  Citation: Omri, M.; Abdel-Khalek, S.; Khalil, E.M.; Bouslimi, J.; Joshi, G.P. Modeling of Hyperparameter Tuned Deep Learning Model for Automated Image Captioning. Mathematics 2022, 10, 288. https://doi.org/10.3390/ math10030288 Academic Editors: Javier Martínez, Bo-Hao Chen and Francisco Chiclana Received: 19 November 2021 Accepted: 11 January 2022 Published: 18 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). mathematics Article Modeling of Hyperparameter Tuned Deep Learning Model for Automated Image Captioning Mohamed Omri 1 , Sayed Abdel-Khalek 2,3 , Eied M. Khalil 4,5 , Jamel Bouslimi 6 and Gyanendra Prasad Joshi 7, * 1 Deanship of Scientific Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Omrimoha2002@yahoo.fr 2 Mathematics Department, Faculty of Science, Taif University, Taif 21944, Saudi Arabia; sabotalb@tu.edu.sa 3 Mathematics Department, Faculty of Science, Sohag University, Sohag 82524, Egypt 4 Department of Mathematics, Faculty of Science, Taif University, Taif 21944, Saudi Arabia; eiedkhalil@tu.edu.sa 5 Mathematics Department, Faculty of Science, Azhar University, Cairo 11884, Egypt 6 Physics Department, Faculty of Science, Taif University, Taif 21944, Saudi Arabia; jamelabouaysem@yahoo.fr 7 Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea * Correspondence: joshi@sejong.ac.kr; Tel.: +82-2-6935-2481 Abstract: Image processing remains a hot research topic among research communities due to its applicability in several areas. An important application of image processing is the automatic image captioning technique, which intends to generate a proper description of an image in a natural language automated. Image captioning is a recently developed hot research topic, and it started to receive significant attention in the field of computer vision and natural language processing (NLP). Since image captioning is considered a challenging task, the recently developed deep learning (DL) models have attained significant performance with increased complexity and computational cost. Keeping these issues in mind, in this paper, a novel hyperparameter tuned DL for automated image captioning (HPTDL-AIC) technique is proposed. The HPTDL-AIC technique encompasses two major parts, namely encoder and decoder. The encoder part utilizes Faster SqueezNet with the RMSProp model to generate an effective depiction of the input image via insertion into a predefined length vector. At the same time, the decoder unit employs a bird swarm algorithm (BSA) with long short- term memory (LSTM) model to concentrate on the generation of description sentences. The design of RMSProp and BSA for the hyperparameter tuning process of the Faster SqueezeNet and LSTM models for image captioning shows the novelty of the work, which helps to accomplish enhanced image captioning performance. The experimental validation of the HPTDL-AIC technique is carried out against two benchmark datasets, and the extensive comparative study pointed out the improved performance of the HPTDL-AIC technique over recent approaches. Keywords: image captioning; deep learning; machine learning; encoder; decoder; hyperparameter tuning 1. Introduction Over the last years, the image processing and computer vision (CV) system has made significant progress in various fields such as object detection and image classification. Benefitting from the advancement of object detection and image classification, it becomes possible to generate more than one sentence automatically for understanding the visual content of an image, called image captioning. Automatically creating natural images and complete description has greater impacts, namely titles description related to healthcare images, attached to news images, accessing data for blind users, human–robot communica- tion, and text-based image retrieval [1]. This application in image captioning has significant practical and theoretical research values. Hence, image captioning is a sophisticated but useful task in the era of artificial intelligence (AI) technology. Provided a novel image, an image captioning method needs to output descriptions based on images at a semantic level. For instance, the input images consist of waves, people, Mathematics 2022, 10, 288. https://doi.org/10.3390/math10030288 https://www.mdpi.com/journal/mathematics