检索结果-内蒙古大学图书馆

International Conference on Emerging Technologies, ICET

作者： Maham Shehzadi Usama Arshad Zainul Abideen Raja Hashim Ali Ammad Aslam Khan Ali Zeeshan Ijaz Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Dept. of Biological Sciences Virtual University of Pakistan Rawalpindi Pakistan Dept. of Computer Science National University of Computer & Emerging Sciences Islamabad Pakistan

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting

PCEE-BERT: Accelerating BERT Inference via Patient and Confi...

引用

2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Zhang, Zhen Zhu, Wei Zhang, Jinfan Wang, Peng Jin, Rize Chung, Tae-Sun Dept. of Artificial Intelligence Ajou University Suwon16499 Korea Republic of School of Software Tiangong University Tianjin300387 China Department of Computer Science East China Normal University Shanghai China Harbin Engineering University Harbin China College of Computer Science Northwest Normal University Lanzhou China

ISBN: (纸本)9781955917766

BERT and other pre-trained language models (PLMs) are ubiquitous in modern NLP. Even though PLMs are the state-of-the-art (SOTA) models for almost every NLP task (Qiu et al., 2020), the significant latency during inference prohibits wider industrial usage. In this work, we propose Patient and Confident Early Exiting BERT (PCEE-BERT), an off-the-shelf sample-dependent early exiting method that can work with different PLMs and can also work along with popular model compression methods. With a multi-exit BERT as the backbone model, PCEE-BERT will make the early exiting decision if enough numbers (patience parameter) of consecutive intermediate layers are confident about their predictions. The entropy value measures the confidence level of an intermediate layer's prediction. Experiments on the GLUE benchmark demonstrate that our method outperforms previous SOTA early exiting methods. Ablation studies show that: (a) our method performs consistently well on other PLMs, such as ALBERT and TinyBERT;(b) PCEE-BERT can achieve different speed-up ratios by adjusting the patience parameter and the confidence threshold. The code for PCEEBERT can be found at https://github. com/michael-wzhu/PCEE-BERT. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Graph Neural Networks for IoT Data Aggregation Scheduling

Graph Neural Networks for IoT Data Aggregation Scheduling

引用

IEEE Symposium on Network Operations and Management

作者： Van-Vi Vo Syed M. Raza Duc-Tai Le Moonseong Kim Hyunseung Choo Convergence Research Institute Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea College of Computing and Informatics Sungkyunkwan University Suwon South Korea Dept. of IT Convergence Software Seoul Theological University Bucheon South Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Data aggregation is an important approach in IoT sensor networks since it reduces data transfer while also preserving energy and bandwidth. This research investigates the challenge of time-efficient data aggregation in wireless sensor networks, which is critical in military, civilian, and industrial applications. Effective data aggregation algorithm design and optimization are required for quick and interference-free data collection. Machine learning has received attention for outperforming classical heuristic techniques. The research presents the first Graph Neural Network (GNN) model for data aggregation in IoT sensor networks, which incorporates Graph Attention Networks (GATs) and fully connected layers. The GNN-based model learns network topology and node attributes, creating node embeddings and correcting sensor node transmitting time slots. With a centralized training procedure and adapted execution for network size change, the proposed approach achieves satisfactory performance compared to the heuristic algorithm.

关键词： Training Adaptation models Wireless sensor networks Machine learning algorithms Job shop scheduling Network topology Machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Women's Safety Through Customized Smartphone Sensor Data: A Human Activity Recognition System

Enhancing Women's Safety Through Customized Smartphone Senso...

引用

IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： Yamina Islam Sanjida Nowrin Majed Ibrahim Faqih Fozia Sultana Erfanul Hoque Bahadur Abdul Kadar Masum Dept. of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh Director of Research and Development Signal Corps Institute Saudi Arabia Artificial Intelligence Engineer Hiperdyne Tokyo Japan Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

Human activity recognition (HAR) research using smartphone sensor technology has changed people's daily lives and opened the door for numerous exciting applications due to its extensive applications in addressing real-world humancentric issues. These studies, however, mostly concentrated on a woman's daily activities and behaviours when she is attacked, such as running, walking, sleeping,eating,drinking,lying, straight punching, front kicking, and so forth. The article offers a method to foresee human behaviour using a deep learning model and evaluates the efficiency of the approach using real-world data in response to this. We offer an LSTM structure for detecting the intensity of HAR in order to track activity patterns and build a neural network with binary classification skills to evaluate the situation of women. The proposed model uses the dataset to identify human activity, which records 30 volunteers' body movements while they engage in 18 physical activities. Feature engineering is used to extract additional variables from the data in addition to that. Our proposed model achieves a 90.89% accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-Free Massive MIMO

Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-F...

