2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)最新文献_第2页

You ask “Durian”, I gave “Musang King, Red Prawn and D24”: A Query Reformulation Behavior Experiment 你问“榴莲”，我给“猫山王、红对虾和D24”:查询改写行为实验

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574131

A. Azizan, Nurazzah Abd Rahman, Z. Bakar, N. Khairudin

{"title":"You ask “Durian”, I gave “Musang King, Red Prawn and D24”: A Query Reformulation Behavior Experiment","authors":"A. Azizan, Nurazzah Abd Rahman, Z. Bakar, N. Khairudin","doi":"10.1109/AiDAS53897.2021.9574131","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574131","url":null,"abstract":"The success of finding relevant information using search engines is highly dependent on the query used by the user. Therefore, an effective query formulation and reformulation is an important strategy in information search. The challenges become harder when finding for more in-depth information in a particular topic or domain. This paper aims to investigate the query behavior when searching for information in a given topic. We chose Durian as the topic of the study. To achieve the goal of the study, a user study was carried out using a task-driven experiment in which 12 scenario-based search tasks were designed for the user to complete using the Google search engine. A total of 4531 queries being analyzed, 3595 reformulation being made by 936 search tasks being submitted by 78 users who engaged in the experiment. The results reveal that 98% of the user performed query reformulation during the search and specification was the most frequent query reformulation strategy used by the users. In terms of the number of query words used, most of the users used 3 to 5 words in their query. In addition, majority of the users prefer to use natural language queries over keyword queries. This work provides insights to the query reformulation behavior in domain-specific information search, as well as the guidance for designing better query formulation assistance in search engine technology.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114865701","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

Comparison of Logistic Regression and XGBoost for Predicting Potential Debtors Logistic回归与XGBoost预测潜在债务人的比较

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574350

Revina Dwidarma, S. D. Permai, Jeklin Harefa

引用次数: 1

Online Shopping Preferences Visualization System Using Web Content Mining 基于Web内容挖掘的在线购物偏好可视化系统

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574386

Nur Fitriyani Che Razali, Masurah Mohamad, K. Salleh, Muhammad Hafizuddin Abd Rahman Sani, Lathifah Alfat

{"title":"Online Shopping Preferences Visualization System Using Web Content Mining","authors":"Nur Fitriyani Che Razali, Masurah Mohamad, K. Salleh, Muhammad Hafizuddin Abd Rahman Sani, Lathifah Alfat","doi":"10.1109/AiDAS53897.2021.9574386","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574386","url":null,"abstract":"Recently, social media sites have been attracting people to start and operate online business. It has been gaining more attention especially during this COVID-19 pandemic, where most activities are conducted via online platform. For instance, Instagram is one of the social media platform where people share and post their pictures and videos on their account, but some people take this opportunity to promote their business and shop online via features provided by Instagram. Based on observation, there is no specific function that visualizes the information of Instagram's business accounts to help people in gaining information and making decisions during shopping process. Thus, a system that specifically visualizes Instagram's extracted data was developed using Web Content Mining technique with the assistance of hashtags as data-points and Phantombuster API. Tableau software integrated with JavaScript library is used as a visualization tool to display the users shopping activities. The result of the extracted data was visualized in the form of Bubble Chart, Bar Chart, and Multiple Bar Chart that were placed in different dashboards. The results from the obtained functional testing and users' feedbacks have indicated 64.3% of the respondents agreed that the proposed system helps users in making the right choice during online shopping. However, this system has some limitation that could be further enhanced such as updating the information automatically in real time without using any manpower and to increase the interactivity between system and user instead of using static visualization graphs.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121238849","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

Pneumonia Radiographic Classification Using Shallow Learning Approach 基于浅学习方法的肺炎影像学分类

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574205

Noraini Hasan, Nurbaity Sabri, Nurfarahim Shaharudin, Shafaf Ibrahim, Z. Ibrahim, Nur Nabilah Abu Mangshor

