Deploying the Minimum Number of Rechargeable UAVs for a Quarantine Barrier

To control the rapid spread of COVID-19, we consider deploying a set of Unmanned Aerial Vehicles (UAVs) to form a quarantine barrier such that anyone crossing the barrier can be detected. We use a charging pile to recharge UAVs. The problem is scheduling UAVs to cover the barrier, and, for any scheduling strategy, estimating theminimum number of UAVs needed to cover the barrier forever. We propose breaking the barrier into subsegments so that each subsegment can be monitored by a single UAV. We then analyze two scheduling strategies, where the first one is simple to implement and the second one requires fewer UAVs. The first strategy divides UAVs into groups with each group covering a subsegment. For this strategy, we derive a closed-form formula for the minimum number of UAVs. In the case of insufficient UAVs, we give a recursive function to compute the exact coverage time and give a dynamic-programming algorithm to allocate UAVs to subsegments to maximize the overall coverage time. The second strategy schedules all UAVs dynamically. We prove a lower and an upper bound on the minimum number of UAVs. We implement a prototype system to verify the proposed coverage model and perform simulations to investigate the performance.

The impact of COVID-19 prevention and control measures on residential burglary hotspots: A case study of the core urban areas of Beijing

In order to analyze the impact of COVID-19 prevention and control measures on the hotspots of residential burglary, the data of crimes that occurred during the First Level Response period of Major Public Health Emergencies in Beijing in 2020 and the same period in 2019 were collected, and the changes of hotspots during the two periods were compared by using kernel density estimation and predictive accuracy index. Consequently, the environmental features such as street network, point of interest (POI) diversity, crime locations, and repeat victimization in significantly varied hotspot areas were investigated. The results show that: 1) After the outbreak of the pandemic, the occurrence of residential burglary in the core urban areas of Beijing dropped significantly, and daily occurrence of crimes during the First Level Response period in 2020 decreased by 66.8% compared with the same days in 2019. 2) The eight major hotspots that existed in 2019 apparently declined during the corresponding days in 2020, five of them basically disappeared, and three hotspots weakened. 3) The declined hotspots were generally clustered around traffic hubs, areas with high diversity of POIs, clustered crimes, and repeat victimizations. 4) Home isolation and social restriction strategies implemented during the First Level Response period reduced the opportunities of offenders, and the real-name inspection adopted in public places increased the exposure risk of offenders, which are the main reasons for the hotspots decline during the pandemic. This work has some implications for crime prevention and police resources optimization during the pandemic. © 2023, Editorial office of PROGRESS IN GEOGRAPHY. All rights reserved.

COVID-19: Agent-Based Simulation-Optimization to Vaccine Center Location Vaccine Allocation Problem

This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.

A hybrid metaheuristic algorithm for the multi-objective location-routing problem in the early post-disaster stage

Disasters such as earthquakes, typhoons, floods and COVID-19 continue to threaten the lives of people in all countries. In order to cover the basic needs of the victims, emergency logistics should be implemented in time. Location-routing problem (LRP) tackles facility location problem and vehicle routing problem simultaneously to obtain the overall optimization. In response to the shortage of relief materials in the early post-disaster stage, a multi-objective model for the LRP considering fairness is constructed by evaluating the urgency coefficients of all demand points. The objectives are the lowest cost, delivery time and degree of dissatisfaction. Since LRP is a NP-hard problem, a hybrid metaheuristic algorithm of Discrete Particle Swarm Optimization (DPSO) and Harris Hawks Optimization (HHO) is designed to solve the model. In addition, three improvement strategies, namely elite-opposition learning, nonlinear escaping energy, multi-probability random walk, are introduced to enhance its execution efficiency. Finally, the effectiveness and performance of the LRP model and the hybrid metaheuristic algorithm are verified by a case study of COVID-19 in Wuhan. It demonstrates that the hybrid metaheuristic algorithm is more competitive with higher accuracy and the ability to jump out of the local optimum than other metaheuristic algorithms.

A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model

Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients' suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP;(2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.

