HELPHED: Hybrid Ensemble Learning PHishing Email Detection

Phishing email attack is a dominant cyber-criminal strategy for decades. Despite its longevity, it has evolved during the COVID-19 pandemic, indicating that adversaries exploit critical situations to lure victims. Plenty of detectors have been proposed over the years, which mainly focus on the contents or the textual information of emails;however, to cope with the evolution of phishing emails more sophisticated approaches should be introduced that will exploit all the emails' traits to enhance the detection capability of Machine Learning/Deep Learning classifiers. To tackle the limitations of existing works, this paper proposes a phishing email detection methodology, named HELPHED that focuses on the detection of phishing emails by combining Ensemble Learning methods with hybrid features. The hybrid features provide an accurate representation of emails by fusing their content and textual traits. We propose two methods of HELPHED, the first one employs the Stacking Ensemble Learning method, while the second method utilizes the Soft Voting Ensemble Learning. Both methods deploy two different Machine Learning algorithms to handle the hybrid features separately, yet in parallel, minimizing the features' complexity and improving the model's performance. A thorough evaluation analysis is carried out considering innovative guidelines that aim to prevent partial and misleading results. Experimental tests verified that the combination of hybrid features with Ensemble Learning, overall, accomplishes better detection performance than when employing only content-based or text-based features. Numerical results on a rich imbalanced dataset (i.e., 32,051 benign and 3,460 phishing email samples) that considers the evolution of phishing emails show that Soft Voting Ensemble Learning outperforms other prominent Machine Learning/Deep Learning algorithms and existing works yielding F1-score equal to 0.9942. © 2022 Elsevier Ltd

Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents a new quantum-inspired differential evolution with explainable artificial intelligence based COVID-19 Detection and Classification (QIDEXAI-CDC) model for HIoT systems. The QIDEXAI-CDC model aims to identify the occurrence of COVID-19 using the XAI tools on HIoT systems. The QIDEXAI-CDC model primarily uses bilateral filtering (BF) as a preprocessing tool to eradicate the noise. In addition, RetinaNet is applied for the generation of useful feature vectors from radiological images. For COVID-19 detection and classification, quantum-inspired differential evolution (QIDE) with kernel extreme learning machine (KELM) model is utilized. The utilization of the QIDE algorithm helps to appropriately choose the weight and bias values of the KELM model. In order to report the enhanced COVID-19 detection outcomes of the QIDEXAI-CDC model, a wide range of simulations was carried out. Extensive comparative studies reported the supremacy of the QIDEXAI-CDC model over the recent approaches. © 2023 Authors. All rights reserved.

Detection of COVID-19 Infection Using Convolutional Neural Network

Coronavirus disease (COVID-19) is a newly discovered viral sickness that can be fatal. The majority of patients will experience mild to severe respiratory problems and will improve without need for special treatment. Persons over 65, and for those who are underlying medical disorders such cardiovascular disease, asthma, respiratory illness, and cancer, are more prone for developing severe symptoms. In these conditions, 3D volumetric imaging has proven to be a useful technique for COVID-19 patient diagnosis and prognosis. We present a new approach for detecting and classifying COVID-19 infection using 3D volumetric lung imaging in this work. For the detection and classification process, we have used 3D volumetric image processing and deep learning techniques, respectively. Early recognition and finding are basic elements to stop COVID-19 spreading. Various profound learning-based approaches had been proposed for COVID-19 separating CT examines as an instrument to computerize and assist with finding. These methods suffer with at least one of the faults listed below: (i) They treat each CT scan individually (ii) These methods are trained and tested on the same dataset. To address these two challenges, we present an accurate deep learning technique for COVID-19 screening using a democratic framework in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A systematic survey on multimodal emotion recognition using learning algorithms

Emotion recognition is the process to detect, evaluate, interpret, and respond to people's emotional states and emotions, ranging from happiness to fear to humiliation. The COVID- 19 epidemic has provided new and essential impetus for emotion recognition research. The numerous feelings and thoughts shared and posted on social networking sites throughout the COVID-19 outbreak mirrored the general public's mental health. To better comprehend the existing ecology of applied emotion recognition, this work presents an overview of different emotion acquisition tools that are readily available and provide high recognition accuracy. It also compares the most widely used emotion recognition datasets. Finally, it discusses various machine and deep learning classifiers that can be employed to acquire high level features for classification. Different data fusion methods are also explained in detail highlighting their benefits and limitations. © 2022 The Author(s)

