Biosynthesised AgCl NPs using Bacillus sp. 1/11 and evaluation of their cytotoxic activity and antibacterial and antibiofilm effects on multi‐drug resistant bacteria

Silver nanoparticles (AgNPs) have attracted the attention of researchers due to their properties. Biological synthesis of AgNPs is eco‐friendly and cost‐effective preferred to physical and chemical methods, which utilize environmentally harmful agents and large amounts of energy. Microorganisms have been explored as potential biofactories to synthesize AgNPs. Bacterial NP synthesis is affected by Ag salt concentration, pH, temperature and bacterial species. In this study, Bacillus spp., isolated from soil, were screened for AgNP synthesis at pH 12 with 5 mM Ag nitrate (AgNO₃) final concentration at room temperature. The isolate with fastest color change and the best ultraviolet‐visible spectrum in width and height were chosen as premier one. AgNO₃ and citrate salts were compared in terms of their influence on NP synthesis. Spherical Ag chloride (AgCl) NPs with a size range of 35–40 nm were synthesized in 1.5 mM Ag citrate solution. Fourier transform infrared analysis demonstrated that protein and carbohydrates were capping agents for NPs. In this study, antimicrobial and antitumor properties of the AgNP were investigated. The resulting AgCl NPs had bacteriostatic activity against four standard spp. And multi‐drug resistant strain of Pseudomonas aeruginosa. These NPs are also cytotoxic to cancer cell lines MCF‐7, U87MG and T293.Inspec keywords: silver compounds, nanoparticles, nanomedicine, nanofabrication, particle size, biomedical materials, microorganisms, ultraviolet spectra, visible spectra, Fourier transform infrared spectra, proteins, macromolecules, antibacterial activity, tumours, cancer, cellular biophysics, toxicologyOther keywords: citrate salts, spherical Ag chloride, particle size, Ag citrate solution, Fourier transform infrared analysis, protein, carbohydrates, capping agents, antitumour properties, bacteriostatic activity, Pseudomonas aeruginosa, multidrug resistant strain, cancer cell lines MCF‐7,U87MG, size 35 nm to 40 nm, temperature 293 K to 298 K, AgCl, ultraviolet‐visible spectrum, colour change, room temperature, Ag nitrate final concentration, soil, bacterial species, temperature effect, pH, Ag salt concentration, biofactories, microorganisms, environmentally harmful agents, chemical methods, physical methods, antibacterial properties, electrical properties, mechanical properties, silver nanoparticles, multidrug resistant bacteria, antibiofilm effects, antibacterial effects, cytotoxic activity, Bacillus sp. 1/11, biosynthesised AgCl NPs     

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a two-layered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.     

Geometry and topology optimization of geodesic domes using charged system search

Dome structures provide cost-effective solutions for covering large areas without intermediate supports. In this article, simple procedures are developed to reach the configuration of the geodesic domes. A new definition of dome optimization problems is given which consists of finding optimal sections for elements (size optimization), optimal height for the crown (geometry optimization) and the optimum number of elements (topology optimization) under determined loading conditions. In order to find the optimum design, the recently developed meta-heuristic algorithm, known as the Charged System Search (CSS), is applied to the optimum design of geodesic domes. The CSS takes into account the nonlinear response of the domes. Using CSS, the optimum design of the geodesic domes is efficiently performed.     

Determining malicious executable distinguishing attributes and low-complexity detection

Detection of rapidly evolving malware requires classification techniques that can effectively and efficiently detect zero-day attacks. Such detection is based on a robust model of benign behavior and deviations from that model are used to detect malicious behavior. In this paper we propose a low-complexity host-based technique that uses deviations in static file attributes to detect malicious executables. We first develop simple statistical models of static file attributes derived from the empirical data of thousands of benign executables. Deviations among the attribute models of benign and malware executables are then quantified using information-theoretic (Kullback-Leibler-based) divergence measures. This quantification reveals distinguishing attributes that are considerably divergent between benign and malware executables and therefore can be used for detection. We use the benign models of divergent attributes in cross-correlation and log-likelihood frameworks to classify malicious executables. Our results, using over 4,000 malicious file samples, indicate that the proposed detector provides reasonably high detection accuracy, while having significantly lower complexity than existing detectors.     

Improving minimax disparity model to determine the OWA operator weights

Determining the Ordered Weighted Averaging (OWA) operator weights is important in decision making applications. Several approaches have been proposed in the literature to obtain the associated weights. This paper provides an alternative disparity model to identify the OWA operator weights. The proposed mathematical model extends the existing disparity approaches by minimizing the sum of the deviation between two distinct OWA weights. The proposed disparity model can be used for a preference ranking aggregation. A numerical example in preference ranking and an application in search engines prove the usefulness of the generated OWA weights.     

Congestion prevention in broadband wireless access systems: An economic approach

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in the next generation broadband wireless access systems (BWASs), it is expected that these systems will severely suffer from congestion, due to the rapid increase in demand of bandwidth-intensive multimedia services. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer an immense revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of call admission control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically, hence, ensuring a congestion-free system. The proposed scheme is general and can accommodate different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.     

Role-based access to facilities lifecycle information on RFID tags

Radio Frequency Identification (RFID) based systems have been used in different applications in the Architecture, Engineering, Construction, Owner and Operator (AECOO) industry. Applications are mainly designed for specific lifecycle stage of the facility and serve the needs of only one of the stakeholders. This would increase the cost and the labor for adding and removing tags and eliminate the chance of using shared resources. In this paper, the usage of tags permanently attached to components is proposed where the memory of the tags is used during the lifecycle by different stakeholders for data storage and handover. A Building Information Model (BIM) database is used for tackling the interoperability issues allowing different users to access and share the data. To securely and efficiently store data on RFID tags in ifcXML format, multi-level encryption together with role-based access control is applied on the data stored on RFID tags. Each user is assigned a certain role and can only access the part of data for which he has authorization according to a predefined role and the Access Control Policy. To explore the technical feasibility of the proposed approach, a case study considering both facilities management and emergency management has been implemented and tested at Concordia University.