This study discusses the development of smart precision farming systems using big data and cloud-based intelligent decision support systems. Big data plays an important role in collecting, storing, and analyzing large...
This study discusses the development of smart precision farming systems using big data and cloud-based intelligent decision support systems. Big data plays an important role in collecting, storing, and analyzing large amounts of data from various sources related to agriculture, including data from weather stations, soil sensors, satellite imagery, crop yield records, pest and disease reports, and other sources. This study highlights the differences between smart farming and precision farming. This study describes key techniques and system architecture, including data collection, processing, analysis, and decision support components. Utilizing a cloud platform enables scalability and optimized performance, which lowers costs and makes it safer and easier to manage. The integration of big data and Alibaba cloud computing in smart precision farming can improve farming productivity by providing timely information and recommendations to farmers for better decision-making. Finally, the system produces smart precision farming, which provides cost-effective real-time monitoring and predictive analytics to increase agricultural production and sustainability.
The proceedings contain 62 papers. The special focus in this conference is on Innovations in Computer Science and Engineering. The topics include: Machine Learning-Based Indian Stock Market’s Price Movement Predictio...
ISBN:
(纸本)9789811974540
The proceedings contain 62 papers. The special focus in this conference is on Innovations in Computer Science and Engineering. The topics include: Machine Learning-Based Indian Stock Market’s Price Movement Prediction and Trend Analysis;Machine Learning-Based Mortality Prediction of COVID-19 Patients;smart Computer Monitoring System Using Neural Networks;using Deep Learning to Perform Payload Classification;malicious Domain Detection Using Memory Augmented Deep Autoencoder;graph Analysis Using Page Rank Algorithm to Find Influential Users;hate Speech and Offensive Language Detection in Twitter data Using Machine Learning Classifiers;secrecy Rate Optimization for Energy Efficient Cognitive Relay Networks;avian Influenza Prediction Using Machine Learning;An Explainable AI Approach for Diabetes Prediction;prediction and Comparison of Diabetes with Logistic Regression, Naïve Bayes, Random Forest, and Support Vector Machine;bone Cancer Detection Using Deep Learning;energy and Buffer Size-Based Routing Protocol for Internet of Things;v-Shaped Binary Version of Whale Optimization Algorithm for Feature Selection Problem;an Energy-Efficient Deep Neural Network Model for Photometric Redshift Estimation;deep Learning-Based Diabetic Retinopathy Screening System;artificial Intelligence-Based dataanalytics Techniques in Medical Imaging;ensuring data Protection Using Machine Learning Integrating with Blockchain Technology;evaluation and Language Training of Multinational Enterprises Employees by Deep Learning in Cloud Manufacturing Resources;development of a Cognitive Question Answering System to Learn Concepts for Placement Assistance;classification of Cervical Cells Using Deep Learning Feature Extraction;cervical Cancer Prediction Using Optimized Meta-Learning;an Ensemble Deep Closest Count and Density Peak Clustering Technique for Intrusion Detection System for Cloud computing;voice-Based intelligent Virtual Assistant for Windows.
In this paper, we present the design, implementation, and deployment of an IoT-based system for machine learning (ML)-based real-time prediction of CO2 exhaust concentrations in road vehicles, by means of transfer lea...
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Edge computing has been extensively used in industries to solve problems such as data explosion. However, in the field of mechanical equipment fault diagnosis, there are still problems such as the large amount of data...
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Since the beginning of the 21st century, the Internet has not only been widely used in various industrial networks, but has gradually extended to every aspect of people’s lives. In recent years, the Internet technolo...
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In the cutting edge domain that is the ever so expanding smart city, the need for development tools that can keep up with such an urban environment is ever so prevalent. Moreover, the rapid expansion of such metropoli...
