Procedia Computer Science最新文献_第10页

Sign Language To Sign Language Translator 手语到手语翻译

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.213

Sharath S R , Suraj S , Abishek Kumar G M , P Siddharth , Nalinadevi K

引用次数: 0

Federated Learning in Detecting Fake News: A Survey 联邦学习在假新闻检测中的应用研究

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.223

Sri Vasavi Chandu , Uma Sankararao Varri , Vamshi A , Vinay Raj

引用次数: 0

LakotaBERT: Transformer based model for Low Resource Lakota Language LakotaBERT：基于转换器的低资源Lakota语言模型

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.226

Kanishka Parankusham , Rodrigue Rizk , K C Santosh

{"title":"LakotaBERT: Transformer based model for Low Resource Lakota Language","authors":"Kanishka Parankusham , Rodrigue Rizk , K C Santosh","doi":"10.1016/j.procs.2025.03.226","DOIUrl":"10.1016/j.procs.2025.03.226","url":null,"abstract":"<div><div>Lakota, a critically endangered language of the Sioux people in North America, faces significant challenges due to declining fluency among younger generations. This paper presents the development of LakotaBERT, the first large language model (LLM) tailored for Lakota, aiming to support language revitalization efforts. Our research has two primary objectives: (1) to create a comprehensive Lakota language corpus and (2) to develop a customized LLM for Lakota. We compiled a diverse corpus of 105K sentences in Lakota, English, and parallel texts from various sources, such as books and websites, emphasizing the cultural significance and historical context of the Lakota language. Utilizing the RoBERTa architecture, we pre-trained our model and conducted comparative evaluations against established models such as RoBERTa, BERT, and multilingual BERT. Initial results demonstrate a masked language modeling accuracy of 51% with a single ground truth assumption, showcasing performance comparable to that of English-based models. We also evaluated the model using additional metrics, such as precision and F1 score, to provide a comprehensive assessment of its capabilities. By integrating AI and linguistic methodologies, we aspire to enhance linguistic diversity and cultural resilience, setting a valuable precedent for leveraging technology in the revitalization of other endangered indigenous languages.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"260 ","pages":"Pages 486-497"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IoT-Driven Smart Farming with Machine Learning for Sustainable Food Systems 物联网驱动的智能农业与可持续粮食系统的机器学习

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.233

Sanjana Murgod , Tanushree Kabbur , Bibijan Matte , Vaibhav Mujumdar , Meenaxi M Raikar

{"title":"IoT-Driven Smart Farming with Machine Learning for Sustainable Food Systems","authors":"Sanjana Murgod , Tanushree Kabbur , Bibijan Matte , Vaibhav Mujumdar , Meenaxi M Raikar","doi":"10.1016/j.procs.2025.03.233","DOIUrl":"10.1016/j.procs.2025.03.233","url":null,"abstract":"<div><div>Integrating the Internet of Things (IoT) and machine learning (ML) technologies in agriculture, commonly called smart farming, is revolutionizing the sector by enhancing productivity, efficiency, and sustainability. This paper explores the application of IoT-driven smart farming using machine learning for sustainable agricultural practices. The system introduces an efficient Soil Moisture Detection System utilizing IoT Technology, revolutionizing modern farming practices. By continuously monitoring crucial parameters such as soil moisture, temperature, and humidity in real-time, the system ensures seamless data transmission to a centralized server. Additionally, integrating motion detection capabilities enhances security measures and promptly alerts farmers to environmental changes. The dataset consisting of 100,000 rows is generated to facilitate the development and training of five ML models to predict soil moisture trends. Decision Trees achieved an accuracy rate of 99.98%, while Random Forests achieved 99.99%. The integration of these predictive models empowers farmers with actionable insights for precise irrigation scheduling and optimal crop yield optimization. These models provide actionable insights for precise irrigation scheduling and optimal crop yield optimization. Field tests have confirmed the efficacy of this approach, demonstrating significant improvements in irrigation efficiency and subsequent crop yields. Thus, the proposed system represents a substantial advancement in leveraging the synergistic potential of IoT and ML technologies to foster sustainable agricultural practices.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"260 ","pages":"Pages 552-560"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy Set of Rules for Optimal Adaptive Selection of OFDM order And FFT Size for NB-IOT NB-IOT OFDM顺序和FFT大小最优自适应选择的模糊规则集

