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The document is an editorial from the Intelligent Data Analysis (IDA) Journal, introducing the third issue of volume 28. The issue includes papers on various theoretical and applied topics in the field of Intelligent Data Analysis. The first half of the issue focuses on theoretical contributions, including papers on multilabel image classification and extended image data analysis. The second half covers papers on adaptive and active learning, data extraction and transformation, recommendation systems, medical applications, code search, and generating magazine layouts. The issue concludes with a paper on a new training dataset for Named Entity Recognition tasks. [Extracted from the article]

BACKGROUND: Oral cancer is a malignant tumor that usually occurs within the tissues of the mouth. This type of cancer mainly includes tumors in the lining of the mouth, tongue, lips, buccal mucosa and gums. Oral cancer is on the rise globally, especially in some specific risk groups. The early stage of oral cancer is usually asymptomatic, while the late stage may present with ulcers, lumps, bleeding, etc. OBJECTIVE: The objective of this paper is to propose an effective and accurate method for the identification and classification of oral cancer. METHODS: We applied two deep learning methods, CNN and Transformers. First, we propose a new CANet classification model for oral cancer, which uses attention mechanisms combined with neglected location information to explore the complex combination of attention mechanisms and deep networks, and fully tap the potential of attention mechanisms. Secondly, we design a classification model based on Swim transform. The image is segmented into a series of two-dimensional image blocks, which are then processed by multiple layers of conversion blocks. RESULTS: The proposed classification model was trained and predicted on Kaggle Oral Cancer Images Dataset, and satisfactory results were obtained. The average accuracy, sensitivity, specificity and F1-Socre of Swin transformer architecture are 94.95%, 95.37%, 95.52% and 94.66%, respectively. The average accuracy, sensitivity, specificity and F1-Score of CANet model were 97.00%, 97.82%, 97.82% and 96.61%, respectively. CONCLUSIONS: We studied different deep learning algorithms for oral cancer classification, including convolutional neural networks, converters, etc. Our Attention module in CANet leverages the benefits of channel attention to model the relationships between channels while encoding precise location information that captures the long-term dependencies of the network. The model achieves a high classification effect with an accuracy of 97.00%, which can be used in the automatic recognition and classification of oral cancer. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the cancers with high morbidity and mortality in the world, which is a serious threat to the health of women. With the development of deep learning, the recognition about computer-aided diagnosis technology is getting higher and higher. And the traditional data feature extraction technology has been gradually replaced by the feature extraction technology based on convolutional neural network which helps to realize the automatic recognition and classification of pathological images. In this paper, a novel method based on deep learning and wavelet transform is proposed to classify the pathological images of breast cancer. Firstly, the image flip technique is used to expand the data set, then the two-level wavelet decomposition and reconfiguration technology is used to sharpen and enhance the pathological images. Secondly, the processed data set is divided into the training set and the test set according to 8:2 and 7:3, and the YOLOv8 network model is selected to perform the eight classification tasks of breast cancer pathological images. Finally, the classification accuracy of the proposed method is compared with the classification accuracy obtained by YOLOv8 for the original BreaKHis dataset, and it is found that the algorithm can improve the classification accuracy of images with different magnifications, which proves the effectiveness of combining two-level wavelet decomposition and reconfiguration with YOLOv8 network model. [ABSTRACT FROM AUTHOR]

The histopathological image classification method, based on deep learning, can be used to assist pathologists in cancer recognition in colon histopathology. The popularization of automatic and accurate histopathological image classification methods in this way is of great significance. However, smaller medical institutions with limited medical resources may lack colon histopathology image training sets with reliable labeled information; thus they may be unable to meet the needs of deep learning for many labeled training samples. Therefore, in this paper, the colon histopathological image set with rich label information from a certain medical institution is taken as the source domain; the colon histopathological image set from a smaller medical institution with limited medical resources is taken as the target domain. Considering the potential differences between histopathological images obtained by different institutions, this paper proposes a classification learning framework, namely unsupervised domain adaptation with local structure preservation for colon histopathological image classification, which can learn an adaptive classifier by performing distribution alignment and preserving intra-domain local structure to predict the labels of the colon histopathological images from institutions with lower medical resources. Extensive experiments demonstrate that the proposed framework shows significant improvement in accuracy and specificity of colon histopathological images without reliable labeled information compared to models without unsupervised domain adaptation. Specifically, in an affiliated hospital in Fuyang City, Anhui Province, the classification accuracy of benign and malignant colon histopathological images reaches 96.21%. The results of comparative experiments also show promising classification performance of our method in comparison with other unsupervised domain adaptation methods. [ABSTRACT FROM AUTHOR]

