Titlebook: Machine Intelligence, Tools, and Applications; Proceedings of the I Satchidananda Dehuri,Sung-Bae Cho,Ashish Ghosh Conference proceedings 2

Demonstrate

mutineer

intuition

Software Maintenance Prediction Using Regression Models, the perofemance of the regression models, Elastic Net Regression and Lasso Regression are perofrmed better tahn otehr models. The Elastic Net and Lasso Regression models achieved RSMSE values are 400.87, 146.86, 80.17,168.79, 272.46 withrespetive datsets.

Talkative

Rough Set, ELM Classifier and Deep Architecture for Remote Sensing Images,tional requirements without compromising model performance. The designed model is evaluated on two data sets i.e., UC Merced and RSSCN7. The model’s superior classification performance is compared with Support Vector Machine (SVM) and similar methods on the ground of overall accuracy, precision, F1 score etc. measures.

粗野

Machine Learning-Based Analysis and Forecasting of Electricity Demand in Misamis Occidental, Philipls to significantly improve energy management in smaller urban areas, addressing the need for accurate and reliable electricity demand forecasting. ARIMA, Machine Learning, Electricity Demand Forecasting, Energy Management, Sustainability, Misamis Occidental.

Brain-Waves

拍下盜公款

Accede

不易燃

點(diǎn)燃

Design of an Efficient Model for Satellite Image Classification Using Graph Neural Networks and Elework extends to land cover monitoring, environmental conservation, urban planning, and disaster management. The fusion of Graph Neural Networks and Elephant Herding Optimization highlights the potential of innovative deep learning techniques in remote sensing and geospatial analysis, aiding in a more sustainable decision-making process.