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Mental arithmetic task load recognition using EEG signal and Bayesian optimized K-nearest neighbor

International Journal of Information technology (Singapore) 2021年第6期13卷 2363-2369页

作者： Sharma, Lakhan Dev Chhabra, Himanshu Chauhan, Urvashi Saraswat, Ritesh Kumar Sunkaria, Ramesh Kumar School of Electronics Engineering VIT-AP University Amaravati India Electronics and Communication Engineering M.L.V. Textile and Engineering College Bhilwara India Instrumentation and Control Engineering Division Netaji Subhas University of Technology Delhi India Electronics and Communication Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India

Cognitive load recognition during mental arithmetic activity facilitates to observe and identify the brain’s response towards stress stimulus. As a result, an efficient mental load characterization approach using electroencephalogram (EEG) signal and Bayesian optimized K-Nearest Neighbor (BO-KNN) has been proposed in this work. The study has been conducted on a recorded EEG dataset of 30 healthy subjects who were exposed to an arithmetic questioner. To obtain artifacts free EEG signal, the Savitzky–Golay filtering approach has been utilized. Further, the decomposition of the extracted EEG signal has been carried out using stationary wavelength transform. In this work, the entropy based feature extraction has been performed followed by F-score based feature selection. Top 40 features having the highest precedence have been used for classification using BO-KNN. The rigorous experimental analysis has been performed to analyze the effectiveness of the proposed method over other state-of-the-art methods and it shows that the classification accuracy is substantially improved. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Bayesian optimized K-nearest neighbor (BO-KNN) Cognitive task EEG Stationary wavelet transformation

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Artificial Neural Network-based State of Charge (SOC) Estimation of a Lithium-Ion Battery under Different Temperatures Conditions

Artificial Neural Network-based State of Charge (SOC) Estima...

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IEEE Power India Conference

作者： Swapnil Tiwari Bhavnesh Kumar Arjun Tyagi Department of Electrical Engineering Netaji Subhash University of Technology New Delhi India Department of Instrumentation and Control Engineering Netaji Subhash University of Technology New Delhi India

ISBN: (纸本)9781665459310

Lithium-Ion batteries are rechargeable batteries used to store energy. State of Charge estimation is necessary to increase the operational viability and reliability of the battery to reduce cost and for proper maintenance. Using a feed-forward backpropagation neural network (FFBPNN) technique combined with the Levenberg Marquardt Training (LM) algorithm, an artificial neural network (ANN) model is proposed in this research that can estimate the state of charge of a lithium-ion battery with greater precision. In the TANSIG transfer function's first layer, the quantity of hidden neurons is compared. The ANN model has been trained and achieved MSE is 0.00039609, RMSE is 0.019902, and R2 of 0.99815 for training, validation, and testing. Using MATLAB software, the effectiveness of the proposed ANN Model scheme has been evaluated for some statistical tools that are signal-based condition indicators based on time domain variables, such as Root Mean Square Error (RMSE), Gradient, Mu, Variance, error histogram, and Regression plot.

关键词： Training Lithium-ion batteries Temperature distribution Neurons Transfer functions Artificial neural networks Mathematical models

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Estimation of Parameters in Fractional order Financial Chaotic system with Nature Inspired Algorithms 1

Estimation of Parameters in Fractional order Financial Chaot...

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1st International Conference on Smart Sustainable Intelligent Computing and Applications, ICITETM 2020

作者： Gupta, Sangeeta Gautam, Gaurava Deepak Vats, Divyanshu Varshney, Pragya Srivastava, Smriti Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Dwarka Sec-3 New Delhi110078 India

Chaotic system parameter modeling is one of the significant topics in the field of non-linear science and therefore has attracted the attention of many researchers in this field. This paper discusses parameter recognition of Fractional Order Financial Chaotic System by employing different metaheuristic algorithms. The implemented algorithms include Artificial bee colony, Grey wolf optimizer, Whale optimization algorithm and Ant colony optimizer. Mean Square Error is employed as objective function to estimate parameters of system under consideration. Results of optimized parameters using the above-mentioned optimization are compared with each other and discussed in the paper. The overall outcome shows that Whale optimization algorithm gives more accurate and robust results with higher convergence rate as compared with other algorithms. © 2020 The Authors. Published by Elsevier B.V.

