检索结果-内蒙古大学图书馆

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

作者： Chaitra, M. Kushal, B. Likhitha, H. Priyanka, H.R. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Deepfake technology, known for its ability to produce convincingly face-swapped videos, presents significant risks to privacy, security, and public trust. Misuse of this technology for spreading misinformation, defamation, and influencing politics is on the rise, creating a pressing need for more effective detection mechanisms. As deepfake generation methods continue to advance, conventional detection techniques are increasingly unable to keep up, often falling short in accuracy and adaptability. This project aims to create an AI-driven detection system specialized in identifying face-swap deepfakes. The system focuses on subtle irregularities in facial details, movements, and the synchronization between audio and visuals. Employing neural network models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Capsule Networks, it pinpoints anomalies like unnatural expressions, atypical skin textures, and lighting inconsistencies. The system achieves high reliability with metrics including accuracy (96%), precision (95%), recall (94%), and F1 score (95%), showing marked improvement compared to traditional techniques. It outperforms existing methods such as XceptionNet (92%) on the Celeb-DF dataset, underscoring its superior generalizability and robustness. This scalable solution has broad applications in media, law enforcement, and cybersecurity, reinforcing efforts to combat deepfake threats and fostering improved trust and security across various sectors. © 2025 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

LegalAI Across Languages: A Comprehensive Survey on Tasks, Methodologies and Challenges 1

LegalAI Across Languages: A Comprehensive Survey on Tasks, M...

引用

1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Deshpande, Anjali Deshmukh, Pratiksha COEP Technological University Dept. of Computer Science and Engineering Pune India PICT Dept. of AI & DS Pune India School of Computational Sciences COEP Technological University Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

Natural Language Processing (NLP) is revolutionizing the legal domain, enabling tasks such as predicting legal outcomes, summarizing complex documents, identifying key entities, and assessing bail risks. This survey paper presents a comprehensive review of the existing research on NLP tasks applied to legal documents and judgments across various languages, with a special emphasis on low-resource languages. Despite the extensive work done in widely spoken languages like English, there remains a critical need to extend these advancements to languages with limited resources and data availability. This paper categorizes and analyzes the NLP tasks in the legal domain based on the languages they target. It highlights the methodologies employed, datasets utilized, and the performance of different models across diverse languages. Furthermore, it identifies the gaps in current research and proposes potential directions for future work, particularly in improving the accessibility and applicability of NLP solutions for legal texts in underrepresented languages. By providing a comprehensive overview of advancements and obstacles, this survey seeks to assist future research endeavors that bridge the disparity between high-resource and low-resource languages in legalAI applications. © 2025 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Smart ANPR: A Checkpoint-Based Toll and Parking Management System Using Hybrid OCR and Blockchain 3

Smart ANPR: A Checkpoint-Based Toll and Parking Management S...

引用

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

作者： Samarth, M.N. Gopisetti, Sampriti Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Automated Number Plate Recognition is one of the most important applications of computer vision, employed in the enforcement of traffic law, vehicular management, security, and even at toll collection. This paper proposed an advanced system for ANPR that can move away from the traditional booth and establish a checkpoint. The strategically located cameras with checkpointing will automatically capture the plates of passing vehicles, permitting road tax and parking fees to be deducted without making contact. A hybrid approach that combines the capabilities of OpenCV, Tesseract OCR, and deep-learningbased OCR models helps tackle such challenges as stable images from live video feeds and highly accurate text recognition. To this end, since the system integrates high-resolution frame stabilization using OpenCV to ensure crystal clear captures, it further employs the use of YOLOv8 to boost the precision of real-time detection. The blockchain technology is further implemented to ensure that transactions are tamper proof, therefore ensuring secure and transparent billing systems. This makes the ANPR solution deployable on edge devices, which opens real-time processing capabilities for high-speed and high-volume environments, thereby offering a scalable and efficient approach for intelligent transportation and smart city applications. © 2025 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Secure Digital Identity Environment 3

Secure Digital Identity Environment

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sankara Mahalingam, M. Banduchode, Uday Balagani, Varun Teja Kuppala, Venkata Sai Akhil Yuvaraj, K.V.V. Dept of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Virudhnagar India

