S-Nav: Safety-Aware IoT Navigation Tool for Avoiding COVID-19 Hotspots

In this article, we present a [Formula Omitted]-learning-enabled safe navigation system—S-Nav—that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.

Incremental Donor Approach for Immune Plasma Algorithm on Solving Path Planning Problem of UCAV

Immune Plasma algorithm (IP algorithm or IPA) that models the implementation details of a medical method popularized with the COVID-19 pandemic again known as the immune or convalescent plasma has been introduced recently and used successfully for solving different engineering optimization problems. In this study, incremental donor (ID) approach was first developed for controlling how many donor individuals will be chosen before the treatment of receivers representing the poor solutions of the population and then a promising IPA variant called ID-IPA was developed as a new path planner. For analyzing the contribution of the ID approach on the solving capabilities of the IPA, a set of experimental studies was carried out and results of the ID-IPA were compared with different well-known meta-heuristic algorithms. Comparative studies showed that controlling the incrementation of donor individuals as described in the ID approach increases the qualities of the final solutions and improves the stability of the IP algorithm. © 2022 IEEE.

Data Mining Techniques for Extraction and Analysis of Covid-19 Data

Artificial intelligence has played a crucial role in medical disease diagnosis. In this research, data mining techniques that included deep learning with different scenarios are presented for extraction and analysis of covid-19 data. The energy of the features is implemented and calculated from the CT scan images. A modified meta-heuristic algorithm is introduced and then used in the suggested way to determine the best and most useful features, which are based on how ants behave. Different patients with different problems are investigated and analyzed. Also, the results are compared with other studies. The results of the proposed method show that the proposed method has higher accuracy than other methods. It is concluded from the results that the most crucial features can be concentrated on during feature selection, which lowers the error rate when separating sick from healthy individuals. © 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.

Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation

Lupus nephritis (LN) is one of the most common and serious clinical manifestations of systemic lupus erythe-matosus (SLE), which causes serious damage to the kidneys of patients. To effectively assist the pathological diagnosis of LN, many researchers utilize a scheme combining multi-threshold image segmentation (MIS) with metaheuristic algorithms (MAs) to classify LN. However, traditional MAs-based MIS methods tend to fall into local optima in the segmentation process and find it difficult to obtain the optimal threshold set. Aiming at this problem, this paper proposes an improved water cycle algorithm (SCWCA) and applies it to the MIS method to generate an SCWCA-based MIS method. Besides, this MIS method uses a non-local means 2D histogram to represent the image information and utilizes Renyi's entropy as the fitness function. First, SCWCA adds a sine initialization mechanism (SS) in the initial stage of the original WCA to generate the initial solution to improve the population quality. Second, the covariance matrix adaptation evolution strategy (CMA-ES) is applied in the population location update stage of WCA to mine high-quality population information. To validate the excellent performance of the SCWCA-based MIS method, the comparative experiment between some peers and SCWCA was carried out first. The experimental results show that the solution of SCWCA was closer to the global optimal solution and can effectively deal with the local optimal problems. In addition, the segmentation experiments of the SCWCA-based MIS method and other equivalent methods on LN images showed that the former can obtain higher-quality segmented LN images.

Tracking the Evolution of Clusters in Social Media Streams

Tracking the evolution of clusters in social media streams is becoming increasingly important for many applications, such as early detection and monitoring of natural disasters or pandemics. In contrast to clustering on a static set of data, streaming data clustering does not have a global view of the complete data. The local (or partial) view in a high-speed stream makes clustering a challenging task. In this paper, we propose a novel density peak based algorithm, TStream, for tracking the evolution of clusters and outliers in social media streams, via the evolutionary actions of cluster adjustment, emergence, disappearance, split, and merge. TStream is based on a temporal decay model and text stream summarisation. The decay model captures the decreasing importance of textual documents over time. The stream summarisation compactly represents them with the help of cells (aka micro-clusters) in the memory. We also propose a novel efficient index called shared dependency tree (aka SD-Tree) based on the ideas of density peak and shared dependency. It maintains the dynamic dependency relationships in TStream and thereby improves the overall efficiency. We conduct extensive experiments on five real datasets. TStream outperforms the existing state-of-the-art solutions based on MStream, MStreamF, EDMStream, OSGM, and EStream, in terms of cluster mapping measure (CMM) by up to 17.8%, 18.6%, 6.9%, 16.4%, and 20.1%, respectively. It is also significantly more efficient than MStream, MStreamF, OSGM, and EStream, in terms of response time and throughput.

