检索结果-内蒙古大学图书馆

14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Joseph, Prashob Yerima, Suleiman Y. School Computer Science and Informatics De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom Cyber Technology Institute De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom

ISBN: (纸本)9781665487719

E-mail and SMS are the most popular communication tools used by businesses, organizations and educational institutions. Every day, people receive hundreds of messages which could be either spam or ham. Spam is any form of unsolicited, unwanted digital communication, usually sent out in bulk. Spam emails and SMS waste resources by unnecessarily flooding network lines and consuming storage space. Therefore, it is important to develop high accuracy spam detection models to effectively block spam messages, so as to optimize resources and protect users. Various word-embedding techniques such as Bag of Words (BOW), N-grams, TF-IDF, Word2Vec and Doc2Vec have been widely applied to NLP problems, however a comparative study of these techniques for SMS spam detection is currently lacking. Hence, in this paper, we provide a comparative analysis of these popular word embedding techniques for SMS spam detection by evaluating their performance on a publicly available ham and spam dataset. We investigate the performance of the word embedding techniques using 5 different machine learning classifiers i.e. Multinomial Naive Bayes (MNB), KNN, SVM, Random Forest and Extra Trees. Based on the dataset employed in the study, N-gram, BOW and TF-IDF with oversampling recorded the highest F1 scores of 0.99 for ham and 0.94 for spam. © 2022 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

An Image Enhancement and Data augmentation of Alzheimer's MRI Data using modified SRGAN 14

An Image Enhancement and Data augmentation of Alzheimer's MR...

引用

14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Rashmi, Uppin Beena, B.M. Preethi, A. Ambesange, Sateesh Amrita School of Computing Amrita Vishwa Vidyapeetham Research Scholar Cse Department Bangalore India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bangalore India Cmr Institute of Technology Department of Ece Bengaluru India Pragyan SmartAI Technology Llp PragyanAI Bengaluru India

ISBN: (纸本)9798350335095

Machine learning (ML) models, Deep Learning (DL) Models rely on labeled data for classification and prediction. Automated or computer-aided medical diagnosis requires a large dataset to maintain clinical accuracy and precision. High-resolution images of different clinical and symptomatic stages are essential for implementing ML /DL algorithms for medical diagnosis. High resolution MRI image aids in better performance of DL Models. Here we propose a model based on Super Resolution Generative Adversarial Networks (SRGAN) with Transfer learning to enhance the MR images for Alzheimer's disease (AD). These images can also be used as data augmentation and increase dataset size for better model performance. AD, an irreversible cognitive impairment disorder, is one of the significant causes of death in the elderly. No cure is yet developed, and diagnosis at an early stage will enable the elderly to make informed decisions. The model enhance low resolution image of lower size to high resolution higher size. The model trained for 5000 epochs resulted in a final G loss of 0.001 and a D loss of 1.006, which is a significantly good result. The high-resolution images generated by the proposed model are validated by the pixel-wise loss function, with a comparable average MSE of 0.009. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

A Critical Analysis of Object Tracking Using Radial Function Networks

A Critical Analysis of Object Tracking Using Radial Function...

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Mishra, Amit Kumar Pandey, Neeraj Kumar Diwakar, Manoj Bahadure, Nilesh Bhaskarrao Dumka, Ankur Patni, Jagdish Chandra Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Dept. of CSE Nagpur Campus Pune India Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India Alliance University Alliance School of Advance Computing Dept. of CSE Karnataka Bengaluru India

ISBN: (纸本)9798350394412

A important method in several fields, including driverless cars, medical imaging, human-computer interaction, and investigation systems, is object tracking. Because of their flexibility to changes in object presence and ambient factors, as well as their capacity to estimated complex, non-linear functions, radial networks have become a potent tool for object stalking in recent years. The article conditions for the analysis of the radical working networking where the algorithm is a tracking segment that aims in moving the object. Object recognition is one of the most basic and centralized factor analyses in determining outputs having relevant values and a strong research background. Radial functioning is a phenomenon that has gained rapid training and desirable properties for improving the presentation of networking sites. radial networks, a form of artificial neural network, utilize radial basis functions as initiation functions. Because of this characteristic, RBF networks are especially well-suited for applications like object tracking that call for high accuracy in function approximation and pattern recognition. We explore the design of RBF networks in detail, emphasizing the functions of the input layer, output layer, and hidden layer using radial basis functions. We examine the mathematical foundations that allow RBF networks to represent intricate connections in the data, emphasizing how well they can generalize and interpolate. © 2024 IEEE.

