Overview
The Fourth Industrial Revolution (Industry 4.0) has introduced a wave of disruptive technologies that are transforming traditional systems and reshaping business strategies across industries. One major shift is the decentralization of decision-making, increasingly delegated to intelligent computer systems. With the rise of Big Data Analytics (BDA) and Artificial Intelligence (AI), organizations now rely on machines that simulate human intelligence—often delivering results that surpass human performance.
AI-powered tools allow for the extraction of meaningful insights from the billions of data points generated every minute. As AI and Machine Learning (ML) continue to evolve, they unlock immense opportunities to boost performance across industries, improve research, and enhance enterprise decision-making.
AI-Driven Mechanisms in Big Data Analytics
Big Data Analytics relies on various subfields of AI to uncover patterns and predictions. The reviewed literature categorizes these into:
- Machine Learning (ML)
- Knowledge-Based and Reasoning Techniques
Each analytical method is evaluated based on four qualitative parameters:
- Scalability – Ability to handle rapid changes without compromising analysis quality.
- Efficiency – Time and cost-effectiveness of the method.
- Precision – Accuracy, including predictive power and error rates.
- Privacy – Safeguards to ensure data is used responsibly and securely.
These parameters help assess the strengths and limitations of different BDA techniques.
Machine Learning Mechanisms
Machine learning programs offer significant benefits to industries like broadcasting, advertising, and content delivery. Traditional forecasting models struggle with high-peak data and require extensive training data and time. In contrast, distributed machine learning enables real-time analytics, especially useful for big data generated via social media.
In the era of massive multimedia data and skyrocketing internet traffic, two major challenges emerge: data processing and network efficiency. Advanced approaches like modified Convolutional Neural Networks (CNNs) have been developed to increase classification accuracy.
Supervised Learning and Predictive Analytics
Supervised learning models, such as those using random forest-based classifiers, address challenges like feedback latency, non-stationary data, and imbalanced datasets. These models demonstrate strong scalability, accuracy, and efficiency for handling high-volume transactions.
Predictive analytics is also used to anticipate societal responses and behavioral trends. Extensive testing confirms the reliability and low classification error of such frameworks.
Conclusion
This study reviewed 32 key research articles across four core AI areas:
- Machine Learning
- Knowledge-Based and Reasoning Techniques
- Decision-Making Algorithms
- Optimization Functions
While these approaches demonstrate high accuracy and efficiency, they often lack in scalability. Knowledge-based methods, however, stand out for their ease of development.
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Top comments (1)
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