检索结果-内蒙古大学图书馆

2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Chaudhry, Beenish Moalla Islam, Muhammad Usama Chawla, Nitesh Vinay School of Computing and Informatics University of Louisiana Lafayette United States Department of Computer Science and Engineering University of Notre Dame United States

ISBN: (纸本)9798400705830

Despite growing interest in mobile games for older adults, there is limited exploration of older adults’ gaming behaviors, perceptions, and experiences as they engage with casual puzzle games over a period of time. To address this, we conducted a 9-month study with 20 older adults, examining training needs, in-situ experiences, and preferences. Participants were trained on tablet PCs and ten selected games. During the study, participants documented their experiences and attended technology workshops. Gaming behaviors were logged and analyzed using descriptive and inferential statistics, revealing patterns and statistically signifcant diferences in play frequency and duration over the course of the study. Thematic analysis identifed facilitators and barriers to engagement such as customization, co-play experiences, and health issues. Based on these fndings, we recommend incorporating educational elements, enhancing user control, leveraging identity and nostalgia, supporting social interactions, designing for tangible interaction, and emphasizing the importance of learning aids. Future research should test the efectiveness of these recommendations in increasing older adults’ engagement with casual games. © 2024 Copyright held by the owner/author(s).

关键词： Personal computers

来源：评论

学校读者我要写书评

暂无评论

Enhancing Protein Language Model With Feature Integration for Anticancer Peptide Prediction

Enhancing Protein Language Model With Feature Integration fo...

引用

2024 IEEE-EMBS International Conference on Biomedical and Health informatics, BHI 2024

作者： Binte Sayuti, Tiara Natasha Cheng, Shen Ajith, Santhisenan Bin Abdul Samad, Abdul Hadi Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Health Informatics Lab Singapore College of Computing and Data Science Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350351552

In the fight against cancer, anticancer peptides (ACP) hold promising therapeutic potential due to their selective cytotoxicity and lower side effects compared to traditional treatments. However, identifying novel ACP is challenged by high costs and labor-intensive processes. Protein language models (PLMs), such as ESM-2 and ProtBERT, have revolutionized peptide prediction by leveraging vast datasets to capture complex biological patterns through pre-training. However, they often struggle to accurately model specific biochemical interactions. To address this limitation, we integrated four sequence-based features: amino acid composition (AAC), dipeptide composition (DPC), composition of k-spaced amino acid group pairs (CKS), and k-mer sparse matrix (k-mer) through a cross-attention mechanism. These features infuse biochemical insights that PLM alone may overlook, enabling a more detailed prediction of anti-cancer properties. This integration enhances biochemical insights, improving prediction accuracy by 15.8% for ProtBERT and 2.9% for ESM-2, with ESM-2 achieving the highest accuracy at 77.8%. SHapley Additive exPlanations (SHAP) analysis confirms the importance of these features, demonstrating that incorporating amino acid features into PLMs enhances ACP prediction. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Design and Develop Automatic Parts of Speech Tagger for Siltigna Language

Design and Develop Automatic Parts of Speech Tagger for Silt...

引用

2024 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Dana, Biniyam Sime Woldeyohannis, Michael Melese Yesuf, Seiyfedin Mohammed Paulos, Eyobed Birhanu Yigezu, Mesay Gemeda Hawassa University School of Computing Ethiopia Addis Ababa University School of Information Science Addis Ababa Ethiopia Wachemo University School of Informatics Hossana Ethiopia Wolaita Sodo University School of Informatics Ethiopia Instituto Politécnico Nacional Mexico

