Streamlit Application for Advanced Ensemble Learning Methods in Crop Recommendation Systems – A Review and Implementation

Indian Journal of Science and Technology, Journal Year: 2023, Volume and Issue: 16(48), P. 4688 - 4702

Published: Dec. 28, 2023

Objectives: This article explores the integration of advanced ensemble machine learning methods within precision agriculture, aiming to enhance reliability and practical utility crop recommendation systems. The incorporation Streamlit framework in development process underpins our objective deliver a user-friendly tool that provides farmers agricultural analysts with actionable insights. Methods: A thorough literature review artificial intelligence applications agriculture serves as foundation study, strong emphasis placed on sophisticated techniques such stacking, an ensembles, federated learning. evaluation methodology entails comparative analysis where these cutting-edge are juxtaposed against standard benchmarks ascertain their performance improvement. In addition conceptual analysis, we implement system using framework, emphasizing usability accessibility for end-users interact predictions based soil data. Findings: empirical results demonstrate chosen significantly improve predictive performance, recording up 15% accuracy increment over traditional algorithms. Their adaptability varied datasets, coupled robust privacy-preserving properties, stand out. When deploying Streamlit-based application, note marked increase 20% user efficiency, solidifying system\'s crucial role bolstering resilient management tactics. Novelty: research pioneers study innovative techniques, married app enhanced experience data-driven agriculture. Our findings emphasize critical need incorporating methodologies into real-world practices, fostering significant paradigm shift data analytics management. synergy between powerful Streamlit-built interactive interface represents step forward translating complex computational practical, on-the-ground tools professionals. Keywords: Machine Learning, Advanced Ensemble Streamlit.

Language: Английский

---

A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102500 - 102500

Published: Jan. 28, 2024

The importance of water quality models has increased as their inputs are critical to the development risk assessment framework for environmental management and monitoring rivers. However, with advent a plethora recent advances in ML algorithms better predictions possible. This study proposes causal effect model by considering climatological such temperature precipitation along geospatial information related agricultural land use factor (ALUF), forest (FLUF), grassland usage (GLUF), shrub (SLUF), urban (ULUF). All these factors included input data, whereas four Stream Water Quality parameters (SWQPs) Electrical Conductivity (EC), Biochemical Oxygen Demand (BOD), Nitrate, Dissolved (DO) from 2019 2021 taken outputs predict Godavari River Basin quality. In preliminary investigation, out SWQPs, nitrate's coefficient variation (CV) is high, revealing close association climate practices across sampling stations. authors' earlier study, using single-layer Feed-Forward Neural Network (FFNN) showed improved performance predicting cause linked metrics. To achieve prediction, stacked ANN meta-model nine conventional machine learning (ML) models, including Extreme Gradient Boosting (XGB), Extra Trees (ET), Bagging (BG), Random Forest (RF), AdaBoost or Adaptive (ADB), Decision Tree (DT), Highest (HGB), Light Method (LGBM), (GB), were compared this study. According study's findings, outperformed stand-alone FFNN same dataset superior predictive capabilities terms accuracy forecasting variable interest. For instance, during testing, determination (R2) (BOD) 0.72 0.87. Furthermore, Artificial (ANN) meta that was reinforced (ET) base performed than individual (from R2 = 0.87 0.91 BOD testing). By new framework, effort hyperparameter tuning can be minimized.

Language: Английский

---

A survey of multimodal hybrid deep learning for computer vision: Architectures, applications, trends, and challenges

Information Fusion, Journal Year: 2023, Volume and Issue: 105, P. 102217 - 102217

Published: Dec. 30, 2023

Language: Английский

---

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 351, P. 119896 - 119896

Published: Jan. 3, 2024

Language: Английский

---

Dissolved Oxygen Forecasting in the Mississippi River: Advanced Ensemble Machine Learning Models

Environmental Science Advances, Journal Year: 2024, Volume and Issue: 3(11), P. 1537 - 1551

Published: Jan. 1, 2024

This study introduces advanced ensemble machine learning models for predicting dissolved oxygen in the Mississippi River, offering high accuracy across various forecast horizons and improving environmental monitoring.