ISBN: (纸本)9798331527549

This research focuses on the development and analysis of a secure data storage platform employing state-of-the-art cybersecurity techniques. With cyber threats growing in complexity, ensuring data integrity, confidentiality, and availability is critical for organizations. The platform integrates advanced encryption algorithms, AI-driven anomaly detection, and compliance mechanisms to protect sensitive data across distributed and cloud environments. A thorough evaluation of existing platforms and the proposed solution highlights its effectiveness in mitigating risks while maintaining scalability and performance. This study contributes to the field by offering insights into the architecture and features of modern secure data platforms. © 2025 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Benchmarking Indian E-Commerce Giants: Insights from Festive Sales Performance

Benchmarking Indian E-Commerce Giants: Insights from Festive...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pranav, H. Harshini, Muppavarapu Sri Tarun, Rachuri Radha, D. Amrita School of Computing Dept of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331512965

This study presents a comparative analysis of the websites of three major e-commerce platforms in India: during the busy shopping sale of Diwali and Navratri. Using metrics derived from Lighthouse audits, the report evaluates the performance, accessibility, SEO, and adherence to best practices of each site. Key factors such as First Contentful Paint (FCP), Largest Contentful Paint (LCP), Speed Index, and overall user experience are examined. The analysis highlights the strengths and areas for improvement of each platform, with recommendations for enhancing load times, user accessibility, and search engine optimization. This comparison aims to provide actionable insights into optimizing the performance and usability of e-commerce websites. © 2025 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

LiDAR-Based Elderly Fall Detection System for Indoor Environments Using Neural Network Algorithms 4

LiDAR-Based Elderly Fall Detection System for Indoor Environ...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vignesh, Puthucode Ganesh Nithin Kumar, S. Senthil Kumar, T. Jeyakumar, G. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331523923

Fall detection systems are critical in elderly care and healthcare monitoring, but traditional camera-based solutions raise significant privacy concerns. This research presents a novel approach utilizing LiDAR (Light Detection and Ranging) sensors for fall detection in indoor environments, addressing privacy issues while maintaining high accuracy. We introduce LiFall, a new dataset captured using a Velodyne VLP 16 Puck LiDAR sensor, comprising 12 scenes and over 500 frames simulating various fall scenarios (including back falls, bed falls, and walking falls), contributing to the scarcity of LiDAR-based fall detection datasets. Our methodology employs two advanced deep learning architectures: Annularly Convolutional Neural Networks (ACNN) and Dynamic Graph Convolutional Neural Networks (DGCNN). Evaluating these models on both the ModelNet40 dataset and our custom LiFall dataset, results show DGCNN outperforms ACNN, achieving an accuracy of 84% on the ModelNet40 dataset with a 70:30 train-test split. On the LiFall dataset, despite challenges with dataset imbalance and achieving lower detection accuracy even after data augmentation, our approach demonstrates the potential of LiDAR-based fall detection systems. Compared to other fall detection methods, our LiDAR-based system offers significant advantages beyond privacy preservation, such as robust performance in low-light conditions and the ability to capture detailed 3D spatial information. While our current implementation is limited to laboratory settings, future work will focus on real-world deployments and further addressing dataset imbalance issues to enhance overall system performance. This research contributes to the field by providing a privacy-preserving, high-performance alternative to camera-based fall detection systems, paving the way for more acceptable monitoring solutions in sensitive indoor environments. © 2025 IEEE.

关键词： Fall detection

来源：评论

学校读者我要写书评

暂无评论

Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

引用

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Saklani, Sumit Manchanda, Mahesh Sharma, Raman Singh, Devendra Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Application Dehradun India

ISBN: (纸本)9798331518578

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

引用

Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

引用

Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation 1

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

引用

1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Patil, Pratima Deshpande, Deepa S. MGM University Jawaharlal Nehru Engineering College Dept. of Computer Science and Engineering Chhatrapati Sambhajinagar431003 India

ISBN: (纸本)9798350355611

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time. © 2025 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：