A COMPREHENSIVE APPLICATION OF ARTIFICIAL INTELLIGENCE BASED HYBRID DEEP LEARNING IN IMAGE CLASSIFICATION

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dc.contributor.advisorKumar, Arun
dc.contributor.authorPandey, Ankita
dc.date.accessioned2024-08-05T09:52:38Z
dc.date.available2024-08-05T09:52:38Z
dc.date.issued2023-02
dc.description.abstractArtificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. Deep learning drives many artificial intelligence applications and services that improve automation, performing analytical and physical tasks without human intervention. Real-world deep learning applications are a part of our daily lives, but in most cases, they are so well-integrated into products and services that users are unaware of the complex data processing that is taking place in the background. The applications for artificial intelligence are endless. In light of the foregoing facts, the present research is centered to propose a comprehensive application of hybrid deep learning models based on artificial intelligence techniques which include transfer learning, deep learning, machine learning, and fuzzy inference system. In this study, six models are introduced. Model [1] presents an effective facial emotion intensity classifier by fusion of the pre-trained deep architecture and fuzzy inference system. The pre-trained architecture VGG16 is used for basic emotion classification and it predicts emotion class with the class index value. By class index value, images are sent to the corresponding Fuzzy inference system for estimating the intensity level of detected emotion. This fusion model effectively identifies the facial emotions (happy, sad, surprise, and angry) and also predict the 13 categories of emotion intensity. Model [2] proposes a casting fault detector to automate the inspection process in casting production. two transfer learning-based convolution neural networks and one lightly structured convolution neural network are created and verified for detecting manufacturing flaws in submersible pump impellers. Model [3] investigate various fusion of deep learning-based feature extractors and machine learningbased classifiers for differentiating two subtypes Adenocarcinoma and Squamous cell carcinoma of nonsmall cell lung cancer. This research generates seven automated integrated models by using five deep feature extractors: InceptionResNetV2, InceptionV3, Xception, VGG16 and, VGG19 and three classifiers: Support vector machine, XGBoost, and Fully connected neural network. Then the most effective optimal model is selected via comparative study and performance. Model [4] construct three transfer learning-based classifiers Binary, Benign, and Malignant. The Binary classifier classifies breast cancer as benign and malignant, the Benign classifier classifies four sub-classes of benign cancer and the Malignant classifier classifies four sub-classes of malignant cancer. All three classifiers are individually trained for their corresponding classification task and then integrated to give the outcome of the combined proposed system. As a result, the proposed system automatically classifies cancer into its major class and then sub-class with greater accuracy. The proposed breast cancer classification is performed on BreaKHis and BACH data. Model [5] develop an automated optimal novel model for knee osteoporosis classification through a comprehensive examination held on various deep learning architectures. This examination starts with the transfer learning model (ResNet50, MobileNet, ResNet50V2, and Xception) and then extended to a concatenated version of the top two models MobileNet and ResNet50V2 based on performance. Finally, on the basis of critical examination, an optimal novel CNN inspired by MobileNet and ResNet50V2 is invented. For classification, FCNN optimized by random search technique is employed. Model [6] develop the Cascade network to identify diabetic retinopathy on the most challenging data IDRiD in literature. The Cascade network extracts the multiscale dense features through the task specific novel CNN and Of-the-self CNN Xception. The proposed model first time in literature construct an architecture to perform combinedly binary and multigrading of DR and DME in a single input. The present study implements a limited data merging strategy with the most relevant DDR data images. To find the optimal network, this research also investigates the various network architectures and Cascade networks with different Of-the-self CNN.
dc.identifier.citationTheses of Ph.D
dc.identifier.urihttps://krishikosh.egranth.ac.in/handle/1/5810212857
dc.language.isoEnglish
dc.pages182 p.p.
dc.publisherG. B. Pant University of Agriculture & Technology, Pantnagar-263145
dc.relation.ispartofseries10838
dc.subMathematics
dc.themeAcademic Research
dc.these.typePh.D
dc.titleA COMPREHENSIVE APPLICATION OF ARTIFICIAL INTELLIGENCE BASED HYBRID DEEP LEARNING IN IMAGE CLASSIFICATION
dc.typeThesis
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