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Published By Blue Eyes Intelligence Engineering And Sciences Engineering And Sciences Publication - BEIESP

2249-8958
Updated Tuesday, 30 November 2021

Author(s):  
Navneet Singh ◽  
◽  
Dr. Amit Jain ◽  
Dr. Dinesh Kumar Singh ◽  
◽  
...  

In this article, a single port with truncated corner and common T-shaped notch loaded microstrip patch antenna for bandwidth enhancement is presented which is useable for mid band of 5G applications. The design of this prototyped antenna is obtained by loading truncated corner and T-shaped notch on rectangular patch antenna having 50 Ω microstrip line feed. The optimized antenna 5 is selected as proposed antenna at design frequency 3 GHz among antenna 1- antenna 5after study of simulated results through IE3D Mentor Graphics simulation software. Proposed antenna covers a wide bandwidth from 2.39 to 4.04 GHz and fractional bandwidth of 51.3% with pair of resonance frequency having return loss of -23.38 dB and -29.65 dB respectively.


Author(s):  
Anupam Agrawal ◽  

The paper describes a method of intrusion detection that keeps check of it with help of machine learning algorithms. The experiments have been conducted over KDD’99 cup dataset, which is an imbalanced dataset, cause of which recall of some classes coming drastically low as there were not enough instances of it in there. For Preprocessing of dataset One Hot Encoding and Label Encoding to make it machine readable. The dimensionality of dataset has been reduced using Principal Component Analysis and classification of dataset into classes viz. attack and normal is done by Naïve Bayes Classifier. Due to imbalanced nature, shift of focus was on recall and overall recall and compared with other models which have achieved great accuracy. Based on the results, using a self optimizing loop, model has achieved better geometric mean accuracy.


Author(s):  
Y Sai Subhash Reddy ◽  
◽  
Sri Krishna Borra ◽  
Koye Sai Vishnu Vamsi ◽  
Nandipati Jaswanth Sai ◽  
...  

COVID-19 is a life-threatening virus taking the lives of thousands of people every day throughout the world. Even though many organizations and companies worked hard and developed vaccines, production of vaccines at large scale to meet today’s demand is not an easy job as there is a shortage of raw materials and cases are rising steeply. Inoculation of every individual cannot be achieved in the foreseeable future. Even the government is vaccinating people in a phased manner prioritizing older people and people who are more vulnerable to the virus. The main objective of this work is to provide an optimum solution for COVID-19 indoor safety for industries, offices, and commercial places where footfall is high. This work focus on automation of temperature sensing and mask detection which is usually carried out by a person. Elimination of human intervention reduces the risk of contraction and spreading and avoids mistakes due to human negligence. Continuous monitoring of a person is not possible and there is no guarantee that a person who is entering a place wearing a mask puts it on until he leaves it. This research intends to implement mask detection along with surveillance which is cost effective as it does not require additional hardware setup.


Author(s):  
Vedang Naik ◽  
◽  
Rohit Sahoo ◽  
Sameer Mahajan ◽  
Saurabh Singh ◽  
...  

Reinforcement learning is an artificial intelligence paradigm that enables intelligent agents to accrue environmental incentives to get superior results. It is concerned with sequential decision-making problems which offer limited feedback. Reinforcement learning has roots in cybernetics and research in statistics, psychology, neurology, and computer science. It has piqued the interest of the machine learning and artificial intelligence groups in the last five to ten years. It promises that it allows you to train agents using rewards and penalties without explaining how the task will be completed. The RL issue may be described as an agent that must make decisions in a given environment to maximize a specified concept of cumulative rewards. The learner is not taught which actions to perform but must experiment to determine which acts provide the greatest reward. Thus, the learner has to actively choose between exploring its environment or exploiting it based on its knowledge. The exploration-exploitation paradox is one of the most common issues encountered while dealing with Reinforcement Learning algorithms. Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. We describe how to utilize several deep reinforcement learning (RL) algorithms for managing a Cartpole system used to represent episodic environments and Stock Market Trading, which is used to describe continuous environments in this study. We explain and demonstrate the effects of different RL ideas such as Deep Q Networks (DQN), Double DQN, and Dueling DQN on learning performance. We also look at the fundamental distinctions between episodic and continuous activities and how the exploration-exploitation issue is addressed in their context.


Author(s):  
Monisha Pathak ◽  
◽  
Mrinal Buragohain ◽  

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.


Author(s):  
Rohit Sahoo ◽  
◽  
Vedang Naik ◽  
Saurabh Singh ◽  
Shaveta Malik ◽  
...  