There are many kinds of Marine organisms and their biological forms differ greatly, so it is difficult to guarantee the accuracy of artificial species identification, which brings great challenges to the work of Marine species identification. In this paper, we propose a recognition method of Marine biological image classification using residual neural network, redefining convolution layer and using batch regularization to avoid gradient parameter disorder. The bottleneck layer is realized by the residual connection in the neural network, and the residual network ResNet50 is constructed by the transfer learning method. The classification training was conducted on 19 common Marine animal data sets, and the experimental results showed that the recognition accuracy of ResNet50 reached about 90%. Compared with the traditional convolutional neural network VGG19, the results showed that the recognition efficiency of ResNet50 was better, thus verifying the effectiveness of the Marine animal classification and recognition model proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of oil painting classification and simulation using an improved embedded learning fusion vision perception algorithm. This paper analyzes and models the image quality evaluation problem by simulating the human visual system and extracting quality perception features as the main entry point to improve the prediction accuracy of the overall algorithm. This paper proposes a multi-classification method of CCNN, which uses the similarity measure based on information first to achieve multi-classification of artwork styles and artists, and this part is the main part of this paper. This paper uses the wiki art repository to construct a dataset of oil paintings, including over 2000 works by 20 artists in 13 styles. CNN achieves an accuracy of 85.75% on the artist classification task, which is far more effective than traditional deep learning networks such as Resnet. Finally, we use the network model of this paper and other network models to train the classification of 3, 4, and 6 categories of art images. The accuracy of art image classification by this paper's algorithm is higher than that of the current mainstream convolutional neural network models, and the extracted features are more comprehensive and more accurate than traditional art image feature extraction methods, which do not rely on researchers to extract image features. Experiments show that the proposed method can achieve excellent prediction accuracy for both synthetic distorted images and distorted images. [ABSTRACT FROM AUTHOR]

Parkinson disease (PD) is a type of neurodegenerative disorder that affects the motor movement of the patient. But each technique has its own advantages or disadvantages. In gene, speech and handwriting data model, the feature extraction and reduction is an important step for efficient classification. These two steps require proper attention for selection and also require high processing time as compared to other data model like images. Because in image modality, the deep learning algorithm can be applied that can perform all process and automate the classification. As compared to these domains, the signal produces better and best results. Because the electroencephalogram (EEG) signal are taken from the brain using electrodes and it helps to observe the brain signals effectively and immediately as compared to the other data modals. Hence, in this paper, the wavelet transform will be used to decompose the signals and statistical features will be extracted from the transformed signal. Here, the satin bower bird optimization will be used for both type of wavelet selection and feature reduction process for final classification. The reduced feature set will be classified using Ensemble Neural Network type including InceptionV3, DenseNet, MobileNet, Xception, and NasNet) recently proposed for medical image classification. The whole process will be realized using MATLAB R2021a software and its performance will be evaluated in terms of Accuracy and is compared against Automated Tunable Q-wavelet transform performance. The proposed ensemble method, employing EEG signal processing and neural networks, achieved a 97% success rate in discriminating PD datasets, surpassing Convolutional Neural Network (CNN) and Machine Learning (ML) classifications (88% –92%). Utilizing MATLAB R2021a, its superiority over Q-wavelet transform was evident, signifying improved PD dataset discrimination. [ABSTRACT FROM AUTHOR]

As a list of remotely sensed data sources is available, the effective processing of remote sensing images is of great significance in practical applications in various fields. This paper proposes a new lightweight network to solve the problem of remote sensing image processing by using the method of deep learning. Specifically, the proposed model employs ShuffleNet V2 as the backbone network, appropriately increases part of the convolution kernels to improve the classification accuracy of the network, and uses the maximum overlapping pooling layer to enhance the detailed features of the input images. Finally, Squeeze and Excitation (SE) blocks are introduced as the attention mechanism to improve the architecture of the network. Experimental results based on several multisource data show that our proposed network model has a good classification effect on the test samples and can achieve more excellent classification performance than some existing methods, with an accuracy of 91%, and can be used for the classification of remote sensing images. Our model not only has high accuracy but also has faster training speed compared with large networks and can greatly reduce computation costs. The demo code of our proposed method will be available at https://github.com/li-zi-qi. [ABSTRACT FROM AUTHOR]

