Pattern Recognition and Machine Learning Project

In machine learning, pattern recognition is the basic and essential the topic which mainly focuses to categorize the data based on the associated patterns. Getting your machine learning projects done by a new scholar is indeed a tuff task get a mind-blowing project done by, we are a leading company who provide worldwide assistance by online for all types of machine learning projects. We work as a team of professionals for thesis writing, we follow your university guidelines so that you get a high rank. Machine Learning Project can be made to several applications. Let us go through the list of capable projects based on pattern recognition in machine learning is mentioned below,

  1. Handwriting Recognition:
  • Objective: The conversion of handwritten text in images to digital text by us is the main goal of handwriting recognition.
  • Tools & Techniques: We can use tools like (CNN) Convolutional Neural Networks, (RNN) Recurrent Neural Networks, (LSTM) Long Short-Term Memory.
  1. Speech Recognition :
  • Objective: By speech recognition, we can able to convert spoken language into digital text.
  • Tools & Techniques: The techniques involved in this area is, (RNN) Recurrent Neural Networks, (LSTM) Long Short-Term Memory, (MFCCs) Mel-frequency cepstral coefficients and transformers.
  1. Facial Recognition:
  • Objective: Facial recognition is used for us to detect or check a person from a digital image.
  • Tools & Techniques: (CNN) Convolutional Neural Networks, One-Shot Learning and Eigenfaces are some of the common tools used in this process.
  1. Anomaly Detection in Network Traffic:
  • Objective: It is utilized for detecting dissimilar patterns which results in fraudulent activities or network intrusion.
  • Tools &Techniques: Here, we use some tools like One-Class SVM (Support Vector Machine) and Auto encoders.
  1. Sentiment Analysis:
  • Objective: Sentiment analysis helps to analyse our sentiment of the part of a text. It can determine positive, negative and neutral statement also.
  • Tools &Techniques: We can perform this process through some tools like (NLP) Natural Language Processing, (LSTM) Long Short-Term Memory, BERT and transformers.
  1. Music Genre Classification:
  • Objective: The music tracks are certainly classified through their categories by us.
  • Tools & Techniques: (MFCCs) Mel-frequency cepstral coefficients, (RNN) Recurrent Neural Networks and (CNN) Convolutional Neural Networks are some of the techniques in Music Genre Classification.
  1. Image Segmentation:
  • Objective: Through this process, we are able to split the image into multiple segments similar to parting an object from the background.
  • Tools & Techniques: Some of the tools are U-Net architecture and Mask R-CNN.
  1. Predictive Maintenance:
  • Objective: Depends on our usage and condition patterns, it detects the equipment which is going to be failed, so that the maintenance of equipment’s are scheduled perfectly.
  • Tools & Techniques: The tools involved in this are Gradient Boosted Trees, (LSTM) Long Short-Term Memory and time series analysis.
  1. DNA Sequence Classification:
  • Objective: The performance of DNA sequences is able to find by us with the help of DNA sequence classification.
  • Tools & Techniques: Tools concludes in this method like Transfer Learning and (CNN) Convolutional Neural Networks.
  1. Stock Market Prediction:
  • Objective: We have the ability to predict stock prices which is based on historical data.
  • Tools & Techniques: Such techniques involved in this area are, ARIMA, (LSTM) Long Short-Term Memory and time series analysis.
  1. Medical Image Diagnosis:
  • Objective: With the help of radiology images, it can predict the diseases or identify the medical conditions.
  • Tools & Techniques: The tools are used by us like, Transfer Learning and CNN (Convolutional Neural Networks).
  1. Gesture Recognition:
  • Objective: We identify the gestures from the sensor data (or) video for human-computer interactions.
  • Tools & Techniques: Hidden Markov Models, LSTM and CNN are some of example tools.
  1. Customer Segmentation:
  • Objective: Based on purchasing behaviour, past interactions or demographics, it enables us to analyse the group customers.
  • Tools & Techniques: Hierarchical Clustering, K-means clustering and DBSCAN are certain tools involves in customer segmentation.
  1. Agricultural Crop Disease Identification:
  • Objective: Classification and detection of crop diseases can be done through images from this process;
  • Tools & Techniques: We make use of some methods such as, Transfer Learning and Convolutional Neural Networks.
  1. Language Detection:
  • Objective: It helps us to identify the language in the given part of the text.
  • Tools & Techniques: The techniques like, LSTM, N-grams and Naive Bayes method.

   We consider the following points which is most important for pattern recognition project,

  • Collect and Pre-process Data: We use this process that includes cleaning, normalizing and dividing the data into training and testing datasets.
  • Model Training: First choose a suitable algorithm, then train the model and finally develop the model with alterations.
  • Evaluation: The list of metrics helps us to calculate the performance of model. They are accuracy, precision, recall and F1-score, etc.
  • Iterate: Emphasis our model based on the function and the real-world application and gather more information or create more characteristics.

This type of project stands in need of domain-specific knowledge, so have a conversation with our research experts to attain proper outcome of the project. Simulation will be well framed by our developers so gain success in all your research work.

