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Associative Learning01:27

Associative Learning

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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.
Classical conditioning, also known...
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Improving Translational Accuracy02:07

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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Related Experiment Video

Updated: Sep 14, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

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Transfer learning with XAI for robust malware and IoT network security.

Ahmad Almadhor1, Shtwai Alsubai2, Natalia Kryvinska3

  • 1Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia.

Scientific Reports
|July 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for detecting privacy-exploiting malware, utilizing transfer learning for enhanced accuracy in cybersecurity threats across memory analysis and network intrusion detection.

Keywords:
Deep neural networksIntrusion detection systemMalware attacksMemory dump analysisShapley additive explanationsTransfer learning

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Digital Forensics

Background:

  • Increasing privacy-exploiting malware necessitates advanced detection methods.
  • Obfuscation techniques make real-time malware detection challenging.
  • Traditional forensic analysis requires sophisticated pattern identification.

Purpose of the Study:

  • Develop a deep learning model for classifying obfuscated malware.
  • Enhance intrusion detection in IoT and network traffic using transfer learning.
  • Improve transparency and interoperability of malware detection models.

Main Methods:

  • Developed a deep learning model trained on the MalwareMemoryDump dataset.
  • Implemented transfer learning to adapt the model for NF-TON-IoT and UNSW-NB15 datasets.
  • Integrated Explainable AI (XAI) for model transparency.

Main Results:

  • Achieved 99.9% accuracy on MalwareMemoryDump and 96% on NF-TON-IoT and UNSW-NB15 datasets.
  • Demonstrated improved accuracy and efficiency in cross-domain detection.
  • Showcased reduced training time and computational costs via transfer learning.

Conclusions:

  • The proposed deep learning model effectively handles diverse cybersecurity threats.
  • Transfer learning significantly enhances malware detection across different domains.
  • The model offers a highly effective and generalizable approach outperforming existing security techniques.