Proposed Convolution Architecture for Monkeypox Detection using Dermoscopy Images

Monkeypox is a skin disease that spreadsfrom animals to people and then people to people, the class of the monkeypox is zoonotic and its genus are othopoxvirus. There is no special treatment for monkeypox but the monkeypox and smallpox symptoms are almost similar, so the antiviral drug developed for prevent from smallpox virus may be used for monkeypox Infected person, the Prevention of monkeypox is just like COVID-19 proper hand wash, Smallpox vaccine, keep away from infected person, used PPE kits. In this paper Deep learning is use for detection of monkeypox with the help of CNN model, The Original Images contains a total number of 228 images, 102 belongs to the Monkeypox class and the remaining 126 represents the normal. But in deep learning greater amount of data required, data augmentation is also applied on it after this the total number of images are 3192. A variety of optimizers have been used to find out the best result in this paper, a comparison is usedbased on Loss, Accuracy, AUC, F1 score, Validation loss, Validation accuracy, validation AUC, Validation F1 score of each optimizer. after comparing alloptimizer, the Adam optimizer gives the best result its total testing accuracy is 92.21%, total number of epochs used for testing is 100. With the help of deep learning model Doctors are easily detect the monkeypox virus with the single image of infected person. © 2023 IEEE.

Multilevel Thresholding of Grayscale Complex Crop Images using Minimum Cross Entropy

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.

Computerized Face Mask Detection System Using Deep CNN and Transfer Learning

Since the COVID-19 pandemic is on the rise again with hazardous effects in China, it has become very crucial for global individuals and the authorities to avoid spreading of the virus. This research aims to identify algorithms with high accuracy and moderate computing complexity at the same time (although conventional machine learning works on low computation power, we have rather used CNN for our research work as the accuracy of CNN is drastically greater than the former), to identify the proper enforcement of face masks. In order to find the best Neural Network architecture we used many deep CNN Methodologies to solve classification problem in regards of masked and non masked image dataset. In this approach we applied different model architectures, like VGG16, Resnet50, Resnet101 and VGG19, on a large dataset to train on and compared the model on the basis of accuracy in which VGG16 came out to be the best. VGG16 was further tuned with different optimizers to determine the one best fit of the model. VGG16 gave an ideal accuracy of 99.37% with the best fit optimizer over a real life data set. © 2023 IEEE.

Bayesian optimization for artificial neural networks: application to Covid-19 image classification

Training a Convolutional Neural Network (CNN) is a difficult task, especially for deep architectures that estimate a large number of parameters. Advanced optimization algorithms should be used. Indeed, it is one of the most important steps to reduce the error between the ground truth and the model prediction. In this sense, many methods have been proposed to solve the optimization problems. In general, regularization, more specifically, non-smooth regularization, can be used in order to build sparse networks, which make the optimization task difficult. The main aim is to develop a novel optimizer based on Bayesian framework. Promising results are obtained when our optimizer is applied on classification of Covid-19 images. By using the proposed approach, an accuracy rate equal to 94% is obtained surpasses all the competing optimizers that do not exceed an accuracy rate of 86%, and 84% for standard Deep Learning optimizers. © 2022 IEEE.

Dual-stage day-ahead optimized performance of renewable-based microgrids

In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost-effective and efficient network service. Various meta-heuristic algorithms have been proposed to allocate WTGs within microgrids. However, the ability of these optimizers might not be guaranteed with uncertainty loads and wind generations. This paper presents novel meta-heuristic optimizers to mitigate extreme voltage drops and the total costs associated with WTGs allocation within microgrids. Arithmetic optimization algorithm (AOA), coronavirus herd immunity optimizer, and chimp optimization algorithm (ChOA) are proposed to manipulate these aspects. The trialed optimizers are developed and analyzed via Matlab, and fair comparison with the grey wolf optimization, particle swarm optimization, and the mature genetic algorithm are introduced. Numerical results for a large-scale 295-bus system (composed of IEEE 141-bus, IEEE 85-bus, IEEE 69-bus subsystems) results illustrate the AOA and the ChOA outperform the other optimizers in terms of satisfying the objective functions, convergence, and execution time. The voltage profile is substantially improved at all buses with the penetration of the WTG with satisfactory power losses through the transmission lines. Day-ahead is considered generic and efficient in terms of total costs. The AOA records costs of 16.575M$/year with a reduction of 31% compared to particle swarm optimization. © 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