HEALTH RESOURCE ALLOCATION METHOD AT CORDON SANITAIRE FOR PANDEMIC CONTROL AND PREVENTION

The outbreak of COVID-19 and its variants has profoundly disrupted our normal life. Many local authorities enforced cordon sanitaires for the protection of sensitive areas. Travelers can only cross the cordon after being tested. This paper aims to propose a method to determine the optimal deployment of cordon sanitaires in terms of minimum queueing delay time with available health testing resources. A sequential two-stage model is formulated where the first-stage model describes transportation system equilibrium to predict traffic ows. The second-stage model, a nonlinear integer programming, optimizes health resource allocation along the cordon sanitaire. This optimization aims to minimize the system's total delay time among all entry gates. Note that a stochastic queueing model is used to represent the queueing phenomenon at each entry link. A heuristic algorithm is designed to solve the proposed two-stage model where the Method of Successive Averages (MSA) is adopted for the first-stage model, and a genetic algorithm (GA) with elite strategy is adopted for the second-stage model. An experimental study is conducted to demonstrate the effectiveness of the proposed method and algorithm. The results show that these methods can find a good heuristic solution, and it is not cost-effective for authorities to keep adding health resources after reaching a certain limit. These methods are useful for policymakers to determine the optimal deployment of health resources at cordon sanitaires for pandemic control and prevention. © 2023.

Reliability-Oriented Route Generation Algorithm for Multimodal Transport: A Perspective from Supply Chain Reliability Enhancement

Sudden infectious diseases and other malignant events cause excessive costs in the supply chain, particularly in the transportation sector. This issue, along with the uncertainty of the development of global epidemics and the frequency of extreme natural disaster events, continues to provoke discussion and reflection. However, transport systems involve interactions between different modes, which are further complicated by the reliable coupling of multiple modes. Therefore, for the vital subsystem of the supply chain-multimodal transport, in this paper, a heuristic algorithm considering node topology and transport characteristics in a multimodal transport network (MTN): the Reliability Oriented Routing Algorithm (RORA), is proposed based on the super-network and improved k-shell (IKS) algorithm. An empirical case based on the Yangtze River Delta region of China demonstrates that RORA enables a 16% reduction in the boundary value for route failure and a reduction of about 60.58% in the route cost increase compared to the typical cost-optimal algorithm, which means that RORA results in a more reliable routing solution. The analysis of network reliability also shows that the IKS values of the nodes are positively correlated with the reliability of the MTN, and nodes with different modes may have different transport reliabilities (highest for highways and lowest for inland waterways). These findings inform a reliability-based scheme and network design for multimodal transportation. Practical Applications: Recently, the COVID-19 epidemic and the frequency of natural disasters such as floods have prompted scholars to consider transport reliability. Therefore, efficient and reliable cargo transportation solutions are crucial for the sustainable development of multimodal transport in a country or region. In this paper, a new algorithm is designed to obtain a reliability-oriented optimal routing scheme for multimodal transport. Using actual data from the Yangtze River Delta region of China as an example for experimental analysis, we obtain that: (1) the proposed algorithm is superior in terms of efficiency, accuracy, and route reliability, which means that the new algorithm can quickly find more reliable routing solutions in the event of urban transport infrastructure failures;and (2) highway hubs have the greatest transport reliability. Conversely, inland waterway hubs are the least reliable. The influence of national highways and railways on the multimodal transport system is unbalanced. These findings provide decision support to transport policymakers on reliability. For example, transport investments should be focused on building large infrastructure and increasing transport capacity, strengthening the connectivity of inland waterway hubs to hubs with higher transport advantages, and leveraging the role of large hubs. © 2023 American Society of Civil Engineers.

A hierarchized green supply chain with customer selection, routing, and nearshoring

