Machine Learning Based Image Processing and Pattern Recognition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 3208

Special Issue Editors


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Guest Editor
College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
Interests: image processing; pattern recognition; facial expression recognition; activity recognition

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Guest Editor
College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
Interests: deep learning; medical imaging; artificial intelligence; healthcare

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Guest Editor
Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar 25130, Pakistan
Interests: machine learning; medical imaging; wireless sensor network; healthcare

Special Issue Information

Dear Colleagues,

The research on machine learning is unceasingly developing at an incredible speed. The “machine learning (ML)-based image processing”, a prequel to the current state of ML, provided us with fascinating techniques for image processing and pattern recognition. However, nowadays, we are experiencing new developments emerging from everywhere in the world and the improvements seen in the algorithms in recent years have completely remodeled the field.

Accordingly, the goal of this Special Issue (SI) is to inspire a systematic study of the recent algorithms of machine learning for image processing and pattern recognition and to associate these algorithms with real-life applications. Moreover, this Special Issue is focused on the applications of image processing and pattern recognition in different domains and in different environments. Recent advancements in image processing and pattern recognition have enabled us to boost the development of intelligent applications in different domains. We see these intelligent applications that are integrated as real-world applications collecting data seamlessly and continuously, and performing pattern recognition on the huge amount of data collected from our environments. These intelligent applications are developed to be adaptable in different unexpected conditions, which makes these applications highly useful in real-world environments.

From the application side, we welcome corresponding papers introducing novel machine learning models for computer vision, machine intelligence, medical imaging, facial expression recognition, activity recognition, and scene understanding. Of course, we are not limiting ourselves to only these topics, and any theoretically solid contribution to the field of machine learning related to image processing or pattern recognition will be considered.

Dr. Muhammad Hameed Siddiqi
Dr. Yousef Alhwaiti
Dr. Asfandyar Khan
Guest Editors

Manuscript Submission Information

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Keywords

  • image processing
  • pattern recognition
  • machine intelligence
  • computer vision
  • segmentation
  • feature extraction and selection
  • classification
  • medical imaging

Published Papers (1 paper)

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Research

36 pages, 9862 KiB  
Article
Prediction of Emotional Empathy in Intelligent Agents to Facilitate Precise Social Interaction
by Saad Awadh Alanazi, Maryam Shabbir, Nasser Alshammari, Madallah Alruwaili, Iftikhar Hussain and Fahad Ahmad
Appl. Sci. 2023, 13(2), 1163; https://doi.org/10.3390/app13021163 - 15 Jan 2023
Cited by 4 | Viewed by 2580
Abstract
The research area falls under the umbrella of affective computing and seeks to introduce intelligent agents by simulating emotions artificially and encouraging empathetic behavior in them, to foster emotional empathy in intelligent agents with the overarching objective of improving their autonomy. Raising the [...] Read more.
The research area falls under the umbrella of affective computing and seeks to introduce intelligent agents by simulating emotions artificially and encouraging empathetic behavior in them, to foster emotional empathy in intelligent agents with the overarching objective of improving their autonomy. Raising the emotional empathy of intelligent agents to boost their autonomic behavior can increase their independence and adaptability in a socially dynamic context. As emotional intelligence is a subset of social intelligence, it is essential for successful social interaction and relationships. The purpose of this research is to develop an embedded method for analyzing empathic behavior in a socially dynamic situation. A model is proposed for inducing emotional intelligence through a deep learning technique, employing multimodal emotional cues, and triggering appropriate empathetic responses as output. There are 18 categories of emotional behavior, and each one is strongly influenced by multimodal cues such as voice, facial, and other sensory inputs. Due to the changing social context, it is difficult to classify emotional behavior and make predictions based on modest changes in multimodal cues. Robust approaches must be used to be sensitive to these minor changes. Because a one-dimensional convolutional neural network takes advantage of feature localization to minimize the parameters, it is more efficient in this exploration. The study’s findings indicate that the proposed method outperforms other popular ML approaches with a maximum accuracy level of 98.98 percent when compared to currently used methods. Full article
(This article belongs to the Special Issue Machine Learning Based Image Processing and Pattern Recognition)
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