Optimal selection of cruise ship waste disposal ports

As cruise ships call at many ports and passengers come from all over the world, it is very easy to carry viruses on cruise ships. Under the control of the epidemic situation on board, the solid waste generated by them should be scientifically treated to prevent the spread of infectious diseases such as COVID-19 pneumonia. Therefore, Reasonable selection of waste disposal ports and formulation of unloading plans are directly related to the resumption of cruise operations. This study considers the cost and risk of waste disposal, uses robust optimization to deal with waste volume, increases the scenarios of port service interruption due to epidemics and other reasons, and proposes a variety of emergency strategies. Finally, the relevant strategies are selected according to the decision-maker's preference for cost and risk;By solving the relevant examples, the optimal choice of the cruise ship waste disposal port under the epidemic situation is given, which verifies the validity and feasibility of the model. The research helps to improve the management of cruise waste during the post-epidemic period, and has practical value and guiding significance for the normal operation and development of the global cruise market. © 2023 SPIE.

AI for Drug Repurposing in the Pandemic Response

Given the time criticality of finding treatments for the novel COVID-19 pandemic disease, drug repurposing has proved to be a vital strategy as the first response while de novo drug and vaccine developments are underway. Furthermore, Artificial Intelligence (AI) has also accelerated drug development in general. Key desirable features of AI that support a rapid and sustained response along the pandemic timeline include technical flexibility and efficiency (i.e. speed, resource-efficiency, algorithm adaptability), and clinical applicability and acceptability (i.e. scientific rigor, physiological applicability and practical implementation of proposed drugs). This chapter reviews a selection of AI-based applications used in drug development targeting COVID-19, including IDentif.AI-a small data platform for a rapid identification of optimal drug combinations, to illustrate the potential of AI in drug repurposing. The benefits and limitations of using Real-World Data are also discussed. The response to the COVID-19 pandemic has offered multiple learnings which highlight the need to strengthen both short- and long-term strategies in developing AI technologies, scientific and regulatory frameworks as well as worldwide collaborations to enable effective preparedness for future epidemic and pandemic risks. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Research on cold chain logistics optimization model considering low-carbon emissions

Cold chain logistics distribution orders have increased due to the impact of COVID-19. In view of the increasing difficulty of route optimization and the increase of carbon emissions in the process of cold chain logistics distribution, a mathematical model for route optimization of cold chain logistics distribution vehicles with minimum comprehensive cost is established by considering the cost of carbon emission intensity comprehensively in this paper. The main contributions of this paper are as follows: 1) An improved hybrid ant colony algorithm is proposed, which combined simulated annealing algorithm to get rid of the local optimal solution. 2) Chaotic mapping is introduced in pheromone update to accelerate convergence and improve search efficiency. The effectiveness of the proposed method in optimizing cold chain logistics distribution path and reducing costs is verified by simulation experiments and comparison with the existing classical algorithms. © 2023 The Author(s). Published by Oxford University Press.

Path Planning of Follow-up Transport Robot Based on Machine Vision

With the upgrading and innovation of the logistics industry, the requirements for the level of transportation smart technologies continue to increase. The outbreak of the COVID-19 has further promoted the development of unmanned transportation machines. Aimed at the requirements of intelligent following and automatic obstacle avoidance of mobile robots in dynamic and complex environments, this paper uses machine vision to realize the visual perception function, and studies the real-time path planning of robots in complicated environment. And this paper proposes the Dijkstra-ant colony optimization (ACO) fusion algorithm, the environment model is established by the link viewable method, the Dijkstra algorithm plans the initial path. The introduction of immune operators improves the ant colony algorithm to optimize the initial path. Finally, the simulation experiment proves that the fusion algorithm has good reliability in a dynamic environment. © 2022 IEEE.

Decision-Dependent Distributionally Robust Markov Decision Process Method in Dynamic Epidemic Control

In this paper, we present a Distributionally Robust Markov Decision Process (DRMDP) approach for addressing the dynamic epidemic control problem. The Susceptible-Exposed-Infectious-Recovered (SEIR) model is widely used to represent the stochastic spread of infectious diseases, such as COVID-19. While Markov Decision Processes (MDP) offers a mathematical framework for identifying optimal actions, such as vaccination and transmission-reducing intervention, to combat disease spreading according to the SEIR model. However, uncertainties in these scenarios demand a more robust approach that is less reliant on error-prone assumptions. The primary objective of our study is to introduce a new DRMDP framework that allows for an ambiguous distribution of transition dynamics. Specifically, we consider the worst-case distribution of these transition probabilities within a decision-dependent ambiguity set. To overcome the computational complexities associated with policy determination, we propose an efficient Real-Time Dynamic Programming (RTDP) algorithm that is capable of computing optimal policies based on the reformulated DRMDP model in an accurate, timely, and scalable manner. Comparative analysis against the classic MDP model demonstrates that the DRMDP achieves a lower proportion of infections and susceptibilities at a reduced cost. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.