Pattern Recognition and Machine Learning Project Ideas

Pattern Recognition and Machine Learning Projects

  1. Investigation of Machine Learning Algorithms for Pattern Recognition in Image Processing.


Support vector machines, deep learning, Histograms, Handwriting recognition, Machine learning algorithms, Image recognition, Forestry

This paper examines the performance of many ML methods like deep learning, SVM, decision trees, and random forests and feature extraction methods like raw pixel values and Histogram of Oriented Gradients using synthetic photographs of handwritten digits to recognize patterns in images. As a result, Histogram of Oriented Gradients feature extraction approach achieved better performance and Deep learning and SVM outperform other methods.

  1. Raman spectral pattern recognition of breast cancer: A machine learning strategy based on feature fusion and adaptive hyperparameter optimization


Raman spectroscopy, Breast cancer, Feature fusion, MSEA, Hyperparameter optimization, Pattern recognition

A feature fusion method is utilized in this paper to minimize the dimensionality of high-dimensional Raman spectra and improve the discriminative information among normal tissues and tumors. For adaptive hyperparameter optimization, they suggested MSEA and combined it with ALHK classification method. The utilization of serial encoding deals with the difficulty of parallel optimization in multi-hyperparameter vector issues.

  1. Selection of Machine Learning Algorithm for Pattern Recognition Based Bionic Devices


Performance evaluation, Force, Grasping

In this paper, the EMG signals are obtained while grasping the cylindrical object at variety of weight levels with two precision prismatic gestures. Force based categorization is performed based on different weight levels for each gesture by utilizing EMG signals. SVM achieved better mean classification accuracy followed by k-NN for each gesture. As a result, complex classifiers provide better performance than simple classifiers.

  1. Machine Learning Pattern Recognition Algorithm with Applications to Coherent Laser Combination


Laser beams, artificial neural networks, Measurement by laser beam, Aerospace electronics

A new ML method was proposed in this study that can recognize patterns and acquire error information from an unstable system. A novel ML method was examined and designed to feedback stabilize coherently integrated lasers. To facilitate learning on initially unstable system, this method learn differential instead of absolute, values of action in phase space. Results show that, this method has ability to control small-scale spatial beam combination with high stability.

  1. Recognition of Recurrent Movement Patterns of Football Players via Machine Learning


Clustering algorithms

In this paper, the author proposed a model to recognize the most frequent velocity and acceleration patterns of football players. To recognize patterns, movements are grouped and labeled and then considered in fixed length sequences. These are compared among each other and grouped by utilizing hierarchical clustering algorithm. At last, common sequence method is utilized to identify the general patterns in movement of the players.

  1. A novel machine learning approach in Image Pattern Recognition under invariance constraints


Time-frequency analysis, Signal processing algorithms, Radar, Radar imaging, Feature extraction, Wavelet analysis

This paper mainly focused on feature extraction and image classification approach by utilizing a Rotation Invariant Wavelet Packet Decomposition and a new entropy-based feature extraction approach to characterize an image. This characterization technique provides improved analysis compared to usual methods like energy of the wavelet sub bands. The computed features are utilized to train a GNN adapted to quad-tree decomposition.

  1. Integration of meta-analysis and supervised machine learning for pattern recognition in breast cancer using epigenetic data


Meta-analysis, Systems biology, Breast cancer, ChIP-Seq

This paper employed a model to detect an epigenomic data pattern of breast cancer by utilizing meta-analysis and ML techniques. The main goal of this study is to discover patterns of epigenome changes in the treatment and diagnosis of breast cancer. At last, pattern recognition was carried out using various attribute weighting algorithms. By utilizing the integration of meta-analysis and data mining, more relevant and reliable information were derived in this study.

  1. Attack Pattern Recognition in the Internet of Things using Complex Event Processing and Machine Learning


Memory management, Real-time systems, Internet of Things

The goal of this paper is to propose a machine learning method. If the aim is to classify attacks, ML method enable for the autonomous creation of CEP patterns based on categorical data. If the aim is to detect anomalies, ML method enable for the autonomous development of CEP patterns based on uncategorized data. A performance evaluation for an automatic creation of patterns for various attack recognition in IoT is also performed in this study.

  1. Cardiac Pattern Recognition from SPECT Images Using Machine Learning Algorithms


Heart, medical treatment.

The goal of this paper is to detect U-shaped left ventricular contraction patterns on GSPECT MPI images using ML approaches and radiomics to achieve an efficient prediction of treatment response. By utilizing maximum relevant minimum redundancy (MRMR) approach, feature selection process is carried out. Various significant features were developed to feed ML approaches such as logistic regression, Random Forest, SVM, and XGBoost.

  1. A pattern recognition model for static gestures in malaysian sign language based on machine learning techniques


Hand gesture recognition, Human-machine interface, Sensory glove, Sign language

A pattern recognition model is proposed in this study for static gestures in Malaysian Sign Language (MSL) utilizing ML techniques. Data acquisition and data processing are comprised in the proposed model. First stage includes capturing of required sign data. DataGlove device is used to measure the motions of the fingers and wrists. Redundant datas are removed in the second stage. Various ML methods are used for SL gesture recognition.

Opening Time


Lunch Time


Break Time


Closing Time


  • award1
  • award2