Sentiment Analysis Using CatBoost Algorithm on COVID-19 Tweets

Sentimental analysis is a study of emotions or analysis of text as an approach to machine learning. It is the most well-known message characterization device that investigates an approaching message and tells whether the fundamental feeling is positive or negative. Sentimental analysis is best when utilized as an instrument to resolve the predominant difficulties while solving a problem. Our main objective is to identify the emotional tone and classify the tweets on COVID-19 data. This paper represents an approach that is evaluated using an algorithm namely—CatBoost and measures the effectiveness of the model. We have performed a comparative study on various machine learning algorithms and illustrated the performance metrics using a Bar-graph. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Exploring the Effect of Social Media and Spatial Characteristics During the COVID-19 Pandemic in China

The declaration of COVID-19 as a pandemic has largely amplified the spread of related information on social platforms, such as Twitter, Facebook and WeChat. In this work, we investigate how the disease and information co-evolve in the population. We focus on COVID-19 and its information during the period when the disease was widely spread in China, i.e., from January 25th to March 24th, 2020. The co-evolution between disease and information is explored via the spatial analysis of the two spreading processes. We visualize the geo-location of both disease and information at the province level and find that disease is more geo-localized compared to information. High correlation between disease and information data is observed, and also people care about the spread of disease only when it comes to their neighborhood. Regard to the content of the information, we obtain that positive messages are more negatively correlated with the disease compared to negative and neutral messages. Additionally, two machine learning algorithms, i.e., linear regression and random forest, are introduced to further predict the number of infected using characteristics, such as disease spatial related and information-related features. We obtain that both the disease spatial related characteristics of nearby cities and information-related characteristics can help to improve the prediction accuracy. The methodology proposed in this paper may shed light on new clues of emerging infections prediction. © 2013 IEEE.

Can NeuroIS improve executive employee recruitment? Classifying levels of executive functions using resting state EEG and data science methods

Based on the assumption that the success of an organization is largely determined by the knowledge and skills of its employees, human resource (HR) departments invest considerable resources in the employee recruitment process with the aim of selecting the best, most suitable employees. Due to the high cost of the recruitment process along with its high rate of uncertainty, HR recruiters utilize a variety of methods and instruments to improve the efficiency and effectiveness of this process. Thus far, however, neurological methods, in which neurobiological signals from an examined person are analyzed, have not been utilized for this purpose. This study is the first to propose a neuro-based decision support system to classify cognitive functions into levels, whose target is to enrich the information and indications regarding the candidate along the employee recruitment processes. We first measured relevant functional and cognitive abilities of 142 adult participants using traditional computer-based assessment, which included a battery of four tests regarding executive functions and intelligence score, consistent with actual recruitment processes. Second, using electroencephalogram (EEG) technology, which is one of the dominant measurement tools in NeuroIS research, we collected the participants' brain signals by administering a resting state EEG (rsEEG) on each participant. Finally, using advanced machine and deep learning algorithms, we leveraged the collected rsEEG to classify participants' levels of executive functions and intelligence score. Our empirical analyses show encouraging results of up to 72.6% accuracy for the executive functions and up to 71.2% accuracy for the intelligence score. Therefore, this study lays the groundwork for a novel, generic (non-stimuli based) system that supports the current employee recruitment processes, that is based on psychological theories of assessing executive functions. The proposed decision support system could contribute to the development of additional medium of assessing employees remotely which is especially relevant in the current Covid-19 pandemic. While our method aims at classification rather than at explanation, our intriguing findings have the potential to push forward NeuroIS research and practice. © 2023 Elsevier B.V.

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.