Medical image classification is an essential task in the fields of computer-aided diagnosis and medical image analysis. In recent years, researchers have made extensive work on medical image classification by computer vision techniques. However, most of the current work is based on deep learning methods, which still suffer from expensive hardware resources, long time consuming and a lot of parameters to be optimized. In this paper, a multi-granularity ensemble algorithm for medical image classification based on broad learning system is proposed, which is an end-to-end lightweight model. On the one hand, the proposed method is designed to address the problem of weak image feature learning ability of broad learning system. The convolution module with fixed weights based on transfer learning is introduced as a feature extractor to extract fusion features of medical images. On the other hand, the multi-granularity ensemble framework is proposed, which learn the fusion features of medical images from fine-grained to coarse-grained respectively, and the prediction results at different granularity levels are integrated by ensemble learning. In this way, the bottom local features can be sufficiently considered, while the global features can also be taken into account. The experimental results show that on the MedMNIST dataset (containing 10 sub-datasets), the proposed method can shorten the training time by tens of times while having similar accuracy to deep convolutional neural networks. On the ChestXRay2017 dataset, the proposed method can achieve an accuracy of 92.5%, and the training time is also significantly better than other methods. [ABSTRACT FROM AUTHOR]

Breast cancer is a widespread and significant health concern among women globally. Accurately categorizing breast cancer is essential for effective treatment, ultimately improving survival rates. Moreover, deep learning (DL) has emerged as a widely adopted approach for precise medical image classification in recent years, showing promise in this domain. However, despite the availability of DL models proposed in the literature for automated classification of breast cancer histopathology images, achieving high accuracy remains challenging. A minor modification to pre-trained models and simple training strategies can further enhance model accuracy. Based on the approach, this paper proposed an anti-aliased filter in a pre-trained ResNet-34 and a novel three-step training process to improve BC histopathology image classification accuracy. The training involves systematically unfreezing layers and imposing additional constraints on the rate of change of learnable parameters. In addition, four-fold on-the-fly data augmentation enhances model generalization. The Ada-Hessian optimizer adjusts learning rates based on first and second-order gradients to improve convergence speed. The training process utilizes a large batch size to minimize the training loss associated with batch normalization layers. Even with the limited GPU size, the gradient accumulation technique achieves a large batch size. Collectively, these strategies minimize training time while maintaining or improving the accuracy of BC histopathology image classification models. In the experimental implementation, the proposed architecture achieves superior results compared to recent existing models, with an accuracy of 98.64%, recall (98.98%), precision (99.35%), F1-Score (99.17%), and MCC (97.36%) for binary classification. Similarly, the model achieves an accuracy of 95.01%, recall (95.01%), precision (94.95%), F1-Score (94.94%), and MCC (93.42%) for the eight-class category of BC images. [ABSTRACT FROM AUTHOR]

Recognizing human activity is the process of using sensors and algorithms to identify and classify human actions based on the data collected. Human activity recognition in visible images can be challenging due to several factors of the lighting conditions can affect the quality of images and, consequently, the accuracy of activity recognition. Low lighting, for example, can make it difficult to distinguish between different activities. Thermal cameras have been utilized in earlier investigations to identify this issue. To solve this issue, we propose a novel deep learning (DL) technique for predicting and classifying human actions. In this paper, initially, to remove the noise from the given input thermal images using the mean filter method and then normalize the images using with min-max normalization method. After that, utilizing Deep Recurrent Convolutional Neural Network (DRCNN) technique to segment the human from thermal images and then retrieve the features from the segmented image So, here we choose a fully connected layer of DRCNN as the segmentation layer is utilized for segmentation, and then the multi-scale convolutional neural network layer of DRCNN is used to extract the features from segmented images to detect human actions. To recognize human actions in thermal pictures, the DenseNet-169 approach is utilized. Finally, the CapsNet technique is used to classify the human action types with Elephant Herding Optimization (EHO) algorithm for better classification. In this experiment, we select two thermal datasets the LTIR dataset and IITR-IAR dataset for good performance with accuracy, precision, recall, and f1-score parameters. The proposed approach outperforms "state-of-the-art" methods for action detection on thermal images and categorizes the items. [ABSTRACT FROM AUTHOR]

