检索结果-内蒙古大学图书馆

International Conference on Smart Computing & Communications (ICSCC)

作者： A Mahesh K. Sangeethalakshmi A. Nithya M. Shafiya Banu N. Gnanavel M. Muthulekshmi Department of Computer Science and Engineering Sri Sairam College of Engineering Bengaluru Karnataka India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Information Technology St.Joseph's Institute of Technology Chennai Tamil Nadu India Deapartment of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Computer Sciennce and Engineering Vinayaka Mission's Research Foundation(DU) Vinayaka Mission's Kirupananda Variyar Engineering College Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Naduz India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

This research presents a novel strategy for improving the treatment of epilepsy by combining neurological monitoring with Internet of Things (IoT) technology. The findings of this study have the potential to significantly advance the field. The methodology that has been developed allows the predictive surveillance of seizure occurrences by the seamless integration of real-time data collected from wearable devices with sophisticated analytics. The system uses algorithms designed to learn from machine data to recognize tiny physiological changes that precede seizures. This enables early intervention. The IoT-enabled platform provides therapeutic insights in addition to seizure prediction. These insights are obtained by combining information supplied by the patient with objective data. This helps to create a full knowledge of both triggers and reactions. This game-changing collaboration between neurology and the Internet of Things gives patients individualized insights, enabling them to proactively manage their illnesses and make choices based on accurate information. Ultimately, this study will alter epilepsy treatment by improving patient wellness, clinical decision-making, and the possibility of developing innovative therapeutic options.

关键词： Technological innovation Neurology Surveillance Epilepsy Medical services Predictive models Prediction algorithms Real-time systems Internet of Things Wearable devices

来源：评论

学校读者我要写书评

暂无评论

Advanced Comparative Analysis of Time-Frequency Signal Processing Techniques and machine learning Models for Optimized Biomedical Classification

Advanced Comparative Analysis of Time-Frequency Signal Proce...

引用

Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Uma Reddy Sorabh Lakhanpal Jay Singh Talari Meena Sarah Abbas Shakir G. Sakthi Lohitha Mallireddy Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Dept. of EEE GL Bajaj Institute of Technology and Management Gr. Noida U.P. India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh India Department of CSE GRIET Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This research aims at comparing the performance of TF analysis in classifying biomedical signals, with special reference to ECG and EEG signals using PhysioNet database as the major source of data. Fourier Transform and Wavelet Transform, are used to extract the feature from signals for the time-frequency analysis. These features are then used for classification using the algorithms like Random Forest and Neural Networks. All computations are performed with SciPy, NumPy, and Scikit-learn libraries. The study assesses the methods considering the classification performance, computational cost, and interpretability toward determining which approaches offer the best solution for the biomedical signal classification. It needs to be mentioned that the presented results are aimed at helping researchers and practitioners in choosing proper methods for biomedical applications of ECG and EEG signals, focused on the PhysioNet database.

关键词： Wavelet transforms Time-frequency analysis Accuracy Biological system modeling Electrocardiography Feature extraction Wavelet analysis Brain modeling Electroencephalography Random forests

来源：评论

学校读者我要写书评

暂无评论

Adaptive hybrid density functionals

arXiv

引用

arXiv 2024年

作者： Khan, Danish Price, Alastair James Arthur Ach, Maximilian L. von Lilienfeld, O. Anatole Trottier, Olivier Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Acceleration Consortium University of Toronto TorontoON Canada Department of Physics University of Toronto St. George Campus TorontoON Canada Department of Physics Ludwig-Maximilians-Universität Munich Germany Department of Materials Science and Engineering University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Exact exchange and correlation contributions are known to crucially affect electronic states, which in turn govern covalent bond formation and breaking in chemical species. Empirically averaging the exact exchange admixture and the amount of correlation over configurational and compositional degrees of freedom, hybrid density functional approximations (DFAs) have been widely successful, yet have fallen short to reach explicitly correlated high level quantum chemistry accuracy in general. Using quantum machine learning, we have adaptified hybrid DFAs by generating optimal admixture ratios of exact exchange or of correlation "on the fly", specifically for any chemical compound. Adaptive exchange in the PBE0 hybrid DFA yields atomization energies sufficiently accurate to effectively cure the infamous spin gap problem in DFT, and also improves electron densities, and HOMO-LUMO gaps in organic molecules. Using adaptive PBE0, we revised the entire QM9 data set presenting more accurate quantum properties that include, on average, stronger covalent binding, larger band-gaps, more localized electron densities, and slightly larger dipole-moments. Adaptive PBE correlation (in conjunction with 100% HF exchange) also affords much improved atomization energies, and even enables covalent bond dissociation. © 2024, CC BY.

