Detecting Ransomware Attacks in Cloud Environment Using Machine Learning-Based Intelligence System in COVID-19 Chaos

2020-2022 provided nearly ideal circumstances for cybercriminals, with confusion and uncertainty dominating the planet due to COVID-19. Our way of life was altered by the COVID-19 pandemic, which also sparked a widespread shift to digital media. However, this change also increased people's susceptibility to cybercrime. As a result, taking advantage of the COVID-19 events' exceedingly unusual circumstances, cybercriminals launched widespread Phishing, Identity theft, Spyware, Trojan-horse, and Ransomware attacks. Attackers choose their victims with the intention of stealing their information, money, or both. Therefore, if we wish to safeguard people from these frauds at a time when millions have already fallen into poverty and the remaining are trying to survive, it is imperative that we put an end to these attacks and assailants. This manuscript proposes an intelligence system for identifying ransomware attacks using nature-inspired and machine-learning algorithms. To classify the network traffic in less time and with enhanced accuracy, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two widely used algorithms are coupled in the proposed approach for Feature Selection (FS). Random Forest (RF) approach is used for classification. The system's effectiveness is assessed using the latest ransomware-oriented dataset of CIC-MalMem-2022. The performance is evaluated in terms of accuracy, model building, and testing time and it is found that the proposed method is a suitable solution to detect ransomware attacks. © 2022 IEEE.

In Situ Electrochemical Trapping and Unraveling the Mechanism of the Toxic Intermediate Metabolite N-Acetyl-p-Benzoquinone Imine of the Acetaminophen Drug and Its Biomimetic Mediated NADH Oxidation Reaction

The integrative study of the pharmacokinetics and dynamics of a drug has been of great research interest due to its authentic description of the biomedical and clinical pros and cons. Acetaminophen (N-acetyl-4-aminophenol, AcAP) is a well-known analgesic having a high therapeutic value, including the Covid-19 treatment. However, an overdose of the drug (>200 mg/kg of men) can lead to liver toxicity. An intermediate, N-acetyl-p-benzoquinone imine (NAPQI), metabolite formation has been found to be responsible for the toxicity. For the detection of NAPQI, several ex situ techniques based on electrochemical methods followed by nuclear magnetic resonance, high-performance liquid chromatography, and LC-MS were stated. For the first time, we report an in situ electrochemical approach for AcAP oxidation and NAPQI intermediate (Mw = 149.1 g mol-1) trapping on a graphitic nanomaterial, carbon black (CB)-modified electrode in pH 7 phosphate buffer solution (CB@NAPQI). The NAPQI-trapped electrode exhibited a surface-confined redox peak at E°′ = 0.350 ± 0.05 V vs Ag/AgCl with a surface excess value of 3.52 n mol cm-2. Physicochemical characterizations by scanning electron microscopy, Raman, FTIR, and in situ electrochemical quartz crystal microbalance (EQCM) techniques supported the entrapment of the molecular species. Furthermore, the scanning electrochemical microscopy (SECM) technique has been adopted for surface-mapping the true active site of the NAPQI-trapped electrode. As a biomimetic study, the mediated oxidation reaction of NADH by CB@NAPQI was demonstrated, and the mechanistic and quantitative aspects were studied using cyclic voltammetry, rotating disc electrode, amperometry, and flow injection analysis techniques. © 2023 American Chemical Society.

Computational analysis of a new biomimetic active ventilation paradigm for indoor spaces

Purpose: Ventilation of indoor spaces is required for the delivery of fresh air rich in oxygen and the removal of carbon dioxide, pollutants and other hazardous substances. The COVID-19 pandemic brought the topic of ventilating crowded indoors to the front line of health concerns. This study developed a new biologically inspired concept of biomimetic active ventilation (BAV) for interior environments that mimics the mechanism of human lung ventilation, where internal air is continuously refreshed with the external environment. The purpose of this study is to provide a detailed proof-of-concept of the new BAV paradigm using computational models. Design/methodology/approach: This study developed computational fluid dynamic models of unoccupied rooms with two window openings on one wall and two BAV modules that periodically translate perpendicular to or rotate about the window openings. This study also developed a time-evolving spatial ventilation efficiency metric for exploring the accumulated refreshment of the interior space. The authors conducted two-dimensional (2D) simulations of various BAV configurations to determine the trends in how the working parameters affect the ventilation and to generate initial estimates for the more comprehensive three-dimensional (3D) model. Findings: Simulations of 2D and 3D models of BAV for modules of different shapes and working parameters demonstrated air movements in most of the room with good air exchange between the indoor and outdoor air. This new BAV concept seems to be very efficient and should be further developed. Originality/value: The concept of ventilating interior spaces with periodically moving rigid modules with respect to the window openings is a new BAV paradigm that mimics human respiration. The computational results demonstrated that this new paradigm for interior ventilation is efficient while air velocities are within comfortable limits. © 2023, Emerald Publishing Limited.