{"title":"Pneumonia Radiographic Classification Using Shallow Learning Approach","authors":"Noraini Hasan, Nurbaity Sabri, Nurfarahim Shaharudin, Shafaf Ibrahim, Z. Ibrahim, Nur Nabilah Abu Mangshor","doi":"10.1109/AiDAS53897.2021.9574205","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574205","url":null,"abstract":"Pneumonia is a common lung infection usually suffered by children under the age of 5 years old. Pneumonia cases occur as a result of air pollution, which is particularly prevalent in developing countries where the World Health Organization (WHO) estimated that there will be more than 4 million casualties caused by pneumonia. Hence, the use of technologies is one of the ways that can effectively improve existing clinical approaches to pneumonia cases. This paper focuses on the implementation of a Shallow Learning (SL) technique to classify normal, viral, and bacterial pneumonia cases from chest radiographic images. To develop the system, Gray-Level Co-Occurrence Matrices (GLCM) texture features extracted the chest radiographic images into four (4) types of features namely energy, contrast, correlation, and homogeneity. Classification features were extracted from a radiographic image using K-Nearest Neighbours (KNN) where three classes were produced by this supervised learning. The finding reveals that the implementation of SL approach, which is KNN, was able to achieve a high accuracy of 86.67%. It shows that KNN can be used to classify the lung radiographic images without utilizing any complex learning approach.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132256205","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

Plants and Their Noise Absorption: Identifying most suitable plants to reduce noise via Impedance Tube Data Analysis 植物及其噪声吸收:通过阻抗管数据分析确定最适合的植物来降低噪声

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574254

Jamaludin S M, H. W, M. R, Mohamad Z, Mohd Kamal N L, Lim A Y K

{"title":"Plants and Their Noise Absorption: Identifying most suitable plants to reduce noise via Impedance Tube Data Analysis","authors":"Jamaludin S M, H. W, M. R, Mohamad Z, Mohd Kamal N L, Lim A Y K","doi":"10.1109/AiDAS53897.2021.9574254","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574254","url":null,"abstract":"Urban areas are always exposed to various noises, especially from vehicles and industrial activities. Plants on the other hand, can act as a medium that can absorb sound. To prove this, a number of plant species have been selected to measure the value of sound absorption based on the thickness, surface area, width and length of the leaves. Plant noise measurements were performed using built-in impedance tubes to identify the effectiveness of the acoustic properties of the leaves. From this study, a leaf such as Pometia Pinnata which has thicker leaves, larger surface area, width as well as longer leaf length than other leaves shows the ability to absorb more sound. Sound absorption measurements for 100% and 50% leaf quantities for Pometia Pinnata were obtained at 4.9 dBA and 4.8 dBA, respectively, which are considered to approach the expected noise attenuation levels reported at 5 - 10 dBA. The motivation from our measurement findings, can help to identify which plants are most suitable for reducing noise based on their characteristics. These measurement results are very important in the soon-to-be-developed intuitive plant tool.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133175372","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

An Overview of Segmentation and Classification Techniques: A Survey of Brain Tumour-Related Research 分割与分类技术综述:脑肿瘤相关研究综述

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574170

Muhammad Bakhtiar Iman Awang, Shafaf Ibrahim

{"title":"An Overview of Segmentation and Classification Techniques: A Survey of Brain Tumour-Related Research","authors":"Muhammad Bakhtiar Iman Awang, Shafaf Ibrahim","doi":"10.1109/AiDAS53897.2021.9574170","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574170","url":null,"abstract":"A brain tumour is a condition in which brain cells develop abnormally. Early treatment can prevent complications, which can be fatal if no early diagnosis or treatment is obtained. The brain tumour has multiple stages and varies in shape and size, necessitating a different type of treatment. It is typically identified through general symptoms such as headache and nausea. To confirm the presence of a potential brain tumour, medical imaging modalities such as Magnetic Resonance Imaging (MRI), Computed Axial Tomography (CAT), and Positron Emission Tomography (PET) are used. Despite the use of high-precision machines to capture brain images, radiologists and specialists are still susceptible to misreading or misinterpretation of medical images due to the large volume of data. Furthermore, manual delineation of brain tumours is tedious and time-consuming. To address these issues, brain tumour segmentation and classification image processing have been extensively researched in recent years due to the increased demand for medical image diagnosis. To address these issues, several new and improved techniques were introduced. Therefore, this paper proposed to overview the algorithms and methods of image enhancement, skull stripping, texture feature extraction, segmentation, and classification involved in an automated MRI brain tumour segmentation and classification. It is expected that this paper would provide new research directions for MRI brain tumour segmentation and classification.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129097691","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