关键词： Carrier concentration

来源：评论

学校读者我要写书评

暂无评论

Handling Data and Model Drift for World Application using Big Data

Handling Data and Model Drift for World Application using Bi...

引用

Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Rajesh Singh Anita Gehlot Finney Daniel Shadrach S. Prabu R Nirmalan V. Sunil Kumar Uttaranchal Institute of Technology Dehradun India Department of ECE KPR Institute of Engineering and Technology Coimbatore India Department of ECE Mahendra Institute of Technology Namakkal India Department of CSE Kalasalingam Academy of Research and Education Krishnankoil India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

It's still unclear how to effectively extract the information concealed inside vast and massive amounts of data. The problem of “idea drift” in stream data flows is one of the difficulties. Random walk is a common issue in data analytics where the statistical features of the characteristics and the categories they are intended for change with time, decreasing the accuracy of the trained model. There are numerous approaches that have been put out for bulk data mining. A new generation of data mining methods called stream mining updates the model in a single pass whenever fresh data is received. Because of its inherent adaptability, this one-pass mechanism may be properly capable than its predecessors of coping with idea drift in data streams. In this study, we assess a group of algorithms for mining the data streams using decision trees. The collection of rules which can be derived from the induced model is the benefit of decision tree learning. The predicate logics that represent the extracted rules can then be applied in a variety of decision-supporting applications. Even in the face of concept drift, the induced decision tree has to be precise and condensed. When dealing with concept-drift data, we evaluate how well three common incremental decision tree methods (random forest, isolation forest, and forest tree) perform. In the experiment, drift data from both synthetic and actual environments are employed. It is discovered that optimization method with big data technique i.e., MapReduce yields better outcomes.

关键词： Adaptation models Optimization methods Forestry Big Data Data models Regulation Data mining

来源：评论

学校读者我要写书评

暂无评论

Predictive Modeling of Dental Health Outcomes Based on Fluoride Concentrations using AI

Predictive Modeling of Dental Health Outcomes Based on Fluor...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Swathi Pai M Annepu Arudra Dankan Gowda V Parashuram Shankar Vadar Mahadeo Ramchandra Jadhav Mandeep Kaur Department of Artificial intelligence and machine learning NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karnataka India Department of Computer Science & Engineering Rajiv Gandhi Institute of Technology Bangalore Karnataka India Department of Electronics & Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India Department of Applied Science Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Electronics and Communication Engineering Punjabi University Patiala Punjab India

Oral health is a crucial aspect of general health, and the presence of fluoride in drinking water has been consistently linked to its improvement. This work utilizes deep learning and machine learning approaches to develop prediction models that can estimate oral health consequences based on fluoride concentrations. Our analysis is based on a comprehensive dataset that includes fluoride levels and oral health indicators from several geographic locations. The dataset covers a broad range of demographic and environmental factors. The study involves a thorough data pretreatment procedure, which includes activities such as data cleaning, standardization, and feature engineering. All of these processes contribute to improving and making the input variables more relevant. The approach used encompasses a range of machine learning algorithms, including as neural networks, decision trees, and ensemble approaches, which are used to create prediction models. Thoroughly adjusting hyperparameters and using cross-validation methods are used to maximize the effectiveness of the model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Big Data Analytics with Wild Horse Optimizer based Deep learning Model for Healthcare Management

Big Data Analytics with Wild Horse Optimizer based Deep Lear...

