Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains.

Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN.

Unmanned Aerial Vehicles (UAVs), have greatly revolutionized the process of gathering and analyzing data in diverse research domains, providing unmatched adaptability and effectiveness. This paper presents a thorough examination of Unmanned Aerial Vehicle (UAV) datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains.

Rakib Hossain Rifat, Abanti Shruti, Marufa Kamal, Farig Sadeque. Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023). 2023.

The field of sentiment and emotion analysis is a challenging problem that has received research attention. The complexity of emotion and sentiment recognition draws from variability in expression, cultural and individual differences, context dependency, etc. This work takes an exploratory approach to the problem by performing an extensive classification of emotion using machine learning (ML) applied to textual and auditory data sources. We create a pipeline that facilitates the examination of textual and auditory inputs, resulting in more reliable emotional classification. The study uses multiple audio and textual datasets for the prediction of four distinct emotions. A four-layer Bi-LSTM model achieved 95% accuracy in emotion analysis from auditory clips. The training set contained 2391 samples, with Angry (20%), Fearful (18%), Happy (38%), and Neutral (24%). In the validation set of 713 samples, emotions were similarly distributed. The test set had 312 samples, with percentages of emotions comparable to the training set. We merged four datasets for textual analysis and utilized the "emotion english distilroberta base" model [5], achieving 90% accuracy on the test data. In the training set, emotions were distributed as follows: Angry (25%), Fearful (23%), Happy (23%), and Neutral (29%). The validation set comprised 305 samples, with similar distributions across emotions. The test set consisted of 712 samples, with percentages of emotions similar to the training set. We develop an application that combines both classifications to obtain a robust classification of arbitrary audio tracks.