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Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

Intelligent Automation & Soft Computing 2023年第3期35卷 3829-3844页

作者： Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoor517127India School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computing College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India

Wind speed forecasting is important for wind energy *** the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed *** main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overﬁtting and non-linearity *** curse of dimensionality and overﬁtting issues are handled by using Boruta feature *** uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed *** model identiﬁes relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time *** proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and *** BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of *** experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

关键词： Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting

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Crop Recommendation and Irrigation System Using Machine Learning with Integrated IoT Devices 1

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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Khan, Mohammad Umair Dangi, Nikhil Kumar, Ankit Saproo, Vishwas Agrawal, Harsh Gaffar, H. Abdul School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (数字)9789819732425

ISBN: (纸本)9789819732418

In agriculture, timely and efficient irrigation is crucial to achieving maximum crop yield. However, traditional irrigation methods are often inefficient and wasteful, leading to water scarcity and abbreviated crop productivity. To solve these problems, we suggest an intelligent irrigation system that combines IoT devices and machine learning techniques to suggest the best crop for a particular area and deliver the best irrigation based on real-time weather data and soil moisture levels. The system accumulates data on soil nutrients, temperature, and sultriness utilizing IoT sensors and uses XGBoost, a popular machine learning algorithm, to recommend the most lucrative crop predicated on historical data. The system additionally incorporates authentic-time weather data from APIs and water level sensors to provide customized irrigation for each crop. Our system aims to improve crop productivity, minimize water waste, and avail farmers to make data-driven decisions to maximize their profits © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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Enhancing Image Super-Resolution with GANs Featuring Enhanced Deep Super-Resolution and Attention Mechanisms 4

Enhancing Image Super-Resolution with GANs Featuring Enhance...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Kumar, G. Aditya Kativar, Aditi Kannan, A. Vellore Institute of Technology Computer Science and Engineering Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350387063

Improving the quality and resolution of low- resolution digital images is an important task with far-reaching implications for a variety of applications, including medical imaging, surveillance, and content retrieval. To solve the high- resolution problem of a single image, this study proposes a new Generative Adversarial Network (GAN) model that includes an Enhanced Deep Super-Resolution (EDSR) generator. Our model includes a sensitivity function and a Squeeze-and- Excitation (SE) block in an EDSR design. These SE blocks directly model channel connections to optimize channel-specific performance responses and improve network capabilities without incurring significant computational costs. After extensive training using complex data processing methods such as rotation, rotation, and rotation, our model shows an impressive ability to enhance image details and traditional culture more accurately. Our strategy outperforms existing models both statistically and qualitatively with better signal-to- noise ratios (PSNRs) on the DIV2K dataset. These results, together with deep learning models, demonstrate the role of attentional processes in improving image resolution and lay the groundwork for further work in this field. © 2024 IEEE.

关键词： Generative adversarial networks

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Empirical techniques for effort estimation in designing effective ML models

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International Journal of computers and Applications 2025年第2期47卷 226-234页

作者： Padmanabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine Learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works and The time to reach the desired level of the model's functionality is essential to gauge the environment training for the model training and pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live (time until 1st code modification) and time-for-modification (time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models and frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the Standard Pipeline. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pipeline codes

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Convolutional Neural Network (CNN) for Fake Logo Detection: A Deep Learning Approach Using TensorFlow Keras API and Data Augmentation 3

Convolutional Neural Network (CNN) for Fake Logo Detection: ...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Murali, Saritha Gupta, Vaishnavi Saha, Soumyojyoti Sharan, Harsh Sinha, Prajwal Baibhav, Kumar Vellore Institute of Technology Department of Database Systems Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350372120

In numerous applications, including image retrieval, brand monitoring, and counterfeit identification, the detection and categorization of logos play pivotal roles. This study proposes a novel methodology employing convolutional neural networks (CNNs) for discerning fraudulent logos. The CNN's structure involves several convolutional layers at its core, accompanied by max-pooling layers to extract features for classification. Training the CNN model involves a varied dataset containing both authentic and fake logo images, and its effectiveness is assessed using well-established metrics like accuracy, precision, recall, and F1-score. The results of the study indicate that the proposed model is proficient in distinguishing counterfeit logos accurately. Furthermore, the study delves into potential applications and future directions of using CNN for identifying counterfeit logos. This research presents a robust methodology for counterfeit logo detection, thereby supporting endeavors aimed at curtailing the proliferation of counterfeit goods and upholding brand integrity. © 2024 IEEE.

