Deleterious Effects of Uncertainty in Color Imagery Streams on Classification Models

In the development of social economy and scientific and technological innovation, the image processing mode and classification model chosen by network technology platform is becoming more and more changeable, but in essence, it is necessary to obtain characteristic information in effective image recognition and choose high-quality network algorithm and processing technology to complete image processing and image classification. Therefore, on the basis of understanding the current research trend of computer image processing and image classification model methods, this paper conducts in-depth discussion on the image processing methods and image classification model training design with artificial intelligence as the core and takes the image classification model of transfer learning as an example for practical exploration. The final results show that the image processing method and image classification model based on artificial intelligence have strong performance advantages in practical application.KeywordsArtificial intelligenceImage processingImage classificationThe migration study

Forest fires pose a significant threat to ecosystems, wildlife, property, and human lives worldwide. Leveraging advancements in artificial intelligence and machine learning, our research presents a comprehensive approach to forest fire detection and management. We employ a state-ofthe-art image classification model, YOLOv8, to swiftly identify fire occurrences within forest imagery, achieving high accuracy rates. Concurrently, we develop a predictive model using logistic regression to forecast the likelihood of fire outbreaks based on environmental factors. Integration of these technologies holds promise for proactive forest fire management. Future prospects include the integration of our YOLOv8 model with UAVs for real-time monitoring and early detection of fire occurrences, thus enhancing environmental conservation and safety measures.

Industrial Revolution (IR) improves the way we live, work and interact with each other by using state of the art technologies. IR-4.0 describes a future state of industry which is characterized through the digitization of economic and production flows. The nine pillars of IR-4.0 are dependent on Big Data Analytics, Artificial Intelligence, Cloud Computing Technologies and Internet of Things (IoT). Image datasets are most valuable among other types of Big Data. Image Classification Models (ICM) are considered as an appropriate solution for Business Intelligence. However, due to complex image characteristics, one of the most critical issues encountered by the ICM is the Concept Drift (CD). Due to CD, ICM are not able to adapt and result in performance degradation in terms of accuracy. Therefore, ICM need better adaptability to avoid performance degradation during CD. Adaptive Convolutional ELM (ACNNELM) is one of the best existing ICM for handling multiple types of CD. However, ACNNELM does not have sufficient adaptability. This paper proposes a more autonomous adaptability module, based on Meta-Cognitive principles, for ACNNELM to further improve its performance accuracy during CD. The Meta-Cognitive module will dynamically select different CD handling strategies, activation functions, number of neurons and restructure ACNNELM as per changes in the data.

Most popular image classification methods mainly focus on classification ability rather than recognizing new things. However, human lay emphasis on cognition first and then classification, which is closely related to human memory system. Though many memory models have been proposed, they are studied in word list whereas the reports about natural images are still limited. This paper proposes a memory model for image recognition and classification based on convolutional neural network and Bayesian decision. First the image feature is extracted by convolutional neural network and stored in binary form. Then the representation, storage and retrieval processes of visual images are modeled. The test image feature vector is matched in parallel to the studied image vectors, and the likelihood values are calculated. Finally, the odd that the test image belongs to a new class is computed based on all likelihood values. If the odd value is greater than a certain threshold, the test image is regarded as new; otherwise, the Bayesian decision rule for image classification is performed. Experimental results on Caltech-101 and Caltech-256 datasets show that the proposed method can perform well in image recognition and classification tasks. And the hit probability of the method is higher than two typical methods, SRC and ELM, at present while the false alarm rate is far lower than them.

Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems

Fused deep neural networks for sustainable and computational management of heat-transfer pipeline diagnosis

This research introduces an innovative approach to image classification, by making use of Vision Transformer (ViT) architecture. In fact, Vision Transformers (ViT) have emerged as a promising option for convolutional neural networks (CNN) for image analysis tasks, offering scalability and improved performance. Vision transformer ViT models are able to capture global dependencies and link among elements of images. This leads to the enhancement of feature representation. When the ViT model is trained on different models, it demonstrates strong classification capabilities across different image categories. The ViT’s ability to process image patches directly, without relying on spatial hierarchies, streamlines the classification process and improves computational efficiency. In this research, we present a Python implementation using TensorFlow to employ the (ViT) model for image classification. Four categories of animals such as (cow, dog, horse and sheep) images will be used for classification. The (ViT) model is used to extract meaningful features from images, and a classification head is added to predict the class labels. The model is trained on the CIFAR-10 dataset and evaluated for accuracy and performance. The findings from this study will not only demonstrate the effectiveness of the Vision Transformer model in image classification tasks but also its potential as a powerful tool for solving complex visual recognition problems. This research fills existing gaps in knowledge by introducing a novel approach that challenges traditional convolutional neural networks (CNNs) in the field of computer vision. While CNNs have been the dominant architecture for image classification tasks, they have limitations in capturing long-range dependencies in image data and require hand-designed hierarchical feature extraction.

We introduce the Plant Disease Detection Platform (PDDP) that allows users to send photos of sick plant leaves or textual descriptions of their appearance to obtain information about an infection that hit the vegetation and treatment tips. The backend of the platform in terms of deep learning includes image classification and text similarity models. The image classification model has two parts: feature extractor and classifier. The feature extractor was trained using the triplet loss function along with transfer learning when the weights of the network are initialized from the MobileNetV2 pretrained on the ImageNet dataset. The classifier is a simple multilayer perceptron. The test on 100 random plant images from the Internet shows 98% of the classification accuracy. We did the post-training static quantization in order to reduce the overall model size and increase the inference performance. The final model has a size of 7 Mb and works 5 times faster than the initial model without significant loss of accuracy. The text similarity model is a BERT-based transformer for obtaining vector representation of input texts for further similarity calculation between user requests and disease descriptions on the PDDP.

Recently, number of medical X-ray images being generated is increasing rapidly due to the advancements in radiological equipment in medical centres. Medical X-ray image classification techniques are needed for effective decision making in the healthcare sector. Since the traditional image classification models are ineffective to accomplish maximum X-ray image classification performance, deep learning (DL) models have emerged. In this study, an Arithmetic Optimization Algorithm with Deep Learning-Based Medical X-Ray Image Classification (AOADL-MXIC) model has been developed. The proposed AOADL-MXIC model investigates the available X-ray images for the identification of diseases. Initially, the AOADL-MXIC model executes the pre-processing step using the Gabor filtering (GF) technique to eliminate the presence of noise. In the next level, the Capsule Network (CapsNet) model is utilized to derive feature vectors from the input X-ray images. Furthermore, for optimizing the hyperparameters related to the CapsNet approach, the AOA is exploited. Finally, the bidirectional gated recurrent unit (BiGRU) model is employed for the classification of medical X-ray images. The experimental result analysis of the AOADL-MXIC technique on a set of medical images stated the promising performance over the other models.KeywordsX-ray imagesArithmetic optimization algorithmDeep learningFeature extractionHyperparameter tuning

With the deep learning (DL) sweeping the world. Traditional image classification methods are difficult to process huge image data and cannot meet people's requirements for image classification accuracy and speed. The image classification method based on convolutional neural network (CNN) breaks through the bottle neck of traditional image classification methods and becomes the mainstream algorithm of image classification at present, how to effectively use convolutional neural network to classify images has become a hot research topic in the field of computer vision at home and abroad. Convolutional neural network (CNN) has performed well in image classification and segmentation, target detection and other applications, and its powerful feature learning and feature expression capabilities are increasingly respected by researchers. However, CNN still has a few problems, such as incomplete feature extraction and overfitting of sample training. In view of these problems, after in-depth research on the application of convolutional neural network in image processing, this paper gives the mainstream structure model, advantages and disadvantages, time/space complexity, problems that may be encountered in the model training process and corresponding solutions used in image classification based on convolutional neural network. Through the overview of the research status of CNN model in image classification, it provides suggestions for the further development and research direction of CNN.

