检索结果-内蒙古大学图书馆

9th International Conference on Systems and Informatics, ICSAI 2023

作者： Zhu, Guanyu Palaoag, Thelma Domingo ShangQiu Normal University College of Information Technology Shangqiu China University of the Cordilleras College of Information Technology and Computer Science Baguio City Philippines

ISBN: (纸本)9798350383706

The aim of this research is to implement and evaluate a smart agriculture system based on big data analytics, which provides a new way to improve the efficiency of agricultural production in the context of glocalization and climate change. Our research utilizes advanced information technology and big data analytics to optimize the agricultural production process. The article begins by introducing the concept of smart agriculture and its importance in modern agriculture. Then, we describe the architecture of the smart agriculture system, including the key aspects of data collection, processing and analysis. The system collects farm data through a network of sensors and uses cloud computing technology for processing and analysis. For the evaluation of the system, we focus on both ease of use and performance. Through collaboration with agricultural practitioners, we collected feedback on the system's user interface, understanding of its functionality, and impact on the agricultural production process. In addition, system performance was assessed through indicators such as processing speed, data accuracy, and system stability. Test results show that the system performs well in terms of ease of use, with users being able to learn quickly and operate effectively. In terms of performance, the system demonstrated efficient data processing capability and stable performance. These results indicate that our smart agriculture system is not only technologically advanced but also user-friendly and can effectively assist in agricultural production decisions. © 2023 IEEE.

关键词： Data Analytics

来源：评论

学校读者我要写书评

暂无评论

VisionEmbedder: Bit-Level-Compact Key-Value Storage with Constant Lookup, Rapid Updates, and Rare Failure 40

VisionEmbedder: Bit-Level-Compact Key-Value Storage with Con...

引用

40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Wu, Yuhan Wang, Feiyu Zhu, Yifan Fan, Zhuochen Xiong, Zhiting Yang, Tong Cui, Bin School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University Beijing China College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9798350317152

In key-value storage scenarios where storage space is at a premium, our focus is on a class of solutions that only store the value, which is highly space-efficient. While these solutions have proven their worth in distributed storage, networking, and bioinformatics, they still face two significant issues: one is that their space cost could be further reduced;the other is their are vulnerable to update failures, which can necessitate a complete table reconstruction. To address these issues, we introduce VisionEmbedder, a compact key-value embedder with constant-time lookup, fast dynamic updates, and a near-zero risk of reconstruction. VisionEmbedder cuts down the storage requirement from 2.2L bits to just 1.6L bits per key-value pair with an L-bit value, and it significantly reduces the chance of update failures by a factor of n, where n is the number of keys (for instance, 1 million or more). The compromise with VisionEmbedder comes with a minor reduction in query throughput on certain data sizes. The enhancements offered by VisionEmbedder have been theoretically validated and are effective across any dataset. Additionally, we have implemented VisionEmbedder on both FPGA and CPU platforms, with all codes made available as open-source. © 2024 IEEE.

关键词： Table lookup

来源：评论

学校读者我要写书评

暂无评论

SEG - Smart Exosuit Glove for Data Driven Physiotherapy 16

SEG - Smart Exosuit Glove for Data Driven Physiotherapy

引用

16th International Conference on Sensing technology, ICST 2023

作者： Anisha, P.R. Kaza, Vijaya Sindhoori Badam, Rithika Reddy, V. Sridhar Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Vignana Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350395341

Physiotherapy and rehabilitation of the human hand play a crucial role in restoring the function and mobility of the hand movement after serious injuries or conditions affecting it. This research aims to develop an exosuit for hands, comprising a Data Glove and E-skin and software for data analysis and prediction, to observe and assess the effects and improvements in hand function among patients undergoing physiotherapy or rehabilitation for hand-related conditions. Through a comprehensive evaluation process, patients' hand functions are assessed before, during, and after the physiotherapy sessions. The exosuit facilitates the collection of quantitative and qualitative data, allowing for a detailed analysis of progress and improvement. These measurements include range of motion, grip strength, fine motor skills, dexterity, and sensory feedback. The collected data is analyzed using advanced machine learning algorithms and statistical methods to identify trends, patterns, and correlations that can help optimize the rehabilitation process and personalize treatment plans for each patient. This integration offers several advantages, such as enhanced accuracy, increased sensitivity, and improved flexibility compared to traditional assessment methods. This research aims to contribute to the development of more effective and personalized physiotherapy and rehabilitation approaches by leveraging the potential of emerging technologies. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Voice-Based Virtual Assistant for Windows (Ziva - AI Companion) 5