ISBN: (纸本)9798331527792

The purpose of this study is to design and develop automatic part-of-speech taggers for the Siltigna language using the deep learning algorithm. Part of speech tagging is the process of assigning words within sentences to the appropriate word categories. In most languages POS tagger is utilized as a pre-processing component in several NLP applications. Considering that several part-of-speech taggers are developed for various foreign and local languages. However, those POST cannot be applied directly with Siltigna due to morphological difference between the languages. To build tagging modelsvarious deep learning algorithms such as Standard RNN, GRU, LSTM, and BiLSTM were implemented. For training and testing purposes, 3500 sentences were collected from different sources, such as Siltigna text books, annual and monthly reports from the tourism o ce, and Siltigna newspapers, in sound format, then converted to text format. Some proverbs, which are written in Siltigna, were also collected to balance the sample corpus, and 22 sets of tags were identified for tagging purposes. The POST tagger is tested using a percentage spilled mechanism. The experimental result indicates that for simple RNN, LSTM, GRU and bidirectional LSTM was 92. 49 %, 92. 55 %, 92. 61 % and 93. 05 % test accuracy, respectively. Based on the result obtained from the accuracy test, conclusions and recommendations are forwarded. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Maintenance of Model Resilience in Distributed Edge Learning Environments 19

Maintenance of Model Resilience in Distributed Edge Learning...

引用

19th International Conference on Intelligent Environments, IE 2023

作者： Wang, Qiyuan Anagnostopoulos, Christos Fornes, Jordi Mateo Kolomvatsos, Kostas Vrachimis, Andreas University of Glasgow School of Computing Science United Kingdom University of Lleida Department of Computer Science Spain University of Thessaly Dept. of Informatics and Telecommunications Greece Glasgow University School of Computing Science United Kingdom

ISBN: (纸本)9798350312225

Distributed Machine Learning (DML) at the edge of the network involves model learning and inference across networking nodes over distributed data. One type of model learning could be the delivery of predictive analytics services to formulate intelligent environments, however, those environments heavily rely on real-time inference and are significantly influenced by changes in the underlying data (concept drifts). Moreover, the quality of service and availability in DML environments are directly tied to each node's reliability, since such environments are highly susceptible to the impact of node failures. Even if such challenges can be tackled with distributed resilience mechanisms, their effectiveness and efficiency, due to concept drifts, should be maintained to ensure continuous and sustained quality of service. DML systems operate in dynamic environments, thus, they require their models to be updated according to the novel trends embedded in the new data they encounter. We, therefore, introduce several model maintenance mechanisms to ensure resilient DML systems in the long term when concept drifts emerge. We provide a comprehensive experimental evaluation of our resilience maintenance mechanisms over synthetic and real data showcasing their importance and applicability in edge learning environments. © 2023 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Extractive Text Summarization for Wolaytta Language Using Recurrent Neural Network

Extractive Text Summarization for Wolaytta Language Using Re...

引用

2024 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Paulos, Eyobed Birhanu Woldeyohannis, Michael Melese Dana, Biniyam Sime Yesuf, Seiyfedin Mohammed Yigezu, Mesay Gemeda Wolaita Sodo University School of Informatics Ethiopia Addis Ababa University School of Information Science Addis Ababa Ethiopia Hawassa University School of Computing Ethiopia Wachemo University School of Informatics Hossana Ethiopia Instituto Politécnico Nacional Mexico

ISBN: (纸本)9798331527792

Information overload is caused by an increasing number of overflowing information sources due to the availability of both online and offline information that doubles over time. Therefore, it is extremely challenging and time-consuming to filter out the information that is relevant from this abundant supply of information. Text summarization is a concept that can help ease this load, which has received a lot of attention in recent NLP studies. Wolaytta language has been written since the Sudan Interior Mission, in the 1940s. The language is utilized in teaching and learning in elementary, secondary, and higher education institutions, as well as processing information transfer in various mass media. But for the advancement of the language, to the best of the researcher's knowledge, despite the fact that there are numerous deep learning-based extractive summarizer models for foreign languages, no deep learning-based text summarization research work has been done for Wolaytta. Therefore, the researchers planned, gathered, and produced 424 text documents and reference summaries for each document in the Wolaytta language. When the datasets were smoothed out, an RNN-based Wolaytta text summarizer was trained and then a relevant model summary was created. The outcome is evaluated using both subjective and objective evaluation techniques. In this research paper, the recall, precision, and f-measure values are 83.3%, 80.4%, and 82.7%, respectively. The research on the Wolaytta language conducted in the past used 92 paragraphs, and the findings revealed 61.16%, 60.69%, and 60.46% recall, precision, and f-measures, respectively. But by using more datasets, doing more preprocessing tasks, using a better strategy, and with better-recorded performance, this work achieves better results than the earlier work. Also, we made a ready-built Wolaytta text dataset available on Kaggle as a pointer for future investigations. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Research on Stock Prediction Model using Mode Decomposition Algorithm and Support Vector Regression 24

Research on Stock Prediction Model using Mode Decomposition ...