引用

International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： M. Vamsikrishna Rajasree RS Gopika G S D. Suganthi Md. Abul Ala Walid Ramu Kuchipudi Department of Information Technology Aditya Engineering College (A) Surampalem Andhra Pradesh India Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bangalore Karnataka India Department of Computer Science Sathyabama Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

Big data analytics in health service is the procedure of research in huge and different datasets, designed for uncovering hidden patterns, correlations, and trends for making the best decisions in medical field. The health and medical services are developed modern and smart healthcare platforms are made the analysis further robust for the treatment. The proper diagnosis of health records depends on primarily disease recognition and the value of accuracy is decreased if the clinical data quality is worse. But, the existing methods failed to use the learning model for handling heterogeneous healthcare information. Therefore, this article introduces Big Data Analytics with Wild Horse Optimizer based deep learning (BDAWHO-DL) model for Healthcare Management. The proposed BDAWHO-DL technique investigates the big data in medical field and makes decisions. To do so, the BDAWHO-DL technique exploits attention based long short term memory (ABLSTM) method for data classification purposes. Moreover, the WHO system was used for the optimal hyperparameter tuning procedure of the ABLSTM algorithm to boost the classifier outcomes. The experimental outcomes indicate the promising outcomes of the BDAWHO-DL algorithm over recent approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NCoRE: Neural counterfactual representation learning for combinations of treatments

arXiv

引用

arXiv 2021年

作者： Parbhoo, Sonali Bauer, Stefan Schwab, Patrick John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge United States Max Planck Institute for Intelligent Systems Tübingen Germany GlaxoSmithKline Artificial Intelligence & Machine Learning Switzerland

Estimating an individual's potential response to interventions from observational data is of high practical relevance for many domains, such as healthcare, public policy or economics. In this setting, it is often the case that combinations of interventions may be applied simultaneously, for example, multiple prescriptions in healthcare or different fiscal and monetary measures in economics. However, existing methods for counterfactual inference are limited to settings in which actions are not used simultaneously. Here, we present Neural Counterfactual Relation Estimation (NCoRE), a new method for learning counterfactual representations in the combination treatment setting that explicitly models cross-treatment interactions. NCoRE is based on a novel branched conditional neural representation that includes learnt treatment interaction modulators to infer the potential causal generative process underlying the combination of multiple treatments. Our experiments show that NCoRE significantly outperforms existing state-of-the-art methods for counterfactual treatment effect estimation that do not account for the effects of combining multiple treatments across several synthetic, semi-synthetic and real-world benchmarks. Copyright © 2021, The Authors. All rights reserved.

关键词： Health care

来源：评论

学校读者我要写书评

暂无评论

Wireless Sensor Networks for Disaster Management and Emergency Response using SVM Classifier

Wireless Sensor Networks for Disaster Management and Emergen...

引用

International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： B. Meenakshi A. Vanathi B. Gopi S. Sangeetha L. Ramalingam S. Murugan Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Rasipuram Tamil Nadu India Department of Computer Science and Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning Chennai Institute of Technology (Autonomous) Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Wireless Sensor Networks (WSNs) are essential for IoT-enabled crisis management and executive response. This paper explores WSNs' function in catastrophe situations and proposes new ways to improve emergency response operations. The article begins with a discussion of disaster management and emergency response scenarios and the necessity for real-time data collecting, monitoring, and analysis. WSNs are suitable for such applications due to their dense and dispersed sensing, tolerance to hostile conditions, and low power consumption. The research provides unique WSN deployment, data aggregation, and resource optimization methodologies for disaster management. These solutions will improve network coverage, dependability, and sensor longevity. The research also examines the synchronization of AI and human brainpower calculations with WSNs to enable smart navigation and robotized crisis response. The research study addresses WSN security and privacy in disaster management. It identifies flaws and suggests ways to classify, verify, and share data. A reenacted fiasco is used to evaluate the proposed alternatives. Situational awareness, response coordination, and emergency operations efficiency increase with the provided techniques. This study sheds light on WSNs' application in IoT-enabled emergency response and disaster management. The proposed solutions advance WSN technology and enable more effective and efficient emergency response systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of a machine learning Model for pulmonary carcinoma Prediction using Computer Tomography (CT) Images

Development of a Machine Learning Model for pulmonary carcin...