Voice-Based Virtual Assistant for Windows (Ziva - AI Compani...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Sunil Patel, Shubham Sonam Srivastav, Vaishnav Galgotias College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

ZIVA is to create a Python-based Desktop Assistant, leveraging Python's versatility and simplicity. Python is a widely used programming language in the field of Dialog Flow and AI, known for its adaptability and longstanding reputation. This Project is also based on the NLP. The primary goal of this project, named Desktop Assistant (ZIVA), is to enable users to issue commands to their local machines using voice recognition, eliminating the need for manual typing. Virtual assistants such as Siri for iOS and Cortana for Windows serve as inspiration for ZIVA. aiming to streamline the process of instructing the computer to perform various operations. ZIVA incorporates intelligent voice recognition to process user input in the form of voice commands and provide outputs such as actions to be executed or search results. Voice commands are the only way that users can engage with the assistant. ZIVA's intelligence includes the ability to store detected voice commands and match them with predefined default commands. As a Desktop Assistant, ZIVA helps with everyday tasks such as engaging in general human conversation, conducting Google searches, opening websites like YouTube, Wikipedia, and Stack Overflow, searching for and playing music, telling the time, interacting with the user, and shutting down the system. User statements and commands are analyzed using machine learning techniques to provide optimal solutions. © 2024 IEEE.

关键词： Artificial intelligence Learning robotics NLP (Natural Language Processing) Advanced Engineering Virtual assistance

来源：评论

学校读者我要写书评

暂无评论

2SWUNet:small window SWin UNet based on tansformer for building extraction from high-resolution remote sensing images

引用

Optoelectronics Letters 2024年第10期20卷 599-606页

作者： YU Jiamin CHAN Sixian LEI Yanjing WU Wei WANG Yuan ZHOU Xiaolong College of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China Key Laboratory of Meteorological Disaster(KLME) Ministry of Education&Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China College of Geographic Information Modern Industry Zhejiang University of TechnologyHangzhou310023China College of Electrical and Information Engineering Quzhou UniversityQuzhou324000China

Models dedicated to building long-range dependencies often exhibit degraded performance when transferred to remote sensing *** transformer(Vi T)is a new paradigm in computer vision that uses multi-head self-attention(MSA)rather than convolution as the main computational module,with global modeling ***,its performance on small datasets is usually far inferior to that of convolutional neural networks(CNNs).In this work,we propose a small window SWin UNet(2SWUNet)for building extraction from high-resolution remote sensing ***,the 2SWUNet is trained based on swin transformer by designing a fully symmetric encoder-decoder U-shaped ***,to construct a reasonable U-shaped architecture for building extraction from high-resolution remote sensing images,different forms of patch expansion are explored to simulate up-sampling operations and recover feature map ***,the small window-based multi-head self-attention(W-MSA)is designed to reduce the computational and memory burden,which is more appropriate for the features of remote sensing ***,the pre-training mechanism is advanced to make up for the lack of decoder ***,comparison experiments with other mainstream CNNs and Vi Ts validate the superiority of the proposed model.

关键词： Net computer resolution

来源：评论

学校读者我要写书评

暂无评论

Facial Emotion Based Automatic Music Recommender System

Facial Emotion Based Automatic Music Recommender System

引用

2023 International Conference on Sustainable Emerging Innovations in Engineering and technology, ICSEIET 2023

作者： Singh, Mahesh Kumar Singh, Pushpendra Sharma, Amit ABES Engineering College Computer Science Department UP Ghaziabad India Ims Engineering College Information Technology Department UP Ghaziabad India

ISBN: (纸本)9798350329186

Listening to music can change the mood of any individual based on type of song like classic, pop or hip-hop. Music can have very strong influence on an individual's emotions. These emotions can be recognized by extracting various facial image features like Eye or Lip movement. Using facial expressions, meaning and context of words can be clearly understood by Humans. With the quick improvement of versatile systems and advanced interactive media innovations, advanced music has gotten to be the standard customer substance looked for by numerous youthful people. People frequently utilize music for mood regulation, more specifically to change a bad mood, to get motivated, to focus on their work, to increase energy levels or to reduce tension. Moreover, tuning in to the correct kind of music at the proper time may progress mental wellbeing. Recommendation of different songs based on an individual's current facial expressions could be latest research problem within the field of image processing. Very few researchers have proposed the solution of this research problem through automatic facial recognition using convolution neural network (CNN) models. In this paper, the proposed system based on CNN model focused on detecting human emotions from face image to identify list of songs to be played through music player to swing user's mood. The objective of this study is to improve the accuracy and reduce time and cost of proposed system for music recommendation. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed System

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

引用

2023 IEEE International Conference on Computing, ICOCO 2023

作者： Saadoon, Muntadher Hamid, Siti Hafizah Ab. Sofian, Hazrina Altarturi, Hamza Nasuha, Nur Azizul, Zati Hakim Khalid, Haqi Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Selangor 43400 Malaysia

ISBN: (纸本)9798350302684

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism. © 2023 IEEE.