引用

IEEE Globecom Workshops

作者： Thong-Nhat Tran Giovanni Interdonato Daniel Benevides Da Costa Beongku An Dept. of Electronics and Computer Engineering in Graduate School Hongik University Republic of Korea Dept. of of Electrical and Information Engineering (DIEI) University of Cassino and Southern Latium Italy Digital Science Research Center Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates Dept. of Software and Communications Engineering Hongik University Republic of Korea

This paper evaluates the uplink spectral efficiency (SE) achieved by a reconfigurable intelligent surface (RIS)-aided cell-free massive MIMO system with max-min fairness (MMF) power allocation. We provide an exact closed-form expression for an achievable SE expression which assumes maximum ratio receive combining at the access points (APs) and centralized large-scale fading decoding at the central processing unit. The derived SE expression also accounts for imperfect channel estimation and spatially correlated Ricean fading channels for both direct (APs-users) and cascaded (APs-RISs-users) links. Alongside this, we provide a generalized SE expression and evaluate the performance achieved by local partial minimum mean-squared error combining. Finally, we optimally solve the RIS phase-shift selection problem in order to maximize the aggregate channel gains, and derive a closed-form solution to the MMF power allocation problem. Simulation results validate the effectiveness of the proposed framework in enhancing the unlink SE with resnect to various benchmarks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

From Alert to Change: Effects of Sensing on Construction Workers’ Health Protection Behaviours

SSRN

引用

SSRN 2024年

作者： Zhou, Na Chang-Richards, Alice Dirks, Kim Natasha Wang, Kevin I-Kai Dept. of Civil and Environmental Engineering Faculty of Engineering the University of Auckland New Zealand Engineering Block 5 - Bldg. 405 5 Grafton Rd Auckland Central Auckland1010 New Zealand Engineering Block 1 - Bldg. 401 20 Symonds St Auckland Central Auckland1010 New Zealand Dept. of Electrical Computer and Software Engineering Faculty of Engineering the University of Auckland New Zealand

Compromised air quality on a construction site could lead to significant adverse respiratory impacts on workers. This paper introduces an alert sensing methodology to develop practical health and wellbeing requirements based on the Health Belief Model (HBM)Health Belief Model:(HBM) and the Theory of Planned Behaviour (TPB)Theory of Planned Behaviour:(TPB). The approach leverages the Internet of Things (IoT)Internet of Things:(IoT) to enable real-time, data-driven evaluation of air quality metrics, facilitating the assessment of workers' behavioural change. Dust sensors were mounted on the helmets of 11 construction workers at two New Zealand sites for 21 days, and pre and post-questionnaires were conducted to gather feedback on their experience before and after using the system. The research findings revealed that sensing systems can significantly increase awareness and positive behavioural change amongst construction workers with respect to particulate matter (PM)particulate matter:(PM) exposure. This research expands the HBM and the TPB by demonstrating how perceived health benefits, perceived barriers, self-efficacy, perceived behavioural control, and attitudes influence construction workers' behavioural intentions toward the sensing system. The case study also highlights the potential of sensing technologies to foster healthier behaviours and workplace environments, offering insights for construction companies to develop comprehensive health and wellbeing programs that reduce PM exposure risks and promote proactive worker actions. © 2024, The Authors. All rights reserved.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

Monitoring and Forecasting Quality Indicators of Predough Production in the Bakery Industry

Monitoring and Forecasting Quality Indicators of Predough Pr...

引用

Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Nataliia Lutska Nataliia Zaiets Lidiia Vlasenko Andrii Zhyltsov dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of electromagnetic systems The Institute of Electrodynamics of the National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The work demonstrates the process of developing a machine learning model for a system for monitoring and controlling the quality of predough at a bakery. This system will provide continuous analysis of the predough parameters, such as temperature, humidity, lifting force and titranic acidity, which will ensure timely detection of deviations in the quality of the predough and avoid production problems. In addition, it will help automate the quality control process and ensure the stability of production processes. Analysis and forecasting of dough parameters will improve the quality of produced bread or other bakery products. Management based also on the predicted values of technological indicators of the predough will allow the enterprise to avoid unnecessary costs of materials and energy and increase economic efficiency. As part of the study, seven machine learning models were created and reviewed, including three linear and four varieties of binary decision trees. An analysis of the best model - a regression decision tree - was carried out, confirming its effectiveness and the validity of the resulting hierarchical structure, in particular, the prediction of important indicators of predough with a high degree of reliability, which indicates its importance in practical applications in production. The created model can be used to monitor and control product quality in real time, allowing operators to quickly identify and correct any deviations in process performance. Also, by analyzing changes in the variables of the predough production process, it is possible to identify precursors of possible production accidents or deviations in product quality, which will allow timely measures to be taken to avoid them.

关键词： Force Process control Production Machine learning Quality control Humidity Predictive models Stability analysis Monitoring Thermal stability

来源：评论

学校读者我要写书评

暂无评论

Mental Health Professionals' Insights on Digital Health Solutions for Anxiety in the UAE

Mental Health Professionals' Insights on Digital Health Solu...

