Feature extraction and performance measure of requirement engineering (RE) document using text classification technique

2018 ◽  
Author(s):  
L. P. Saikia ◽  
Shilpi Singh
Keyword(s):  
Feature Extraction ◽  
Text Classification ◽  
Requirement Engineering ◽  
Performance Measure ◽  
Classification Technique ◽  
And Performance

Melanoma Detection and Classification using Computerized Analysis of Dermoscopic Systems: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 794-822
Author(s):  
Muhammad Nasir ◽  
Muhammad Attique Khan ◽  
Muhammad Sharif ◽  
Muhammad Younus Javed ◽  
Tanzila Saba ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Performance Measure ◽  
Diagnostic Methods ◽  
High Survival ◽  
Features Selection ◽  
Melanoma Detection ◽  
And Performance ◽  
Contrast Stretching ◽  
Improved Accuracy

Malignant melanoma is considered as one of the most deadly cancers, which has broadly increased worldwide since the last decade. In 2018, around 91,270 cases of melanoma were reported and 9,320 people died in the US. However, diagnosis at the initial stage indicates a high survival rate. The conventional diagnostic methods are expensive, inconvenient and subject to the dermatologist’s expertise as well as a highly equipped environment. Recent achievements in computerized based systems are highly promising with improved accuracy and efficiency. Several measures such as irregularity, contrast stretching, change in origin, feature extraction and feature selection are considered for accurate melanoma detection and classification. Typically, digital dermoscopy comprises four fundamental image processing steps including preprocessing, segmentation, feature extraction and reduction, and lesion classification. Our survey is compared with the existing surveys in terms of preprocessing techniques (hair removal, contrast stretching) and their challenges, lesion segmentation methods, feature extraction methods with their challenges, features selection techniques, datasets for the validation of the digital system, classification methods and performance measure. Also, a brief summary of each step is presented in the tables. The challenges for each step are also described in detail, which clearly indicate why the digital systems are not performing well. Future directions are also given in this survey.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

Does What We See Shape History? Examining Workload History as a Function of Performance and Ambient/Focal Visual Attention

2021 ◽  
Vol 18 (2) ◽  
pp. 1-17
Author(s):  
Shannon P. Devlin ◽  
Jennifer K. Byham ◽  
Sara Lu Riggs
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Performance Measure ◽  
Task Demands ◽  
Unexpected Finding ◽  
Impact Performance ◽  
History Effects ◽  
And Performance ◽  
Workload History ◽  
Over Time

Changes in task demands can have delayed adverse impacts on performance. This phenomenon, known as the workload history effect, is especially of concern in dynamic work domains where operators manage fluctuating task demands. The existing workload history literature does not depict a consistent picture regarding how these effects manifest, prompting research to consider measures that are informative on the operator's process. One promising measure is visual attention patterns, due to its informativeness on various cognitive processes. To explore its ability to explain workload history effects, participants completed a task in an unmanned aerial vehicle command and control testbed where workload transitioned gradually and suddenly. The participants’ performance and visual attention patterns were studied over time to identify workload history effects. The eye-tracking analysis consisted of using a recently developed eye-tracking metric called coefficient K , as it indicates whether visual attention is more focal or ambient. The performance results found workload history effects, but it depended on the workload level, time elapsed, and performance measure. The eye-tracking analysis suggested performance suffered when focal attention was deployed during low workload, which was an unexpected finding. When synthesizing these results, they suggest unexpected visual attention patterns can impact performance immediately over time. Further research is needed; however, this work shows the value of including a real-time visual attention measure, such as coefficient K , as a means to understand how the operator manages varying task demands in complex work environments.

GUI-CAD Tool for Segmentation and Classification of Abnormalities in Lung CT Image

2019 ◽  
Vol 8 (1) ◽  
pp. 9-31
Author(s):  
V. Vijaya Kishore ◽  
R.V.S. Satyanarayana
Keyword(s):  
Region Of Interest ◽  
Performance Measure ◽  
Morphological Operations ◽  
Interactive 3D ◽  
And Performance ◽  
Aided Diagnosis ◽  
Lung Ct ◽  
Clinical Expert ◽  
Clinical Determination

A vital necessity for clinical determination and treatment is an opportunity to prepare a procedure that is universally adaptable. Computer aided diagnosis (CAD) of various medical conditions has seen a tremendous growth in recent years. The frameworks combined with expanding capacity, the coliseum of CAD is touching new spaces. The goal of proposed work is to build an easy to understand multifunctional GUI Device for CAD that performs intelligent preparing of lung CT images. Functions implemented are to achieve region of interest (ROI) segmentation for nodule detection. The nodule extraction from ROI is implemented by morphological operations, reducing the complexity and making the system suitable for real-time applications. In addition, an interactive 3D viewer and performance measure tool that quantifies and measures the nodules is integrated. The results are validated through clinical expert. This serves as a foundation to determine, the decision of treatment and the prospect of recovery.

