ABSTRACT
Crop image segmentation plays a key step in the field of agriculture. The crop images present near the environs have complex backgrounds and their grayscale histogram is mostly multimodal. Hence, multilevel segmentation of grayscale crop images may be helpful for better analysis. This paper proposed multilevel thresholding of grayscale crop images incorporated with minimum cross entropy as an objective function. The time complexity of this technique increases with the threshold levels. Hence, the coronavirus herd immunity optimizer (CHIO) has been amalgamated with the objective function. This technique improves the image's accuracy. The CHIO is a humanbased algorithm that separates the foreground and background efficiently with multiple thresholds value. The simulation has been performed on grayscale crop images. It is. compared with bacterial foraging algorithm (BFO), and beta differential algorithm (BDE) to validate the accuracy. The results validates that the proposed method outperforms BFO and BDE for grayscale crop images in terms of fidelity parameters. The qualitative and quantitative results evidence the proficiency of suggested method. © 2023 IEEE.
ABSTRACT
The COVID-19 pandemic has been going on for more than two years. Vaccination is believed to be one of the most efficient ways to achieve herd immunity and end pandemic. However, the contents about COVID-19 vaccines on social media have impacts on personal attitude towards vaccination. The present study aims to examine the current scenario and the echo chamber effect of COVID-19 vaccine videos on YouTube. A total of 1,646 videos with comments and replies were identified. An approach combining topic modeling, sentiment analysis, and social network analysis was employed to explore users' attitude towards COVID-19 vaccines and whether the echo chamber effect existed. The results indicate that, even if the misleading and anti-vaccination videos were removed by the platform, "anti-vaccination"contents still widely appear in the comments. Moreover, the community of "anti-vaccination"users was more homogeneous compared with that of "pro-vaccination"users. The findings of this study advanced theories of echo chamber effect and the network perspective to examine echo chambers. We propose that should be paid more attention ideology echo chamber, compared with exposure echo chamber. © 2022 IEEE.
ABSTRACT
Blockchain based microgrid mechanisms can be designed efficiently to provide uninterrupted power supply and to balance load demands dynamically. In this present work, a conceptual design of a microgrid system is proposed in power system modeling. A blockchain based trading mechanism has been implemented on this system. Various optimization algorithms have been used to maximize economic profit. Finally, the Coronavirus Herd Immunity Optimizer (CHIO) algorithm is described to accommodate the impression that arises for the optimal power flow (OPF) and energy capacity. A case study has been provided to authenticate the performance of this method. The result expresses that the present scheme can largely improve the power dispatch and trading system. © 2022 IEEE.
ABSTRACT
In load frequency control (LFC) study of a large power system, the key concept is control area, which is the segment of the system consisting of strongly interconnected buses, generator buses thereof working in unison. For accurate linearization of load frequency control problem, proper determination of control area is important. In the present work, a novel deterministic method is proposed and formulated to calculate the sharing of load changes by the generators to determine the control areas for LFC study of multimachine systems. This method is applied on a weakly interconnected two-area system and then on the 10-Machine New England Test System for area segmentation of each of the two systems. Furthermore, LFC studies are carried out with proposed Fuzzy Rule-tuned PID controllers (FRT-PID Controllers) for both the systems incorporated with Dish-Stirling Solar thermal system (DSTS) in each area. The scaling factors and the controller gains are optimized using Coronavirus Herd Immunity Optimizer Algorithm (CHIOA). Performance of the proposed FRT-PID controllers is compared with that of the Conventional PID controllers for the LFC studies of the systems. To test effectiveness of the FRT-PID controllers, effect of random step load perturbation (SLP) in load buses located in different areas are considered. © 2023 Elsevier B.V.
ABSTRACT
A new blackbox technique has been presented in this article for model estimation of solid oxide fuel cells (SOFCs) for providing better results. The proposed method is based on a hierarchical radial basis function (HRBF). The presented method is then developed by a new modified metaheuristic called developed coronavirus herd immunity algorithm (DCHIA). The suggested model has been named DCHIA-HRBF. The proposed model is then trained by some data and prepared for identification and prediction. The model is then analyzed and put in comparison with several latest techniques for validation of the efficiency of the technique. It is also verified by the empirical data to prove its validation with the real data. The results show that the best cost for the performance index which is the network error, is achieved by the proposed developed coronavirus herd immunity algorithm with about 119.442, which is satisfying for the considered function and target against the other state-of-the-art methods. As a result, the simulation results specified that the suggested DCHIA-HRBF delivers high effectiveness as an identifier and prediction tool for the SOFCs. © 2023 John Wiley & Sons, Ltd.