Cancer is one of humanity’s biggest challenges. Although there are many ways to prevent cancer, some types of cancer are still largely incurable. One of the most common types of cancer is breast cancer and the most important action toward a favorable outcome is early diagnosis. Correct diagnosis is one of the most critical processes in breast cancer treatment. There are many studies in the literature to predict the type of breast tumors. In this study, Artificial Intelligence and machine learning were used to increase the incidences of correct diagnosis. A convolutional neural network (CNN) was used in the models to classify and segment breast ultrasound images. Two separate models were created; one for image classification, and another for image segmentation, using TensorFlow. The models were based on current Convolutional Neural Network models published previously. The image segmentation model was based on a customized U-Net architecture. Attention gates were added to this customized U-Net model. The image classification model was constructed using a 50-layer ResNet model. The resultant CNN yielded an accuracy of 99.35% for image segmentation. For image classification, the separately constructed CNN model yielded an accuracy of 97.43%. Hence, it was possible to obtain high accuracies to classify and segment breast cancer from ultrasound images using our CNN models.

Deep learning-based image classification for online multi-coal and multi-class sorting

Comparing HMAX and BoVW Models for Large-Scale Image Classification

Background and aimsVideo capsule endoscopy (VCE) is widely used in the detection of abnormalities in the small intestine. However, there remains the challenge to correctly identify a limited number of possible abnormal images from tens of thousands of total images, and this impediment has limited the expansion of the technology. More recently artificial intelligence (AI) technology has been used in classifying VCE images from patients, but a clinical-grade diagnostic accuracy (greater than 99%) has not been achieved. MethodsThis study proposes a system for the automatic classification of a number of categories of unbounded VCE images with high accuracy by means of a transfer learning approach using multiple convolutional neural networks (CNNs). With this new approach, it is not necessary to implement image segmentation, so that the feature extraction becomes automatic and the existing models can be fine-tuned to obtain specific classifiers. ResultsOver 16,000 VCE gastrointestinal (GI) images from normal individuals including those with normal clean mucosa, the pylorus, the ileocecal valve, reduced mucosal view due to luminal contents and lymphangiectasia (a normal variant), and patients with five pathological states including angioectasia, bleeding, erosion/s, ulcers and foreign bodies, were obtained from a publicly available data set. These were used in building, testing and validating AI models for evaluating the diagnostic accuracy of our combined 17-CNN deep learning approach. Compared to a single CNN approach used by other research groups, our AI method, using 17 CNNs, achieved an overall diagnostic accuracy of 99.79%, with an accuracy of 100% for identifying bleeding and foreign bodies. The high accuracy was further demonstrated in the confusion matrices, precision, recall, and F1 score. ConclusionsWe have developed accurate AI deep learning models for unbounded VCE image classification of various medical conditions in medical practice.

As an important form of expression in modern civilization art, printmaking has a rich variety of types and a prominent sense of artistic hierarchy. Therefore, printmaking is highly favored around the world due to its unique artistic characteristics. Classifying print types through image feature elements will improve people’s understanding of print creation. Convolutional neural networks (CNNs) have good application effects in the field of image classification, so CNN is used for printmaking analysis. Considering that the classification effect of the traditional convolutional neural image classification model is easily affected by the activation function, the T-ReLU activation function is introduced. By utilizing adjustable parameters to enhance the soft saturation characteristics of the model and avoid gradient vanishing, a T-ReLU convolutional model is constructed. A better convolutional image classification model is proposed based on the T-ReLU convolutional model, taking into account the issue of subpar multi-level feature fusion in deep convolutional image classification models. Utilize normalization to analyze visual input, an eleven-layer convolutional network with residual units in the convolutional layer, and cascading thinking to fuse convolutional network defects. The performance test results showed that in the data test of different styles of artificial prints, the GT-ReLU model can obtain the best image classification accuracy, and the image classification accuracy rate is 0.978. The GT-ReLU model maintains a classification accuracy above 94.4% in the multi-dataset test classification performance test, which is higher than that of other image classification models. For the use of visual processing technology in the field of classifying prints, the research content provides good reference value.