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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sahu, Richa Srivastava, Smriti Kumar, Rajesh Netaji Subhas University of Technology Department of Electrical Engineering Delhi India Netaji Subhas University of Technology Department of Control and Instrumentation Engineering Delhi India National Institute of Technology Department of Electrical Engineering Kurukshetra India

ISBN: (纸本)9798350306118

Long Short-Term Memory (LSTM) is a Recurrent Neural Network (RNN) that overcomes typical neural network constraints. Because of its ability to record temporal dependencies and solve nonlinear equations, long-Term data can be simply managed. LSTM is intended to alleviate the problems of RNN's vanishing gradient and exploding gradient difficulties. In this paper, we used LSTM to solve nonlinear plant equations with a gradient descent-based back propagation approach and retrieved the plant's output as well as other performance measures including Average Mean Square Error (AMSE) and Total Mean Average Error (TMAE). When the output of an LSTM is compared to that of a feed-forward Neural Network (FFNN), the LSTM outshines the FFNN. All the parameters of both models, such as iteration count and learning rate are kept constant. © 2023 IEEE.

关键词： Backpropagation

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Nonlinear complex dynamic system identification based on a novel recurrent neural network

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Soft Computing 2025年第2期29卷 957-976页

作者： Saini, Kartik Kumar, Narendra Bhushan, Bharat Kumar, Rajesh Department of Electrical Engineering Delhi Technological University Shahbad Daulatpur Main Bawana Road Delhi110042 India Netaji Subhas University of Technology New Delhi India Department of Electrical Engineering National Institute of Technology Kurukshetra Kurukshetra136119 India

This paper proposed a novel Modified Jordan Recurrent Neural Network (MJRNN) model to identify complex nonlinear dynamical systems. Due to its capabilities, nonlinear dynamic system identification using artificial neural networks is the most commonly used method in control system engineering. The structure of the proposed model is an extended version of the original Jordan recurrent neural network model. The parameter update equations are obtained using the back-propagation optimization algorithm, the most frequently used method as a learning approach for the training of the proposed model’s parameters. The effectiveness of the proposed neural network model is evaluated in comparison to other neural network models such as the Jordan recurrent neural network (JRNN), Elman recurrent neural network (ERNN), Diagonal recurrent neural network (DRNN), and feed-forward neural network (FFNN) models. The robustness of the proposed model is also tested with parameter variation and disturbance signals. The simulation results have shown that the proposed model performs better than the other neural network models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Neural network models

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Heart Disease Prediction Evaluation of Machine Learning Models with PSO-Optimized K-Fold Cross-Validation

Heart Disease Prediction Evaluation of Machine Learning Mode...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Gupta, Nikita Jadon, Kuldeep Singh Soni, Preeti Gupta, Nitin Dhurandher, Sanjay Kumar Department of Computer Science & Engineering India Netaji Subhas University of Technology Department of Information Technology New Delhi India National Institute of Electronics and Information Technology New Delhi India

ISBN: (纸本)9798350364866

Heart disease prediction is a critical aspect of modern healthcare and demands accurate and efficient models for timely diagnosis. Machine learning models in heart disease prediction analyze patient data, with cross-validation ensuring reliable performance, aiding model selection, and optimizing parameters for enhanced accuracy. The k value in cross-validation determines the number of folds into which the dataset is divided for training and validation. In the proposed work, optimization of the k-value in cross-validation is done using Particle Swarm Optimization (PSO), which is utilized to improve the evaluation of the predictive accuracy of various models. PSO dynamically adjusts the k-value, balancing computational efficiency with accurate model evaluation. The result shows that among various ML models, Random Forest, with an 86.95% mean accuracy, emerges as the preferred choice, offering reliable predictions for improved diagnostics. © 2024 IEEE.

关键词： Prediction models

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Modelling of a MIMO Non-Linear System using a Double Internal Loop Recurrent Neural Network

Modelling of a MIMO Non-Linear System using a Double Interna...

