Image classification is a basic procedure in computer vision and it includes categorizing pictures into built-in classes depends on their content. Machine Learning is the most trending topics of current time we aim in offering trending research and dissertation ideas that matches to your needs. Unique and Novel research ideas will be shared we work in a different framework that you attain success in your academics. By using the Deep Learning (DL), Convolutional Neural Networks (CNNs) we design the go-to model for image classification due to their extraordinary performance.
The following is the processing steps on how we produce an image classification project using Machine Learning (ML).
- Objective Definition
We define the main aim of our work is to design a ML framework the classifies images into predefined categories.
- Data Collection
- Dataset: Based on our application we need to gather our own dataset. There are some famous datasets such as CIFAR-10, CIFAR-100, ImageNet, and MNIST which is better to begin our model.
- Ensure we labeled data, defining every image has a related category.
- Data Preprocessing
- Image Cropping: We make sure that entire images have the identical dimensions which are necessary in most ML models.
- Normalization: The pixel values should in the range [0, 1] when they are starting in [0,255]. For this we normalize and standardize the data.
- Augmentation of Data: By preparing more training data by deploying scattered conversions such as rotations, flips, shifts, and zooms which we make use of it to generalize our model.
- Model choosing and Construction
- Convolutional Neural Networks (CNNs): These are the most usual DL models utilized for image classification contains the famous structures such as VGG, ResNet, and MobileNet that help us.
- Transfer Learning: We implement a pre-trained model which is trained on a large dataset like ImageNet and fine-tune it for our particular task rather than instructing it from initial stage. This leads us in fast and better outcomes.
- Existing ML models such as SVM, k-NN are useful to us when the dataset is small, but their robustness is basically inferior to CNNs for large-scale image classification.
- Training the Framework
- Split the Data: We divide our dataset into training, validation and test sets.
- Instructing: When using CNNs we analyze GPU acceleration to train our framework by training dataset.
- Validation: Testing and fine-tune our model using test set for avoiding overfitting.
- Model Validation
- To evaluate the model’s efficiency we utilize the test dataset.
- General metrics like accuracy, precision, recall, F1-score, and confusion matrix are tested in our framework.
- Optimization & Hyperparameter Tuning (optional)
- We adapt hyperparameters such as learning rate, batch size, number of layers, and number of filters for our model.
- Analyzing methods such as dropout and batch normalization for enhancing CNN performance and balancing.
- Deployment
- Once we get satisfied of our model’s efficiency, we apply it as part in mobile app, web service and environments where image categorization is required.
- User Interface (if suitable)
- Developing an interface where our users can upload and capture pictures and receive classification outcomes in real-world scenarios.
- The review systems assist us in refining our framework by gathering user-tested samples.
- Conclusion & Future Enhancements
- We outline our project’s results, challenges faced and the possible applications.
- Understand the additional developments like,
- Incorporating real-time classifications abilities.
- We combine the classifier into huge systems like an e-commerce product categorizer.
- Extending the number of categories in our model.
Guidelines:
- Large-scale Training: We analyze instructing on cloud platforms and specialized hardware for huge datasets and difficult frameworks.
- Class Imbalance: When few classes have certainly some examples we consider approaches like oversampling, under sampling and using stable periodic samplers.
Image classification has a vast number of applications from medical imaging to autonomous cars. By using the appropriate method and techniques, we design an effective image classification system that fulfills our particular requirements.
Image Classification using Machine Learning Thesis Topics
Get to know the relevant thesis ideas and the thesis topics that we have worked .Contact us for more details we also carry out tailored thesis topics assistance.
- Machine Learning Algorithms for Satellite Image Classification Using Google Earth Engine and Landsat Satellite Data: Morocco Case Study
Keywords:
Remote sensing, satellite image, supervised classification, Google earth engine, LANDSAT satellite, machine learning algorithms.
Our paper uses Landsat 8 satellite data to perform a land cover classification of morocco by utilizing ML methods. We proposed six supervised ML techniques such as SVM, CART, RF, MD, DT, GTB o built-up areas, water areas etc… to deduce at the end best performing classifier have high accuracy. To enhance the outcome we additionally used NDVI, NDBI, BSI and MNDWI. We compare these metrics to get the high accuracy.
- GLCM Based Feature Extraction and Medical X-ray Image Classification Using Machine Learning Techniques
Keywords:
GLCM, Machine Learning, random forest, SMOTE
We proposed medical x-ray images that are classified and we can extract the features by utilizing an ensemble learning method. We can extract the image features by utilizing the GLCM feature extraction method. Our proposed method can able to find the difference among sick and healthy images. To enhance the efficiency of ensemble learning classification method we can compare them with different methods by utilizing the performance indicators LR, GNB as well as RF.
- Machine Learning and Deep Learning Models for Vegetable Leaf Image Classification Based on Data Augmentation
Keywords:
Classification, Vegetable plant leaf, Image preprocessing, RGB image, Data augmentation, Deep learning, Computer vision
Our paper collects types of RGB images after that we have to utilize data augmentation techniques, we found that the blending of rotation, flipping, shift, and zoom approaches to enhance the performance of DL model. These can go along the augmented data of DL and ML to process and train different model. The DL method (resnext50) has the high accuracy.
