2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)最新文献_第3页

Acute Lymphoblastic Leukemia Classification in Nucleus Microscopic Images using Convolutional Neural Networks and Transfer Learning 基于卷积神经网络和迁移学习的急性淋巴细胞白血病核显微图像分类

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574176

Dheannisa Ramadhani Putri, A. Jamal, Ali Akbar Septiandri

{"title":"Acute Lymphoblastic Leukemia Classification in Nucleus Microscopic Images using Convolutional Neural Networks and Transfer Learning","authors":"Dheannisa Ramadhani Putri, A. Jamal, Ali Akbar Septiandri","doi":"10.1109/AiDAS53897.2021.9574176","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574176","url":null,"abstract":"Leukemia is a disease caused by the abnormal production of abnormal blood cells. In Acute Lymphoblastic Leukemia (ALL), lymphoblast cells do not develop into lymphocytes. To diagnose the disease, we need to differentiate between lymphocytes and lymphoblasts. However, lymphocytes and lymphoblasts have similar morphologies. Several studies using computer vision have been developed to distinguish lymphocytes from lymphoblasts. This study aims to compare deep and wide deep learning architectures to classify segmented blood cell images. The “deep” architecture employed in this study was DenseNet201, whereas the “wide” architecture was Wide-ResNet-50-2. We also employed ResNet50 as a baseline. This study utilizes transfer learning to reduce the training steps needed. In addition, we measured the impact of dataset preprocessing using histogram equalization on the classifier performance. We found that DenseNet201 model has the best performance with an AUC score of 86.73% and that histogram equalization makes the performance worse.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"6 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":"125877442","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: 3

Artificial Intelligence (Ai) In The Food And Beverage Industry: Improves The Customer Experience 人工智能(Ai)在食品和饮料行业:改善客户体验

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574261

Yun Seng Cheong, Choon Sen Seah, Y. Loh, Li Har Loh

引用次数: 4

Development of Open Source-based Threat Hunting Platform 基于开源的威胁搜索平台的开发

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574308

Denny Hermawan, Nugroho Ganda Novianto, Digit Octavianto

{"title":"Development of Open Source-based Threat Hunting Platform","authors":"Denny Hermawan, Nugroho Ganda Novianto, Digit Octavianto","doi":"10.1109/AiDAS53897.2021.9574308","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574308","url":null,"abstract":"Threat hunting is a process that focuses on activities that are repeated in nature, by taking an approach to identify and understand threat actors who may have entered and are in the computer network infrastructure. It becomes the focus of the Central Security Operation Centers (SOCs) as a survey conducted by the SANS Institute on threat hunting, show 91% of enterprises reported increasing the implementation of threat hunting. Therefore, this research attempts to design a threat hunting platform using Elasticsearch, Logstash, and Kibana (ELK) by implementing rules and alerts obtained from sigma rules for attack detection and performing penetration testing using the Atomic Red Team method and Web Application Vulnerability to obtain attack logs. In the log mapping process, an analysis of the previously obtained attack detection was carried out. Then we create a threat hunting framework using MITRE ATT&CK, Pyramid of pain and Diamond Models of Intrusion Analysis. By getting the results of attack detection from 10 rules and alerts detected on ELK and obtaining 3 tactics and 4 attack techniques on the attack method with the Atomic Red Team, and on the Web Application Vulnerability we discover 2 tactics and 3 attacks.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"63 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":"132877603","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: 3

Comparison of Face Coverings Detection Methods using Deep Learning 基于深度学习的人脸覆盖物检测方法比较

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574318

Muhammad Nizam Abdul Aziz, S. Mutalib, S. Aliman

{"title":"Comparison of Face Coverings Detection Methods using Deep Learning","authors":"Muhammad Nizam Abdul Aziz, S. Mutalib, S. Aliman","doi":"10.1109/AiDAS53897.2021.9574318","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574318","url":null,"abstract":"End of the year 2019, the world has been shocked by a novel coronavirus disease 2019 (COVID-19), and the world is facing a huge health crisis due to the rapid transmission of COVID-19. World Health Organization (WHO) advised wearing a face cover in public places and crowded areas as a part of a comprehensive element to preventive and control measures to limit the spread of COVID-19. However, it is not easy to monitor people manually in these areas. In this paper, a detector model using deep learning for face coverings detection will be presented based on DEtection TRansformer (DETR) algorithm. The custom dataset has been used through this research, which comprises different face covering such as face mask, face shield, niqab, and purdah. The results concluded that the proposed model achieved the highest accuracy percentage of 92.38% and the highest recall percentage of 86.21% as a detector model. Finally, a comparative result with other face coverings model has been presented at the end of the research. The proposed model has achieved higher accuracy, precision, recall and specificity than the other model.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"47 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":"129523967","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

Sentiment Analysis for Informal Malay Text in Social Commerce 社交商务中马来语非正式文本的情感分析

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574436

A. Nabiha, S. Mutalib, A. Malik

{"title":"Sentiment Analysis for Informal Malay Text in Social Commerce","authors":"A. Nabiha, S. Mutalib, A. Malik","doi":"10.1109/AiDAS53897.2021.9574436","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574436","url":null,"abstract":"Sentiment Analysis (SA) is opinion mining which often defines as the study of emotions, opinions, or feedback that relates to the usage of computational linguistics, text analytics, and natural language processing. With the rise of social media posts, it is becoming more challenging to evaluate brief, casual, and non-structured texts to optimize consumer feedback and spot patterns. Meanwhile, social commerce involves social media for social interaction in assisting customers and merchants to do business transactions. From a social media perspective, the informal Malay Text is less explored by the researchers. Thus, it will directly yield difficulties in conducting and preparing the SA processes. Cross-Industry Standard Process for Data Mining (CRISP-DM) was adapted as a reference model for the methodology of this work with machine learning approaches in classifying the informal Malay textual data based on sentiment. The dataset was extracted from the Facebook platform of Pos Laju Malaysia pages. The comparison of the classification technique performances was analyzed in identifying the most accurate classifier for SA, within three different machine learning classifiers was experimented by using 1200 instances from an informal Malay textual dataset. The results of Decision Tree (J48), Support Vector Machine (SVM), and Naïve Bayes (NB) were analyzed and discussed. The result of the highest accuracy of Ten-Fold Cross-Validation is 69.7% and meanwhile, for the Percentage Split method, the highest accuracy result is 70.9%. It shows that Support Vector Machine (SVM) is the best classifier compared to other classifiers of text classification based on sentiment.","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":"130814739","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: 4