Optimized speaker change detection approach for speaker segmentation towards speaker diarization based on deep learning

Speaker diarization is the partitioning of an audio source stream into homogeneous segments according to the speaker's identity. It can improve the readability of an automatic speech transcription by segmenting the audio stream into speaker turns and identifying the speaker's true identity when used in combination with speaker recognition systems. Generally, the automatic speaker diarization is done based on two phases, like the transformation of audio segments into feature representation and the clustering. In this paper, clustering along with a hybrid optimization technique is carried out for performing the speaker diarization. For that, the extracted features from the audio signal is processed under speech activity prediction in order to identify the speak segments. The diarization process is done by Deep Embedded Clustering (DEC) in which the constants are trained by the developed Fractional Anticorona Whale Optimization Algorithm (FrACWOA). The FrACWOA is a hybrid optimization technique, which is designed by adapting the concept of fractional theory, precaution behaviour of COVID-19 and hunting performance of whales. DEC performs the diarization, which concurrently learns the representation of features as well as cluster assignments with neural networks. Using a mapping from the information space to a lower-dimensional feature space, DEC repeatedly discovers the most effective solution for a clustering objective. On the basis of testing accuracy, diarization error, false discovery rate (FDR), false negative rate (FNR), and false positive rate (FPR) of 0.902, 0.627, 0.276, 0.117, and 0.118, respectively, the developed FrACWOA+DEC algorithm performed much better with six speakers using the EenaduPrathidwani dataset. Comparing the accuracy of the proposed method to existing approaches such as Active learning, DE+K-means, LSTM, MCGAN, ANN-ABC-LA, and ACWOA+DFC, the accuracy of the proposed method is 12.97%, 10.31%, 9.75%, 7.53%, 4.32%, and 2.106% higher when using 6 speakers. © 2022 Elsevier B.V.

Jump forecasting in foreign exchange markets: A high-frequency analysis

Using tick data for 14 emerging and developed market currencies covering the period from January 2018 until April 2021, we first detect jumps by Lee and Mykland methodology then apply various machine learning algorithms to forecast out of sample jump occurrences and their direction. Our results show that the arrival and the direction of intraday jumps in the foreign exchange market can be predicted with these algorithms combined with liquidity metrics and technical indicators, even for the Covid pandemic period where volatility in the foreign exchange market is very high. Among all the methods considered, multilayer perceptron has the highest average accuracy for jump prediction overall, followed by support vector machine and random forest methodologies with slightly less average accuracy results. Results are robust to alternative sampling schemes. Accordingly, central bankers can adjust liquidity injection timing with these jump prediction models in the foreign exchange markets where they can try to minimize jump strength if not completely eliminate its occurrence. For investors, having information regarding jump occurrence timings gives an opportunity to hedge against foreign exchange risks more efficiently. © 2023 John Wiley & Sons Ltd.

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019 (COVID -19) is the current global buzzword, putting the world at risk. The pandemic's exponential expansion of infected COVID-19 patients has challenged the medical field's resources, which are already few. Even established nations would not be in a perfect position to manage this epidemic correctly, leaving emerging countries and countries that have not yet begun to grow to address the problem. These problems can be solved by using machine learning models in a realistic way, such as by using computer-aided images during medical examinations. These models help predict the effects of the disease outbreak and help detect the effects in the coming days. In this paper, Multi-Features Decease Analysis (MFDA) is used with different ensemble classifiers to diagnose the disease's impact with the help of Computed Tomography (CT) scan images. There are various features associated with chest CT images, which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia. The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results. The model's performance is assessed using Receiver Operating Characteristic (ROC) curve, the Root Mean Square Error (RMSE), and the Confusion Matrix. It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient. © 2023 CRL Publishing. All rights reserved.

COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well. Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world. However, with the advancement of technology, the Internet of Things (IoT) and social IoT (SIoT), the versatile data produced by smart devices helped a lot in overcoming this lethal disease. Data mining is a technique that could be used for extracting useful information from massive data. In this study, we used five supervised ML strategies for creating a model to analyze and forecast the existence of COVID-19 using the Kaggle dataset” COVID-19 Symptoms and Presence.” RapidMiner Studio ML software was used to apply the Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (K-NNs) and Naive Bayes (NB), Integrated Decision Tree (ID3) algorithms. To develop the model, the performance of each model was tested using 10-fold cross-validation and compared to major accuracy measures, Cohan's kappa statistics, properly or mistakenly categorized cases and root means square error. The results demonstrate that DT outperforms other methods, with an accuracy of 98.42% and a root mean square error of 0.11. In the future, a devised model will be highly recommendable and supportive for early prediction/diagnosis of disease by providing different data sets. © 2023 Tech Science Press. All rights reserved.

