检索结果-内蒙古大学图书馆

Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Rishi Kumar J. Praveen Gujjar M.S. Guru Prasad Raghavendra M. Devadas Preethi Utsav Kumar Department of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed to be University) Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education India Department of Computer Applications Graphic Era (Deemed to be University) Dehradun India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Machine learning tasks with imbalanced datasets present several difficulties, especially when trying to solve the underlying imbalance issue in the data. Insufficient data in the minority class can result in biased models that are not very good at generalizing to real-world situations. Moreover, because they must take into account class imbalance, traditional evaluation metrics like accuracy can be deceptive. It is imperative to tackle these issues with methods such as Imbalance-Learn, which offers instruments to manage unbalanced datasets efficiently through the modification of class weights, resampling techniques, and algorithmic methods. Data scientists can guarantee equitable and precise model predictions by taking into account the number of samples in the training set and managing the imbalance issue with great care. This paper shows various strategies to handle class imbalance, including data-level techniques such as resampling. This paper highlights the challenges of the imbalanced dataset and also it presents the various techniques to balance the imbalance dataset.

关键词： Training Measurement Accuracy Instruments Machine learning Information processing Predictive models Prediction algorithms Data models Robustness

来源：评论

学校读者我要写书评

暂无评论

Predictive Analytics for Hospital Readmissions Using Logistic Regression and IoT Sensor Data 6

Predictive Analytics for Hospital Readmissions Using Logisti...

引用

6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Kiruthiga, B. Riazulhameed, Arshadh Ariff Mohamed Abuthahir Rajapriya, M. Sarasu, R. Mohankumar, N. Kasthuri, A. Srm Valliammai Engineering College Department of Management Studies Tamil Nadu Chennai India Blackstraw Technologies Pvt Ltd Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics Tamil Nadu Chennai India Maharashtra Nagpur India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350379297

Hospital readmissions affect healthcare systems globally, increasing costs and morbidity. Readmissions must be predicted and prevented to improve patient outcomes and save healthcare costs. This research leverages the Internet of Things (IoT) sensor data and Logistic Regression (LR) to reduce hospital readmissions using predictive analytics. Patient health data from IoT devices during and after hospitalization is used in the paper. Heart rate, blood pressure, and oxygen saturation are constantly monitored. LR is used to examine this dataset and create a prediction model. The LR algorithm trains us to recognize IoT sensor data patterns and correlations that indicate readmission risks. Our methodology provides developed risk estimates incorporating patient-specific parameters, including age, multiple illnesses, and medical history. The temporal data permits dynamic predictions, considering patient health changes over time. This research detected hospital readmission risk with promising accuracy. These predictions allow healthcare practitioners to proactively intervene and deliver targeted treatments, including prescription modifications, lifestyle advice, and follow-up consultations to decrease readmission. This proactive strategy may improve patient outcomes and save healthcare expenses. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Natural Language Processing: Bridging the Gap between Human Language and Machine Understanding

Natural Language Processing: Bridging the Gap between Human ...

引用

Trends in Quantum Computing and Emerging Business technologies (TQCEBT), International Conference on

作者： Keerthipati Kumar Mohammad Haider Syed Saumya Bhargava Lalit Lalitav Mohakud Mohammad Serajuddin Melanie Lourens Computer Science and Engineering Department S.V. College of Engineering Tirupati Andhra Pradesh Department of Computer Science Saudi Electronic University Indira School Of Business Studies PGDM Pune Department of Education Jadavpur University Kolkata College of Dentistry King Saud University Faculty of Management Sciences Durban University of Technology South Africa

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

NLP in relation to deep learning models is focused on the review of NLP evolution from the middle of the 1950s up to present days. Today, NLP remains the pillar on which rests our digital age, connecting speech nuances of a human language and the computational power of machines. It is tracked along with the historical evolution from rule based systems to the paradigm shift to the machine learning decade since the nineties and constellations of the NLP components. The methodology involves using a conventional linguistic model alongside ultra-modern machine learning models to overcome ambiguity as well as contexts associated with other languages. The technical specifics and continuity loops under the Human-in-the-loop (HITL) framework are discussed. For example, they look at words like "uncommon" words, which have cultural implications in showing how involved human speech is. For instance, the strength of NPL can be depicted by being used in diverse sectors like health and social media. Finally, this article illustrates an advanced time when smart NPL units give topnotch solutions proving that interconnection between linguistics, computing and artificial intelligence is ongoing.

关键词： Deep learning Quantum computing Social networking (online) Reviews Computational modeling Knowledge based systems Semantics

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Diverse ML Techniques in Disasters Twitter classification

Performance Evaluation of Diverse ML Techniques in Disasters...

