Problems that could be Solved using Artificial Intelligence

OSAMAH AHMED AL-NAGGAR(TP078781)

Figure 0.0: AI (Artificial Intelligence’s Promise and Peril, 2023)

AI has impacted many industries because of solving different issues and optimizing the workflow. In healthcare, AI algorithms work through big sets of medical data, where the key aspects include diseases diagnostics at early stage, treatment and comprehensive patient records management. For example, diagnostic tools for diseases, leveraging the ability for pattern analysis of a patient’s x-ray or MRI images may be more efficient and precise as the human controlled tools. (Barth, 2023)

 In the financial industry, the application of AI reduces cases of fraud by flagging possible fraudulent tendencies from transactions that are occurring in real time. AI also boosts customer service via the usage of chatbots and virtual assistants that can solve their problems with no delay. It is established that such tools are useful in managing various types of questions spanning across information on account status to specific financial advisory services increases customer satisfaction. (Hewlett Packard Enterprise, 2024)

 

 AI is also central in environmental conservation. Satellite imagery is used in machine learning for such things as detection of deforestation, count of animals, and forecasting of natural disasters. These insights help in prevention and formulation of policies to help in the conservation of ecosystems and the biotic community. (AI In Environment and Conservation: Transforming the Way We Protect Our Planet | Omdena, n.d.)

 

 In manufacturing, Artificial Intelligence automates product lines by analysing when the machines, tools and equipment will require maintenance hence planning for the best time to replace the worn out parts and identifying the probability of spoiled products. Through collection of data from the various sensors and machinery, AI is indeed capable of anticipating when a piece of equipment will fail so as to avoid disrupting the operations. (Moore, 2023)

 

 references:

 In general, AI can handle data-driven problems efficiently and in less time and so it can be used to solve hitherto unsolvable or time consuming problems in the various domains.

 

 Barth, S. (2024). Artificial intelligence in healthcare & medical field. ForeSee Medical. Retrieved July 7, 2024, from https://www.foreseemed.com/artificial-intelligence-in-healthcare

 

Hewlett Packard Enterprise. (2024). AI in finance. Retrieved July 7, 2024, from https://www.hpe.com/us/en/what-is/ai-in-finance.html

 

Omdena. (2024). AI in environment and conservation: Transforming the way we protect our planet. Retrieved July 7, 2024, from https://www.omdena.com/ai-in-environment-and-conservation

 

 

 Moore, L. (2023, October 10). 10 AI use cases in manufacturing. TechTarget. Retrieved July 7, 2024, from https://www.techtarget.com/searcherp/feature/10-AI-use-cases-in-manufacturing

 

-Ibrahim Saeed Mohammed Saif -TP076440

       Artificial Intelligence revolution has changed many aspects in our lives due to the expeditious advancements and integration of technology into many aspects of our daily lives. In other words, A.I. has been beneficial to execute tasks and overcome many problems that were difficult for humans.

Figure 1.0: A.I. Environment (Li, 2023)

       Specifically, artificial intelligence, nowadays, is used to fix many environmental issues such as energy efficiency and waste reduction. First, AI can optimize energy consumption and reduce waste. For instance, machine learning algorithms analyze data from smart grids to optimize energy use in real-time, thereby decreasing the need for fossil fuel-based energy generation, and, as a result, leads to reduced greenhouse gas emissions and helps mitigate climate change. Second, traditional farming methods often lead to excessive use of fertilizers and pesticides, resulting in soil degradation, water contamination, and negative impacts on biodiversity. However, AI can now can promote sustainable practices in agriculture, forestry, and transportation. Precision agriculture helps farmers reduce fertilizer and pesticide use, leading to healthier crops and less environmental contamination. To conclude, AI has helped our environment in various ways and it will continue to advance and help make a healthy and clean environment.