An Efficient COVID-19 Disease Outbreak Prediction Using BI-SSOA-TMLPNN and ARIMA

Globally, people's health and wealth are affected by the outbreak of the corona virus. It is a virus, which infects from common fever to severe acute respiratory syndrome. It has the potency to transmit from one person to another. It is established that this virus spread is augmenting speedily devoid of any symptoms. Therefore, the prediction of this outbreak situation with mathematical modelling is highly significant along with necessary. To produce informed decisions along with to adopt pertinent control measures, a number of outbreak prediction methodologies for COVID-19 are being utilized by officials worldwide. An effectual COVID-19 outbreaks' prediction by employing Squirrel Search Optimization Algorithm centric Tanh Multi-Layer Perceptron Neural Network (MLPNN) (SSOA-TMLPNN) along with Auto-Regressive Integrated Moving Average (ARIMA) methodologies is proposed here. Initially, from the openly accessible sources, the input time series COVID-19 data are amassed. Then, pre-processing is performed for better classification outcomes after collecting the data. Next, by utilizing Sine-centered Empirical Mode Decomposition (S-EMD) methodology, the data decomposition is executed. Subsequently, the data are input to the Brownian motion Intense (BI) - SSOA-TMLPNN classifier. In this, the diseased, recovered, and death cases in the country are classified. After that, regarding the time-series data, the corona-virus's future outbreak is predicted by employing ARIMA. Afterwards, data visualization is conducted. Lastly, to evaluate the proposed model's efficacy, its outcomes are analogized with certain prevailing methodologies. The obtained outcomes revealed that the proposed methodology surpassed the other existing methodologies. © 2023 World Scientific Publishing Company.

Feedstock planning and optimization of a sustainable distributed configuration biorefinery for biojet fuel production via ATJ process

In recent years, the production and consumption of fossil jet fuel have increased as a consequence of a rise in the number of passengers and goods transported by air. Despite the low demand caused by the coronavirus 2019 pandemic, an increase in the services offered by the sector is expected again. In an economic context still dependent on scarce oil, this represents a problem. There is also a problem arising from the fuel's environmental impact throughout its life cycle. Given this, a promising solution is the use of biojet fuel as renewable aviation fuel. In a circular economy framework, the use of lignocellulosic biomass in the form of sugar-rich crop residues allows the production of alcohols necessary to obtain biojet fuel. The tools provided by process intensification also make it possible to design a sustainable process with low environmental impact and capable of achieving energy savings. The goal of this work was to design an intensified process to produce biojet fuel from Mexican lignocellulosic biomass, with alcohols as intermediates. The process was modeled following a sequence of pretreatment/hydrolysis/fermentation/purification for the biomass-ethanol process, and dehydration/oligomerization/hydrogenation/distillation for ethanol-biojet process under the concept of distributed configuration. To obtain a cleaner, greener, and cheaper process, the purification zone of ethanol was intensified by employing a vapor side stream distillation column and a dividing wall column. Once designed, the entire process was optimized by employing the stochastic method of differential evolution with a tabu list to minimize the total annual cost and with the Eco-indicator-99 to evaluate the sustainability of the process. The results show that savings of 5.56% and a reduction of 1.72% in Eco-indicator-99 were achieved with a vapor side stream column in comparison with conventional distillation. On the other hand, with a dividing wall column, savings of 5.02% and reductions of 2.92% in Eco-indicator-99 were achieved. This process is capable of meeting a demand greater than 266 million liters of biojet fuel per year. However, the calculated sale price indicates that this biojet fuel still does not compete with conventional jet fuel produced in Mexico. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach

