scholarly journals An Efficient Software Error Prediction and Recommend System

2019 ◽  
Vol 9 (2) ◽  
pp. 5300-5303
Keyword(s):  
Machine Learning ◽  
Modern Technology ◽  
Element Determination ◽  
Software Defect ◽  
Learning Technique ◽  
Prediction Technique ◽  
Information Sets ◽  
Information Set ◽  
Efficient Software

This present paper proposes the while its beginning, and past years software testing has been involved. Modern technology in software is using Artificial Intelligent and machine learning for advancing the technology. According to software engineering various techniques are analysed depending on the required predictions. Here in order to give the importance for the development of software defect prediction technique helps foe testers to focus on modules that defect prone. Depending on the development aspect the literature survey states various techniques based on features that are mostly captured for the prediction of defects. So in this paper we give a novel machine learning technique which is the foremost objective for finding prospective areas defects by considering various parameters like system testing metrics and unique parameters called ‘Component Dependency Score’. By applying element determination method we can reduce the words present in defect information and also there will be an expansion in precision so that both systems can build the additional qualities like precision and reducing defect reports or words. Depending on this new technique we can reduce the defect information sets for getting 71.8 percent exactness for reducing the request. The present issue reducing information to defect and increase the information set of defect in two viewpoints such as all the while diminish the sizes of defect extent and the word extent and to enhance the precision of defect triage. So we propose a mix way for dealing with of attention of issuing for reducing information. This is viewed as an example for purpose of choice highlighting in defecting store house. So we construct a parallel categoriser for expecting the request in determination of applying example and highlighting choices. Here the request for applying occurrence in highlighting the choices is not yet related to the research space.

scholarly journals Computer Vision Based Detection and Quantification of Extraneous Water in Raw Milk

2021 ◽  
Author(s):  
Bezuayehu Gutema Asefa ◽  
Legesse Hagos ◽  
Tamirat Kore ◽  
Shimelis Admassu Emire
Keyword(s):  
Machine Learning ◽  
Digital Image Analysis ◽  
Raw Milk ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Machine Learning Technique ◽  
Milk Adulteration ◽  
Learning Technique ◽  
Total Accuracy

Abstract A rapid method based on digital image analysis and machine learning technique is proposed for the detection of milk adulteration with water. Several machine learning algorithms were compared, and SVM performed best with 89.48 % of total accuracy and 95.10 % precision. An increase in the classification performance was observed in extreme classes. Better quantitative determination of the extraneous water was achieved using SVMR with R2(CV) and R2(P) of 0.65 and 0.71 respectively. The proposed technique can be used to screen raw milk based on the level of added extraneous water without the necessity of any additional reagent.

GEOMETRIC TECHNIQUES FOR HUMANOID PERCEPTION

2010 ◽  
Vol 07 (03) ◽  
pp. 429-450
Author(s):  
ALBERTO PETRILLI-BARCELÓ ◽  
HERIBERTO CASARRUBIAS-VARGAS ◽  
MIGUEL BERNAL-MARIN ◽  
EDUARDO BAYRO-CORROCHANO ◽  
RÜDIGER DILLMAN
Keyword(s):  
Machine Learning ◽  
Humanoid Robot ◽  
Robot Vision ◽  
Machine Learning Technique ◽  
Localization And Mapping ◽  
Perception System ◽  
Learning Technique ◽  
Robot Head ◽  
Geometric Techniques

In this article, we propose a conformal model for 3D visual perception. In our model, the two views are fused in an extended 3D horopter model. For visual simultaneous localization and mapping (SLAM), an extended Kalman filter (EKF) technique is used for 3D reconstruction and determination of the robot head pose. In addition, the Viola and Jones machine-learning technique is applied to improve the robot relocalization. The 3D horopter, the EKF-based SLAM, and the Viola and Jones machine-learning technique are key elements for building a strong real-time perception system for robot humanoids. A variety of interesting experiments show the efficiency of our system for humanoid robot vision.

scholarly journals Machine learning determination of atomic dynamics at grain boundaries

2018 ◽  
Vol 115 (43) ◽  
pp. 10943-10947 ◽  
Author(s):  
Tristan A. Sharp ◽  
Spencer L. Thomas ◽  
Ekin D. Cubuk ◽  
Samuel S. Schoenholz ◽  
David J. Srolovitz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Grain Boundaries ◽  
High Energy ◽  
Polycrystalline Materials ◽  
Large Variability ◽  
Atomic Structures ◽  
Learning Technique ◽  
Glassy Materials ◽  
Dominant Influence

In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. Here, we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity (softness) that captures the propensity of an atom to rearrange. This approach correctly identifies crystalline regions, stacking faults, and twin boundaries as having low likelihood of atomic rearrangements while finding a large variability within high-energy grain boundaries. As has been found in glasses, the probability that atoms of a given softness will rearrange is nearly Arrhenius. This indicates a well-defined energy barrier as well as a well-defined prefactor for the Arrhenius form for atoms of a given softness. The decrease in the prefactor for low-softness atoms indicates that variations in entropy exhibit a dominant influence on the atomic dynamics in grain boundaries.

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection
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