What Is ANN, And What Are The Commonalities And Differences ✓ Solved

What is ANN, and what are the commonalities and differences between biological and artificial neural networks? What types of business problems can be solved with ANN?

Artificial Neural Networks (ANNs) are computational systems inspired by the structure and function of biological neural networks found in the human brain. They are designed to enable computers to learn from data and make human-like judgments, functioning as advanced AI tools that mimic some aspects of biological neural processing (Da Silva et al., 2017). ANNs are essentially software applications that emulate processes similar to neural activity, allowing machines to analyze complex patterns and relationships within data sets.

Biological neurons are highly sophisticated, involving complex internal machinery, chemicophysical processes, and mechanical activities. These neurons are interconnected within vast networks, capable of high levels of plasticity, fault tolerance, and self-healing abilities. In contrast, artificial neurons are simplified computational units that emulate certain features of biological neurons but lack many of their complexity and resilience. Despite these differences, both systems share common features, such as interconnected nodes (neurons), weighted connections, and learning processes.

Comparison of Biological and Artificial Neural Networks

Size and Scale

Biological neural networks in the human brain contain approximately 86 billion neurons, interconnected through over 100 trillion synapses (Richard, 2018). In comparison, artificial neural networks are composed of far fewer neurons—typically between ten and one thousand—making them vastly smaller but still capable of processing complex data (Richard, 2018).

Processing Speed

Biological neurons fire at an average rate of about 1,200 times per minute, with signal transmission speeds varying from 0.61 m/sec to 119 m/sec depending on factors such as age, sex, and physiological conditions. In contrast, artificial neurons operated via digital computers process signals at much higher speeds, constrained by hardware capabilities and floating-point calculations, enabling rapid data processing and learning without fatigue or fatigue-related issues (Richard, 2018).

Fault Tolerance and Resilience

The human brain exhibits fault tolerance due to its redundant and overlapping networks. Minor damage does not typically impair overall function, owing to redundancy and the brain's ability to recover partially—an attribute known as neuroplasticity. Artificial neural networks, however, lack this innate fault tolerance. They do not regenerate automatically following damage or data corruption, and their performance depends heavily on the integrity of their trained weights and architecture (Richard, 2018).

Learning Processes

Biological neural networks learn through complex mechanisms involving synaptic plasticity, neurogenesis, and neurotransmitter activity. They adapt based on experience, often in a continuous, real-time manner. Artificial neural networks learn primarily through the adjustment of weights during training, often employing back-propagation algorithms to minimize error and improve performance iteratively. This process reinforces existing connections or creates new ones based on data exposure, leading to pattern recognition and prediction capabilities (Da Silva et al., 2017).

Business Applications of Artificial Neural Networks

1. Spam Detection and Email Filtering

One of the common applications of ANNs in business is in filtering spam emails. ANNs analyze email patterns, identify characteristic features of spam messages, and distinguish them from legitimate emails, thereby protecting organizations from phishing threats and data breaches. This automation enhances efficiency and security in communication channels (Livieris et al., 2019).

2. Sales Forecasting

ANNs are integral to predictive analytics, especially in sales forecasting. They analyze historical sales data, identify trends, and predict future sales, helping companies optimize inventory, production, and marketing strategies. The capability to handle nonlinear relationships in data makes ANNs valuable for accurate demand planning and revenue prediction (Livieris et al., 2019).

3. Product Promotion and Customer Behavior Analysis

By tracking purchase histories and transaction data, ANNs help businesses understand consumer preferences and behavior patterns. This insight facilitates targeted marketing, personalized recommendations, and effective product promotions, especially in e-commerce platforms. Tracking customer choices enables businesses to optimize product placements and increase conversion rates (Livieris et al., 2019).

4. Image and Pattern Recognition

ANNs excel in recognizing patterns in images, handwriting, and voice data. They are used in facial recognition systems, fraud detection, quality control in manufacturing, and medical diagnostics. These applications benefit from ANNs' ability to classify complex data accurately and swiftly, significantly improving efficiency and accuracy in various sectors.

5. Financial Modeling and Risk Assessment

Financial institutions utilize ANNs to predict stock market trends, assess credit risk, detect fraudulent activities, and optimize investment portfolios. The neural network’s ability to process vast amounts of financial data and recognize subtle patterns assists in making informed decisions and managing risk effectively.

6. Healthcare Diagnostics

In healthcare, ANNs contribute to disease detection, medical imaging analysis, and personalized treatment planning. By learning from medical data, ANNs can detect anomalies and predict disease outcomes, assisting clinicians in early diagnosis and improving patient care.

7. Supply Chain Optimization

Artificial neural networks optimize supply chain logistics by forecasting demand, managing inventory levels, and predicting delivery times. These applications reduce costs, enhance efficiency, and improve responsiveness to demand fluctuations.

8. Customer Service Automation

Chatbots and virtual assistants powered by ANNs provide 24/7 customer support, handling inquiries, complaints, and service requests. This automation improves customer satisfaction while reducing operational costs.

Conclusion

Artificial Neural Networks represent a significant advancement in AI, emulating certain functions of biological neural networks. Despite differences in complexity, scale, and resilience, ANNs have proven highly effective for numerous business applications, including prediction, pattern recognition, and automation. Understanding the similarities and differences between biological and artificial neural networks enhances our appreciation of their capabilities and limitations, guiding better implementation and future development in technology and business.

References

• Da Silva, I. N., Spatti, D. H., Flauzino, R. A., Liboni, L. H. B., & dos Reis Alves, S. F. (2017). Artificial neural networks. Cham: Springer International Publishing.
• Richard, N. (2018). The differences between Artificial and Biological Neural Networks. Towards Data Science.
• Livieris, I. E., Kiriakidou, N., Kanavos, A., Vonitsanos, G., & Tampakas, V. (2019). Employing Constrained Neural Networks for Forecasting New Product’s Sales Increase. IFIP International Conference on Artificial Intelligence Applications and Innovations. Springer.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
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