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ISBN:
(数字)9798350359091
ISBN:
(纸本)9798350359107
In the cutting edge domain that is the ever so expanding smart city, the need for development tools that can keep up with such an urban environment is ever so prevalent. Moreover, the rapid expansion of such metropolitan areas requires even more specialists than are currently available. This study presents a pioneering cloud-based solution for intelligent transportation and crime prevention, emphasizing the seamless integration of machine learning techniques within a web deploy-ment framework. Utilizing data from sources like Automated Speed Enforcement, police crime statistics, and traffic moni-toring programs, our approach employs advanced predictive analytics to accurately identify potential crime hotspots and optimize traffic management. A significant innovation of this research is the development of a scalable Software as a Service model, which allows for the effective predictions of traffic sensors across urban settings. The proposed system features a user-friendly graphical user interface and employs Dynamic Load Balancing to enhance computational efficiency, making it accessible to a wide range of users. By harnessing cloud computing, our solution offers a versatility for government officials, law enforcement, and researchers, promising improvements in road safety, crime prevention, and the overall quality of life.
The major purpose of the study topic is to use data science to anticipate the future effect of COVID-19 using existing data. The goal of this research is to use data science and analytics to generate precise forecasts...
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The use of machine learning techniques for developing intelligent energy management systems for buildings. Because of the growing emphasis placed on energy efficiency and sustainability, optimizing the amount of energ...
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ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
The use of machine learning techniques for developing intelligent energy management systems for buildings. Because of the growing emphasis placed on energy efficiency and sustainability, optimizing the amount of energy that is consumed in buildings has become an extremely important task. Tominimize energy waste and reduce operational expenses, smart energy management systems offer a viable alternative. These systems leverage advanced dataanalytics and automation to achieve their goals. Throughout the course of this research project, we investigate the possibility of incorporating machine learning algorithms into building energy management systems. These algorithms include neural networks, decision trees, and reinforcement learning. These algorithms examine past data on energy usage, weather trends, occupancy schedules, and building attributes to estimate energy demand, optimize HVAC (heating, ventilation, and air conditioning) operations, and intelligently schedule tasks that use a significant amount of energy. We demonstrate the efficacy of machine learning approaches through case studies and performance evaluations. These techniques are utilized to improve energy efficiency, reduce carbon emissions, and enhance occupant comfort in buildings. The findings of this study contribute to the development of intelligent energy management methods, which in turn paves the way for buildings that are more sustainable and kind to the environment.
The adoption of distributed reasoning through ubiquitous instrumentation in the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault tolerance, traffic, h...
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The adoption of distributed reasoning through ubiquitous instrumentation in the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault tolerance, traffic, healthcare, etc. Using a ubiquitous controller to interconnect devices in the IoT is still in the embryonic stage. However, it has the potential to create distributed-intelligent IoT solutions that are more efficient and secure than centric intelligence. It is essential to take a new direction to design a distributed intelligent controller for task scheduling that can firstly dynamically interact with a smart environment in efficient real-time data processing and secondly respond to flexible changes. To address these issues, we outline a two-level intelligence scheme that leverages edge computing to improve distributed IoT. The edge scheme shifts the capability of streaming processing from the cloud to edge devices to alleviate latency, support better reliable streaming analytics, and improve smart IoT applications' performance. In this work, to enable better, reliable, and flexible streaming analytics and overcome the data uncertainties, we proposed an IoT gateway controller that provides low-level intelligence by using a fuzzy abductive reasoner. Numerical simulations support the feasibility of our proposed approaches. (C) 2021 The Authors. Published by Elsevier B.V.
The main problem is that employees leaving the company has a big affect on its costs, efficiency, and effectiveness. By accurately estimating employee turnover, businesses can take proactive steps to keep good employe...
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ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
The main problem is that employees leaving the company has a big affect on its costs, efficiency, and effectiveness. By accurately estimating employee turnover, businesses can take proactive steps to keep good employees and lower the costs of hiring and teaching new ones. This research looks at how machine learning techniques can be used to predict staff turnover by looking at things like employee demographics, job satisfaction, success indicators, and engagement levels. Training and testing of the models were done with a dataset that included both old and new data from a big organisation. The results of this research are very helpful for human resource managers who are trying to fix problems with disengaged employees and make policies that keep employees. The research say that machine learning systems can accurately predict how many employees will leave a company.
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