Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

     

Financial assessment of health and safety programmes in the workplace

In evaluating health and safety improvements for performance improvement, it is necessary to account for both the contributions of a healthy workforce and the resources required supporting it.

The Economic Assessment of the Work Environment (EAWE) is a financial framework that helps management forecast the financial benefits of health and safety implementations. The five-step process comprises (1) a health assessment to identify critical elements in the work environment, (2) an action plan to address gaps, (3) performance targets based on internal goals and external benchmarks, (4) transformation of the expected improvements in health and safety into expected performance outcomes, and (5) implementation in stages, starting from individual jobs to entire organisation.

EAWE offers a dynamic framework for corporate decision-makers when evaluating health and safety programmes. Further research should explore the bounds of EAWE across different types of organisations and the evolution of performance over time.     

Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.     

Effect of bioactive dental adhesive on periodontal and endodontic pathogens

The objectives of this study were to: (1) develop a new bioactive dental bonding agent with nanoparticles of amorphous calcium phosphate and dimethylaminohexadecyl methacrylate for tooth root caries restorations and endodontic applications, and (2) investigate biofilm inhibition by the bioactive bonding agent against eight species of periodontal and endodontic pathogens for the first time. Bonding agent was formulated with 5?% of dimethylaminohexadecyl methacrylate. Nanoparticles of amorphous calcium phosphate at 30?wt% was mixed into adhesive. Eight species of biofilms were grown on resins: Porphyromonas gingivalis, Prevotella intermedia, Prevotella nigrescens, Aggregatibacter actinomycetemcomitans, Fusobacterium nucleatum, Parvimonas micra, Enterococcus faecalis, Enterococcus faecium. Colony-forming units, live/dead assay, biomass, metabolic activity and polysaccharide of biofilms were determined. The results showed that adding dimethylaminohexadecyl methacrylate and nanoparticles of amorphous calcium phosphate into bonding agent did not decrease dentin bond strength (P?>?0.1). Adding dimethylaminohexadecyl methacrylate reduced the colony-forming units of all eight species of biofilms by nearly three orders of magnitude. The killing efficacy of dimethylaminohexadecyl methacrylate resin was: P. gingivalis?>?A. actinomycetemcomitans?>?P. intermedia?>?P. nigrescens?>?F. nucleatum?>?P. micra?>?E. faecalis?>?E. faecium. Dimethylaminohexadecyl methacrylate resin had much less biomass, metabolic activity and polysaccharide of biofilms than those without dimethylaminohexadecyl methacrylate (P?<?0.05). In conclusion, a novel dental adhesive was developed for root caries and endodontic applications, showing potent inhibition of biofilms of eight species of periodontal and endodontic pathogens, and reducing colony-forming units by three orders of magnitude. The bioactive adhesive is promising for tooth root restorations to provide subgingival margins with anti-periodontal pathogen capabilities, and for endodontic sealer applications to combat endodontic biofilms.     

Central Aggregator Intrusion Detection System for Denial of Service Attacks

Vehicle-to-grid technology is an emerging field that allows unused power from Electric Vehicles (EVs) to be used by the smart grid through the central aggregator. Since the central aggregator is connected to the smart grid through a wireless network, it is prone to cyber-attacks that can be detected and mitigated using an intrusion detection system. However, existing intrusion detection systems cannot be used in the vehicle-to-grid network because of the special requirements and characteristics of the vehicle-to-grid network. In this paper, the effect of denial-of-service attacks of malicious electric vehicles on the central aggregator of the vehicle-to-grid network is investigated and an intrusion detection system for the vehicle-to-grid network is proposed. The proposed system, central aggregator–intrusion detection system (CA-IDS), works as a security gateway for EVs to analyze and monitor incoming traffic for possible DoS attacks. EVs are registered with a Central Aggregator (CAG) to exchange authenticated messages, and malicious EVs are added to a blacklist for violating a set of predefined policies to limit their interaction with the CAG. A denial of service (DoS) attack is simulated at CAG in a vehicle-to-grid (V2G) network manipulating various network parameters such as transmission overhead, receiving capacity of destination, average packet size, and channel availability. The proposed system is compared with existing intrusion detection systems using different parameters such as throughput, jitter, and accuracy. The analysis shows that the proposed system has a higher throughput, lower jitter, and higher accuracy as compared to the existing schemes.     

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers.     

Numerical study of asymmetric laminar flow of micropolar fluids in a porous channel

A numerical study is presented for the two-dimensional flow of a micropolar fluid in a porous channel. The channel walls are of different permeability. The fluid motion is superimposed by the large injection at the two walls and is assumed to be steady, laminar and incompressible. The micropolar model due to Eringen is used to describe the working fluid. The governing equations of motion are reduced to a set of non-linear coupled ordinary differential equations (ODEs) in dimensionless form by using an extension of Berman’s similarity transformations. A numerical algorithm based on finite difference discretization is employed to solve these ODEs. The results obtained are further improved by Richardson’s extrapolation for higher order accuracy. Comparisons with the previously published work are performed and are found to be in a good agreement. It has been observed that the velocity and microrotation profiles change from the most asymmetric shape to the symmetric shape across the channel as the parameter R or the permeability parameter A are varied between their extreme values. The results indicate that larger the injection velocity at a wall relative to the other is, smaller will be the shear stress at it than that at the other. The position of viscous layer has been found to be more sensitive to the permeability parameter A than to the parameter R. The micropolar fluids reduce shear stress and increase couple stress at the walls as compared to the Newtonian fluids.