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

An Ensemble Framework for Dropout Prediction in Online Learning

An Ensemble Framework for Dropout Prediction in Online Learn...

引用

2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Srinivasan, Sruthi Dewan, M. Ali Akber School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computing and Information Systems Athabasca University Faculty of Sceinece and Technology AB Canada

ISBN: (纸本)9781665456371

Online learning has gained traction over recent years, especially since online education has become more widespread. However, it comes with its own set of challenges of which high dropout is still a major one. Identifying at-risk learners at an early stage is pivotal to offering personalized attention that can potentially prevent them from dropping out from the online courses. This work proposes two methods to analyze students' progress in an online course and subsequently identify dropout prone students. The first method performs fusion of course activity features by concatenating previous weeks' features before training. The second method extracts course activity features from the start date of the courses to a current week instead of concatenating as the first method does. A set of machine learning models and an ensemble framework were trained and tested on these two types of feature sets. On evaluating the models, the benchmark dataset KDDCup15 has been used, where the first method yielded an F1-score 91% while the second method yielded a score 92%. It was observed that both feature fusion methods produce comparable results although we expected that the concatenation of the features over time would produce better results. We also found that using features over a longer duration of time can help in achieving better performance. Further, ensemble model consistently outperformed the base classifiers. © 2022 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Enhancing Traffic engineering Policy in SDN Addressing TCAM limitations using Bloom Filters 4

Enhancing Traffic Engineering Policy in SDN Addressing TCAM ...

引用

4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Ghosh, Akash Barik, Lalbihari Rout, Suchismita Rawat, Bhupender Singh Jena, Prakash Chandra Patra, Sudhansu Shekhar School of Computer Applications KIIT Deemed To be University Bhubaneswar India Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Saudi Arabia College of Smart Computing COER University Roorkee India Department of Computer Sc and Engg. Hi-Tech College of Engineering Bhubaneswar India

ISBN: (纸本)9798350305258

With the increasing demand for new technologies, the speed of delivering a packet is becoming the major focus of various industries. The generation of huge traffic is becoming the bottleneck of the current scenario. By segregating the control plane from the data plane, Software Defined Networking (SDN) is able to many such issues to certain extent. But the traffic engineering problem still remains as an important topic to focus on. Ternary Content Addressable Memory (TCAM) is capable of solving many such issues with the cost of its cost and power issues. For the effective use of TCAM, various algorithms and mechanisms have been proposed by many researchers. Bloom filter is one of the popular ways which is able to provide effective solutions after collaborating with TCAM. An algorithm has been proposed which is able to find the exact match of the entry in TCAM. The use of a CRC generator and multiple hashing tables makes it effective at finding the presence of a flow rule in TCAM, in very little time. The major focus is on the fast search procedure for finding the exact match of the flow rule. The number of false positives (FPs) is reduced to a lower percentage value as compared to the existing approaches. © 2023 IEEE.

关键词： Bloom Filter False positive IP addressing multi-hashing Prefix match SDN TCAM Traffic engineering

来源：评论

学校读者我要写书评

暂无评论

Federated Learning Assisted Covid-19 Detection Model

Federated Learning Assisted Covid-19 Detection Model

引用

作者： Banerjee, Sourav Barik, Sudip Das, Debashis Ghosh, Uttam Debnath, Narayan C. Computer Science and Engineering Kalyani Government Engineering College Nadia Kalyani India Computer Science and Engineering University of Kalyani Nadia Kalyani India Department of CS and DS Meharry Medical College NashvilleTN United States School of Computing and Information Technology Eastern International University Binh Duong Viet Nam

Federated learning (FL) ensures data privacy and security, which has been emerging as a promising approach for developing an accurate and efficient model for COVID-19 detection. This paper highlights the FL-assisted systematic review of the current state-of-the-art COVID-19 detection methods. This work conducts a comprehensive study of the relevant literature and identifies relevant articles. The included articles described various FL-based COVID-19 detection models that utilized different types of data and machine learning algorithms. Furthermore, these models have several advantages over traditional centralized models, including privacy protection, data security, and scalability. The findings of this systematic review suggest that FL-based COVID-19 detection models have great potential for accurate and efficient COVID-19 detection while protecting data privacy and security. FL requires further research to validate the findings on different metrics and enhance the performance of the models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

引用

Procedia computer Science 2025年 258卷 3970-3979页

作者： Rekha R Nair Tina Babu Kishore S Vishal S Sheila Mahapatra Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to be university Nitte Institute of Professional Education Mangalore Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R 2 , AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day’s stock price would be higher or lower than the previous day’s price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability.