The method for document image classification presented in this paper mainly focuses on six different Malayalam palm leaf manuscripts categories. The proposed approach consists of three phases: dataset analysis, building a bag of words repository followed by recognition and classification using a voting approach. The palm leaf manuscripts are initially subject to pre-processing and subjective analysis techniques to create a bag of words repository during the dataset analysis phase. Next, the textual components from the manuscripts are extracted for recognition using Tesseract 4 OCR with default and self-adapted training sets and a deep-learning algorithm. The Bag of Words approach is used in the third phase to categorize the palm leaf manuscripts based on textual components recognized by OCR using a voting process. Experimental analysis was done to analyze the proposed approach with and without the voting techniques, varying the size of the Bag of Words with default/self-adapted training datasets using Tesseract OCR and a deep learning model. Experimental analysis proves that the proposed approach works equally well with/ without voting with a bag of words technique using Tesseract OCR. It is noticed that, for document classification, an overall accuracy of 83% without voting and 84.5% with voting is achieved with an F-score of 0.90 in both cases using Teserract OCR. Overall, the proposed approach proves to be high generalizable based on trial wise experiments with Bag of Words, offering a reliable way for classifying deteriorated Malayalam handwritten palm manuscripts. [ABSTRACT FROM AUTHOR]

The rapid and effective identification of leaf diseases of woody fruit plants can help fruit farmers prevent and cure diseases in time to improve fruit quality and minimize economic losses, which is of great significance to fruit planting. In recent years, deep learning has shown its unique advantages in image recognition. This paper proposes a new type of network based on deep learning image recognition method to recognize leaf diseases of woody fruit plants. The network merges the output of the convolutional layer of ResNet101 and VGG19 to improve the feature extraction ability of the entire model. It uses the transfer learning method to partially load the trained network weights, reducing model training parameters and training time. In addition, an attention mechanism is added to improve the efficiency of network information acquisition. Meanwhile, dropout, L2 regularization, and LN are used to prevent over-fitting, accelerate convergence, and improve the network's generalization ability. The experimental results show that the overall accuracy of woody fruit plant leaf diseases identification based on the model proposed in this paper is 86.41%. Compared with the classic ResNet101, the accuracy is improved by 1.71%, and the model parameters are reduced by 96.63%. Moreover, compared with the classic VGG19 network, the accuracy is improved by 2.08%, and the model parameters are reduced by 96.42%. After data set balancing, the overall identification accuracy of woody fruit plant leaf diseases based on the model proposed in this paper can reach 86.73%. [ABSTRACT FROM AUTHOR]

In order to allow users to incorrectly identify images by manipulating them using deep neural networks, this paper analyses the shortcomings of deep learning for image classification. It also develops a game that uses this technique. In the game, players can select one of their preferred product categories, causing the model to classify other product categories incorrectly as the one they selected. The goal of this game is to demonstrate to players the limitations of AI. We evaluate these programs based on their overall effectiveness, user satisfaction, and achievement of their objectives. The results show that this program is a successful method for arousing curiosity and stimulating thought. They can learn to appreciate the limitations of AI and the need to prioritize AI security in their daily activities. [ABSTRACT FROM AUTHOR]

In the field of image classification, the Convolutional Neural Networks (CNNs) are effective. Most of the work focuses on improving and innovating CNN's network structure. However, using labeled data more effectively for training has also been an essential part of CNN's research. Combining image disturbance and consistency regularization theory, this paper proposes a model training method (PairTraining) that takes image pairs as input and dynamically modify the training difficulty according to the accuracy of the model in the training set. According to the accuracy of the model in the training set, the training process will be divided into three stages: the qualitative stage, the fine learning stage and the strengthening learning stage. Contrastive learning images are formed using a progressively enhanced image disturbance strategy at different training stages. The input image and contrast learning image are combined into image pairs for model training. The experiments are tested on four public datasets using eleven CNN models. These models have different degrees of improvement in accuracy on the four datasets. PairTraining can adapt to a variety of CNN models for image classification training. This method can better improve the effectiveness of training and improve the degree of generalization of classification models after training. The classification model obtained by PairTraining has better performance in practical application. [ABSTRACT FROM AUTHOR]