关键词： Ant colony optimization

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Classification of Parkinson's Disease using CNN and ANN with the aid of Drawing and Acoustic Feature 2

Classification of Parkinson's Disease using CNN and ANN with...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Fiza, Syed Kumar, Ashish Yadav, Jatin Reddy, Sainandan Sidda, Tej Kurma Sai, Kaarthik Dayananda Sagar College of Engineering Department of Medical Electronics Karnataka Bengaluru India Delhi Technological University Department of Information Technology Shabad Daulatpur Delhi India Netaji Subhas University of Technology Department of Instrumentation and Technology Dwarka Delhi India Indian Institute of Technology Department of Civil Engineering Hyderabad India Gandhi Institute of Technology and Management Department of Computer Science and Engineering Andhra Pradesh Vishakhapatnam India Data Analytics Vellore Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9781665437899

Parkinson's disease manifests itself in a variety of ways. slurred speech, muscle rigidity, and tremors. The condition can also affect the production of the brain's dopamine. Therapies can help treat conditions after a diagnosis, however, there is no remedy. The paper primarily analyses the many drawing evaluation methods and disease diagnoses that have been used. The AI deep learning idea was used, along with convolutional neural networks and artificial neural networks. However, this article proposes a model for detecting disease using drawings of different persons from two classes. Disease detection on photos has been accomplished using a variety of techniques such as data augmentation, sequential, and CNN. The acoustic feature data was processed using a Decision tree and ANN with GridsearchCV, and the overall accuracy on testing data was 98 percent. © 2022 IEEE.

关键词： Convolutional neural networks

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MeFunX: A novel meta-learning-based deep learning architecture to detect fungal infection directly from microscopic images

Franklin Open

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Franklin Open 2024年 6卷

作者： Rawat, Shubhankar Bisht, Bhanvi Bisht, Virender Rawat, Nitin Rawat, Aditya Department of Instrumentation and Control Engineering Netaji Subhas University of Technology India Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University For Women India Department of Mathematics Al-Falah University India Department of Industrial Management Indian Institute of Management Mumbai India School of Engineering and the Built Environment Birmingham City University United Kingdom

Fungal infections are a growing threat to human health. They can lead to a range of health problems and can be life-threatening. There are many impediments to the traditional diagnosis of fungal infections, such as a diminishing number of clinical mycologists, expensive procedures, high time consumption, and requirements for sensitivity and specificity. Therefore, early diagnosis of fungal infection is critical to effective treatment. In this paper, a novel meta-learning-based deep learning architecture, termed MeFunX, is proposed for the early detection of fungal infections from microscopic images. MeFunX architecture consisted of two convolutional neural network-based models as base learners and XGBoost as the meta-learner. To assess the proposed approach, standard metrics namely, accuracy, precision, recall and f1-score, were used. The fungal disease identification performance of MeFunX was compared with state-of-the-art architectures like VGG16, InceptionV3, ResNet, AlexNet, DenseNet, and EfficientNet. In addition, MeFunX was also benchmarked against its base learners and other meta-learning model with EfficientNet and ResNet as the base learners to demonstrate the effectiveness of the meta-learning architecture. Rigorous experimentation clearly signifies the superior performance of MeFunX, which achieved an overall accuracy of 92.49 % for the early diagnoses of fungal infections in microscopic images. © 2023 The Authors

关键词： Deep learning Early detection Fungal infection Meta-learning XGBoost

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Image Analysis for E-Healthcare Systems using Multi-Biometric Recognition Model

Image Analysis for E-Healthcare Systems using Multi-Biometri...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Naman Bansal Pranav Arora Deepak Kumar Sharma Koyel Datta Gupta Chandana Kuntala Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology University of Delhi New Delhi India Department of Information Technology Indira Gandhi Delhi Technical University for Women New Delhi India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Information Technology Indira Gandhi Delhi Technical University for Women New Delhi India

With the dawn of e-Healthcare systems, Medical Record Management has become an important research problem. The storage and organization of medical records have made relatively little progress in a world of constantly emerging new technologies and continuous innovation. Many hospitals keep the records on paper, which raises many challenges, including but not limited to a significant amount of time for searching and retrieving a specific record, high maintenance costs, lack of backup, and limited security. Although the inclusion of technology has made this task far more efficient and secure, there is still much that can be done to improve it. This paper proposes a double index-based approach for mapping medical records directly to a patient’s biometrics which would take advantage of the uniqueness of biometrics to identify a patient. The proposed multi-biometric model achieves an accuracy of 97% and an F 1 score of 0.9814.

