The communication system involved in the various networking it becomes decentralized and automatic, network articles is to make decisions for increasing network performance in uncertainty network environment. The chal...
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The proceedings contain 24 papers. The special focus in this conference is on intelligent Systems and Applications. The topics include: Analyzing Various CNN Architectures for Detection of Early Blight and Late Blight...
ISBN:
(纸本)9789819798384
The proceedings contain 24 papers. The special focus in this conference is on intelligent Systems and Applications. The topics include: Analyzing Various CNN Architectures for Detection of Early Blight and Late Blight in Infected Potato Plants;GrainLog: An IoT-ML Based Food Grain Monitoring and Shelf-Life Prediction System for the Farmer;intelligent Systems for Digitalization and Automation of Agriculture Equipment;machine learning Methods for Crop Yield Prediction Across Diverse Agricultural Environments;soil Health Analysis and Automatic Prediction of Macro and Micronutrient Using Machine learning Algorithms;A Deep CNN Model for Analytical Prediction of Tomato Diseases Using Multiple Disease Dataset;synergistic Solutions: Imagen-Based Dataset Augmentation and Yolo-V8-Driven Disease Detection for Enhanced Cherry Tree Health;advanced Detection of Diseases in Tomato Plant Leaf Using Deep learning;Application of CNN for Separation Between No Rain and Rain from Remote Sensing Data;Comparative Analysis of Ensemble-Based Nowcasting Models for Precipitation Prediction in the UAE;AI-Driven Approach for Optimal Soil-Based Crop Recommendations;Employing Machine learning Methods and RFM Model for Customer Clustering: Case Study of an Agricultural Retailer;Enhanced Vegetation Cover Assessment Using Sentinel-2A: A Unified Perspective on NDVI, SAVI, and GNDVI;agroGenie: A Smart Approach to Agriculture Using Machine learning;Implementation of DCNN Framework—Auto Identification and Categorization of Various Stress in Paddy Crop and Resource Management System;Optimized CNN Model to Develop a Decision-Making System for Spraying on Cotton Crop;a Robust optimization Approach for Agricultural Waste Supply Chain Network Design: A Real Case Study;finding the Non-destructive Monitoring System of Papaya by Machine learning Algorithm.
Fingerprint recognition has become an invaluable and rapidly advancing technology that plays a crucial role in various daily applications. From identity authentications to justice systems, mobile payment, and even acc...
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Distributed systems are critical infrastructures for many enterprises and organizations, yet they also present challenges in fault diagnosis. To improve the efficiency and accuracy of fault diagnosis in distributed sy...
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ISBN:
(纸本)9798350352634;9798350352627
Distributed systems are critical infrastructures for many enterprises and organizations, yet they also present challenges in fault diagnosis. To improve the efficiency and accuracy of fault diagnosis in distributed systems, this paper proposes a fault diagnosis model based on the RF-LGBM algorithm. The model first utilizes LGBM for initial diagnosis and then employs RF to reclassify the two fault types with lower accuracy. The results indicate that the RF-LGBM model achieves an AUC greater than 0.96, effectively diagnosing faults in distributed systems.
As modern engineering optimisation problems become increasingly complex, a significant number of them exhibit multimodal characteristics. This implies that there are multiple local optimal solutions and a single globa...
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With the increasingly complex business environment, the rational allocation of human resources and the improvement of personnel utilization efficiency have become key factors for enterprises to maintain competitivenes...
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This study explores the integration of federated learning and Transformer models to enable collaborative and privacy-preserving deep learning for vision tasks. Federated learning is a distributed learning paradigm tha...
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ISBN:
(纸本)9798400717048
This study explores the integration of federated learning and Transformer models to enable collaborative and privacy-preserving deep learning for vision tasks. Federated learning is a distributed learning paradigm that allows training models on distributed data without sharing the original data, and Transformer models have demonstrated impressive performance in a variety of applications. We leverage the strengths of federated learning and Transformer models and apply the clustering techniques to address the challenge of privacy protection in large-scale deep learning. By combining these approaches, this research aims to develop efficient and private deep learning systems for vision applications, providing data privacy guarantees and maintaining the practicality of trained models. In the experiments, we show the proposed model can successfully identify clustering membership and perform local tasks with satisfactory accuracy and efficiency.
Currently, attacks in the networks are the most vital issue in modern society and the networks from minor to huge networks are susceptible to network threats. Various approaches exist with several advantages and limit...
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This paper explores the integration of Artificial Intelligence (AI) with that of Social-Emotional learning (SEL) interventions to address bedtime procrastination and promote better sleep hygiene. Bedtime procrastinati...
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This paper introduces an advanced short-term electricity demand forecasting model based on Recurrent Neural Networks (RNN) combined with Long Short-Term Memory (LSTM) units. This hybrid model effectively captures the ...
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