关键词： Image classification

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An Efficient Real Time Decision Making System for Autonomous Vehicle Using Timber Chased Wolf Optimization Based Ensemble Classifier

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Journal of engineering science and technology Review 2023年第1期16卷 75-84页

作者： Jaiswal, Swati Mohan, B. Chandra School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

An autonomous vehicle is anticipated to increase comfort, safety, energy efficiency, emissions reduction, and mobility. The development of autonomous vehicles depends on decision-making algorithms that can handle complex and dynamic urban intersections. Hence in this research, a TCWO based ensemble classifier-based instantaneous decision-making model involved in driverless vehicles is devised. The ensemble classifier is designed through the combination of the Timber chased wolf optimization (TCWO) for detecting traffic sign along with decision process is performed. The bidirectional long short term (BiLSTM) and convolutional neural network (CNN) is combined to create the hybrid ensemble classifier, which is more effective. The TCWO is developed by hybridizing the characteristics of GWO and COA that helps to optimizing the classifiers and boots the classification performance. The TCWO based GAN helps for finding lane in the data that effectively reduces the problems of misclassification by generating synthetic data and training the data to differentiate the original and the generated data. The TCWO-based ensemble classifier attained the values of 98.88%, 98.36%, 98.88% while detecting the traffic sign, and 2.41%, 2.41%, 7.39% while predicting the lane using TCWO-based GAN, which is significantly higher than the competent technique © 2023 school of science, IHU. All rights reserved

关键词： Generative adversarial networks

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Medical Plant Detection using Transfer learning Coupled with K-Nearest Neighbour Algorithm 3

Medical Plant Detection using Transfer learning Coupled with...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Srambickal, Sai Subramanian Srivastava, Aman Somasundaram, S.K. School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Information Security Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Medical Plants are being used for more than a thousand year, with evidences dating before the Mauryain Era (around 322 BCE), they are widely used in the Ayurvedic school of medicine for therapeutic as well as medicinal purposes, it requires an expert to identify the species as well as the medical property associated with a given plant. This paper aims to use deep learning in order to identify a given plant using the leaves of the plant by using a pretrained Convolutional Neural Network (CNN) model namely ResNet50 and MobileNet to extract the latent vector which will then be passed to K-Nearest Neighbour (KNN) algorithm in order to identify the plant class, the best accuracy of the proposed models comes out to be 97.65%. © 2024 IEEE.

关键词： Neural network models

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Nuclei Segmentation in Histopathology Images Using Structure-Preserving Color Normalization Based Ensemble Deep Learning Frameworks

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computers, Materials & Continua 2023年第12期77卷 3077-3094页

作者： Manas Ranjan Prusty Rishi Dinesh Hariket Sukesh Kumar Sheth Alapati Lakshmi Viswanath Sandeep Kumar Satapathy Centre for Cyber-Physical Systems Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Centre for Advanced Data Science Vellore Institute of TechnologyChennai600127India

This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology *** purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping *** this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation ***,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain *** proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ *** findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.

关键词： Nuclei segmentation image segmentation ensemble U-Net deep learning histopathology image convolutional neural networks

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Elevating IDS Capabilities: The Convergence of SVM, Deep Learning, and RFECV in Network Security 2

Elevating IDS Capabilities: The Convergence of SVM, Deep Lea...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Kumar, G Aditya Katiyar, Aditi Srinivasan, Kathiravan Computer Science Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

By presenting an improved Intrusion Detection System (IDS) that combines deep learning with support vector machines (SVM), this research increases network security. The main goal is to increase the accuracy of SVM detection by using a methodical feature selection and optimization technique that is tailored to the complexity of network intrusions. 35 out of the 42 features were chosen for feature selection using RFECV, an algorithmic feature selection technique in machine learning. To ensure that the features preserved are those that contribute most to the model's predictive capacity and that the redundant features are deleted, techniques such as RFECV and feature priority ranking using ExtraTreesClassifier take the model's performance into account during the feature selection process. To improve classifier performance, optimization strategies such as hyperparameter tuning are used, focusing on important data and cutting down on redundancies. The performance of several SVM kernel functions, including linear, polynomial, RBF, and sigmoid, is compared in the study. The Linear model combined with the deep learning model was shown to perform the best. Our model outperforms current IDS frameworks, as demonstrated by comparative analysis, confirming the efficacy of integrating SVMs with deep learning for real-time threat detection. The KDD Cup 99 dataset, which has been widely used as a benchmark for assessing the performance of different IDS models, was used for the work. It offers a consistent, varied, and large dataset so that researchers may evaluate and contrast their methods. Researchers can experiment with feature selection and reduction strategies to enhance IDS performance because of the dataset's broad set of features. © 2024 IEEE.

关键词： Support vector machines

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

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