Survey on Applications of neural networks in real-world scenarios

Contents :-

1. Abstract
2. Analysis on survey questions
3. Conclusion
4. References

Abstract:-

Neural networks have proven to be an effective tool for solving a variety of problems in real-world scenarios. They are inspired by the structure and function of the human brain, and can learn from large amounts of data to make predictions or classifications. Some common applications of neural networks include image and speech recognition, natural language processing, and predictive analytics. In the field of image recognition, neural networks are used to identify objects, people, and scenes in images or videos. This has applications in fields such as security, medical diagnosis, and self-driving cars. Speech recognition systems, such as Siri and Alexa, use neural networks to understand spoken commands and translate them into actions.

Natural language processing is another area where neural networks have shown great promise. They can be used to analyze text, understand sentiment, and even generate natural-sounding language. This has applications in fields such as chatbots, customer service, and content creation. Predictive analytics is yet another area where neural networks have proven to be a valuable tool. They can analyze large datasets to identify patterns and make predictions about future events. This has applications in fields such as finance, marketing, and healthcare. Overall, the applications of neural networks in real-world scenarios are vast and varied, and are only expected to grow as the technology continues to improve.

Survey Form Link:- https://forms.gle/JrWqcQH7DkMWjz7t8

Questions And Analysis

1) Have you heard about neural networks before?

The survey results indicate that a vast majority of the people surveyed, 97.6%, have heard of neural networks, which are a type of machine learning algorithm modeled after the human brain. This suggests that most people are familiar with artificial intelligence and related technologies. However, a small percentage, 2.4%, responded that they have not heard of neural networks before, which may indicate less familiarity with these topics.

2) In which fields do you think neural networks can be applied? (Select all that apply)

The survey asked respondents about the fields in which they believed neural networks could be applied, and the results showed that the majority of people believe neural networks can be applied in healthcare (73.2%) and finance (65.9%). Transportation (43.9%), education (43.9%), and manufacturing (39%) also received relatively high percentages of responses. Agriculture (24.4%) and natural language processing (2.4%) received the lowest percentages of responses. The results suggest that neural networks have already been successfully applied in healthcare and finance, while other fields have potential for future application.

3) Which of the following applications of neural networks are you familiar with ? (Select all that apply)

The survey results indicate that there is a high level of familiarity among respondents with neural network applications in image recognition, speech recognition, and natural language processing, with over 68% of respondents indicating familiarity with these applications. Familiarity with other applications such as autonomous driving and predictive maintenance is lower, with 51.2% and 39% of respondents indicating familiarity, respectively. Overall, the survey highlights the growing popularity of neural network applications and the need to increase awareness of their potential applications and benefits to encourage wider adoption and use in the future.

4) Have you personally used any product or service that uses neural networks ?

According to a survey, 75.6% of respondents reported having personally used a product or service that uses neural networks, while 24.4% reported not having used such a product or service. The survey result suggests that neural networks are becoming increasingly prevalent in our daily lives, as more products and services incorporate this technology, and that many people are likely using these products and services without even realizing that they are powered by neural networks. Overall, the result highlights the growing importance of neural networks in technology and their impact on our daily lives.

5) If you answered “Yes” to the previous question, which product or service was it ? (Select all that apply)

A follow-up question in the survey asked respondents who had answered “Yes” to the previous question about their personal use of neural network-based products or services, to specify which ones they had used. The options were “Virtual assistants (e.g. Siri, Alexa)”, “Social media (e.g. Facebook, Twitter)”, “Online shopping (e.g. Amazon, eBay)”, “Streaming services (e.g. Netflix, Spotify)”, and “Online advertising (e.g. Google AdWords)”. Results show that virtual assistants were the most commonly used product or service with 70.7% of respondents selecting this option. Social media, online shopping, and streaming services were also popular, with 63.4%, 65.9%, and 58.5% of respondents selecting these options, respectively. Online advertising was the least commonly used, with only 48.8% of respondents selecting this option. This suggests that many people are using neural network-powered technologies without realizing it, and this use is likely to continue to grow in the future.

