Text Extraction Research Articles

Verifiable privacy-preserving semantic retrieval scheme in the edge computing

Edge computing, with its characteristics of low latency and low transmission costs, addresses the storage and computation challenges arising from the surge in network edge traffic. It enables users to leverage nearby edge servers for data outsourcing and retrieval. However, data outsourcing poses risks to data privacy. Although searchable encryption is proposed to secure search of outsourced data, existing schemes generally cannot meet the requirements of semantic search, and they also exhibit security risks and incur high search costs. In addition, edge servers may engage in malicious activities such as data tampering or forgery. Therefore, we propose a verifiable privacy-preserving semantic retrieval scheme named VPSR suitable for edge computing environments. We utilize the Doc2Vec method to extract text feature vectors and then convert them into matrix form to reduce storage space requirements for indexes, queries, and keys. We encrypt matrices using an improved secure k-nearest neighbor (kNN) algorithm based on learning with errors (LWE) and calculate text similarity by solving the Hadamard product between matrices. Additionally, we design an aggregable signature scheme and offload part of the result verification tasks to edge servers. Security and performance analysis results demonstrate that the VPSR scheme is suitable for edge computing environments with high encryption and search efficiency and low storage cost while ensuring security.

A Bi-LSTM and Attention Based Approach for Developing an Assamese AI Chatbot

Objectives: The main objective of this study is to develop, evaluate, and analyses a Bi-LSTM and attention-based model and decide whether it is the best deep learning hybrid model for developing the Assamese AI Conversational Agent. Methods: A dataset for the Assamese language was created for this research. A bi-LSTM and attention-based model was developed and trained with the dataset. We have compared the accuracy and performance of the proposed model with those of other deep learning models. The bag of words technique was used for feature extraction of text. For natural language processing, a custom stemmer, stop word functions and root word and stop word database were developed. The performance of the model is measured using metrics such as precision, recall, and F1-score. Findings: From the research study, we have found that the bi-LSTM and attention-based model performed better in terms of all the metrics compared to other models. This model was able to attain an accuracy of 89.99%. Novelty: This research is novel as it is the first attempt to develop an AI chatbot in Assamese based on a deep learning model. Assamese is a language that has not received much attention in the field of AI chatbots. This research has great significance for the society where Assamese is spoken, as it can provide a platform for communication, information, and education in their native language. The Assamese language is in its beginning stages on the digital platform and has limited research done in the field of AI chatbots. This research will contribute a lot to this research area and open a new door for researchers who are interested in this topic. It will also allow them to further explore, improve, and develop new ideas. Keywords: Deep Learning, Natural language processing, Chatbot, Assamese

Multimodal Social Media Fake News Detection Based on 1D-CCNet Attention Mechanism

Due to the explosive rise of multimodal content in online social communities, cross-modal learning is crucial for accurate fake news detection. However, current multimodal fake news detection techniques face challenges in extracting features from multiple modalities and fusing cross-modal information, failing to fully exploit the correlations and complementarities between different modalities. To address these issues, this paper proposes a fake news detection model based on a one-dimensional CCNet (1D-CCNet) attention mechanism, named BTCM. This method first utilizes BERT and BLIP-2 encoders to extract text and image features. Then, it employs the proposed 1D-CCNet attention mechanism module to process the input text and image sequences, enhancing the important aspects of the bimodal features. Meanwhile, this paper uses the pre-trained BLIP-2 model for object detection in images, generating image descriptions and augmenting text data to enhance the dataset. This operation aims to further strengthen the correlations between different modalities. Finally, this paper proposes a heterogeneous cross-feature fusion method (HCFFM) to integrate image and text features. Comparative experiments were conducted on three public datasets: Twitter, Weibo, and Gossipcop. The results show that the proposed model achieved excellent performance.

