Recently, IoT has greatly influenced our daily lives through various applications. One of the most promising application is smart city that leverages IoT devices to manage cities without any human intervention. The high possibility of sensing and publishing sensitive data in this smart environment leads to three significant issues: (1) privacy-preserving (2) heterogeneity, and (3) real-time services.We observe that current studies are in lack of addressing these challenges. In this paper, we propose a new privacy-preserving architecture for IoT devices in the smart city by leveraging ontology, a data model, at the edge of the network. At first, we propose an ontology that consists of privacy information of devices. Then, we mount a real-time privacy-preserving method on top of it that is achieved by providing a dynamic environment from the privacy-preserving point of view. Based on the simulation results using Protege and Visual Studio on a synthetic dataset, we find that our solution provides privacy at real-time while addressing heterogeneity issue so that many IoT devices can afford it. Thus, our proposed solution can be widely used for smart cities.

Rainfall is a natural demolishing phenomenon. On the other side, it also serves as a major source of water when conserved through proper channel. For this issue, estimation of rain fall is of at utmost importance. The present study employed on rain fall forecasting in annual as well as non-moon session in Odisha (India). The total annual rainfall and relative humidity data were collected from period 1991-2015 from Department of Forest and Environment Govt. of Odisha. Support Vector Regression and Multilayer perception implemented for prediction of maximum rainfall in annual and non-monsoon session. Input parameter like average temperature in month, wind velocity, humidity, and cloud cover was conceder for predicting rainfall in non-monsoon session. The performance of the results was measure with MSE (mean squared error), correlation coefficient, coefficient of efficiency and MAE (mean absolute error). The results of SVR were compared to those of MLP and simple regression technique. MLP being a computationally intensive method, SVR could be used as an efficient alternative for runoff and sediment yield prediction under comparable accuracy in predictions.SVR-MLP may be used as promising alternative forecasting tool for higher accuracy in forecasting and better generalization ability.

Stakeholder satisfaction is the main motive of the software industry. Consumer and producer satisfaction mean, achieving the software quality product. There are so many techniques, methods and models are present for the software quality prediction. Computational Intelligence is also playing a crucial role for the prediction of quality characteristics. This study gives a new optimal mathematical model for the prediction of degree of stakeholder satisfaction (Q). Optimal models validate the real data using the relationship impacts of various quality attributes. It uses the equations of constraints. Optimal model gives the maximum and minimum values for Q. Constraints constitutes of software quality characteristics. In given case study example, the idle value of Q is 30 but using optimal model it gives the maximum optimal value of Q =22.788020075 for xLAB IT Consulting Services on their project “In-Reg Molecule Registration” using MATLAB. It means if there is any change in the value of any software quality characteristics than it will decrease the value of Q. It proves that the given result is an optimal solution.

Limitations of the pinhole method and astigmatic defocus detection have difficulties in determining the direction of defocus and a restricted linear correction range. Here, a distance-measuring approach based on the laser triangulation principle is proposed with a corresponding mathematical model to overcome these problems and to satisfy the requirements of an industrial TFT-LCD (thin film transistor liquid crystal display) Inspection Microscope System in active auto-focus applications. The approach combines infrared light at a wavelength of 808nm with visible light. The light reflected from the surface of the object reaches a CCD camera and forms a spot via a set of optical devices. To control a PZT to achieve real-time auto-focusing, a Gaussian curve fitting method is used to process the spot information with the presented mathematical model giving the relationship between the detection value and the defocus value FES. Preliminary experiments with a 50x objective lens show that the measured values are consistent with the theoretical values, have good linearity and possess a focusing accuracy of 0.2μm within ±30μm of the focal plane. The analysis of the experimental results proves the feasibility of using this method in industrial applications. Compared with the traditional method of microscope focus correction, this method has the advantages of high accuracy, good linearity and fast focus speed for the actual requirements of industrial inspection processes. This method has been applied to circuit board component defect detection in our laboratory.

The main purpose of this research is the allocation of orders to suppliers in an agile and flexible manner suitable to the automobile industry. In this paper, parts supplied by a single source were eliminated from the set of parts. In this paper, using mathematical modeling and through Interval-Valued Fuzzy-Rough Numbers Best Worst Method (IVFRNBWM), we try to achieve the results that can meet the proposed model’s needs and provide ideal results by introducing new modes. The present study addressed some new aspects of the subject and achieved robust results by considering five objective functions. These five functions are as follows: minimization of production line disruptions due to the performance of suppliers, minimization of the complaints of production line about supplied parts, minimization of defective parts received from suppliers (PPM), maximization of on-time delivery services and minimization of overall costs of supplied parts. Reviewing the literature, the originality of this study are as follows: 1. Identifying the structure of a supply chain (SC) in general and particularly in an automobile industry SC. 2. Investigating the modeling techniques of the existing SC models for coordinating all the members of a product SC. 3. Building a hybrid model of IVFRN-BWM and a robust goal programming agile and flexible supply chain in uncertain situation. 4. Identifying the suitable scenarios/cases for testing the proposed models to validate the models. This paper can help decision makers and managers to opt the best suppliers and also allocate right numbers of parts to those supplier(s) based on a real situation of each firm.