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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Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: Jan. 24, 2024

Abstract Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate using screening methods improves outcomes, but balance between overdiagnosis early remains debated. Using Deep Learning (DL) algorithms for offers a promising solution accurate efficient diagnosis, particularly cases where imaging challenging. In this paper, we propose Cancer Detection Model (PCDM) model automatic diagnosis cancer. It proves its clinical applicability to aid management real-world healthcare environments. The PCDM modified ResNet50-based architecture that integrates faster R-CNN dual optimizers improve performance process. trained on large dataset annotated medical images, experimental results show proposed outperforms both ResNet50 VGG19 architectures. Specifically, achieves high sensitivity, specificity, precision, accuracy rates 97.40%, 97.09%, 97.56%, 95.24%, respectively.

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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Using deep learning on microscopic images for white blood cell detection and segmentation to assist in leukemia diagnosis

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(2)

Published: Jan. 21, 2025

Language: Английский

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Intelligent wearable vision systems for the visually impaired in Saudi Arabia

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

Published: Feb. 12, 2025

Language: Английский

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Toward precision cardiology: a transformer-based system for adaptive prediction of heart disease

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

Published: May 2, 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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Towards sustainable energy management: Leveraging explainable Artificial Intelligence for transparent and efficient decision-making

Sustainable Energy Technologies and Assessments, Journal Year: 2025, Volume and Issue: 78, P. 104348 - 104348

Published: May 12, 2025

Language: Английский

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VisTA: vision transformer-attention enhanced CNN ensemble for optimized classification of acute lymphoblastic leukemia benign and progressive malignant stages

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 5, 2024

Language: Английский

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Explainable Enhanced Recurrent Neural Network for lie detection using voice stress analysis

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(11), P. 32277 - 32299

Published: Sept. 20, 2023

Abstract Lie detection is a crucial aspect of human interactions that affects everyone in their daily lives. Individuals often rely on various cues, such as verbal and nonverbal communication, particularly facial expressions, to determine if someone truthful. While automated lie systems can assist identifying these current approaches are limited due lack suitable datasets for testing performance real-world scenarios. Despite ongoing research efforts develop effective reliable methods, this remains work progress. The polygraph, voice stress analysis, pupil dilation analysis some the methods currently used task. In study, we propose new algorithm based an Enhanced Recurrent Neural Network (ERNN) with Explainable AI capabilities. ERNN, long short-term memory (LSTM) architecture, was optimized using fuzzy logic hyperparameters. LSTM model then created trained dataset audio recordings from interviews randomly selected group. proposed ERNN achieved accuracy 97.3%, which statistically significant problem analysis. These results suggest it possible detect patterns voices individuals experiencing explainable manner.

Language: Английский

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