检索结果-内蒙古大学图书馆

Communications and Information Technologies (GCCIT), Global Conference on

作者： Udhayakumar Pasupathi A. Gopi Saminathan A. Manikandan M. Subhashini C. Gowrishankar T. S. Poornappriya Department of Computer Science Srimad Andavan Arts and Science College (Autonomous) (Affiliated to Bharathidasan University Tiruchirappalli) Tamilnadu India NPR College of Engineering and Technology Dindigul ECE SRM Institute of Science and Technology Chennai Department of Computer Science Srimad Andavan Arts and Science College (Autonomous) Trichy-5 Department of Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Tiruchirappalli Module Lead Data and Analytics Aspire Systems Pvt Ltd Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Wireless sensor networks (WSNs) are an innovative technology that can be used in critical situations such as battlefields as well as commercial applications including buildings, traffic monitoring, smart homes, habitat monitoring, and more. A wireless sensor network (WSN) comprises small nodes capable of wireless sensing, processing, and communication. One of the most pressing concerns plaguing WSNs today is security. This paper suggests and investigates an energy-efficient cluster head (CH) selection approach using the Whale Optimization Algorithm (WOA) (called WOA-Clustering (WOA-C)) and the Geographic and Energy Aware Routing Algorithm (GEAR). As a result, the suggested method simplifies the decision-making process of energy-aware CHs by utilizing the fitness function that assesses node residual energy and the total energy of neighbouring nodes. Compared to the GWO-C, GABEEC, and PSOMSB protocols, the proposed WOA-GEAR protocol consumes 28%, 22%, and 7.5% less power. The suggested algorithm's network lifetime, power efficiency, buffer occupancy, throughput, and end-to-end latency were evaluated.

关键词： Wireless communication Wireless sensor networks Gears Query processing Clustering algorithms Energy efficiency Whale optimization algorithms Routing protocols Security Monitoring

来源：评论

学校读者我要写书评

暂无评论

Protecting Deep Neural Networks in Cloud-Based Environments Towards Backdoor Assaults

Protecting Deep Neural Networks in Cloud-Based Environments ...

引用

Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Manjunatha R Naresh Kaushik Yaduvir Singh Department of Data analytics and Mathematical Sciences School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of uGDX ATLAS SkillTech University Mumbai Maharashtra India Department of Computer Science and Engineering (AI) Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

When it comes to training deep neural networks, especially those with complicated architectures, the accompanying financial and temporal costs often force resource-constrained customers to outsource their learning process via cloud service providers. But there's a risk that dishonest cloud providers may include backdoors and jeopardise the security of the overall model. Users may examine the model closely and utilise state-of-the-art defence techniques to reduce this danger. The goal of this research is to create a robust backdoor attack technique called RobNet that can circumvent current defences from the perspective of malevolent cloud providers. Furthermore, multi-trigger backdoor assaults are introduced, which enlarge the attack area by misclassifying inputs with different triggers into target labels that are either identical or dissimilar. RobNet is tested against four popular defence techniques (Pruning, NeuralCleanse, Strip, and ABS) using datasets from MNIST, GTSRB, and CIFAR-10. RobNet has a higher attack success rate and more robustness against possible defensive measures when compared to two state-of-the-art baselines: Hidden Backdoors and BadNets.

关键词： Training Technological innovation Strips Neurons Artificial neural networks Robustness Service-oriented architecture Security Biological neural networks Resilience

来源：评论

学校读者我要写书评

暂无评论

Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework

arXiv

引用

arXiv 2024年

作者： Averly, Reza Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States College of Pharmacy The Ohio State University United States

Clinical named entity recognition (NER) aims to retrieve important entities within clinical narratives. Recent works have demonstrated that large language models (LLMs) can achieve strong performance in this task. While previous works focus on proprietary LLMs, we investigate how open NER LLMs, trained specifically for entity recognition, perform in clinical NER. Our initial experiment reveals significant contrast in performance for some clinical entities and how a simple exploitment on entity types can alleviate this issue. In this paper, we introduce a novel framework, entity decomposition with filtering, or EDF. Our key idea is to decompose the entity recognition task into several retrievals of entity sub-types and then filter them. Our experimental results demonstrate the efficacies of our framework and the improvements across all metrics, models, datasets, and entity types. Our analysis also reveals substantial improvement in recognizing previously missed entities using entity decomposition. We further provide a comprehensive evaluation of our framework and an in-depth error analysis to pave future works. © 2024, CC BY.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things

A Secured and Optimized Broadcast Authentication Scheme for ...

