检索结果-内蒙古大学图书馆

2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Robinson Joel, M. Ranganathan, Chitra Sabapathy Narendruni, Lakshmi Priya Srinivasan, S. Latha, N. Department of Information Technology Kings Engineering College Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Department of Computer Science Engineering Nalla Malla Reddy Engineering College Telangana Hyderabad India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The coal mine is experiencing difficult circumstances due to global warming and other climate change effects. Automation in the area of a coal mine is required since it will lower the amount of effort put in by mineworkers while also reducing costs, improving productivity and product quality, and increasing overall production. This work aims to utilize Raspberry Pi to create a monitoring system based on the MQTT protocol for usage in a coal mine. Raspberry Pi is the system's brain, processing information from the many sensors placed throughout the coal mine. To guarantee the mine's safety and productivity, sensors monitor variables, including temperature, humidity, gas concentration, and air quality. An MQTT broker, acting as a message broker between the Raspberry Pi and subscribing customers, receives and distributes the gathered data. To set up the MQTT client on the Raspberry Pi and have it regularly gather sensor data and publish it to the broker, the system uses the Paho MQTT library and Python scripting. The released data is subsequently received and processed by subscribers, allowing for continuous monitoring and study of the coal miner’s environment. Improve coal mining safety and situational awareness with this scalable and effective MQTT-based monitoring system. © 2023 IEEE.

关键词： Coal Mining Communication Protocols Internet of Things technology Monitoring Real-time Data Collection Safety

来源：评论

学校读者我要写书评

暂无评论

Breaking Language Barriers for Real-Time Translation Solutions in Tourism Using Cloud Computing and AI 3

Breaking Language Barriers for Real-Time Translation Solutio...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Jame, S. Lourdu Nageswari, C. Shobana Jayaprakash, S. Punarselvam, E. Meenakshi, R. Muthulekshmi, M. SRM Institute of Science and Technology Kattankulathur Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology School of Electrical and Electronics Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Namakkal India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

To improve tourism experiences and enable cultural communication in today's worldwide tourism industry, it is essential to communicate effectively across linguistic boundaries. Cloud computing and artificial intelligence (AI) are the focus of this paper to create tourism-specific real-time translation systems. These solutions use cloud computing and advanced AI algorithms to better connect visitors with local businesses and provide accurate and contextually aware translations. Important technical parts include cloud computing for scalability and accessibility, machine learning models for language comprehension, and natural language processing (NLP). It uses empirical research or case study techniques to assess the solutions' practicability, performance, and user acceptability. Through the efficient and effective removal of language barriers, the results highlight the promise of cloud-based AI translation systems to revolutionize the tourist industry. This research investigates real-time translation innovations, highlighting scalability, context awareness, and multilingual support, with implications for future advances in AI and cloud-based tourist applications. Also covered are potential avenues for further study and application, with the growth of the tourist industry's continuous development and use of AI-driven solutions. The proposed approach enhances real-time translation using cloud computing and AI, enabling scalability and context-aware translations. However, there are other challenges, such as overcoming latency and advanced, idiomatic expressions. © 2025 IEEE.

关键词： Tourism

来源：评论

学校读者我要写书评

暂无评论

Federated Machine Learning for Cardiac Disease Detection Using Internet of medical Things

Federated Machine Learning for Cardiac Disease Detection Usi...

引用

2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Aggarwal, Kapil Vimal, V.R. Patil, Upasana Senthilkumar, C. Ingle, Anup Kumar, T. Sampath Department of Computer Science & Engineering Guntur Andhra Pradesh Vaddeswaram India Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Thandalam India Lingaraj Appa Engineering College Karnataka Bidar India Sri Krishna College of Technology Department of Ece Tamilnadu Coimbatore India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunications Maharashtra Pune India School of ComputerScience and Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350300857

The abundant availability of medical data from healthcare organizations increases the application of Machine learning (ML) techniques in medical field. Reliable and trustworthy ML models needs to be developed in order to maintain the efficacy of the healthcare providers. ML models are used extensively to make reliable disease predictions while also safeguarding the data collected through Internet of medical Things (IoMT) devices. Federated Learning (FL) techniques are suitable for preserving IoMT data since it retains only the trained models and progresses with information from dispersed consumers. FL techniques have the caliber to drastically transform medical industry by making prompt disease diagnosis to enhance efficacy of therapy. The accuracy of these FL techniques decreases due to the massive data being exchanged between local and remote sites. In order to mitigate this issue, a novel approach FedEDFA, that combines Federated Machine learning with meta-heuristic optimization algorithm is proposed. Enhanced Dragonfly Optimization algorithm is utilized to optimally choose the relevant characteristics and employ it further for disease prediction. This approach improves the resilience of the system in insecure networking environments. The performance of the proposed FedEDFA is assessed by applying it to UCI Cleveland dataset for predicting cardiovascular disease in a secure and privacy preserving way. The accuracy of the proposed approach is 98.3% which is superior to the other existing methods taken for comparison. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Application of Multiple End member Mixing Models for Hyper spectral Image Analysis

Application of Multiple End member Mixing Models for Hyper s...

