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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

共 7139 条记录，以下是31-40 订阅

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

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

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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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CH and CMiGIN 2024 - Proceedings of the 3rd international conference on Cyber Hygiene and Conflict Management in Global Information Networks

CH and CMiGIN 2024 - Proceedings of the 3rd International Co...

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3rd international conference on Cyber Hygiene and Conflict Management in Global Information Networks, CH and CMiGIN 2024

The proceedings contain 34 papers. The topics discussed include: a comparative analysis of cyber threat intelligence models;cloud shared hosting DDoS resistance and potential ways of protection;protection of IoT networks: cryptographic solutions for cybersecurity management;development of a modified steganographic model of data transmission using IPv6 protocol;optimization of the integrated video surveillance system with elements of data analysis;ecological disasters as a result of cyber-attacks in the energy sector;estimation of the aircraft's position based on optical channel data;analysis of cross-sectional neural network recognition of satellite and aerial survey data;and predictive control for failure risk assessment during navigation equipment operation.

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Generation of Pseudo X-Ray and Digitally Reconstructed Radiograph from CT images 3

Generation of Pseudo X-Ray and Digitally Reconstructed Radio...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Arya, R. Kochuvila, Sreeja Dept. of Artificial Intelligence Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Computed Tomography (CT) scanning offers 3D insights into the human body but involves higher radiation exposure than traditional X-Rays, increasing the relevance of 3D reconstructions from 2D data. In this work, datasets of CT- pseudo-X-Ray and CT- Digitally Reconstructed Radiograph (DRR) pairs are generated using ray-sum tracing and projection techniques. This can be helpful for implementing machine learning and deep learning architecture as 2-D data availability is limited. The described approach generates CT–pseudo-X-Ray and CT–Digitally Reconstructed Radiograph (DRR) pairs using ray-sum tracing and projection methods. The pseudo-X-Ray is created by combining bone and soft tissue projections, while the DRR is derived via ray-sum tracing. By retaining the CT images in Digital Imaging and Communications in Medicine (DICOM) format, the created dataset enhances robustness and efficiency, supporting 3D reconstruction—a rapidly advancing field. © 2025 IEEE.

关键词： X ray radiography

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Artificial intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced Natural Language Processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 IEEE.

关键词： Natural language processing systems

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Smartphone Continuous Authentication via Deep Behavioral Features and Isolation Forest 24

Smartphone Continuous Authentication via Deep Behavioral Fea...

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3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Ma, Hangyuan Li, Zhongrui Mou, Shiyu School of Computer Science and Artificial Intelligence Southwest Minzu University Sichuan Chengdu China Engineering Faculty Biomedical Engineering Department University of Malaya Petaling Jaya Malaysia

ISBN: (纸本)9798400710834

Smartphone authentication typically relies on user input of passwords or biometric features such as facial recognition, fingerprint scanning, or iris recognition. However, these methods only authenticate the user at specific points in time, meaning that once the password is compromised or the device is unlocked, unauthorized individuals can access all information on the device or perform unauthorized actions. To resolve this issue, continuous authentication has emerged, continuously verifying the user’s identity by covertly monitoring the ongoing interactions between the user and the device. This paper proposes a continuous authentication method that utilizes smartphone sensor data to extract users’ deep behavioral features through Siamese Convolutional Neural Networks (Siamese CNN). These features are then input into the Isolation Forest (iForest) algorithm for classification. Experimental results demonstrate that the proposed method can authenticate the user within one second, achieving an average accuracy of 93.79% and an average Equal Error Rate (EER) of 8.37%, significantly outperforming other comparative methods used in this study. © 2024 Copyright held by the owner/author(s).

关键词： Smartphones

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Water Quality Prediction and Suggesting Filter using machine Learning 3

Water Quality Prediction and Suggesting Filter using Machine...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chaudhari, Archana Mane, Rutuja Khot, Anant Kadam, Anup Rajam, Nagraj Department of Instrumentation Engineering Vishwakarma Institute of technology Bibwewadi Maharashtra Pune India

ISBN: (纸本)9798331527549

Survival of all living beings and efficient working of industries and agriculture are largely dependent on the availability of clean water. However, contamination is a major issue that requires dependable methods for assessing the quality of water before its use in drinking, chemical applications, or animal hydration. This research utilizes machine learning to predict water quality based on parameters such as pH, turbidity, hardness, conductivity, and dissolved particles. Based on a broad dataset, models such as time-series analysis and Random Forest classifier are applied to categorize water as safe or unsafe for domestic use. A hardware system equipped with sensors captures real-time data, which enables dynamic assessment of water quality. The proposed solution shows high accuracy in its evaluation using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Regression Analysis, offering a practical approach to ensure water safety in diverse applications. © 2025 IEEE.