Entangling of Peptide Nanofibers Reduces the Invasiveness of SARS-CoV-2.

The viral spike (S) protein on the surface of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to angiotensin-converting enzyme 2 (ACE2) receptors on the host cells, facilitating its entry and infection. Here, functionalized nanofibers targeting the S protein with peptide sequences of IRQFFKK, WVHFYHK and NSGGSVH, which are screened from a high-throughput one-bead one-compound screening strategy, are designed and prepared. The flexible nanofibers support multiple binding sites and efficiently entangle SARS-CoV-2, forming a nanofibrous network that blocks the interaction between the S protein of SARS-CoV-2 and the ACE2 on host cells, and efficiently reduce the invasiveness of SARS-CoV-2. In summary, nanofibers entangling represents a smart nanomedicine for the prevention of SARS-CoV-2.

A novel COVID-19 predictor model inspired by Flower Pollination Algorithm

The COVID-19 outbreak turned the world upside down by infecting hundred million people, killing more than five million and disrupting everyday life across the planet. The Wuhan virus shattered the global economy and brought daily life to a grinding halt in much of the world. The second largest populated country India had no escape as well. Since the very beginning of 20th century, machine learning based methodologies have been largely applied in epidemiological data analysis in order to control diseases and other health issues. In this regard, researchers have come up with various predictor models to forecast the future impact of the Wuhan virus, so that further spreading of virus can be controlled by implementing precautionary measures. The purpose behind this work is to investigate the prediction capability of Legendre Polynomial Neural Network (LEPNN) trained using the very popular bio-inspired Flower Pollination Algorithm on the real data set of three categories of COVID cases in India as well as Odisha. The three types are the confirmed, deceased and recovery cases of daily basis. The prediction performance of the LEPNN-FPA model has been assessed with respect to the performance of two other models. © 2022 IEEE.

Recombinant Proteins for Assembling as Nano- and Micro-Scale Materials for Drug Delivery: A Host Comparative Overview.

By following simple protein engineering steps, recombinant proteins with promising applications in the field of drug delivery can be assembled in the form of functional materials of increasing complexity, either as nanoparticles or nanoparticle-leaking secretory microparticles. Among the suitable strategies for protein assembly, the use of histidine-rich tags in combination with coordinating divalent cations allows the construction of both categories of material out of pure polypeptide samples. Such molecular crosslinking results in chemically homogeneous protein particles with a defined composition, a fact that offers soft regulatory routes towards clinical applications for nanostructured protein-only drugs or for protein-based drug vehicles. Successes in the fabrication and final performance of these materials are expected, irrespective of the protein source. However, this fact has not yet been fully explored and confirmed. By taking the antigenic RBD domain of the SARS-CoV-2 spike glycoprotein as a model building block, we investigated the production of nanoparticles and secretory microparticles out of the versions of recombinant RBD produced by bacteria (Escherichia coli), insect cells (Sf9), and two different mammalian cell lines (namely HEK 293F and Expi293F). Although both functional nanoparticles and secretory microparticles were effectively generated in all cases, the technological and biological idiosyncrasy of each type of cell factory impacted the biophysical properties of the products. Therefore, the selection of a protein biofabrication platform is not irrelevant but instead is a significant factor in the upstream pipeline of protein assembly into supramolecular, complex, and functional materials.