关键词： Independent Component Analysis Principal Component Analysis Probable Neural Network Expectation Maximization algorithm Natural language processing

来源：评论

学校读者我要写书评

暂无评论

Energy efficient machine learning based SMART-A-BLE implemented Wireless Battery Management System for both Hybrid Electric Vehicles and Battery Electric Vehicles

Energy efficient machine learning based SMART-A-BLE implemen...

引用

2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Pannerselvam, Srikalaivani Narayanan, Vanantha Gireesh Kumar, T. Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore641112 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore641112 India

Electric vehicles have a larger battery pack with more than 100's cells connected in series or parallel combinations to form many battery modules. A Battery Management system (BMS) continuously monitors Current, Voltage, and Temperature data to trigger control algorithms like cell balancing and thermal management using wired communication which is then sent to the cloud for real-time analytics [1]. Wired communication restricts the flexibility in battery module assembly and makes it difficult for battery swapping. The proposed wireless communication, along with the SMART-A-BLE algorithm for BMS in the electric vehicle, uses Bluetooth Low Energy [2], [3], [4], and [5]. SMART-A-BLE algorithm predicts the best connection interval using the Decision tree model based on the link quality factor captured based on real time latency. The model uses threshold latency, expected retransmission factor, data packet length, and the number of peripherals as input features. A look-up table-based algorithm is also proposed for resource constraint devices to achieve the optimal operating point where the slaves could transmit at both low latency and low current consumption with less congestion. SMART-A-BLE control algorithm outperforms the mathematical model-based algorithm in both ideal and interference conditions within a metallic container by maintaining robust connectivity. © 2023 The Authors. Published by Elsevier B.V.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent computing Techniques for Sustainable Cybersecurity: Enhancing Threat Detection and Response 1

引用

1st International Conference on Intelligent computing for Sustainable Development, ICICSD 2023

作者： Praneeth, A. Siva Ramakrishna Shyashyankhareddy, G. Niranjan, D.K. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9783031612879

ISBN: (纸本)9783031612862

Cyberthreats including hacking, data breaches, and malware assaults have significantly increased as a result of digitalization and the Internet of Things’ (IoT) extensive use. As a consequence, the significance of cybersecurity measures has increased in order to shield crucial information technology assets and shield people and organisations from monetary losses. In order to improve cybersecurity’s capacity to recognise and react to cyberattacks, this research makes a contribution by investigating intelligent computing methodologies that make use of technologies like data analytics, machine learning (ML) and artificial intelligence (AI). The integration of intelligent computing techniques with current security architecture, proactive defence strategies, and ethical cybersecurity practises are highlighted. The study uses the UNSW-NB15 dataset and a mix of feature selection methods based on correlation and k-means clustering, followed by support vector machine (SVM) classification, to show the efficacy of the suggested strategy. According to the findings, the recommended technique has good accuracy, sensitivity, specificity, precision, and F1-score, making it a reliable option for successfully addressing dynamic cyberthreats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Indoor Navigation Using Ultra Wide Band

Indoor Navigation Using Ultra Wide Band

引用

2023 Intelligent computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Divya, B. Veeramakali, T. Revathi, S. Harisharajan, R.R. Adhirooban, D. Ebishdon, G.V. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India School of Computing Srm Institute of Science and Technology Dept of Data Science and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9798350394580

Indoor positioning is a significant and intriguing topic in navigation systems that present numerous use cases for investigation. Researchers are exploring technologies capable of providing accurate locations in indoor spaces where GNSS positioning systems are ineffective. In the numerous solutions available for indoor location services, Ultra-wideband (UWB) technology was chosen for its ability to provide high-precision positioning information for multiple points in real-time. This paper presents a UWB indoor positioning system that utilizes time of flight (TOF) algorithm. At times, time difference of arrival (TDOA) algorithms compute range measurements, this facilitates the trans-receiver (TAG) plane position algorithm's location determination. UWB technology was chosen because it is the latest technology available on Android 12 and further also offers high accuracy, resistance to interference and minimal signal reflection from walls. UltraWide Band(UWB) localization encounters various obstacles, such as multipath distortions, additional signal delays, time discrepancies in clocks, signal interference, and the computational resources necessary to determine the user's location. © 2023 IEEE.

关键词： Indoor Positioning System(IPS) Time of Flight(TOF) Ultra Wide Band(UWB)

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：