Language: Английский

---

A Novel Deep Stacking-Based Ensemble Approach for Short-Term Traffic Speed Prediction

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 15222 - 15235

Published: Jan. 1, 2024

Advanced technologies, driven by extensive data analysis, support the concept of intelligent cities, which aim to enhance quality people's lives, minimize consumption energy, reduce pollution, and promote economic growth. The transportation network is a crucial component this vision in urbanized cities. However, massive increase road traffic poses significant challenge achieving vision. Developing an system requires accurately predicting speed. This paper proposes novel deep stacking-based Ensemble model with two-layer structure address problem forecasting speed networks solve congestion problems. Firstly, advanced machine learning such as eXtreme Gradient Boosting(XGB), Random Forest(RF), Extra Tree(ET) base learners are used predict short-term In next phase, Multi-Layer Perceptron (MLP) meta-learner technique, employing various combinations aforementioned approaches accuracy prediction. proposed approach has capability analyze, extract, aggregate features from primary order generate more refined accurate forecasts. study publicly available dataset Floating Cars Data collected real for evaluation. Mutual information regression feature selection technique obtain training these models. performance results compared state-of-the-art prediction Results show that ensemble strategy outperforms conventional large margin HA, KNN, SVR, DT, T-GCN, A3TGCN demonstrate notable reduction 9.71% RMSE 15.4% MAE, indicating enhanced accuracy. Furthermore, our achieved substantial improvement 13.80% R ² 11.64% EV 15-minute horizon.

Language: Английский

---

Probe into the volumetric properties of binary mixtures: Essence of regression-based machine learning algorithms

Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 399, P. 124498 - 124498

Published: March 15, 2024

Language: Английский

---

Probabilistic coastal wetland mapping with integration of optical, SAR and hydro-geomorphic data through stacking ensemble machine learning model

Ecological Informatics, Journal Year: 2023, Volume and Issue: 77, P. 102273 - 102273

Published: Aug. 20, 2023

Language: Английский

---

Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 10865 - 10885

Published: Jan. 1, 2024

Deep learning excels at managing spatial and temporal time series with variable patterns for streamflow forecasting, but traditional machine algorithms may struggle complicated data, including non-linear multidimensional complexity. Empirical heterogeneity within watersheds limitations inherent to each estimation methodology pose challenges in effectively measuring appraising hydrological statistical frameworks of variables. This study emphasizes forecasting the region Johor, a coastal state Peninsular Malaysia, utilizing 28-year streamflow-pattern dataset from Malaysia's Department Irrigation Drainage Johor River its tropical rainforest environment. For this dataset, wavelet transformation significantly improves resolution lag noise when historical data are used as lagged input variables, producing 6% reduction root-mean-square error. A comparative analysis convolutional neural networks artificial reveals these models' distinct behavioral patterns. Convolutional exhibit lower stochasticity than dealing complex transformed into format suitable modeling. However, suffer overfitting, particularly cases which structure is overly simplified. Using Bayesian networks, we modeled network weights biases probability distributions assess aleatoric epistemic variability, employing Markov chain Monte Carlo bootstrap resampling techniques. modeling allowed us quantify uncertainty, providing confidence intervals metrics robust quantitative assessment model prediction variability.

Language: Английский

---

Data-Based Solar Radiation Forecasting with Pre-Processing Using Variational Mode Decomposition

2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), Journal Year: 2023, Volume and Issue: unknown, P. 2061 - 2066

Published: July 3, 2023

This paper presents a hybrid method for accurately predicting Global Horizontal Irradiance (GHI) over the following 24 hours to forecast energy production from photo-voltaic system in positive building. The input data is preprocessed using Variational Mode Decomposition (VMD) extract wide-bandwidth features and decompose them into smooth modes focused on specific frequency ranges. Salp Swarm Algorithm (SSA) utilized identify optimal VMD parameters accurate extraction. analysis employed most critical of features. model's efficiency further enhanced by performing residual preprocessing step between observed solar radiance decomposed modes. Stacking technique (ST) predict 24-hour GHI residual, which are summed reconstruct final signal. proposed method's performance evaluated Normalized Root Mean Square Error (NRMSE) Absolute (NMAE) metrics three years available (2019–2022) Rabat, compared with model based raw data. results show that achieved promising an NRMSE 1.35% NMAE 0.82% cloudy day.

Language: Английский

---