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2023 International Conference on Recent Advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023

ISBN: (纸本)9781665493826

A Double Internal Loop Recurrent Neural Network (DILRNN) model is proposed for Multiple Input Multiple Output (MIMO) system to obtain improved output using Gradient Descent based Back Propagation Algorithm. In the modelling of DILRNN, three feedback loops are taken i.e., the first feedback is taken from the unit delay of the output to the hidden layer, the second feedback is taken from the previous value of hidden layer to itself and the last feedback is taken from the previous value of output to output. The weight is updated with the help of the algorithm after every iteration. The output of the proposed DILRNN model reduces error significantly vis-à-vis other Recurrent Neural Network (RNN) models for constant value of iterations and epochs. The statistical parameters such as RMSE and TMAE for the proposed model are greatly in comparison to other models and thereby enhances the accuracy of the model. © 2023 IEEE.

关键词： Recurrent neural networks

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PDNet: A Novel Approach to Plant Disease Detection using CNN and Keras 6

PDNet: A Novel Approach to Plant Disease Detection using CNN...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Jain, Garima Shukla, Amita Chaudhary, Anamika Sharma, Nidhi Bisht, Sovers Singh Jain, Ankush Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Delhi India Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9798331522667

This research addresses the pressing global demand for food by leveraging cutting-edge deep learning techniques for automating plant disease detection. Focusing on tomato and potato leaf diseases, the study utilized the PlantVillage dataset to compare classical machine learning methods, such as Local Binary Patterns (LBP) combined with classifiers like kNN, SVM, and Extreme Learning Machines, against modern Convolutional Neural Network (CNN) architectures. These included MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16/19. A custom-designed CNN model was developed with unique configurations implemented using Keras and TensorFlow. The model was tested for binary, 6-class, and 10-class classifications, achieving remarkable accuracy rates of 99.6%, 98.4%, and 97.2%, respectively. Notably, after 50 training epochs, the model achieved a peak accuracy of 99.78% for plant disease classification. A practical web-based application was also developed, integrating the best-performing model to facilitate real-time leaf disease detection and notify farmers via email. This approach demonstrates a significant advancement in sustainable agriculture by providing an efficient, accurate, and scalable solution for early disease detection, which is critical for improving crop yield and productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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Monitoring maritime traffic with ship detection via Yolov4 2

Monitoring maritime traffic with ship detection via Yolov4

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2nd International Conference on Artificial Intelligence and Signal Processing, AISP 2022

作者： Verma, Gaurav Gupta, Aditya Bansal, Shobhit Dhiman, Himanshu Computer Engineering Netaji Subhas Institute of Technology Delhi India Electronics and Communications Engineering Netaji Subhas Institute of Technology Delhi India

ISBN: (纸本)9781665442909

In India a large part of goods transportation is carried out by sea, leading to an emerging requirement for remote maritime patrolling system, which also serves as an asset during wartime and peacetime for defence. In this research paper, we propose an automated maritime patrolling solution by making a Deep Learning Model Pipeline for Ship Detection from satellite images using existing State of the art Object Detection Algorithms like Faster-RCNN, SSD, YOLOv3, and YOLOv4. We compare results based on various Evaluation Metrics. Further we also release our own dataset which consists of around 300 satellite images of the top 13 busiest Sea-ports of India. After performing the validations, we found that the YOLO v4 displayed the best results with a balanced mAP and FPS score to detect the ships in the satellite images. © 2022 IEEE.

关键词： Object detection

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Analysis of Ring Radius and Q-Factor for Enhanced Bandpass Filter performance in Racetrack Ring Resonators

Analysis of Ring Radius and Q-Factor for Enhanced Bandpass F...

