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Optical Character Recognition using YOLO4, Tesseract and Paddleocr for Text Extraction 3

Optical Character Recognition using YOLO4, Tesseract and Pad...

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kusuma, T. Jyothi, S. Shruthi, G. Sheela, S. Naveen Kumar, C. Ramaiah Institute of Technology Department of Artificial Intelligence & Data Science Bangalore India Global Academy of Technology Department of Computer Science & Engg Bangalore India Ramaiah Institute of Technology Department of Information Science Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

The recent advancement Tesseract OCR engine and the YOLO4 (You Only Look Once version 4) object detection framework provide an innovative approach to optical character recognition (OCR) with a focus on table extraction. Because of their complicated layouts, traditional OCR algorithms frequently have difficulty accurately retrieving tabular data from photos. The suggested technique successfully extracts tables from photos by utilizing Tesseract for precise character recognition and YOLO4 for reliable object detection. Table borders, cell boundaries, and text sections inside tables are all recognized by the YOLO4 model after it has been trained. The textual information inside these areas is then identified using Tesseract. The suggested solution outperforms conventional OCR techniques in experimental results that show how well it extracts tables from photos with different layouts and levels of complexity. © 2025 IEEE.

关键词： data Analytics

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Coffee Leaf Disease and Severity Prediction Using Deep Learning 38

Coffee Leaf Disease and Severity Prediction Using Deep Learn...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Chaubey, Harshit Kumar Arelli, Siri Raj, Aryan Naik, Nenavath Srinivas Iiit Naya Raipur Computer Science and Engineering Raipur India Iiit Naya Raipur Data Science and Artificial Intelligence Raipur India Iiitdm Kurnool Computer Science and Engineering Kurnool India

ISBN: (纸本)9798350302196

Coffee production is a vital industry in many countries, but diseases affecting coffee leaves can lead to significant losses for farmers. To mitigate these losses, timely disease detection and accurate assessment of disease severity are crucial. This work proposes a deep learning approach for classifying coffee leaf diseases based on their severity levels. The proposed methodology involves several steps. Initially, U2Net removes the background from the coffee leaf images. Subsequently, the background-removed images are converted into BGR format to identify the diseased regions. DeepLabV3 is then trained to extract and mark the diseased portions of the leaves in red. Using these annotated images, various convolutional neural network (CNN) models, including VGG-16, Inception V3,and MobileNetV2, were trained to classify the diseases based on their severity levels. These models are carefully modified and fine-tuned with hyperparameters to achieve the best performance metrics. Upon model training, the modified MobileNetV2 model performs better than the other CNN models, achieving an impressive F1-score of 97.99%. This outcome highlights the effectiveness of this paper's approach in accurately classifying coffee leaf diseases according to their severity levels. The proposed methodology has significant implications for coffee farmers, enabling them to swiftly detect diseases and assess their severity, allowing for timely and appropriate actions. Furthermore, the findings indicate the superiority of the modified MobileNetV2 model in achieving high accuracy in disease severity classification. This research contributes to advancing deep learning techniques for agricultural applications, offering practical solutions for disease management in the coffee industry. © 2023 IEEE.

关键词： Deep learning

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A Combination Model of Periodic and Non-periodic Events for Temporal Knowledge Graph Event Prediction

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Chinese Journal of Electronics 2025年第3期34卷 952-961页

作者： Chen Yue Huang Yongzhong School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China School of Data Science and Software Engineering Wuzhou University Wuzhou 543002 China

Temporal knowledge graph (TKG) reasoning aims to predict missing facts or future events at given timestamps and has attracted more and more attention in recent years. Existing TKG reasoning methods mainly focus on the interactions between entities and ignore the associations between events where the entities involve. In addition, the characteristics of different types of events have not been studied and exploited, which reduces the performance of event prediction. To address these problems, this paper proposes a combination model of periodic and non-periodic events (CM-PNP). Specifically, there are two basic components designed to process different types of events. The periodic component of CM-PNP learns the recurrent pattern of periodic events and encodes the temporal information in the manner of timespan to prevent the unseen timestamp issue. The non-periodic component of CM-PNP introduces extra information (e.g., entity attributes) to represent non-periodic events, and predicts this type of events according to the related historical events. A combination model of multiple sub-models that focus on encoding different parts of the event is used to improve the performance of single model. The periodic and non-periodic components are combined by a gate block. The experimental results on three real-world datasets demonstrate that CM-PNP outperforms the existing baselines.