关键词： Fault tolerance

来源：评论

学校读者我要写书评

暂无评论

Improving students' cognitive abilities in online environment based on neurofeedback 14

Improving students' cognitive abilities in online environmen...

引用

14th IEEE Global Engineering Education Conference, EDUCON 2023

作者： Jamil, Nuraini Lakas, Abderrahmane Belkacem, Abdelkader Nasreddine College of Information Technology United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates College of Information Technology United Arab Emirates University Department of Computer and Network Engineering United Arab Emirates

ISBN: (纸本)9798350399431

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style. © 2023 IEEE.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

Virtual Nursing Using Deep Belief Networks for Elderly People (DBN-EP)

引用

computer Systems science & Engineering 2022年第9期42卷 985-1000页

作者： S.Rajasekaran G.Kousalya Department of Information Technology KGiSL Institute of TechnologyCoimbatore641035India Department of Computer Science and Engineering Coimbatore Institute of TechnologyCoimbatore641014India

The demand for better health services has resulted in the advancementof remote monitoring health, i.e., virtual nursing systems, to watch and supportthe elderly with innovative concepts such as being patient-centric, easier to use,and having smarter interactions and more accurate conclusions. While virtual nursing services attempt to provide consumers and medical practitioners with continuous medical and health monitoring services, access to allied healthcare expertssuch as nurses remains a challenge. In this research, we present Virtual NursingUsing Deep Belief Networks for Elderly People (DBN-EP), a new framework thatprovides a virtual nurse agent deployed on a senior citizen’s home, workplace, orcare centre to help manage their health condition on a continuous basis. Using thismethod, healthcare providers can assign various jobs to nurses by utilizing a general task definition mechanism, in which a task is defined as a combination ofmedical workflow, operational guidelines, and data gathered from a remotelymonitored virtual nursing system. Practitioners are in charge of DBN-EP andmake treatment decisions for patients. This allows a DBN-EP to act as a personalized full-time nurse for a client by carrying out practitioner support activitiesbased on information gathered about the client’s health. An electronic PersonalHealth Record (ePHR) system, such as a specialized web portal and mobile apps,could provide such patient information to elderly person family members and carecentres. We created a prototype system using a DBN-EP system that allows traditional client applications and healthcare provider systems to collaborate. Finally,we demonstrate how this system may benefit the elderly through a result anddebate.

关键词： Deep belief networks RBM video mining elder people elder care

来源：评论

学校读者我要写书评

暂无评论

Fault Detection System Using Machine Learning in IoT Based Automobile Industries 2

Fault Detection System Using Machine Learning in IoT Based A...

引用

2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Kumuthini, C. Kavitha, K. Thangarasu, N. Tamilarasi, P. Kavitha, M. Vikkram, R. Dr.N.G.P Arts and Science College Department of Computer Applications TamilNadu Coimbatore India Karpagam Academy of Higher Education Department of Computer Science TamilNadu Coimbatore India Sri Sarada college for Women Department of Computer Science TamilNadu Salem16 India Panimalar Engineering college Department of Mathematics TamilNadu Chennai India Karpagam Academy of Higher Education Department of information technology TamilNadu Coimbatore India

ISBN: (纸本)9798331542573

Billions of physical devices around the world connected to the internet refer to the latest booming technology such as internet of things (IoT). Without human intervention, all collecting and sharing data around the globe. An automobile is one of the familiar mechanized industries around the world. For the past four years, introduce various pioneering features in vehicles that result is enormously high car sales in the automobile sector. Internet of Things (IoT) is one of the most recent technologies that plateful automobile industry to make next-generation of vehicles. Using the IoT based automobiles techniques are more renovate the way that people can feel easily to interact with the vehicles. Sensors play a crucial role in IoT systems for predicting various types of information within vehicles. If a sensor malfunctions, it can jeopardize the entire system. Therefore, detecting sensor faults is essential in modern IoT technology. In the proposed work, a fault detection system is implemented using machine learning within IoT-integrated automobiles. The Random Forest Classifier (RFC) algorithm is utilized to predict and identify sensor faults in vehicles. The performance metrics shows the random forest classifier produces best performance when compared to the other existing Logistic Regression for sensor fault detection and other conventional algorithm. © 2024 IEEE.

关键词： Automotive industry

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：