The growth of the "Internet of Medical Things (IoMT)” allows for the collection and processing of data in healthcare systems. At the same time, it is challenging to study the requirements of public health prevention. Here, mask-wearing is considered an efficient preventive measure for avoiding virus transfer. Hence, it is necessary to implement an automated mask identification model to prevent public epidemics. The main scope of the proposed method is to design a face mask detection model with IoT using a "Single Shot Multi-box Detector (SSD)” and a hybrid deep learning method. The novelty of the proposed model is that the enhancement made in the face detection and face classification with the developed ASMFO by optimizing the parameters like the threshold in SSD, steps per execution in ResNet, and learning rate in MobileNet, which makes it more efficient and to perform better the conventional models. Here, the parameter optimization is carried out using a hybrid optimization algorithm named Adaptive Sailfish Moth Flame Optimization (ASMFO). Then, the detected face images are given to the hybrid approach named Hybrid ResMobileNet (HResMobileNet)-based classification, where the parameters are tuned using the same ASMFO algorithm for achieving accurate mask detection results. However, the suggested mask identification model with IoT based on three standard datasets is compared with the conventional meta-heuristic algorithms and existing classifiers with various measures. Thus, the experimental analysis is conducted to analyze the effectiveness of the proposed framework over different meta-heuristic algorithms and existing classifiers. The implemented ASMFO-HResMobileNet provides 18.57%, 15.67%, 17.56%, 16.24%, and 19.2% elevated accuracy than SVM, CNN, VGG16-LSTM, ResNet 50, MobileNetv2, and ResNet 50-MobileNetv2. © 2022 Elsevier B.V.

Lung disease detection using Self-Attention Generative Adversarial Capsule network optimized with sun flower Optimization Algorithm

Lung cancer is the uncontrolled growth of abnormal cells in one or both lungs. This is one of the dangerous diseases. A lot of feature extraction with classification methods were discussed previously regarding this disease, but none of the methods give sufficient results, not only that, those methods have high over fitting problem, as a result, the detection accuracy was minimizing. Therefore, to overcome these issues, a Lung Disease Detection using Self-Attention Generative Adversarial Capsule Network optimized with Sun flower Optimization Algorithm (SA-Caps GAN-SFOA-LDC) is proposed in this manuscript. Initially, NIH chest X-ray image dataset is gathered through Kaggle repository to diagnose the lung disease. Then, the chests X-ray images are pre-processed by using the contrast limited adaptive histogram equalization (CLAHE) filtering method to eliminate the noise and to enhance the image quality. These pre-processed outputs are fed to feature extraction process. In the feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Self-Attention based Generative Adversarial Capsule classifier for detecting the lung disease. The hyper parameters of SA-Caps GAN classifier is optimized using Sun flower Optimization Algorithm. The simulation is implemented in MATLAB. The proposed SA-Caps GAN-SFOA-LDC method attains higher accuracy 21.05%, 33.28%, 30.27%, 29.68%, 32.57% and 44.28%, Higher Precision 30.24%, 35.68%, 32.08%, 41.27%, 28.57% and 34.20%, Higher F-Score 32.05%, 31.05%, 36.24%, 30.27%, 37.59% and 22.05% analyzed with the existing methods, SVM-SMO-LDC, CNN-MOSHO-LDC, XGboost-PSO-LDC respectively. © 2022 Elsevier Ltd

Hybrid Approach to Predict the Death Rate of COVID-19 Patients

From closedown of December 2019, coronavirus has directly exhibited a lofty rate of transmission, coercing the World Health Organization to contend in the month of March 2020 that this unbeknownst coronavirus can be depicted as a pandemic. COVID-19 epidemic has guided to an operatic misplacement of deathly life over the public and presents an unbeknownst complaint to public fitness. It also affects the food systems of the person and the world of work. Once the person is infected by COVID, the metabolic exertion of vulnerable cells in his or her body is enhanced, similar as the one driven by COVID-19. The country's dietary habits are analyzed to predict the particular person's death rate. By using KNN algorithm, the performance metrics such as accuracy, precision, recall, and F1 score are evaluated for the country's dietary habits. In this research, both clustering and classification are combined to increase the accuracy of the prediction of death rate of the person. K-means is used for the clustering of the countries, and KNN is used for classifying the countries. The 170 countries are clustered based on the country's dietary habits, and other disease affected rate using K-means clustering algorithm. Countries are clustered into high and normal death rate countries based on the country's dietary habits and another cluster into high and normal death rate based on the other disease affected rate rather than COVID-19. Using the country's dietary habits and other disease affected clusters, the death rate of the person is predicted. After clustering the data based on the country's dietary habits and other disease affected rate, the KNN algorithm is used to classify and identify the person's death rate. Using clustering and classification algorithms in a combined way, an accuracy of 79% is achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A partial coverage hierarchical location allocation model for health services

We consider a hierarchical maximal covering location problem (HMCLP) to locate health centres and hospitals so that the maximum demand is covered by two levels of services in a successively inclusive hierarchy. We extend the HMCLP by introducing the partial coverage and a new definition of the referral. The proposed model may enable an informed decision on the healthcare system when dynamic adaptation is required, such as a COVID-19 pandemic. We define the referral as coverage of health centres by hospitals. A hospital may also cover demand through referral. The proposed model is solved optimally for small problems. For large problems, we propose a customised genetic algorithm. Computational study shows that the GA performs well, and the partial coverage substantially affects the optimal solutions. © 2023 Inderscience Enterprises Ltd.

