Collaborative energy price computing based on sarima-ann and asymmetric stackelberg games

T Zhang, Y Wu, Y Chen, T Li, X Ren - Symmetry, 2023 - mdpi.com
The energy trading problem in smart grids has been of great interest. In this paper, we focus
on two problems: 1. Energy sellers' inaccurate grasp of users' real needs causes information …

IoT-based prediction and classification framework for smart farming using adaptive multi-scale deep networks

B Padmavathi, A BhagyaLakshmi, G Vishnupriya… - Expert Systems with …, 2024 - Elsevier
Agriculture is one of the prime economical sources of India and most of the people directly or
indirectly depend on farming. The researchers are focusing on plant ailment detection and …

Medical Material Allocation Using Multi-Queue Scheduling During Pandemics

DD Priya, S Subramaniam, PV Kumar… - 2024 International …, 2024 - ieeexplore.ieee.org
In this study, we implemented a multi-level queue scheduling algorithm for a hospital with
three wards: General, Pandemic, and Arogya Sree. The Pandemic ward uses priority …

Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality

MM Ahmed, M Amjad, MA Qureshi, MO Khan… - Energies, 2024 - mdpi.com
The optimal PMU placement problem is placing the minimum number of PMUs in the
network to ensure complete network observability. It is an NP-complete optimization …

Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information

M Wen, H Mei, W Wang, X Zhang - Applied Sciences, 2023 - mdpi.com
Temporal knowledge graph completion (TKGC) refers to the prediction and filling in of
missing facts on time series, which is essential for many downstream applications. However …

Comment Analyzer by Sentimental Analysis through Natural Language Processing

P Chinnasamy, RK Ayyasamy… - 2024 10th …, 2024 - ieeexplore.ieee.org
Comment analyzers were widely employed across industries for sentiment analysis, social
media monitoring, and customer feedback evaluation. These tools facilitated insight into …

Growing Greener: Exploring the Creative Potential of Deep Learning in Agricultural Disease Control

A Kiran, SZ Rahman, P Sreelatha… - 2024 International …, 2024 - ieeexplore.ieee.org
According to estimates, by mid-2030, there will be 8.6 billion people on the planet. The
human population has been growing exponentially. There will be a rise in food production …

An Extensive Study on Precision Farming Based on Crop Yield Using Integrated Approaches to Learning

K Geetha, BV Vidhya, A Kiran - 2023 International Conference …, 2023 - ieeexplore.ieee.org
Precision agriculture aims to optimize the use of resources by tailoring them to specific field
conditions. Previous research has investigated the use of IoT devices, remote sensing …

Alzheimer's Disease Classification and Severity Level Identification in Image Processing Using Deep Learning Algorithms

TS Devi, V Raghavendran - 2024 2nd International Conference …, 2024 - ieeexplore.ieee.org
Alzheimer's disease (AD) is a neurodegenerative condition that deteriorates brain cells and
impairs a patient's memory. It is progressive and incurable. Early identification can shield the …

Plant Disease Detection Using Convolution Neural Network

KP Rani, G Lavanya, KS Madhuri… - … on Advancements in …, 2024 - ieeexplore.ieee.org
Today the persistence of plant and plant diseases is a substantial problem for cultivators,
disease diagnosis is important in the farm. To preserve the production of crops, adequate …