Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles (UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The results suggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published.

This comprehensive review explores the implications of artificial intelligence (AI) in addressing cochlear implant (CI) issues and revolutionizing the landscape of auditory prosthetics. It begins with an overview of ear anatomy and hearing loss, then explores a review of CI technology and its current challenges. The review emphasizes how advanced AI algorithms and data‐driven approaches enhance CI adaptability and functionality, enabling personalized rehabilitation strategies and improving speech enhancement. It highlights diverse AI applications in auditory rehabilitation, including real‐time adaptive control mechanisms and cognitive hearing assistants that help users manage their auditory health. By outlining innovative pathways and future directions for AI‐enhanced CIs, the paper sets the stage for a transformative shift in auditory prosthetics, aiming to improve the quality of life for individuals with hearing loss.

This research was conducted with the objective of identifying and ranking the tourism sector development strategies for the Changbai Mountain. The SWOT approach was used to construct strategies and the QSPM matrix and AHP method were employed to rank selected strategies. A questionnaire and the Delphi technique were used to collect and analyze research data from forty specialists. The effectiveness of 16 internal factors and 12 external factors in the business region was assessed. According to the results obtained, “Existence of beautiful natural features with distinctive scenery” is the most significant strength of Changbai Mountain. Also, “Inadequate amenities and weakness of infrastructure construction” has been established as the most significant weakness. The term “Adapting the development of the region to the national strategy” is among the most significant external opportunities. Additionally, the “islanding phenomenon” is one of the most significant threats. Sixteen plans were recommended for the growth of the Changbai Mountain’s tourism business. On the basis of the matrix of internal–external components in the SWOT model, an offensive strategy was identified as the optimal approach. We used the combined SWOT-AHP model with 4 criteria and 28 sub-criteria to determine the optimum strategy in the second model, and offensive methods were given the highest priority. The results showed that the “Taking advantage of the natural, historical potentials, etc.” and “Establishing an appropriate mechanism for public and private sector investment” strategies are the most crucial for improving the condition in Changbai Mountain. Therefore, special consideration should be given to the tourism potential in this region, and it should be placed on the agenda of managers and planners in order to strengthen the tourism industry, the region’s economic status, and create employment opportunities.

In a large-scale epidemic, reducing direct contact among medical personnel, attendants and patients has become a necessary means of epidemic prevention and control. Intelligent vehicles and mobile robots in the hospital environment, such as disinfection vehicles, logistics vehicles, nursing robots, and guiding robots, play an important role in improving the operational efficiency of the medical system and promoting epidemic prevention and governance. Powerful capabilities of environmental spatial perception and reconstruction are the keys to accurate localization, navigation, and obstacle avoidance for intelligent vehicles and autonomous robots in such operations. Omnidirectional perception is becoming increasingly important and proliferative in autonomous vehicles and robots since its wide field of view significantly enhances the perception ability. However, the lack of dense and accurate 360° depth datasets has brought the challenge to the omnidirectional perception. In this paper, we propose a depth-sensing and reconstruction system to address this challenge in the large-scale indoor environment. First, we design an omnidirectional depth completion convolutional neural network model, in which a spherical normalized convolutional and the unit sphere area-based loss are introduced to extract features from cross-modal omnidirectional input with unequal sparsity and deal with the imbalanced data distribution and distortion in the panoramic input. In addition, we present a 3D reconstruction system by integrating our depth completion into omnidirectional localization and dense mapping. We evaluate our method on 360D large-scale indoor datasets and real-world sequences of a challenging hospital scene. Extensive experiments show that the proposed method outperforms the other state-of-the-art (SoTA) approaches in terms of depth completion and 3D reconstruction.