Heart disease instances are rising at an alarming rate, and it is critical and essential to predict any such ailments in advance. This is a challenging diagnostic that must be done accurately and swiftly. Lack of relevant data is often the impeding factor when it comes to various areas of research. Data augmentation is a strategy for improving the training of discriminative models that may be accomplished in a variety of ways. Deep generative models, which have recently advanced, now provide new approaches to enrich current data sets. Generative Models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are frequently used to generate high quality, realistic, synthetic data essential for machine learning algorithms as they play a critical role in various classification problems. In our case, we were provided with 304 rows of heart disease data to create a robust model for predicting the presence of an ailment in the patient. However, the identification of heart disease would not be efficient given the small amount of available training data. We used GAN, CGAN, and VAE to generate data to tackle this problem, thus augmenting the original data. This additional data will help in increasing the accuracy of the models created using the new dataset. We applied classification-based Machine Learning models such as Logistic Regression, Decision Trees, KNN, and Random Forest. We compared the accuracy of the said models, each of which was supplied with the original dataset and the augmented datasets that used the data generation techniques mentioned above. Our research suggests that using data generation techniques significantly boosts the accuracy of the machine learning techniques applied to them.


Author(s):  
D.S.T. Ramesh ◽  
◽  
D. Angel Jovanna ◽  

In this article, our main topic is about the existence of relaxed skolem mean labeling for a 5 – star graph G = K1,α ∪ K1,α ∪ K1,α ∪ K1, β ∪ K1, β 1 2 3 1 2 with partition 3, 2 with a certain condition. By using the trial and error method we find the existence of the relaxed skolam mean labeling of 5 - star graph with partition 3, 2 with a specific condition.


Author(s):  
Mong Hien Thi Nguyen ◽  
◽  
Minh Hieu Tran ◽  

This paper presents the research results of automatic estimation of the neck girth and inside leg to extract the size and body shape from the male sizing system table. The data used in the study is the 3D scan file *.obj from the 3D body scanner. The author uses the interpolation and optimization method in the algorithm to automatically extract 2 primary dimensions combined with the fuzzy logic method to extract sizes, body shapes. Besides, rotate matrix method combines with the optimal function used to write an algorithm to estimate the neck girth, inside leg measurements. Furthermore, a simple approach based on vertices and surface normal vectors data and optimal search was adapted to estimate the neck girth and inside leg measurements. These extraction results will be linked to the algorithm of the fuzzy logic to run for the automated process. This automatic algorithm will be very useful in face-to-face clothing purchases or online or for garment manufacturers in reducing shopping time and choosing sizes to design samples for customers.


Author(s):  
Gayatri Mahajan ◽  

Technology plays a pivotal role in shaping construction industry. Adoption of new trends, tools, software and technology would motivate to minimize problems that arise during use of drones in construction. The paper not only elaborates previous reviews on Drone Technology (DT) in Construction Industry (CI), but also explores extensive literature review on (i) classification of drones, construction software used with drone, (iii) overview of utility of DT in construction and related industries (iv) recent construction technology trends, tools and techniques accomplish with drone technology. This is basically a review paper. The aim of this paper is to study the potential of DT in construction industry, extended it to understand the following issues in better way(i) benefits and impacts of drone in CI, (ii) record disadvantage of drone in CI(iii) integration of BIM with DT at substantial length and volume (iv)extensive description and enumeration on applications and uses of drones in CI(v) use of drone at each stage of construction stage to monitor the progress of construction rightly from the purchase of land to close out the project(vi)lastly appended a note on the impact of COVID-19 on construction. This study (2012-2021) also discusses challenges, opportunities, limitations, and strategies for the adoption of drones in construction. It assists to contractors, building planners, designers, academicians, engineers, and architects to improve the construction activities for greater efficiency and better performance. It also motivates towards inclusion of these technologies in the curriculum in Architecture Engineering


Author(s):  
Jitendra Khatti ◽  
◽  
Kamaldeep Singh Grover ◽  

The Gaussian Process Regression (GPR), Decision Tree (DT), Relevance Vector Machine (RVM), and Artificial Neural Network (ANN) AI approaches are constructed in MATLAB R2020a with different hyperparameters namely, kernel function, leaf size, backpropagation algorithms, number of neurons and hidden layers to compute the permeability of soil. The present study is carried out using 158 datasets of soil. The soil dataset consists of fine content (FC), sand content (SC), liquid limit (LL), specific gravity (SG), plasticity index (PI), maximum dry density (MDD) and optimum moisture content (OMC), permeability (K). Excluding the permeability of soil, rest of properties of soil is used as input parameters of the AI models. The best architectural and optimum performance models are identified by comparing the performance of the models. Based on the performance of the AI models, the NISEK_K_GPR, 10LF_K_DT, Poly_K_RVM, and GDANN_K_10H5 models have been identified as the best architectural AI models. The comparison of performance of the best architectural models, it is observed that the NISEK_K_GPR model outperformed the other best architectural AI models. In this study, it is also observed that GPR model is outperformed ANN models because of small dataset. The performance of NISEK_K_GPR model is compared with models available in literature and it is concluded that the GPR model has better performance and least prediction error than models available in literature study.


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