A Hybrid Method for Analyzing the Situation Based on Cumulative Fully Vaccinated and Confirmed Cases of Covid-19 in Malaysia

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters. © 2022 IEEE.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format. Besides, the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network (LSTM-RNN) model for fake news detection and classification. Finally, hunter prey optimization (HPO) algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model. The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets. The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57% and 93.53% on Covid19Fakes and satirical datasets, respectively. © 2023 Tech Science Press. All rights reserved.

Blockchain Based Trading Mechanism in Microgrid using CHIO Algorithm

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.

Determination of control area and design of fuzzy rule-tuned PID controller for LFC of multimachine power system

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.

A novel COVID-19 herd immunity-based optimizer for optimal accommodation of solar PV with battery energy storage systems including variation in load and generation

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.

Comparative Analysis of Swarm Algorithms to Classification of covid19 on X-Rays

The use of Chest radiograph (CXR) images in the examination and monitoring of different lung disorders like infiltration, tuberculosis, pneumonia, atelectasis, and hernia has long been known. The detection of COVID-19 can also be done with CXR images. COVID-19, a virus that results in an infection of the upper respiratory tract and lungs, was initially detected in late 2019 in China's Wuhan province and is considered to majorly damage the airway and, thus, the lungs of people afflicted. From that time, the virus has quickly spread over the world, with the number of mortalities and cases increasing daily. The COVID-19 effects on lung tissue can be monitored via CXR. As a result, This paper provides a comparison regarding k-nearest neighbors (KNN), Support-vector machine (SVM), and Extreme Gradient Boosting (XGboost) classification techniques depending on Harris Hawks optimization algorithm (HHO), Salp swarm optimization algorithm (SSA), Whale optimization algorithm (WOA), and Gray wolf optimizer (GWO) utilized in this domain and utilized for feature selection in the presented work. The dataset used in this analysis consists of 9000 2D X-ray images in Poster anterior chest view, which has been categorized by using valid tests into two categories: 5500 images of Normal lungs and 4044 images of COVID-19 patients. All of the image sizes were set to 200 × 200 pixels. this analysis used several quantitative evaluation metrics like precision, recall, and F1-score. © 2022 IEEE.

COVID 19 Prediction Using Chest X-Ray And CNN Hyperparameter Optimizer

According to mid-June 2020, the abrupt escalation of coronavirus reported widespread fear and crossed 16 million confirmed cases. To fight against this growth, clinical imaging is recommended, and for illustration, X-Ray images can be applied for opinion. This paper categorizes chest X-ray images into three classes- COVID-19 positive, normal, and pneumonia affected. We have used a CNN model for analysis, and hyperparameters are used to train and optimize the CNN layers. Swarm-based artificial intelligent algorithm - Grey Wolf Optimizer algorithm has been used for further analysis. We have tested our proposed methodology, and comparative analysis has been done with two openly accessible dataset containing COVID- 19 affected, pneumonia affected, and normal images. The optimized CNN model features delicacy, insight, values of F1 scores of 97.77, 97.74, 96.24 to 92.86, uniqueness, and perfection, which are better than models at the leading edge of technology. © 2022 IEEE.

Specific mining of COVID-19 reviews using deep learning methods

Sentiment classification is a valid measure to monitor public opinion on the COVID-19 epidemic. This study provides a significant basis for preventing the spread of adverse public opinion. Firstly, in epidemic texts, we use a convolutional neural network and bidirectional long short-term memory neural network BiLSTM model to classify and analyze the sentiment of the comment texts about the epidemic situation on Weibo. Secondly, embedded in the model layer to generate adversarial samples and extract semantics. Then, semantic information is weighted using the attention mechanism. Finally, the RMS optimizer is used to update the neural network weights iteratively. According to comparative experiments, the experimental results show that such four evaluation metrics as accuracy, precision, recall, and f1-score with our proposed model have obtained better classification performance. © 2022 IEEE.

