - The Visualization Stable Analysis Pattern
    2007 IEEE International Conference on Information Reuse and Integration, 2007
    Co-Authors: Mohamed E. Fayad
    Abstract:

    The main purpose of this paper is to extract and document the core knowledge of visualization, as an Analysis Pattern that provides a recurring and viable solution for the problem of visualization in any domain, and in any of its several applications. Now, people use the concept of visualization in every other field of study as an effective communication medium for an easy understanding of the subject. The usage of visualization in different domains or in different applications motivates us to design an efficient Pattern shared by the application, as the core concept. The Pattern presented in this paper relies on software stability model (Fayad and Altman, 2001), which also helps us to design a stable Analysis Pattern based on the core concept. The paper also provides a detailed documentation of the proposed Pattern, and demonstrates the benefits of the Pattern by using it as the core logic in several different applications throughout several case studies.

  • IRI - The Classification Stable Analysis Pattern
    2007 IEEE International Conference on Information Reuse and Integration, 2007
    Co-Authors: Mohamed E. Fayad
    Abstract:

    The main goal of this paper is to extract and document the core knowledge of the classification technique, as an Analysis Pattern that is usable in many different applications, where classification concept is required, rather than repeatedly building the concept from its scratch. The motivation behind the development of this Pattern is to write a generic Pattern, based entirely on the core classification concept, which is applicable in any application, and in any domain where classification is needed. To achieve this goal, this paper uses the concept of "software stability model" (SSM) to identify and isolate the core knowledge of classification from the application specific knowledge. Several scenarios chosen here to explore and probe will demonstrate the applicability and reusability of the Pattern. This paper also provides a detailed documentation of the proposed Pattern and demonstrates a number of benefits and advantages by demonstrating two case studies, which describe two completely different applications that use classification to solve specific problem in a specific domain.

Yuan Lu - One of the best experts on this subject based on the ideXlab platform.

  • Tree Analysis Pattern of mass spectral urine profiles in differential diagnosis of bladder transitional cell carcinoma
    Chinese journal of oncology, 2007
    Co-Authors: Deng-long Wu, Ming Guan, Yuanfang Zhang, Yue-min Xu, Jiong Zhang, Yuan Lu
    Abstract:

    OBJECTIVE: To develope a tree Analysis Pattern of mass spectral urine profiles to discriminate bladder transitional cell carcinoma (TCC) from non-cancer lesions using surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology. METHODS: Urine samples from 61 bladder transitional cell carcinoma (TCCs) patients, 53 healthy volunteers and 42 patients with other urogenital diseases were analyzed using IMAC-Cu-3 ProteinChip. Proteomic spectra were generated by SELDI-TOF- MS. A preliminary "training" set of spectra derived from Analysis of urine from 46 TCC patients, 32 patients with benign urogenital diseases (BUD), and 40 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm which identified a fine-protein mass Pattern that discriminated cancers from non-cancers effectively. A blinded test set including 38 cases was used to determine the sensitivity and specificity of the classification system. RESULTS: The algorithm identified a cluster Pattern that, in the training set, segregated cancer from non-cancer with a sensitivity of 84.8% and specificity of 91.7%. The discriminatory Pattern was correctly identified. A sensitivity of 93.3% and a specificity of 87% for the blinded test were obtained when compared the TCC versus non-cancers. CONCLUSION: SELDI-TOF-MS technology is a rapid, convenient and high-throughput analyzing method. The urine tree Analysis proteomic Pattern as a screening tool is effective for differential diagnosis of bladder cancer. More detailed studies are needed to further evaluate the clinical value of this Pattern.

  • Using Tree Analysis Pattern and SELDI-TOF-MS to Discriminate Transitional Cell Carcinoma of the Bladder Cancer from Noncancer Patients
    European Urology, 2004
    Co-Authors: Ming Guan, Deng-long Wu, Yuanfang Zhang, Zhong Wu, Ming Xu, Yuan Lu
    Abstract:

    OBJECTIVE: To determine whether SELDI protein profiling of urine coupled with a tree Analysis Pattern could differentiate TCC from noncancer patients. METHODS: The ProteinChip Arrays were performed on a ProteinChip PBS II reader of the ProteinChip Biomarker System. The study was divided into two phases: a preliminary phase with construction of tree Analysis Pattern, and a testing phase with test urine samples. Generation of the tree Analysis Pattern was performed by a training data set consisting of 104 samples. The validity of the tree Analysis Pattern was then challenged with a test set of 68 samples. RESULTS: Average of 187 mass peaks was detected in the urine samples, and five of these peaks were used to construct the tree Analysis Pattern. The classification Pattern correctly predicted 91.67-94.64% of the samples for both of the two groups in the training set, for an overall correct classification of about 93%. The Pattern correctly predicted 72.0% (49 of 68) of the test samples, with 71.4% (25 of 35) of the TCC samples, 72.7% (24 of 33) of the noncancer samples. CONCLUSIONS: The high sensitivity and specificity obtained by the urine protein profiling approach demonstrate that SELDI-TOF-MS combined with a tree Analysis Pattern can both facilitate discriminate TCC bladder cancer with noncancer and provide an innovative clinical diagnostic platform improve the detection of TCC bladder cancer patients.

