Vehicular Network Security: Threats, Vulnerabilities and Countermeasures DOI
Dharmesh Dhabliya, N. Thangarasu,

Shivam Khurana

et al.

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 6

Published: June 24, 2024

Language: Английский

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study DOI Creative Commons

Sumarsono Sumarsono,

Fatma Ayu Nuning Farida Afiatna,

Nur Muflihah

et al.

International Journal on Advanced Science Engineering and Information Technology, Journal Year: 2024, Volume and Issue: 14(1), P. 118 - 130

Published: Feb. 14, 2024

In Indonesia, the agriculture industry has been more reluctant than other sectors to adopt IoT, IT, and AI technology. Utilizing this technology will enable precision agriculture. This research aims make implement an IoT-Webserver-Android Machine Learning-based soil PH factor monitoring tool system. The steps for making system are divided into three subsystems. first is a multiple sensors data acquisition subsystem, consisting of PH-Moisture, Temperature-Humidity, Sunlight. connected Arduino Uno microcontroller serial communication with ESP 8266 Wi-Fi module. second part subsystem local web application, which contains MySQL database page. third Android includes real-time Firebase application mobile display. results have implemented display expected outcomes. It clear from performance outcomes system's evaluation provide precise statistical values. Then, Learning analysis generates accurate prediction models. demonstrated that applicable favorable impact on factor. implication future should be added Nitrogen-Phosphorus-Potassium measure nutrients. Also, edge-analysis integrated in analyzing

Language: Английский

Citations

5
A comprehensive review towards resilient rainfall forecasting models using artificial intelligence techniques DOI Creative Commons
Md. Abu Saleh, H. M. Rasel,

Briti Ray

et al.

Green Technologies and Sustainability, Journal Year: 2024, Volume and Issue: 2(3), P. 100104 - 100104

Published: May 27, 2024

Rainfall is one of the remarkable hydrologic variables that directly connected to sustainable environment for any region over globe. The present study aims review different research papers on rainfall forecasting using artificial intelligence (AI) models including a bibliographic assessment most popular AI and comparison results based accuracy parameters. 39 journal papers, published in renowned international journals from 2000 2023, were studied extensively categorize modeling techniques, best models, characteristics input data, period variables, data division, so forth. Although certain drawbacks still exist, reviewed studies suggest may help simulate various geographic locations. In some cases, splitting mechanism was delivered model itself gets improved. recommendations will future researchers fill gaps, especially tuning hyperparameters while building training models. Hybrid advised cases minimize gap between simulated observed data. All aimed achieve resilient era climate change.

Language: Английский

Citations

4
Optimizing non-linear autoregressive networks with Bird Sea Lion algorithms for effective rainfall forecasting DOI

C. Vijayalakshmi,

M. Pushpa

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 22, 2025

Language: Английский

Citations

0
CNN–RNN Hybrid Deep Learning Model for Monthly Rainfall Prediction DOI
Shuvajit Bhattacharya, Padmalini Singh

Smart innovation, systems and technologies, Journal Year: 2025, Volume and Issue: unknown, P. 549 - 559

Published: Jan. 1, 2025

Language: Английский

Citations

0
Results with the Use of Artificial Intelligence with OpenAI Applied to Internet of Things Classes DOI
Antonio Carlos Bento,

Elsa Yolanda Torres-Torres,

Sergio Camacho-Léon

et al.

Published: May 27, 2024

Language: Английский

Citations

0
Aprendizaje por refuerzo como soporte a la predicción de la precipitación mensual. Caso de estudio: Departamento de Boyacá - Colombia DOI Creative Commons
Jimmy Alejandro Zea Gutiérrez, Marco Javier Suárez Barón, Juan-Sebastián González-Sanabria

et al.

TecnoLógicas, Journal Year: 2024, Volume and Issue: 27(60), P. e3017 - e3017

Published: June 27, 2024

La emisión de gases efecto invernadero, atribuida directa o indirectamente a la actividad humana, es principal causa del cambio climático nivel mundial. Entre los emitidos, el dióxido carbono (CO2) que más contribuye variación espacio temporal magnitudes físicas como humedad relativa, presión atmosférica, temperatura ambiente y, manera significativa, precipitación. El objetivo investigación fue presentar un análisis predicción precipitación mensual en departamento Boyacá mediante uso modelos basados aprendizaje reforzado (RL, por sus siglas inglés). metodología empleada consistió extraer datos desde CHIRPS 2,0 (Climate Hazards Group InfraRed Precipitation with Station data, versión 2,0) con una resolución espacial 0,05° posteriormente fueron preprocesados para implementación enfoques simulación Montecarlo y profundo (DRL, inglés) proporcionar predicciones mensual. Los resultados obtenidos demostraron DRL generan significativas Es esencial reconocer convencionales Aprendizaje profundo, Memoria Corto Plazo (LSTM) Redes Convolucionales (ConvLSTM), pueden superar términos precisión predicción. Se concluye técnicas refuerzo detecta patrones información ser usados soporte estrategias dirigidas mitigar riesgos económicos sociales derivados fenómenos climáticos.

Citations

0
A performance and interpretability assessment of machine learning models for rainfall prediction in the Republic of Ireland DOI Creative Commons

Menatallah Abdel Azeem,

Soumyabrata Dev

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 12, P. 100515 - 100515

Published: Aug. 24, 2024

Rainfall prediction significantly impacts agriculture, water reserves, and preparations for flooding conditions. This research examines the performance interpretability of machine learning (ML) models rainfall in Republic Ireland. The study uses a brute force approach Leave One Feature Out (LOFO) methodology to evaluate model under highly correlated variables. Results reveal consistent across ML algorithms, with average Area Under Curve Precision-Recall (AUC-PR) scores ranging from 0.987 1.000, certain features such as atmospheric pressure soil moisture deficits demonstrating significant influence on outcomes.SHapley Additive exPlanations (SHAP) values provide insights into feature importance, reaffirming significance prediction. underscores importance selection enhancing accuracy usability

Language: Английский

Citations

0
Compression-Based Image Retrieval for Efficient Networked Applications DOI

T. Kuppuraj,

Paramjit Baxi,

N.T. Velusudha

et al.

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 6

Published: June 24, 2024

Language: Английский

Citations

0
Crafting Communications Software Design Parameters for Network Applications DOI

Divya Paikaray,

Deepak Kumar,

M M Rekha

et al.

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 5

Published: June 24, 2024

Language: Английский

Citations

0
Unsupervised Feature Learning for Image Segmentation DOI
B. Umamaheswari, Divya Aggarwal,

B Spoorthi

et al.

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 6

Published: June 24, 2024

Language: Английский

Citations

0