检索结果-内蒙古大学图书馆

Advances in AI-based genomic data analysis for cancer survival prediction

Multimedia Tools and Applications 2025年第14期84卷 14139-14166页

作者： Deepali Goel, Neelam Padmavati Khandnor University Institute of Engineering and Technology Panjab University Chandigarh160014 India Chandigarh160012 India Department of Computer Science Guru Nanak College Budhlada151502 India

Problem: Cancer is one of the deadliest diseases prevalent in the world. Survivability, early diagnosis, and accurate prognosis are of utmost importance for the therapeutics and clinical management of cancer patients. To achieve accurate and timely prediction of the survival of cancer patients, several machine-learning models based on genomic data have been proposed but a comprehensive review of recent applications in cancer survival prediction is lacking. This paper represents a review of the most recent application of machine learning and deep learning on cancer survival prediction, with a particular focus on the use of genomic data. It specifically targets the most prominent cancer types such as breast cancer, glioblastoma, lung cancer, renal cell cancer, and oral cancer. Methods: A systematic review approach is employed to analyze recent studies on machine learning techniques applied to cancer survival prediction. Emphasis is placed on methodologies utilizing genomic data due to its effectiveness in predicting survival outcomes. Results: This review highlights the efficacy of different machine/deep learning-based techniques in predicting survival outcomes for different cancer types with genomic data. It also provides a summary of the contributions made by different research groups, critically examines the associated challenges, and suggests potential areas for further investigation. Conclusion: Machine learning and deep learning techniques, especially those utilizing genomic data, hold significant promise for accurate cancer survival prediction across diverse cancer types. Despite advancements, challenges such as data heterogeneity and model interpretability still persist. Further research is warranted to address these challenges and develop a comprehensive framework for cancer survival prediction applicable to various cancer types. This review lays the foundation for future investigations in the area of cancer research. © The Author(s), under exclusive licence

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

AR-GeoShapes: Development and Evaluation of a Marker Less Augmented Reality Based Geometrical Shapes Learning Framework on Memory Retention and Learning Achievement 18

AR-GeoShapes: Development and Evaluation of a Marker Less Au...

引用

18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Azam, Ayesha Naz, Zubaira Khan, Muhammad Usman Ghani Din, Irfanud Al-Khwarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Information Technology University Lahore Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan New Uzbekistan University Department of Computer Science School of Computing Tashkent Uzbekistan

ISBN: (纸本)9798331508692

A large number of students struggle to grasp geometric principles, especially in three-dimensional (3D) space. In turn, this is why digital educational tools have been integrated more and more into the learning environment: because they entertain students. One of these technologies is Augmented Reality (AR) which has become a widely researched and effective tool for education. Where learning is situated with content embedded within the physical environment (support), AR better enables offerings to be adapted. This paper presents AR-GeoShapes, an augmented reality-based educational tool developed to help students learn solid geometry concepts. Built with the Vuforia marker less SDK in Unity, AR-GeoShapes is an exercise that runs on mobile devices for users to learn shapes interactively. Further to evaluate whether using AR-GeoShapes helps increase memory retention and improve learning achievement, a comparative study involving 58 students was conducted. The results demonstrate that the experimental group, utilizing AR-GeoShapes, exhibited significantly higher memory retention and learning outcomes compared to the control group. These findings suggest that AR-GeoShapes are a valuable tool for enhancing students' comprehension of geometric concepts. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

DDoS Attacks Detection in IoT Networks using Naive Bayes and Random Forest 3

DDoS Attacks Detection in IoT Networks using Naive Bayes and...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Srivastava, Animesh Tiwari, Shweta Rawat, Bhupender Singh Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Institute of Engineering Chandigarh University Department of Computer Science & Engineering Department / University Punjab Gharuan Mohali India Coer University College of Smart Computing Roorkee India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350375190

