Figure - uploaded by Muhammad Zohaib Khan
Content may be subject to copyright.
Download
View publication
SVM Classification Malignant and Benign Outcomes

SVM Classification Malignant and Benign Outcomes

Source publication
Figure 1. Classification [17]
Figure 3. Clustering [18]
Figure 6. K-means Clustering [21]
Figure 12. Predicted Label and True Label SVM
+5 Figure 16. Breast Cancer & Clusters Dataset
COMPARATIVE CASE STUDY: AN EVALUATION OF PERFORMANCE COMPUTATION BETWEEN SUPPORT VECTOR MACHINE, K-NEAREST NEIGHBORS, K-MEAN, AND PRINCIPAL COMPONENT ANALYSIS
Article
Full-text available
  • Apr 2022
The rapid advancement in Information Technology (IT) and the development of Artificial Intelligence (AI) makes systems more efficient and effective in performing day-today tasks, such as identification, extraction, detection, and recognition-related problems. These pose a serious indication towards the concept of Machine learning (ML) and the propo...
Cite
Download full-text

Similar publications

Figure 2. Flowchart of the proposed algorithm.
Figure 3. Sectioning the collected point cloud to small same-length...
Figure 4. Displaying the distance-and elevation-based errors.
Figure 5. Collecting the point cloud by the MLS system from the...
+2 Figure 6. Sectioning the collected point cloud.
Fast extraction of power lines from mobile LiDAR point clouds based on SVM classification in non-urban area
Article
Full-text available
  • Apr 2022
Mobile Laser Scanning (MLS) systems have been used for power line inspection in a fast and precise fashion. However, manually processing of huge LiDAR point clouds is tedious and time-consuming. Thus, an automated method is needed. This study proposes a machine learning-based method for automated detection of power lines from MLS point clouds. The...
View

Citations

Performance Analysis of Classification Algorithms for Software Defects Prediction by Mathematical Modelling & Simulations
Article
Full-text available
  • Oct 2023
This study explores machine learning (ML) techniques for Software defects prediction (SDP) by using Mathematical Modelling & Simulation. The SDP is also used in the critical systems of aviation, healthcare, manufacturing, and robotics. Many organizations face difficulty in forecasting the accurate defect before software deployment which is actually very crucial for estimating delivery time, maintenance efforts, and ensuring quality expectations. SDP enhances software quality by spotting potential defects in the upkeep phase. The current models of SDP rely on static program metrics for machine learning classifiers, but manual feature engineering may miss vital information impacting defect prediction accuracy. This study initially explores the past SDP results then aims to develop methods by adapting to future anomaly detection techniques. The study explores the various approaches of SDP which include K-Means methodology, Support Vector Machines (SVM) linear, Random Forest (RF) & Multi-layer Perceptron (MLP) algorithms and discussed the current models of SDP. The proposed SDP models are rigorously evaluated by using metrics like false alarm rate, precision, and detection rate. The results show high accuracy for K-Means and MLP (99.67%), K-Means and SVML (99.19%), and K-Means and RF (97.76%) for defect prediction.
View