Hello all,
We have collected few(>5) years of data related to dairy supply chain industry which involves milk quantity and quality from collection at farmer end until it gets as a dairy product. We are working on creating an AI/ML model to bring various business insights out of this data and different predictions.
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Hi Team,
I could think of 2 use cases in 4G/5G networks Telecom domain as of today which are **Anomaly detection & NLP for parsing the logs ** to build log analyzer tool.
Use Case: Automated Anomaly Detection in eNodeB Performance Testing
Background: In 4G/5G networks, eNodeB plays a crucial role in providing wireless connectivity. Ensuring the optimal performance of eNodeB is essential for a seamless and reliable network experience. Traditional testing methods involve manually configuring test scenarios and analyzing performance metrics, which can be time-consuming and may not efficiently identify subtle anomalies.
Objective: Implement machine learning algorithms to automate the anomaly detection process during eNodeB performance testing. The goal is to proactively identify irregularities, deviations, or potential issues in real-time, leading to faster issue resolution and improved network reliability.
Key Components:
Data Collection:
- Gather performance data from various eNodeB parameters, including signal strength, latency, throughput, and resource utilization.
- Collect historical data for training the machine learning model.
Feature Engineering: - Extract relevant features from the collected data, such as time of day, network load, and environmental conditions.
- Normalize and preprocess the data to make it suitable for training.
Machine Learning Model: - Train a supervised machine learning model (such as a neural network, decision tree, or ensemble methods) using historical data.
- Utilize labeled datasets to teach the model to distinguish between normal and anomalous behavior.
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I am a Subject Matter expert in the Listed derivatives space since 23 years for all global markets and i am trying to implement via AI/Gen AI/ML the below concepts into the Listed Exchanges Executions (FO) and Middle office (MO) and back office (BO) .
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Fully automated Auto fix(Root cause Idetification & bot fixes for all Data quality production issues on all listed derivative clearing and straight through processing)
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Market standardization of brokers across the board.
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Auto calculations of volumes and weightages of Indexâs for any user for free without signing up for any application
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Auto calculation of cash/position/transaction at teh EOD- Endy of the day for a perfect NAV (Net asset value) instead of depending upon market data and bookkeepers and other 3rd party subscription to do the whole manual work which has very high charges and needs co-relation to next days market research.
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Creation of UAT/SIT environment which are ONLY AI based and without any human inteference so that production machines are safeguarded from AI while still utilizing AI in the lower env for all kinds of coding/development/testing/support fixes/data quality fixes and across platforms and domains and Super fast issue resolution to any applications production issues mianly for applications which use Oracle/Unix/Linux/Java/Groovy script/Apiâs/GIT/Repo & service now or any MS with other software integrated platforms.
6)Front to back auto testing and data comparison tool development for any level of end user, Some of these are already available in SAP,Informatica etc but practically many of the end users need instatineous results and we cannot expect any user to get trained on softwares or platforms, while here utilizing AI to do all the reporting and testing and generate based upon given tools to the AI the end user can be rest assured that the results will be given in a comfortable timely manner.
- No one wants overrides and and or falling into traps of Risk & Compliance, Hence i am also working on training AI to auto read all the latest Risk and compliance documents and provide the end user of any production system managers/owners/sponsors to happily guide them through AI bots or via auto tutorials and speech which will save time and also avoid any user falling into an override or a Risk & compliance issue.
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Hi, I work in Global Finance Department, specifically to Deposits, the major challenge we often face is regarding the variance commentaries. I would like to use AI/ML and build such a model through which we can quickly and accurately get the commentaries.
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Hello,
Currently I am a student and am deeply fascinated by AI and its applications as it is âHuman intelligenceâ in a humanâs creation. Since a really long time, I have this dream of making something like âJarvisâ (from movie Iron Man) and AI/ML/DL would be best in this project. But then, there is a question: How intelligent will my Jarvis be? Of course, I would want him to have answers to all the scientific questions I ask him (like Google) and behave like a human. To behave like human, Jarvis will have to have the understanding of human emotions and communication which includes humor, sensible reasoning, morals and values. For this, we need the datasets to train Jarvis. We have two ways of obtaining the dataset: to create a dataset (If the behavior of the modal is expected in some desired way), or to get it from chats (say, Instagram chats as they are informal). But there is a catch: creating a dataset will take a lot of time and may be applicable to certain situations only while the dataset we obtain from chats will be easily available and will consist of many situations involving many people, many incidents and many outcomes of same situations. Some outcomes to a situation might be âgood outcomesâ while other outcomes might be âbad outcomesâ. This means, after training, the modal with dataset from chats will have the âaverage solutionâ to an event/situation because the dataset had both good and bad solutions. In a situation, the solutions provided to me by Jarvis might be the best ones for me but the same may not be the case with someone else. To solve this issue, ML and DL can greatly contribute in my project where the Jarvis asks himself âwhy this solutionâ and âwhat other solutionsâ before providing a result. With some additional data, the modal (Jarvis) can provide satisfactory solutions to not just one person but anyone who seeks him. Take for example the MBTI personality which classifies human personalities in 16 different types, each type having different characteristics. If Jarvis understands which personality type is seeking his help, he will find the outcomes which are most likely to satisfy the userâs expectations.
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- Introduction:
- Provide an overview of the importance of inventory forecasting in supply chain management.
