检索结果-内蒙古大学图书馆

Whisper of Understanding: Age Differences in Algorithmic Literacy Across Generations

Whisper of Understanding: Age Differences in Algorithmic Lit...

2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ghani, Miharaini Md Mustafa, Wan Azani Hashim, Mohd Ekram Alhafis Bin Hanafi, Hafizul Fahri Alabdeli, Haider Universiti Sains Malaysia School of Communication Penang Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau Malaysia Universiti Pendidikan Sultan Idris Faculty of Art Sustainability and Creative Industry Department of Creative Multimedia Perak Tanjong Malim Malaysia Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Department of Computer Science and Digital Technology Perak Tanjong Malim Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350336474

In today's digital landscape, algorithms play a pivotal role in shaping our experiences and decision-making processes. From social media feeds to online recommendations and search results, algorithms influence our daily interactions with technology. However, the understanding and literacy surrounding these algorithms vary significantly across different age groups, leading to a potential divide in how individuals engage with and comprehend the digital world. This research delves into the age differences in algorithmic literacy, exploring the contrasting experiences and challenges faced by younger generations, often referred to as "digital natives,"and older generations, termed "digital immigrants."By examining factors such as access to technology, educational opportunities, and cultural attitudes, this study aims to shed light on the root causes of these disparities. Through a multifaceted approach, including surveys, interviews, and observational studies, the research investigates the varying levels of algorithmic literacy across different age groups. It explores the potential advantages of younger generations, who have grown up immersed in digital environments, as well as the challenges faced by older generations, who may encounter resistance or difficulties in adapting to new technologies. Furthermore, this study examines the implications of age-related algorithmic literacy gaps, including the potential for digital exclusion, ethical concerns surrounding algorithmic bias and transparency, and the need for inclusive design and education initiatives. By highlighting these issues, the research aims to foster intergenerational dialogue and collaborative efforts to bridge the algorithmic literacy divide. Ultimately, this exploration of age differences in algorithmic literacy aims to contribute to the ongoing discourse on digital literacy, promoting a more inclusive and equitable digital landscape where individuals of all ages can engage with and critically understand t

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis of Local Tourism in Thailand from YouTube Comments Using BiLSTM 1

引用

15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Khruahong, Sanya Surinta, Olarik Lam, Sinh Cong Department of Computer Science and Information Technology Faculty of Science Naresuan University Phitsanulok Thailand Faculty of Informatics Mahasarakham University Mahasarakham Thailand Faculty of Electronics and Telecommunication VNU - University of Engineering and Technology Hanoi Viet Nam

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

Currently, social networks, where people can express their opinion through content and comments, are fast developing and affect various areas of daily life;Particularly, some research on YouTube travel channels found that almost tourists and audiences leave comments about their attitudes to that place. Thus, mining the emotional recognition of comments through artificial intelligence can bring knowledge about the tourists’ general view. This article analyzes the relationship(s) between social media use and its effect on community-based tourism in Thailand using the Social Media Sensing framework (S-Sense) as sentiment analysis and the Bidirectional Long Short-Term Memory (BiLSTM) methods to analyze the text comments. This research collected 51,280 comments on 114 Youtube Videos, which are tourist attractions in various provinces in Thailand. The approach categorizes attractions based on sentiment analysis of 60% or more, including restaurants, markets, historical sites, temples, or natural attractions. The results show that 67.51% of the 19,391 clean-processed comments were satisfied with those attraction places. Therefore S-Sense and BiLSTM models can be sufficient to analyze the attitude of comments about attraction places with from 43 to remain 33 keywords of 1,603 comments. Furthermore, the offered sentiment analysis method has higher precision, recall, and F1 scores. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Cross-View Mutual Learning for Semi-Supervised Medical Image Segmentation 24

Cross-View Mutual Learning for Semi-Supervised Medical Image...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Song Wei, Xiaoyu Chen, Xinyue Ren, Yazhou He, Jing Pu, Xiaorong School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Guangdong Shenzhen China Faculty of Medicine & Biomedical Sciences The University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798400706868

