When we are planning for a capstone related research project with the deployment of AI and Deep Learning, an efficient concept selection is significant and it offers an optimistic effect. In case if you choose deep learning merging with capstone it isn’t easy and it’s even harder but we at get all details of your areas of interest and develop you an apt project that it might be able to help you more in your research encounter. We discussed various research concepts by considering different fields in our below description:

  1. Farming:
  • Yield Forecasting: By considering the environments such as quality of soil, weather condition and crop types, we forecasted the yield of crops.
  • Identification of Crop Disease: Crops impacted by pests and crop disease is found in our approach by examining drones and satellite-based images of farms.
  1. Energy:
  • Prediction of Energy Utilization: In organizations or households, we predicted the utilization of energy in terms of previous data. From this, grid management gets more beneficial.
  • Solar Panel Defect Identification: By analyzing images, we identified the artifacts or noises in the solar panel which causes ineffective performance.
AI Capstone Topics with Deep Learning
  1. Healthcare:
  • Disease Forecasting and Categorization: In our study, we examined patient’s data such as genomic sequence, MRI scans and X-rays for the identification of various diseases including cancer, genetic disorders or pneumonia.
  • Finding Drugs: DL based method is employed by us for molecular bioactivity forecasting to enhance the drug creation procedure.
  1. Entertainment and Media:
  • DeepFake Recognition: We constructed a framework to identify the altered or modified images and video data.
  • Movie Suggestion Framework: By considering user’s area of interest, movies and TV programs are suggested in our research.
  1. Retail and Ecommerce:
  • Inventory Optimization: Assist the retailers to maintain their sales efficiently by carried out the forecasting performance in our study through analyzing the highly demanding products among customers.
  • Product Suggestion Model: We proposed a recommendation framework to suggest products related to the customer’s search history, previous purchase data.
  1. Education:
  • Adaptive Learning: We developed a content delivery-based model by considering the student’s capacity and interpretation.
  • Automatic Essay Scoring: To rank the student’s essay content and to offer the grammatical reviews, a framework is suggested by us.
  1. Finance:
  • Forecasting in Stock Market: A future stock market prices are forecasted in our research based on the previous stock market prices and some economic measures.
  • Credit Scoring: We employed DL system to forecast the loan defaulters in terms of historical information like transaction data, credit data and some personal information.
  1. Telecommunications:
  • Churn Forecasting: In that, we understand the reason behind why the customers break the business with some groups.
  • Network Fault Identification: We considered the previous data and actual time metrics to forecast and identify the network disconnections and degradations.
  1. Smart Cities:
  • Waste Management: In our study, a model is proposed to arrange the waste based on various terms like recyclables, trash and organics autonomously through the utilization of robotics and cameras.
  • Traffic Management Approach: We optimize the traffic signals timing and minimize the traffic by examining actual time traffic camera data.
  1. Environment:
  • Forecasting Air Quality: Based on the present conditions such as environmental activities, weather status, and traffic and previous historical information, we predicted the air quality.
  • Wildlife Monitoring: Wildlife are identified and categorized by us through monitoring the captured images in forests that assist to protect the wildlife.
  1. Automotive:
  • Driver Fatigue Identification: We identified the fatigue stage of driver by examining the car cameras and notify them.
  • Automated Driving: To drive the car automatically by identifying traffic signals, various lanes and other vehicles, a framework is suggested in our research that makes use of recorded feeds.

Here we need to ensure about the following criteria after choosing a certain field and issues we are intended to solve in our capstone project.

  1. Problem Interpretation: First, we should thoroughly examine the selected research problem, its deployment and possible outcomes.
  2. Data Collection: We must choose the significant dataset and also make use of publicly available datasets such as Kaggle, UCI ML, or domain related database.
  3. Framework Construction: DL based frameworks including TensorFlow, PyTorch, or Keras may employ in our approach for various phases like development, training and evaluation.
  4. Iterate: Commonly, multiple iterations are needed for AI related projects and based on the findings and reviews, we retrain the framework.
  5. Work Presentation: We can discuss the utilized techniques, challenges, findings and possible future research in our paper.

Finally, we should sure about some conditions like whether we followed the proper guidelines or not. Specifically, when we are working with real world data or providing some forecasting methods.

How do I start a deep learning project?

      A DL based domain is an in-depth concept and also an interesting field to work in. Here we suggested procedural steps to construct a deep learning framework.