RBCA-ETS: enhancing extractive text summarization with contextual embedding and word-level attention

RBCA-ETS: enhancing extractive text summarization with contextual embedding and word-level attention

Lung cancer classification model using convolutional neural network with feature ranking process

Abstract Lung cancer is the leading cause of cancer-related deaths worldwide, highlighting the importance of early detection to improve patient outcomes. The goal of this study is to create a computer-aided diagnosis (CAD) system that detects and classifies lung cancer based on medical images using a Convolutional Neural Network (CNN) and feature extraction techniques. The study employs the LIDC-IDRI dataset, a comprehensive collection of lung cancer-related medical images, to achieve this goal. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to improve contrast and detail while reducing noise. The Sobel operator is used to refine the segmentation process by enhancing edges and contours in binary images. Morphological operations such as dilation and filling are used to refine the segmented regions even further. Feature extraction is used to extract statistical data and texture characteristics from segmented regions. Mean and variance calculations reveal information about brightness and variability within regions, whereas co-occurrence matrices and Gray-Level Co-occurrence Matrix (GLCM) properties quantify texture features. The correlation between different regions is also evaluated to assess their relationships. Using the pre-processed and ranked features as inputs, a CNN model with five hidden layers is trained. To classify the segmented regions into cancerous and non-cancerous classes, the model learns patterns and relationships in the data. A confusion matrix is used to assess the accuracy, specificity, and sensitivity of the model's predictions, with an emphasis on correctly identifying lung cancer-affected regions. The results show promising results, with the proposed CAD system identifying lung cancer-affected regions with an accuracy of 99.4375%. The system also outperforms other existing methods with a specificity of 99.12% and a sensitivity of 99.26%. These findings highlight the developed system's potential as a valuable tool for early lung cancer detection, assisting doctors in making accurate diagnoses and improving patient outcomes.

Multi-Feature Extraction and Selection Method to Diagnose Burn Depth from Burn Images

Burn wound depth is a significant determinant of patient treatment. Typically, the evaluation of burn depth relies heavily on the clinical experience of doctors. Even experienced surgeons may not achieve high accuracy and speed in diagnosing burn depth. Thus, intelligent burn depth classification is useful and valuable. Here, an intelligent classification method for burn depth based on machine learning techniques is proposed. In particular, this method involves extracting color, texture, and depth features from images, and sequentially cascading these features. Then, an iterative selection method based on random forest feature importance measure is applied. The selected features are input into the random forest classifier to evaluate this proposed method using the standard burn dataset. This method classifies burn images, achieving an accuracy of 91.76% when classified into two categories and 80.74% when classified into three categories. The comprehensive experimental results indicate that this proposed method is capable of learning effective features from limited data samples and identifying burn depth effectively.

Extractive text summarization on medical insights using fine-tuned transformers

ABSTRACT Text summarization is a fundamental Natural language processing task that plays a crucial role in efficiently condensing large textual documents into concise and clear summaries for human comprehension. The amount of data being generated in the medical domain nowadays requires substantial application of the current deep learning approaches such as transformers. The main goal of this research is to extract relevant summaries from the abstracts of the research articles published related to cancer, blood cancer, tinnitus, and Alzheimer's. As the domain-related data requires special attention, our approach uses a fine-tuned transformer model, to guarantee that the summaries produced are not only brief but also accurate. Moreover, as a part of this research, we have effectively collected the information from PubMed and also prepared the data for analysis. A comparative analysis of the Bidirectional and Auto-Regressive Transformers (BART), Text-to-Text Transfer Transformer (T5), Textrank, and Lexrank models on the dataset is carried out in this study to understand the medical insights effectively. The fine-tuned transformer's performance in comparison with other models brings out a newer dimension for future studies.

