The main goal of the research is to make agricultural yield predictions more accurate through the use of the Naive Bayes (NB) method with a Recurrent Neural Net The main goal of the research is to make agricultural yield predictions more accurate through the use of the Naive Bayes (NB) method with a Recurrent Neural Net

Change detection from time series multispectral Landsat imagery has been an active research in remote sensing for several years to monitor the ecosystem, environment, climate and so on. This study is focused on detecting the changes in surface water by the integration of fusion and image classification techniques in multi-temporal multispectral Landsat images. The panchromatic band and the multispectral band of Landsat OLI and TM images respectively, were fused using undecimated wavelet transform to get the pan-sharpened image. Then classification techniques like Maximum Likelihood, Support Vector Machine, Artificial Neural Network and Random Forest were employed for extracting the water pixels and changed pixels. The performances of these classification techniques were analyzed based on metrics such as overall error, commission error, precision, recall, overall accuracy, kappa coefficients and the results show that the application of random forest classifier on pansharpened image outperforms in extracting the water pixels and also in highlighting the changes with maximum accuracy.

Carbon Monoxide Detection Based IoT was selected to be the title for this project. IoT platform in recent year have grown and expanded to different applications. These Carbon Monoxide Detection System was designed and implemented with the use of problem statements, investigating previous work done by other people whilst reviewing the work that was done and proposed methodology was given. The finalised design was built and thus enhanced with real environmental testing that took place in several locations. The overall system was tested and evaluated. Thus, based on the design and implementation of the system, the results were obtained.

The aim of this project is to apply Fuzzy C-means clustering for detecting the presence of gallbladder stones, and then compare the outcomes with those obtained The aim of this project is to apply Fuzzy C-means clustering for detecting the presence of gallbladder stones, and then compare the outcomes with those obtained

This paper introduces an enhanced Capon beamforming approach that is further integrated with the Multiple Signal Classification (MUSIC) method to achieve superior Direction of Arrival (DOA) estimation using coprime arrays. The proposed enhancement to the standard Capon beamformer focuses on improving its robustness against steering vector mismatches, which significantly boosts its performance in complex signal environments. By optimizing the beamformer's spatial filtering capability, the improved method mitigates signal distortion and enhances resolution. Building on this enhanced Capon beamformer, this study integrates it with the MUSIC method to leverage the strengths of both approaches. The coprime array configuration allows for an increased number of virtual sensors, enabling higher degrees of freedom and improving the resolution of both coherent and uncorrelated signals. This combined Capon-MUSIC framework provides an efficient solution for accurate DOA estimation, even in scenarios where traditional methods fail. The effectiveness of this hybrid approach is evaluated in disaster management applications, where precise signal localization is crucial for tasks such as emergency communication, search and rescue operations, and resource deployment. The simulation results demonstrate that the integrated method outperforms conventional techniques, delivering improved accuracy, robustness, and computational efficiency, making it ideal for real-world disaster response scenarios.

Driver drowsiness and fatigue are leading causes of road accidents, particularly due to drivers having to operate vehicles for long hours under challenging phys Driver drowsiness and fatigue are leading causes of road accidents, particularly due to drivers having to operate vehicles for long hours under challenging phys