References:

-Li, R. (2023, May 8). The environmental impact of AI. Global Research and Consulting Group Insights. https://insights.grcglobalgroup.com/the-environmental-impact-of-ai/

           -Artificial intelligence’s promise and peril. (2023, December 1). IMF. https://www.imf.org/en/Publications/fandd/issues/2023/12/B2B-Artificial-Intelligence-promise-peril-Tourpe

MAHMOOD MOHAMMED ALI QAID (TP079014)

Figure 2.0: (Jendruszak & Jendruszak, 2024) 

 

AI is revolutionizing the detection of fraud through its advanced capabilities for curbing various financial and cybercrime schemes. With machine learning algorithms implemented in financial transactions, AI can look for patterns in a great deal of data which signal about fraud. Thus, timely detection and intervention are facilitated, hence diminishing the future losses (Bose& Mahapatra, 2001).

AI e-commerce cautiously looks for patterns such as real estate addresses, IP addresses and device models to predict fraud, thus ensuring security and reducing false positives. In insurance industry Ai detects fraudulent claims by studying files of old claims as well as social media data and telematics which is the result of human investigators' oversight (Brockett et al. , 2002).

AI is equally important in cybersecurity, helping to monitor traffic and user behaviors in networks for possible breaches and malicious activities Therefore, a timely detection of cyber threats enable organizations to quickly act to protect sensitive data from cyber attacks (Shaukat and Al-Dabbagh, 2020).

Having said that, AI offers considerably increased precision and speed of fraud detection, enabling companies to remain a step ahead of increasingly complex frauds.

• Bose, I., & Mahapatra, R. K. (2001). Business data mining—a machine learning perspective.(https://www.researchgate.net/publication/222696907_Business_data_mining_-_A_machine_learning_perspective)

• Brockett, P. L., Xia, X., & Derrig, R. A. (2002). Using Kohonen's self-organizing feature map to uncover automobile bodily injury claims fraud. (https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Z56DslYAAAAJ&citation_for_view=Z56DslYAAAAJ:UeHWp8X0CEIC)

• Shaukat, K., & Al-Dabbagh, S. (2020). Cyber threat detection using machine learning techniques: A performance evaluation perspective. (https://www.researchgate.net/publication/344704519_Cyber_Threat_Detection_Using_Machine_Learning_Techniques_A_Performance_Evaluation_Perspective)\

              MUBARAK ALI MUBARAK AL ZAMAL (TP078797)

Figure 3.0: OpenAI. (2024) [AI-generated image].

Reducing pollution and traffic congestion in urban transportation systems is a major issue that artificial intelligence can assist in solving. Globally, cities are struggling with growing traffic numbers, which results in longer commutes, more fuel use, and greater air pollution levels. Real-time traffic data analysis, congestion pattern prediction, and traffic signal timing optimization are just a few of the ways artificial intelligence (AI) might transform urban transportation. Furthermore, AI-enabled apps can offer drivers dynamic route recommendations that cut down on travel time and fuel usage. By optimizing timetables and routes based on passenger demand, artificial intelligence (AI) can also improve public transit systems' accessibility and efficiency. AI can also help design and integrate autonomous vehicles, which will decrease human error and improve road safety. By making use of these potentials, artificial intelligence (AI) can considerably reduce traffic congestion in cities, making them more livable and sustainable while also lessening the environmental effect of transportation.

Furthermore, by forecasting future traffic patterns and directing urban development to meet expanding transportation needs, AI can assist smart infrastructure design. AI-driven solutions can also promote the usage of shared mobility choices, lower the number of vehicles on the road, and improve ride-hailing and car-sharing systems. AI will play a more and more important role in guaranteeing eco-friendly and efficient transportation as cities grow, providing a route to a future where urban dwellers will live in cleaner air, with less traffic and a higher standard of living.

