检索结果-内蒙古大学图书馆

6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Digumarthi, Suseela Singidi, Sugunasri Paladugu, Triveni Mattaparthi, Ramakrishna Chandra Sekhar Reddy, N. Department of Computer Science and Engineering Godavari Institute of Engineering and Technology Andhra Pradesh Rajahmundry India Department of Computer Science and Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Tamil Nadu Chennai India

ISBN: (纸本)9789819619801

Cloud computing stands at the forefront of contemporary advancements in distributed computing, providing a range of data storage, including on-demand services, platform services, and network capabilities. Despite the compelling advantages, many enterprises exhibit hesitancy in embracing cloud services, primarily stemming from apprehensions about data security, given that the data are housed on servers belonging to the cloud service provider. Addressing this concern has prompted researchers worldwide to explore various avenues for fortifying and ensuring the security of data stored in cloud computing. Among these approaches, the bi-directional DNA encryption algorithm is gaining prominence as a notable contender. However, a notable limitation of the current methodology lies in its exclusive focus on the American Standard Code for Information Interchange character set, neglecting the broader scope of non-English users engaging with cloud computing. Consequently, this proposed endeavor seeks to augment the BDEA to accommodate Unicode characters, ensuring a more inclusive and robust security framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed cloud

来源：评论

学校读者我要写书评

暂无评论

Ground-Glass Lung Nodules Recognition Based on CatBoost Feature Selection and Stacking Ensemble Learning

引用

Journal of Shanghai Jiaotong University (science) 2024年 1-10页

作者： Miao, Jun Chang, Yiru Chen, Chen Zhang, Maoxuan Liu, Yan Qi, Honggang Guo, Zhijun Xu, Qian School of Computer Science Beijing Information Science and Technology University Beijing100101 China Pathological Information Engineering Technology Center Jinan Supercomputing Technology Research Institute Jinan250100 China School of Computer Science and Technology University of the Chinese Academy of Sciences Beijing100049 China Department of Radiology Tianjin Kanghui Hospital Tianjin300385 China Department of Radiology Huabei Petroleum General Hospital Hebei Renqiu062550 China

Aimed at the issue of high feature dimensionality, excessive data redundancy, and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition, a recognition method based on CatBoost feature selection and Stacking ensemble learning was proposed. First, the method uses a feature selection algorithm to filter important features and remove features with less impact, achieving the effect of data dimensionality reduction. Second, random forests classifier, decision trees, K-nearest neighbor classifier, and light gradient boosting machine were used as base classifiers, and support vector machine was used as meta classifier to fuse and construct the ensemble learning model. This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers. The experimental results show that the recognition accuracy of the proposed method reaches 94.375%. Compared to the random forest algorithm with the best performance among single classifiers, the accuracy of the proposed method is increased by 1.875%. Compared to the recent deep learning methods (ResNet+GBM+Attention and MVCSNet) on ground-glass pulmonary nodule recognition, the proposed method’s performance is also better or comparative. Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules. © Shanghai Jiao Tong University 2024.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Transforming Classroom Instruction: Embracing Machine Learning-Assisted Teaching Pedagogy 3rd

Transforming Classroom Instruction: Embracing Machine Learni...

引用

3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Bhatt, Vinod Gupta, Dev Brat Jain, Divya Jain, Manisha Mathew, Geethu Anne Sirohi, Gajendra School of Advanced Sciences and Languages VIT Bhopal University Madhya Pradesh Bhopal India Jaypee University of Engineering and Technology Guna India National University of Science and Technology Muscat Oman Department of Commerce Acropolis Institute of Management Studies and Research Indore India