引用

2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Mohammed, Raja Salih Dhabliya, Dharmesh Hemalatha, S. Anicama Navarrete, Evelyn Alina Vigenesh, M. Huaman Flores, Elio Javier Middle Technical University Computer Science Institute of Medical Technology AI-Mansour Baghdad Iraq Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Docente Universitario Ica Universidad Autonoma de Ica Peru Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India

ISBN: (纸本)9798350357769

This paper provides software for multiple cease-member mixing fashions (MEMM) to evaluate hyperspectral photos. MEMM helps isolate distinct additives of an image of flowers or soils with unique spectral signatures. The version is based on the linear blending version (LMM) and can unravel the spectral and spatial distributions of cease contributors inside the image. The proposed MEMM approach is carried out to a hyperspectral image of a woodland scene. The model can become aware of photo components and reconstruct their fractions present in the image. The hassle of appropriately estimating the fractions is addressed using a Bayesian approach. Outcomes show that the MEMM technique can effectively pick out and estimate the components present in a hyperspectral picture. © 2023 IEEE.

关键词： appropriately components estimating hyperspectral provides

来源：评论

学校读者我要写书评

暂无评论

Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

来源：评论

学校读者我要写书评

暂无评论

Analysis of Blockchain Technology to Protect Data Access Using Intelligent Contract Mechanism for 5G Networks 6

Analysis of Blockchain Technology to Protect Data Access Usi...

引用

6th International Conference on Contemporary Computing and informatics, IC3I 2023

作者： William, P. Rai, Anjani Kumar Madan, Parul Kumar, C Praveen Shrivastava, Anurag Rana, Ajay Sanjivani College of Engineering Department of Information Technology Savitribai Phule Pune University Maharashtra Pune India Institute of Engineering & Technology GLA University Uttar Pradesh Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

With constantly changing internet models automating and digitising countless residential and industrial applications, the world of today has made significant progress in the sharing of healthcare data and the implementation of crucial actions. The two most crucial factors in the setting of healthcare data exchange today are privacy and data integrity. In order to prevent unauthorised individuals from viewing or accessing sensitive data, such as medical information, encryption is essential. The intermediary nodes shouldn't be given too much weight because conventional encryption techniques could not be deployable through them. The current security alternatives for IoT-driven healthcare monitoring frameworks are discussed in this article along with the numerous security challenges that exist in the current 5G network design and how blockchain technology may be used to encrypt data between nodes. The suggested approach outperformed the alternatives and the present strategies for IoT-driven healthcare monitoring frameworks'security solutions, according to a variety of performance factors utilised to test it. A number of performance parameters were used to test the suggested technique, and the results showed that it performed better than the alternatives. © 2023 IEEE.

关键词： Block chain Data Integrity Encryption Healthcare IoT Security

来源：评论

学校读者我要写书评

暂无评论

Speaking from Experience: Co-designing E-textile Projects with Older Adult Fiber Crafters 23

Speaking from Experience: Co-designing E-textile Projects wi...

引用

17th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2023

作者： Jelen, Ben Lazar, Amanda Harrington, Christina Pradhan, Alisha Siek, Katie A. Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre HauteIN United States College of Information Studies University of Maryland College ParkMD United States HCI Institute Carnegie Mellon University College ParkMD United States School of Informatics Computing Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9781450399777

Researchers support race, gender, and age diverse groups of people to create with maker electronics. These groups include older adults, who are often overlooked as not interested or capable of learning new technologies due to ageist stereotypes. One approach, often involving e-textiles, leverages crafting as a bridge to broaden participation in making. We investigated ways to broaden participation in maker electronics for older adults by remotely co-designing e-textile projects with 6 older adult crafters over the course of 5 workshop sessions for a total of 45 hours. We developed a deeper understanding of their practices, identifying a Planner-Improviser Spectrum for how they approached their craft, and created medium fidelity prototypes. Our design implications draw on our participants' crafting experience and their experience in the workshop to highlight what e-textile toolkit designers can learn from skilled older adult crafters, such as selecting familiar materials, supporting aesthetic goals, and making electronics more attainable. © 2023 ACM.