The proliferation of Internet of Things (IoT) devices has resulted in numerous benefits, including streamlined tasks and improved connectivity but has also raised concerns regarding security vulnerabilities. Distributed Denial of Service (DDoS) attacks targeting IoT systems pose a significant threat, potentially disrupting operational activities. This study conducts a comparative analysis of existing methodologies to detect DDoS attacks in IoT environments, focusing on the efficacy of machine learning algorithms. Specifically, this study evaluates the performance of Random Forest and Naive Bayes classifiers in detecting DDoS attacks using an IoT dataset. The evaluation involves training and testing these classifiers and assessing their accuracy as a key performance metric. Empirical findings demonstrate enhanced precision in identifying DDoS attacks in IoT environment, indicating the effectiveness of the proposed approach. By leveraging machine learning algorithms, particularly Random Forest and Naive Bayes, traditional techniques for detecting and mitigating DDoS attacks in IoT environments are accelerated, thus enhancing the security of IoT systems. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection System Using Deep Learning Techniques for Internet of Medical Things (IoMT) 5th

Intrusion Detection System Using Deep Learning Techniques fo...

引用

5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Saran, Naveen Kesswani, Nishtha Saharan, Ravi Department of Computer Science Central University of Rajasthan NH-8 Rajasthan Bandarsindri305817 India Department of Computer Science and Engineering Central University of Rajasthan NH-8 Rajasthan Bandarsindri305817 India Department of Data Science & Analytics Central University of Rajasthan NH-8 Rajasthan Bandarsindri305817 India

ISBN: (纸本)9783031609343

The Internet of Things (IoT) has tremendously impacted people’s daily lives in recent years. Researchers and corporations also show interest in diverse IoT applications, such as the Internet of Medical Things (IoMT). In various aspects, security issues are the most important area of IoT, specifically for IoMT. The healthcare industry’s primary concern is safeguarding patient data from numerous intrusion attacks. It is essential to develop an effective and secure Intrusion Detection System (IDS) for IoMT. In this research article, we have proposed an efficient Intrusion Detection System (IDS) to identify intrusion attacks on the Internet of Medical Things (IoMT). We have developed a robust Intrusion Detection System (IDS) by employing a variety of Deep Learning (DL) techniques such as Simple Recurrent Neural Networks (S-RNN), Long Short-Term Memory Networks (LSTM) and Gated Recurrent Units (GRU) on a publicly available healthcare dataset. The experiment’s findings showed very high accuracy rates of intrusion detection using Simple Recurrent Neural Network (S-RNN), Long Short Term Memory Network (LSTM), Gated Recurrent Units (GRU) classification techniques compared to the state-of-art techniques. The overall accuracy of the experimented Intrusion Detection Systems was more than 99 % for all the implemented DL classification techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

POD-QUERY: Schema Mapping and Query Rewriting for Solid Pods 22

POD-QUERY: Schema Mapping and Query Rewriting for Solid Pods

引用

22nd International Semantic Web Conference on Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters-Demos-Industry 2023

作者： Vandenbrande, Maarten Jakubowski, Maxime Bonte, Pieter Buelens, Bart Ongenae, Femke Van den Bussche, Jan IDLab Ghent University – imec Belgium Data Science Institute Hasselt University Belgium Belgium Departement of Computer Science KU Leuven Kulak Belgium

We envisage a decentralized Web architecture in which access to data in Solid pods is mediated by Web agents. Leveraging methods from information integration and data exchange, Web agents can give clients access to views defined by schema mappings. Queries formulated by clients over the view are rewritten by the agent to queries that work correctly over the base data. We demonstrate POD-QUERY, our prototype implementation of such an architecture, where schema alignment is specified by RDF-to-RDF mappings written in RML, and SPARQL queries are rewritten based on these mappings. We demonstrate our system in a personal health scenario. © 2023 Copyright for this paper by its authors.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

data-Driven Analysis of EV Energy Prediction and Planning of EV Charging Infrastructure 9

Data-Driven Analysis of EV Energy Prediction and Planning of...