Building Detection in Earthquake Prone Urban Area with Satellite Imagery Data Using Deep Learning 基于深度学习的卫星图像数据在地震多发城市地区的建筑检测

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574257

E. Irwansyah, A. A. Gunawan, C. Surya, Dewa Ayu Defina Audrey Nathania

{"title":"Building Detection in Earthquake Prone Urban Area with Satellite Imagery Data Using Deep Learning","authors":"E. Irwansyah, A. A. Gunawan, C. Surya, Dewa Ayu Defina Audrey Nathania","doi":"10.1109/AiDAS53897.2021.9574257","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574257","url":null,"abstract":"Earthquakes have the potential to be extremely damaging, resulting in significant loss of life and property damage, particularly in urban areas. The ability to observe damaged buildings is critical for emergency management professionals because it allows them to quickly direct rescue teams to the right place. Currently deep learning models have achieved cutting-edge accuracy in extracting natural image shapes from large, labeled data sets. This success has included implementation in the field of remote sensing despite problems with the lack of valid label data supply, especially those that are fully segmented. The goal of this work is to create damage assessment in earthquake-prone urban areas using satellite imagery and the deep learning model, with each image pixel labeled as either a building or a non-building label. Compared to other deep learning models such as UNet and DeepLab, the results show that PSPnet is the best model in this regard, as it achieves training and testing accuracy of 94.3 percent and 89.8 percent, respectively.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122566111","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

Price Prediction Using Time-Series Algorithms for Stocks Listed on Bursa Malaysia 利用时间序列算法预测马来西亚交易所上市股票的价格

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574445

Yi-Tou Choy, M. Hoo, Kok-Chin Khor

引用次数: 4

Sentiment Analysis of Snapchat Application's Reviews Snapchat应用评论的情感分析

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574379

W. Wong, S. Ismail, Muhammad Amirul Arifin, Siti Salwa Abdullah Make, M. Wahab, S. M. Shaharudin

{"title":"Sentiment Analysis of Snapchat Application's Reviews","authors":"W. Wong, S. Ismail, Muhammad Amirul Arifin, Siti Salwa Abdullah Make, M. Wahab, S. M. Shaharudin","doi":"10.1109/AiDAS53897.2021.9574379","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574379","url":null,"abstract":"Sentiment analysis is a process of extracting opinion and subjectivity knowledge from user generated text content without the need to monitor the reviews manually. It can help to obtain an overview on performance of a product or subject based on the reviews from users. The aim of this study is to classify the Snapchat application's reviews into different polarities which are positive, neutral or negative. Next, the most frequent words are identified. Furthermore, Multinomial Naïve Bayes and Random Forest classification algorithm are used to predict the user's rating. The performances of the classification models are evaluated using accuracy, precision, recall and F1-score. The results showed majority of the Snapchat users had a positive experience with the total of 6037 positive reviews. Based on the performance measures, Multinomial Naïve Bayes classification algorithm performed slightly better than the Random Forest classification algorithm in predicting the rating of Snapchat application. Overall, both of the classification algorithms have average performance in predicting user's rating for Snapchat application.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"125 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129671166","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

Predicting Company Bankruptcy Using Random Forest Method 用随机森林方法预测公司破产

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574384

Ishika Gurnani, Vincent, Febryan Stefanus Tandian, Maria Susan Anggreainy

引用次数: 6