引用

Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Basudev Nath Deepak Sahoo Bhramara Bar Biswal Sudhansu Shekhar Patra Rakesh Kumar Ray Faculty of Emerging Technologies Sri Sri University Cuttack India Dept. of Computer Science & Engineering GIET University Gunpur India School of Computer Applications KIIT Deemed to be University Bhubaneswar India MCA Department Centurian University of Technology and Management Khordha India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Social media has grown to be an essential and significant aspect of our lives in the modern day. Due to the widespread usage of laptops, mobile phones, and other devices, individuals find it simpler to communicate with one another and to post their opinions, concerns, and other information on social media sites like Facebook, Instagram, and tweeter. Several government and commercial organizations use these social media platforms to gather text and assess people’s sentiments during times of crisis. Tracking the tweets relating to the accident is too tough for them to do. To categories tweets as “disaster” or “non-disaster,” the paper implemented three models, the Naïve-Bayes, KNN and support vector machine classification models. The Naïve-Bayes algorithm shows the highest accuracy of 86.591.

关键词： Analytical models Accuracy Social networking (online) Disasters Blogs Support vector machine classification Nearest neighbor methods Vectors Classification algorithms Web sites

来源：评论

学校读者我要写书评

暂无评论

An Involuntary Liver Cancer Recognition in Abdominal Liver Imageries with Soft Optimization Methods in Biomedical Application

An Involuntary Liver Cancer Recognition in Abdominal Liver I...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： M. Vinodh Kumar Raj Kumar Patra Veena K A Siva Kumar Reddy V Sunil Kumar Inventuriz Labs Pvt Ltd Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Telangana India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

A medical image recovery system can be used for a variety of useful purposes, one of which is the classification of medical images. When numerous different types of medical imaging evidence are accessible, it is essential to have accurate categorization models. Because of the CT paradigm in clinical diagnosis, radiologists are able to properly detect and monitor changes in the body's physiological state. The ability to differentiate between different types of tissues in a CT image based on the various grey rates along each line contributes significantly to the accuracy of a medical diagnosis. The standard practice for diagnosing cancer relies on scientific and histological data, both of which have the potential to lead to incorrect or erroneous findings. The liver, which may be found in the upper abdominal region of the external human form, is responsible for filtering the blood and getting rid of any toxins it discovers in the process. The distinction between the two types of cancer is taught through an artificial neural network in this method. A number of different parameters, including accuracy, area, correlation, entropy, homogeneity, contrast, and the similarity index, are employed to conduct an analysis of the impact. After that, researchers tried to enhance the early identification and treatment of liver cancer by segmenting and classifying liver tumors from CT images. This was done in order to gain a better understanding of the disease. The accuracy of the identification was raised to 99.63 percent thanks to the classifier and optimizer that was proposed.

关键词： Liver cancer Image segmentation Computed tomography Software algorithms Liver Software Biomedical monitoring

来源：评论

学校读者我要写书评

暂无评论

Performance LoRa Technology for Autonomous Vehicles 24

Performance LoRa Technology for Autonomous Vehicles

引用

24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Adi, Puput Dani Prasetyo Purnama, Iwan Siregar, Arfanda Anugrah Juledi, Angga Putra Edi, Firman Karim, Abdul Wahyu, Yuyu Maulana, Fairuz Iqbal Susilo, Suhardi Atmoko Budi Toni Harahap, Ilham Maliki Patria, Lintang Bandung Indonesia Labuhanbatu University Faculty of Science and Technology North Sumatra Labuhanbatu Indonesia Politeknik Negeri Medan Telecommunication Engineering Study Program North Sumatra Medan City Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Riau Islands Batam City Indonesia Budi Darma University Department of Information Technology North Sumatra Medan Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia University Labuhanbatu Faculty of Teacher Training and Education North Sumatra Labuhanbatu Indonesia Universitas Terbuka Faculty of Engineering Jakarta Indonesia

ISBN: (纸本)9798350313956

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss. © 2023 IEEE.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

A Smart Capacity Enhancement and Estimation Model for Hybrid Buildings by using Light Weight Deep Learning Model

A Smart Capacity Enhancement and Estimation Model for Hybrid...