Everything becomes smart in the modern era, for everything we need a better plan or arrangements. In the olden days, essential information was noted as a document with the help of paper and pen or printed texts. But the intelligent world needs a paperless environment by converting handwritten or printed text documents into software copies. This can be achieved by the electronic data conversion concept called Optical Character Recognition (OCR). OCR of some documents is complex because of different writing styles and quality of scanned image issues, which can be solved by adopting a deep learning technique for better accuracy. We employed Long Short Term Memory (LSTM) for English Optical Character Recognition for paperless and effortless data storage and fast access in this work. Still, the records may contain the entities like names, contact details, drug details, diseases, educational qualifications, dates, etc. These entities cannot be separated by employing OCR alone; we need an entity recognition framework for deeper and faster data analysis. For efficient Named Entity Recognition, we utilize the Adaptive Fuzzy Inference System (ANFIS) powered by the algorithms CRF and BERT to automatically label each entity by training the vast amount of unlabeled text data. The ANFIS model is equipped with both linguistic and numerical knowledge. It is more accurate than the ANN when it comes to identifying patterns and classification data. Also, it is more transparent to the user. Our proposed framework aims to improve the performance of the character recognition system by using a feed-forward network. One of the main issues that have been identified in the development of this system is noise. Through this network, we can provide a single input and one output layer. The main components of the system are the training and recognition sections. These two sections are mainly focused on image acquisition and feature extraction. Besides these, they also include training and simulation of the classifier. The first step in the process of image recognition is to extract the features from the normalized image matrix. We then train the network using a proposed training algorithm. Experimentation on medical records attains a higher accuracy value of 0.9637, recall value of 0.9627, and f1 score of 0.9627, respectively. [ABSTRACT FROM AUTHOR]

The clothing images on the Internet is growing rapidly, and there is an increasing demand for the clothing images' intelligent classification. In this paper, Region-Based Fully Convolutional Networks (R-FCN) is introduced into the clothing image recognition. In the clothing image classification, because the network training time is long and the recognition rate of deformed clothing images is low, an improved framework HSR-FCN is proposed. The regional suggestion network and HyperNet network in R-FCN are integrated in the new framework, the learning approach of image features is changed in HSR-FCN, the higher accuracy can be achieved in a shorter training time. A spatial transformation network is introduced into the model, the input clothing image and feature map are spatially transformed and aligned, the feature learning is strengthened for multi-angle clothing and deformed clothing. The experimental results show that the improved HSR-FCN model is used to strengthen effectively the learning of deformed clothing images, and with a shorter training time, the average accuracy rate of the original network model R-FCN is increased by about 3%, it reachs 96.69%. [ABSTRACT FROM AUTHOR]

Insulator determines the insulation level and power supply reliability of transmission lines. The traditional operation and maintenance method of insulators has a large workload. This paper presents an insulator recognition and fault diagnosis system based on image recognition and machine learning. Firstly, the composite insulators in complex backgrounds have been identified by Faster RCNN algorithm, which helps to extract the image of insulators by drone shot. Then, the cracking of umbrella skirts has been carried out by means of image processing. Also, the contamination of composite insulator umbrella skirts is also identified. The appropriate feature quantity by Fisher's discrimination is recommended, and the insulator contamination level has been identified by S component. Lastly, analyzing the infrared image of the insulator provides the basis for the normalization of insulator temperature rise detection results in different environments. Insulator image autonomous recognition and defect intelligent detection, which is based on deep reinforcement learning, is helpful for the operation, maintenance and repair of line insulators. [ABSTRACT FROM AUTHOR]

Due to the wide range of diseases and imaging modalities, a retrieving system is a challenging task to access the corresponding clinical cases from a large medical repository on time. Several computer-aided systems (CADx) are developed to recognize medical imaging modalities (MIM) based on various standard machine learning (SML) and advanced deep learning (DL) algorithms. Pre-trained models like convolutional neural networks (CNN) are used in the past as a transfer learning (TL) architecture. However, it is a challenging task to use these pre-trained models for some unseen datasets with a different domain of features. To classify different medical images, the relevant features with a robust classifier are needed and still, it is unsolved task due to MIM-based features. In this paper, a hybrid MIM-based classification system is developed by integrating the pre-trained VGG-19 and ResNet34 models into the original CNN model. Next, the MIM-DTL model is fine-tuned by updating the weights of new layers as well as weights of original CNN layers. The performance of MIM-DTL is compared with state-of-the-art systems based on cancer imaging archive (TCIA), Kvasir and lower extremity radiographs (LERA) datasets in terms of statistical measures such as accuracy (ACC), sensitivity (SE) and specificity (SP). On average, the MIM-DTL model achieved 99% of ACC, SE of 97.5% and SP of 98% along with smaller epochs compare to other TL. The experimental results show that the MIM-DTL model is outperformed to recognize medical imaging modalities and helps the healthcare experts to identify relevant diseases. [ABSTRACT FROM AUTHOR]