- Introduce the use of AI and ML in improving inventory forecasting accuracy and efficiency.
- Background:
- Describe your company or organization and its inventory management processes.
- Explain the challenges faced in traditional inventory forecasting methods, such as manual forecasting or simplistic models.
- Implementation of AI and ML:
- Detail how AI and ML techniques were integrated into the inventory forecasting process.
- Discuss the selection of AI/ML algorithms and technologies used, such as neural networks, decision trees, or time series forecasting models.
- Explain the data collection and preprocessing steps involved, including gathering historical sales data, inventory levels, and external factors like market trends or seasonality.
- Highlight any customizations or modifications made to the algorithms to suit your specific business needs.
- Results and Benefits:
- Present the outcomes of implementing AI and ML in inventory forecasting.
- Showcase improvements in forecast accuracy, reduction in stockouts or overstock situations, and optimization of inventory levels.
- Quantify the benefits achieved, such as cost savings, improved customer satisfaction, and increased operational efficiency.
- Case Study Examples:
- Provide specific examples or case studies illustrating successful inventory forecasting outcomes using AI and ML.
- Include before-and-after comparisons, demonstrating the impact of AI-driven forecasting on inventory management metrics.
- Challenges and Lessons Learned:
- Discuss any challenges or limitations encountered during the implementation process.
- Reflect on lessons learned and strategies for overcoming obstacles in adopting AI and ML for inventory forecasting.
- Conclusion:
- Summarize the key takeaways from the case study.
- Emphasize the importance of AI and ML in modernizing inventory forecasting practices and driving business success.
- Future Directions:
- Offer insights into future developments or enhancements planned for your AI-driven inventory forecasting system.
- Discuss potential areas for further optimization or expansion of AI and ML applications in supply chain management.
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Case Study: Using AI and ML for Bike Demand Prediction in a Bike-Sharing System
Industry: Transportation
Challenge: Accurately predict bike demand at different locations and times to optimize resource allocation and user experience in a bike-sharing system.
Solution: Develop a machine learning model that can predict bike demand using historical rental data and various influencing factors.
Data:
- Rental data: Includes information like timestamps, user types (casual, registered), locations, and weather conditions.
- External data: Weather data, holidays, and seasonality information.
Machine Learning Model:
- A LightGBM (Light Gradient Boosting Machine) regression model is chosen due to its efficiency and accuracy in handling large datasets with various feature types.
- The model is trained on historical data, considering factors like:
- Temporal features: Day of the week, hour, month, season, year.
- Weather features: Temperature, humidity, wind speed, and weather description.
- User type: Casual or registered user.
- Location: Specific docking station or area.
Benefits:
- Improved resource allocation: By predicting demand accurately, the system can distribute bikes efficiently across different locations, reducing situations of empty stations or overflowing stations with no available bikes.
- Enhanced user experience: Users can easily find available bikes at their desired locations, reducing waiting times and frustration.
- Data-driven decision making: The modelâs insights can inform strategic decisions like expanding the network to high-demand areas, optimizing pricing strategies, and improving maintenance schedules.
Challenges:
- Data quality and availability: Ensuring the accuracy and completeness of historical data is crucial for model performance.
- Model interpretability: Understanding how the model arrives at its predictions can be challenging, making it difficult to identify and address potential biases.
- Continuous improvement: The model needs to be constantly updated with new data and re-trained to adapt to changing user behavior and environmental factors.
Overall, AI and ML offer a powerful solution for bike-sharing systems to predict demand, optimize resource allocation, and improve user experience. By addressing data quality, interpretability, and continuous improvement, this technology can play a significant role in the future of sustainable transportation.
Hello All,
I would like to lay emphasis on product recommendation engines and analyzing the purchasing habits and shopping patterns for various customers through data analytics. This is not only applicable online, but every store company these days uses this data to arrange products in stores accordingly. You may find that the store has rearranged the items after probably a week or so. These days almost ever website we access gathers cookies to understand the consumer behaviour which can be further used for marketing, analyzing the most popular content and showing targeted ads.
AI-powered voice recognition and NLP technologies can enable customers to interact with banking services using natural language commands and speech recognition. This improves accessibility and enhances the user experience for customers who prefer voice-based interactions.
Currently, my research focuses on formulating a mathematical model using a partial differential equation to predict the growth of solid malignant tumours. As I am learning AI and ML, I aim to design an ML pipeline to integrate growing tumour imaging data (such as Magnetic resonance imaging (MRI) scan, Computed tomography (CT) scan, and Mammography, etc) with the mathematical model.
Consider a scenario where we aim to predict the growth of a solid tumour, such as a brain tumour, breast cancer, liver cancer, etc. To achieve this, we will start by collecting MRI data, including multiple tumour size measurements taken over time. Then, we will create a partial differential equation (PDE) using the provided clinical data, typically a form of reaction-diffusion equation. This equation will encompass parameters like cell proliferation rate, diffusion coefficients, and nutrient availability. Subsequently, we will develop a machine-learning pipeline to combine the MRI data with the mathematical model. Integrating AI, ML, and mathematical modelling based on PDEs presents a robust approach to forecasting cancer growth and enhancing the treatment. By harnessing expertise from multiple fields, such as mathematics, computer science, and oncology, this methodology has significant potential to advance precision medicine in cancer care.