引用

9th IEEE International Conference on Big data Computing Service and Applications, BigdataService 2023

作者： Palaniappan, Arul Bhukya, Purnima Chitti, Sai Kiran Gao, Jerry San Jose State University Department of Applied Data Science San Jose United States San Jose State University Department of Applied Data Science Department of Computer Engineering San Jose United States

ISBN: (纸本)9798350333794

To reduce the current crisis on energy and environmental problems, the adoption of electric vehicles is an effective way. But limited access and availability of charging infrastructure are restricting the EV market development. This paper presents data-driven analysis of current EV charging infrastructure to plan future EV infrastructure in the city. This paper tackles the problem using three approaches. The first approach that is covered in this paper is to predict the number of EV charging stations that need to be implemented in a geographic location. This is done using the SARIMAX model on the California EV charging station dataset that is preprocessed to be split into zip codes. The second approach that is tackled by this paper is the energy consumption prediction by an EV charging station. This information is useful to allow planners to understand how much energy is consumed by an EV station across multiple different time periods. An optimized Meta-Fusion regression model based on a stacked model is proposed for this approach and is applied to the Palo Alto charging station dataset. The third approach utilized the K-means clustering algorithm on the Palo Alto charging station dataset to predict the optimal location for EV charging stations in that city. The SARIMAX mode had an R2 score of 0.97, a MAE of 6.50, and RMSE of 17.43 when predicting EV station counts in the zip code 92101. The stacked model had an R2 score of 0.98, a MAE of 0.91 and an RMSE of 2.26 when predicting the daily energy consumption by an EV station. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Classification of Opioid Patients Based on Drug Usage Using Machine Learning

Classification of Opioid Patients Based on Drug Usage Using ...

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ramesh, Ch Raja Sumanjali, A. Shreya, A. Saipraneeth, D. Sharma, P. Muralidhar Vignan Institute of Institute of Technology & Sciences Department of Data Science Department of Cse Hyderabad Deshmukhi India Vignan Institute of Institute of Technology & Sciences Department of Cse Hyderabad Deshmukhi India

ISBN: (纸本)9798350373783

There have been more drug overdoses in the past few years because of the opioid problem. A number of ways have been created to guess how often doctors will prescribe opioids. But because the problem is so complicated, these methods are not yet accurate enough. The way that opioid-dependent people are categorized from stable data sources needs to be reliable and honest. Most of the studies that have been done in the past have looked at the mental health of opioid users in connection to how much they use. Some of the newest deep learning techniques, like the attention and knowledge distillation mechanism, are not used in these works. These would help them get better results. This study classified drugs using machine and deep learning. MIMIC-III organized and unstructured data was used to identify purposeful and accidental opioid users. The 41 characteristics that are used are based on machine learning and deep learning to identify the drug users. We were able to accurately predict both intentional and accidental users from organized datasets better than from unstructured datasets. A good test accuracy is also reached by using a distilled body of knowledge that comes from combining the two machine learning algorithms CNN+LSTM. Ablation analysis gives us new opioid patient data for our investigation. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Overview of Shared Task on Caste and Migration Hate Speech Detection 4

Overview of Shared Task on Caste and Migration Hate Speech D...

引用

4th Workshop on Language Technology for Equality, Diversity, Inclusion, LT-EDI 2024

作者： Rajiakodi, Saranya Chakravarthi, Bharathi Raja Ponnusamy, Rahul Kumaresan, Prasanna Kumar Thangasamy, Sathiyaraj Sivagnanam, Bhuvaneswari Rajkumar, Charmathi Central University of Tamil Nadu India School of Computer Science University of Galway Ireland Data Science Institute University of Galway Ireland Department of Tamil Sri Krishna Adithya College of Arts and Science Tamil Nadu India The American College Tamil Nadu Madurai India

