Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian Mutation–Spider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset.

The Adder is the important part in any processor/controller design. Till date there are a plenty of 1-bit full-adder circuits which have been proposed and designed. In this paper we have a analytic and comparative description of various full adder circuits, considering various constraints like power consumption, speed of operation and area. The circuits are designed in the virtuoso platform, using cadence tool with the available GPDK - 45nm kit. The Fulladder circuits with the most 28 transistor to the one with only 6 transistors are successfully designed, simulated and compared for various parameters like power consumption, speed of operation(delay) and area (transistor count), and finally concluded the best designs, that suite for the particular specifications.

In recent years, the accurately and timely predict the crop yield has become hard to achieve because crop growth has complex and changing patterns across space and time. However, the existing model struggle to capture dynamic spatial temporal, nonlinear and external environmental factors such as temperature, soil conditions and precipitation, due to its graph performance. To overcome this problem, this paper proposed a Knowledge guided Graph Attention network with a Mixture Density Network (KGAT-MDN) to predict crop yield with more flexibility. The proposed KGAT-MDN first uses a 3D Convolutional Neural Network (CNN) to extracts important features. Next, the graph attention network used to model, the proposed method as a node-level regression problem on a graph built from the distribution system. After, mixture density networks are used to predict multiple possible outcomes at once, along with their importance, which are represented as Gaussian distributions. At last, location-aware Spatial Attention Graph Network is used, which uses geospatial knowledge to combine the features of nearby areas used for the final result. The experiments results obtained 0.50 for RMSE, 0.95 for R² and 10.11 for MAPE on Crop yield prediction dataset by outperforming the existing KSTAGE in predicting crop yield in agriculture.

In recent years, intelligent plant disease detection has become an essential task in agriculture for ensuring crop health and improving yield. Detection and classification of plant diseases remain a challenging problem due to the vast numbers of plants species worldwide and the numerous diseases that negatively affect crop production. However, existing Deep Learning (DL) methods are used for plant disease detection and classification suffer from overfitting, limited robustness, and lack of interpretability. To overcome these issues, this paper proposes a Hybrid Vision Graph Neural Network with Ontology-Driven Knowledge Graph Embedding (HV-GNN-OKGE) method for effective plant disease detection and classification. The HV-GNN method captures both local and global structural features through isotropic and pyramid layers. While the ontology-driven embedding integrates semantic relationships among plant species, symptoms, and diseases to enrich classification with domain knowledge. The performance of the proposed HV-GNN-OKGE method is evaluated using two publicly available datasets: Plant Leaf Dataset (4,590 images, 22 classes) and PlantVillage (2,052 images, 2 classes) are used for training and evaluation, with preprocessing techniques including augmentation, resizing, and normalization to enhance image quality. Experimental results demonstrate that the proposed HV-GNN-OKGE achieves an accuracy of 99.95%, significantly outperforming existing deep learning models in plant disease detection and classification.

This project introduces a comprehensive home automation system leveraging Wi-Fi connectivity and ESP32 micro controllers, aimed at enhancing modern living through seamless automation and user-centric design. The background emphasizes the fusion of IoT technology with home automation, highlighting Wi-Fi's significance in enabling remote monitoring and control of household devices. Methodologically, the project integrates relay modules and DHT11 sensors, orchestrated through a user-friendly smartphone application. Results demonstrate successful implementation and testing, showcasing functionality, reliability, and user-friendliness. Real-time environmental monitoring and remote appliance control fulfill the objectives of enhancing home comfort, convenience, and energy efficiency. The conclusion underscores the project's contribution to advancing smart home technologies, offering users a scalable and customizable solution. Future research opportunities include enhancing system capabilities and addressing security and privacy concerns.