检索结果-内蒙古大学图书馆

Enabling technology Integrated Learning for Autistic Children Using Augmented Reality Based Cognitive Rehabilitation

引用

SN computer science 2024年第1期5卷 151页

作者： Abdullah, A. Sheik Karthikeyan, J. Gomathi, V. Parkavi, R. Rajarajeswari, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering National Engineering College Kovilpatti India Department of Information Technology Thiagarajar College of Engineering Madurai India

Augmented reality (AR)-based rehabilitation is an excellent teaching tool (ICT tool) for simulating daily activities for autistic children. Children with autism may become more mentally capable with this type of AR-based rehabilitation. It is intended to observe the children's performance in terms of concentration, attention, and identification. The observation has been done through placards as a target image to display the 3D objects on a mobile phone or tablet. In this project, observations are made for 21 autism children in the age group of 7–14, out of whom 17 are boys and 5 are girls. Those 21 children are given practice identifying 15 different objects in an augmented reality environment. Their performance was initially evaluated using conventional instructional techniques. The majority of the kids were having more difficulty identifying things during that observation. Then, with an Augmented Reality environment, the identical observation has been made once more. Using a mobile device or tablet, the 3D objects from the provided placard photos are produced in an augmented reality environment with animation and voice in the languages of English and Tamil. Children with autism are able to recognize and also grasp the behaviors of those objects while viewing them in 3D. Their efforts are measured using a two-point scale (0, 1, 2). The pre-assessment and post-assessment reports for the above observations are tabulated. All the observations are made in the presence of the special education teacher (therapist). However, the children observed in this project fall into three different categories: mild, moderate, and severe. In the Mild category, statistical significance is evident with p values of 0.002 in pre-assessment and 0.014 in post-assessment. Likewise, in the Moderate category, where p values are 0.023 in pre-assessment and 0.033 in post-assessment, significance is observed, as all p values fall below the chosen significance level of 0.05. This leads to rejecti

关键词： Augmented reality (AR) Autism ICT tools Observation Rehabilitation Teaching methodology

来源：评论

学校读者我要写书评

暂无评论

Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

引用

computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

来源：评论

学校读者我要写书评

暂无评论

An Earthquake Alert system using Internet of Things 10

An Earthquake Alert system using Internet of Things

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Teja, N. Vishnu Akshay, S.P. Khaleed, K.Abu Kumar, C.Santan Manoj Kumar, G.P. Bhavadharini, R.M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

This project introduces an innovative earthquake detection system leveraging accelerometers for seismic sensing. By incorporating accelerometers from widely available devices like smartphones into a distributed sensor network, the system aims to provide a cost-effective and scalable solution for enhancing seismic monitoring capabilities. Real-time data processing, supported by advanced signal processing and machine learning algorithms, enables the system to differentiate between normal vibrations and seismic activities. Upon detecting anomalies, the system generates alerts to facilitate swift response and early warning mechanisms. Noteworthy features of this system include its affordability, easy deployment, and suitability for implementation in diverse environments, both urban and remote. The project contributes to advancing earthquake detection technologies, thereby bolstering preparedness and resilience against seismic events © 2024 IEEE.

关键词： Accelerometers

来源：评论

学校读者我要写书评

暂无评论

Leveraging Data Analytics in Azure for Effective Churn Management 2

Leveraging Data Analytics in Azure for Effective Churn Manag...

引用

2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Gokulnath, Shreejith Suthraye Revathi, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

This research aims to develop optimized and cost-effective Azure architecture solutions by strategically selecting and utilizing Microsoft Azure services for specific use cases. The study leverages a simulated data-driven churn management case study within a social network context, emulating real-world data scenarios. The methodology employed involves a comprehensive understanding of the existing churn management script, enabling adaptability to diverse scenarios. This necessitates an in-depth exploration of Microsoft Azure's service offerings to ascertain their necessity and relevance. As the case study entails real-time streaming of event data, it is channeled into a staging area while concurrently undergoing batch processing for both account and subscription data. This synthesis culminates in a unified storage space tailored for analytics purposes. The implications of this research are twofold. First, the study yields a high-impact automated asset from a business standpoint. Second, it contributes expertise in the intricate process of migrating an entire asset from on-premises infrastructure to the cloud. Furthermore, using streamlined cost optimization achieved by judiciously selecting and configuring only essential resources, culminating in efficient outcomes. This paves the way for client-side stakeholders to make informed, data-driven decisions, leveraging the insights harnessed from the system. © 2024 IEEE.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

Empowering Limited Data Domains: Innovative Transfer Learning Strategies for Small Labeled Datasets

Empowering Limited Data Domains: Innovative Transfer Learnin...

引用

2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Navaneetha Krishnan, S. Mohnish Karthikeyan, B. Arun, Shriman K. Christopher Columbus, C. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529765

Traditional Indian Medicine faces the risk of extinction due to insufficient transfer of knowledge. This research highlights the critical need for preservation through modern technology and techniques. It addresses the challenges of limited labeled data by employing pioneering transfer learning approaches for classifying Indian Medicinal Plants, specifically leaves. The study utilizes diverse trained models and a bespoke model tailored for this task, systematically investigating transfer learning dynamics to mitigate issues such as overfitting and underfitting in neural network training. Rooted in Artificial General Intelligence (AGI) concepts, the research demonstrates how transfer learning enhances neural network efficiency, especially when labeled data is scarce. Practical application is illustrated with a dataset of plant images, providing a real-world context for evaluating transfer learning strategies. By leveraging models trained with large data in relevant domains, the research facilitates effective feature extraction and generalization despite limited labeled data. A significant contribution of the research is the comparison of outcomes from various models with the proposed custom CNN model, HerboraNet, which achieves a remarkable accuracy of 95%. This research underscores the superiority of transfer learning over training models with limited input, particularly in data-constrained domains, highlighting its pivotal role in enhancing model efficiency and adaptability. © 2024 IEEE.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Super Long Range CNN For Video Enhancement in Handball Action Recognition 3

Super Long Range CNN For Video Enhancement in Handball Actio...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Pratik, V. Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

This paper explores the fusion of Longterm Recurrent Convolutional Neural Network (LRCNN) for action classification and Single Image Super-Resolution Convolutional Neural Network (SRCNN) for enhancing image quality in handball video sequences. Building upon previous work utilizing Mask R-CNN and an Optical Flow-Based Method for handball action detection, our study extends the framework by integrating LRCNN for precise action classification and SRCNN for improving spatial resolution and image quality as a hybrid model, Super Long Range Convolutional Neural Network (SLRCNN). LRCNN accurately classifies handball actions, while SRCNN enhances visual fidelity, leading to higher Peak Signal-to-Noise Ratio (PSNR) values. Through thorough experimentation and analysis, we assess the effectiveness of SLRCNN in capturing and classifying subtle handball actions, advancing sports analytics in computer vision with a holistic approach optimizing both action recognition accuracy and image quality metrics. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Transaction Fraud Detection Using SMOTE Oversampling 1

Transaction Fraud Detection Using SMOTE Oversampling

引用

1st International Conference on Software, systems and information technology, SSITCON 2024

作者： Marimuthu, M. Lekshmi, K. Saravanan, P. Nagaveni, Dasari Manikandan, P. Natarajan, B. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Credit card fraud detection remains a critical challenge in financial transactions, particularly due to the prevalence of unbalanced datasets and the limitations of traditional algorithms. This paper suggests a novel hybrid ensemble approach that reduces false negatives and improves fraud detection accuracy by merging the Random Forest (RF) and XGBoost (XGB) algorithms. Unbalanced data presents challenges for traditional algorithms such as Random Forest, Decision Trees, XGBoost, Logistic Regression, and Deep Neural Networks (DNN). Our hybrid technique, which is enhanced by under-sampling and the Synthetic Minority Over-sampling Technique (SMOTE), capitalizes on the benefits of RF and XGB while limiting their drawbacks. We prioritize recall over accuracy in order to reduce false negatives, which are important for detecting fraud. With an emphasis on recall, we evaluate the performance of Decision Trees, Random Forest, Logistic Regression, XGBoost, DNN, and our hybrid model. Utilizing techniques to address data imbalance, the evaluation makes use of an actual credit card dataset with different attributes. The results demonstrate the effectiveness of our hybrid ensemble approach in enhancing fraud detection accuracy and reducing false negatives, highlighting its potential for practical application in financial security systems. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Examination of Toxic Tweet Classification on Twitter 9

A Comprehensive Examination of Toxic Tweet Classification on...

引用

9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Gokulnath, Shreejith Suthraye Jayaraman, Shrikar Nathezhtha, T. School of Computer Science And Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350365092

In recent times, daily interactions have been taking place largely on online platforms, with forms of interactions such as ***,an important problem lies in the understanding of these messages. These textual interchanges can be interpreted in multiple different ways by different people which can cause significant misunderstandings and negative perceptions. The aim of this project is to address this challenge and overcome it by developing a system that is capable of identifying the negativity and toxicity in the tone of the messages. Through rigorous analysis the model aims to identify parts or sequences of a message that might appear as offensive or negative, giving the user constructive feedback on how they can better improve their messages. This helps users to write messages that are not only clearer but also easily received in the intended manner by their audience, thereby increasing the effectiveness of the communication process and reducing misunderstandings. The main goal of this paper is to contribute to the development of an inclusive and understanding online community by using techniques of Natural Language Processing and Machine Learning to solve the complexities of human communication. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Real-Time Gesture Based Sign Language Recognition System

Real-Time Gesture Based Sign Language Recognition System

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Debnath, Jeet Praveen Joe, I.R. School Of Computer Science And Engineering Vellore Institute Of Technology Chennai India

ISBN: (纸本)9798350364828

Sign language is a vital mode of communication for the deaf and dumb community. This research presents a robust and real-time Gesture-Based Sign Language Detection System that leverages computer vision and deep learning techniques. The system is designed to recognize and interpret American Sign Language (ASL) gestures, enabling efficient communication between individuals who are proficient in ASL and those who are not. The core of the system utilizes Python, OpenCV (Open-Source computer Vision Library), and MediaPipe Holistic for real-time hand and body pose estimation. By accurately tracking the movements of hands and key body parts, the system captures the nuances of sign language gestures. The captured data is then fed into a Long Short-Term Memory (LSTM) neural network, which excels in sequence modeling tasks. The LSTM model is trained on a comprehensive dataset of ASL gestures, encompassing a wide range of signs and expressions. Transfer learning techniques are also employed to fine-tune the model for improved performance in sign language recognition. The model's architecture allows it to learn the temporal dependencies and context inherent in sign language, making it capable of recognizing gestures within sentences or phrases. The system's evaluation demonstrates its effectiveness in real-world scenarios, achieving high accuracy and low latency in sign language recognition. It opens new avenues for accessible and inclusive communication, aiding both deaf and hearing individuals in bridging the communication gap. Future work may explore the integration of natural language processing (NLP) to facilitate two-way communication between sign and spoken language. In conclusion, this Gesture-Based Sign Language Detection System represents a significant step towards harnessing the power of computer vision and deep learning to make sign language more accessible and inclusive in various domains, including education, accessibility, and social interaction. © 2024 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

Congestion Based Adaptive Association Mechanism for IEEE 802.11ah Based Large IoT Network 5th

Congestion Based Adaptive Association Mechanism for IEEE 80...

引用

5th EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： R, Nandhini R, Radha School of Computer Science and Engineering Vellore Institute of Technology–Chennai Chennai India

ISBN: (纸本)9783031770807

The IEEE 802.11 ah standard is the prominent protocol for medium range communication in the domain of Internet of Things (IoT), facilitating interaction between wireless sensor nodes and access points. Supporting up to approximately 8192 nodes connected to a single access point through single hop strategies, IEEE 802.11 ah employs CSMA/CA for the authentication and association process of sensor nodes with access point. However, the standard encounters challenges such as significant collisions and prolonged network setup times when attempting to associate an excessive number of wireless sensor nodes with a single access point, primarily due to authentication thresholds set at intervals. In this research, we propose a dynamic adjustment of the authentication threshold based on network congestion status. Learning parameters including the number of requests served successfully by the access point in the past, present and a congestion threshold are leveraged to adapt to current network congestion levels. Through evaluation using the LILD method in the ns-3 simulator, our approach demonstrates considerable enhancements in terms of collision reduction and network setup efficiency. This research endeavours to promote responsible network management practices for improving the performance and reliability of IEEE 802.11 ah based IoT deployments. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Internet of things

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：