ABSTRACT
The world has now looked towards installing more renewable energy sources type distributed generation (DG), such as solar photovoltaic DG (SPVDG), because of its advantages to the environment and the quality of power supply it produces. However, these sources' optimal placement and size are determined before their accommodation in the power distribution system (PDS). This is to avoid an increase in power loss and deviations in the voltage profile. Furthermore, in this article, solar PV is integrated with battery energy storage systems (BESS) to compensate for the shortcomings of SPVDG as well as the reduction in peak demand. This paper presented a novel coronavirus herd immunity optimizer algorithm for the optimal accommodation of SPVDG with BESS in the PDS. The proposed algorithm is centered on the herd immunity approach to combat the COVID-19 virus. The problem formulation is focused on the optimal accommodation of SPVDG and BESS to reduce the power loss and enhance the voltage profile of the PDS. Moreover, voltage limits, maximum current limits, and BESS charge-discharge constraints are validated during the optimization. Moreover, the hourly variation of SPVDG generation and load profile with seasonal impact is examined in this study. IEEE 33 and 69 bus PDSs are tested for the development of the presented work. The suggested algorithm showed its effectiveness and accuracy compared to different optimization techniques. © 2023 TÜBÍTAK.
ABSTRACT
The Reactive Power Reserve (RPR) is a very important indicator for voltage stability and is sensitive to the operating conditions of power systems. Thorough understanding of RPR, specifically Effective Reactive Reserve (ERR) under intermittent Wind Power (WP) and uncertain demand is essential and key focus of this research. Hence, a stochastic multivariate ERR assessment and optimization problem is introduced here. The proposed problem is solved in three stages: modeling of multivariate uncertainty, studying the stochastic behavior of ERR and optimizing ERR. The volatilities associated with WP generation and consumer demand are modeled explicitly, and their probability distribution function is discretized to accommodate structural uncertainty. A combined load modeling approach is introduced and extended further to accommodate multi-variability. The impact of these uncertainties on ERR is assessed thoroughly on modified IEEE 30 and modified Indian 62 bus system. A non-linear dynamic stochastic optimization problem is formulated to maximize the expected value of ERR and is solved using ‘Coronavirus Herd Immunity Optimizer (CHIO)'. The impact of the proposed strategy on stability indices like the L-index, Proximity Indicator (PI) are analyzed through various case studies. Further, the effectiveness of the proposed approach is also compared with the existing mean value approach. Additionally, the performance of CHIO is confirmed through exhaustive case studies and comparisons. © 2023 Elsevier B.V.
ABSTRACT
A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE
ABSTRACT
With the growth and development of COVID-19 and its variants, reaching a level of herd immunity is critically important for national security in public health. To deal with COVID-19, the United States has implemented phased plans to distribute COVID-19 vaccines. As of November 2022, over 80% of Americans had received their first shot to guard against COVID-19, and 68.6% were considered fully vaccinated, according to the dataset provided by CDC. However, a significant number of American people still hesitate to receive a shot of the COVID-19 vaccine. This paper aims to demystify COVID-19 vaccine hesitancy by analyzing various socioeconomic characteristics among individuals and communities, including unemployment rate, age groups, median household income, and education level. A multiple regression modeling and data visualization analysis show patterns with an increasing trend of vaccine hesitancy associated with a lower median household income, a younger age group, and a lower education level, which would help policymakers to make policies accordingly to target vaccine support information and remove this hurdle to end the COVID-19 pandemic effectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Purpose: To cope with the COVID-19 pandemic, contact tracing mobile apps (CTMAs) have been developed to trace contact among infected individuals and alert people at risk of infection. To disrupt virus transmission until the majority of the population has been vaccinated, achieving the herd immunity threshold, CTMA continuance usage is essential in managing the COVID-19 pandemic. This study seeks to examine what motivates individuals to continue using CTMAs. Design/methodology/approach: Following the coping theory, this study proposes a research model to examine CTMA continuance usage, conceptualizing opportunity appraisals (perceived usefulness and perceived distress relief), threat appraisals (privacy concerns) and secondary appraisals (perceived response efficacy) as the predictors of individuals' CTMA continuance usage during the pandemic. In the United States, an online survey was administered to 551 respondents. Findings: The results revealed that perceived usefulness and response efficacy motivate CTMA continuance usage, while privacy concerns do not. Originality/value: This study enriches the understanding of CTMA continuance usage during a public health crisis, and it offers practical recommendations for authorities. © 2023, Chenglong Li, Hongxiu Li and Shaoxiong Fu.
ABSTRACT
The permutation flow shop scheduling problem (PFSSP) is well-applied in the industry, which is confirmed to be an NP-Hard optimization problem, and the objective is to find the minimum completion time (makespan). A modified coronavirus herd immunity optimizer (CHIO) with a modified solution update is suggested in this work. Meanwhile, the simulated annealing strategy is used on the updating herd immunity population to prevent trapping on local optima, and an adjusted state mechanism is involved to prevent fast state change/ convergence. Nine instances of different problem scales on the FPSSP dataset of Taillard were tested. The experimental results show that the proposed method can find the optimal solutions for the tested instances, with ARPDs no more than 0.1, indicating that the proposed method can effectively and stably solve the PFSSP. © 2022 IEEE.