ISBN: (纸本)9798891760813

We present an overview of the first shared task on "Caste and Migration Hate Speech Detection." The shared task is organized as part of LT-EDI@EACL 2024. The system must delineate between binary outcomes, ascertaining whether the text is categorized as a caste/migration hate speech or not. The dataset presented in this shared task is in Tamil, which is one of the under-resource languages. There are a total of 51 teams participated in this task. Among them, 15 teams submitted their research results for the task. To the best of our knowledge, this is the first time the shared task has been conducted on textual hate speech detection concerning caste and migration. In this study, we have conducted a systematic analysis and detailed presentation of all the contributions of the participants as well as the statistics of the dataset, which is the social media comments in Tamil language to detect hate speech. It also further goes into the details of a comprehensive analysis of the participants’ methodology and their findings. © 2024 Association for Computational Linguistics.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Efficient Protein Structural Class Prediction Via Chaos Game Representation and Recurrent Neural Networks 48

Efficient Protein Structural Class Prediction Via Chaos Game...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zervou, Michaela Areti Doutsi, Effrosyni Tsakalides, Panagiotis Institute of Computer Science Foundation for Research and Technology-Hellas Heraklion Greece University of Crete Department of Computer Science Heraklion Greece

ISBN: (纸本)9781728163277

Predicting the structural class of a protein from its amino acid sequence is among the most significant problems in bioinformatics, especially for proteins with a low sequence similarity. While current methods using recurrent neural networks achieve a notable accuracy in this task, their approach relies on extracting a large quantity of features, impacting the efficiency and reliability of the prediction. In this work, we introduce an efficient and accurate classification scheme based on chaos game representation and recurrent neural networks. The proposed scheme achieves comparable results with state-of-the-art methods, while using a significantly lower-dimensional representation of the feature space. © 2023 IEEE.

关键词： Chaos Game Representation Gated Recurrent Unit Multidimensional Time-series Protein Structural Class Prediction

来源：评论

学校读者我要写书评

暂无评论

Semantics Preserving Emoji Recommendation with Large Language Models

Semantics Preserving Emoji Recommendation with Large Languag...

引用

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Qiu, Zhongyi Qiu, Kangyi Lyu, Hanjia Xiong, Wei Luo, Jiebo Georgia Institute of Technology School of Computational Science and Engineering AtlantaGA United States Georgia Institute of Technology School of Computer Science AtlantaGA United States University of Rochester Department of Computer Science RochesterNY United States

ISBN: (纸本)9798350362480

Emojis have become an integral part of digital communication, enriching text by conveying emotions, tone, and intent. Existing emoji recommendation methods are primarily evaluated based on their ability to match the exact emoji a user chooses in the original text. However, they ignore the essence of users' behavior on social media in that each text can correspond to multiple reasonable emojis. To better assess a model's ability to align with such real-world emoji usage, we propose a new semantics preserving evaluation framework for emoji recommendation, which measures a model's ability to recommend emojis that maintain the semantic consistency with the user's text. To evaluate how well a model preserves semantics, we assess whether the predicted affective state, demographic profile, and attitudinal stance of the user remain unchanged. If these attributes are preserved, we consider the recommended emojis to have maintained the original semantics. The advanced abilities of Large Language Models (LLMs) in understanding and generating nuanced, contextually relevant output make them well-suited for handling the complexities of semantics preserving emoji recommendation. To this end, we construct a comprehensive benchmark to systematically assess the performance of six proprietary and open-source LLMs using different prompting techniques on our task. Our experiments demonstrate that GPT-4o outperforms other LLMs, achieving a semantics preservation score of 79.23%. Additionally, we conduct case studies to analyze model biases in downstream classification tasks and evaluate the diversity of the recommended emojis (https://***/VIStA-H/SemanticsPreservingEmojiRec). © 2024 IEEE.

关键词： Semantics

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：