Digital Global Classroom, a Collaborative Online International Learning (COIL) Approach: An Innovative Pedagogical Strategy for Sustainable Competency Development and Dissemination of SDGs in Engineering Higher Education

One of the most popular strategies to develop skills such as collaborative work, critical thinking, and problem-solving is the application of Collaborative Online International Learning (COIL), in which Professors from at least two universities from different countries and cultures develop a period known as "Global Classroom” (GC) in which, through the Challenge-Based Learning (CBL) approach, they solve a real challenge, using digital communication tools. This study held four-week global courses between groups from the Tecnológico de Monterrey in Mexico and groups from the Corporación Universitaria Minuto de Dios in Colombia. The challenges were related to two fundamental issues in sustainability: 1) Management of natural resources and climate change and 2) Biomimetics. Students were able to solve the challenges, develop skills to communicate effectively through online interaction with people from different cultures and disciplines, and use technological tools that facilitate distance learning in multicultural virtual environments. Current teaching models involve active and experiential learning, developing soft and hard skills. The GC experience is a tool that allowed continuity in the preparation of students during the COVID-19 pandemic. The use of GC is available to those interested as a valuable tool to provide students with the opportunity to live sustainable international experiences and promote the Sustainable Development Goals (SDG). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.

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.

A Decision-Making System for Dynamic Scheduling and Routing of Mixed Fleets with Simultaneous Synchronization in Home Health Care

Globally, the growing number of elderly people, chronic disorders and the spread of COVID-19 have all contributed to a significant growth of Home Health Care (HHC) services. One of HHC's main goals is to provide a coordinated set of medical services to individuals in the comfort of their own homes. On the basis of the current demand for HHC services, this paper attempts to develop a novel and effective mathematical model and a suitable decision-making technique for reducing costs associated with HHC service delivery systems. The proposed system of decision making identifies the real needs of HHCs which incorporate dynamic, synchronized services and coordinates routes by a group of caregivers among a mixed fleet of services. Initially, this study models the optimization problem using Mixed Integer Linear Programming (MILP). The Revised Version of the Discrete Firefly Algorithm is designed to address the HHC planning decision-making problem due to its unique properties and its computational complexity. To evaluate the scalability of this proposed approach, random test instances are generated. The results of the experiments revealed that the algorithm performed well even with the different scenarios such as dynamic and synchronized visits. Furthermore, the improved version of nature-inspired solution methodology has proven to be effective and efficient. As a result, the proposed algorithm has significantly reduced costs and time efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Stretchable, self‐healable, and breathable biomimetic iontronics with superior humidity‐sensing performance for wireless respiration monitoring

Stretchable, self‐healing, and breathable skin‐biomimetic‐sensing iontronics play an important role in human physiological signal monitoring and human–computer interaction. However, previous studies have focused on the mimicking of skin tactile sensing (pressure, strain, and temperature), and the development of more functionalities is necessary. To this end, a superior humidity‐sensitive ionic skin is developed based on a self‐healing, stretchable, breathable, and biocompatible polyvinyl alcohol–cellulose nanofibers organohydrogel film, showing a pronounced thickness‐dependent humidity‐sensing performance. The as‐prepared 62.47‐μm‐thick organohydrogel film exhibits a high response (25,000%) to 98% RH, excellent repeatability, and long‐term stability (120 days). Moreover, this ionic skin has excellent resistance to large mechanical deformation and damage, and the worn‐out material can still retain its humidity‐sensing capabilities after self‐repair. Humidity‐sensing mechanism studies show that the induced response is mainly related to the increase of proton mobility and interfacial charge transport efficiency after water adsorption. The superior humidity responsiveness is attributed to the reduced thickness and the increased specific surface area of the organohydrogel film, allowing real‐time recording of physiological signals. Notably, by combining with a self‐designed printed circuit board, a continuous and wireless respiration monitoring system is developed, presenting its great potential in wearable and biomedical electronics.

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.

A Novel Machine Learning Framework for Covid-19 Image Classification with Bio-heuristic Optimization

Due to its rapidly advancing spread, the world is still reeling from COVID-19 (coronavirus 2019), which is categorized as a highly infectious disease. An early diagnosis is very critical in treating COVID-19 patients due to its lethal implications. However, the shortage of X-ray machines has resulted in life-threatening conditions and delays in diagnosis, increasing the number of deaths around the world. Therefore, in order to avoid such fatalities, COVID-19 has to be detected earlier and diagnosed faster using an intelligent computer-aided diagnosis system than with traditional screening programs. We present a novel framework for COVID-19 image categorization in this article that utilizes deep learning (DL) and bio-inspired optimization techniques. A bio-heuristic optimizer algorithm MoFAL is utilized as a feature selector to decrease the dimensionality of the image representation and increase the accuracy of the classification by ensuring that only the most essential selected features are used. Furthermore, the feature extraction is realized using the MobileNetV3 DL model. The experimental results deduced indicate that our proposed approach drastically improves performance in terms of classification accuracy and reduction in dimensions reflected during the period of feature extraction and its phases of selection. We propose that our COVID-Classifier can be deployed in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.

