检索结果-内蒙古大学图书馆

Spectrum Sensing Methodology Using Artificial Intelligence for Cognitive Radio Network 1

Spectrum Sensing Methodology Using Artificial Intelligence f...

1st International Conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Bhavani, M. Monica Balamurugan, P. Sankaran, Vikram Nattamai Majumder, Sohom Rakshitha, L.P. Nandagopal, K. SRM University Data Science & Business Systems Department Tamil Nadu Chennai India Institute: Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mathematics Tamil Nadu Chennai India Department of Industry Experts AtlantaGA United States JIS College of Engineering Department of Business Administration West Bengal Kalyani Kolkata India Brindavan Group of Institutions Department of Mathematics Karnataka Bangalore India Mohan Babu University Department of Mathematics Andhrapradesh Tirupati India

ISBN: (纸本)9798331518578

Spectrum sensing is an essential part of cognitive radio networks (CRNs) that enables efficient utilisation of the radio frequency spectrum and dynamic spectrum access. Conventional spectrum sensing technologies, such as energy detection, matched filtering, and cyclostationary features recognition, are often computationally complex, noise-sensitive, and unable to detect signals at low signal-to-noise ratio (SNR) levels. Artificial intelligence (AI)-based approaches have become a viable substitute to deal with these issues. CRNs can provide more precise, flexible, and effective spectrum sensing owing to AI approaches. In intricate and dynamic situations, these algorithms can identify primary users, predict spectrum usage, and categorise spectrum accessibility. Major AI-driven techniques for spectrum sensing are reviewed in this work, with an emphasis on supervised, unsupervised, and reinforcement learning techniques. The suggested approach supports the creation of intelligent, independent CRNs for next-generation wireless networks by improving spectrum efficacy, cutting down on sensing time, and facilitating real-time decision-making. © 2025 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

引用

Electronics and Renewable systems (ICEARS), International Conference on

作者： Velmurugan M Shinzeer C K G. Maya R. Ramya R Kanimozhi Syed Ismail Abdul Lathif Department Of Computer Science & Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science And Technology Chennai Department of School of Computer Applications Lovely Professional University Phagwara Punjab Faculty of Management SRM Institute of Science and Technology Kattankulathur Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Samayapuram Computer Science and Engineering Muthayammal Engineering College Rasipuram Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions.

关键词： Resistance Privacy Fault tolerance Smart contracts Fault tolerant systems Throughput Energy efficiency Blockchains Identity management systems Interoperability

来源：评论

学校读者我要写书评

暂无评论

Sign Language to Text Conversion using RNN-LSTM

Sign Language to Text Conversion using RNN-LSTM

引用

data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Amutha Seviappan Kothai Ganesan Anna Anbumozhi A. Sameer Reddy B. Vamsi Krishna D.Sampath Reddy Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Department of CSE (AIML) KPR Institute of Engineering and Technology Coimbatore India Department of Data Science and Business systems SRM Institute of Science and Technology Chennai India

The speech-impaired community only uses sign language; the rest of society interacts verbally. Our research intends to fill this communication gap by proposing a state-of-the-art method for comprehending both static and moving signs in Indian Sign Language and translating them into text. data is collected, pre-processed, and hand recognition is finished using media pipe holistic before being categorized into suitable speech output. Because LSTM networks can develop long-term dependencies, they were investigated and employed for the classification of gesture data. The constructed model exhibited a 100% accuracy rate while categorizing 26 motions, highlighting the usefulness of LSTM-based neural networks for sign language translation. For those who are deaf or dumb, translating sign language into text allows them to interact with each other or with people in the general public by using hand gestures. According to a survey we conducted on sign language comprehension, most people are unable to identify the hand gestures and mode of communication used by sign language users. After hearing about all the difficulties, they have communicating with regular people, we developed this initiative on sign language transcription. The community of deaf and mute people will benefit from this initiative because it explains the meaning of each hand gesture and uses hand gestures to communicate various signs. In this project, hand gestures were recognized using a pipeline method called media pipe, and training and testing were carried out using deep learning techniques. Sign language was then converted to text format.

