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检索条件"机构=Dept. of Engineering and Computing"

共 3721 条记录，以下是1-10 订阅

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A Data-Driven Approach in predicting Heart Disease Detection in earlier stage 2

A Data-Driven Approach in predicting Heart Disease Detection...

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Nagarjuna, N. Kavitha, S. Lakshmi, H.N. SRM Institute of Science and Technology School of Computing Dept. of Computing Tamilnadu Kattankulathur India CVR College of Engineering Dept. of CSIT Hyderabad India CVR College of Engineering Dept. of Emerging Technologies Hyderabad India

ISBN: (纸本)9798350365269

In the healthcare field accumulation of vast amounts of data, known as big data, that contains valuable insights important for decision-making is available. The Data-driven approaches consistently proves their effectiveness by highlighting the pivotal roles of data analysis in error detection, valuable information identification, healthcare variables assumption and validation, and correlation establishment among health-related System. The proposed system Heart-Risk predict (HRP) serves as a data analysis tool by using statistical modeling techniques. The proposed novelistic model focuses on prediction of heart disease and risk factors using the k-means grouping algorithm by employing data analytics and visualization tools, which is further tested on publicly available datasets. Some of basic health related attributes like patient age, chest pain category, blood pressure, glucose levels and heart rate aid in early-stage chest pain type of determination. Through preprocessing methods and classifier-based evaluations, the current study showcases heart disease prediction accuracy at a higher level. Furthermore, the proposed system integrates the Tabu search mechanism and the K-means algorithm in addressing traditional method limitations. This integration enhances efficacy in handling large datasets by reducing computational complexity and give improvement in prediction accuracy. Thorough experimental validation depict system's efficacy and superiority over existing methods by offering a promising implications for early heart disease detection and proactive management, thus improvising public health outcomes. © 2024 IEEE.

关键词： Data visualization

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Uniandes at the Regulations Challenge Task: A Scalable Framework for Legal Text Understanding in Regulatory and Financial Contexts 9

Uniandes at the Regulations Challenge Task: A Scalable Frame...

引用

Joint Workshop of the 9th Financial Technology and Natural Language Processing, FinNLP 2025, the 6th Financial Narrative Processing, FNP 2025, and the 1st Workshop on Large Language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Carrión, Santiago Martínez Castañeda, Juan Manuel Manrique, Rubén Francisco Dept. of Systems and Computing Engineering Universidad de los Andes Bogotá Colombia

ISBN: (纸本)9798891762091

This study presents a comprehensive approach to developing a domain-specific large language model (LLM) for regulatory and financial text interpretation. A specialized corpus was constructed through large-scale scraping of financial and regulatory documents across domains such as compliance, licensing, and financial reporting. The data was preprocessed using GPT-4o-mini with prompt engineering to retain critical information and remove noise. We further pre-trained a LLaMA-3.1-8B model on the curated corpus and fine-tuned it using an instruction dataset covering nine tasks from the Coling 2025 Regulations Challenge (Wang et al., 2024), including acronym expansion, regulatory question-answering, and XBRL-based financial analytics, employing QLoRA to reduce memory requirements. The model exhibits a slight improvement from baseline answering complex regulatory questions (detailed QA) and expanding acronyms. This study demonstrates the potential of domain-specific LLMs in regulatory text interpretation and lays the groundwork for future research in specialized NLP evaluation methodologies. © 2025 Association for Computational Linguistics.

关键词： Finance

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Autonomous Defect Inspection and Mapping for Building Maintenance

Autonomous Defect Inspection and Mapping for Building Mainte...

引用

ASCE International Conference on computing in Civil engineering 2023: Resilience, Safety, and Sustainability, i3CE 2023

作者： Herbers, Patrick Çelik, Firdes König, Markus Computing in Engineering Dept. of Civil and Environmental Engineering Ruhr Univ. Bochum Germany

ISBN: (数字)9780784485248

ISBN: (纸本)9780784485248

As structures age, the continuous surveying of defects to structural elements becomes a central part of the life cycle. Automated and frequent inspections could allow for an enriched defect history for faster decisions and reduce the time until problems are first reported. This requires recording the defect on an autonomous platform and automatically classifying areas of interest. We propose and test a prototype that uses a defect segmentation network for classifying defect instances deployed on a Spot mobile robot for autonomous maintenance missions. The robot maps its surroundings using a continuous laser scan and annotates the resulting environment mesh using the defect segmentation network. We test the feasibility of entirely autonomous, repeat missions and the possible use cases of such a system. © ASCE *** rights reserved.

