## 1 Introduction  
Scene recognition, which is a crucial aspect of computer vision and artificial intelligence, has been the subject of extensive research across multiple disciplines and has received significant interest in recent decades [1]. This discipline centers on the advancement of algorithms, models, and procedures that possess the ability to classify and comprehend the content of photographs or videos by analyzing the scenes they portray automatically and precisely [2]. Recognition of scenes is of great significance in various domains, encompassing autonomous navigation, retrieval of images and videos, robotics, surveillance, and augmented reality, among other fields [3, 4, 5, 6, 7]. The main goal of scene recognition is to provide machines with the capacity to understand the visual world in a manner similar to humans through the recognition and interpretation of intricate environmental scenarios [8]. This involves the identification of different components present in a given scene, including objects, spatial arrangements, and contextual associations, followed by the deduction of the scene's overarching semantic significance. The attainment of this particular talent is a multifaceted undertaking that involves various obstacles, such as comprehending scenes, detecting and recognizing objects, modeling context, and segmenting images. Traditionally, the field of scene recognition has predominantly employed a unimodal methodology, wherein image or video input is analyzed using a solitary model [9]. Recently, there has been a notable increase in research attention and progress in the field of scene identification [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. This rise can be attributed to the advancements made in deep learning techniques, the widespread availability of extensive datasets with annotations, and the accessibility of high-performance computer resources. CNNs and other deep learning architectures have exhibited notable achievements in the domain of scene recognition tasks, outperforming conventional computer vision techniques in both accuracy and efficiency. The aforementioned technological advancements have facilitated the development of novel applications, such as autonomous vehicles, intelligent surveillance systems, and augmented reality encounters, which heavily depend on the comprehension and analysis of visual scenes. In recent times, multimodal deep learning techniques have demonstrated remarkable success in scene recognition tasks, surpassing the capabilities of traditional unimodal approaches [21; 22; 23]. These advances have paved the way for innovative applications such as self-driving cars, smart surveillance systems, and augmented reality experiences, all of which rely on scene understanding and interpretation. These methodologies make use of several sources of information, including text, audio, and depth data, in order to improve the comprehension of scenes. This paper centers on the utilization of multimodal deep learning techniques for scene recognition. The objective is to leverage the advantages of different modalities in order to enhance the robustness and accuracy of the findings obtained. The core of our methodology revolves around the novel idea of integrating many modalities into a single input for scene recognition. This approach deviates from traditional models that predominantly rely on processing data from a single modality. This innovative methodology utilizes multimodal deep learning on the identical input image data, facilitating the concurrent analysis of many forms of information, such as text, audio, and depth data, all derived from the same image. In this particular framework, we effectively incorporate many modalities into a unified model, leveraging their complementary nature to attain a more thorough comprehension of the scenario. In order to enhance the quality of the feature representations resulting from this fusion, we utilize the technique of _MI_-based feature selection [24].