In this paper, a time based fuzzy multi objective reliability redundancy allocation problem (FMORRAP) is proposed for the xj-out-of -m series-parallel system. The main objective is to maximize the system reliability with minimization of system cost and system repairing cost by optimizing the number of redundant components at each stage. The objectives are achieved by satisfying entropy constraints with limited redundant components at each stage and same for the whole system. Here the uncertainty of reliability, cost and repairing cost of each component is maintained by using triangular fuzzy number (TFN). The decreasing factor of component reliability and cost follows the change in the length of radius for the inverse logarithmic spiral with respect to time. Similarly the increasing factor of component repairing cost follows the same for the logarithmic spiral. Tuning and Neighborhood based Fuzzy Multi-Objective Particle Swarm Optimization (TNF-MOPSO) algorithm is proposed to solve fuzzy multi-objective optimization problems (MOOP). The proposed problem and proposed algorithm are illustrated by using a bench mark problem. The proposed algorithm produced better membership of objective functions for most of the time values and high satisfaction level of reliability for all values of the time parameter. It also outperforms the standard approaches MOPSO and NF-MOPSO.

In this paper, a time based fuzzy multi objective reliability redundancy allocation problem (FMORRAP) is proposed for the xj−out−of−m series–parallel system. The main objective is to maximize the system reliability with minimization of system cost and system repairing cost by optimizing the number of redundant components at each stage. The objectives are achieved by satisfying entropy constraints with limited redundant components at each stage and same for the whole system. Here the uncertainty of reliability, cost and repairing cost of each component is maintained by using triangular fuzzy number (TFN). The decreasing factor of component reliability and cost follows the change in the length of radius for the inverse logarithmic spiral with respect to time. Similarly the increasing factor of component repairing cost follows the same for the logarithmic spiral. Tuning and Neighborhood based Fuzzy Multi-Objective Particle Swarm Optimization (TNF-MOPSO) algorithm is proposed to solve fuzzy multi-objective optimization problems (MOOP). The proposed problem and proposed algorithm are illustrated by using a bench mark problem. The proposed algorithm produced better membership of objective functions for most of the time values and high satisfaction level of reliability for all values of the time parameter. It also outperforms the standard approaches MOPSO and NF-MOPSO.

Over time, there has been a significant increase in the amount of text data, leading to an alarming rise in the spread of fake news globally. This has had a detrimental impact on various aspects of society, including the economy, politics, organizations, and individuals. To combat this issue, it is crucial to identify fake news early on. Fake news propagators often target innocent people, making it necessary to develop effective techniques for detecting and preventing the spread of fake news. One approach is to use supervised machine learning algorithms, which can classify news articles as true or false by analyzing their language and features. In this article, natural language processing techniques and feature extraction techniques are implemented to analyze and predict the spread of fake news. The XGBoost algorithm yields the best results during testing, with an accuracy of 99.62%.

This study introduces a time-dependent fuzzy multi-objective reliability redundancy allocation problem (TF-MORRAP) for the n -stage (level) series–parallel system. System reliability maximization and system cost minimization according to time by optimizing the redundant components counting at every stage of the system is the main objective of this study. This optimization is done by satisfying the entropy constraints with limited redundant components at every stage and in the whole system. The reliability and cost of every component are represented as triangular fuzzy numbers (TFN) to handle the uncertainty of input information of the system. According to time, the component reliability and cost decrease by some factor of their previous existing value. This factor follows the change in the length of radius of the inverse logarithmic spiral with respect to angle which is regarded as time here. The proposed problem is analyzed by using an over-speed protection system of a gas turbine. We compare the membership values of optimal solutions obtained by using two well-known techniques namely non-dominated sorting genetic algorithm-II (NSGA-II) and a multi-objective particle swarm optimization algorithm called NF-MOPSO. Various performances of the algorithms are compared to solve the aforementioned problem by using some performance metrics. NF-MOPSO shows the high satisfaction level of objective functions and better performance than NSGA-II.

The main goal of this paper is to solve a fuzzy multi-objective reliability redundancy allocation problem (MORRAP) for $$x_{j}$$ -out-of- $$m_{j}$$ series-parallel system. We consider that system reliability and system cost are two conflicting objectives. Due to the incompleteness and uncertainty of input information, we formulate the objectives by considering the reliability and cost of each component as a triangular fuzzy number (TFN). Here, the fuzzy multi-objective optimization problem of system reliability and cost is analyzed simultaneously using our proposed fuzzy rank-based multi-objective particle swarm optimization (FRMOPSO) algorithm. Comparing the results of FRMOPSO with standard particle swarm optimization (PSO), we see that better optimum reliability and cost have been achieved in the FRMOPSO technique. To illustrate the effectiveness of our proposed technique, we consider the problem of the over-speed protection system of gas turbines containing two mutually conflicting reliability and cost objectives with entropy and several other constraints. We present graphically the effect of optimum system reliability and cost with respect to the percentage change of different parameters. We also compare the convergence rate of FRMOPSO with PSO. Our proposed algorithm shows better results.

With the advent of new technologies and the internet around the globe, many cities in different countries are involving the local residents (or city dwellers) for making decisions in various government policies and projects. In this paper, the problem of detecting tourist spots in a city with the help of city dwellers, in strategic setting, is addressed. The city dwellers vote against the different locations that may act as a potential candidate for the tourist spot. For the purpose of voting, the concept of single peaked preferences is utilised, where each city dweller reports a privately held single peaked value that signifies the location in a city. Given the above discussed scenario, the goal is to determine the location in the city as a tourist spot. For this purpose, we have designed the mechanisms (one of which is truthful). For measuring the efficacy of the proposed mechanisms the simulations are done.

Number of devices needing wireless communication is more than ever. The main ingredient of wireless communication-the spectrum-is limited and due to the unprecedented growth of smart devices the demand for spectrum is high. To serve all the devices needing wireless communication, intelligent spectrum sensing and its trading has been the hot topic of research in this decade. In spectrum trading, so far in the literature, two layers are considered in terms of primary and secondary users. However, it may be the case, that the secondary users (may be NGOs) may redistribute their spectrum to some third party (downtrodden people of the rural areas) freely. To the best of our knowledge, this environment is not addressed in the literature so far. In this paper this three layer potentially demanding architecture is studied and algorithms are proposed based on the theory of mechanism design without money. Our algorithm is also simulated with a specially designed benchmark algorithm.

Today internet has become a trusted factotum of everyone. Almost all payments like tax, insurance, bank transaction, healthcare payment, payment in e-commerce are done digitally through debit or credit card or through e-wallet. People share their personal information through social media like Facebook. Twitter, WhatsApp etc. The government of every developing country is going to embrace e-Governance system to interact with people more promptly. The information shares through these applications are the burning target to intruders. This paper utilized the imperceptibility as well as the robustness of steganography techniques which are increased by embedding multiple bits in a particular region selected either based on some image attributes or by Human Visual Perception.

Recently spectrum trading has got a serious attention as it is observed that a significant amount of the spectra that are held by the primary users (spectrum owners or sellers) are remaining un-utilized. Open, market-based spectrum trading motivates the primary users to lease their un-utilized spectrum to new spectrum users (secondary users or buyers) on demand basis. This spectrum trading provides secondary users the spectrum they desperately need. Of late, in wireless communications, auction has played a central role for modelling the reallocation process of the un-utilized spectra that are held by the primary users. In this paper, we propose a truthful double auction mechanism for heterogeneous spectrum. Here, heterogeneity signifies the fact that the secondary users can give demand for the spectrum for multiple primary users. Results from extensive simulation studies demonstrate the effectiveness and efficiency of our proposed work.