Bangla Optical Character Recognition for Mobile Platforms: A Comprehensive Cross-Platform Approach

The development of Optical Character Recognition (OCR) systems for Bangla script has been an area of active research since the 1980s. This study presents a comprehensive analysis and development of a cross-platform mobile application for Bangla OCR, leveraging the Tesseract OCR engine. The primary objective is to enhance the recognition accuracy of Bangla characters, achieving rates between 90% and 99%. The application is designed to facilitate the automatic extraction of text from images selected from the device's photo library, promoting the preservation and accessibility of Bangla language materials. This paper discusses the methodology, including the preparation of training datasets, preprocessing steps, and the integration of the Tesseract OCR engine within a Dart programming environment for cross-platform functionality. This integration provides that the application could be introduced on mobile platforms without substantial alterations. The results demonstrate significant improvements in recognition accuracy, making this application a valuable tool for various practical applications such as data entry for printed Bengali documents, automatic recognition of Bangla number plates, and the digital archiving of vintage Bangla books. These improvements are crucial to further enhance the usability and reliability of Bangla OCR on mobile devices. Our cross-platform method for Bangla OCR on mobile devices provides a strong solution with exceptional identification accuracy, which helps in preserving and making Bangla language information accessible in digital format. This study has significant implications for future research and advancement in the field of optical character recognition (OCR) for intricate writing systems, especially in mobile settings.

Graphic association learning: Multimodal feature extraction and fusion of image and text using artificial intelligence techniques

With the advancement of technology in recent years, the application of artificial intelligence in real life has become more extensive. Graphic recognition is a hot spot in the current research of related technologies. It involves machines extracting key information from pictures and combining it with natural language processing for in-depth understanding. Existing methods still have obvious deficiencies in fine-grained recognition and deep understanding of contextual context. Addressing these issues to achieve high-quality image-text recognition is crucial for various application scenarios, such as accessibility technologies, content creation, and virtual assistants. To tackle this challenge, a novel approach is proposed that combines the Mask R-CNN, DCGAN, and ALBERT models. Specifically, the Mask R-CNN specializes in high-precision image recognition and segmentation, the DCGAN captures and generates nuanced features from images, and the ALBERT model is responsible for deep natural language processing and semantic understanding of this visual information. Experimental results clearly validate the superiority of this method. Compared to traditional image-text recognition techniques, the recognition accuracy is improved from 85.3% to 92.5%, and performance in contextual and situational understanding is enhanced. The advancement of this technology has far-reaching implications for research in machine vision and natural language processing and open new possibilities for practical applications.

Infrared Image Enhancement Method of Substation Equipment Based on Self-Attention Cycle Generative Adversarial Network (SA-CycleGAN)

During the acquisition of infrared images in substations, low-quality images with poor contrast, blurred details, and missing texture information frequently appear, which adversely affects subsequent advanced visual tasks. To address this issue, this paper proposes an infrared image enhancement algorithm for substation equipment based on a self-attention cycle generative adversarial network (SA-CycleGAN). The proposed algorithm incorporates a self-attention mechanism into the CycleGAN model’s transcoding network to improve the mapping ability of infrared image information, enhance image contrast, and reducing the number of model parameters. The addition of an efficient local attention mechanism (EAL) and a feature pyramid structure within the encoding network enhances the generator’s ability to extract features and texture information from small targets in infrared substation equipment images, effectively improving image details. In the discriminator part, the model’s performance is further enhanced by constructing a two-channel feature network. To accelerate the model’s convergence, the loss function of the original CycleGAN is optimized. Compared to several mainstream image enhancement algorithms, the proposed algorithm improves the quality of low-quality infrared images by an average of 10.91% in color degree, 18.89% in saturation, and 29.82% in feature similarity indices. Additionally, the number of parameters in the proposed algorithm is reduced by 37.89% compared to the original model. Finally, the effectiveness of the proposed method in improving recognition accuracy is validated by the Centernet target recognition algorithm.

Charting the Universe of Metal Music Lyrics and Analyzing Their Relation to Perceived Audio Hardness

We analyze the relationship between the musical and the lyrical content of metal music by combining automated audio feature extraction and quantitative text analysis on a corpus of 124,288 song lyrics from this genre. Based on this text corpus, a topic model is first constructed using Latent Dirichlet Allocation (LDA). For a subsample of 503 songs, scores for predicting perceived musical hardness (heaviness) are extracted using an audio feature model and corroborated via listening tests. By combining both audio feature and text analysis, we (1) offer a comprehensive overview of the lyrical topics present within the metal genre and (2) are able to establish whether levels of hardness are associated with the occurrence of particularly harsh (and other) textual topics. Twenty typical topics are identified and projected into a topic space using multidimensional scaling (MDS). Positive correlations are found between musical hardness and textual topics dealing with “brutal death,” “dystopia,” “archaisms and occultism,” “religion and satanism,” “battle,” and “(psychological) madness,” while there is a negative association with topics like “personal life” and “love and romance.” Further, we show different prevalences of the identified topics within different metal subgenres. In summary, this article presents the first comprehensive computational corpus study of musical and lyrical content in metal music.