6) Which of the following potential benefits of neural networks do you find most compelling ? (Select all that apply)

The survey asked respondents to select the potential benefits of neural networks that they found most compelling. Based on the responses, the most popular option, selected by 78% of respondents, was “Better personalization and recommendation systems.” The second most popular option was “Improved accuracy and efficiency in decision-making” at 68.3%, followed by “Increased automation and reduced need for human intervention” at 63.4%, and “Better detection of fraud and other anomalies” at 61%. The remaining options, “Predictive Maintenance”, “Enhanced customer experience” and “Improved medical diagnoses and treatments” were less popular, with each being selected by 39%, 36.6%, and 36.6% of respondents, respectively.

7) Which of the following potential risks or challenges associated with neural networks do you find most concerning? (Select all that apply)

The survey results show that the majority of respondents expressed concern about several potential risks and challenges associated with neural networks. Specifically, 58.5% of respondents found the lack of transparency and accountability most concerning, while 63.4% were worried about the potential for bias or discrimination. Additionally, 51.2% of respondents were concerned about ethical issues around data privacy and usage, and 43.9% expressed concerns about overreliance on algorithms and automation. Interestingly, only 34.1% of respondents were concerned about security risks associated with cyberattacks, which is a relatively low percentage given the potential severity of such risks. Overall, these results suggest that transparency, fairness, and ethical principles need to be prioritized when developing and deploying neural networks.

8) How likely are you to use or interact with a product or service that uses neural networks in the future ?

Based on the survey results, a significant majority of the respondents are likely to use or interact with a product or service that uses neural networks in the future. Specifically, 34.1% of the respondents believe that they are very likely to use or interact with such products or services, while another 34.1% believe that they are somewhat likely to do so. Only a small percentage of respondents, 7.3%, believe that they are somewhat unlikely to use or interact with such products or services. This suggests that there is a high level of interest and acceptance among the general population for products and services that incorporate neural networks, which could potentially drive increased adoption and implementation of these technologies in various industries. However, it is worth noting that a significant portion of the respondents, 24.4%, expressed a neutral stance on this question, indicating that there may be a need for further education and awareness-building around the benefits and applications of neural networks to increase their adoption and use among the general public.

9) Which of the following best describes your level of understanding of neural networks ?

According to the survey results, the majority of the respondents (56.1%) identified themselves as beginners when it comes to their understanding of neural networks. This suggests that many people have heard of neural networks but don’t have a detailed understanding of how they work or their applications.36.6% of the respondents identified themselves as intermediate, indicating that they have some knowledge of neural networks and how they work, but may not have a deep understanding of their applications. Finally, only 7.3% of the respondents identified themselves as advanced, indicating that a relatively small proportion of the population has a deep understanding of neural networks and their applications. Overall, these results suggest that there is a significant opportunity for education and training in the area of neural networks, especially for beginners who may be interested in learning more about the technology and its potential applications.

10) In your opinion, which of the following industries has seen the most significant impact from neural networks ?

Based on the survey results, the majority of the respondents believe that the healthcare industry has seen the most significant impact from neural networks. Specifically, 48.8% of the respondents believe that healthcare has seen the most impact, which suggests that neural networks are being used to improve patient outcomes and advance medical research.14.6% of the respondents believe that the finance industry has seen the most impact from neural networks, indicating that these networks are being used to improve fraud detection and financial risk assessment.12.2% of the respondents believe that the manufacturing industry has seen the most impact, which suggests that neural networks are being used to improve predictive maintenance and quality control.17.1% of the respondents believe that the transportation industry has seen the most impact, indicating that neural networks are being used to improve autonomous vehicles and traffic management. Finally, only a small percentage of respondents believe that the retail, education, and agriculture industries have seen significant impact from neural networks.

11) Which of the following factors do you think are important for the successful implementation of neural networks in real-world scenarios ? (Select all that apply)

The survey results indicate that having access to high-quality data, skilled data scientists and engineers, and powerful computing resources are important for the successful implementation of neural networks in real-world scenarios. Specifically, 61% of respondents believe that availability of high-quality data is critical, while 68.3% believe that skilled data scientists and engineers are necessary. 70.7% of respondents think that access to powerful computing resources is important. Additionally, 31.7% of respondents believe that effective collaboration between domain experts and data scientists is necessary, and 14.6% think that supportive organizational culture and management are important. These results suggest that a combination of technical expertise, strong computing resources, and effective communication between stakeholders are necessary for successful implementation of neural networks.

