Abstract Beauty face masks (BFM) are becoming increasingly popular among both men and women since they provide quick refreshment and nurture the skin. Given the wide range of skin types and the chemicals used in their formulation, it can be difficult to find a product that not only complements the skin type but is also free of potentially harmful ingredients that could endanger the consumer's health. When dealing with ambiguous situations, the multi‐attribute decision making (MADM) approach combined with fuzzy set theory is more effective. Type‐2 fuzzy sets (T2FS) provide greater flexibility in dealing with uncertainty in real‐world issues since they are characterised by a main and secondary membership function. In this research, we present the innovative idea of type‐2 linear diophantine fuzzy set (T2LDFS) as an intriguing tool for capturing expert reluctance about an issue. For analysing the discussed problem, a hybrid fuzzy VIKOR enhanced with the proposed fuzzy logic is suggested. A sensitivity and comparative analysis is carried out to establish the validity of the recommended approach.

Trust between sensor nodes is very essential to improve security, dependability, energy efficiency, scalability, and cooperation in Industrial Wireless Sensor Networks (IWSNs). In order to enhance cooperation and security on a large scale in IWSNs, we suggest a Trust-based Integrated Techniques for Anomaly Neutralization (TITAN), where unequal clustering can be used to detect and shrink unacceptable sensor nodes and save resources. Disparate strategic clustering helps in boosting energy efficiency through the creation of smaller clusters close to the sink and larger ones at the distances, therefore encouraging the more tolerable distribution of power and communication encumbrance. Heads of the clusters are dynamically selected depending on the fitness of the nodes within the cluster through a new Optimal Cluster Representative Election Algorithm (OCREA). The fitness of a node is based on its residual power, connection quality, signal strength and distance to the sink. TITAN applies distributed intra-cluster trust to make decisions combining with centralized inter-cluster methodologies, merging attack-resistant trust evaluations and effective trust aggregation. In addition, TITAN applies an appealing dynamic logarithmic trust fund distribution of rewards and sanctions based on the actions of sensor nodes, it is possible to distinguish between reliable and faulty nodes. Also, it includes key indicators of communication trust, data trust, and energy measurement to allow proper trust measurement. TITAN considers a dynamic aging factor and damping factor, which ensures that reliability of sensor nodes is considered on account of recent exchanges, and, therefore, minimizing the influence of old information. To a larger extent, the model incorporates a logarithmic penalty term that punishes the node when the rate of unsuccessful interactions goes up hence effectively isolating with untrustworthy nodes. TITAN enhances better and reliable and robust trust assessment as it incorporates feedback provided by trustworthy neighbor’s nodes and the manipulation of trust levels using an extensive analysis. The combination of these features contributes to the overall performance and improves it’s security of IWSNs, which enables them to be better resistant to attacks and use less resource to run in resource-constrained environments. Due to its communication overhead, trust evaluation and detected malicious nodes, the solution proposed is superior in its capabilities compared to other solutions authenticated with extensive simulations. TITAN manages to recognize the presence of the malicious nodes with 87 percent even when the malicious nodes are less than 60 percent, meaningfully better than such comparative models as SDTS and DTMS. The positive error rate and the negative error rate is minimized with a precision in detection increased to 9% and 6% respectively. TITAN also maintains high packets delivery ratio of above 89% and reduced the average packet loss to only 36 as compared to more than 60 in the baseline schemes. Also, the energy consumption is lowered by about 14% that confirms the effectiveness of TITAN. These findings all indicate the strength and scalability of TITAN Energy-constrained environment performance and threat-prone IWSN environments performance.

Abstract Background The COVID‐19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare infrastructure and accessibility the world over. Consequently, the importance of timely prediction and treatment of the disease to reduce transmission and mortality rates cannot be emphasized enough. Various symptoms of the disease have been identified as it progresses from the time it is contracted. COVID‐19 has been found to internally affect the lungs, and the four progressive stages of the infection can be categorized as mild, moderate, severe, and critical. Therefore, an accurate analysis of the current stage of the disease that can help predict its progression has become critical. X‐ray imaging has been found to be an effective screening procedure for predicting the various stages of this epidemic. Although many different approaches using machine learning, as well as deep learning were utilized to predict and classify diseases in general, till date, such an approach has not been used to predict the various stages of COVID‐19 by using X‐ray imaging to identify and classify those stages. Materials and method The proposed hybrid method used three public datasets for its implementation. In this work, extensive images were used for the purposes of testing and training. The dataset‐1 consists of 1200 COVID‐19 as well as 1200 Non‐COVID‐19 images, while dataset‐2 used 700 COVID‐19 as well as 700 Non‐COVID‐19 images, and finally, dataset‐III utilized 1900 COVID‐19 as well as 1900 Non‐COVID‐19 images for purposes of testing and training. The proposed work undertook the task of pre‐processing using textual and morphological features, while the segmentation and prediction of COVID‐19 as well as Non‐COVID‐19 images were undertaken using VGG‐16 with light GBM for better prediction and handing of huge datasets, and finally, the classification of the various stages of COVID‐19 images was performed using Deep Belief Network. Results The outcomes of the proposed work were subjected to several iterations which were then compared using different parameters such as accuracy, specificity, and sensitivity. In general, the prediction and grouping of the various stages of COVID‐19 by using affected images were found to be 99.2%, 99.4% and 99.5%, respectively. The bacterial pneumonia prediction rates were observed to be 98.5%, 99.4% and 98.3%, respectively. The average classification of the stages were found to be 98.1%, 98.6% and 98.3%, while the combined multi‐classification prediction rates were observed to be 98.6%, 99.1% and 98.7%, respectively.