引用

International Conference on Image Information Processing

作者： Jampani Chandra Sekhar Priyanshu Singh T. Rajesh Kumar G. Mahendran Katta Subba Rao Ramya Maranan Department of Computer Science and Engineering NRI Institute of Technology Guntur Andhra Pradesh India Department of Computer Science and Engineering R.V. College of Engineering Bengaluru Karnataka India Department of Artificial Intelligence and Machine Learning Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of Mechanical Engineering R.M.K Engineering College Kavaraipettai Chennai Tamil Nadu India Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Medak (District) Telangana India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

In this study, computer tomography (CT) scans are used to build a machine learning model for predicting lung cancer. A dataset of 10,000 CT scan images from 900 patients was used to train a group of machine learning algorithms. The objective is to identify the most reliable algorithm for lung cancer detection. The algorithms CNN, CNN with gradient descent, VGG-19, and VGG-16 have all been used in real-world applications. Each algorithm is trained using the dataset to create a prediction model. Performance metrics including accuracy, precision, specificity, sensitivity, and F-Score are taken into account when assessing how effective the algorithms are. The results demonstrate that the CNN model outperforms the competition with a 97% accuracy rate. The superior accuracy, specificity, sensitivity, and F-Score of the CNN model enable accurate lung cancer detection in CT scan images. The outcomes demonstrate that, utilising the well-established machine learning paradigm, the CNN algorithm, in particular, has substantial potential for early lung cancer diagnosis and prediction. Correctly identifying lung cancer from CT scan images allows for the implementation of focused treatment programmes and prompt interventions, ultimately leading to better patient outcomes. The study emphasises the requirement for applying machine learning methods to identify relevant characteristics and patterns in CT scan images. machine learning models can now be used in clinical settings to support the precise detection and treatment of lung cancer. The use of powerful algorithms and extensive datasets allowed for this accomplishment. In order to validate and enhance the models that have been developed, more study is needed in this field. The models will therefore be more useful and suitable for use in actual clinical settings. Utilising larger datasets, deep learning architectures, and other possible machine learning advancements can significantly increase the precision and generalizability of

关键词： machine learning algorithms Sensitivity Computed tomography Computational modeling Lung cancer machine learning Predictive models

来源：评论

学校读者我要写书评

暂无评论

IoT Security Based on machine learning

IoT Security Based on Machine Learning

引用

International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Rama Krishna Vanakamamidi L. Ramalingam N. Abirami S. Priyanka C. Sasi Kumar S. Murugan HCL Technology LTD Vijayawada Andhra Pradesh India Department of Artificial Intelligence and Machine Learning Chennai Institute of Technology (Autonomous) Chennai Tamil Nadu India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Shree Sathyam College of Engineering and Technology Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

The protection of user privacy and mitigation of threats like spoofing, DoS, jamming, and eavesdropping are essential for the Internets of Things (IoT) to fulfill its promise of bringing improved and intelligent services to users via the integration of diverse devices into networks. machine learning (ML) approaches, including supervised learning, unsupervised learning, and Reinforcement learning (RL), are explored as potential solutions for securing the IoT. This article focuses on the use of ML in IoT authentications, access controls, secured offloading, and malware detection strategies to safeguard sensitive information. The challenges of incorporating these ML-based security strategies into real-world IoT systems are also discussed. A complete analysis of the threats facing IoT networks is provided, as the methods already in use to protect against them and the need for securing such networks. Then, highlight the areas where these security solutions are lacking and might benefit from ML and DL methods. Finally, go through in-depth the current ML and DL methods for dealing with various IoT network security issues. Several potential research possibilities for IoT securities based on ML and DL are also discussed.

关键词：

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：