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.265

Amrita Khera , Susheel Kumar Gupta , Urwashi Sad , Sandeep Sahu , Hemant Choubey

{"title":"Fuzzy Set of Rules for Optimal Adaptive Selection of OFDM order And FFT Size for NB-IOT","authors":"Amrita Khera , Susheel Kumar Gupta , Urwashi Sad , Sandeep Sahu , Hemant Choubey","doi":"10.1016/j.procs.2025.03.265","DOIUrl":"10.1016/j.procs.2025.03.265","url":null,"abstract":"<div><div>The huge growth is being observed in case of Narrow-Band-IOT (NB-IOT) based futuristic communication technology. The sharing of spectrum is the primary benefit. The system makes use of the 5G mobile communication spectrum that is freely available. When using any kind of communication technology, the physical layer is mostly in charge of data communication. The objective of this work is to enhance the effectiveness of OFDM, which serves as the physical layer of the NB-IOT, a futuristic approach. In the end, fast Fourier transform (FFT) is used to generate orthogonal frequencies, which forms the foundation of OFDM. Increasing the modulation order also addresses the excess signal transition need. In order to construct an efficient OFDM system, the goal of this research is to define a fuzzy set of guidelines for the best selection of the modulation order and the FFT size. These fuzzily defined criteria might provide improved capacity and bandwidth efficiency. A is taken into account to deal with these issues. Selecting the lower order modulation and ideal FFT size in accordance with data need is suggested. The BER parameter is chosen for performance assessment, considering modulation parameters, FFT sizes, and signal duration’s impacts. The research suggests increasing FFT size and order modulation to accommodate increased capacity and demand, while comparing the BER efficiency of M-QAM and M-PSK modulation schemes.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"260 ","pages":"Pages 839-846"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Ensembles of Pre-trained CNNs for Improved Lung Cancer Detection and Classification 利用预训练cnn集合改进肺癌检测和分类

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.188

Dasari Bhulakshmi , Dharmendra singh rajput

引用次数: 0

Brain Tissue Segmentation from MRI Scans using Digital Image Processing 利用数字图像处理从MRI扫描中分割脑组织

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.174

Sushmita Chauhan , Poonam Saini , Sanjeev Sofat

{"title":"Brain Tissue Segmentation from MRI Scans using Digital Image Processing","authors":"Sushmita Chauhan , Poonam Saini , Sanjeev Sofat","doi":"10.1016/j.procs.2025.03.174","DOIUrl":"10.1016/j.procs.2025.03.174","url":null,"abstract":"<div><div>The brain is one of the most unexplored parts of the human body and its complex and delicate structure has scientists worldwide looking for answers about its intricacies. Also, since the advent of deep learning techniques as well as imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), analysis of the brain has become the most intriguing and researched area in healthcare, as well as deep learning sectors of artificial intelligence. The extraction of the brain from the skull forms the basis and source of study for the prediction of age-related diseases like Alzheimer’s disease. Nowadays With the increase in life expectancy and the extravagant use of technology, it is evident that neurological diseases are on the rise. Therefore, it becomes essential that such diseases can be diagnosed at an early stage of their occurrence. The proposed work presents brain extraction from the skull with the help of three basic steps, data acquisition, pre-processing, and largest connected component extraction using contours. The data acquired is using the ADNI data repository. The preprocessing step involves contrast enhancement using CLAHE, binarization of the scan using Otsu thresholding, and de-blurring so that the noise that might be there in the scans can be removed and a clear image of the brain is available for further processing and classification of Alzheimer’s disease.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"260 ","pages":"Pages 32-39"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in AI for Medical Imaging: A Review of Machine and Deep Learning in Disease Detection 人工智能在医学成像中的进展：疾病检测中的机器和深度学习综述

