scholarly journals Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques

2017 ◽  
Vol 1 (3) ◽  
pp. 101
Author(s):  
Haitham A.M Salih ◽  
Hany H Ammar
Keyword(s):  
Machine Learning ◽  
Resource Utilization ◽  
Performance Prediction ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Software System ◽  
Performance Risk ◽  
Learning Techniques ◽  
Uml Diagrams ◽  
And Performance

The growing complexity of modern software systems makes the performance prediction a challenging activity. Many drawbacks incurred by using the traditional performance prediction techniques such as time consuming and inability to surround all software system when large scaled. To contribute to solving these problems, we adopt a model-based approach for resource utilization and performance risk prediction. Firstly, we model the software system into annotated UML diagrams. Secondly, performance model is derived from UML diagrams in order to be evaluated. Thirdly, we generate performance and resource utilization training dataset by changing workload. Finally, when new instances are applied we can predict resource utilization and performance risk by using machine learning techniques. The approach will be used to enhance work of human experts and improve efficiency of software system performance prediction. In this paper, we illustrate the approach on a case study. A performance training dataset has been generated, and three machine learning techniques are applied to predict resource utilization and performance risk level. Our approach shows prediction accuracy within 68.9 % to 93.1 %.

Pedestrian Safety Performance Prediction using Machine Learning Techniques

2021 ◽  
Author(s):  
Bharat Kaushik ◽  
Pratap Daphal ◽  
Pratyush Khare ◽  
Sivaprasad Koralla ◽  
Satadru Bera
Keyword(s):  
Machine Learning ◽  
Performance Prediction ◽  
Pedestrian Safety ◽  
Safety Performance ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Malicious web domain identification using online credibility and performance data by considering the class imbalance issue

2019 ◽  
Vol 119 (3) ◽  
pp. 676-696 ◽  
Author(s):  
Zhongyi Hu ◽  
Raymond Chiong ◽  
Ilung Pranata ◽  
Yukun Bao ◽  
Yuqing Lin
Keyword(s):  
Machine Learning ◽  
Class Imbalance ◽  
Performance Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Real World Data ◽  
Content Type ◽  
Domain Identification ◽  
Learning Techniques ◽  
And Performance

Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones). Design/methodology/approach The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling. Findings By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification. Originality/value Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

scholarly journals Machine Learning Generalisation across Different 3D Architectural Heritage

2020 ◽  
Vol 9 (6) ◽  
pp. 379 ◽  
Author(s):  
Eleonora Grilli ◽  
Fabio Remondino
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
High Complexity ◽  
Architectural Heritage ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Point Cloud Classification

The use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D heritage data, the diversity of the possible scenarios, and the different classification purposes that each case study might present, makes it difficult to realise a large training dataset for learning purposes. An important practical issue that has not been explored yet, is the application of a single machine learning model across large and different architectural datasets. This paper tackles this issue presenting a methodology able to successfully generalise to unseen scenarios a random forest model trained on a specific dataset. This is achieved looking for the best features suitable to identify the classes of interest (e.g., wall, windows, roof and columns).

scholarly journals Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques

2020 ◽  
Vol 10 (7) ◽  
pp. 2406
Author(s):  
Valentín Moreno ◽  
Gonzalo Génova ◽  
Manuela Alejandres ◽  
Anabel Fraga
Keyword(s):  
Machine Learning ◽  
Software Reuse ◽  
Unified Modeling Language ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Unified Modeling ◽  
Learning Techniques ◽  
Web Images ◽  
Time Required ◽  
Automated Software

Our purpose in this research is to develop a method to automatically and efficiently classify web images as Unified Modeling Language (UML) static diagrams, and to produce a computer tool that implements this function. The tool receives a bitmap file (in different formats) as an input and communicates whether the image corresponds to a diagram. For pragmatic reasons, we restricted ourselves to the simplest kinds of diagrams that are more useful for automated software reuse: computer-edited 2D representations of static diagrams. The tool does not require that the images are explicitly or implicitly tagged as UML diagrams. The tool extracts graphical characteristics from each image (such as grayscale histogram, color histogram and elementary geometric forms) and uses a combination of rules to classify it. The rules are obtained with machine learning techniques (rule induction) from a sample of 19,000 web images manually classified by experts. In this work, we do not consider the textual contents of the images. Our tool reaches nearly 95% of agreement with manually classified instances, improving the effectiveness of related research works. Moreover, using a training dataset 15 times bigger, the time required to process each image and extract its graphical features (0.680 s) is seven times lower.

