2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)最新文献_第2页

Clustering Algorithms Analysis Based on Arcade Game Player Behavior 基于街机游戏玩家行为的聚类算法分析

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00026

Daniel Shamsudin, M. Leow, Lee-Yeng Ong

引用次数: 0

Detecting Pneumonia Based On Chest X-Ray Images Using Reinforcement Learning 基于强化学习的胸部x线图像肺炎检测

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00018

Rafa Alenezi, Simone A. Ludwig

{"title":"Detecting Pneumonia Based On Chest X-Ray Images Using Reinforcement Learning","authors":"Rafa Alenezi, Simone A. Ludwig","doi":"10.1109/AIKE55402.2022.00018","DOIUrl":"https://doi.org/10.1109/AIKE55402.2022.00018","url":null,"abstract":"While early detection of diseases helps in managing and improving patient outcomes, most detection methods employed today are largely manual, costly, and time-consuming. Accordingly, computer-aided diagnosis is emerging as an innovative solution to improving the accuracy of detection by eliminating human errors and lowering the cost of diagnosis. One of the diseases that can benefit immensely from computer-aided diagnosis is pneumonia, which is an acute pulmonary infection accounting for thousands of hospitalizations and deaths globally. Current pneumonia detection approaches entail manually examining radiology images such as X-rays. Because of subjective variability, the outcomes of the examination are not always accurate. As a result, researchers have started to develop models based on machine learning to aid in detecting pneumonia based on chest X-ray images. Most of the models developed are based on deep learning, especially convolutional neural networks. However, these models require vast data sets for training and their accuracy values can be improved. For that reason, this paper developed a detection model based on Reinforcement Learning (RL) with convolutional neural network (CNN). The chest X-ray images of pneumonia is a data set that is used for the experiments. The obtained results confirm that applying the RL model is a good choice for detecting pneumonia. The efficacy of these model's performance was evaluated by measuring the precision, recall, F1-score, accuracy, and confusion matrix.","PeriodicalId":441077,"journal":{"name":"2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121654422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Sustainability in Machine Learning-based Android Malware Detection using API calls 使用API调用增强基于机器学习的Android恶意软件检测的可持续性

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00028

Hojun Lee, Seong-je Cho, Hyoil Han, Woosang Cho, Kyoungwon Suh

引用次数: 0

Ethical and Sustainability Considerations for Knowledge Graph based Machine Learning 基于知识图的机器学习的伦理和可持续性考虑

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00015

C. Draschner, Hajira Jabeen, Jens Lehmann

{"title":"Ethical and Sustainability Considerations for Knowledge Graph based Machine Learning","authors":"C. Draschner, Hajira Jabeen, Jens Lehmann","doi":"10.1109/AIKE55402.2022.00015","DOIUrl":"https://doi.org/10.1109/AIKE55402.2022.00015","url":null,"abstract":"Artificial Intelligence (AI) and Machine Learning (ML) are becoming common in our daily lives. The AI-driven processes significantly affect us as individuals and as a society, spanning across ethical dimensions like discrimination, misinformation, and fraud. Several of these AI & ML approaches rely on Knowledge Graph (KG) data. Due to the large volume and complexity of today's KG-driven approaches, enormous resources are spent to utilize the complex AI approaches. Efficient usage of the resources like hardware and power consumption is essential for sustainable KG-based ML technologies. This paper introduces the ethical and sustainability considerations, challenges, and optimizations in the context of KG-based ML. We have grouped the ethical and sustainability aspects according to the typical Research & Development (R&D) lifecycle: an initial investigation of the AI approach's responsibility dimensions; technical system setup; central KG data analytics and curating; model selection, training, and evaluation; and final technology deployment. We also describe significant trade-offs and alternative options for dedicated scenarios enriched through existing and reported ethical and sustainability issues in AI-driven approaches and research. These include, e.g., efficient hardware usage guidelines; or the trade-off between transparency and accessibility compared to the risk of manipulability and privacy-related data disclosure. In addition, we propose how biased data and barely explainable AI can result in discriminating ML predictions. This work supports researchers and developers in reflecting, evaluating, and optimizing dedicated KG-based ML approaches in the dimensions of ethics and sustainability.","PeriodicalId":441077,"journal":{"name":"2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131703101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Hybrid Video-to-Text Summarization Framework and Algorithm on Cascading Advanced Extractive- and Abstractive-based Approaches for Supporting Viewers' Video Navigation and Understanding