REDUCED TRANSMISSION RISKS FOR PASSENGER OPERATIONS DURING COVID-19 PANDEMIC

In the aircraft cabin, passengers must share a confined environment with other passengers during boarding, flight, and disembarkation, which poses a risk for virus transmission and requires risk-appropriate mitigation strategies. Spacing between passenger groups during boarding and disembarkation reduces the risk of transmission, and optimized sequencing of passenger groups helps to significantly reduce boarding and disembarkation time. We considered passenger groups to be an important factor in overall operational efficiency. The basic idea of our concept is that the members of a group should not be separated, since they were already traveling as a group before entering the aircraft. However, to comply with COVID-19 regulations, different passenger groups should be separated spatially. For the particular challenge of disembarkation, we assume that passenger groups will be informed directly when they are allowed to leave for disembarkation. Today, cabin lighting could be used for this information process, but in a future digitally connected cabin, passengers could be informed directly via their personal devices. These devices could also be used to check the required distances between passengers. The implementation of optimized group sequencing has the potential to significantly reduce boarding and disembarkation times, taking into account COVID-19 constraints. © 2022 ICAS. All Rights Reserved.

Multi-objective Evolutionary Discretization of Gene Expression Profiles: Application to COVID-19 Severity Prediction

Machine learning models can use information from gene expressions in patients to efficiently predict the severity of symptoms for several diseases. Medical experts, however, still need to understand the reasoning behind the predictions before trusting them. In their day-to-day practice, physicians prefer using gene expression profiles, consisting of a discretized subset of all data from gene expressions: in these profiles, genes are typically reported as either over-expressed or under-expressed, using discretization thresholds computed on data from a healthy control group. A discretized profile allows medical experts to quickly categorize patients at a glance. Building on previous works related to the automatic discretization of patient profiles, we present a novel approach that frames the problem as a multi-objective optimization task: on the one hand, after discretization, the medical expert would prefer to have as few different profiles as possible, to be able to classify patients in an intuitive way;on the other hand, the loss of information has to be minimized. Loss of information can be estimated using the performance of a classifier trained on the discretized gene expression levels. We apply one common state-of-the-art evolutionary multi-objective algorithm, NSGA-II, to the discretization of a dataset of COVID-19 patients that developed either mild or severe symptoms. The results show not only that the solutions found by the approach dominate traditional discretization based on statistical analysis and are more generally valid than those obtained through single-objective optimization, but that the candidate Pareto-optimal solutions preserve the sense-making that practitioners find necessary to trust the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Social Economic Impacts for Covid-19 Pandemics Using Machine Learning Based Optimization Algorithm

As the number of COVID-19 patients grows exponentially, not all cases are likely dealt with by doctors and medical professionals. Researchers will add to the fight against COVID-19 by developing smarter strategies to achieve accelerated control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus that causes disease. Proposed method suggests best ways to optimize protection and avoid COVID-19 spread. Big benefit of the hybrid algorithm is that COVID-19 is diagnosed and treated more rapidly. Pandemic diseases possibilities are handling with help of Computational Intelligence, using cases and applications from current COVID-19 pandemic. This work discusses data that can be analyzed based on optimization algorithm which provides betterCOVID-19 detection and diagnosis. This algorithm uses a machine learning model to decide how the hazard function changes concerning characteristics of potential methods to find parameters in optimization of machine learning model, which has in many cases been shown to be accurate for actual clinical datasets. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system

This research attempts to study the Supplier Selection and Order Allocation Problem (SSOAP) considering three crucial concepts, namely responsiveness, sustainability, and resilience. To do so, the current research develops a Multi-Stage Decision-Making Framework (MSDMF) to select potential suppliers and determine the quantity of orders. The first stage aims at computing the scores of the suppliers based on several indicators. To do this, a novel decision-making approach named the Stochastic Fuzzy Best–Worst Method (SFBWM) is developed. Then, in the second stage, a Multi-Objective Model (MOM) is suggested to deal with supplier selection and order allocation decisions. In the next step, a data-driven Fuzzy Robust Stochastic (FRS) optimization approach, based on the fuzzy robust stochastic method and the Seasonal Autoregressive Integrated Moving Average (SARIMA) methods, is employed to efficiently treat the hybrid uncertainty of the problem. Afterwards, a novel solution method named the developed Chebyshev Multi-Choice Goal Programming with Utility Function (CMCGP-UF) is developed to obtain the optimal solution. Moreover, given the crucial role of the Medical Equipment (ME) industry in society's health, especially during the recent Coronavirus disease, this important industry is taken into account. The outcomes of the first stage demonstrate that agility, cost, GHG emission, quality, robustness, and Waste Management (WM), respectively, are the most important criteria. The outcomes of the second stage determine the selected suppliers, utilized transportation systems, and established sites. It is also revealed that demand directly affects all the objective functions while increasing the rate of disruptions has a negative effect on the sustainability measures.