- Hybrid Approach for Classification of Medical Imaging Data Using Machine Learning Techniques
Keywords:
Supervised machine learning, Hybrid approach, Support vector machine, Medical imaging chest X-ray pneumonia dataset
Our proposed model implements a high approach on the medical imaging data through ML methods. We used the supervised ML methods like DT, Ensemble classification, RF and KNN. Our proposed method comprises of two states: first state classifies the data into two classes namely pneumonia image or normal image with various ML methods. Our proposed hybrid approach gives the best accuracy.
- Comparative Analysis of Machine Learning Algorithms in Vehicle Image Classification
Keywords:
Vehicle detection, Image filtering, Data mining
Our paper uses weka to classify vehicle images into three vehicle groups based on data mining methods. In feature extraction we used three image filtering methods that are Colour Layout, Edge Histogram, and Pyramid Histogram of Oriented Gradients (PHOG). We can extract the feature to fed into five classification methods like MLP, Simple Logistic, SMO, LMT, RF. Our PHOG filter method gives the best accuracy.
- A Comparison of Machine Learning and Deep Learning in Hyperspectral Image Classification
Keywords:
Hyperspectral image classification, Convolutional neural network (CNN), k-nearest neighbors (k-NN), Artificial Neural Networks (ANN), Fully Convolutional Network (FCN)
We use different ML and DL methods to perform classification on hyperspectral images are investigated and related. We used three popular ML methods like SVM, KNN, ANN are utilized for hyperspectral image classification then another two deep structures in CNN. Three datasets were used and we have to perform excellent in 3D CNN DL methods and this method is more beneficial when compare to ML methods.
- Maize Leaf Healthy and Unhealthy Classification Using Image Processing Technique and Machine Learning Classifiers
Keywords:
Feature extraction, PNN
Our paper proposes an effective ML method to classify the healthy or unhealthy classification according to the leaf image present. We also evaluate the color feature extraction by utilizing RGB mean and standard deviation and classification by utilizing PNN and KNN techniques. We also used five image processing methods like image preprocessing, image segmentation, feature extraction, classification and performance analysis.
- Performance Comparison of Various Machine Learning Algorithms for Ultrasonic Fetal Image Classification Problem
Keywords:
Principal component analysis (PCA), Decision tree classifier (DT), Classifier, Multi-layer perceptron (MLP)
We offer a novel approach performance by the comparison of ML classifiers for classification of ultrasonic fetal images. We also offer Gabor feature extraction and different classification methods to classify three various class of ultrasound image. At first Gabor features are gained from raw images and to remove redundancy in feature and dimensionality PCA can be used. At last the features were gained from PCA were fed to different ML methods and the performance can be estimated.
- Machine Learning-Based Multi-temporal Image Classification Using Object-Based Image Analysis and Supervised Classification
Keywords:
Object-based approach, Land cover classification, Change detection, Remote sensing
We offer a hybrid method of object-based image analysis and the supervised classification can be utilized. We used the data that is high-resolution multispectral 4-band images offered by PlanetScope satellite of region. Initially we preprocess the data and that can pass through pipeline followed by multi-resolution segmentation method and we can classify the image into seven class based on spectral signature using algorithms like maximum likelihood (ML), SVM, MD.
- Fashion Images Classification using Machine Learning, Deep Learning and Transfer Learning Models
Keywords:
deep learning (DL), image classification, transfer learning, Fashion classification
The aim of our paper is to use ML and DL methods to classify and find the image. We used the ML methods like SVM, KNN, DT, RF and the DL methods like CNN, AlexNet, GoogleNet, LeNet, LeNet5 and the transfer learning methods like VGG16, MobileNet and ResNet50. We train and test our model by online google colaboratory that support ML and DL methods.
Milestones
MILESTONE 1: Research Proposal
Finalize Journal (Indexing)
Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.
Research Subject Selection
As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.
Research Topic Selection
We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other
Literature Survey Writing
To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)
Case Study Writing
After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.
Problem Statement
Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.
Writing Research Proposal
Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)
MILESTONE 2: System Development
Fix Implementation Plan
We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.
Tools/Plan Approval
We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.
Pseudocode Description
Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.
Develop Proposal Idea
We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.
Comparison/Experiments
We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.
Graphs, Results, Analysis Table
We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.
Project Deliverables
For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.
MILESTONE 3: Paper Writing
Choosing Right Format
We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.
Collecting Reliable Resources
Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.
Writing Rough Draft
We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources
Proofreading & Formatting
We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on
Native English Writing
We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.
Scrutinizing Paper Quality
We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).
Plagiarism Checking
We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.
MILESTONE 4: Paper Publication
Finding Apt Journal
We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.
Lay Paper to Submit
We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.
Paper Submission
We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.
Paper Status Tracking
We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.
Revising Paper Precisely
When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.
Get Accept & e-Proofing
We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.
Publishing Paper
Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link
MILESTONE 5: Thesis Writing
Identifying University Format
We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.
Gathering Adequate Resources
We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.
Writing Thesis (Preliminary)
We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.
Skimming & Reading
Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.
Fixing Crosscutting Issues
This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.
Organize Thesis Chapters
We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.
Writing Thesis (Final Version)
We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.
How PhDservices.org deal with significant issues ?
1. Novel Ideas
Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.
2. Plagiarism-Free
To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.
3. Confidential Info
We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.
- Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
- Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.
CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.
4. Publication
Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.
5. No Duplication
After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.
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