Rong Peng - One of the best experts on this subject based on the ideXlab platform.

  • An Analysis Pattern Driven Analytical Requirements Modeling Method
    Communications in Computer and Information Science, 2020
    Co-Authors: Jingjing Ji, Rong Peng
    Abstract:

    Analytical requirements are the basis for building the enterprise data models that are used to develop the IT assets that deliver the analytical requirements to business users [10]. Due to the difficulties existed in the modeling and Analysis process, reusing existing Analysis experiences becomes a good choice to find an optimal way from problem domain to solution domain efficiently. To help data analysts use previous experience to elicit and model analytical requirements and find satisfactory solutions, an Analysis Pattern driven analytical requirements modeling method is proposed. It utilizes Analysis Patterns to help analysts model the relationships between data domains and machine domains, and select available Analysis models under the guidance of measurable analytical goals. The modeling process is an interactive and iterative process, which uses the feedbacks from analysts to adjust its Analysis behavior on real time. To illustrate the method more specifically, we apply it on requirements tracing.

  • RE Workshops - An Analysis Pattern Driven Requirements Modeling Method
    2016 IEEE 24th International Requirements Engineering Conference Workshops (REW), 2016
    Co-Authors: Jingjing Ji, Rong Peng
    Abstract:

    Enormous commercial value brought by big data Analysis promotes the vigorous development of big data Analysis industry. Due to the difficulties existed in the modeling process, reusing existing Analysis experiences becomes a good choice to find an optimal way from problem domain to solution domain efficiently. To help data analysts reuse previous experiences to elicit and model Analysis requirements and find satisfactory solutions, an Analysis Pattern driven Analysis requirements modeling method is proposed. It utilizes Analysis Patterns to help analysts model the relationships between data domains and machine domains, and select available Analysis models under the guidance of measurable Analysis goals. The modeling process is an interactive and iterative process, which uses the feedbacks from analysts to adjust its Analysis behavior.

  • An Analysis Pattern Driven Requirements Modeling Method
    2016 IEEE 24th International Requirements Engineering Conference Workshops (REW), 2016
    Co-Authors: Jingjing Ji, Rong Peng
    Abstract:

    Enormous commercial value brought by big data Analysis promotes the vigorous development of big data Analysis industry. Due to the difficulties existed in the modeling process, reusing existing Analysis experiences becomes a good choice to find an optimal way from problem domain to solution domain efficiently. To help data analysts reuse previous experiences to elicit and model Analysis requirements and find satisfactory solutions, an Analysis Pattern driven Analysis requirements modeling method is proposed. It utilizes Analysis Patterns to help analysts model the relationships between data domains and machine domains, and select available Analysis models under the guidance of measurable Analysis goals. The modeling process is an interactive and iterative process, which uses the feedbacks from analysts to adjust its Analysis behavior.

Deng-long Wu - One of the best experts on this subject based on the ideXlab platform.

  • Proteomic evaluation of urine from renal cell carcinoma using SELDI-TOF-MS and tree Analysis Pattern.
    Technology in Cancer Research & Treatment, 2020
    Co-Authors: Deng-long Wu, Wen-hong Zhang, Wen-jing Wang, San-bao Jing, Yue-ming Xu
    Abstract:

    There is no useful marker in screening and early diagnosis for renal cell carcinomas (RCCs), especially in the urine. To screen for specific markers in the urine of RCCs patients, surface enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOF-MS) was used and coupled with a tree Analysis Pattern to develop SELDI protein profiling of urine. Urine samples from 58 RCC patients, 45 healthy volunteers, and 56 patients with other urogenital diseases were analyzed using IMAC-Cu ProteinChip capable of specifically binding metal interesting proteins. Proteomic spectra were generated by mass spectrometry. Bioinformatic calculations were performed with Biomarker Wizard software 3.0 (Ciphergen). Four differentially expressed potential biomarkers from urine were identified with the relative molecular weights of 4020, 4637, 5070, and 5500. The discriminatory classifier with a panel of the four biomarkers determined in the training set could precisely detect 24 of 30 (sensitivity, 80.0%) RC...

