Neural Network Machine Learning Projects

Neural networks are a subset of Machine Learning (ML) that is significantly good at learning from unstructured data like images and text. Regarding neural network project we give scholars a wide variety of thesis ideas according to their interest your satisfaction plays a vital importance to us. In depth research will be conducted for the successful completion of research word. We have a wide resource team fully equipped with necessary tools and techniques and have earned on line trust for more than 18+ years.

The following are the wide range of project plans across various difficulties that we can implement in neural networks:

Beginner Projects

  • Handwritten Digit Recognition: We include a convolutional neural network (CNN) to categorize digits from the MNIST dataset.
  • Basic Sentiment Analysis: To detect the Sentiment of film feedbacks from the IMDB dataset we can utilize the recurrent neural network (RNN).
  • Share Market Prediction: Utilizing an easy feedforward neural network for the prediction of stock rates from previous data.
  • Character-Level Text Generation: By constructing a mini RNN to prepare text character by character.
  • Image Colorization: We design a neural network that can colorize black and white pictures.

Intermediate Projects

  • Facial Recognition: For analyze and classify various faces we can implement a CNN.
  • Object Detection in Images: To forecast and categorize multiple things within a picture we can utilize CNN.
  • Language Translation: We can construct a sequence-to-sequence framework that can translate words from one language to another.
  • Music Genre Classification: By improving the neural network we can classify audio tracks into themes based on their spectrogram images.
  • Traffic Sign Recognition: Classification of traffic signs from the German Traffic Sign Recognition Benchmark (GTSRB) dataset can assist us in instructing the CNN.

Advanced Projects

  • Voice Command Recognition: We can design a neural network that can remember spoken commands using spectrograms.
  • Autonomous Car Simulation: Enhancing a deep reinforcement learning framework to locate our car in an assumed platform.
  • Real-Time Object Tracking: By utilizing the DL for real-world applications like multi-object detecting in video series can support us.
  • Neural Style Transfer: We construct a neural network which can deploy the template of one picture to the content of another such as making our photo look like a Van Gogh painting.
  • Forecasting Text Keyboard: Developing a neural network that recommends the next text in a sentence which is relevant to what is used in our smartphone keyboards.

Expert Projects:

  • AI Composer: To compose music and perform instrumental tracks we can design the DL framework methods.
  • Deepfake Detection: For comparing the real and AI- generated fake videos we develop neural networks.
  • Reinforcement Learning for Robotics: We implement reinforcement learning methods to teach robots for executing complicated tasks like picking up and operating objects.
  • Automated Essay Scoring: Designing a framework that can result essays on different topics such as grammar, coherence and architecture that can help us in future.
  • Medical Image Diagnosis: To predicting diseases like pneumonia from X-ray images and identify cancer affected tissue in MRI scans we can train our CNN.
  • General Opposed Networks for Art Creation: We can build the GANs for producing art pieces that are identical from manual artworks.

It becomes essential to have a perfect interpretation of the issue that we want to address, the working dataset, and the particular neural network that is suitable for our requirements while designing these projects. It is also necessary to consider the executional resources including the training needs, cloud-based environments and GPU-accelerated hardware for our project. We can make use of the platforms like TensorFlow, PyTorch, or Keras to integrate neural networks which provides built-in layers, instructing loops and so on.

Neural Network Machine Learning Projects Ideas

Neural Network Machine Learning Thesis Topics

The very frequent neural network topics are listed you may understand that searching for the right topic us crucial but we help you to land up on the right place. Full working explanation will be given no matter where ever you are.

  1. Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation
  2. High-dimensional aerodynamic data modeling using a machine learning method based on a convolutional neural network
  3. A novel improved extreme learning machine algorithm in solving ordinary differential equations by Legendre neural network methods
  4. New approach for the diagnosis of extractions with neural network machine learning
  5. Scientific machine learning through physics–informed neural networks: Where we are and what’s next
  6. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection
  7. A comparative evaluation of Stacked Auto-Encoder neural network and Multi-Layer Extreme Learning Machine for detection and classification of faults in transmission lines using WAMS data
  8. Artificial neural networks and machine learning techniques applied to ground penetrating radar: A review
  9. Machine learning material properties from the periodic table using convolutional neural networks
  10. SkyNet: an efficient and robust neural network training tool for machine learning in astronomy
  11. Breast cancer diagnosis using evolving deep convolutional neural network based on hybrid extreme learning machine technique and improved chimp optimization algorithm
  12. Integrated process for simulation of gasification and chemical looping hydrogen production using Artificial Neural Network and machine learning validation
  13. Intriguing of pharmaceutical product development processes with the help of artificial intelligence and deep/machine learning or artificial neural network
  14. Robust machine learning modeling for predictive control using Lipschitz-Constrained Neural Networks
  15. Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks
  16. Establishment of a flow-induced vibration power database based on deep neural network machine learning method
  17. Partial discharge based recognition of water droplets location in high voltage insulator using convolutional neural network – Bacterial foraging algorithm based optimized machine learning classifier
  18. Determination of material parameters in constitutive models using adaptive neural network machine learning
  19. Estimation of service length with the machine learning algorithms and neural networks for patients who receiving home health care
  20. Prediction of biological nutrients removal in full-scale wastewater treatment plants using H2O automated machine learning and back propagation artificial neural network model: Optimization and comparison
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