The COVID-19 pandemic forced upon the world, severe social distancing restrictions, which led to prolonged confinement across populations. The latter directly impacted actors along the supply chain in a variety of industrial sectors (for instance, raw material suppliers, manufacturers, distributors, and customers, among others). Some actors involved had to cease participation altogether due to closures. As a result, the supply chain requires restructuring and its reactivation requires careful consideration. In addition to the pandemic, poor air quality has brought about an environmental crisis in recent years. Primary polluters include greenhouse gas (GHG) emissions caused by manufacturers and distributors. Therefore, this research studies the problem of restructuring a particular multicommodity and hierarchized supply chain. Specifically for companies dealing with situations derived from a reduction in manufacturing capacity and service level in light of the pandemic. In this case, a company (leader) is faced with selecting customers that it will service in pursuit of maximizing profit, all while looking to minimize GHG emissions. The consolidated demand is nearshored once the leader company decides on the customers to be supplied. That is, an order is placed on a company with a lower hierarchy (follower). The follower, in turn, aims to minimize its own manufacturing costs without exceeding the pollution limits imposed by the government. However, its manufacturing plan inevitably pollutes and incurs different costs. In addition, the follower's decisions impact both leader's objective functions. We propose a bi-objective bi-level programming model to study this situation. To solve the problem in reasonable computational time, a heuristic algorithm that takes into account existing asynchrony between leader and follower companies is proposed to approximate the Pareto front. Computational experimentation reveals that the proposed algorithm provides good trade-off solutions, which can reduce GHG emissions by 67% on average without significantly affecting company revenue. Moreover, the algorithm is able to provide solutions for instances of up to 1000 nodes in a competitive computational timeframe. In addition, we discuss the advantages of computing GHG emissions proposed herein. Finally, useful managerial insights are discussed by performing a sensitivity analysis regarding the distribution company's minimum acceptable level of profit. © 2023 Elsevier Ltd

Detecting -Vertex Cuts in Sparse Networks via a Fast Local Search Approach

The k-vertex cut (k-VC) problem belongs to the family of the critical node detection problems, which aims to find a minimum subset of vertices whose removal decomposes a graph into at least k connected components. It is an important NP-hard problem with various real-world applications, e.g., vulnerability assessment, carbon emissions tracking, epidemic control, drug design, emergency response, network security, and social network analysis. In this article, we propose a fast local search (FLS) approach to solve it. It integrates a two-stage vertex exchange strategy based on neighborhood decomposition and cut vertex, and iteratively executes operations of addition and removal during the search. Extensive experiments on both intersection graphs of linear systems and coloring/DIMACS graphs are conducted to evaluate its performance. Empirical results show that it significantly outperforms the state-of-the-art (SOTA) algorithms in terms of both solution quality and computation time in most of the instances. To evaluate its generalization ability, we simply extend it to solve the weighted version of the k-VC problem. FLS also demonstrates its excellent performance. IEEE

SRC: Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients (GFCC) for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network (DHO-ANN). The noisy crowdsourced cough datasets were collected from the public domain. This research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech corpora. The proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures, F1 score, confusion matrix, specificity, and sensitivity parameters. Besides, it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset, COUGHVID. Moreover, the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5% compared to the existing methods. © 2023 CRL Publishing. All rights reserved.

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.

Delivery Strategy Under Epidemic Based on Neural Network and ALNS

Under the background of the continuous spread of covid-19, fresh food delivery platforms need to make decisions on how to incorporate epidemic factors into their delivery strategies. In this paper, considering the factors of large activity range, long path, low efficiency and high risk of delivery staff in reservation-type fresh food delivery, combined with the perspective of delivery platform, a path planning model is constructed. we apply the ALNS algorithm to the proposed model and compares it with other classical heuristic algorithms. The results show that our proposed model can effectively reduce risks and improve delivery efficiency. © 2022 SPIE.

Delivery Strategy Under Epidemic Based on Neural Network and ALNS

Under the background of the continuous spread of covid-19, fresh food delivery platforms need to make decisions on how to incorporate epidemic factors into their delivery strategies. In this paper, considering the factors of large activity range, long path, low efficiency and high risk of delivery staff in reservation-type fresh food delivery, combined with the perspective of delivery platform, a path planning model is constructed. we apply the ALNS algorithm to the proposed model and compares it with other classical heuristic algorithms. The results show that our proposed model can effectively reduce risks and improve delivery efficiency. © 2022 SPIE.

A Solution of Freshness Constraint Order Batching Problem for Fresh Food E-Commerce

Influenced by the Covid-19 pandemic, fresh food e-commerce market in China developed quickly. Efficient solution for Order Batch Problem (OBP) could achieve efficient batching operation and then reduce costs and control risks. However, the OBP model proposed by the previous researches did not consider the characteristics of fresh food products such as the less demand of orders, the large variety of products, perishability of products and etc. Therefore, this paper proposed a model of OBP with freshness constraint of perishable food products, and proposed a two-stage heuristic algorithm to solve the target problem of the model. Our solution could improve the efficiency of the sorting process while ensuring the freshness of food products. © 2022 The authors and IOS Press.

