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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Levels of Use of a GIS01:29

Levels of Use of a GIS

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

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Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
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Related Experiment Video

Updated: Sep 18, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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Published on: July 11, 2025

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Smart waste management and classification system using advanced IoT and AI technologies.

Abdullah Alourani1, M Usman Ashraf2, Mohammed Aloraini3

  • 1Department of Management Information Systems, College of Business and Economics, Qassim University, Buraydah, Saudi Arabia.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent solid waste management system (iSSWMs) using AI and IoT for smart collection and segregation of plastic, glass, and metal waste. The system achieved 99.7% accuracy in segregating recyclable materials.

Keywords:
Artificial intelligenceCloud manufacturing serviceIndustrial Internet of ThingsIntelligent manufacturing systemMachine learningSolid waste recyclingWaste management system

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Area of Science:

  • Environmental Science
  • Computer Science
  • Engineering

Background:

  • Municipal solid waste management is a critical global challenge.
  • Ineffective waste segregation hinders recycling and resource recovery.
  • Emerging technologies offer solutions for efficient waste management.

Purpose of the Study:

  • To develop an intelligent and smart solid waste management system (iSSWMs).
  • To enable smart collection and segregation of recyclable materials (plastic, glass, metal).
  • To contribute to environmental protection and public health goals.

Main Methods:

  • Phase 1: Smart waste collection using IoT-enabled bins and a mobile application.
  • Phase 2: Deep learning model (VGG-19) for automated waste segregation.
  • Integration of artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT).

Main Results:

  • The iSSWMs effectively integrates smart collection and segregation.
  • The VGG-19 model achieved 99.7% accuracy in segregating plastic, glass, and metal.
  • The system demonstrates high efficiency in identifying and separating recyclable waste.

Conclusions:

  • iSSWMs offers a promising framework for modernizing solid waste management.
  • The integration of AI, ML, and IoT enhances recycling efficiency.
  • This approach contributes to a cleaner environment and supports broader public health objectives.