Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(11), P. 5695 - 5714

Published: Jan. 11, 2024

Abstract Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical performance, and prevailing weather patterns, provide personalized recommendations. In response the growing demand transparency interpretability agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective is empower farmers with comprehensible insights into recommendation process, surpassing opaque nature conventional machine learning models. rigorously compares prominent models, Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), Multimodal (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Absolute (MAE), R-squared (R2). empirical results unequivocally establish superior performance XAI-CROP. It achieves impressively low MSE 0.9412, indicating highly accurate yield predictions. Moreover, MAE 0.9874, consistently maintains errors below critical threshold 1, reinforcing its reliability. robust R 2 value 0.94152 underscores XAI-CROP's ability explain 94.15% data's variability, highlighting explanatory power.

Language: Английский

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Exploring the effects of pandemics on transportation through correlations and deep learning techniques

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(3), P. 7295 - 7316

Published: June 7, 2023

The COVID-19 pandemic has had a significant impact on human migration worldwide, affecting transportation patterns in cities. Many cities have issued "stay-at-home" orders during the outbreak, causing commuters to change their usual modes of transportation. For example, some transit/bus passengers switched driving or car-sharing. As result, urban traffic congestion changed dramatically, and understanding these changes is crucial for effective emergency management control efforts. While previous studies focused natural disasters major accidents, only few examined pandemic-related patterns. This paper uses correlations machine learning techniques analyze relationship between authors simulated models five different networks proposed Traffic Prediction Technique (TPT), which includes an Impact Calculation Methodology that Pearson's Correlation Coefficient Linear Regression, as well Module (TPM). paper's main contribution introduction TPM, Convolutional Neural Network predict results indicate strong correlation spread patterns, CNN high accuracy rate predicting impacts.

Language: Английский

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Toward interpretable credit scoring: integrating explainable artificial intelligence with deep learning for credit card default prediction

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 36(9), P. 4847 - 4865

Published: Dec. 21, 2023

Language: Английский

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A machine-learning approach for stress detection using wearable sensors in free-living environments

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 179, P. 108918 - 108918

Published: July 18, 2024

Language: Английский

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Utilizing social media and machine learning for personality and emotion recognition using PERS

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(33), P. 23927 - 23941

Published: Sept. 5, 2023

Language: Английский

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HCNNet: a hybrid convolutional neural network for abnormal human driver behaviour detection

Sadhana, Journal Year: 2025, Volume and Issue: 50(1)

Published: Jan. 25, 2025

Language: Английский

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A deep learning approach to analyse stress by using voice and body posture

Soft Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Language: Английский

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Learning-based estimation of operators’ psycho-physiological state

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127097 - 127097

Published: March 1, 2025

Language: Английский

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Language: Английский

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CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease

Bioengineering, Journal Year: 2024, Volume and Issue: 11(8), P. 822 - 822

Published: Aug. 12, 2024

The global prevalence of cardiovascular diseases (CVDs) as a leading cause death highlights the imperative need for refined risk assessment and prognostication methods. traditional approaches, including Framingham Risk Score, blood tests, imaging techniques, clinical assessments, although widely utilized, are hindered by limitations such lack precision, reliance on static variables, inability to adapt new patient data, thereby necessitating exploration alternative strategies. In response, this study introduces CardioRiskNet, hybrid AI-based model designed transcend these limitations. proposed CardioRiskNet consists seven parts: data preprocessing, feature selection encoding, eXplainable AI (XAI) integration, active learning, attention mechanisms, prediction prognosis, evaluation validation, deployment integration. At first, preprocessed cleaning handling missing values, applying normalization process, extracting features. Next, most informative features selected categorical variables converted into numerical form. Distinctively, employs learning iteratively select samples, enhancing its efficacy, while mechanism dynamically focuses relevant precise prediction. Additionally, integration XAI facilitates interpretability transparency in decision-making processes. According experimental results, demonstrates superior performance terms accuracy, sensitivity, specificity, F1-Score, with values 98.7%, 99%, respectively. These findings show that can accurately assess prognosticate CVD risk, demonstrating power surpass conventional Thus, CardioRiskNet's novel approach high advance management CVDs provide healthcare professionals powerful tool care.

Language: Английский

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