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関連する概念動画

Classifying Matter by Composition03:35

Classifying Matter by Composition

91.7K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
91.7K
Classifying Matter by State02:49

Classifying Matter by State

104.8K
Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
104.8K
Machines01:19

Machines

581
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
581
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.4K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.4K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

45.2K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
45.2K
Machines: Problem Solving II01:30

Machines: Problem Solving II

678
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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ディープ・フィーチャー・エクストラクションとマシン・ラーニング・クラシフィアー・インテグレーションを用いた自動マンゴー品種分類

Ibrar Ahmad1, Aftab Khaliq2, Bushra Siddique1

  • 1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.

Foods (Basel, Switzerland)
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まとめ
この要約は機械生成です。

人工知能を用いた自動マンゴーの品種分類は,エラーと収穫後の損失を大幅に削減します. ハイブリッドのディープラーニングと機械学習モデルは,リアルタイムアプリケーションの処理がはるかに高速で100%の精度を達成します.

キーワード:
人工知能 (AI) とは,人工知能 (AI) に関する機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) と呼ばれるものです.収穫後の作業についてです.精密農業 精密農業について学習の移転を学習に移行する.

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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科学分野:

  • 農業技術 農業技術について
  • コンピュータ・ビジョン コンピュータ・ビジョン
  • 人工知能 (AI) は,人工知能 (AI) を利用する.

背景:

  • マンゴーの手動分類は非効率で,開発途上国では収穫後の損失が大きい.
  • 自動化されたシステムの開発は,効率を改善し,経済的影響を軽減するために不可欠です.

研究 の 目的:

  • 自動化されたマンゴーの品種分類のための計算効率の良い,高度に正確な人工知能の枠組みを開発する.
  • 果物の分類システムにおけるリアルタイムアプリケーションを可能にします.

主な方法:

  • 特徴抽出器として8つのディープ・トランスファー・ラーニングモデルを評価した.
  • これらを10の古典的な機械学習分類器と組み合わせた.
  • 精度,ログ損失,メモリ使用量,トレーニング時間,推論遅延を使用してパフォーマンスを評価します.

主要な成果:

  • ハイブリッドモデルであるEfficientNetB0-Linear Discriminant Analysis (LDA) とResNet50-Logistic Regressionでは,テストの精度が100%に達した.
  • 推論時間は,完全なコンボリューションニューラルネットワーク (CNN) モデルと比較して最大330倍短縮されました.
  • 計算コストが大幅に低く,最先端の精度が実証されています.

結論:

  • ハイブリッドのディープラーニングと機械学習アーキテクチャは,効率的で正確な自動マンゴーの分類のための実行可能なソリューションを提供します.
  • 開発されたフレームワークは,リアルタイムアプリケーションと産業用果物分類に適しています.
  • 将来の作業には,現実世界の検証と組み込みハードウェアの展開が含まれます.