Semi-supervised medical image segmentation has gained increasing attention due to its potential to alleviate the manual annotation burden. Mainstream methods typically involve two subnets, and conduct a consistency objective to ensure them producing consistent predictions for unlabeled data. However, they often ignore that the complementarity of model predictions is equally crucial. To realize the potential of the multi-subnet architecture, we propose a novel cross-view mutual learning method with a two-branch co-training framework. Specifically, we first introduce a novel conflict-based feature learning (CFL) that encourages the two subnets to learn distinct features from the same input. These distinct features are then decoded into complementary model predictions, allowing both subnets to understand the input from different views. More importantly, we propose a cross-view mutual learning (CML) to maximize the effectiveness of CFL. This approach requires only modifications to the model inputs and supervisory signals, and implements a heterogeneous consistency objective to fully explore the complementarity of model predictions. Consequently, the aggregated predictions can effectively capture both consistency and complementarity across two subnets. Experimental results on three public datasets demonstrate the superiority of CML over previous SoTA methods. Code is available at https://***/SongwuJob/CML. © 2024 ACM.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Bee Colony-Reptile Search Optimization Technique for Blood Cell Cancer Detection

Bee Colony-Reptile Search Optimization Technique for Blood C...

引用

International Conference on computer and Automation engineering, ICCAE

作者： Aziza I. Hussein Mohammed Ali Saleh Rabab Hamed M. Aly Energy and Technology Research Center College of Engineering Effat University Jeddah Saudi Arabia Computer and Systems Engineering Department Faculty of Engineering Helwan University Egypt Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt The Higher Institute for Management and Information Technology Minya Egypt

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

The biosystem is a crucial system grounded in classification and detection, utilizing Artificial Intelligence (AI) approaches or metaheuristic techniques. Currently, cancer of the blood cells is among the deadliest cancers in the world. Acute lymphoblastic leukemia (ALL) is a cancer of blood cells that causes excessive proliferation of lymphocytes. It is extremely time-consuming and expensive to conduct diagnostic calculations. The number of platelets in a patient's blood is computed by a platelet count. A lacking number of platelets can indicate cancer, infection, or other health problems. A patient with too many platelets is at risk for blood strokes. A single drop of blood includes tens of thousands of platelets. The main goal of this paper is how to detect the features of blood cells and classify with predicting cancer type based on platelets analysis by using Bee Colony followed by Reptile Search Optimization (BCRSO) technique. According to the results, BCRSO algorithm performed better in terms of classification efficacy and accuracy rate than other algorithms. Based on simulation results, the proposed method is more effective than previously published research for classification optimization.

关键词： Accuracy Blood platelets Simulation Metaheuristics Cells (biology) Prediction algorithms Cancer detection Classification algorithms Artificial intelligence Cancer

来源：评论

学校读者我要写书评

暂无评论

Quantifying the Impact of AI and Machine Learning on Data Access Optimization

Quantifying the Impact of AI and Machine Learning on Data Ac...

引用

2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Khandari, Harsimrat Sahoo, Girija Shankar Tr, Mahesh Rajeswari, M. Jadhav, Prranjali Meena, Mansingh Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka 562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350329773

Quantifying the impact of synthetic Intelligence (AI) and machine getting to know (ML) on information access Optimization (DAO) and its ability to enhance overall performance and performance inside organizations has been a topic of research for decades. This newsletter investigates how such technology is essential in optimizing records access and its effect on the rate and accuracy of operations. We speak about how cutting-edge advances in AI and ML can be utilized to detect patterns in facts, reduce latency, and improve the general quality of data. Furthermore, we discuss how that technology can reduce manual techniques and get rid of errors due to human mistakes, hence enhancing performance and productiveness. Sooner or later, we talk about the main challenges associated with enforcing AI and ML within a company and identify capacity areas of development. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Attack-Agnostic Deep Face Anti-Spoofing

Attack-Agnostic Deep Face Anti-Spoofing

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Ajian Tan, Zichang Liang, Yanyan Wan, Jun Institute of Automation Chinese Academy of Sciences Mais Beijing China Institute of Deep Learning Baidu Research Beijing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350302493