Building a hybridised meta-heuristic optimisation algorithm for efficient cluster analysis

Nature-inspired algorithms are a relatively recent field of meta-heuristics introduced to optimise the process of clustering unlabelled data. In recent years, hybridisation of these algorithms has been pursued to combine the best of multiple algorithms for more efficient clustering and overcoming their drawbacks. In this paper, we discuss a novel hybridisation concept where we combine the exploration and exploitation processes of the vanilla bat and vanilla whale algorithm to develop a hybrid meta-heuristic algorithm. We test this algorithm against the existing vanilla meta-heuristic algorithms, including the vanilla bat and whale algorithm. These tests are performed on several single objective CEC functions to compare convergence speed to the minima coordinates. Additional tests are performed on several real-life and artificial clustering datasets to compare convergence speeds and clustering quality. Finally, we test the hybrid on real-world cases with unlabelled clustering data, namely a credit card fraud detection dataset, and a COVID-19 diagnosis dataset, and end with a discussion on the significance of the work, its limitations and future scope. © 2023 Inderscience Enterprises Ltd.

Risk Propagation Decision-Making for Product and Supply Chain Change Systems Under COVID-19: An Assessment-to-Control Support Scheme

Dynamics in supply chains (SCs) can trigger risks due to the changing and propagating nature. In the context of COVID-19, this article presents an assessment-to-control decision-making support scheme to tackle propagation effect uncertainties of SCs considering product changes. First, a new decision model is proposed for risk warnings, with the potential advantages that: 1) propagation effects can be assessed generally and objectively and 2) permitting control theory to integrate and identify the interrelations between propagation effects. More specifically, the bullwhip effect (BE) with operational and behavioral causes is quantified as cascading amplified inventory fluctuations. The ripple effect (RE) from large-scale supplier disruptions driven by COVID-19 is quantified as increased entropy rates (ERs). Then, the system studied is integrated as a closed-loop control system under provided change control. Moreover, some criteria are derived for the existence of controller gains/decision coefficients to stabilize the closed-loop system with the BE mitigation under the RE. Finally, a mask SC case study under COVID-19 is performed for examining the effectiveness of the proposed scheme. IEEE

Research on Airline Pilot Allocation Based on Safety Considerations

With the continuous impact of COVID-19, the task of the airline changes so rapidly that the pilots' estimation and allocation were needed. In this paper, a mathematical model for the pilots' allocation is established based on the real air fleet to estimate the pilots' demand and to arrange the flight tasks in the airline. This approach serves as an alternative for the human resource management in the air company for the long-term plan. The innovative contributions in this paper include: a) The pilots' allocation with the international long-distance flights and the domain short-distance flights were considered in this paper. b) The minimum number of pilots to finish the flights in an air company was estimated to support the air company's long-term plan. Additionally, a heuristic algorithm is developed to get an approximate resolution to the model with less time. Computational result is approximate to the theoretical results, which proved its effectiveness. © 2022 IEEE.

Multi-Level Image Thresolding Entropy Based Methods for Covid X-Ray Image Segmentation Using FS-AOSMA

The Technology of Image Processing has been incredibly used in many era of application like Medical Diagnosis using Image Segmentation, Face Recognition, HandWriting Analysis using Pattern Recognition. It has created its' own identity and has been fascinating all over the Research studies. Our paper is based on Image Processing called as "MULTI-LEVEL IMAGE THRESOLDING METHODS FOR COVID X-RAY IMAGE SEGMENTATION ". The mirror of the whole paper is summarized in this part. The people life has been affected due to the ongoing commotion due to COVID-19.The Researcher's left no stone unturned to deal out with Corona virus. Many methods had been applied like RT-PCR, CT Scan, Image Segmentation, uses of Meta-heuristic Algorithm: PSO, Cuckoo search, MRFO, MRFO Algorithm, MRFO-OBL, etc. © 2022 IEEE.

AdaBias: An Optimization Method With Bias Correction for Differential Privacy Protection

A continuous increase in privacy attacks has caused the research and application of differential privacy (DP) to gradually increase. We can improve the efficiency of the DP model by Optimizing its parameters significantly. Inspired by the performance of various optimization methods for differential privacy, this paper proposes an improved RDP-AdaBound optimization method with bias correction, which is called "AdaBias", to increase the performance of Renyi differential privacy (RDP). The bias correction is used to realize the learning rate and speed up the convergence by upper and lower bound functions. We evaluate our method on the three datasets by training two different privacy model. We further compare three traditional optimization algorithms, namely, RDP-SGD, RDP-Adagrad, and RDP-Adam. And we use AdaBias to verify the performance of privacy protection on the COVID-19 dataset. Experimental results show that the new variant better implements learning rate adjustment to accommodate updates of noisy gradients. As a result, it can achieve higher accuracy and lower losses with a lower privacy budget, thereby better protecting data privacy.