Assessment of effective reactive power reserve in power system networks under uncertainty applying coronavirus herd immunity optimizer (CHIO) for operation simulation

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.

CNN-OSBO Encoder-Decoder Architecture for Drug-Target Interaction (DTI) Prediction of Covid-19 Targets

Drug Target Interaction (DTI) prediction is an important factor is drug discovery and repositioning (DDR) since it detects the response of a drug over a target protein. The Coronavirus disease 2019 (COVID-19) disease created groups of deadly pneumonia with clinical appearance mostly similar to SARS-CoV. The precise diagnosis of COVID-19 clinical outcome is more challenging, since the diseases has various forms with varying structures. So predicting the interactions between various drugs with the SARS-CoV target protein is very crucial need in these days, which may leads to discovery of new drugs for the deadly disease. Recently, Deep learning (DL) techniques have been applied by the researches for DTI prediction. Since CNN is one of the major DL models which has the ability to create predictive feature vectors or embeddings, CNN-OSBO encoder-decoder architecture for DTI prediction of Covid-19 targets has been designed Given the input drug and Covid-19 target pair of data, they are fed into the Convolution Neural Networks (CNN) with Opposition based Satin Bowerbird Optimizer (OSBO) encoder modules, separately. Here OSBO is utilized for regulating the hyper parameters (HPs) of CNN layers. Both the encoded data are then embedded to create a binding module. Finally the CNN Decoder module predicts the interaction of drugs over the Covid-19 targets by returning an affinity or interaction score. Experimental results state that DTI prediction using CNN+OSBO achieves better accuracy results when compared with the existing techniques. © 2022 IEEE.

Transfer learning-based CNN diagnostic framework for diagnosis of COVID-19 from lung CT images

COVID-19 is an infectious disease that affects the respiratory system. To assist the physician in diagnosing lung disorders from chest CT images various systems have been developed and used. Detection of COVID-19 remains a challenging area of research. The objective of the work is to develop an inductive parameter-transfer learning-based approach for the prediction of COVID-19, pneumonia, from lung CT images. Our proposed approach is built on layer wise and convolution block-wise fine-tuning which designs the CNN architecture highly specific to lung CT image. We implemented the DenseNet201, InceptionV3, Xception, VGG19, and ResNet50 as baseline models. The network architectures are developed to learn feature representation of lung CT images. For the experimental analysis, five datasets are used. From the experimental results, it is inferred that the DenseNet201 model yields higher accuracy of 0.94 for Adam optimizer and 0.93 for the RMSprop optimizer compared to other models. © 2023 The Royal Photographic Society.

Modified Coronavirus Herd Immunity Optimizer for Permutation Scheduling Problems

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.

Chaotic Binary Ant Lion Optimizer Approach for Feature Selection on Medical Datasets with COVID-19 Case Study

Binary version of the ant lion optimizer (ALO) are suggested and utilized in wrapper-mode to pick the best feature subset for classification. ALO is a recently developed bio-inspired optimization approach that mimics ant lion hunting behavior. Furthermore, ALO balances exploration and exploitation utilizing a unique operator to explore the space of solutions adaptively for the best solution. The difficulties of a plethora of noisy, irrelevant, and misleading features, as well as the capacity to deal with incorrect and inconsistent data in real-world subjects, provide rationale for feature selection to become one of the most important requirements. A difficult machine learning problem is to choose a subset of important characteristics from a vast number of features that characterize a dataset. Choosing the most informative markers and conducting a high-accuracy classification across the data may be a difficult process, especially if the data is complex. The feature selection task is usually expressed as a bio-objective optimization challenge, with the goal of enhancing the performance of the prediction model (data training fitting quality) while decreasing the number of features used. Various evaluation criteria are employed to determine the success of the suggested approach. The findings show that the suggested chaotic binary algorithm can explore the feature space for optimum feature set efficiently. © 2022 IEEE.

Multihop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

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.

Intelligent IoT-BOTNET attack detection model with optimized hybrid classification model

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