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.201

Rnjai Lamba

{"title":"Advances in AI for Medical Imaging: A Review of Machine and Deep Learning in Disease Detection","authors":"Rnjai Lamba","doi":"10.1016/j.procs.2025.03.201","DOIUrl":"10.1016/j.procs.2025.03.201","url":null,"abstract":"<div><div>This review provides an exhaustive overview of the impact of machine learning (ML) and deep learning (DL) methods on medical imaging. This paper focuses on how AI is revolutionizing the field of disease detection and diagnosis. These advances have enhanced the precision and ability to diagnose various medical conditions, including cancer, neurological diseases, and retinal disorders. Autonomous ML and DL techniques have enhanced the accuracy of diagnostic processes while simplifying them, eliminating potential human errors, and supporting better clinical judgment by automating intricate image processing functions. The paper presents a comprehensive analysis of the main techniques of ML and DL, such as Support Vector Machines (SVM), Random Forests, Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Numerous case studies have demonstrated the remarkable accuracy of these techniques when compared with traditional diagnostic methods. However, the broad adoption of these techniques in medical imaging faces obstacles because of low data quality, lack of interpretability of models, and the requirement of additional computational resources. These problems can be mitigated by creating interpretable AI systems, optimizing the efficiency of computational resources, and establishing ethical guidelines for utilizing these algorithms in healthcare. The review concludes by evaluating the potential of these technologies to transform individualized treatment and healthcare delivery. Ongoing collaboration among technologists, healthcare practitioners, and policy specialists is necessary to guarantee the responsible assimilation of AI into clinical practice.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"260 ","pages":"Pages 262-273"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep CNN-Based Multi-Grade Brain Tumor Classification with Enhanced Data Augmentation 基于cnn的深度多级别脑肿瘤分类与增强数据增强

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.205

Immaculate Joy S , Sriram G , Sriram Venkatesan S

{"title":"Deep CNN-Based Multi-Grade Brain Tumor Classification with Enhanced Data Augmentation","authors":"Immaculate Joy S , Sriram G , Sriram Venkatesan S","doi":"10.1016/j.procs.2025.03.205","DOIUrl":"10.1016/j.procs.2025.03.205","url":null,"abstract":"<div><div>Innovations in the field of medical imaging techniques, especially in magnetic resonance imaging (MRI), have significantly enhanced diagnostic capabilities. However, the accurate classification of brain tumors from MRI scans remains a difficult task due to the subtle variations between different tumor types and the presence of non-tumorous regions. The primary challenges in automated MRI classification include the high variability in tumor appearance, similarities between benign and malignant tumor features, and the inherent imbalance in medical datasets. The proposed model architecture includes multiple convolutional layers with normalizing batches and removing outliers to enhance generalization and control for overfitting. The dataset was artificially expanded using data augmentation techniques like flipping, zooming, and rotating from 5,712 original images to 142,800 images, allowing the model to learn from a more diverse set of examples. The model demonstrated promising results, obtaining a training precision of 99% and a validation accuracy of 91.5% after 50 epochs, suggesting effective learning and generalization capabilities.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"260 ","pages":"Pages 300-307"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

eDiagnosing mental health in neurodevelopmental disorders: Experiences with person centered development of a self-report tool. 电子诊断神经发育障碍的心理健康：以人为中心的自我报告工具的开发经验。

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.178

Oddbjørn Hove , Heidi Breistrand Bringsvor

{"title":"eDiagnosing mental health in neurodevelopmental disorders: Experiences with person centered development of a self-report tool.","authors":"Oddbjørn Hove , Heidi Breistrand Bringsvor","doi":"10.1016/j.procs.2025.02.178","DOIUrl":"10.1016/j.procs.2025.02.178","url":null,"abstract":"<div><div>The MindMe project, related to the development of a tool system Inventory for Supported Psychological Evaluation (iSpe®), aims to design and develop a digital tool for the assessment of mental illness in people with cognitive challenges. In this paper, we describe and discuss experiences from the project over the last three years, with a primary focus on end-user involvement.</div><div>Results indicated that active user participation provided new insights at all stages of development. User feedback supplemented previous research findings and accessibility guidelines, ensuring that the solutions met the actual needs of the target audience. Usability testing and feasibility studies confirmed the tool’s effectiveness in enhancing comprehension and articulation of mental health symptoms among users. However, challenges such as response difficulties to symptom severity and reluctance to use audio support were also identified.</div><div>The project advances the understanding of user-centered design in psychological test development for individuals with NDD by documenting iterative feedback loops and user-centered approach that offers a practical framework for future projects. The collaborative approach ensured that the developed tool was clinically relevant, user-friendly and accessible, and can serve as a model for future projects.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"256 ","pages":"Pages 772-780"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0