关键词： Defects

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Seamless Service Evolution: Enhancing Customer Satisfaction Using AWS-Driven AI Chatbots for Restaurant Ordering 4

Seamless Service Evolution: Enhancing Customer Satisfaction ...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ali, Mohammed Jaffer Vyshnavi, M.R.V. Kumar, Konda V.S. Harshith Vishnubhatla, Sravya Rajagopal, Shinu M. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331540364

In the current restaurant business environment, factors have emerged in the manner in which consumers are attended to and the ways orders are placed. As good as these traditional systems may be, they do not offer the optimum flexibility and consumer convergence that today's client demands for fast and convenient services. Recent approaches using only basic form of automation and rule-based chatbots do offer some enhancements but lack adequate capabilities to deal with huge volumes of orders, custom recommendations and scalability according to demand. Further, the existing systems have problems with scalability, versatility to different customers' trends and needs, and real-time interactions. To overcome these challenges our work, will utilize modern advances in artificial intelligence and cloud technology. The proposed system uses Amazon Lex for NLU, A WS Lambda for processing multiple booking requests simultaneously without any intervention from the users and Amazon Polly for making interaction more interactive with personalized audio. In addition, Amazon Rekognition and A WS S3 for images will be used for analysis of images and proper storage of data respectively, enhancing the customer satisfaction by recommending products based on a past order. All these technologies integrated with A WS ecosystem in our solution will provide convenience, efficiency and passion to restaurant business proprietors to fulfill the increasing demands of modern customer. The system also supports linkage with some commonly used applications such as WhatsApp, guaranteeing that the customers receive timely updates. Thus, this solution is designed to create an improved status of restaurant ordering and eliminate the problematic experiences placed in current schemes. © 2024 IEEE.

关键词： Scalability

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Enhanced Security Measures for Image Privacy: Encryption Perspectives 2

Enhanced Security Measures for Image Privacy: Encryption Per...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Omshith, R. Balaji, Vemisetti Pavan Rahul, Rayapudi Venkata Kumaran, U. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. Using a variety of cryptographic techniques and algorithms, the original image is transformed into an encrypted format throughout this process, making it more difficult for unauthorized users to view or understand the content. Secure communication, picture sharing, medical imaging, and many more fields requiring the protection of sensitive visual data find widespread use in image encryption. A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. It also discusses the difficulties and potential paths for picture encryption, highlighting the rising demand for reliable, effective, and secure image encryption solutions in a world that is becoming more and more networked and data-driven. Advanced Encryption Standard (AES) is more secure than (Exclusive OR) XOR, Fernet. © 2024 IEEE.

关键词： Advanced Encryption Standard Confidentiality Encryption Exclusive OR (XOR) Fernet Integrity

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ConfigMaster: An Interactive Solution for System Management 8

ConfigMaster: An Interactive Solution for System Management

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ganapathi, Ananya Sivakumar, Parikshith Divya, K.V. Elango, Aditya Gupta, Hardik Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331505462

The 'ConfigMaster: An interactive solution for system management' is a simple utility written using Bash scripting. It is designed for Unix-like systems. The tool lets users easily configure system settings and do administrative tasks without having to remember complex commands. The tool minimizes user's work by automating tasks. The tool can be used even by users with very little technical knowledge, as it has a simple menu-based interface to manage things like network settings, software packages, and wireless connections directly from the terminal. By automating common tasks, the tool makes system management more efficient and accessible. By automating common tasks, the tool makes system management more efficient and accessible. It's very useful to simplify system configuration on Unix-like systems using scripting languages like Bash. 'ConfigMaster' aims to offer a smooth user experience with the incorporation of core management principles from an operating system, such as disk scheduling, process synchronization, and memory management etc. Being flexible itself, this tool will bring further improvements to make effective responses to emerging user needs. The framework being flexible, it can be expanded to add new features to configure under different scenarios. By integrating essentials operating system operations, 'ConfigMaster' enables users to efficiently perform system configurations along with promoting ease of use. © 2024 IEEE.

