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Due to the COVID-19 epidemic, human civilization and the corporate world confront unfathomable risks to their continued existence on the planet. People all over the world are losing employment or seeing their incomes diminish due to the COVID-19 virus influence. As a result, its influence has a direct effect on the purchasing ability of consumers. When a customer’s purchasing capability is diminished, his desire for items decreases. Again, retailers or suppliers of deteriorative goods face a risk in this uncertain environment, since deteriorative products have a limited shelf life. They should have an appropriate business strategy in this circumstance to maintain their commercial profession. Taking all of these factors into account, an inventory model is created that incorporates the COVID-19 influence on consumers’ demand for perishable commodities with preservation capability. Customers are granted a partial trade credit term to enable them to sell the items on time. The proposed model is very intricate and not amenable to an analytical solution. Two numerical cases are explored and solved to demonstrate the proposed model’s practical usefulness. The eigenvalues of that numerical example are determined to demonstrate that the Hessian matrix is positive definite, i.e., to determine the average cost function’s convexity. Finally, we do a post-optimality study to find out how different inventory characteristics affect the best policies.

In this paper, we study the marginal distribution of concomitants of k-record (KR) values from generalized Farlie–Gumbel–Morgenstern (GFGM) of bivariate distributions. In addition, the joint distribution of concomitants of KR for this family is obtained. Furthermore, some useful recurrence relations between moments of concomitants are derived. In addition, the hazard rate, the reversed hazard rate, and mean residual life functions of concomitants for this family are obtained. Some recent new measures of information, such as weighted extropy, weighted cumulative past extropy, and weighted cumulative residual extropy, are investigated for the concomitant of KR under the GFGM family. A non-parametric estimator of the proposed measure is provided by combining the empirical method with the concurrent use of KR in the GFGM family. Finally, we analyzed real-world data to examine our findings. [ABSTRACT FROM AUTHOR]

Urban logistics implementation causes environmental pollution; therefore, it is necessary to consider the impact on the environment when carrying out such logistics. Electric vehicles are alternative vehicles that reduce the impact on the environment. For this reason, this study investigated which electric vehicle has the best indicators for urban logistics. An innovative approach when selecting such vehicles is the application of a fuzzy–rough method based on expert decision making, whereby the decision-making process is adapted to the decision makers. In this case, two methods of multi-criteria decision making (MCDM) were used: SWARA (stepwise weight assessment ratio analysis) and MARCOS (measurement alternatives and ranking according to compromise solution). By applying the fuzzy–rough approach, uncertainty is included when making a decision, and it is possible to use linguistic values. The results obtained by the fuzzy–rough SWARA method showed that the range and price of electric vehicles have the greatest influence on the selection of an electric delivery vehicle. The results of applying the fuzzy–rough MARCOS method indicated that the Kangoo E-Tech Electric vehicle has the best characteristics according to experts' estimates. These results were confirmed by validation and the application of sensitivity analysis. In urban logistics, the selection of an electric delivery vehicle helps to reduce the impact on the environment. By applying the fuzzy–rough approach, the decision-making problem is adjusted to the preferences of the decision makers who play a major role in purchasing a vehicle. [ABSTRACT FROM AUTHOR]

Material selection is a complex process that involves selecting the best material for a given application. It is a critical process in engineering, and the importance of selecting the right material for the job cannot be overstated. Multi-criteria decision-making (MCDM) is an important tool that can be used to help engineers make informed decisions about material selection. The logistic function can be extended using the soft-max function, which is widely used in stochastic classification methods like neural nets, soft-max extrapolation, linear differential analysis, and Naïve Bayes detectors. This has inspired researchers to develop soft-max-based fuzzy aggregation operators (AOs) for q-rung orthopair fuzzy sets (q-ROPFS) and to propose an MCDM approach based on these AOs. To test the effectiveness of this approach, the researchers applied it to a practical problem using q-rung orthopair fuzzy data and conducted a numerical example to validate the suggested procedures. [ABSTRACT FROM AUTHOR]

Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural products. The monitoring of plant health continuously and detecting the diseases is a significant for sustainable agriculture. Manual system to monitor the diseases in plant is time consuming and report a lot of errors. There is high demand for technology to detect the plant diseases automatically. Recently image processing approach and deep learning approach are highly invited in detection of plant diseases. The diseases like late blight, bacterial spots, spots on Septoria leaf and yellow leaf curved are widely found in plants. These are the main reasons to affects the plants life and yield. To identify the diseases earliest, our research presents the hybrid method by combining the region based convolutional neural network (RCNN) and region based fully convolutional networks (RFCN) for classifying the diseases. First the leaf images of plants are collected and preprocessed to remove noisy data in image. Further data normalization, augmentation and removal of background noises are done. The images are divided as testing and training, training images are fed as input to deep learning architecture. First, we identify the region of interest (RoI) by using selective search. In every region, feature of convolutional neural network (CNN) is extracted independently for further classification. The plants such as tomato, potato and bell pepper are taken for this experiment. The plant input image is analyzed and classify as healthy plant or unhealthy plant. If the image is detected as unhealthy, then type of diseases the plant is affected will be displayed. Our proposed technique achieves 98.5% of accuracy in predicting the plant diseases. [ABSTRACT FROM AUTHOR]

Data management becomes essential component of patient healthcare. Internet of Medical Things (IoMT) performs a wireless communication between E-medical applications and human being. Instead of consulting a doctor in the hospital, patients get health related information remotely from the physician. The main issues in the E-Medical application are lack of safety, security and privacy preservation of patient's health care data. To overcome these issues, this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage. Patients health data is collected from physician, smart devices etc. The main goal is to store this highly valuable health related data in a secure, safety, easy access and less cost-effective manner. In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work (PBFT-PoW). The implementation is done using cloud technology setup with smart contracts (PBFT-PoW). The accuracy rate of PBFT is 90.15%, for PoW is 92.75% and our proposed work PBFT-PoW is 99.88%. [ABSTRACT FROM AUTHOR]

Brain tumor is considered as an unusual cell that presents and grows in the brain. Similarly, it may lead to cancerous or non-cancerous. So, to improve the survival rate of the patient and to give the best treatment at the earliest, it's very necessary for early prediction of tumor. Accurate classification of tumor in the brain is important for improving the diagnosis. In accordance with that, various research programs are invited for the better treatment of the patients. Machine Learning (ML) algorithms are applied to help the health associates for the classification of brain tumor and present their diagnosis. This paper focuses primarily on brain tumors of meningioma, Glioma, and pituitary. Moreover, the manual evaluation of Magnetic Resonance Image (MRI) is a difficult process. For accessing MRI brain image in the aspects of its volume, boundaries, detecting tumor size, shape and classification are the challenging tasks. To overcome these difficulties, this paper proposes a novel approach in feature selection using bat algorithm with Extreme Learning Machine (ELM) and for enhancing the accurate classification by Transfer Learning (BA + ELM-TL). Here the data is pre-processed to remove noises; Stationary Wavelet Transforms (SWT) is used to extract the features from the MRI brain image. This paper has collected the dataset from fig share, whole brain atlas and TCGA-GBM data set. Therefore, it is proved that 92.6% is the accuracy of Bat algorithm, 90.4% for Extreme Learning algorithm and 98.87% for BA + ELM-TL. [ABSTRACT FROM AUTHOR]