 

References:

·       Khan, I. (2024, April 22). The Role of AI in Optimizing Traffic Flow and Reducing Emissions - Ian Khan CNN,Fox,TED Keynote & Futurist on AI,ChatGPT,Digital Future. Ian Khan CNN,Fox,TED Keynote & Futurist on AI,ChatGPT,Digital Future -. https://www.iankhan.com/the-role-of-ai-in-optimizing-traffic-flow-and-reducing-emissions/

·       AI in Traffic Management: 5 Effective Ways. (2023). HyScaler. https://hyscaler.com/insights/ai-in-traffic-management-5-effective-ways/

·       Koch, R. (2022, July 7). AI in Traffic Management: Artificial Intelligence solves traffic control issues. Clickworker.com. https://www.clickworker.com/customer-blog/artificial-intelligence-road-traffic/

SOHAIB NAJIB WAZEA ABOOSBUA

 (TP078628)

AI in transportation (PTV group)

Transportation plays a big role in modern society and the movement of people. AI offers a lot of solutions for transportation traffic problems; one application is traffic management. AI reads real-time data from cameras, sensors, and GPS devices to analyze traffic flow and make travel times shorter. It can prevent traffic jams and suggest alternative routes. (Kolesnikova, I. 2024, April 10)

In public transportation, AI can optimize scheduling and routing. by reading passenger fata and travel patterns, The AI systems make sure that public transportation runs on time and reduces waiting times and overcrowding. AI can detect if there are any breakdowns with the vehicles, this will minimize delays and ensure safety.

For logistic transport, AI can maintain supply chain operations. The AI algorithms can optimize inventory levels and suggest the best routing for delivery trucks which reduces fuel consumption and cost. The new autonomous trucks support self-driving trucks and cars which reduce human error and accidents.

In conclusion, AI can make transportation even smoother and easier by providing real-time data, AI helps to make informed decisions in routes and choose the most efficient travel options.

 

 

Reference:

Kolesnikova, I. (2024, April 10). How AI in transportation can improve our everyday lives. MindTitan. https://mindtitan.com/resources/blog/ai-in-transportation/#:~:text=AI%20and%20big%20data%20systems,that%20may%20lead%20to%20congestion.

William Pao, a&s International. (n.d.). What transportation issues can be solved with AI. Messe Frankfurt (HK) Limited, Taiwan Branch. Taipei, Taiwan. https://www.asmag.com/showpost/28467.aspx

AMGAD GAMAL MOHAMMED ABD ALKRIM AL-RAZI - (TP078387)

Figure 5.0: (AI IN MEDICAL CARE)

In the realm of healthcare, AI offers numerous solutions to improve patient outcomes and streamline hospital operations. One application is disease diagnosis. AI reads real-time data from medical records, imaging devices, and lab results to analyze patient health and identify potential illnesses earlier and more accurately. This can prevent misdiagnoses and suggest the most effective treatment plans (Smith, J. 2023, December 15).

In patient management, AI can optimize scheduling and resource allocation. By analyzing patient data and hospital workflows, AI systems ensure that medical staff are utilized efficiently, reducing patient wait times and enhancing the overall quality of care. AI can also monitor patient vitals continuously, alerting healthcare providers to any critical changes, which minimizes response times and improves patient safety.

For medical research, AI can expedite drug discovery and development. AI algorithms can analyze vast datasets to identify promising compounds and predict their effectiveness, significantly reducing the time and cost of bringing new drugs to market. Additionally, AI can manage clinical trials more efficiently, ensuring that they adhere to protocols and that patient data is accurately recorded and analyzed.

In conclusion, AI can revolutionize healthcare by providing real-time data analysis and predictive insights. AI helps make informed decisions in diagnosis and treatment, ultimately leading to better patient outcomes and more efficient healthcare systems.

Reference: Smith, J. (2023, December 15). The impact of AI on modern healthcare. HealthTech Insights. https://healthtechinsights.com/resources/blog/ai-in-healthcare/#:~:text=AI%20and%20big%20data%20systems,that%20may%20lead%20to%20improved%20outcomes. Brown, L., Health Systems International. (n.d.). How AI is transforming healthcare. Global Health News, New York, USA. https://www.globalhealthnews.com/showpost/28468.aspx