关键词： Wi-Fi

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Satellite Image Classification using CNN with Particle Swarm Optimization Classifier 5

Satellite Image Classification using CNN with Particle Swarm...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Vidhya, S. Balaji, M. Kamaraj, V. Dept. of Computing Technologires School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

Disaster relief, police work, and environmental monitoring all benefit from satellite images. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that need to be searched and the limited number of accessible analysts, automation is essential. However, the accuracy and dependability of existing object recognition and classification algorithms renders them inadequate for the task. One family of machine learning algorithms called "deep learning" has showed immense potential for automating these kinds of jobs. Convolutional neural networks have been successful in the area of image recognition. Here, convolutional neural networks (CNNs) and a particle swarm optimization classifier is utilized to develop efficient algorithms for classifying satellite images. The results of this classifier model are better than those of existing approaches. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Biometric Authentication using lightweight Convolutional Neural Network

Biometric Authentication using lightweight Convolutional Neu...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Sharma, Reena Manipal University Dept. of Computer & Comm. Engineering Jaipur India Poornima College of Engineering Dept. of Advance Computing Jaipur India

ISBN: (纸本)9798350348460

Biometric technology like face recognition, retina scan and finger print mapping are very useful in identification and verification to recognize a person. In recent years, many deep learning algorithms have proposed for various applications specially face recognition. Hierarchical architecture of deep learning methods significantly improves the Face Recognition performance and encourages diverse and efficient real - world application. In Face Recognition Technology mainly four phases are exist: face detection, face alignment, feature extraction and classification. Many challenges are faced in face recognition like: pose, age, illumination, gender etc. Convolutional neural network (CNN) achieves good performance in face recognition system. In Deep learning especially convolutional neural network models required large data sets to train the deep layers and provide more accuracy. In CNN, Many models work for pattern recognition. One of them is Visual Geometry Group (VGG), which have large number of layers in architecture. In this research, light VGG - 16 models is proposed to recognize faces. The proposed model achieves good accuracy and low loss value in comparison to traditionally VGG -16 and comparatively reduces the number of parameters because large number of parameter takes significant memory size. © 2024 IEEE.

关键词： Biometric Authentication Convolutional Neural Network Face Recognition VGG -16

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Nova: Revolutionizing Agile Project Management with Scrum and Kanban Integration for Enhanced Team Collaboration and Productivity 5

Nova: Revolutionizing Agile Project Management with Scrum an...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Manaswini, Poluri Shaik, Sajjad Ashwani, Siddhant Balakrishnan, Roshni M. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

In the realm of software project management, Agile methodologies such as Scrum and Kanban have become quintessential for fostering adaptability and efficiency. "Nova,"a novel project management tool, embodies these methodologies to offer a user-friendly interface that supports the dynamic needs of modern software development teams. This paper explores Nova's unique approach that integrates the foundational principles of Agile which are Scrum, and Kanban, thereby facilitating a seamless and effective management platform. Through a comparative analysis with traditional project management tools, this study evaluates how Nova enhances team collaboration, project tracking, and outcome predictability. Emphasizing its ease of use and adaptability, the research highlights Nova's potential to improve project delivery times and overall team productivity. © 2024 IEEE.

关键词： Lean production

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Pattern Recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features 15

Pattern Recognition in Lung Disease Classification: Leveragi...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sree, M. Ramya Siddhartha, Mettu Reddy, Poli Vamsi Vardhan Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance School of Advanced Computing Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. Recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and Local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction. © 2024 IEEE.

关键词： CNNs Deep learning Early detection Feature extraction GLCM Local Phase Quantization Lung disease diagnosis Medical imaging Texture analysis

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