ISBN: (纸本)9789819727155

The quality of teaching in the classroom significantly affects students’ learning results, highlighting its critical role in education. One progressive teaching approach involves the use of machine learning, demonstrating a major technological influence on educational methods. This approach mixes machine learning principles to enrich learning spaces and teaching methods. By applying machine learning tools, educators can tailor teaching methods, delve into student data to tackle specific educational needs, and craft engaging learning activities. These machine learning tools help teachers enhance how they instruct. These machine learning techniques enrich the educators’ methods of instruction as they can be employed to fasten machine learning based teaching strategies in K-12 classrooms. They can pinpoint the weak areas, then create the lessons that are adjusted, and finally direct the help to where it is really needed the most. Integration of a game-like aspect and interactive approach makes learning more involving and enjoyable. These methods invite students to interact with learning from one’s own experience. The advantages of this teaching strategy are the enhancement of the teachers’ content delivery and the development of lessons’ design. Through machine learning, teachers can produce and distribute learning documents in a short time that meet the individual needs of learners. Immediate feedback and assessment are the qualities, which natural language processing makes possible, and serve teachers in finding the best ways of teaching. However, along with machine learning-backed training, a collaborative approach among instructors is also endorsed. Through automation of the repetitive jobs, teachers can now devote more energy to classroom instruction and student support. Data empowering insights provide teachers with valuable help to become successful in their teaching with the confirmation that the educational methods used are quite effective. In addition, techno

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Use of IOT in computer Control Using Hand Gesture Using AT Mega 328 over the Cloud

Use of IOT in Computer Control Using Hand Gesture Using AT M...

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Ishwarlal Rathod Kunal Rathod Neha Gupta Computer Science and Information Technology Symbiosis University of Applied Sciences Indore India Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

In the realm of remote control, my preference is computer manipulation through hand gestures. Recent strides in technology showcase the feasibility of using manual gestures on Arduino platforms, facilitated by cloud computing. A Wi-Fi-based hand gesture computer control system was developed using an ESP-01 Wi-Fi module and an Arduino microcontroller, enabling remote control and monitoring without geographical constraints or time limitations. The ESP8266 Wi-Fi module played a crucial role in establishing wireless communication with an Android application (S Remote) for transmitting commands to regulate domestic appliances. The AT mega 328 AVR series Microcontroller R3, lacking built-in Wi-Fi, required the ESP-01 Wi-Fi module for wireless communication and programming. The Arduino IDE facilitated programming, incorporating ESP-01 and LCD components. Hand gesture-controlled robots and PC/laptop hand gesture control systems, both utilizing Arduino technology, exemplify similar applications. The Human Machine Interface (HMI) concept involves physical components and software enabling communication between users and machines. HMI devices, including LED indicators and touch screens, traditionally use keyboards and mice. This research implemented a hand gesture controller using Arduino technology, specifically for web browser features, video playback, and VLC media player audio adjustments. The ESP-01 Wi-Fi module, configured via AT instructions through a UART serial connection, played a key role in integrating a hand gesture system for effective computer control. Users can perform actions like playing or pausing videos and adjusting audio amplitude within the VLC media player solely through manual gestures.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Study of Machine Learning Classifiers for Intrusion Detection System 1

引用

3rd International Conference on Data, engineering, and Applications, IDEA 2021

作者： Mishra, Akshita Thakur, Archana Acropolis Institute of Technology and Research Madhya Pradesh Indore India School of Computer Science & IT Devi Ahilya University Madhya Pradesh Indore India

ISBN: (数字)9789811946875

ISBN: (纸本)9789811946868

Intrusion detection systems are an important part of our network security system. Furthermore, with the development in technologies, new attacks have been developed and attack types have also changed, hereby affecting more people and organizations. Cyber security research studies have been working on strengthening intrusion detection systems and intrusion detection methods. Machine learning has become a boon for this area also and is being actively applied to improving intrusion detection systems. Zero-day attacks or novel attacks have become a major problem. Therefore, machine learning methods need to be trained with newer attacks for detecting novel attacks quickly. Hence, we have used the CICIDS 2017, dataset which consists of many cutting-edge intrusions for testing and training purposes. In this work, we have concentrated on Supervised Machine Learning Classifiers and have selected 8 classifiers based on various works of literature available. These classification models have been evaluated on the basis of training time, testing time, train score, accuracy, and area under curve. As the result of this work, it has been found that decision trees and random forest have achieved an accuracy of almost 99% and within a considerable time limit. This work of ours is mainly concentrated on the use of classifiers for the intrusion detection system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A Review of Social Media Sentiment Analysis Using Machine Learning To Enhance Stock Market Prediction 2

A Review of Social Media Sentiment Analysis Using Machine Le...

