A detection method of stagnation symptoms by using project progress models generated from project reports
The purpose of this research is to extract “stagnation symptoms” from progress reports related to a research project. A stagnation symptom is defined in a portion where remarkable stagnation is seen during the progress of a project. Specifically, according to project managers, stagnation symptoms can be classified into the following three kinds: first one is a project bottleneck grasped from one document; the second is clarified by comparing it with the most recent document; and the third is clarified from changes to a working object in a series of documents. We propose a method of extracting stagnation symptoms using the structural analysis of a project’s progress. A progress model that is a structural chart to expressing the progress of a project is generated from documents with label tags, which indicate prior contexts or attributes. This progress model has the following features: a multilevel layer model using detailed degrees and situation analysis using color, and relation analysis of these details and basis using color propagation. Stagnation symptoms are automatically extracted by applying stagnation symptom extraction rules to the progress model. This proposed method was been applied to a set of real progress reports. It could extract stagnation symptoms that were extracted manually.
Managing project quality
This work discusses with the superior quality and project management that optimize the performance excellence of organizations, so every quality practitioner needs to be able to manage project quality effectively. Unfortunately, the combined leverage of quality and project management is often underutilized due to inadequate experience in both fields, time pressures or budgetary cutbacks. In the project quality initiation stage there are many activities involved, in the first stage, such as development of ethical work culture values, an agreement to make fact based decisions, adoption or development of a quality policy, determination of team operating principles and identification of assumptions and risks are considered. Then it will focus on: identification, alignment, selection, chartered commitment to the selected project.
Early Stage Diagnosis of Parkinson’s Disease Using HOS Features of EEG Signals
Parkinson’s disease (PD) is a common neurodegenerative disease that causes involuntary muscle movements and tremor among other symptoms. One approach to diagnosing this disease is by analyzing the electroencephalography (EEG) signals of the patients. However, due to the complexity of this type of signal, more advanced feature extraction and classification methods are required. The goal of this study is to combine six well-known features in EEG analysis with eight higher order statistical features and use them for classification of early stage PD (1st and 2nd stage) from a healthy control group. After extracting the required features, eight classifiers are employed to classify the signals.