关键词： artificial intelligence data visualization dataset filter hardware machine learning preprocessed data Random forest algorithm TDS water quality

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Energizing the Future: A Comprehensive Review and Evaluation of Artificial intelligence based Solar Irradiance Forecasting Model with Swot Analysis of Solar Energy 3

Energizing the Future: A Comprehensive Review and Evaluation...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, Gautam Goyal, Sandip Kumar Cse Department Mullana Haryana Ambala India

ISBN: (纸本)9798331509675

Meeting the fast-growing need of energy is paramount;therefore, solar energy can contribute to playing a very important role in reducing greenhouse gas emissions. Solar radiation depends on several factors such as the availability of sunlight, cloud cover, and panel orientation, so it is the key to a solar energy system. Proper prediction of solar irradiance allows better reliability and efficiency of the system. AI and machine learning models, which are trained by use of historical data on weather, are increasingly applied for this purpose using techniques such as regression, neural networks, and ensemble methods. This review presents a review of the state-of-the- art models for solar irradiance forecasting, covering machine learning, numerical weather prediction, and hybrid approaches, by assessing their accuracy, strengths, and weaknesses. It also delineates the prospective future development possibilities while enunciating the role of interdisciplinary collaboration and technology in merging solar power into the main electricity system. Therefore, this work is going to be a core analysis for future researchers in finding optimum methods leading into medium- and long-term solar irradiance forecasting. © 2025 IEEE.

关键词： Solar irradiance

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An Analysis of Face based eCrime Identification using First Information Report 3

An Analysis of Face based eCrime Identification using First ...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. Vanarasan, S. Dharani, S. Dharshini, R. Harani, S. Selvarathi, C. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Sri Sairam College of Engineering Department of Artificial Intelligence and Machine Learning Banglore562 106 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639 113 India

ISBN: (纸本)9798331509675

Face based e-crime identification initiative combines cutting-edge technologies to detect criminals in video footage, allowing for quick and effective online FIR (First Information Report) reporting. Furthermore, it uses blockchain technology to securely store and manage sensitive data, as well as to ensure the integrity of evidence and to facilitate robust facial recognition using the Inception V3 algorithm. The proliferation of electronic crimes (eCrimes) in today's digital age poses a huge challenge to law enforcement organizations worldwide. In the face of continuously shifting criminal strategies, traditional means of identifying and reporting offenders frequently fall short. The fundamental functionality of the technology is around identifying offenders using video footage. It makes use of the potent Inception V3 algorithm, a cutting-edge deep learning model noted for its efficient image recognition skills. This algorithm analyses video frames, detects and matches facial traits, and identifies suspects. This not only improves the accuracy of criminal identification but also speeds up the investigation. In addition to identifying criminals, the streamlines the FIR reporting process. Victims and witnesses can now conveniently file FIRs online, eliminating the need for physical visits to police stations. The integration of digital reporting platforms enhances the efficiency of law enforcement agencies, making the process more accessible to the public. Blockchain technology is used to provide data security and immutability. A blockchain-based database stores all data relevant to criminal identification and FIR reporting. The combination of improved facial recognition, online reporting, and secure blockchain data management promises to make identifying and reporting criminals more efficient and secure. © 2025 IEEE.

关键词： Deep learning

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De-Styling Bias in #Climatical Tweets: A Neural Language Style Transfer Approach 3

De-Styling Bias in #Climatical Tweets: A Neural Language Sty...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： George, Elizabeth Leah Parthasarathy, Subashini Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore India Centre for Machine Learning and Intelligence Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore India

ISBN: (纸本)9798331515911

The spread of biased and misleading opinions on social media regarding climate change necessitates robust solutions to counteract misinformation and promote balanced discourse. In this study, we introduce the Semantic Neural Style Transfer Bias (SNLT) Framework, an AI-driven solution designed to identify and mitigate bias in climate-related tweets. Leveraging a curated dataset of climate change tweets collected from Twitter, we employ the all-MiniLM-L6-v2 sentence transformer for feature extraction and BERT-based embeddings to capture semantic nuances. Our methodology integrates semantic clustering models to evaluate clustering performance using silhouette scores, including KMeans, Gaussian KMeans, keyNMF clustering, and an Autoencoder-based model. DistilBERT is further employed to classify tweets as biased or unbiased, while a Named Entity recognition (NER) model trained on RobERTa transformers identifies word-level bias indicators. The fundamental basis of our framework is a transformer-based style transfer model, such as GPT-2, which adjusts the stylistic tone of biased text while preserving its semantic meaning. This approach employs a style-specific decoder to ensure grammatical accuracy and contextual relevance, producing text with a neutral tone through refined phrasing and word choice. Evaluation results demonstrate a substantial reduction in bias without compromising fluency or naturalness, validated through statistical metrics and human assessments. Additionally, Explainable AI techniques are integrated to enhance the transparency of model decisions, offering insights into bias mitigation processes. Our findings highlight the framework's efficacy in addressing climate misinformation and fostering constructive conversations on social media, underscoring its potential as a responsible AI tool for digital discourse. © 2025 IEEE.

关键词： Tweets

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An Artificial intelligence Based Attendance Monitoring System for Malpractice Control 3rd

An Artificial Intelligence Based Attendance Monitoring Syste...

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3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Gadey, Nishanth Pande, Sagar Dhanraj School of Computer Science Engineering VIT-AP University Andhra Pradesh Amaravati India Pune India

ISBN: (纸本)9789819743582

In all organizations, attendance monitoring is essential for monitoring student performance, and checking whether every student is present or not is not easy. Each organization takes attendance manually like calling with their registration numbers or student names and noting it in attendance records given by the school/college principal as a proof of attendance. While some consider student signature. These are the methodologies which are old, complex, and time consuming for students and teachers. This study explores how AI simplifies attendance monitoring. Several methods of attendance tracking are discussed in this study, including face detection, which may be used to identify students continually and regularly. Numerous additional approaches are also mentioned. The approaches used for Facial Biometric Framework attendance are covered in this study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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