Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

https://www.mdpi.com/1424-8220/19/23/5170/pdf

A Survey on Machine-Learning Techniques for UAV-Based Communications

Communication and networking technologies for UAVs: A survey

A risk defense method based on microscopic state prediction with partial information observations in social networks

With the advancement in drone technology, in just a few years, drones will be assisting humans in every domain. But there are many challenges to be tackled, communication being the chief one. This paper aims at providing insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures. Additionally, we explore techniques such as machine learning and path planning to enhance existing drone communication methods. Encryption and optimization techniques for ensuring long lasting and secure communications, as well as for power management, are discussed. Moreover, applications of UAV networks for different contextual uses ranging from navigation to surveillance, URLLC (Ultra reliable and low latency communications), edge computing and work related to artificial intelligence are examined. In particular, the intricate interplay between UAV, advanced cellular communication, and internet of things constitutes one of the focal points of this paper. The survey encompasses lessons learned, insights, challenges, open issues, and future directions in UAV communications. Our literature review reveals the need for more research work on drone to drone and drone to device communications.

Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a new breed of unprecedented applications in different domains. This technology's ongoing trend is departing from large remotely-controlled drones to networks of small autonomous drones to collectively complete intricate tasks time and cost-effectively. An important challenge is developing efficient sensing, communication, and control algorithms that can accommodate the requirements of highly dynamic UAV networks with heterogeneous mobility levels. Recently, the use of Artificial Intelligence (AI) in learning-based networking has gained momentum to harness the learning power of cognizant nodes to make more intelligent networking decisions. An important example of this trend is developing learning-powered routing protocols, where machine learning methods are used to model and predict topology evolution, channel status, traffic mobility, and environmental factors for enhanced routing. 
This paper reviews AI-enabled routing protocols designed primarily for aerial networks, with an emphasis on accommodating highly-dynamic network topology. To this end, we review the basics of UAV technology, different approaches to swarm formation, and commonly-used mobility models, along with their impact on networking paradigms. We proceed with reviewing conventional and AI-enabled routing protocols, including topology-predictive and self-adaptive learning-based routing algorithms. We also discuss tools, simulation environments, remote experimentation platforms, and public datasets that can be used for developing and testing AI-enabled networking protocols for UAV networks. We conclude by presenting future trends, and the remaining challenges in AI-based UAV Networking, for different aspects of routing, connectivity, topology control, security and privacy, energy efficiency, and spectrum sharing.

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook.

Pathology reports contain the essential data for both clinical and research purposes. However, the extraction of meaningful, qualitative data from the original document is difficult due to the narrative and complex nature of such reports. Keyword extraction for pathology reports is necessary to summarize the informative text and reduce intensive time consumption. In this study, we employed a deep learning model for the natural language process to extract keywords from pathology reports and presented the supervised keyword extraction algorithm. We considered three types of pathological keywords, namely specimen, procedure, and pathology types. We compared the performance of the present algorithm with the conventional keyword extraction methods on the 3115 pathology reports that were manually labeled by professional pathologists. Additionally, we applied the present algorithm to 36,014 unlabeled pathology reports and analysed the extracted keywords with biomedical vocabulary sets. The results demonstrated the suitability of our model for practical application in extracting important data from pathology reports.

/pdf/validation-of-deep-learning-natural-language-processing-2h3357d7y5.pdf

Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records.

Drones cooperate with each other by transmitting and receiving packets. Therefore, it is important to conjecture the packet transmission rates within the network. However, the conventional methods are not suitable to describe the transmission patterns with satisfactory computing speed and accuracy. In this paper, we demonstrated that machine learning can successfully predict the transmission patterns in drone network. The packet transmission rates of a communication network with twenty drones were simulated, of which results were used to train the linear regression and Support Vector Machine with Quadratic Kernel (SVM-QK). We found out SVM-QK can precisely predict the communication between drones.

Prediction of information propagation in a drone network by using machine learning

With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language models has been conducted. The Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language, as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train the language models. In this paper, we present a Korean medical language model based on deep learning NLP. The model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. The pre-trained model showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation, and the evaluation for the Korean medical named entity recognition showed a 0.053 increase in the F1-score. 

/pdf/a-pre-trained-bert-for-korean-medical-natural-language-2b2006ec.pdf

A pre-trained BERT for Korean medical natural language processing

Drones transmit and receive packets with each other in a wireless network setting. Packet transmission among drones fails for various reasons. The pattern of information propagation through the packet transmission in a drone network can be considered similar to the pattern of infectious disease transmission in a human interaction network. In this work, we use a Microscopic Markov Chain Approach (MMCA), which has been applied to model the patterns of disease epidemics in a human network, to investigate the packet transmission pattern in a microscopic scale. Throughout the simulation studies, we investigated the usefulness of MMCAs for a drone network.

Information propagation modeling in a drone network using disease epidemic models

Motivation Despite the potential usefulness, the association analysis of gene expression with interval times of two events has been hampered because the occurrence of events can be censored and the conventional survival analysis is not suitable to handle two censored events. However, the recent advances of multivariate survival analysis considering multiple censored events together provide an unprecedented chance for this problem. Based on such advances, we have developed a software tool, GAIT, for the association analysis of gene expression with interval time of two events. Results The performance of GAIT was demonstrated by simulation studies and the real data analysis. The result indicates the usefulness of GAIT in a wide range of biomedical applications. Availability and implementation http://cdal.korea.ac.kr/GAIT/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

/pdf/gait-gene-expression-analysis-for-interval-time-1bdqmnl3ew.pdf

Yoojoong Kim

Papers

Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records.

Prediction of information propagation in a drone network by using machine learning

A pre-trained BERT for Korean medical natural language processing

Information propagation modeling in a drone network using disease epidemic models

GAIT: Gene expression Analysis for Interval Time.