引用

2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Raut, Rohini Saratkar, Saniya Thakre, Gayatri Thute, Trupti Chaudhari, Aarti Gourshettiwar, Palash Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Predicting stock prices is a critical financial activity that is frequently impacted by sentiment analysis, machine learning, and data mining approaches. This examination looks at a variety of stock price prediction methods, including artificial neural networks, graph-based approaches, time series analysis, and classic machine learning techniques such as Support Vector Machine, K-Nearest Neighbor, and Random Forest. The article assesses the problems and efficacy of various methodologies, including a thorough examination of sentiment analysis in social media to enhance stock market predictions. By examining multiple research, this study provides a complete evaluation of various prediction algorithms and reveals their shortcomings, such as overfitting, non-stationary data, and the complexity of financial transactions. It helps academics and practitioners construct more accurate and dependable stock forecasting models by merging financial data with sentiment analysis. The study also discusses rising trends and new potential for further study, especially hybrid models and advanced deep learning approaches to improve the accuracy of stock forecasts. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

A Framework for Collaborative Computing on Top of Mobile Cloud Computing to Exploit Idle Resources

引用

Annals of Data science 2023年第6期10卷 1635-1651页

作者： Babu, A. Ramesh Upadhayaya, Niraj Research Scholar Department of Computer Science & Engineering Jawaharlal Nehru Technological University Kukatpally Telangana Hyderabad500085 India J.B.Institute of Engineering & Technology Moinabad Telangana Hyderabad India

In the contemporary era, collaborative computing is the widely used model to exploit geographically distributed heterogeneous computing resources. Mobile Cloud Computing (MCC) offers an infrastructure that helps in offloading storage and computing resources to a public cloud. It has several advantages. However, in the context of modern Internet of Things based applications, it is essential to exploit idle resources of mobile devices as well. However, it is a challenging problem as mobile devices are resource-constrained and have mobility. Many existing MCC solutions concentrated on offloading tasks to outside mobile devices. In this paper, we investigate the possibility of using idle resources in mobile devices besides offloading tasks to the cloud. We proposed a novel algorithm known as Delay-aware Energy-Efficient Task Scheduling. The algorithm analyses locally available idle resources and schedules tasks over heterogeneous cores in mobile devices and also the cloud. In the process, it achieves strict deadlines associated with tasks and promotes energy conservation. A prototype application is built to simulate and evaluate the proposed algorithm. The experimental results revealed that the algorithm outperforms the existing baseline algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Mobile cloud computing

来源：评论

学校读者我要写书评

暂无评论

Unstructured Text Classification Using NLP and LSTM Algorithms 7th

Unstructured Text Classification Using NLP and LSTM Algorith...

引用

7th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2023

作者： Prusty, Sashikanta Patnaik, Srikanta Sahoo, Ghanashyam Rautaray, Jyotirmayee Prusty, Sushree Gayatri Priyadarsini Department of Computer Science and Engineering Siksha ‘O’ Anusandhan [Deemed to be University Bhubaneswar751030 India Interscience Institute of Management and Technology Kantabada Bhubaneswar752024 India Department of Computer Science and Engineering GITA Autonomous College Bhubaneswar751017 India Department of Computer Science and Engineering Odisha University of Technology and Research Bhubaneswar751003 India

