Jyotshana Kanti

Voting system plays an important role in any democratic country. Vote means to elect the best political pioneer. In India, currently two types of voting systems are depleted, which are secret Ballet paper and Electronic Voting Machines(EVM), but both of them have severalrestrains. The current voting system is jeopardizing, voters visits poolingbooth and stand in long line up to cast their votes, because of which there are possibilities that many people miss their chance of voting. Additionally, social gathering isn’t much preferred in this COVID-19 situation. Voters who are ineligible may also use fraudulent methods to cast their ballots, which may result in hardship. Therefore, in this project, we suggest a voting system that is efficient and has three layers of security. The verification of the voter’s unique identification number (UID) comes first, then the verification of their election identification number (EID), and finally the verification of their face, which is the highest level of security.

Business-based decision support systems have been proposed for a few decades in the e-commerce and textile industries. However, these Decision Support Systems (DSS) have not been so productive in terms of business decision delivery. In our proposed model, we introduce a content-based image retrieval model based on a DSS and recommendations system for the textile industry, either offline or online. We used the Fashion MNIST dataset developed by Zalando to train our deep learning model. Our proposed hybrid model can demonstrate how a DSS can be integrated with a system that can separate customers based on their personal characteristics in order to tailor recommendations of products using behavioral analytics, which is trained based on MBTI personality data and Deap EEG data containing numerous pre-trained EEG brain waves. With this hybrid, a DSS can also show product usage analytics. Our proposed model has achieved the maximum accuracy compared to other proposed state-of-the-art models due to its qualitative analysis. In the first section of our analysis, we used a deep learning algorithm to train our CBIR model based on different classifiers such as VGG-net, Inception-Net, and U-net which have achieved an accuracy of 98.2% with a 2% of minimized error rate. The result was validated using different performance metrics such as F-score, F-weight, Precision, and Recall. The second part of our model has been tested on different machine learning algorithms with an accuracy rate of 89.9%. Thus, the entire model has been trained, validated, and tested separately to gain maximum efficiency. Our proposal for a DSS system, which integrates several subsystems with distinct functional sets and several model subsystems, is what makes this study special. Customer preference is one of the major problems facing merchants in the textile industry. Additionally, it can be extremely difficult for retailers to predict customer interests and preferences to create products that fulfill those needs. The three innovations presented in this work are a conceptual model for personality characterization, utilizing an amalgamation of an ECG classification model, a suggestion for a textile image retrieval model using Denoising Auto-Encoder, and a language model based on the MBTI for customer rating. Additionally, we have proposed a section showing how blockchain integration in data pre-processing can enhance its security and AI-based software quality assurance in a multi-model system.

The load frequency control of a microgrid is one of the emerging areas due to the changes in demand and supply in power system. So the controllers’ implementation must be changed accordingly. This paper proposes an interval type-2 fuzzy logic-based, dual-mode gain scheduling (DMGS) of the proportional and integral controller in which the gains of the PI controller werescheduled through the dynamic selector. This proposed controller was implemented ina hybrid microgrid power system in which nonconventional energy sources wereadded to each area of the conventional power plant, which madethe system much more prone to frequency variations. The controller was designed for three areas, consisting of a photovoltaic (PV) system, a wind power system, a fuel cell and a diesel engine/hydropower generator in which the generation rate constraint (GRC) was considered as a nonlinearity. The proposed power system was investigated under various load conditions in the MATLAB/SIMULINK environment. A comparative evaluation of changes in frequency, tie-line power fluctuations and variations in area control errors for the test system showed the effectiveness of the current approach over simple fuzzy PI and a conventional PI-controlling approach.

In this paper, an improved-two-stage detection (improved-TSD) technique for spectrum sensing (SS) has been proposed. In improved-TSD technique, there are two stages, the first stage has two detectors, i.e. energy detector using a single adaptive threshold (ED_SAT) and energy detector using two adaptive thresholds (ED_TAT), organized in a parallel way, and the second stage carries decision device (DD) who decide the final decision using OR-rule. Graphical results confirm that proposed improved-TSD technique improves detection performance and outperforms the energy detection technique for adaptive spectrum sensing-2015 (EDT-ASS-2015) and conventional-ED (C-ED) SS techniques by 10.4 % and 31.3 % at –10 dB signal-to-noise ratio (SNR) respectively. Further, in terms of sensing time, the presented scheme performs well as compared to EDT-ASS-2015 SS scheme i.e. takes 0.5 ms sensing time at –20 dB SNR.

With a rapid growth in the number of users, the demand for a higher throughput in wireless communication systems is also increased, which in turn requires an excessive use of the available spectrum. Scarcity of the available spectrum has become a big issue today as it is signicantly underutilized due to the xed spectrum licensing policies (Federal Communications Commission [FCC], 2005). With this motivation, the concept of cognitive radio (CR) has been proposed as a promising approach for efcient utilization of the available spectrum (Haykin, 2005; Kolodzy, 2005). CR relies on the fact that some frequency bands of the primary (licensed) users (PUs) are unoccupied or partially occupied most of the times. These unoccupied bands are called as white spaces. The main idea behind the CONTENTS 12.1 Introduction ........................................................................................................................227 12.2 Interference Management in CRNs .................................................................................229

In this paper, a reliable spectrum sensing (SS) scheme using dual detectors for IEEE 802.22 WRAN has been presented. In dual or double detectors, first one detector is energy detector (ED) with fixed threshold (FT), while second one is ED with adapti