  • Tree Analysis Pattern of mass spectral urine profiles in differential diagnosis of bladder transitional cell carcinoma
    Chinese journal of oncology, 2007
    Co-Authors: Deng-long Wu, Ming Guan, Yuanfang Zhang, Yue-min Xu, Jiong Zhang, Yuan Lu
    Abstract:

    OBJECTIVE: To develope a tree Analysis Pattern of mass spectral urine profiles to discriminate bladder transitional cell carcinoma (TCC) from non-cancer lesions using surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology. METHODS: Urine samples from 61 bladder transitional cell carcinoma (TCCs) patients, 53 healthy volunteers and 42 patients with other urogenital diseases were analyzed using IMAC-Cu-3 ProteinChip. Proteomic spectra were generated by SELDI-TOF- MS. A preliminary "training" set of spectra derived from Analysis of urine from 46 TCC patients, 32 patients with benign urogenital diseases (BUD), and 40 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm which identified a fine-protein mass Pattern that discriminated cancers from non-cancers effectively. A blinded test set including 38 cases was used to determine the sensitivity and specificity of the classification system. RESULTS: The algorithm identified a cluster Pattern that, in the training set, segregated cancer from non-cancer with a sensitivity of 84.8% and specificity of 91.7%. The discriminatory Pattern was correctly identified. A sensitivity of 93.3% and a specificity of 87% for the blinded test were obtained when compared the TCC versus non-cancers. CONCLUSION: SELDI-TOF-MS technology is a rapid, convenient and high-throughput analyzing method. The urine tree Analysis proteomic Pattern as a screening tool is effective for differential diagnosis of bladder cancer. More detailed studies are needed to further evaluate the clinical value of this Pattern.

  • Using Tree Analysis Pattern and SELDI-TOF-MS to Discriminate Transitional Cell Carcinoma of the Bladder Cancer from Noncancer Patients
    European Urology, 2004
    Co-Authors: Ming Guan, Deng-long Wu, Yuanfang Zhang, Zhong Wu, Ming Xu, Yuan Lu
    Abstract:

    OBJECTIVE: To determine whether SELDI protein profiling of urine coupled with a tree Analysis Pattern could differentiate TCC from noncancer patients. METHODS: The ProteinChip Arrays were performed on a ProteinChip PBS II reader of the ProteinChip Biomarker System. The study was divided into two phases: a preliminary phase with construction of tree Analysis Pattern, and a testing phase with test urine samples. Generation of the tree Analysis Pattern was performed by a training data set consisting of 104 samples. The validity of the tree Analysis Pattern was then challenged with a test set of 68 samples. RESULTS: Average of 187 mass peaks was detected in the urine samples, and five of these peaks were used to construct the tree Analysis Pattern. The classification Pattern correctly predicted 91.67-94.64% of the samples for both of the two groups in the training set, for an overall correct classification of about 93%. The Pattern correctly predicted 72.0% (49 of 68) of the test samples, with 71.4% (25 of 35) of the TCC samples, 72.7% (24 of 33) of the noncancer samples. CONCLUSIONS: The high sensitivity and specificity obtained by the urine protein profiling approach demonstrate that SELDI-TOF-MS combined with a tree Analysis Pattern can both facilitate discriminate TCC bladder cancer with noncancer and provide an innovative clinical diagnostic platform improve the detection of TCC bladder cancer patients.

James M Ragusa - One of the best experts on this subject based on the ideXlab platform.

  • forecasting the nyse composite index with technical Analysis Pattern recognizer neural networks and genetic algorithm a case study in romantic decision support
    Decision Support Systems, 2002
    Co-Authors: William Leigh, Russell L Purvis, James M Ragusa
    Abstract:

    The 21st century is seeing technological advances that make it possible to build more robust and sophisticated decision support systems than ever before. But the effectiveness of these systems may be limited if we do not consider more eclectic (or romantic) options. This paper exemplifies the potential that lies in the novel application and combination of methods, in this case to evaluating stock market purchasing opportunities using the "technical Analysis" school of stock market prediction. Members of the technical Analysis school predict market prices and movements based on the dynamics of market price and volume, rather than on economic fundamentals such as earnings and market share. The results of this paper support the effectiveness of the technical Analysis approach through use of the "bull flag" price and volume Pattern heuristic. The romantic approach to decision support exemplified in this paper is made possible by the recent development of: (1) high-performance desktop computing, (2) the methods and techniques of machine learning and soft computing, including neural networks and genetic algorithms, and (3) approaches recently developed that combine diverse classification and forecasting systems. The contribution of this paper lies in the novel application and combination of the decision-making methods and in the nature and superior quality of the results achieved.