Optimal gene prioritization and disease prediction using knowledge based ontology structure

Prioritizing candidate genes is essential for genome-based diagnostics of various hereditary disorders. Furthermore, it is a difficult task with particular and noisy information about genes, illnesses, and relationships. Although several computer methods for disease gene prioritization have been developed, their efficiency is limited by manually created traits, network architecture, or pre-established data fusion criteria. Hence, this research proposes a unique gene prioritization and disease prediction model. Initially, the gathered information is pre-processed by a data cleaning model. In the proposed gene prioritization phase, the pre-processed data are tokenized. Then a new knowledge-based ontology structure is constructed with the improved skewness-based semantic similarity function. The ensemble classifier is constructed along Recurrent Neural Network (RNN), optimized fuzzy logic, and also Deep Belief Network (DBN) to forecast the gene disorders in the prediction phase. The retrieved features from the feature extraction phase are used to train RNN;while the extracted knowledge bases are used to train the DBN, then the results are fed into the optimized fuzzy logic. The fuzzy logic is the primary indication;its fuzzification function is fine-tuned employing a methodology to improve illness prediction accuracy. A recommended new hybrid system, named as Cauchy's Mutated Corona Virus Optimization Algorithm (CMCOA), is the upgraded version of the CVOA, a typical coronavirus optimization technique. Finally, to evaluate the efficiency of the projected model, a comparison of the suggested and existent models is performed with respect to various measures. In particular, the proposed model has recorded the highest accuracy as 93 % at 60 % of training, which is 42.5 %, 36.1 %, 33.3 %, 41.1 %, 48.5 %, 48.5 %, 9 %, 8 %, 8 %, 8 %, 8 %, and 14.5 % improved over existing models like GCN, GCN [6], SVM, CNN, Bi-LSTM, LSTM, GRU, fuzzy, EC + GOA, EC + SSO, EC + CMBO, EC + SMA and EC + CCVOA, respectively. The precision of the suggested work with improved features &CMCOA is 15.5 %, and 14.42 % superior to the proposed work without existing features & CMCOA and proposed work with existing features & CMCOA approaches. © 2022 Elsevier Ltd

Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network

Accurate prediction of domestic waste generation is a challenging task for municipalities to implement sustainable waste management strategies. In the present study, domestic waste generation in the Kingdom of Bahrain, representing a Small Island Developing State (SIDS) case study, has been investigated during successive COVID-19 lockdowns due to the pandemic in 2020. Temporal trends of daily domestic waste generation between 2019 and 2020 and their statistical analyses exhibited remarkable variations highlighting the impact of consecutive COVID-19 lockdowns on domestic waste generation. Machine learning has great potential for predicting solid waste generation rates, but only a few studies utilized deep learning approaches. The state-of-the-art Bidirectional Long Short-Term Memory (BiLSTM) network model as a deep learning method is applied to forecast daily domestic waste data in 2020. Bayesian optimization algorithm (BOA) was hybridized with BiLSTM to generate a super learner approach. The performance of the BOA-BiLSTM super learner model was further compared with the statistical ARIMA model. Performance indicators of the developed models using ARIMA and BiLSTM showed that the latter yielded superior performance for short-term forecasts of domestic waste generation. The MAE, RMSE, MAPE, and R2 were 47.38, 60.73, 256.43, and 0.46, respectively, for the ARIMA model, compared to 3.67, 12.57, 0.24, and 0.96, respectively, for the BiLSTM model. Additionally, the relative errors for the BiLSTM model were lower than those of the ARIMA model. This study highlights that the BiLSTM can be a reliable forecasting tool for solid waste management policymakers during public health emergencies. © 2022 Elsevier B.V.