引用

IEEE International Requirements engineering Conference Workshops (REW)

作者： Mariam Al Meqbaali Sofia Ouhbi Reem K Jan Leena Amiri Fadwa Al Mugaddam Mohamed Adel Serhani Nazar Zaki Dept. Computer Science & Software Engineering CIT UAEU Al Ain UAE Dept. Information Technology Uppsala University Uppsala Sweden Mohammed Bin Rashid University of Medicine and Health Sciences Dubai UAE Dept. Psychiatry and Behavioral Science CMHS UAEU Al Ain UAE College of Computing and Informatics University of Sharjah Sharjah UAE

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

In recent years, anxiety disorders have become more prevalent worldwide, leading to an increased demand for mental healthcare services. Barriers such as stigma and cost, limit access to these services, prompting the exploration of alternative solutions. Digital technology shows significant promise in improving access to mental healthcare services. This study aims to explore the preferences, and concerns of Mental Health Professionals (MHPs) regarding digital health solutions for self-management of anxiety disorders in the UAE via an online survey. Participation was voluntary and anonymous, with 18 MHPs taking part. The survey included 12 questions covering demographics and key design elements. Most respondents displayed positive attitudes toward integrating digital anxiety solutions in the UAE, providing feedback and highlighting considerations. 61% endorsed the use of digital technology in anxiety disorder treatment, while 94% favored mindfulness exercises for anxiety management. 72% expressed interest in utilizing technology for therapy. Notable concerns raised included privacy and dependency. Guided by MHPs insights, we propose a conceptual framework for the design of digital anxiety solutions in the UAE. This framework prioritizes cultural appropriateness and language accessibility, offering solutions in Arabic and English. It encompasses diverse features, including a blended approach, treatment approaches, and relaxation techniques, to address anxiety disorders and ensures user safety through risk assessment and management. This framework can assist developers of digital anxiety solutions by highlighting the features and elements that MHPs deem most important.

关键词： Surveys Privacy Anxiety disorders Medical treatment Mental health Safety Electronic healthcare

来源：评论

学校读者我要写书评

暂无评论

Real Data and Application-based Interactive Modules for Data Science Education in engineering

Real Data and Application-based Interactive Modules for Data...

引用

2021 ASEE Virtual Annual Conference, ASEE 2021

作者： Suthar, Kerul Mitchell, Thomas Hartwig, Anna Claire Wang, Jin Mao, Shiwen Parson, Laura Zeng, Peng Liu, Bo He, Peter Dept. of Chemical Engineering Auburn University AuburnAL36849 United States Dept. of Electric and Computer Engineering Auburn University AuburnAL36849 United States Dept. of Educational and Organizational Leadership North Dakota State University FargoND58105 United States Dept. of Mathematics and Statistics Auburn University AuburnAL36849 United States Dept. of Computer Science and Software Engineering Auburn University AuburnAL36849 United States

The importance of data science and engineering (DSE) education cannot be overstated and undergraduate education offers a critical link in providing more DSE exposure to students and expanding the supply of DSE talent. Currently significant progress has been made in classwork, while progress in hands-on research experience is still lacking. To help fill this gap, we propose to create data-enabled engineering project (DEEP) modules in the form of interactive Jupyter Notebooks based on real data and applications. We hypothesize that this web-based interactive development and learning environment (IDLE) will enable easy and wide adoption of the DEEP modules by other educators and institutions. In this work, we will present our ideas, the rationale behind the proposed approach, the work in progress, and the future plans for the project. © American Society for engineering Education, 2021

关键词： Data Science

来源：评论

学校读者我要写书评

暂无评论

Blockchain-based Reputation System For IoT Data Ecosystem: A Utility Maximization Approach

Blockchain-based Reputation System For IoT Data Ecosystem: A...

引用

International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Seyednima Khezr Abdulsalam Yassine Rachid Benlamri Dept. of Electrical and Computer Engineering Lakehead University Thunder Bay ON Canada Dept. of Software Engineering Lakehead University Thunder Bay ON Canada Dept. of Information Technology University of Doha for Science and Technology Doha Qatar

ISBN: (纸本)9781665461986

Recently, blockchain technology has been used to build private and secure internet of things (IoT) data markets and trading systems. Reputation of the trading parties is an important attribute that affect their profitability and trading prosperity. However, current reputation systems are prone to malicious manipulation. Such an issue can be avoided by registering all reputation scores in the blockchain which guarantees trans-parency, encryption, privacy, and security. To this end, this paper proposes a blockchain-based reputation system where market players can maximize their utility while engaged in the IoT data ecosystem. We also propose an adaptive learning mechanism that allows the data providers and consumers to enhance their reputation and review credibility scores. We provide extensive experiments to ensure our system satisfies the feasibility, usability, and availability properties. These properties are essential for functional deployment in real IoT data markets.

关键词： Privacy Smart cities Profitability Ecosystems Blockchains Encryption Internet of Things

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：