关键词： Older adults electronic toolkits maker electronics remote workshop co-design crafting crafters crafting technology.

来源：评论

学校读者我要写书评

暂无评论

Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides

引用

ACS Chemical Biology 2023年第3期18卷 476-483页

作者： Jian, Bo-Siyuan Chiou, Shao-Lun Hsu, Chun-Chia Ho, Josh Wu, Yu-Wei Chu, John Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Chemistry National Taiwan University Taipei 10617 Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 10675 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei 10675 Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei 10675 Taiwan

The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms. © 2023 The Authors. Published by American Chemical Society.

关键词： Algorithms Bacteria Bioinformatics and computational biology Peptides and proteins Pharmaceuticals

来源：评论

学校读者我要写书评

暂无评论

Cognitive Solutions: Implementation of AI for Fault Detection and Reduction in High-Voltage DC Transmission Systems

Cognitive Solutions: Implementation of AI for Fault Detectio...

引用

2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Dasari, Kavitha Gundlapalle, Rajesh Gupta, Manish Bhutani, Vijay Nirisha, A. Albawi, Ali Institute Of Aeronautical Engineering Hyderabad India New Horizon College Of Engineering Department Of Electronics And Communication Engineering Bangalore India Lovely Professional University Phagwara India Amity University Greater Noida Department Of Mechanical Engineering India Mlr Institute Of Technology Department Of Computer Science And Information Technology Telangana Hyderabad India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

The rise of high-voltage direct current (HVDC) transmission systems has heralded a new era in the field of electrical engineering, offering superior efficiency over long distances compared to traditional alternating current (AC) systems. However, the complexity and sensitivity of these systems necessitate advanced fault detection and mitigation strategies to ensure reliability and stability. This paper introduces a novel artificial intelligence (AI)-based framework for the detection and mitigation of faults within HVDC systems. Utilizing a combination of machine learning algorithms and deep neural networks, the proposed method achieves rapid identification and classification of faults, significantly reducing detection times. Furthermore, the integration of predictive analytics allows for preemptive actions to mitigate potential disruptions. Simulation results demonstrate the effectiveness of the AI-based approach in enhancing the operational reliability of HVDC transmission systems, showcasing improvements in fault detection accuracy and response times. The methodology's adaptability to various types of HVDC configurations and fault conditions underscores its potential for widespread application in modern power systems. This research contributes to the body of knowledge by providing a robust solution to one of the pressing challenges in the deployment of HVDC technologies, thereby supporting the transition towards more efficient and reliable power transmission infrastructures. © 2024 IEEE.

关键词： HVDC power transmission

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Models for Understanding Audience Sentiments in Movie Tweets 5

Deep Learning Models for Understanding Audience Sentiments i...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Prasath, S. Ram Jeevitha, J.K. Margret, S. Krishnan, R. Santhana Raj, J. Relin Francis Nithila, Ezhil E Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India

ISBN: (纸本)9798350379945

This study explores the application of deep learning models for analyzing audience sentiments in movie tweets, aiming to provide a comprehensive understanding of audience reception. Leveraging Twitter data collected using Tweepy and supplemented with web-scraped content via BeautifulSoup, the study employs advanced Natural Language Processing (NLP) techniques. Text preprocessing using SpaCy ensures standardized input for subsequent analysis. Sentiment analysis utilizes RoBERTa, an advanced deep learning transformer model known for its robust performance in NLP tasks. RoBERTa effectively classifies tweets into positive, negative, or neutral sentiments, leveraging its contextual understanding to interpret nuanced language expressions prevalent in social media discourse. Simultaneously, RoBERTa is employed for emotion detection, identifying emotional states such as joy, anger, sadness, and more, crucial for gauging audience emotional responses to various aspects of movies. This dual-analysis framework provides detailed insights into audience reactions, facilitating a nuanced evaluation of movie reception across diverse demographic segments. Additionally, the study explores aspect-based sentiment analysis to delve deeper into specific movie attributes like plot, acting, and special effects. Visualizations using Plotly enhance the interpretability of sentiment and emotion distributions over time, supporting informed decision-making in the entertainment industry. © 2024 IEEE.

关键词： Tweets

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：