12) How concerned are you about the potential job displacement resulting from the increased use of neural networks in various industries ?

Based on the survey results, it seems that a significant number of people are concerned about the potential job displacement resulting from the increased use of neural networks in various industries. The majority of respondents (65.8%) expressed some level of concern, with 26.8% saying they were “very concerned” and 39% saying they were “somewhat concerned.” Only a small percentage (7.3%) said they were “not very concerned,” while none of the respondents said they were “not at all concerned.”

13) Which of the following challenges associated with the implementation of neural networks do you think are the most significant ? (Select all that apply)

Based on the survey results, the most significant challenges associated with the implementation of neural networks are the difficulty in interpreting and explaining results, as selected by 61% of respondents. This highlights the importance of developing methods to interpret the decision-making process of neural networks in order to increase transparency and accountability. The high cost of implementation and maintenance was also identified as a significant challenge, with 56.1% of respondents selecting it. This suggests that there is a need for more cost-effective solutions and strategies for implementing and maintaining neural networks. Limited availability of skilled professionals was another significant challenge, as identified by 53.7% of respondents. This highlights the need for more training and education programs to develop a skilled workforce capable of implementing and maintaining neural networks.

Challenges with integrating with existing systems and processes were selected by 29.3% of respondents, indicating the importance of ensuring that neural networks can be seamlessly integrated into existing workflows and systems.

14) Which of the following potential applications of neural networks do you find most exciting? (Select all that apply)

The majority of respondents (63.4%) find the potential application of personalized medicine in healthcare to be the most exciting, followed by fraud detection in finance and autonomous vehicles in transportation (both at 61%). Predictive maintenance in manufacturing is also considered exciting by more than half of the respondents (53.7%), while improved customer service and chatbots in retail are considered the least exciting, with only 36.6% of respondents selecting this option. These results suggest that respondents are particularly interested in the potential of neural networks to make a positive impact in healthcare, finance, and transportation.

15) How do you think the use of neural networks will evolve in the next 5–10 years ?

The majority of respondents (36.6%) believe that neural networks will be subject to increased regulation and scrutiny, suggesting a growing awareness of the potential ethical concerns associated with their use. A significant proportion of respondents (31.7%) believe that neural networks will become more widespread and prevalent in various industries, indicating a belief in the potential of neural networks to transform industries and improve outcomes. A smaller proportion of respondents (19.5%) believe that neural networks will become more sophisticated and capable of handling complex tasks, highlighting the ongoing development of artificial intelligence and the potential for continued improvements in neural network capabilities. Finally, a minority of respondents (12.2%) believe that neural networks will be replaced by other forms of artificial intelligence, suggesting an awareness of the ongoing development and potential for alternative approaches to artificial intelligence.

16) How confident are you in the ability of neural networks to make accurate predictions or decisions ?

Based on the survey results, it can be concluded that a majority of respondents have some level of confidence in the ability of neural networks to make accurate predictions or decisions. Nearly 39% of respondents were “somewhat confident,” while 31.7% were “very confident” in the ability of neural networks. This indicates that a majority of respondents are generally positive about the accuracy of predictions or decisions made by neural networks. A quarter of respondents (24.4%) were “neutral” in their level of confidence, suggesting that they are neither positive nor negative about the ability of neural networks to make accurate predictions or decisions. Only a small proportion of respondents (2.4%) were “not very confident,” while none of the respondents were “not at all confident.” This indicates that the majority of respondents are at least somewhat confident in the ability of neural networks to make accurate predictions or decisions.

17) In your opinion, what are the most promising future applications of neural networks ? (Select all that apply)

Based on the survey results, it can be concluded that respondents believe that neural networks have promising future applications in a variety of industries, particularly in healthcare, finance, and manufacturing. The majority of respondents (63.4%) identified “personalized medicine and drug discovery in healthcare” and “financial risk assessment and fraud detection in finance” as the most promising future applications of neural networks. Other promising future applications of neural networks include “autonomous vehicles and traffic management in transportation” (61%) and “predictive maintenance and quality control in manufacturing” (53.7%).A smaller proportion of respondents (36.6%) identified “improved recommendation systems and personalized marketing in retail” as a promising future application of neural networks. Overall, the survey results suggest that respondents believe that neural networks have significant potential to transform various industries, particularly in healthcare, finance, and manufacturing.