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00012

Aishwarya Ramakrishnan, Chun-Kit Ngan

{"title":"A Hybrid Video-to-Text Summarization Framework and Algorithm on Cascading Advanced Extractive- and Abstractive-based Approaches for Supporting Viewers' Video Navigation and Understanding","authors":"Aishwarya Ramakrishnan, Chun-Kit Ngan","doi":"10.1109/AIKE55402.2022.00012","DOIUrl":"https://doi.org/10.1109/AIKE55402.2022.00012","url":null,"abstract":"In this work, we propose the development of a hybrid video-to-text summarization (VTS) framework on cascading the advanced and code-accessible extractive and abstractive (EA) approaches for supporting viewers' video navigation and understanding. More precisely, the contributions of this paper are three-fold. First, we devise an automated and unified hybrid VTS framework that takes an arbitrary video as an input, generates the text transcripts from its human dialogues, and then summarizes the text transcripts into one short video synopsis. Second, we advance the binary merge-sort approach and expand its use to develop an intuitive and heuristic abstractive-based algorithm, with the time complexity $O(T_{L}logT_{L})$ and the space complexity $O(T_{L})$, where TL is the total number of word tokens on a text, to dynamically and successively split and merge a long piece of text transcripts, which exceeds the input text size limitation of an abstractive model, to generate one final semantic video synopsis. At the end, we test the feasibility of applying this proposed framework and algorithm in conducting the preliminarily experimental evaluations on three different videos, as a pilot study, in genres, contents, and lengths. We show that our approach outperforms and/or levels most of the individual EA methods stated above by 75% in terms of the ROUGE F1-Score measurement.","PeriodicalId":441077,"journal":{"name":"2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127698032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning Algorithm for Non-invasive Blood Pressure Estimation Using PPG Signals 基于PPG信号的无创血压估计的机器学习算法

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00022

Gengjia Zhang, Siho Shin, Jaehyo Jung, Meina Li, Y. Kim

引用次数: 1

OCI Runtime Comparison and Analysis Study OCI运行时比较与分析研究

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00017

SungHwan Jeon, Simon Shim, Haejin Chung, Yunmook Nah

引用次数: 0

Interpretability of Knowledge Graph-based Explainable Process Analysis 基于知识图的可解释过程分析的可解释性

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00008

Anne Füßl, V. Nissen

{"title":"Interpretability of Knowledge Graph-based Explainable Process Analysis","authors":"Anne Füßl, V. Nissen","doi":"10.1109/AIKE55402.2022.00008","DOIUrl":"https://doi.org/10.1109/AIKE55402.2022.00008","url":null,"abstract":"The last decade produced rapid developments and powerful new technologies that are creating a huge upsurge in artificial intelligence research. However, for critical operational decisions (e.g., consulting services), the need for explanations and interpretable results are becoming a necessity. The integration of knowledge graphs that provide relevant background knowledge in machine-readable form, and machine learning methods represents a new form of hybrid intelligent systems that benefit from each other's strengths. Our research aims at an explainable system with a specific knowledge graph architecture that can generate human-understandable results even when no suitable domain experts are available. Against this background, the interpretability of a knowledge graph-based explainable artificial intelligence approach for business process analysis is focused. We design a framework of interpretation, and show how interpretable models are generated. Result paths on weaknesses and improvement measures related to a business process are used to produce stochastic decision trees, which improve the interpretability of results. This can lead to interesting consulting self-services for clients or be applied as a device for accelerating classical consulting projects.","PeriodicalId":441077,"journal":{"name":"2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116454182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Sentiment Analysis Based Stock Recommendation System 基于情绪分析的股票推荐系统

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00020

Jayanth Rao, V. Ramaraju, James Smith, Ajay Bansal

{"title":"A Sentiment Analysis Based Stock Recommendation System","authors":"Jayanth Rao, V. Ramaraju, James Smith, Ajay Bansal","doi":"10.1109/AIKE55402.2022.00020","DOIUrl":"https://doi.org/10.1109/AIKE55402.2022.00020","url":null,"abstract":"There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance's conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities which represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics (PEG Ratio, Forward EPS, etc.) to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary “Buy” (1) or “Not Buy” (0) recommendation for each stock. The model achieved an F1 accuracy of 82.5% and for most major stocks, the model's recommendations were aligned with the stock analysts' ratings from the NASDAQ website. The logistic regression model would improve from leveraging a historical compass of data, given the hive-mind behavior that online discussion forums exhibit.","PeriodicalId":441077,"journal":{"name":"2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126325961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-shot Text Classification with Saliency-equivalent Concatenation 具有显著性等效连接的少射文本分类

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00019

Ying-Jia Lin, Yu Chang, Hung-Yu kao, Hsin-Yang Wang, Mu Liu

{"title":"Few-shot Text Classification with Saliency-equivalent Concatenation","authors":"Ying-Jia Lin, Yu Chang, Hung-Yu kao, Hsin-Yang Wang, Mu Liu","doi":"10.1109/AIKE55402.2022.00019","DOIUrl":"https://doi.org/10.1109/AIKE55402.2022.00019","url":null,"abstract":"In few-shot text classification, the lack of significant features limits models from generalizing to data not included in the training set. Data augmentation is a solution to the classification tasks; however, the standard augmentation methods in natural language processing are not feasible in few-shot learning. In this study, we explore data augmentation in few-shot text classification. We propose saliency-equivalent concatenation (SEC)11Our code is available at https://github.com/IKMLab/SEC.. The core concept of SEC is to append additional key information to an input sentence to help a model understand the sentence easier. In the proposed method, we first leverage a pre-trained language model to generate several novel sentences for each sample in datasets. Then we leave the most relevant one and concatenate it with the original sentence as additional information for each sample. Our experiments on the two few-shot text classification tasks verified that the proposed method can boost the performance of meta-learning models and outperform the previous unsupervised data augmentation methods.","PeriodicalId":441077,"journal":{"name":"2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130288189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0