A text guided cross modal joint inpainting algorithm for ancient murals

Existing deep learning algorithms for ancient mural inpainting only consider prior information of mural images to guide the inpainting of damaged areas, lacking guidance from textual semantic information, which often leads to problems such as inconsistent semantics and disordered textures in the inpainting results. In this work, we propose a text guided cross modal joint inpainting algorithm for ancient murals. Firstly, a cross modal joint inpainting network for ancient murals was constructed, which introduced text semantic information as the control guidance for mural inpainting, providing contextual information for mural image inpainting and guiding the inpainting of damaged areas. Then, design a text encoder and an image encoder to extract text and image features respectively, including word and sentence features. Input the sentence features into the conditional enhancement module, resample the text sentences, and solve the problem of text sparsity. Secondly, design a text decoupling module that utilizes a multimodal attention mechanism to extract the semantic information of the missing parts of damaged murals, and design a text semantic enhancement module to improve the semantic representation ability of the text, achieve the repair of local details of murals, and solve the problem of texture disorder. Finally, the reconstruction and repair of murals are completed through a residual discriminator game and a spectral normalized Markov dual discriminator against the mural generation network. Through inpainting experiments on real Dunhuang murals, it has been shown that the proposed method is more effective than comparative methods in repairing damaged murals, achieving better visual perception and coordination in reconstruction, and outperforming comparative algorithms in both subjective and objective evaluations.

Cloud OCR implementation in Iraqi government SaaS: A step towards digital transformation

The present paper examines the deployment of Cloud Optical Character Recognition (OCR) technology within the Software as a Service (SaaS) platforms of the Iraqi government, which can be considered a strategic step in digitalization in notary uses. The benefits, challenges, and possible outcomes of Cloud OCR incorporation into different government services are discussed in order to outline the opportunities it creates in terms of accelerating citizens’ efficient and transparent access to available services. Cloud OCR allows the Iraqi government to facilitate document processing, enhance the accuracy of provided data, and accelerate decision-making, especially for notarized processing. The paper depicted the MATLAB simulations for processing times of OCR and paper-based text extraction methods across multiple iterations along with accuracy rates for both cases for 25 documents that validate the main objective of this study. The significance of microstrip devices, IoT, AI, and cybersecurity in Cloud OCR are as well determined in this study.

Scoping Review of Pediatric Primary Care Educational Literature.

Primary care pediatrics is an important element of pediatric training at all levels, yet trainees often lack clinical competence in this area. This scoping review sought to identify the current research available related to pediatric primary care education for medical students and residents and gaps/areas for improvement. Searches were completed on PubMed MEDLINE, Embase, Scopus, and MedEdPORTAL using the search terms "pediatrics," "education," "curricula," and "primary care." An initial total of 6499 articles was screened down to 102 articles for the final full text review and extraction. The most frequently encountered educational subjects were child development and developmental screening (8.8%), adolescent substance use (7.8%), and vaccines/vaccine hesitancy (6.9%). Several subject areas were underrepresented, including pubertal assessment (0.9%), transgender medicine (0.9%), child passenger safety (0.9%), discipline (0.9%), and sports/orthopedic medicine (0%). This study shows that future pediatric primary care educational studies and innovations are needed to improve trainee competence and comfort.