Over recent years, the Industrial Internet of Things and connectivity of the various sensors on the industrial and automaton front have played a crucial role in the manufacturing process. Production ventures are predominantly represented by Industry 4.0 so produce colossal information. Data outsourcing is one of the ways to manage the overhead of the massive data generated from the various resource-constrained devices utilized in the industrial environment. Therefore, the crowdsourced data from many organizations are outsourced to the cloud system. However, privacy and security challenges such as illegal admittance, data leakage are raised by the outsourced storage. Data authentication is an optimistic approach to establishing the integrity, confidentiality, and authenticity of the data. The certificateless signcryption scheme is most appropriate for lightweight devices established in the industrial ecosystem. In this article, we propose a privacy-conserving, lightweight data aggregation scheme to attain security in an industrial network. In the proposed model, the data owner collects the industrial data from various resource-constrained devices and sends this data to the data aggregator and proficiently data obtained by the industrial data user securely. Particularly, in this article, we propose a proficient certificateless aggregated signcryption scheme, which provides a data aggregation element in comparison to existing schemes. Our proposed scheme includes mutual authentication, public viability, integrity and confidentiality of data, volatile to key escrow, and privacy-preserving aspects for the industrial data. Performance evaluation and result analysis demonstrate that the proposed protocol performs better than other schemes significantly.

An accurate electricity price forecasting (EPF) plays a vital role in the deregulated energy markets and has a specific effect on optimal management of the power system. Considering all the potent factors in determining the electricity prices-some of which have stochastic nature-makes this a cumbersome task. In this article, first, Grey correlation analysis is applied to select the effective parameters in EPF problem and eliminate redundant factors based on low correlation grades. Then, a deep neural network with stacked denoising auto-encoders has been utilized to denoise data sets from different sources individually. After that, to detect the main features of the input data and putting aside the unnecessary features, dimension reduction process is implemented. Finally, the rough structure artificial neural network (ANN) has been executed to forecast the day-ahead electricity price. The proposed method is implemented on the data of Ontario, Canada, and the forecasted results are compared with different structures of ANN, support vector machine, long short-term memory-benchmarking methods in this field-and forecasting data reported by independent electricity system operator (IESO) to evaluate the efficiency of the proposed approach. Furthermore, the results of this article indicate that the proposed method is efficient in terms of reducing error criterion and improves the forecasting error about 5-10 percent in comparison with IESO. This is a remarkable achievement in EPF field.

In this paper, a new formula of fuzzy Caputo fractional-order derivatives (0 <; v ≤ 1) in terms of shifted Chebyshev polynomials is derived. The proposed approach introduces a shifted Chebyshev operational matrix in combination with a shifted Chebyshev tau technique for the numerical solution of linear fuzzy fractional-order differential equations. The main advantage of the proposed approach is that it simplifies the problem alike in solving a system of fuzzy algebraic linear equations. An approximated error bound between the exact solution and the proposed fuzzy solution with respect to the number of fuzzy rules and solution errors is derived. Furthermore, we also discuss the convergence of the proposed method from the fuzzy perspective. Experimentally, we show the strength of the proposed method in solving a variety of fractional differential equation models under uncertainty encountered in engineering and physical phenomena (i.e., viscoelasticity, oscillations, and resistor-capacitor (RC) circuits). Comparisons are also made with solutions obtained by the Laguerre polynomials and the fractional Euler method.

In this paper, we propose a novel approach for the numerical solution of fuzzy fractional differential equations (FFDEs) under fuzzy Caputo-type derivative. More specifically, we first obtain the equivalent integral form of original problem, then the fractional integral equation is approximated using Laplace transforms. Afterwards, we can get the solution by employing any numerical method. Indeed, the proposed approach introduces an efficient and practical way to solve a wide range of fractional models under uncertainty. The most important advantage of this procedure is that the complexity of dealing with the fractional derivative is removed from the calculations, which can reduce the computational costs, considerably. Illustrative examples address the validity and appropriateness of this technique.

The main study of this paper is focused on the solutions of a class of fuzzy sequential fractional differential equations in the form of (0Dxβy)'(x) = b(x)y(x), where (0Dxβy)(x) is the fuzzy Riemann-Liouville derivative of order β ∈ (0, 1). On this subject, a new fuzzy complete metric space is introduced. Finally, we proof the existence and uniqueness of our solution using the contraction principle.

Fractional calculus has a long successful history of 300 years, as it able to model natural phenomena states more accurately than the differential equations of integer order. With this, it plays an important role in variant disciplines. Recently, variant fractional models for the Bloch equations have been proposed, however, effective numerical methods for the fractional Bloch equation (FBE) are still in the infancy stage. In this paper, we extend the time-fractional Bloch equation (TFBE) to fuzzy field under the generalized Caputo differentiability, such that these extensions have natural relationship between crisp. For this purpose, we adopted the fractional Adams-Bashforth-Moulton (FABM) type predictorcorrector method, and introduced a new variant - the fuzzy fractional ADM (FFABM) to find the numerical solution. In this case, a new theorem concerning the error of our proposed FFADM method is also presented. Finally, the capability of the newly developed numerical methods is demonstrated in a fuzzy fractional-order problem, and it achieves satisfactorily in terms of numerical stability.