关键词：

来源：评论

学校读者我要写书评

暂无评论

iA∗: Imperative Learning-based A∗ Search for Pathfinding

arXiv

引用

arXiv 2024年

作者： Chen, Xiangyu Yang, Fan Wang, Chen Lab Institute for Artificial Intelligence and Data Science Department of Computer Science and Engineering University at Buffalo NY14260 United States Robotic Systems Lab ETH Zürich Zürich8092 Switzerland

The pathfinding problem, which aims to identify a collision-free path between two points, is crucial for many applications, such as robot navigation and autonomous driving. Classic methods, such as A∗ search, perform well on small-scale maps but face difficulties scaling up. Conversely, data-driven approaches can improve pathfinding efficiency but require extensive data labeling and lack theoretical guarantees, making it challenging for practical applications. To combine the strengths of the two methods, we utilize the imperative learning (IL) strategy and propose a novel self-supervised pathfinding framework, termed imperative learning-based A∗ (iA∗). Specifically, iA∗ is a bilevel optimization process where the lower-level optimization is dedicated to finding the optimal path by a differentiable A∗ search module, and the upper-level optimization narrows down the search space to improve efficiency via setting suitable initial values from a data-driven model. Besides, the model within the upper-level optimization is a fully convolutional network, trained by the calculated loss in the lower-level optimization. Thus, the framework avoids extensive data labeling and can be applied in diverse environments. Our comprehensive experiments demonstrate that iA∗ surpasses both classical and data-driven methods in pathfinding efficiency and shows superior robustness among different tasks, validated with public datasets and simulation environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

USEFUSE: UTILE STRIDE FOR ENHANCED PERFORMANCE IN FUSED LAYER ARCHITECTURE OF DEEP NEURAL NETWORKS

arXiv

引用

arXiv 2024年

作者： Ibrahim, Muhammad Sohail Usman, Muhammad Lee, Jeong-A Department of Mechanical Systems Engineering Kumoh National Institute of Technology Gumi-Si Korea Republic of Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Department of Computer Engineering Chosun University Gwangju Korea Republic of

Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The study proposes a methodology for fusing multiple convolution layers to reduce off-chip memory communication and increase overall performance. An effective mechanism detects and skips inefficient convolutions after ReLU layers, minimizing power consumption without compromising accuracy. Furthermore, efficient tile movement guarantees uniform access to the fusion pyramid. An analysis demonstrates the utile stride strategy improves operational intensity. Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications, and another focuses on resource-constrained devices with comparable latency. This approach notably reduced redundant computations, improving the efficiency of CNN deployment on edge devices. © 2024, CC BY.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical data

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

引用

Communication, Control and Intelligent systems (CCIS)

作者： Prasanna V Suresh Kumar N Divyaprabha P R Sundaram Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Information Technology Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Ganesh College of Engineering Salem India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN–GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors’ information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python.

关键词： Analytical models Accuracy data analysis Computational modeling Predictive models Feature extraction data models Convolutional neural networks Cardiovascular diseases Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

引用

Electronics and Renewable systems (ICEARS), International Conference on

作者： Pradeep Kumar S CH Hussaian Basha C. Arun Prasath G. Amirthayogam Syed Ismail Abdul Lathif R. Leelavathi Computer Science Design Department Rajalakshmi Engineering College Thandalam Chennai Department of Electrical and Electronics Engineering SR University Warangal Department of ECE Mahendra Engineering College Namakkal Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Department of Biomedical Engineering Velalar College of Engineering and Technology Erode

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods.

关键词： Training Costs Supply chain management Supply chains Smart contracts Key performance indicator Blockchains Stakeholders Internet of Things Optimization

来源：评论

学校读者我要写书评

暂无评论

The Smart Application of data Mining in the Detection of Fraudulent Transactions 3

The Smart Application of Data Mining in the Detection of Fra...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Bodhankar, Aniruddha Aswal, Upendra Singh Arivazhagn, D. Pund, Sachin S. Kulkarni, Nandini Chaturvedi, Sudhir Kumar Dr. Ambedkar Institute of Management Studies and Research- Nagpur Department of Data Science & Information Systems Nagpur India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Symbiosis International University Department of Symbiosis School of Planning Architecture and Design Nagpur Nagpur India Business School Academy of Maritime Education and Training Deemed to Be University Department of Amet Chennai India Upes Department of School of Engineering Dehradun India

ISBN: (纸本)9798350319125

Records mining is a powerful analytical device used to find out styles, correlations, and traits in large facts sets. Smart application of data mining methods within the detection of fraudulent transactions has finished an extensive quantity of success. It normally entails statistical algorithms, facts evaluation, and machine getting to know techniques to stumble on suspicious sports associated with fraud. Using statistics mining techniques, it's far possible to music the behaviour of clients, discover anomalous transaction styles and developments in huge datasets, come across frauds and generate signals on suspicious activities. It also allows to identify which forms of merchandise are frequently centered, which price methods are used, which places are problem to danger, and which traders have capacity credit card fraud. The utility of facts mining within the detection of fraudulent sports entails principal techniques. The first method makes use of conventional techniques used by banks, credit groups and other retailers, which includes account reconciliation, annualized billing and complex question processing. Those methods are used to generate reviews that are used by investigators to perceive probably suspicious activities. The second technique entails the usage of superior analytical techniques such as neural networks, decision timber, and clustering algorithms. These strategies have the functionality to stumble on facts irregularities in several conditions that were formerly unseen or unknown. © 2023 IEEE.