Abstract 3429: Implementation of Electronic Medical Record Stroke Templates Increases Compliance with Stroke Performance Measures

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Crismely A Perdomo ◽  
Vepuka E Kauari ◽  
Elizabeth Suarez ◽  
Olajide Williams ◽  
Joshua Stillman ◽  
...  
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Performance Measures ◽  
New York State ◽  
Stroke Care ◽  
Performance Measure ◽  
Performance Comparison ◽  
York State Department ◽  
Free Text ◽  
And Performance

Background and Purpose The literature demonstrates how utilizing evidence-based, standardized stroke care can improve patient outcomes; however, the contribution of electronic medical record (EMR) systems may also impact outcomes by ensuring utilization and compliance with established stroke performance measures, facilitating and improving documentation requirements, and standardizing approach to care. In 2008, documentation in patients’ medical records was done in combination of paper and a template free EMR. Originally, the EMR was used for order entry, then transitioned to full electronic documentation in 2009. At that time we implemented our stroke templates and performance measures based on regulatory standards. We hypothesized that the stroke template implementation would help us achieve performance measure criteria above state benchmarks as set out by the New York State Department of Health (NYS DOH). Methods Implementation was phased in [over 18 months], initially using a template that only included neurological assessment and free text fields for stroke measures. By July 2010, existing templates were modified and additional stroke templates were implemented to meet new regulatory requirements and meaningful use criteria. Retrospective data review was conducted for performance comparison between 2008 -- one year prior to EMR/template implementation -- and 2010. In Quarter 1 of 2011 EMR was also implemented in the Emergency Department (ED). Data was reviewed for compliance with stroke measures. Results Documentation compliance substantially improved between 2008 and Quarter 1 2011: Compliance for these measures has been maintained ≥ 85% since November 2010, ≥ 90% Q1 2011 Conclusions The EMR implementation of stroke templates and performance measures can produce substantial improvement in performance measure compliance. Future steps will include automated documentation alerts to retrieve information and real time discovery of missing documentation for concurrent quality review and improvement

Headnote Prediction Using Machine Learning

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Sarmad Mahar ◽  
Sahar Zafar ◽  
Kamran Nishat
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Active Learning ◽  
Text Classification ◽  
Extraction Methods ◽  
Text Summarization ◽  
Training Data ◽  
Second Step ◽  
Support Vector ◽  
Classification Algorithms

Headnotes are the precise explanation and summary of legal points in an issued judgment. Law journals hire experienced lawyers to write these headnotes. These headnotes help the reader quickly determine the issue discussed in the case. Headnotes comprise two parts. The first part comprises the topic discussed in the judgment, and the second part contains a summary of that judgment. In this thesis, we design, develop and evaluate headnote prediction using machine learning, without involving human involvement. We divided this task into a two steps process. In the first step, we predict law points used in the judgment by using text classification algorithms. The second step generates a summary of the judgment using text summarization techniques. To achieve this task, we created a Databank by extracting data from different law sources in Pakistan. We labelled training data generated based on Pakistan law websites. We tested different feature extraction methods on judiciary data to improve our system. Using these feature extraction methods, we developed a dictionary of terminology for ease of reference and utility. Our approach achieves 65% accuracy by using Linear Support Vector Classification with tri-gram and without stemmer. Using active learning our system can continuously improve the accuracy with the increased labelled examples provided by the users of the system.

scholarly journals Quality of life issues and occupational performance of persons with epilepsy

2012 ◽  
Vol 70 (2) ◽  
pp. 140-144 ◽  
Author(s):  
Renato Nickel ◽  
Carlos Eduardo Silvado ◽  
Francisco Manoel Branco Germiniani ◽  
Luciano de Paola ◽  
Nicolle Lucena da Silveira ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Performance Measure ◽  
Occupational Performance ◽  
Canadian Occupational Performance Measure ◽  
Perceived Quality Of Life ◽  
Life Issues ◽  
Perception Of Quality ◽  
Performance Limitations ◽  
And Performance

Epilepsy causes restrictions in the performance of various daily activities. The aiming of this study was to investigate whether these restrictions affect the perceived quality of life. The assessments Quality of Life in Epilepsy-31 (QOLIE-31) and Canadian Occupational Performance Measure (COPM) were applied in a sample that consisted of a single group of 34 subjects with at least two years of uncontrolled seizures. The results indicated that the most affected domains of QOLIE-31 were seizure worry, 29.77 (±21.72), and effects of drugs, 49.75 (±28.58), and for the COPM, the average of performance and satisfaction were respectively 3.10 (±3.07) and 4.45 (±3.29), and performance limitations most frequently cited were maintain employment (18), left home alone (15) and courses (15). The application of the Spearman correlation coefficient showed that the three main performance limitations posed by the COPM, especially regarding the level of satisfaction, influence the perception of quality of life. Thus, occupational performance proves to be an important area of intervention with subjects with epilepsy.

Do Anxious Swimmers Swim Slower? Reexamining the Elusive Anxiety-Performance Relationship

1988 ◽  
Vol 10 (1) ◽  
pp. 45-61 ◽  
Author(s):  
Damon Burton
Keyword(s):  
Low Complexity ◽  
Performance Measure ◽  
Somatic Anxiety ◽  
Self Confidence ◽  
Cognitive Anxiety ◽  
Long Duration ◽  
Two Samples ◽  
And Performance ◽  
Moderate Complexity ◽  
The Relationship

The purpose of this investigation was to utilize a multidimensional measure of anxiety and a more sensitive intraindividual performance measure to evaluate the relationship between anxiety and performance. Three hypotheses were tested. First, cognitive anxiety is more consistently and strongly related to performance than is somatic anxiety. Second, somatic anxiety demonstrates an inverted-U relationship with performance, whereas self-confidence and performance exhibit a positive linear relationship and cognitive anxiety and performance exhibit a negative one. Finally, short duration and high and low complexity events demonstrate stronger relationships between somatic anxiety and performance than do long duration or moderate complexity events. Two samples of swimmers completed the CSAI-2 prior to competition, and performance data were obtained from meet results. Correlational and multiple regression analyses generally supported Hypotheses 1 and 3, while polynomial trend analyses on standardized CSAI-2 scores confirmed trends predicted in Hypothesis 2. Overall, these results not only revealed that improved instrumentation allows demonstration of consistent anxiety-performance relationships, but they also provided additional construct validity for the CSAI-2.

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