引用

Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Syed Waleed Riaz Fazlullah Khan Insaf Ullah Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China Institute for Analytics and Data Science University of Essex Colchester UK Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes.

关键词： Performance evaluation Computational modeling Authentication Surgery Internet of Medical Things Public key cryptography Real-time systems Information management Monitoring Read only memory

来源：评论

学校读者我要写书评

暂无评论

Classification of Parkinson's Disease using CNN and ANN with the aid of Drawing and Acoustic Feature 2

Classification of Parkinson's Disease using CNN and ANN with...

引用

2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Fiza, Syed Kumar, Ashish Yadav, Jatin Reddy, Sainandan Sidda, Tej Kurma Sai, Kaarthik Dayananda Sagar College of Engineering Department of Medical Electronics Karnataka Bengaluru India Delhi Technological University Department of Information Technology Shabad Daulatpur Delhi India Netaji Subhas University of Technology Department of Instrumentation and Technology Dwarka Delhi India Indian Institute of Technology Department of Civil Engineering Hyderabad India Gandhi Institute of Technology and Management Department of Computer Science and Engineering Andhra Pradesh Vishakhapatnam India Data Analytics Vellore Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9781665437899

Parkinson's disease manifests itself in a variety of ways. slurred speech, muscle rigidity, and tremors. The condition can also affect the production of the brain's dopamine. Therapies can help treat conditions after a diagnosis, however, there is no remedy. The paper primarily analyses the many drawing evaluation methods and disease diagnoses that have been used. The AI deep learning idea was used, along with convolutional neural networks and artificial neural networks. However, this article proposes a model for detecting disease using drawings of different persons from two classes. Disease detection on photos has been accomplished using a variety of techniques such as data augmentation, sequential, and CNN. The acoustic feature data was processed using a Decision tree and ANN with GridsearchCV, and the overall accuracy on testing data was 98 percent. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of Language Disorders in Children Using NLP and Machine Learning

Automated Detection of Language Disorders in Children Using ...

引用

Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： P. Malathi N. Legapriyadharshini Shruthi S Nair M. P. Sujatha Sadaieswaran R T.Thirumalaikumari Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Application Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Applications Tagore College of Arts and Science Chennai India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The purpose of the study is to address the essential need for a more efficient and objective technique for detecting language disorders in children, overcoming the constraints of standard manual evaluation systems. Existing systems, which utilize subjective evaluations and manual scoring, frequently result in lengthy processes and possible errors. To solve these issues, the proposed system automates the identification process by combining Natural Language Processing (NLP) and Machine Learning (ML). The system achieves a breakthrough in accuracy by normalizing text and extracting a comprehensive collection of linguistic features using advanced NLP techniques. The implementation involves difficult data preprocessing, extensive feature extraction, and the application of a cutting-edge Support Vector Machine (SVM) classifier. The proposed system achieves a 15% increase in accuracy, a 12% increase in precision, a 10% increase in recall, and an 11% increase in F1 score over existing systems. The confusion matrices show improved prediction skills, while accuracy across varied linguistic samples demonstrates the system's adaptability. Finally, the proposed system not only speeds up the detection of language disorders but also provides the groundwork for deeper insights into linguistic development, with revolutionary potential in educational and therapeutic settings.

关键词： Support vector machines Adaptation models Technological innovation Accuracy Confusion matrices Manuals Linguistics Feature extraction Natural language processing Standards

来源：评论

学校读者我要写书评

暂无评论

An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm 1

An Efficient Algorithm to Classify the Mitotic Cell using An...

引用

1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Vidhya, R.G. Seetha, J. Ramadass, Sudhir Dilipkumar, S. Sundaram, Ajith Saritha, G. Hkbk College of Engineering Department of Ece Bangalore India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Data Analyst Sterck Systems Chennai India Vit Department of Analytics Scope Vellore India School of Business Selection Grade Kochi India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India

ISBN: (纸本)9798350397840

Cancer that originates in the breast tissue then spreads to the chest wall is called breast cancer. Doctors routinely examine mammograms for signs of cancer;however, aberrant macrocalcifications and microcalcifications might appear on mammograms when the picture quality is subpar. Always get checked out if you see anything out of the ordinary, especially if it involves your breasts, such abnormal calcium deposits. For this mammographic deposit to be properly interpreted, top-notch picture quality is necessary. Many different breast cancer screening methods and the many breast cancer phases are still the subject of active study. In order to construct effective medical image processing systems, experts use methods including the Ant Colony Algorithm (ACA), the Improved Adaptive Fuzzy C-Means (IAFCM), and TNM (the size of the breast tumor (T), the lymph nodes around the tumor, and metastasized). Classes were determined using an MPIG, or a modified Poisson inverse gradient classifier. More than five hundred picture modalities are used across all methods. Medical professionals that rely on images to establish diagnoses or treatments might find the results of this research useful. © 2022 IEEE.

