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Related Concept Videos

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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Cognitivism01:17

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Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Extraction: Advanced Methods00:56

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Associative Learning01:27

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Related Experiment Video

Updated: Dec 21, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Diverging deep learning cognitive computing techniques into cyber forensics.

Nickson M Karie1, Victor R Kebande2,3, H S Venter3

  • 1Cyber Security and Forensics Research Group, Department of Computer Science, University of Eswatini, Private Bag 4, Kwaluseni, Eswatini.

Forensic Science International. Synergy
|May 16, 2020
PubMed
Summary
This summary is machine-generated.

Deep Learning (DL) offers solutions for cyber forensics by analyzing Big Data to find digital evidence. This AI subset can enhance cybercrime investigations and improve evidence admissibility in legal proceedings.

Keywords:
Artificial intelligenceCyber forensicsCyberattacksCybercrimesDeep learningFrameworkInvestigations

Related Experiment Videos

Last Updated: Dec 21, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Area of Science:

  • Cybersecurity
  • Digital Forensics
  • Artificial Intelligence

Background:

  • Increasing cyber-attacks necessitate advanced methods for combating cybercrime.
  • Digital forensic investigators face challenges analyzing large, complex datasets (Big Data) from diverse sources.
  • Traditional methods struggle with the speed, volume, and complexity of data in modern cyber investigations.

Purpose of the Study:

  • To propose a generic framework, the Deep Learning in Cyber Forensics (DLCF) Framework, for applying DL techniques to cyber forensics.
  • To explore the potential of Deep Learning (DL), a subset of Artificial Intelligence (AI), in enhancing cybercrime investigations.
  • To address the challenges faced by forensic investigators in handling Big Data for uncovering potential digital evidence (PDE).

Main Methods:

  • The study focuses on the application of Deep Learning (DL) cognitive computing techniques.
  • DL utilizes machine learning and neural networks that mimic human decision-making processes.
  • A generic framework (DLCF) is proposed to integrate DL into cyber forensics workflows.

Main Results:

  • Deep Learning (DL) demonstrates significant potential to transform cyber forensics.
  • DL can assist in reducing bias within forensic investigations.
  • DL may offer solutions for determining the admissibility of digital evidence in legal settings.

Conclusions:

  • Deep Learning (DL) provides valuable tools for enhancing the fight against cybercrime.
  • The proposed DLCF Framework can aid forensic investigators in managing and analyzing Big Data.
  • DL holds the potential to significantly improve the efficiency and effectiveness of cyber forensic processes.