关键词： Temporal knowledge graph Temporal knowledge graph reasoning Event prediction Embedding learning data mining

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Nuclei Segmentation in Histopathology Images Using Structure-Preserving Color Normalization Based Ensemble Deep Learning Frameworks

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computers, Materials & Continua 2023年第12期77卷 3077-3094页

作者： Manas Ranjan Prusty Rishi Dinesh Hariket Sukesh Kumar Sheth Alapati Lakshmi Viswanath Sandeep Kumar Satapathy Centre for Cyber-Physical Systems Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Centre for Advanced Data Science Vellore Institute of TechnologyChennai600127India

This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology *** purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping *** this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation ***,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain *** proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ *** findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.

关键词： Nuclei segmentation image segmentation ensemble U-Net deep learning histopathology image convolutional neural networks

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Enhancing Hadoop Performance with Q-Learning for Optimal Parameter Tuning 38

Enhancing Hadoop Performance with Q-Learning for Optimal Par...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Akshay, Garika Naik, Nenavath Srinivas Vardhan, Jai Iiit Naya Raipur Data Science and Artificial Intelligence Raipur India Iiitdm Kurnool Computer Science and Engineering Kurnool India Iiit Naya Raipur Computer Science and Engineering Raipur India

ISBN: (纸本)9798350302196

This paper presents an approach to enhance Hadoop performance by leveraging deep Q-Learning, a form of Reinforcement Learning, to optimize parameter settings. The performance of Hadoop, a widely adopted distributed computing framework, relies heavily on configuring numerous parameters. However, the complex and extensive search space poses challenges in determining the optimal settings. In response, we propose an innovative deep Q-Learning algorithm that iterative discovers and applies the most effective parameter configurations, thereby improving Hadoop's performance. Our approach involves defining state and action spaces, learning from performance feedback, and identifying the optimal configuration to maximize Hadoop's efficiency. Our proposed model consistently outperforms existing solutions by benchmarking with popular jobs such as TeraSort, WordCount, Hive Aggregate, and WordCooccur, demonstrating significant reductions in execution times and improved computational efficiency. This research accelerates big data processing in Hadoop and provides a scalable and efficient solution applicable to similar distributed computing frameworks. Furthermore, our paper reinforces the effectiveness and applicability of Reinforcement Learning in addressing complex optimization problems. By harnessing the power of deep Q-Learning, we showcase its capability to navigate the intricate parameter space of Hadoop, resulting in enhanced performance and streamlined data processing. This research contributes to advancing distributed computing frameworks, offering a novel and efficient approach to optimize parameter settings and improve overall system performance. © 2023 IEEE.

关键词： Reinforcement learning

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Predicting Employee Attrition: A Comparative Analysis of Machine Learning Models Using the IBM Human Resource Analytics dataset

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Procedia computer science 2025年 258卷 4084-4093页

作者： Rajkumar Govindarajan N. Komal Kumar Sudhakar Reddy P Sai Pravallika E Dhatri B Pavan Kumar G Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh-517