CLIP-FSSC: A transferable visual model for fish and shrimp species classification based on natural language supervision

Fish and shrimp species classification is a practical need in the field of aquaculture. Traditional classification method includes extracting modal features of the single image and training them on downstream datasets. However, this method has the disadvantage of requiring manual annotated image data and significant training time. To address these issues, this paper introduces a method named CLIP-FSSC (Contrastive Language–Image Pre-training for Fish and Shrimp Species Classification) for zero-shot prediction using a pre-trained model. The proposed method aims to classify fish and shrimp species in the field of aquaculture using a multimodal pre-trained model that utilizes semantic text description as an image supervision signal for transfer learning. In the downstream fish dataset, we use natural language labels for three types of fish - grass carp, common carp, and silver carp. We extract text category features using a transformer and compare the results obtained from three different CLIP-based backbones for the image modality - vision transformer, Resnet50, and Resnet101. We compare the performance of these models with previous methods that performed well. After performing zero-shot predictions on samples of the three types of fish, we achieve similar or even better classification accuracy than models trained on downstream fish datasets. Our experiment results show an accuracy of 98.77 %, and no new training process is required. This proves that using the semantic text modality as the label for the image modality can effectively classify fish species. To demonstrate the effectiveness of this method on other species in the field of aquaculture, we collected two sets of shrimp data - prawn and cambarus. Through zero-shot prediction, we achieve the highest classification accuracy of 92.00 % for these two types of shrimp datasets. Overall, our results demonstrate that using a multimodal pre-trained model with semantic text description as an image supervision signal for transfer learning can effectively classify fish and shrimp species with high accuracy, while reducing the need for manual annotation and training time.

Synergizing frequency domain and texture-aware implicit module for MRI arbitrary-scale super-resolution

Arbitrary-scale MR image super-resolution (SR) methods have emerged as a prominent research topic in medical imaging due to their ability to address the demand for continuous resolution in medical image analysis and diagnostics. Although several methods currently employ Implicit Neural Representations (INR) to achieve arbitrary-scale SR for MR images, they still face the following issues that constrain further enhancements in SR performance. (1) They solely perform feature extraction on MR images in the image domain, disregarding the frequency domain information in MR images, which results in aliasing artifacts and blurriness in the reconstructed SR images. (2) They utilize a standard multi-layer perceptron in INR, which tend to learn global structures (i.e., low-frequency components) and fail to capture and restore detailed texture information (i.e., high-frequency components) in MR images adequately. To address these issues, we propose a brand-new method called FTNet, which introduces the frequency domain information and employs the texture-aware implicit module for MR image arbitrary-scale SR. Specifically, we design an additional branch to extract the feature of MR images in the frequency domain to maximize removing aliasing artifacts. To further improve the quality of SR images, we introduce a texture extraction branch and a texture-aware implicit module to capture and restore detailed texture information within the MR images. Extensive experiments demonstrate the superiority of our network over state-of-the-art methods and its great potential for clinical practice.

Texture feature-aware consistency for semi-supervised honeycomb lung lesion segmentation

Accurate segmentation of honeycomb lung lesions from lung CT images is crucial for the clinical diagnosis of a wide range of lung diseases. Existing semi-supervised segmentation methods ignore the rich texture feature information in honeycomb lung CT images, resulting in inaccurate predictions in the lesion region. Therefore, this paper proposes a semi-supervised honeycomb lung lesion segmentation framework using texture feature-aware consistency. The framework consists of a shared encoder, a main decoder, and three auxiliary decoders. The different outputs produced by the decoders are used to compute the Grey Level Co-occurrence Matrix and extract texture features, respectively, and further to compute the loss of texture feature-aware consistency. In pseudo-labeling supervision, the uncertainty of each output is computed to bootstrap the pseudo-labeling supervision loss. For the small lesion recognition omission problem, a pseudo-label refinement module is constructed to reuse the multi-scale features of the encoder, focusing on small-scale lesions, and refining the pseudo-labels. Experimental results show that the proposed method outperforms other methods in the honeycomb lung segmentation task, achieving a Dice coefficient of 77.62 using 20% labeled data. Additionally, experiments on publicly available datasets further demonstrate the generalizability of the proposed method. The code is available at https://github.com/Oran9er/TFC-Net-main.