ISBN: (纸本)9789819990177

In the last two decades, cancer has found as the most severe disease that causes a large number of deaths worldwide. However, it seems that the medical data are in the unstructured form, which makes it challenging for the pathologist to classify the disease in the beginning stages. This article provides a novel technique using natural language processing (NLP) for the classification of three common cancers thyroid, lung, and colon from the unstructured medical data, removing at-stop words, and articles such as ‘a,’ ‘an,’ and ‘the’ to form tokens. Tokenization is performed on larger text to divide it into smaller parts with the help of the TensorFlow technique. Besides that, long short-term memory networks (LSTM) model has been proposed to handle long sentences, especially in sequence prediction problems. This article also discusses the basic workflow of model design and prediction of three cancers. Finally, a confusion_matrix has been drawn to evaluate our model performance and found 99.41% at classifying three individual cancers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Machine Learning based Human Stress Detection through Sleep Patterns 5

Machine Learning based Human Stress Detection through Sleep ...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Santhosh Krishna, B.V. Mai, Kiran Aswini, S. Biju, J. Mohanraj, S. Kaliappan, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Bangalore Technological Institute Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technology Information Science and Engineering Erode Tamilnadu Sathyamangalam India KGiSL institute of Technology Artificial Intelligence and Data Science Tamilnadu Coimbatore India Lovely Professional University Division of Research and Development Punjab India

ISBN: (纸本)9798331504403

Stress is a critical factor in overall wellbeing, and understanding its patterns during sleep can provide valuable insights into improving sleep quality and managing stress-related health issues. Stress has become an increasingly prevalent issue in modern society, impacting both mental and physical well-being. The ability to accurately detect stress levels in individuals is crucial for timely intervention and management. Sleep patterns serve as a valuable indicator of stress, as disruptions in sleep quality and duration often correlate with heightened stress levels. Leveraging advancements in machine learning techniques, this study proposes a novel linaer regression based approach to detect human stress by analyzing sleep patterns extracted from wearable devices. The proposed system consists of several key components. Firstly, data collection involves the continuous monitoring of sleep parameters such as sleep stages, heart rate variability, and movement using wearable sensors. Next, feature extraction techniques are applied to transform raw sleep data into informative features that capture relevant aspects of sleep patterns indicative of stress. © 2024 IEEE.

关键词： Sleep research

来源：评论

学校读者我要写书评

暂无评论

On-Road Drivers and People with Visual Impairments: Real-Time Machine Learning-based Speed Breaker and Pothole Detection 15

On-Road Drivers and People with Visual Impairments: Real-Tim...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Fathima, Chahath A. Suhas, G.K. Bhagappa Deepak, N.R. Research center for Computer & Information Sciences Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur India Research center for Computer & Information Sciences Brindavan College of Engineering Affiliated to visvesvaraya technological university Bangalore India Department of Computer Science Engineering Atria Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9798331300579

Although well-maintained roads are an important source of transportation, their economic impact is rarely felt in a ***, drivers who aren't convinced of their safety are seriously at risk from potholes and speed ***, poor night vision, inaccurate speed breaker signage, and the driver's carelessness all contributed to this *** of these elements harm the vehicle's suspension system in addition to having an impact on drivers who are on the *** days, cars with reduced ground clearance have trouble on uneven *** this paper, we proposed to use a Recurrent Neural Network (RNN) in a machine learning method to create a device that can identify bumps and *** device uses an ESP32 GPS Speedometer to locate these bumps and potholes and transmits the position, along with speed, latitude, and longitude values, to a database via *** device also chooses to save the data it collects in the *** the aid of machine learning, it can be classified into various categories based on *** also has a speech module that allows audio notifications to be played over the *** research suggests using a Recurrent Neural Network (RNN), a machine learning method, to train parameters autonomously in order to enhance vehicle suspension health and prevent driver fatigue. © Grenze Scientific Society, 2024.

关键词： Recurrent neural networks

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：