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack Pattern Generation (Adv-APG) module and a Supervised learning-based Attack Pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets. © 2023 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Accurate Phase Detection for ZigBee Using Artificial Neural Network

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 2505-2518页

作者： Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

关键词： Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of Machine Learning and Deep Learning for Disaster Prediction

A Comparative Study of Machine Learning and Deep Learning fo...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Waleed Amer Ola Nasir Ala Hamarsheh Ali Mahmoud Ali Amani Abu Zaid Walaa Shtayah Department of Cyber Secyrity Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of computer science Faculty of Information Technology Arab American University Department of Computer Network Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The analytical procedure known as Disaster Prediction (DP) makes estimates regarding future incident and disaster events before they actually occur. The present research seeks to determine which method produces the best results for DP. The research finds that Deep Learning surpasses Machine Learning and Artificial Intelligence methods when predicting disaster occurrence including or excluding events. The results indicate Deep Learning stands as the top DP method. CNN turned out to be the DL model with the best performance results which produced Accuracy at 0.993 and Precision at 0.986 along with Recall at 0.978. Among the models tested the LSTM model performed as the runner-up with accuracy reaching (0.975), precision at (0.959), while recall obtained (0.992). The research shows how DL technologies prove advantageous for disaster risk assessment plus their capability for developing proactive disaster response strategies.

关键词： Deep learning Accuracy Disasters Risk management Long short term memory Computational intelligence

来源：评论

学校读者我要写书评

暂无评论

Device-Based Cellular Throughput Prediction for Video Streaming: Lessons from a Real-World Evaluation

IEEE Transactions on Machine Learning in Communications and ...

引用

IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 318-334页

作者： Raca, Darijo Zahran, Ahmed H. Sreenan, Cormac J. Sinha, Rakesh K. Halepovic, Emir Gopalakrishnan, Vijay University of Sarajevo Faculty of Electrical Engineering Sarajevo71000 Bosnia and Herzegovina University College Cork School of Computer Science and Information Technology CorkT12 XF62 Ireland AT&T Labs-Research Florham ParkNJ07932 United States

AI-driven data analysis methods have garnered attention in enhancing the performance of wireless networks. One such application is the prediction of downlink throughput in mobile cellular networks. Accurate throughput predictions have demonstrated significant application benefits, such as improving the quality of experience in adaptive video streaming. However, the high degree of variability in cellular link behaviour, coupled with device mobility and diverse traffic demands, presents a complex problem. Numerous published studies have explored the application of machine learning to address this problem, displaying potential when trained and evaluated with traffic traces collected from operational networks. The focus of this paper is an empirical investigation of machine learning-based throughput prediction that runs in real-time on a smartphone, and its evaluation with video streaming in a range of real-world cellular network settings. We report on a number of key challenges that arise when performing prediction 'in the wild', dealing with practical issues one encounters with online data (not traces) and the limitations of real smartphones. These include data sampling, distribution shift, and data labelling. We describe our current solutions to these issues and quantify their efficacy, drawing lessons that we believe will be valuable to network practitioners planning to use such methodologies in operational cellular networks. © 2023 CCBY.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

AMBO:All Members-Based Optimizer for Solving Optimization Problems

引用

computers, Materials & Continua 2022年第2期70卷 2905-2921页

作者： Fatemeh Ahmadi Zeidabadi Sajjad Amiri Doumari Mohammad Dehghani Zeinab Montazeri Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

There are many optimization problems in different branches of science that should be solved using an appropriate *** optimization algorithms are one of the most efficient approaches to solve this type of *** this paper,a new optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization *** main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population ***,in AMBO,any member of the population can play a role in updating the population *** theory of AMBO is described and then mathematically modeled for implementation on optimization *** performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also *** optimization results demonstrate the ability of AMBO to solve various optimization ***,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.

关键词： Algorithm all members optimization optimization algorithm optimization problem population-based algorithm

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：