关键词： Deposits

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Outlier Elimination Algorithms on Pulsar Stars dataset

Comparative Analysis of Outlier Elimination Algorithms on Pu...

引用

Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Tejaswini Katale Waidehi Gautam Aakash Chotrani Aishwarya Katale Parikshit N. Mahalle Gitanjali R. Shinde Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India Department of Information Technology University of Cumberlands Williamsburg KY USA Department of Business Analytics Mercer University Atlanta Georgia USA Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune Maharashtra India Computer Science and Engineering Vishwakarma Institute of Information Technology Pune Maharashtra India

Outliers are uncommon observations that are dispersed from most of the other data Points. These points, which can be abnormally high or low, can significantly affect the entire analysis and forecasting of the data. The purpose of this Research Paper is to examine the implementation of Outlier Elimination Algorithms on Pulsar Stars dataset. It is attributed as an HTRU2 (High Time Resolution Universe Survey) project, which focuses on identifying new pulsar candidates based on the data from Parkes radio telescope in Australia. On this dataset, a total of 6 Outlier Elimination Algorithms are implemented; Z-Score, DBSCAN(Density-based spatial clustering of applications with noise), Mahalanobis Distance, Median Absolute Deviation, Multivariate Normal Distribution for N Dimension, Angle-based Outlier Detection (ABOD). After removal of the identified Outliers, to determine which Outlier Technique was more effective, 4 Machine Learning Techniques are deployed - Random Forest, Decision Tree, Naive Bayes and XGBoost. Furthermore, Accuracies, Recall, Precision and F1 Score values were compared. Every deployed Outlier algorithm must have a consistent quantity of Outliers in order to be evaluated for efficacy. For the same around 2600–2800 number of Outliers are eliminated for each Technique. Multivariate Normal Distribution for N Dimension Outlier Algorithm with Random Forest yields Highest Accuracy of 98.2%. ABOD with Naive Bayes gives Highest Recall Value of 0.93, Z-Score with Random Forest results in Highest Precision value of 0.96 and ABOD with Random Forest yields Highest F1-Score value of 0.92. From the information provided above, it is also clear that Random Forest produces the greatest Accuracy, Precision, and F1-Score values among all the Machine Learning algorithms that have been deployed. Therefore, for improved outcomes, the Random Forest technique should be utilized after the outliers are removed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Nonblocking execution in GraphBLAS

Nonblocking execution in GraphBLAS

引用

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Aristeidis Mastoras Sotiris Anagnostidis Albert– Jan N. Yzelman Computing Systems Laboratory Zurich Research Center Huawei Technologies Switzerland Data Analytics Laboratory Department of Computer Science ETH Zurich Switzerland

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

GraphBLAS is a recent standard that allows the expression of graph algorithms in the language of linear algebra and enables automatic code parallelization and optimization. GraphBLAS operations are executed either in blocking or in non-blocking mode. Although there exist multiple implementations of GraphBLAS for efficient blocking execution on both shared-and distributed-memory systems, none of these implementations supports full nonblocking execution to improve data locality. In this paper, we present a preliminary evaluation for two algorithms, Pagerank and Conjugate Gradient, that confirms the importance of nonblocking execution, by showing promising speedups over the corresponding blocking execution.

关键词： Analytical models Distributed processing Heuristic algorithms Instruction sets Distributed databases Manuals Linear algebra

来源：评论

学校读者我要写书评

暂无评论

Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced data: Improving Machine Failure Prediction

引用

computers, Materials & Continua 2023年第6期75卷 4821-4841页

作者： Yap Bee Wah Azlan Ismail Nur Niswah Naslina Azid Jafreezal Jaafar Izzatdin Abdul Aziz Mohd Hilmi Hasan Jasni Mohamad Zain Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA(UiTM)Shah Alam40450SelangorMalaysia School of Computing Sciences College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)40450Shah AlamSelangorMalaysia Mathematical Sciences Studies College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)Kelantan BranchMachang CampusBukit Ilmu18500MachangKelantan Darul NaimMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer and Information Sciences(DCIS)Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610PerakMalaysia UNITAR International University Jalan SS6/3SS6Petaling Jaya47301SelangorMalaysia

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure *** common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)*** paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas *** original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level *** accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to *** performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and *** showed that the sensitivity of all classifiers increases as the imbalance rate *** with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.

关键词： Machine failure machine learning imbalanced data SMOTE classification

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：