Hybrid Words Representation for Airlines Sentiment Analysis

Trans Emerging Tel Tech

Siraj

et al. 2021

Self Cite

Next‐generation wireless communication networks, in particular, the densified 5G will bring many developments to the existing telecommunications industry. The key benefits will be the higher throughput and very low latency. In this context, the usage of unmanned aerial vehicle (UAV) is becoming a feasible option for deploying 5G services on demand. At the same time, the immense bandwidth potential of mmWave has strengthened its performance in radio communication. In this article, we provide a consolidated synthesis on the role of UAVs and mmWave in 5G, emphasis on recent developments and challenges. The review focuses on UAV relay architectures, identifies the relevant problems and limitations in the deployment of UAVs using mmWave in both access and backhaul links simultaneously. There is a critical analysis of the optimum placement of the UAVs as a relay with a focus on the mmWave band. The distinctive rich characteristics of the mmWave propagation and scattering are presented. We also synthesis mmWave path loss models. Then, the scope of artificial intelligence and machine learning techniques as an efficient solution for combating the dynamic and complex nature of UAV‐based cellular communication networks are discussed. In the end, security and privacy issues in UAV‐based cellular network are spotlighted. It is believed that the literature discussed, and the findings reached in this article are of significant importance to researchers, application engineers and decision‐makers in the designing and deployment of UAV‐supported 5G network.

Section: State‐of‐the‐artmentioning

confidence: 99%

The role of unmanned aerial vehicles and mmWave in 5G: Recent advances and challenges

Khan

Trans Emerging Tel Tech

Siraj

et al. 2021

Self Cite

“…Si et al [36], trained a clinical note corpus BERT language model, and enhancements over both traditional and ELMo embeddings were obtained on the 12b2 2010 and 2020 tasks by utilising complex taskspecific models, thus giving rise to new cutting edge results. Different hybrid models such as DICE [24], DICE+ [23], hybrid contextual word representation [25] and transformer based Deep Intelligent Contextual Embedding [26] were also presented to capture complex language ambiguities in previous studies.…”

Section: Related Workmentioning

confidence: 99%

Biomedical Named-Entity Recognition by Hierarchically Fusing BioBERT Representations and Deep Contextual-Level Word-Embedding

2020 International Joint Conference on Neural Networks (IJCNN)

Musiał

Eklund

et al. 2020

Self Cite

Text mining in the biomedical domain is increasingly important as the volume of biomedical documents increases. Thanks to advances in natural language processing (NLP), extracting valuable information from the biomedical literature is gaining popularity among researchers, and deep learning has enabled the development of effective biomedical text mining models. However, directly applying advancements in NLP to biomedical sources often yields unsatisfactory results, due to a word distribution drift from the general language domain corpora to specific biomedical corpora, and this drift introduces linguistic ambiguities. To overcome these challenges, this paper presents a novel method for biomedical named entity-recognition (BioNER) through hierarchically fusing representations from BioBERT, which is trained on biomedical corpora and Deep contextual-level word embeddings to handle the linguistic challenges within biomedical literature. Proposed text representation is then fed to attention-based Bi-directional Long Short Term Memory (BiLSTM) with Conditional random field (CRF) for the BioNER task. The experimental analysis shows that our proposed end-to-end methodology outperforms existing state-ofthe-art methods for the BioNER task.

“…Rules are limited as the parameters are divided into low, medium, high category or few more. Few more survey can be found in [20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Logic in the Ranking of Academic Institutions

Das¹,

Samanta²,

Fuzzy Information and Engineering

et al. 2019

Self Cite

For the development of any organization, evaluation of performance is an important task. To analyze the performance, Das et al. introduced a ranking framework based on fuzzy logic. That framework compared the current NIRF system of Indian Institutions with the fuzzy logic-based Mamdani system, KSM index. In that study, uniform membership functions are not taken. This study will focus on the ranking system based on Mamdani and Sugeno with fuzzy triangular numbers as input values of membership functions. Also, the updated results are compared with existing NIRF systems and considered Top 20 institutions of India for the year 2019 as per NIRF.