scholarly journals Machine Learning Algorithms Performance Analysis for VLSI IC Design

2021 ◽  
Vol 3 (2) ◽  
pp. 80-92
Author(s):  
Joy Iong-Zong Chen ◽  
Kong-Long Lai
Keyword(s):  
Machine Learning ◽  
Performance Prediction ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Ic Design ◽  
Technical Developments ◽  
Learning Techniques ◽  
Improved Performance ◽  
Automated Tools

The design of an analogue IC layout is a time-consuming and manual process. Despite several studies in the sector, some geometric restrictions have resulted in disadvantages in the process of automated analogue IC layout design. As a result, analogue design has a performance lag when compared to manual design. This prevents the deployment of a large range of automated tools. With the recent technical developments, this challenge is resolved using machine learning techniques. This study investigates performance-driven placement in the VLSI IC design process, as well as analogue IC performance prediction by utilizing various machine learning approaches. Further, several amplifier designs are simulated. From the simulation results, it is evident that, when compared to the manual layout, an improved performance is obtained by using the proposed approach.

Analysis of Crime Report by Data Analytics Using Python

2021 ◽  
pp. 54-79
Author(s):  
G. Maria Jones ◽  
S. Godfrey Winster
Keyword(s):  
Machine Learning ◽  
New Technologies ◽  
Rapid Development ◽  
Performance Measure ◽  
Machine Learning Techniques ◽  
Complex Data ◽  
Time Data ◽  
Related Data ◽  
Learning Techniques ◽  
And Performance

The ever-rapid development of technology in today's world tends to provide us with a dramatic explosion of data, leading to its accumulation and thus data computation has amplified in comparison to the recent past. To manage such complex data, emerging new technologies are enabled specially to identify crime patterns, as crime-related data is escalating. These digital technologies have the potential to manipulate and also alter the pattern. To combat this, machine learning techniques are introduced which have the ability to analyse such voluminous data. In this work, the authors intend to understand and implement machine learning techniques in real time data analysis by means of Python. The detailed explanation in preparing the dataset, understanding, visualizing the data using pandas, and performance measure of algorithm is evaluated.

Using Machine Learning Techniques for Performance Prediction on Multi-Cores

2013 ◽  
pp. 259-273 ◽  
Author(s):  
Jitendra Kumar Rai ◽  
Atul Negi ◽  
Rajeev Wankar
Keyword(s):  
Machine Learning ◽  
Performance Studies ◽  
Performance Prediction ◽  
Memory Hierarchy ◽  
Performance Degradation ◽  
Machine Learning Techniques ◽  
System Level ◽  
Shared Resources ◽  
Learning Techniques ◽  
Multi Core Processor

Sharing of resources by the cores of multi-core processors brings performance issues for the system. Majority of the shared resources belong to memory hierarchy sub-system of the processors such as last level caches, prefetchers and memory buses. Programs co-running on the cores of a multi-core processor may interfere with each other due to usage of such shared resources. Such interference causes co-running programs to suffer with performance degradation. Previous research works include efforts to characterize and classify the memory behaviors of programs to predict the performance. Such knowledge could be useful to create workloads to perform performance studies on multi-core processors. It could also be utilized to form policies at system level to mitigate the interference between co-running programs due to use of shared resources. In this work, machine learning techniques are used to predict the performance on multi-core processors. The main contribution of the study is enumeration of solo-run program attributes, which can be used to predict concurrent-run performance despite change in the number of co-running programs sharing the resources. The concurrent-run involves the interference between co-running programs due to use of shared resources.

Sign in / Sign up

Export Citation Format

Share Document