引用

2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Sun, Yu Mutalib, Sofianita Omar Peng Guangdong University of Science and Technology Dongguan Guangdong523083 China School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798400717147

Stock market forecasting has always been one of the most difficult challenges faced by the stock market. In the issue of stock price prediction, its noisy nature, complexity, as well as the country's governance system, economic situation, epidemic will be mentioned. In recent years, scholars have conducted many stock market prediction studies using traditional and artificial intelligence technologies since the lower the error in stock prediction, the more investment risk can be reduced. In this research, a novel prediction method based on mode decomposition algorithm and optimized Support Vector Regression (SVR) is proposed. Mode decomposition algorithm is used to smooth the original signal and reduce the impact of noise. SVR extracts knowledge from data and ultimately utilizes the knowledge to predict new stock data. Bayesian optimization (BO) algorithm is adopted to optimize the core parameters of SVR and prevent overfitting so as to improve predictive performance. Finally, The CEEMDAN-BO-SVR model proposed in this study is compared with several other methods, including CEEMDAN-SVR, BO-SVR, SVR, RNN, LSTM, and ensemble learning algorithms, then analyzed through evaluation indicators. The experimental results comparing different models indicate that the CEEMDAN-BO-SVR model has the best fitting ability. © 2024 ACM.

关键词： Investments

来源：评论

学校读者我要写书评

暂无评论

Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

引用

Computers, Materials & Continua 2022年第6期71卷 5855-5873页

作者： Reya Sharma Baijnath Kaushik Naveen Kumar Gondhi Muhammad Tahir Mohammad Khalid Imam Rahmani School of Computer Science and Engineering SMVDUJ&KIndia College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Even though several advances have been made in recent years,handwritten script recognition is still a challenging task in the pattern recognition *** field has gained much interest lately due to its diverse application ***,different methods are available for automatic script *** most of the reported script recognition techniques,deep neural networks have achieved impressive results and outperformed the classical machine learning ***,the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error,which renders them *** approach often requires manual intervention with domain expertise which consumes substantial time and computational *** alleviate this shortcoming,this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization(QPSO),which is capable of automatically evolving the meaningful convolutional neural network(CNN)*** computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts,namely Bangla,Devanagari,and Dogri,consisting of handwritten characters and ***,the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.

关键词： Neuro-evolution quantum particle swarm optimization deep learning convolutional neural networks handwriting recognition

来源：评论

学校读者我要写书评

暂无评论

Zirkus Empathico 2.0: a multiplayer serious mobile game for children with autism spectrum disorder (ASD), with a focus on enhancing social and emotional development

引用

Multimedia Tools and Applications 2025年 1-24页

作者： Hassan, Ahmed Pinkwart, Niels Shafi, Muhammad Department of Computer Science and Informatics Berlin School of Business and Innovation Karl Marx Strasse 97 - 99 Berlin12043 Germany Institute for Informatic Humboldt University Berlin Rudower Chaussee 25 Berlin Berlin12489 Germany School of Computing Ulster University Belfast Campus United Kingdom Faculty of Computing & Information Technology Sohar University Sohar Oman