  1. Problem Description:
  • Understand the issue you are intended to address. Is it a categorization problem, regression, or something else?
  • Check whether the problem is appropriate foe deep learning or not. Because some problems can be addressed by utilizing simple models rather than using deep learning.
  1. Literature Survey:
  • We can explore some previous research papers related to our problem by utilizing websites such as Google Scholar or arXiv.
  • Understand various research approaches or methods that are suitable for our problem field.
  1. Data Gathering:
  • In DL researches, data holds an essential place. Therefore, in our study, we can employ available datasets or also developed by our own.
  • From various environments such as Kaggle, UCI ML database or other particular field database, we can utilize public datasets.
  • We need to check whether we have rights to use that dataset or not. Because some may comprise of personal data.
  1. Preprocessing of Data:
  • Data are cleaned in our research by managing the missing values, outliers and artifacts.
  • If there is a need, we can normalize or standardize the characteristics.
  • Here, data are divided into three phases like training, testing and validation.
  1. Model Selection:
  • We must select proper DL algorithms like CNN for image based projects, RNN and LSTM for series tasks by considering our project problem and literature survey.
  • To construct a baseline, we utilize some simple framework at first.
  1. Training:
  • We described and trained our framework by employing several techniques like TensorFlow, PyTorch or Keras.
  • Framework weights are saved often and we evaluate the various training and verification-based performance metrics like loss and accuracy.
  • Overfitting issue is eliminated in our work by establishing several methods including dropout, regularization and augmenting data.
  1. Evaluation:
  • We utilized test dataset to examine the efficiency of our framework. Various metrics like accuracy, F1-score, ROC curve and so on related to our specified problems are considered.
  • For efficient analysis, cross validation approaches are employed by us.
  1. Fine-Tuning and Optimization:
  • Our framework carried out more iteration based on the performance evaluation by altering its techniques, hyperparameters and training plans.
  • In the case of inadequate data, we can utilize methods such as transfer learning.
  1. Implementation:
  • After we successfully evaluated our framework, we have to think about various environments such as cloud, edge devices or server in which where we are going to implement.
  • For implementation, we transformed our framework into an optimized one. For instance: TensorFlow Lite, ONNX.
  1. Feedback Loop:
  • We should analyze efficiency of our framework in the real time after the implementation.
  • To enhance and rebuild the framework, we examined the data and reviews.
  1. Documentation and Presentation:
  • If we carried out this research for some organizations or for professional experts, we need to document the procedures, techniques and findings.
  • We develop a representation, journal or description to discuss about our results and techniques.

We should ensure the followings throughout the process:

  • We should know about the modern enhancements of deep learning
  • Various environments such as Stack overflow, AI related fields, Reddit’s or Machine learning is also assisted to achieve our goal.
  • We need to be confidential about the real world or personal data that are utilized in our research.

Mostly, DL related models always require more time for implementation and more complicated one but we can achieve efficient findings through this. So, we should be patient when working with DL approaches it takes up a lot of time and energy. There are more than 50+ trained experts to run your AI capstone deep learning projects.


 Looking for M.Sc. dissertation topics. Here we have listed some of the hot topics that we have worked out. You can either get it or customized topics by integrating capstone and deep learning will be developed by our researchers. Our developers make use of the correct algorithms to achieve the desirable result.

  1. Comparing the performance of machine learning and deep learning algorithms classifying messages in Facebook learning group
  2. Research and Practice on the construction of deep learning algorithm experimental platform
  3. Detecting Port Scan Attempts with Comparative Analysis of Deep Learning and Support Vector Machine Algorithms
  4. Research on Abstractive Summarization Technology Based on Deep Learning
  5. Knowledge Transferring in Deep Learning of Wearable Dynamics
  6. An emotional EEG signal classification research based on deep learning
  7. Runway Detection and Localization in Aerial Images using Deep Learning
  8. A Statistical Learning Model with Deep Learning Characteristics
  9. Intrusion Detection and Prevention in Networks Using Machine Learning and Deep Learning Approaches: A Review
  10. Automatic Speaker Recognition using Transfer Learning Approach of Deep Learning Models
  11. Analysis of Multi-Class Weather Classification using deep learning models and machine learning classifiers
  12. Research On Deep Learning Based End-To-End Wireless Communication System Technology
  13. A Deep Learning Method on Audio and Text Sequences for Automatic Depression Detection
  14. Transfer Optimistic Outcome-based Learning for Mature Behavior of Machine in Deep Learning
  15. A Novel Method for Detection of ECG with Deep Learning
  16. Data Augmentation in Training Deep Learning Models for Malware Family Classification
  17. Machine Learning and Deep Learning Algorithms for Network Data Analytics Function in 5G Cellular Networks
  18. Semi-Supervised Deep Learning Seismic Impedance Inversion Using Generative Adversarial Networks
  19. An Improved System for Brain Pathology Classification using Hybrid Deep Learning Algorithm
  20. Deep Learning Algorithm Based Remote Sensing Image Classification Research


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