In employee turnover research and workforce management, addressing the impacts of suboptimal employee performance is crucial for organizations of all sizes and industries. Utilizing advanced machine learning classification models to predict potential employee resignations can enhance human resource departments’ intervention strategies, effectively mitigating attrition challenges. This research investigates the performance of various machine learning algorithms in classification tasks, focusing on their accuracy in predicting outcomes from a given dataset. Five models were evaluated: Random Forest, Support Vector Machine (SVM), Decision Tree, Gradient Boosting, and a Hybrid Model that integrates multiple algorithms. The goal was to identify which model yields the highest accuracy and to understand the strengths and weaknesses of each approach. Results showed that the Hybrid Model achieved the highest accuracy at 95.0%, suggesting that combining different algorithms effectively harnesses their strengths while mitigating individual weaknesses. The SVM accurately classified the instance with 88.6%, demonstrating its capability to manage complex decision boundaries in high-dimensional spaces. Both Random Forest and Gradient Boosting attained an accuracy of 87.3%, reflecting their ensemble techniques that enhance predictive performance by reducing overfitting and optimizing error reduction. In comparison, the Decision Tree classifier exhibited the least accuracy at 80.5%, highlighting its susceptibility to overfitting and limited generalizability. The superior performance of the Hybrid Model indicates a promising direction for future research, where integrating diverse algorithms could lead to more robust predictions. Overall, this study provides valuable insights for practitioners and researchers seeking to optimize model selection and improve predictive accuracy in their domains.

关键词： Employee turnover Workforce Predictive Human Resourse Intervention

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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Correspondence Learning via Group-wise Motion Enhancement 14

Correspondence Learning via Group-wise Motion Enhancement

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Zhang, Yifan Kang, Zheng Lin, Xiaolang Lai, Taotao Lin, Lihui Li, Zuoyong Fuzhou University College of Computer and Data Science Fuzhou China Fujian Agriculture and Forestry University College of Computer and Information Sciences Fuzhou China Fujian University of Technology School of Transportation Fuzhou China Minjiang University School of Computer and Big Data Fuzhou China Wuyi University School of Mathematics and Computer Science Nanping China

ISBN: (纸本)9798350356670

The outlier removal methods are usually based on Multi-Layer Perceptron (MLP) for capturing context, which neglect the underlying motion information in images. Recently, CNN-based methods have attempted to address this issue by estimating motion fields and have made significant progress. However, CNNs tend to diminish the discontinuities of motion fields, making it difficult for the network to capture multiple motion patterns in complex scenes. Therefore, we propose a Group-wise Motion Enhancement (GME) block that groups motion fields along the channel dimension to learn multiple motion patterns. This enables the network to identify inliers under different motion patterns more accurately. Experiments show that compared with several existing methods, our method achieves advanced performance on the relative pose estimation task. © 2024 IEEE.

关键词： Motion estimation

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A Systematic Literature Review of CNN Approaches in Classifying Brain Tumor 6

A Systematic Literature Review of CNN Approaches in Classify...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Paulindino, Aedentrisa Yasmanda Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

The ability of Convolutional Neural Networks (CNNs) to accurately discriminate between normal and tumorous brain tissues has been promising. The review focuses on the different CNN models, pre-processing methods, data augmentation, and Transfer Learning (TL) strategies used in this research. This Systematic Literature Review (SLR) collected the data from Google Scholar. The results of this study indicate that open-source datasets from Kaggle and Brain MRI Images for Brain Tumor Detection are the most used datasets. However, limited data and imbalanced class problems remain common challenges across various datasets. To overcome those challenges, using a larger dataset, oversampling, Generative Adversarial Network (GAN), federated learning, and Self-Supervised Learning (SSL) to handle the imbalance are the potential solution. Additionally, popular CNN architectures for brain tumor classification extensively use pre-trained models such as VGG16, VGG19, DenseNet121, DenseNet201, GoogleNet, ResNet-50, and Inception-v3. TL strategies are preferred, allowing CNNs to leverage knowledge from large datasets, improving generalization even with limited labeled data. © 2023 IEEE.

关键词： brain tumor classification cnn mri pre-trained transfer learning

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Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review 6

Pap Smear Medical Image Classification Using Deep Learning: ...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Selvano, Elvan Elwiehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability. © 2023 IEEE.

关键词： cervical cancer deep learning machine learning pap test

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