This study explores the effectiveness of the multiplayer serious mobile game "Zirkus Empathico (ZE) 2.0" in enhancing social-emotional behaviors in children diagnosed with autism spectrum disorder (ASD). It aims to assess how such games can foster emotional recognition, empathy, and prosocial behavior, thereby supporting the holistic development of children with ASD. An eight-week randomized controlled trial (RCT) was conducted between January and April 2021 at autism centers in Islamabad, Pakistan, and Berlin, Germany. The study involved 107 children aged 5–11 years, with 60 diagnosed with ASD and 47 typically developing children serving as an Active Control Group (ACG). Participants were engaged in sessions using the bilingual"ZE 2.0"game, incorporating multiplayer and emotional learning modules. The intervention required 120 min of weekly training supplemented by daily sessions. Post-intervention assessments demonstrated significant improvements in the ASD group compared to the control group in areas of empathy and emotional awareness, as evidenced by higher scores on the Griffith Empathy Measure (GEM) and multiplayer module evaluations. Notable was the ability of participants to apply learned social skills in dynamic, real-world contexts, which suggests the game’s effectiveness transcends the controlled settings typically associated with ASD interventions. ZE 2.0 adds to the social and teamwork competencies of children with ASD. Besides encouraging peers to be proactive in communications and cooperation, the multiplayer mode provides an immersion learning atmosphere for facilitating social and emotional development. Such an outcome underlines the perspectives of serious games for use as a framework for the treatment and education of children who have ASD. In this manner, serious games can be utilized as a supplementary or alternative means of treating ASD. Future research may focus on distinguishing the effects of intervention from those of other therapy modalit

关键词： Serious games

来源：评论

学校读者我要写书评

暂无评论

Hybrid Approaches to Emotion Recognition: A Comprehensive Survey of Audio-Textual Methods and Their Application 4

Hybrid Approaches to Emotion Recognition: A Comprehensive Su...

引用

4th International Conference on Advanced Research in computing, ICARC 2024

作者： Wewelwala, Sahan Hewage Sumanathilaka, T.G.D.K. School of Computing Informatics Institute of Technology 06 Colombo Sri Lanka Swansea University Department of Computer Science Wales Swansea United Kingdom

ISBN: (纸本)9798350384864

This survey study provides a comprehensive analysis of emotion recognition, documenting its progression from conventional techniques to innovative hybrid algorithms that effectively combine textual and audio information. This work distinguishes itself with its thorough examination of the innovative combination of deep learning-based textual sentiment analysis and sophisticated aural signal processing approaches. This unique combination greatly improves the precision of emotion recognition from intricate sources like social media and consumer feedback. The study addresses the difficulties of processing real-time data and reducing bias in varied datasets. It provides new perspectives on the powers and limitations of neural networks and machine learning in this specific scenario. Our research represents a notable advancement in the utilization of these technologies for human-computer interaction, facilitating the creation of digital interfaces that are more empathic and attuned to human emotions. The final statements underscore the pressing requirement for sophisticated multimodal methodologies and the incorporation of developing technology, underscoring our distinctive contribution to the progress of emotion identification systems. This report offers a thorough analysis of the present condition of the subject and outlines a direction for future advancements, highlighting the crucial significance of emotion identification in improving human-computer interaction. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

A Brief Survey on Data Aggregation and Data Compression Models using Blockchain Model in Wireless Sensor Network

A Brief Survey on Data Aggregation and Data Compression Mode...

引用

2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Sudheer, Bandlamudi Naga Sujatha, Kamepalli School of Computing and Informatics Vignan's Foundation for Science Technology & Research Guntur Andhra Pradesh Vadlamudi India

ISBN: (纸本)9798350397208

Most of the time, nodes in Wireless Sensor Networks (WSNs) are set up in an unmonitored, potentially dangerous setting. Battery life, storage space, processing power, and network connectivity are all severely lacking in these nodes. Therefore, it is difficult to protect a WSN without negatively impacting the efficiency of the network. The issues listed above can be effectively addressed with a well-designed plan that makes use of Data Aggregation (DA) in conjunction with a robust security system In this work, we provide a thorough analysis of Secure Data Aggregation (SDA) in WSNs, highlighting both the security benefits sought and the challenges currently faced by this approach. Both established and up-and-coming network topologies are covered, with comparisons drawn between them to highlight key differences in application landscapes and security provisions afforded by each. Compressive data gathering (CDG), which employs Compressive Sensing (CS), can significantly enhance energy effieiency with the advantages gained from data compression. The data collection performance and longevity of Internet of Things (IoT) based WSNs are found to be significantly improved by CS based data collecting techniques. However, in hostile and sophisticated network deployments, they encounter significant difficulties with key distribution and adversary attacks. This research explores how wireless sensor networks' security measures affect their ability to collect and analyze data. This research provides a brief survey on data aggregation and data compression using blockchain in WSN for secure data transmission. © 2023 IEEE.

关键词： Compressed sensing

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：