Optical character recognition system using artificial intelligence

Abstract A technique termed optical character recognition, or OCR, is used to extract text from images. An OCR the system's primary goal is to transform already present paper-based paperwork or picture data into usable papers. Character as well as word detection are the two main phases of an OCR, which is designed using many algorithms. An OCR also maintains a document's structure by focusing on sentence identification, which is a more sophisticated approach. Research has demonstrated that despite the efforts of numerous scholars, no error-free Bengali OCR has been produced. This issue is addressed by developing an OCR for the Bengali language using the latest 3.03 version of the Tesseract OCR engine for Windows.

Incorporating Patient Values in Large Language Model Recommendations for Surrogate and Proxy Decisions.

Surrogates, proxies, and clinicians making shared treatment decisions for patients who have lost decision-making capacity often fail to honor patients' wishes, due to stress, time pressures, misunderstanding patient values, and projecting personal biases. Advance directives intend to align care with patient values but are limited by low completion rates and application to only a subset of medical decisions. Here, we investigate the potential of large language models (LLMs) to incorporate patient values in supporting critical care clinical decision-making for incapacitated patients in a proof-of-concept study. We simulated text-based scenarios for 50 decisionally incapacitated patients for whom a medical condition required imminent clinical decisions regarding specific interventions. For each patient, we also simulated five unique value profiles captured using alternative formats: numeric ranking questionnaires, text-based questionnaires, and free-text narratives. We used pre-trained generative LLMs for two tasks: 1) text extraction of the treatments under consideration and 2) prompt-based question-answering to generate a recommendation in response to the scenario information, extracted treatment, and patient value profiles. Model outputs were compared with adjudications by three domain experts who independently evaluated each scenario and decision. Automated extractions of the treatment in question were accurate for 88% (n = 44/50) of scenarios. LLM treatment recommendations received an average Likert score by the adjudicators of 3.92 of 5.00 (five being best) across all patients for being medically plausible and reasonable treatment recommendations, and 3.58 of 5.00 for reflecting the documented values of the patient. Scores were highest when patient values were captured as short, unstructured, and free-text narratives based on simulated patient profiles. This proof-of-concept study demonstrates the potential for LLMs to function as support tools for surrogates, proxies, and clinicians aiming to honor the wishes and values of decisionally incapacitated patients.

Highly accurate brain tumor detection with high sensitivity using transform-based functions and machine learning algorithms.

Brain tumor is an extremely dangerous disease with a very high mortality rate worldwide. Detecting brain tumors accurately is crucial due to the varying appearance of tumor cells and the dimensional irregularities in their growth. This poses a significant challenge for detection algorithms. Currently, there are numerous algorithms utilized for this purpose, ranging from transform-based methods to those rooted in machine learning techniques. These algorithms aim to enhance the accuracy of detection despite the complexities involved in identifying brain tumor cells. The major limitation of these algorithms is the mapping of extracted features of a brain tumor in the classification algorithms. To employ a combination of transform methods to extract texture feature from brain tumor images. This paper employs a combination of transform methods based on sub band decomposition for texture feature extraction from MRI scans, hybrid feature optimization methods using firefly and glow-worm algorithms for selection of feature, employment of MKSVM algorithm and stacking ensemble classifier for classification and application of the feature of fusion of different feature extraction methods. The algorithm under consideration has been put into practice using MATLAB, utilizing datasets from BRATS (Brain Tumor Segmentation) for the years 2013, 2015, and 2018. These datasets serve as the foundation for testing and validating the algorithm's performance across different time periods, providing a comprehensive assessment of its effectiveness in detecting brain tumors. The proposed algorithm achieves maximum detection accuracy, detection sensitivity and specificity up to 98%, 99% and 99.5% respectively. The experimental outcomes showcase the efficiency of the algorithm in detection of brain tumor. The proposed work mainly contributes in brain tumor detection in the following aspects: a) use of combination of transform methods for texture feature extraction from MRI scans b) hybrid feature selection methods using firefly and glow-worm optimization algorithms for selection of feature c) employment of MKSVM algorithm and stacking ensemble classifier for classification and application of the feature of fusion of different feature extraction methods.