关键词： developments entails reconciliation statistics transaction

来源：评论

学校读者我要写书评

暂无评论

Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification 1

Equilibrium Optimizer with Deep Learning Model for Autism Sp...

引用

1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Praveena, A. Senthamilarasi, N. Karthik, T.S. Abirami, S.K. Vijayakrishna Rapaka, E. Das, Shyamali Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Tamilnadu Coimbatore India Department of Information Technology Panimalar Engineering College Tamilnadu Chennai India Department of Electronics and Communication Engineering Aditya College of Engineering and Technology Andhrapradesh Surampalem India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Tamilnadu Coimbatore India Data Science Indian Institute of Technology Madras Tamilnadu Chennai India Karnataka Bangalore India

ISBN: (纸本)9781665460842

Autism Spectrum Disorder (ASD) is a developing disorder if the symptoms develop obvious in the initial years of age but it could be present in some age groups. ASD is mental health problem that affects communicational, social, and non-verbal performances. It could not be cured entirely but is decreased when identified initially. The primary analysis was hampered by the difference and severity of ASD symptoms and containing symptoms usually realized in other mental health problems as well. With the application of machine learning (ML) for the predictive and recognition of several diseases with optimum accuracy, a ray of hope to initial recognition of ASD dependent upon many physiological and physical parameters is projected. This article designs an Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification (EODL-ASDC) technique. The presented EODL-ASDC technique mainly focuses on the identification and classification of ASD. To attain this, the presented EODL-ASDC technique exploits the deep belief network (DBN) system to perform the classification procedure. In addition, the EO algorithm is employed for the optimal hyperparameter tuning of the DBN approach. To demonstrate the enhanced ASD classification result of the EODL-ASDC approach, an extensive range of experimental evaluates was executed. The experimental results demonstrate the improvements of the EODL-ASDC technique over other approaches. © 2022 IEEE

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

POS0144 PREDICTING INFECTION RISK IN RHEUMATOID ARTHRITIS PATIENTS TREATED WITH BIOLOGIC AND TARGETED SYNTHETIC DMARDS: A MACHINE LEARNING APPROACH

引用

Annals of the Rheumatic Diseases 2025年 84卷 433-434页

作者： K. Peng D. Yang J. Wang C.Y. Shen M.C.Y. Cheng S.C.W. Chan I.Y.K. Tang Q. Zhang E.C. Lai N.L. Pratt C.S. Lau X. Li The University of Hong Kong Department of Medicine Hong Kong Hong Kong China The University of Hong Kong HKU Musketeers Foundation Institute of Data Science Hong Kong Hong Kong China The University of Hong Kong Deparment of Pharmacology and Pharmacy Hong Kong Hong Kong China National Cheng Kung University School of Pharmacy Tainan Taiwan China University of South Australia Clinical and Health Sciences Adelaide Australia Hong Kong Science Park Laboratory of Data Discovery for Heath Hong Kong Hong Kong China

Background: Patients with rheumatoid arthritis (RA) treated with biologic and targeted synthetic disease-modifying antirheumatic drugs (b/ts DMARDs) face an increased risk of serious infections, a leading cause of morbidity and mortality [1]. Current evidence on infection risk stratification across different b/ts DMARDs is fragmented, and predictive tools are lacking. Objectives: This study aimed to develop a machine learning (ML) model leveraging electronic medical records (EMR) to predict serious infection risk among b/ts DMARD users. Methods: data from the Hong Kong Clinical data Analysis and Reporting System (CDARS) were used to develop the model, with external validation performed using the U.S.-based All of Us database. Adult RA patients initiating b/ts DMARDs between 2010 and 2023 were included. Serious infection was defined as infection-related hospitalization requiring anti-infection drugs within ±14 days of admission. Predictive features encompassed demographics, comorbidities, laboratory tests, prior infection history, and medication use. Six ML algorithms, including random forest, XGBoost, support vector machine, neural network, LASSO regression, and logistic regression, were evaluated. The final model was selected based on the highest area under the receiver operating characteristic curve (AUROC). SHAP (Shapley Additive Explanations) values were used for clinical feature interpretation and biomarker thresholds for infection risk were determined to guide high risk patients' identification. Results: A total of 3159 and 1845 b/ts DMARD users were identified in the CDARS and All of Us cohorts, respectively. The random forest model performed best in the training cohort (AUROC: 0.84; accuracy: 0.76; sensitivity: 0.73; specificity: 0.77) and achieved robust external validation on All of Us database (AUROC: 0.73; accuracy: 0.61; sensitivity: 0.71; specificity: 0.60). Previous infection, the type of b/ts DMARD, and diabetes mellitus were the top predictors of in

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：