An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

Anbananthen, Kalaiarasi Sonai Muthu; Subbiah, Sridevi; Deisy, C.; Sivakumar, Prithika; Somasundaram, Varsha; Velshankar, Kethaarini Harshana; Khan, Mohamed K.

doi:10.12688/f1000research.73009.1

Cited by 22 publications

(4 citation statements)

References 15 publications

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“…They added a Multi-Scale Feature Pyramid Network that works supported by a simple U-shaped structure. Anbananthen et al [13] and Subbiah et al [14] used feature selection methods and hybrid machine learning algorithms for crop yield prediction. Sridevi et al [15] used pattern-matching algorithms for predicting time series data.…”

Section: -Literature Reviewmentioning

confidence: 99%

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

Anbananthen,

Subbiah,

Baskar

et al. 2023

Emerg Sci J

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The eye is an essential sensory organ that allows us to perceive our surroundings at a glance. Losing this sense can result in numerous challenges in daily life. However, society is designed for the majority, which can create even more difficulties for visually impaired individuals. Therefore, empowering them and promoting self-reliance are crucial. To address this need, we propose a new Android application called “The Eye” that utilizes Machine Learning (ML)-based object detection techniques to recognize objects in real-time using a smartphone camera or a camera attached to a stick. The article proposed an improved YOLOv5l algorithm to improve object detection in visual applications. YOLOv5l has a larger model size and captures more complex features and details, leading to enhanced object detection accuracy compared to smaller variants like YOLOv5s and YOLOv5m. The primary enhancement in the improved YOLOv5l algorithm is integrating L1 and L2 regularization techniques. These techniques prevent overfitting and improve generalization by adding a regularization term to the loss function during training. Our approach combines image processing and text-to-speech conversion modules to produce reliable results. The Android text-to-speech module is then used to convert the object recognition results into an audio output. According to the experimental results, the improved YOLOv5l has higher detection accuracy than the original YOLOv5 and can detect small, multiple, and overlapped targets with higher accuracy. This study contributes to the advancement of technology to help visually impaired individuals become more self-sufficient and confident. Doi: 10.28991/ESJ-2023-07-05-011 Full Text: PDF

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Section: -Literature Reviewmentioning

confidence: 99%

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

Anbananthen,

Subbiah,

Baskar

et al. 2023

Emerg Sci J

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“…As a result, this work proposes a novel hate speech detection as a ternary classification task to reduce the complexity and computation of NLP processing by reducing the dataset dimension. [8] inference from the stacked regression model, two-level attentions are implemented as a stacked model. The novelty of the proposed approach is to use dual-level cross attention on POS tagging and aspectbased sentiment polarity as the pattern-based on deep hate speech detection (PDHS).…”

Section: Bpattern Featuresmentioning

confidence: 99%

PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation

Sharmila¹,

Anbananthen

Chelliah³

et al. 2022

IEEE Access

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Automatic hate speech identification in unstructured Twitter is significantly more difficult to analyze, posing a significant challenge. Existing models heavily depend on feature engineering, which increases the time complexity of detecting hate speech. This work aims to classify and detect hate speech using a linguistic pattern-based approach as pre-trained transformer language models. As a result, a novel Pattern-based Deep Hate Speech (PDHS) detection model was proposed to detect the presence of hate speech using a cross-attention encoder with a dual-level attention mechanism. Instead of concatenating the features, our model computes dot product attention for better representation by reducing the irrelevant features. The first level of Attention is extracting aspect terms using predefined parts-of-speech tagging. The second level of Attention is extracting the sentiment polarity to form a pattern. Our proposed model trains the extracted patterns with term frequency, parts-of-speech tag, and Sentiment Scores. The experimental results on Twitter Dataset can learn effective features to enhance the performance with minimum training time and attained 88%F1Score.

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“…The prediction system was constructed using a five-fold cross-validation method on the training set [28]. Based on Anbananthen et al [29], the entire data set is split into two parts: 70% of the data set is used to train the model, whereas 30% is used to test it. The data was divided into five equal portions at random.…”

Section: -2-data Partitioningmentioning

confidence: 99%

A Comparative Performance Analysis of Hybrid and Classical Machine Learning Method in Predicting Diabetes

Anbananthen

Busst²,

Kannan³

et al. 2022

Emerg Sci J

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Diabetes mellitus is one of medical science’s most important research topics because of the disease’s severe consequences. High blood glucose levels characterize it. Early detection of diabetes is made possible by machine learning techniques with their intelligent capabilities to accurately predict diabetes and prevent its complications. Therefore, this study aims to find a machine learning approach that can more accurately predict diabetes. This study compares the performance of various classical machine learning models with the hybrid machine learning approach. The hybrid model includes the homogenous model, which comprises Random Forest, AdaBoost, XGBoost, Extra Trees, Gradient Booster, and the heterogeneous model that uses stacking ensemble methods. The stacking ensemble or stacked generalization approach is a meta-classifier in which multiple learners collaborate for prediction. The performance of the homogeneous hybrid models, Stacked Generalization and the classic machine learning methods such as Naive Bayes and Multilayer Perceptron, k-Nearest Neighbour, and support vector machine are compared. The experimental analysis using Pima Indians and the early-stage diabetes dataset demonstrates that the hybrid models achieve higher accuracy in diagnosing diabetes than the classical models. In the comparison of all the hybrid models, the heterogeneous model using the Stacked Generalization approach outperformed other models by achieving 83.9% and 98.5%. Doi: 10.28991/ESJ-2023-07-01-08 Full Text: PDF

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An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

Cited by 22 publications

References 15 publications

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation

A Comparative Performance Analysis of Hybrid and Classical Machine Learning Method in Predicting Diabetes

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