ABSTRACT
As COVID-19 spreads globally and generates an unprecedented pandemic, COVID-19 fake news is born and quickly disseminated on the internet. Misinformation and disinformation of COVID-19 can distort public perception of the virus and have a serious negative influence on society. To increase vaccine coverage rates and achieve herd immunity, eliminating fake news becomes an urgent need worldwide. Our research aims at using the Transformer model to implement COVID-19 fake news detection. We use the dataset of COVID-19 fake news, extract features through the embedding method of one hot representation, and construct the transformer model to implement text classification on the binary problem. Then we analyze results through loss curve and confusion matrix and show performance parameters, including accuracy, AUC score, and F1 score. We conclude that the model can achieve an accuracy of 72% for COVID-19 fake news detection. This research provides insight for transformer learning dealing with fake news detection of COVID-19. © 2022 IEEE.
ABSTRACT
Distribued Generations (DG) have economic, financial, and environmental benefits. DG reduces power losses in the distribution system but has a negative impact on the protection devices. In this article, the IEEE 33 bus system will be used and tested by adding up to three DG units using MATLAB/SIMULINK software. the optimization techniques that will be used are Grey Wolf Optimizer, Whale Optimization Algorithm, Genetic Algorithm, and Coronavirus Herd Immunity or COVID-19 optimization techniques to select the optimal site and size of the DG units based on the lowest pay-back period considering the voltage limits and power losses. The paper proposes a modified mutation operator for COVID-19 based on Gaussian and Cauchy mutations to have better performance and lower variance. The proposed algorithm is compared with the other optimization techniques. The proposed algorithm achieved better results, which proved to have competitive performance with state-of-the-art evolutionary algorithms. © 2022 IEEE.
ABSTRACT
Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and cannot be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. First, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Second, the cluster heads (CHs) are selected according to the energy and location factors in the clusters, and a reasonable CH replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of CHs. Finally, a multihop routing mechanism between the CHs and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption, and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9%, and 162.2% compared with IGWO, ACA-LEACH, and DEAL in the monitoring area of $300×300 m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. © 2001-2012 IEEE.
ABSTRACT
The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users' lack of security awareness. By default, several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively. © 2022
ABSTRACT
COVID-19 has a huge impact on the economic and social development of the world. In this paper, a simulation model of virus transmission was developed using the netlogo platform based on the characteristics of humans developing acquired immunity to a virus they once had, and predicted the prospects of achieving herd immunity under two scenarios: no vaccination and vaccination of a certain percentage of the population. The results show that failure to vaccinate leads to a longer duration of the outbreak, as well as an increase in the number of infected individuals and overall mortality. If vaccination is administered, the time to achieve herd immunity will be substantially reduced. © 2022 ACM.
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In an era of depleting fossil fuels and a contaminated environment, legislators, governments, industries, academics, and other energy organizations have focused their attention on renewable energy distributed generation (REDG). REDGs' appropriate size and location should be determined optimally. Since, the operating characteristics of the distribution system (DS) such as losses, voltage profile depends upon placement and sizing of DG in DS. Optimal accommodation includes placement and sizing of PV-DG is implemented using a novel Coronavirus herd immunity optimizer in the present work. This model is aiming to minimize total power loss and improve the voltage profile of the whole DS. Further, the constraints used for this study are voltage limits and current limits. Also, the seasonal load and PV generation variation for a typical year is included during the optimization. The results and performance of the proposed technique have been compared with well-known methods in the literature. The results obtained show the efficacy of the suggested method. © 2022 IEEE.
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This chapter illustrates the SIR model-based exploration of the second wave dynamics of Covid-19 for the same selected regions of India. The chapter explains the model-based variability in the growth trend, lockdown impact, vaccination, herd immunity, etc. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Social media has become a primary source of news, providing a fertile environment for spreading misinformation. Since the outbreak of the COVID-19 pandemic, misleading information related to COVID-19 has been spreading rapidly and widely on social media. Several conspiracy theories have emerged regarding the origin of the COVID-19, potential treatments, and vaccines posing a real threat to the public health of people. Fake news that promotes vaccine hesitancy might jeopardize achieving the levels of vaccination needed to reach herd immunity and end the pandemic. The need for automatic tools that detect COVID-19 related misinformation has encouraged researchers to propose several Machine learning (ML) and Deep Learning (DL). Many datasets have been released since the start of the pandemic, aiming to assess the performance of misinformation detection methods. This survey reviews the datasets that have been released to analyze the related to COVID-19 in general and COVID-19 misinformation detection in particular released in Arabic, English, and other languages. We also provide an overview of the different methods used to detect COVID-19 fake news. In this paper, the terms 'misleading information', 'misinformation', and 'fake news' are used interchangeably. © 2022 IEEE.
ABSTRACT
Large-scale injections of COVID-19 vaccine and formation of herd immunity are currently the most effective way to combat COVID-19 epidemic in the world. During the vaccination process, unexpected security incidents are likely to occur, and the government and relevant emergency departments have the responsibility to deal with these emergent security incidents. This paper proposes an AHP-TOPSIS method, establishes the evaluation index system, determines the index weight combined with AHP, and uses TOPSIS to rank the evaluation objects, so as to evaluate the emergency management ability of the government and relevant departments on the emergency safety events of COVID-19 vaccine. This is of great positive significance for continuously strengthening the emergency management capacity of the government and relevant departments and ensuring people's life, health and safety. © 2022 SPIE