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2024 JSAP-Optica Joint Symposia, JSAP 2024

作者： Gupta, Naveen Kumar Vardhan, Shalini Singh, Ritu Raj Department of Electronics and Communication Engineering Indian Institute of Information Technology Ranchi India Department of Electronics and Communication Engineering Netaji Subhas University of Technology Delhi India

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Robust Frequency Regulation of Power System Through Aggregated Variable Speed Air-Conditioner Systems 8

Robust Frequency Regulation of Power System Through Aggregat...

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8th International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Harish, V.S.K.V. Routh, Avijit Netaji Subhas University of Technology Department of Electrical Engineering Dwarka New Delhi110078 India Haldia Institute of Technology Department of Electrical Engineering West Bengal Haldia721657 India

ISBN: (纸本)9798331520762

Frequency regulation services through demand-side measures such as thermostatically controlled loads have gained popularity in the recent decade. However, there is a significant need for a control strategy that has the capability to provide regulation even under parametric uncertainties. In this paper, a robust PI controller has been designed to homogeneously integrate aggregated Variable Speed Air-Conditioning (VSAC) systems into a Single-Area Power System (SAPS) with a reheat steam turbine. Based on parametric uncertainties, a worst-case model of the VSAC-SAPS system is obtained and its stability margins in terms of Phase and Gain margins are evaluated. Simulation results show that the dynamic responses of the power grid's frequency and power deviation of the grid were steady-state stable. Considering 30,000 homogeneous VSACs with 1.5 tonnes capacity, the rated power of the aggregated VSACs is 59MW (approx.). Simulation results showed that without a robust controller, the uncertain system was stable but sensitive to perturbations due to lower gain and phase margins. Frequency support services were utilized from the grid and aggregated VSAC systems, thereby significantly reducing the strain on the conventional grid. The robust PI controller in the VSAC system enhances transient stability by reducing overshoot and achieving faster damping, complementing the slower grid response. © 2025 IEEE.

关键词： Step response

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Modelling of CO2 Gas Sensing using Spectral Envelope of SoI Integrated Racetrack Resonator

Modelling of CO2 Gas Sensing using Spectral Envelope of SoI ...

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2024 JSAP-Optica Joint Symposia, JSAP 2024

作者： Vardhan, Shalini Gupta, Naveen Kumar Kushwaha, Aditya Verma, Priyanka Singh, Ritu Raj Department of Electronics and Communication Engineering Netaji Subhas University of Technology Delhi India Department of Electronics and Communication Engineering Indian Institute of Information Technology Ranchi India

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An Accurate Speech Emotion Analysis using Gradient Boosting and BiLSTM Techniques 1

An Accurate Speech Emotion Analysis using Gradient Boosting ...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Raina, Ineesh Singh Gupta, Nikhil Kumar, Gulshan Rathee, Geetanjali Netaji Subhas University of Technology Main Campus Department of Computer Science Engineering New Delhi India

ISBN: (纸本)9798350335774

In the field of speech emotion recognition, machine learning (ML) algorithms face a challenging problem in recognizing emotional states. Speech emotion recognition (SER) is crucial in many real-Time solutions, including human-robot interaction, assessing human behavior, and analyzing the emotional state of speakers. Due to its intricacy, SER is a task that is still difficult to complete but one that is becoming more and more crucial in emotional computing. Although there are many datasets available for SER, we are selecting RADVESS since we believe it to be the most ap-propriate. For many applications, such as a speech-To-speech translation system, feature selection can increase the accuracy of speech emotion recognition. Although tens of thousands of features can be retrieved from speech signals, it can be challenging to determine which elements are most closely associated to speaker emotional state. Most characteristics that are connected to emotional states are yet to be discovered. Mel Spectrogram and MFCC feature selection are the major topics of this research. Then passing these combined features through a modified CNN. In this paper we have used various neural network techniques (NN) for SER. The models we used include CNN, MLP, CNN-Attention-BiLSTM, Gradient Boosting. CNN-Attention-BiLSTM gives the best results among these models with accuracy of about 86 percent, precision of 0.834, recall of 0.802, F1-Score of 0.802. © 2023 IEEE.

关键词： Speech recognition

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