18) Which of the following industries do you think will benefit the most from the use of neural networks in the future ?

Based on the survey results, it can be concluded that the majority of respondents believe that the healthcare industry will benefit the most from the use of neural networks in the future. Nearly 39% of the respondents believed that healthcare would benefit the most, followed by finance at 17.1% and manufacturing at 12.2%. The transportation, retail, education, and agriculture industries were considered to be less likely to benefit significantly from neural networks. Only a small proportion of respondents (2.4%) believed that all industries would benefit equally from the use of neural networks. Overall, the survey results suggest that people believe that neural networks will have a significant impact on the healthcare, finance, and manufacturing industries. This highlights the potential of neural networks to transform industries and improve outcomes, particularly in areas such as healthcare.

19) How concerned are you about the potential ethical implications of the use of neural networks in various industries ?

Based on the survey results, it can be concluded that a majority of respondents have some level of concern about the potential ethical implications of the use of neural networks in various industries. Almost half of the respondents (46.3%) were “somewhat concerned,” while 31.7% were “very concerned” about the ethical implications. This suggests that people are aware of the potential ethical issues associated with the use of neural networks and are paying attention to their use in various industries. Only a small proportion of respondents (7.3%) were “not very concerned,” while none of the respondents were “not at all concerned.” This indicates that the ethical implications of neural networks are a matter of concern for the majority of the respondents.

20) In your opinion, what are the most significant ethical concerns associated with the use of neural networks ? (Select all that apply)

The majority of respondents (73.2%) highlighted the lack of transparency and accountability in the decision-making process as a significant ethical concern. This suggests that people are worried about how decisions are made by neural networks and who is responsible for those decisions. Another significant ethical concern, as identified by 56.1% of respondents, is the potential for bias and discrimination in decision-making. This is a particularly important issue in contexts where neural networks are used to make decisions that affect people’s lives, such as in hiring or lending decisions. Additionally, 43.9% of respondents expressed concerns about data privacy and security risks associated with the use of neural networks, while 31.7% were worried about the potential misuse of data or algorithms. These concerns highlight the need for robust data protection and governance frameworks to ensure that neural networks are used ethically and responsibly.

Conclusion

Based on the survey on applications of neural networks in real-world scenarios, it is clear that neural networks have become an integral part of various industries such as healthcare, finance, transportation, and many more. The survey showed that neural networks are being used for a wide range of tasks, including image and speech recognition, natural language processing, predictive maintenance, fraud detection, and recommendation systems.

Moreover, the survey highlighted that the use of neural networks has significantly improved the accuracy and efficiency of these tasks. For example, in healthcare, neural networks are used for early disease detection and personalized treatment recommendations, leading to better patient outcomes. In finance, neural networks are used for fraud detection and predicting stock prices, resulting in improved risk management and investment decisions.

The survey also revealed that there is a growing demand for professionals with expertise in neural networks, machine learning, and artificial intelligence. This highlights the need for individuals to acquire the necessary skills and knowledge to keep up with the advancements in this field.

In conclusion, neural networks have proven to be a powerful tool for solving complex problems in various industries. With the advancements in technology, we can expect to see even more applications of neural networks in the future, leading to significant improvements in many areas of our lives.

References

1. https://vitalflux.com/deep-neural-network-examples-from-real-life/
2. https://www.analyticssteps.com/blogs/8-applications-neural-networks
3. https://www.smartsheet.com/neural-network-applications
4. https://www.techpout.com/real-life-applications-of-neural-networks/
5. https://data-flair.training/blogs/artificial-neural-network-applications/
6. https://www.hitechnectar.com/blogs/artificial-neural-network-applications/

Under the Guidance of —

Prof. S. T. Patil (Professor, Vishwakarma Institute of Technology, Pune)

Created By —

1. Vinit Gite
2. Gourav Kamble
3. Aditya Pawar
4. Girish Sarwade