en Machine Learning Machine Learning Closed - 2024 Special Issue on Applications of Deep Learning in Electrical and Electronic Engineering Closed - 2024 Special Issue on Applications of Deep Learning in Electrical and Electronic Engineerin <span style="font-size:10pt"><span style="line-height:200%"><span new="" roman="" style="font-family:" times=""><o:wrapblock><v:shape coordsize="4719320,9525" fillcolor="black" id="Graphic_x0020_12" path="m4719193,l,,,9144r4719193,l4719193,xe" stroked="f" style="position:absolute; left:0; text-align:left; margin-left:133px; margin-top:568px; width:417.6pt; height:.7pt; z-index:-251657728; v-text-anchor:top"> <v:path arrowok="t"> <w:wrap anchorx="page" type="topAndBottom"> </w:wrap></v:path></v:shape></o:wrapblock></span></span><span new="" roman="" style="font-family:" times="">Robots have become integral to modern society, taking over both complex and routine human tasks. Recent advancements in depth camera technology have propelled computer vision-based robotics into a prominent field of research. Many robotic tasks&mdash;such as picking up, carrying, and utilizing tools or objects&mdash;begin with an initial grasping step. Vision-based grasping requires the precise identification of grasp locations on objects, making the segmentation of objects into meaningful components a crucial stage in robotic grasping. In this paper, we present a system designed to detect the graspable parts of objects for a specific task. Recognizing that everyday household items are typically grasped at certain sections for carrying, we created a database of these objects and their corresponding graspable parts. Building on the success of the Dynamic Graph CNN (DGCNN) network in segmenting object components, we enhanced this network to detect the graspable areas of objects. The enhanced network was trained on the compiled database, and the visual results, along with the obtained Intersection over Union (IoU) metrics, demonstrate its success in detecting graspable regions. It achieved a grand mean IoU (gmIoU) of 92.57% across all classes, outperforming established networks such as PointNet++ in part segmentation for this dataset. Furthermore, statistical analysis using analysis of variance (ANOVA) and<u> </u>T-test validates the superiority of our method.</span><span style="line-height:200%"><span new="" roman="" style="font-family:" times=""><o:wrapblock><v:shape coordsize="4719320,9525" fillcolor="black" id="Graphic_x0020_12" path="m4719193,l,,,9144r4719193,l4719193,xe" stroked="f" style="position:absolute; left:0; text-align:left; margin-left:133px; margin-top:568px; width:417.6pt; height:.7pt; z-index:-251657728; v-text-anchor:top"></v:shape></o:wrapblock></span></span></span> Robotic Grasp, Grasp Area, Point Cloud, Part Segmentation, Dynamic Graph CNN 134 146 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-2204-2&slc_lang=en&sid=1 Haniye Merrikhi Ha_merrikhi@sut.ac.ir 1800319475328460014922 1800319475328460014922 No Computer Vision Res. Lab., Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran. Hossein Ebrahimnezhad ebrahimnezhad@sut.ac.ir 1800319475328460014923 1800319475328460014923 Yes Computer Vision Res. Lab., Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran.