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Internet of Things Based Health Monitoring System Using Smart Glove 3

Internet of Things Based Health Monitoring System Using Smar...

引用

3rd IEEE international conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things, RAAICON 2024

作者： Ahmed, Mohammad Faisal Hasan, Nowshad Ullah, Mohammad Woli Maskat Sharif, Md. Rahaman Khan, Md Tohedur Fahad Khan, Md Rokibul Bahar, Anamul Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chittagong Bangladesh International Islamic University Chittagong Department of Electronics and Telecommunication Engineering Chittagong Bangladesh University of London Department of Computer Science Enginnering London United Kingdom

ISBN: (纸本)9798331534400

The current healthcare and emergency response systems face significant challenges in preventing fatalities and serious injuries among elderly individuals and those living alone due to falls, delayed emergency care, and insufficient continuous health monitoring. This research proposes the development and evaluation of a cost-effective smart hand glove designed to enhance the safety and health monitoring of these vulnerable populations. The smart hand glove is built using an Arduino Uno microcontroller, an ESP8266 Wi-Fi module, two MPU6050 sensors for fall detection and gesture recognition, and a MAX30100 sensor for heart rate and SpO2 monitoring. These components work together to detect falls, recognize specific hand gestures, and continuously monitor vital signs. Upon detecting a fall or receiving a gesture input, the system triggers automatic SMS and email alerts to designated contacts. Additionally, health monitoring data is transmitted to a secure, password-protected website accessible by healthcare providers. The smart hand glove demonstrated its potential in improving safety and health outcomes for elderly individuals and those living alone by enabling timely medical intervention and continuous health monitoring. This innovative solution addresses critical gaps in emergency response and healthcare for vulnerable populations, ensuring better safety and wellbeing. © 2024 IEEE.

关键词： Fall detection

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A Novel Methodology to Predict Flood and Landslide Disasters using Learning Assisted Artificial intelligence (AI) Logic 3

A Novel Methodology to Predict Flood and Landslide Disasters...

引用

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

作者： Raj, G. Bhupal Patan, Muzeeb Khan Gupta, Ashwani Kumar Lakshmi, C. Gopinath, D. Yadav, Ajay Singh Sr University School of Agriculture Warangal India University of Technology and Applied Sciences College of Engineering and Technology Department of Eee Muscat Oman Chandigarh University Faculty of Business and Commerce Uttar Pradesh Unnao India Saveetha Engineering College Chennai India Department of Computer Science Karnataka Bengaluru India Srm Institute of Science and Technology Department of Mathematics Delhi-NCR Campus U.P Ghaziabad India

ISBN: (纸本)9798331509675

Comprehending the dynamics of flood and landslide prediction in current and adjacent regions is crucial for the formulation and advancement of effective predictive models, conservation, and management techniques. Conventional forecasting techniques, typically dependent on statistical models and empirical data, may prove inadequate because of the intrinsic complexities and variability of environmental elements. The complex interaction of climatic conditions, hydrological processes, geological attributes, and topographical aspects complicates precise forecasting. The constraints of traditional methods highlight the pressing necessity for more advanced and efficient predictive instruments. Artificial intelligence, especially machine learning (ML) and deep learning (DL), presents a robust paradigm that utilizes sophisticated algorithms to examine extensive datasets, discern intricate patterns, and produce more precise and prompt predictions. This proposed ViT-DL model prediction ability can greatly boost catastrophe preparedness, mitigation strategies, and ultimately, preserve lives and reduce economic damages. This study will comprehensively examine contemporary research trends, emphasizing the methodology used, model efficacy, related problems, and potential future research avenues in AI-based flood and landslide prediction. The experimental results of our proposed model produces 96.6% of Accuracy and 96.31% of Precision, comparision shows our model outperforms the overall accuracy to conventions model techniques. © 2025 IEEE.

关键词： Landslides

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Behind the Scenes: An Explainable Artificial intelligence (XAI) on the Service Classification of the 5G/B5G Network 3

Behind the Scenes: An Explainable Artificial Intelligence (X...

引用

3rd international conference on Digital Transformation and Applications, ICDXA 2024

作者： Allias, Noormadinah Kadir, Diyana Ab Abdullahi, Akibu Mahmoud Ismail, Shahrinaz Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Kuala Lumpur Malaysia Computer Engineering Technology Universiti Kuala Lumpur- Malaysia Institute of Information Technology Kuala Lumpur Malaysia School of Computing and Informatics Albukhary International University Kedah Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

Fifth generation and beyond (5G/B5G) is part of the next generation of mobile technology, which is expected to offer more capacity and faster speeds than the previous generation Long-Term Evolution (LTE) network. These features enable 5G/B5G to offer users a wide range of services, including smart cities, entertainment and multimedia, healthcare and mission-critical applications that benefit the entire economy and communities. However, due to growing demand, telecom service providers need to provide better Quality of service (QoS) on their networks to meet user expectations. Therefore, it is necessary to implement service classification that enables service providers to select the right network slices for each service to improve network QoS. Previous studies have predicted the classification of 5G/B5G services using traditional machine learning algorithms such as random forest and decision tree, achieving good accuracy. However, prediction based on black-box machine learning models was not transparent enough, so mobile service providers could not understand the reliability and interpretation of the results. Therefore, this article aims to model the system performance of 5G Key Performance Indicators (KPI) and extend the experiment to Explainable Artificial intelligence (XAI) by using the Explainable AI to look behind the scenes of the chosen black-box models for service classification of 5G/B5G networks, as this is crucial for telecom service providers to make critical investment decisions around customer and employee experience. The results from the experiments show that the random forest has the highest confidence towards class Label 3 with the probability of 35 percent. © 2024 IEEE.

关键词： Decision trees

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Enhancing use of BERT information in neural machine translation with Masking-BERT attention 3

Enhancing use of BERT information in neural machine translat...

引用

3rd international conference on Artificial intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Chen, Xi Wu, Lin Zhang, Yuanhao Yunnan Provincial Artificial Intelligence Key Laboratory Kunming University of Science and Technology Yunnan Kunming China Cybersecurity and Safety Research Institute Beijing Tiandi Hexing Technology Co. Ltd. Beijing China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

BERT has shown remarkable performance in several natural language processing tasks, but it fails to exhibit the same high performance in cross-lingual tasks, particularly machine translation. To address this issue, we propose a BERT-enhanced neural machine translation (BE-NMT) model that optimizes the use of the information contained in BERT by NMT. Our proposed model comprises three components: (1) A MASKING strategy to mitigate knowledge forgetting caused by fine-tuning of BERT on the NMT task. (2) Serial and parallel processing of multi-attention models for incorporating BERT into the NMT system. (3) Fusing multiple hidden layer outputs of BERT to supplement the missing linguistic information of its final hidden layer output. We conducted experiments on several translation tasks, and our proposed model notably outperforms the strong baseline by improving 1.93 BLEU points on the United Nations Parallel Corpus English→Chinese task. Additionally, our model also achieves remarkable performance on other translation tasks. © 2023 SPIE.

关键词： Neural machine translation

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Artificial intelligence in Current Education: Roles, Applications & Challenges 3

Artificial Intelligence in Current Education: Roles, Applica...

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Bhatt, Chandradeep Singh, Sanjeev Chauhan, Rahul Singh, Teekam Uniyal, Anita Graphic Era Hill University Cse Department Dehradun India Graphic Era Deemed to Be University Cse Department Dehradun India

ISBN: (纸本)9798350322842

This review was undertaken to measure what effects the artificial intelligence has had on the modern-day education. AI is a field that includes machines along with the features of human intelligence features such as adaptability, learning abilities, cognitive abilities, and decision-making abilities. The result of this study is that AI is widely applied and used in various ways in education, especially in educational institutions. The inception of AI was in the form of computers which evolved into use of humanoid robots and web chatbots that individually performed the tasks and functions of instructors and used integrated computer systems along with other technologies or a trainer with them. With the help of these platforms, teachers were able to perform a host of administrative tasks like improving the quality of educational activities by conducting research and made the evaluation of student challenges more efficient and effective. Meanwhile, the system incorporates machine learning to ensure that the curriculum is tailored and personalized to an individual's need, fostering acceptance and retention, improving the learning experience, and augmenting the overall learning quality. © 2023 IEEE.

关键词： Human computer interaction

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Proceedings - 2023 3rd international Signal Processing, Communications and Engineering Management conference, ISPCEM 2023

Proceedings - 2023 3rd International Signal Processing, Comm...

引用

3rd international Signal Processing, Communications and Engineering Management conference, ISPCEM 2023

ISBN: (纸本)9798350307023

The proceedings contain 153 papers. The topics discussed include: the control of dual-input DC-DC converter system based on fuzzy MPPT;research on the product automatic pricing model based on grey relation analysis;prediction of water quality of Xincheng bridge in Lanzhou based on CNN-LSTM model;research on optimization of power grid planning for comprehensive transmission and distribution tariff reform;research on the collaborative interaction between electricity and computing power for green development — taking data centers as an example;categorizing pain intensity through facial recognition using deep learning;the application of IoT+Artificial intelligence technology in adoption agriculture;a study on train schedule planning based on simulated annealing algorithm;research on digital system of collaborative cycle integration information transformation in electric power enterprises;and research and design of a deep learning-based free trade zone data analysis system.

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Artificial intelligence Chatbots for Customer Service & Their Impacts on Consumer Compliancy 3

Artificial Intelligence Chatbots for Customer Service & Thei...

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

ISBN: (纸本)9798350399264

E-commerce utilize to interacting with clients using live chat interface and has gaining popularity which offer live customer care. The interactive software agents, often known as chatbots, which are tools meant to connect with humans by way of human speech and are frequently based on machine learning. This system frequently replaces the human chat program agents (AI). Because of their cost & time-saving prospects which led to a wider adoption of intelligence chatbots. This system commonly falls short of customer expectation, which may make consumers less likely to follow the chatbot's instructions. Here the work experimentally investigates through a randomised online study how vocal anthropomorphic model cues as well as the foot-in-the-door (FITD) method effect user request conformity, relying on social reaction and dedication theory. System findings show that users are more likely to cooperate with such a chatbot's demand for bringing improvement when it is anthropomorphic and when there is a need to maintain consistency. The outcomes additionally demonstrate that social interaction mediates the impact of humanistic model signals on customer adherence. © 2023 IEEE.

关键词： Software agents

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Novel Power Load pattern recognition Algorithm Based on Unbalanced Data Clustering 3

Novel Power Load Pattern Recognition Algorithm Based on Unba...

引用

3rd international conference on New Energy and Power Engineering, ICNEPE 2023

作者： Zhu, Dongyuan Sun, Yifei Cui, Jia Hu, Zhen Huang, Jingbo Zhang, Jiahui Shenyang Institute of Engineering School of Electric Power Liaoning Shenyang110136 China State Grid Yingkou Electric Power Supply Liaoning Yingkou115000 China Shenyang University of Technology School of Electrical Engineering Liaoning Shenyang China

ISBN: (纸本)9798350393224

An improved fuzzy C-means clustering algorithm is proposed for the imbalance between different date types in the power load dataset and the uniformity effect of the cluster size faced by the fuzzy C-means clustering algorithm when dealing with the unbalanced dataset. Firstly, in the improved algorithm, the sum of the membership values of all samples belonging to a certain class is taken as the volume of the class, and the objective function of the traditional algorithm is normalized by its inverse ratio to overcome the problem of uniformity. Secondly, through the Lagrange multiplier method, a new membership matrix update formula is obtained. Finally, Dynamic Time Warping (DTV) was selected as the distance measure in order to solve the Euclidean distance cannot measure the shape similarity of time series. Experimental results show that the proposed algorithm can effectively identify the load pattern of holidays from the historical load data set. © 2023 IEEE.

关键词： clustering algorithms imbalance data load pattern recognition

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Deep Learning for Automated Image Captioning: A CNN and Transformer Model Analysis 3

Deep Learning for Automated Image Captioning: A CNN and Tran...

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

作者： Saarika, Kathula Varsha, Vasaraju Sri Harsha, P. Sheikh, Anjum Nabi Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology India

ISBN: (纸本)9798331527549

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and IEEEXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning. © 2025 IEEE.

关键词： Convolutional neural networks

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An Improvement of Vehicle and Passerby recognition Based on YOLO-V3 Algorithm 3

An Improvement of Vehicle and Passerby Recognition Based on ...

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3rd international conference on Artificial intelligence and Computer Engineering, ICAICE 2022

作者： Ling, Tianyang Tian, Shuo Gao, Songyuheng Xing, Zhixue Lai, Jianshao Li, Zhenzhai School of Information and Communication Guilin University of Electronic Technology China

ISBN: (纸本)9781510663473

In order to reduce the incidence of traffic accidents, the use of computer vision to identify vehicles and passers-by in the process of driving can achieve the effect of assisting driving. This paper mainly introduces the performance improvement brought by the introduction of the SPP module in YOLO-V3 for object recognition. Model training is performed on the VOC dataset based on YOLO-V3-SPP. Finally, 300 photos were used to test the accuracy of the algorithm. The results show that the recognition accuracy of YOLO-V3-SPP for vehicles and pedestrians can reach 94.19% and 90.68%, and the accuracy of YOLO-V3 is improved by nearly ten under the same equipment. percentage point. The research on this technology can effectively reduce the probability of traffic accidents and provide reference value for the future driving safety warning field. © 2023 SPIE.

关键词： Computer vision

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