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).
We define the main aim of our work is to design a ML framework the classifies images into predefined categories.
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.
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.
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, 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.
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.
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.
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.
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.
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.
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.
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.
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.
Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.
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.
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
To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)
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.
Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.
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)
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.
We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.
Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.
We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.
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.
We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.
For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.
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.
Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.
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
We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on
We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.
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).
We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.
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.
We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.
We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.
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.
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.
We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.
Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link
We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.
We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.
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Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.
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We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.
We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.
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.
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