Brownian Motion Fractal Nature Frontiers Within the Matter

One of the main motivations for our research was to find a connection between the Brownian motion of microorganisms within fractal nature, with the idea of developing an appropriate procedure and method to control the microorganism's motion direction and predict the position of the microorganism in time. In this paper, we have followed the results of the very rear microorganism's motion sub-microstructures in the experimental microstructure analysisFractals already observed and published. All of these data have been good basis to describe the motion trajectory by time interval method and fractals. We successfully defined the diagrams in two and three-dimensions and we were able to establish the control of Brownian chaotic motion as a bridge between chaotic disorders to control disorder. This significant study opens a new possibility for future investigation and the new potential of total control of the microorganism motion. These perspectives and findings provide significant data for getting more information from these bio systems. They can also be applied, based on self-similarities and biomimetics, to particle physical systemMatterFractalss and matter, generally. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Internal Virus Detection Framework Based on IoT Semantic Interoperability

Since the outbreak of the COVID-19 pandemic, indoor air quality has become increasingly important. The interdisciplinary grouping of academic majors focused on the pursuit of solutions that identify or prevent the airborne transmission and inhalation, initially of Coronavirus and secondarily of viruses such as influenza. Throughout the research work, we aim to contribute by elaborating the teaching-learning technique to select and identify the optimal attributes of viruses' variants of the indoor atmosphere. The novelty is based on the objective to enable real-time identification of the density of the airborne molecules to prevent virus propagation. Several sensors and systems came into the spotlight by conducting a systematic literature review that, in conjunction with our innovative idea, could construct a revolutionary new solution that could eliminate the risk of exposure to viable viruses. The proposed teaching-learning based attribute selection optimisation is among the most popular bio-inspired meta-heuristic methods. Therefore, evolutionary logic and provocative performance can be widely utilised to solve the aforementioned humanitarian problem. The proposed frame constitutes three pivotal steps: the new update mechanism, the novel method of selecting the principal teacher in the teacher's phase, and the support vector machine method to compute the fitness function of optimisation. © 2022 IEEE.

Bio-Inspired Modelling of Disease Through Delayed Strategies

In 2020, the reported cases were 0.12 million in the six regions to the official report of the World Health Organization (WHO). For most children infected with leprosy, 0.008629 million cases were detected under fifteen. The total infected ratio of the children population is approximately 4.4 million. Due to the COVID-19 pandemic, the awareness programs implementation has been disturbed. Leprosy disease still has a threat and puts people in danger. Nonlinear delayed modeling is critical in various allied sciences, including computational biology, computational chemistry, computational physics, and computational economics, to name a few. The time delay effect in treating leprosy delayed epidemic model is investigated. The whole population is divided into four groups: those who are susceptible, those who have been exposed, those who have been infected, and those who have been vaccinated. The local and global stability of well-known conclusions like the Routh Hurwitz criterion and the Lyapunov function has been proven. The parameters’ sensitivity is also examined. The analytical analysis is supported by computer results that are presented in a variety of ways. The proposed approach in this paper preserves equilibrium points and their stabilities, the existence and uniqueness of solutions, and the computational ease of implementation.

Elucidating the neuropathophysiology of COVID-19 using quantum dot biomimetics of SARS-CoV-2

Quantum dots were encapsulated in polymeric phospholipid micelles conjugated to multiple ligands of SARS-CoV-2 spike protein to form fluorescent biomimetic nanoparticles for SARS-CoV-2 (COVID-QDs). Phosphatidylethanolaminepolyethylene glycol (PE:PEG) was appended with bis(4-methylphenyl)sulfone to form PE:PEG:bis-sulfone and self-assembled into micelles around CdSe/CdS core/shell quantum dots via thin-film rehydration. The introduction of the bis-sulfone group the surface of the micelle-encapsulated quantum dots provides multiple sites for conjugation to his-tagged SARS-CoV-2 spike protein via a bisalkylation mechanism. Based on the eluted unconjugated fraction, we estimate that an average of seven spike proteins are conjugated per COVID-QD. We treated an in-vitro model system for the neurovascular unit (NVU) with these COVID-QD constructs to investigate the COVID-QDs, and by proxy SARS-CoV-2, may modulate the NVU leading to the COVID-19 associated neuropathophysiology. © 2022 SPIE