Optimization and Characterization of Quercetin Vitamin C Nano-Phytosome Formulation

Objective: To design an optimal formulation for quercetin and vitamin C nano-phytosome. Method(s): Nano-phytosomes are prepared by the thin layer hydration technique using a 2-level-5-factor design experimental. A total of 32 experimental formulas were used for data analysis. The ratio of quercetin: soy lecithin (X1), the ratio of quercetin: cholesterol (X2), stirring speed (X3), stirring temperature (X4), and stirring time (X5) were used as independent factors, while globule size as a dependent factor. Data analysis was carried out by Design Expert12 application. Characterization of the optimal formula included physicochemical evaluation, globule size analysis, zeta potential, polydispersity index, entrapment efficiency, Transition Electron Microscopy (TEM) analysis, and FTIR analysis. Result(s): The optimal formula consisted of quercetin: vitamin C: lecithin: cholesterol ratio of 1: 1: 1.046: 0.105 mol;stirring speed 763.986 rpm;stirring time of 59 min, at temperature 51.73 degreeC which produced 59.26 nm average globule size, PDI value 0.66;zeta potential value-35.93+/-0.95 mV and average SPAN value 0.61. This formulation showed entrapment efficiency of quercetin 91.69+/-0.18 % and vitamin C 90.82+/-0.13 %. The TEM and FITR analysis showed the morphological of the globules and interactions between the drugs, soy lecithin, and cholesterol to form nano-phytosomes. Conclusion(s): The conditions to obtain the optimal formula for quercetin vitamin C nano-phytosome consisted of quercetin: vitamin C: lecithin: cholesterol ratio of 1: 1: 1.046: 0.105 mol;stirring speed 763.986 rpm;stirring time of 59 min, and at temperature 51.73 degreeC.Copyright © 2023 The Authors.

A Hybrid Grey Wolf-Whale Optimization Algorithm for Classification of Corona Virus Genome Sequences using Deep Learning

Genome sequence data is widely accepted as complex data and is still growing in an exponential rate. Classification of genome sequences plays a crucial role as it finds its applications in the area of biology, medical and forensics etc. For classification, Genome sequences can be represented in terms of features. More number of less significant features leads to lower accuracy in classification task. Feature selection addresses this issue by selecting the most important features which aids to improve the accuracy and lessens the computational complexity. In this research, Hybrid Grey Wolf-Whale Optimization Algorithm (HGWWOA) is proposed for Genome sequence classification. The proposed algorithm is evaluated using 23 benchmark objective functions along with Convolutional Neural Network classifier and its efficiency is verified using a novel metric namely "Feature Reduction Rate”. The proposed optimization algorithm can be applied for any optimization problems. In this research work, the proposed algorithm is used for classification of Corona Virus genome sequences. Performance comparison of the proposed and existing algorithms was carried out and it is evident that the performance of proposed algorithm exceeds the previous algorithms with an accuracy of 98.2%. © 2023, Zarka Private University. All rights reserved.

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies, investigates, and offers solutions to language-related real-life problems. The new coronavirus disease, otherwise known as Coronavirus disease (COVID-19), has severely affected the everyday life of people all over the world. Specifically, since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection, the country has initiated the appropriate preventive measures (like lockdown, physical separation, and masking) for combating this extremely transmittable disease. So, individuals spent more time on online social media platforms (i.e., Twitter, Facebook, Instagram, LinkedIn, and Reddit) and expressed their thoughts and feelings about coronavirus infection. Twitter has become one of the popular social media platforms and allows anyone to post tweets. This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis (SCOBGRU-SA) on COVID-19 tweets. The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic. The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this. Moreover, the BGRU model is utilized to recognise and classify sen-timents present in the tweets. Furthermore, the SCO algorithm is exploited for tuning the BGRU method's hyperparameter, which helps attain improved classification performance. The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset, and the results signify its promising performance compared to other DL models.