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Comparative Analysis of Ant Colony and Particle Swarm Optimization Algorithms for Distance Optimization 1

Comparative Analysis of Ant Colony and Particle Swarm Optimi...

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1st International Conference on Smart Sustainable Intelligent Computing and Applications, ICITETM 2020

作者： Gupta, Arushi Srivastava, Smriti Accenture Technologies Pune411028 India Division of Instrumentation and Control Netaji Subhas University of Technology New Delhi110078 India

In this paper, we have solved one of the widely popular problems in the domain of control systems-distance optimization using two separate swarm intelligence algorithms-particle swarm and ant colony optimization. The problem is successfully solved with both algorithms and subsequently quantitative comparison between their performances is done. Simulated results show the more recently developed ant colony optimization algorithm to be better and more robust of the two. The requisite simulations are carried out and results are obtained in the MATLAB environment. © 2020 The Author(s).

关键词： Ant colony optimization

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Drought prediction using artificial neural network 2

Drought prediction using artificial neural network

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Metta, Pardha Saradhi Chintamaneni, Anish Kumar, Ashish Yadav, Jatin Kumar, Vishesh Bhaskar, Bharath Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Netaji Subhas University of Technology Dwarka Department of Information Technology Delhi India Karunya Institute of Technology and Sciences Department of Instrumentation and Technology Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Kattankulathur India Pes University Department of Electronics and Communications Karnataka Bangalore India

ISBN: (纸本)9781665437899

Drought has been one of the most frightening natural calamity that is being faced by the humans., and it is generally faced by the farmers all around the world. It can occur anywhere in the world and is also known as one of the slow disaster as it stays for a very longer time period and maybe even more if it wants to be harsher. Drought not only impacts the human life but also the agriculture, world economies and energy that had been infiltrated by the farmers. Everything goes on stakes during this calamity and the basic needs such as food are not easy to get during these times and the inflammation is at peak due to supply and demand instability. There are many ways which can be done to avoid the drought, which include drip irrigation to save water during the times of drought, rainwater harvesting can also be a saviour, crop engineering, desalination of water and many more things. The main solution for this would be to analyse the weather and the future outcomes of it which would help in preparing for the worse situation and also the soil prediction can help a lot when it comes to predicting this situation. The paper aims to take the data of weather and soil together to predict how droughts can be avoided. Deep learning techniques will help to get a great idea if the drought is going to happen or not. © 2022 IEEE.

关键词： Drought

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Modelling and study of SPV module under partial shading condition with simulation and experimental results 7

Modelling and study of SPV module under partial shading cond...

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7th International Conference on Signal Processing and Integrated Networks, SPIN 2020

作者： Ilyas, Afshan Rizwan Khan, M. Ayyub, Mohammad Beg, MirzaTabish Shah Aligarh Muslim University Department of Electrical Engineering Aligarh U.P. India Netaji Subhas University of Technology Deparment of Instrumentation and Control Engineering New Delhi India

ISBN: (纸本)9781728154756

In this work, the comprehensive modelling, simulation and analysis of solar photovoltaic (SPV) module for different shading conditions is presented. The SPV module is designed using the mathematical equations in Simulink environment using MATLAB. The simulink model is used to determine the static characteristics of the SPV array under different environmental conditions. The key motive of this work is to analyze the effect of partial shading (PS) on a SPV module under different operating conditions. Initially, the simulation modelling of the PV system has been done, proceeds with experimental demonstration of the effect of PS on a single 37 W SPV module. © 2020 IEEE.

关键词： Simulink

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2023年 956卷 v-vi页

作者： Tomar, Anuradha Gaur, Prerna Jin, Xiaolong Department of Instrumentation and Control Engineering Netaji Subhas University of Technology Delhi New Delhi India Department of Electrical Engineering Technical University of Denmark Lyngby Denmark West Campus Netaji Subhas University of Technology New Delhi India

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