Ventilation‐induced epithelial injury drives biological onset of lung trauma in vitro and is mitigated with prophylactic anti‐inflammatory therapeutics

Mortality rates among patients suffering from acute respiratory failure remain perplexingly high despite the maintenance of blood oxygen homeostasis during ventilatory support. The biotrauma hypothesis advocates that mechanical forces from invasive ventilation trigger immunological mediators that spread systemically. Yet, how these forces elicit an immune response remains unclear. Here, a biomimetic in vitro three‐dimensional (3D) upper airways model allows to recapitulate lung injury and immune responses induced during invasive mechanical ventilation in neonates. Under such ventilatory support, flow‐induced stresses injure the bronchial epithelium of the intubated airways model and directly modulate epithelial cell inflammatory cytokine secretion associated with pulmonary injury. Fluorescence microscopy and biochemical analyses reveal site‐specific susceptibility to epithelial erosion in airways from jet‐flow impaction and are linked to increases in cell apoptosis and modulated secretions of cytokines IL‐6, ‐8, and ‐10. In an effort to mitigate the onset of biotrauma, prophylactic pharmacological treatment with Montelukast, a leukotriene receptor antagonist, reduces apoptosis and pro‐inflammatory signaling during invasive ventilation of the in vitro model. This 3D airway platform points to a previously overlooked origin of lung injury and showcases translational opportunities in preclinical pulmonary research toward protective therapies and improved protocols for patient care.

COVID-19 inflammation and implications in drug delivery.

Growing evidence indicates that hyperinflammatory syndrome and cytokine storm observed in COVID-19 severe cases are narrowly associated with the disease's poor prognosis. Therefore, targeting the inflammatory pathways seems to be a rational therapeutic strategy against COVID-19. Many anti-inflammatory agents have been proposed; however, most of them suffer from poor bioavailability, instability, short half-life, and undesirable biodistribution resulting in off-target effects. From a pharmaceutical standpoint, the implication of COVID-19 inflammation can be exploited as a therapeutic target and/or a targeting strategy against the pandemic. First, the drug delivery systems can be harnessed to improve the properties of anti-inflammatory agents and deliver them safely and efficiently to their therapeutic targets. Second, the drug carriers can be tailored to develop smart delivery systems able to respond to the microenvironmental stimuli to release the anti-COVID-19 therapeutics in a selective and specific manner. More interestingly, some biosystems can simultaneously repress the hyperinflammation due to their inherent anti-inflammatory potency and endow their drug cargo with a selective delivery to the injured sites.

Smart Data Analytics on COVID-19 Data

Advances in computers, information and networks has brought a digital cyber world to our daily lives. They have generated numerous digital things (or cyber entities), which have resided in the cyber world. Meanwhile, countless real things in the conventional physical, social and mental worlds have possessed cyber mappings (or cyber components) to have a cyber existence in cyber world. Consequently, cyberization has been an emerging trend forming the new cyber world and reforming conventional worlds towards cyber-enabled hyper-worlds. As such, cybermatics helps build systematic knowledge about new phenomena, behaviors, properties and practices in the cyberspace, cyberization and cyber-enabled hyper-worlds. Cybermatics is characterized by catching up with the human intelligence (e.g. intelligent sensing, making decision and control, etc.), as well as learning from the nature-inspired attributes (e.g., dynamics, self-adaptability, energy saving). As a cybermatics technique, smart data analytics helps filter out the noise data and produce valuable data. In this paper, we focus on smart data analytics on health data related to coronavirus disease 2019 (COVID-19). It builds temporal and demographic hierarchies, which capture characteristics of COVID-19 patients, to discover valuable knowledge and information about temporal-demographic characteristics of these patients. Evaluation on real-life COVID-19 epidemiological data demonstrates the practicality of our solution in conducting smart data analytics on COVID-19 data. © 2021 IEEE.