引用

2023 International Conference on Disruptive technologies, ICDT 2023

作者： Kumar, Senthil Atyam, Nageswara Rao Khadar Nawas, K. Awasthi, Shashank Kotteeswaran, M. Thirunavukkarasu, K.S. Koneru Lakshmanan Education Foundation Kl School of Architecture Andhra Pradesh Guntur India Cmr Institute of Technology Electrical and Electronics Department Karnataka Bengaluru India Vellore Institute of Technolog School of Computing Science and Engineering Tamil Nadu Chennai India Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering Uttar Pradesh Greater Noida India School of Management and Commerce Tamil Nadu Pallavaram Chennai India School of Computing Science Tamil Nadu Pallavaram Chennai India

ISBN: (纸本)9798350323887

Generally, for the buildings constructed in our areas, the conventional foundation known as 'shallow', i.e. from the ground level downwards, is set up in three dimensions such as length, width and depth. Foundations are not the same for all types of structures. The size and structure of the foundation varies depending on the nature of the prevailing soil, ground water level of the plot, type of building, and total load of the structure. In this paper an innovation estimation model was proposed based on light weigth deep learning technique. The proposed model introduces capacity estimation and energy efficiency plans and designs. These are all relating to commercial buildings, craft facilities and mass housing projects to enable design, construction. It also maintains the buildings under minimum energy consumption without stressing the operation of buildings. The health and comfort of the occupants are establishing compliance standards and ensure the maximum standards for energy efficiency in the plans or designs of commercial buildings. The proposed model also focuses the capacity estimation and energy efficiency programs beyond maximum standards. © 2023 IEEE.

关键词： Foundations

来源：评论

学校读者我要写书评

暂无评论

VHASR: A Multimodal Speech Recognition System With Vision Hotwords

arXiv

引用

arXiv 2024年

作者： Hu, Jiliang Li, Zuchao Wang, Ping Ai, Haojun Zhang, Lefei Zhao, Hai Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China School of Information Management Wuhan University Wuhan China Department of Computer Science and Engineering Shanghai Jiao Tong University China

The image-based multimodal automatic speech recognition (ASR) model enhances speech recognition performance by incorporating audio-related image. However, some works suggest that introducing image information to model does not help improving ASR performance. In this paper, we propose a novel approach effectively utilizing audio-related image information and set up VHASR, a multimodal speech recognition system that uses vision as hotwords to strengthen the model’s speech recognition capability. Our system utilizes a dual-stream architecture, which firstly transcribes the text on the two streams separately, and then combines the outputs. We evaluate the proposed model on four datasets: Flickr8k, ADE20k, COCO, and OpenImages. The experimental results show that VHASR can effectively utilize key information in images to enhance the model’s speech recognition ability. Its performance not only surpasses unimodal ASR, but also achieves SOTA among existing image-based multimodal ASR.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Challenges in Metaverse in Problem-Based Learning as a Game-Changing Virtual-physical Environment for Personalized Content Development 6

Challenges in Metaverse in Problem-Based Learning as a Game-...

引用

6th Smart Cities Symposium, SCS 2022

作者： Dutta, P.K. Bose, Manoswita Sinha, Akash Bhardwaj, Ramakant Ray, Samrat Roy, Sudipta Prakash, Kolla Bhanu School of Engineering and Technology Amity University Kolkata India School of Computer Application Amity University Kolkata India Department of Mathematics Amity University Kolkata India The Institute of Industrial Management Economics and Trade Peter the Great Saint Petersburg Polytechnic University Russia Artificial Intelligence & Data Science Jio Institute India Department of Computer Science and Engineering Konneru Lakshmaih Education Foundation India

ISBN: (纸本)9781839538544

The benefits and drawbacks of using the metaverse in the field of education are discussed, along with the possibilities and potential issues it will open up. Utilizing the idea of personalised knowledge transfer and the use of an immersive environment to match students' learning pace using horizon classroom, the Metaverse environment can add a new dimension to the field of educational technologies. We have also highlighted the virtualization-based redesign of educational environments using a personalised cloud, which is anticipated to improve learning for students by making it more affordable, collaborative, and useful. The goal is to build a metaversity that can serve as a communication tool for teachers and students anywhere in the world by providing each with an individual avatar. This platform will offer a virtual setting for students to socialise with their friends and peers and receive peer-to-peer learning, which is lacking in the current e-learning environment. The students won't need an entrance score card;instead, they will need critical learning skills to advance and mature. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric 4th

Highsimb: A Concrete Blockchain High Simulation with Contra...

引用

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Pengfei Jie, Wanqing Voundi Koe, Arthur Sandor Hou, Ruitao Yan, Hongyang Nouioua, Mourad Thien, Phan Duc Mbous Ikong, Jacques Lancine, Camara Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Pazhou Lab Guangzhou510330 China Faculty of Mathematics and Computer Science University of Mohamed Bachir El Ibrahimi Bordj Bou Arreridj34030 Algeria Faculty of Information Technology Nam Dinh University of Technology Education Nam Dinh420000 Viet Nam Department of Electrical and Telecommunications Engineering National Advanced School of Engineering - University of Yaounde I Yaounde337 Cameroon Department of Computer Science University of Social Sciences and Management Bamako2575 Mali

ISBN: (纸本)9783031200984

Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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：