A5047312965- Smart Agriculture and AI
- Photovoltaic System Optimization Techniques
- Plant Disease Management Techniques
- AI in cancer detection
- Spectroscopy and Chemometric Analyses
- Smart Systems and Machine Learning
- Artificial Intelligence in Healthcare
- Energy Efficient Wireless Sensor Networks
- Network Security and Intrusion Detection
- COVID-19 diagnosis using AI
- Solar Thermal and Photovoltaic Systems
- Photovoltaic Systems and Sustainability
- Imbalanced Data Classification Techniques
- Mobile Ad Hoc Networks
- Energy Harvesting in Wireless Networks
- IoT-based Smart Home Systems
- Underwater Vehicles and Communication Systems
- Microbial bioremediation and biosurfactants
- Vehicle License Plate Recognition
- Brain Tumor Detection and Classification
- Distributed Control Multi-Agent Systems
- Internet of Things and AI
- Music and Audio Processing
- Nematode management and characterization studies
- Solar Radiation and Photovoltaics
Graphic Era University
2021-2025
National Institute of Solar Energy
2015-2024
Sri Sri University
2024
GNA University
2023-2024
Mody University of Science and Technology
2022-2024
Chitkara University
2024
Alliance University
2023
Conference Board
2023
Indian Institute of Technology Ropar
2021-2022
Institute of Microbial Technology
2020-2021
A 10nm logic technology using 3rd-generation FinFET transistors with Self-Aligned Quad Patterning (SAQP) for critical patterning layers, and cobalt local interconnects at three interconnect layers is described. For high density, a novel self-aligned contact over active gate process elimination of the dummy cell boundaries are introduced. The feature rectangular fins 7nm fin width 46nm height, 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML"...
The identification and severity evaluation of peach leaf diseases using Convolutional Neural Networks (CNN) inside a federated learning (FL) framework is presented in detail this study. Precision, recall, F1-score, accuracy were used to measure the model's performance over various levels. Concerning four customers, local data analysis showed good precision (ranging from 93.53% 96.80 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ),...
Powdery mildew is a fungal disease that affects kiwi fruit plants and leads to reduction in yield quality. Early detection classification of the can help farmers take necessary measures curb transmission infection. In this research, we conducted binary multi-classification powdery (KPMD) using 12000 images fruit. The was done two classes, healthy inf, while four different classes mildew. An integrated CNN LSTM model developed for multi-classification, which resulted an accuracy 92.14% 95.91%...
The fungal disease known as cucumber leaf spot (CLS) is capable of causing substantial damage to crops, leading a decrease in production and quality. Early detection management the are critical for minimizing its impact on crop productivity. In this study, authors developed Residual Next-50 (ResNext-50) deep learning (DL) model based 5 different severity levels multi-classification CLS disease. work collected dataset 50,000 digital images leaves from multiple sources, including local farmers...
For the purpose of assisting farmers in making knowledgeable choices on crop management, this research suggests a recommendation system based machine learning algorithms. To assess soil data and suggest optimal management practices to farmers, makes use Decision Tree, Naive Bayes, KNN, Random Forest, XG-Boost. The discovered that agricultural yields may be reliably predicted using algorithms, these algorithms can also recommend best techniques, resulting higher output at reduced cost....
Cervical cancer is a major cause of mortality for women, and early detection crucial successful treatment Recent studies have investigated the use machine learning cervical cancer, but challenges remain. This paper evaluates performance different algorithms, including logistic regression, bagging, random forest, XG Boost, predicting cancer. The study analyzes in working with data, such as dealing imbalanced datasets limited data availability. To address these challenges, proposes an approach...
This study provides a comprehensive analysis of the effectiveness eight different machine learning algorithms for predicting water quality. The algorithms, which include Gaussian Naive Bayes, Extreme Gradient Boost Classifier, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression, Random Forest, and Decision Tree, were tested using potability dataset. study's main goals to identify best accurate algorithm quality present thorough comparison these methods. Algorithm's...
Energy storage systems are utilized to enhance energy security and improve photovoltaic (PV) system performance. The can be coupled at the rear surface of a PV panel in order cool maintain its high efficiency. latent heat enhanced through nanoparticles phase change materials for storing thermal energy. This study presents bibliometric thematic analysis cooling with an publication trends, prominent authors, countries, affiliations, most cited articles, sources, keywords this field over last...
ASD, often known as an autism spectrum disorder, is a neurodevelopmental condition that impairs person's capacity for successful social interaction and communication. The long-term results individuals affected can be significantly improved by early detection proper diagnosis of this disorder. Traditional diagnostic techniques, however, might take while are not always accurate. In order to solve these challenges, study investigates the use machine learning diagnose ASD. research uses big...
Multi-core processing is extensively used in every sector for its performance efficiency, with the advent of multi-core architecture have to modify existing primitive algorithms. This study analyses feasibility K-mean data-mining technique, which applied a hybrid cluster programming. The algorithm developed using Message Passing Interface (MPI) and C programming languages parallel sets uses CPU maximum power sets. heterogeneous clusters are confirmed by usage MPICH2 (High portability...
Early diagnosis of illnesses affecting spinach leaves is essential for maintaining agricultural output and food security. The accuracy scalability conventional disease detection techniques, such as human inspection remote sensing, are constrained. Convolutional neural networks (CNNs) federated learning used in this study to categorise leaf into four severity categories. Four customers participated the research, each with a separate dataset comprising pictures various degrees severity. system...