Multi-Criteria Decision Making based on Z-Number Valuation for Uncertain Information 基于z数估值的不确定信息多准则决策

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574134

T. Zamali, M. Lazim

引用次数: 1

Review representation learning and profiling for Recommendation System 推荐系统的评论表示学习与分析

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574349

N. Khairudin, N. Sharef, A. Azizan

{"title":"Review representation learning and profiling for Recommendation System","authors":"N. Khairudin, N. Sharef, A. Azizan","doi":"10.1109/AiDAS53897.2021.9574349","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574349","url":null,"abstract":"The utility function of users and items in a traditional recommender system is based on rating. However, data sparsity is a fundamental issue in which the user rates a small number of items in comparison to the available items. This rating alone is sometimes insufficient for precisely understanding users' behaviours. A user's general preferences on an item can be shown through the overall ratings, yet they may be dissatisfied with a specific feature of the item. Since most commercial website nowadays, allows user to express their opinion through the review text, then there is an opportunity to precisely understand the user preferences via this element. However, it is essential to represent the review texts in a suitable representation to be used as one of the recommendation components. Therefore, this research aim is to create a relevant review profiling framework based on the data analytic assessment and some observation of the user behaviours reviewing pattern. The review texts are classified into groups of word count, and the consistency of the relevant category is identified. This selected category is used to generate a representation of reviews as one of the components of recommendation processes and resulted in less computational complexity and processing time. The experimental results show that review text count distribution fall in the range of 10 to 30 with a range from 20–30 word count has the highest number of users for almost all datasets.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"47 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":"115787836","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

Exploring Classification For Sentiment Analysis From Halal Based Tweets 探索基于清真推文的情感分析分类

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574255

Roziyani Setik, R. M. T. R. L. Ahmad, S. Marjudi

{"title":"Exploring Classification For Sentiment Analysis From Halal Based Tweets","authors":"Roziyani Setik, R. M. T. R. L. Ahmad, S. Marjudi","doi":"10.1109/AiDAS53897.2021.9574255","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574255","url":null,"abstract":"Globally, social media is gaining popularity and redefining how people interact with one another online. Malaysian individuals, for example, are increasingly reliant on social media platforms such as Facebook and Twitter as well as LinkedIn, Pinterest, Instagram, and other similar sites. Consider sentiment analysis to be a sub-category of social listening. A social media sentiment analysis has uncovered the public's current feelings on a particular topic or brand. Sentiment analysis is a technique for characterizing and capturing emotional states from unstructured text. The most important part of sentiment analysis is to evaluate a body of text to comprehend the opinion expressed by it. It usually assigns a polarity of “positive”, “negative” or “neutral”. It uses an algorithmic technique to capture people's thoughts, sentiments, and emotions by incorporating Natural Language Processing and Machine Learning technology. Sentiment analysis in Malaysia's social media is challenging to perform since posts are frequently written in a mixed language, usage of English and Malay with embedded jargon and various district dialect. The classification was performed based on Malaysia halal certification scheme for each tweet to acquire the class label's frequency value based on the sentiment analysis process's polarity results. It will demonstrate social media users' proclivity for posting and can act as a reference point for users when making decisions. An analysis of amounted 500 tweets with the hashtag #sijilhalal elicited information regarding people's feelings, preconceptions, and attitudes toward various issues related to halal certification in Malaysia. The discovery of a person's emotions concerning halal topics is visualized. Muslims' views are of importance to #sijilhalal awareness.","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":"125947048","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: 2

Increasing Student's Engagement in Study Skills Course via Gamification 通过游戏化提高学生对学习技巧课程的参与度

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574396

A. F. M. Moshiur Rahman, Ini Imaina Binti Abdullah, Nor Azizah Talkis, Nur Faezah Jamal, Samsiah Abdul Razak

引用次数: 0

The DEMATEL Approach to Analyzing The Factors Influencing University Students' Purchase of Smartphones DEMATEL法分析大学生智能手机购买影响因素

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574256

Z. Rodzi, Muhammad Nazmi Haqim bin Kamarulamirin, Nur Qasfareeny binti Abdul Halim, Yasmin binti Ya'akop

{"title":"The DEMATEL Approach to Analyzing The Factors Influencing University Students' Purchase of Smartphones","authors":"Z. Rodzi, Muhammad Nazmi Haqim bin Kamarulamirin, Nur Qasfareeny binti Abdul Halim, Yasmin binti Ya'akop","doi":"10.1109/AiDAS53897.2021.9574256","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574256","url":null,"abstract":"A smartphone is a necessity for everyone because it enables users to stay connected and entertained while travelling by browsing websites, listening to music, and staying connected. They can use any location to access email, play games, and work. Numerous smartphones from various manufacturers are available on the market. Purchasing a smartphone entails a great deal of thought. Thus, the primary objective of this paper is to analyse smartphone specifications that affect buyer behaviour when purchasing a product using the Decision-making Trial and Evaluation Laboratory (DEMATEL) method. The study was conducted with one hundred seventy students at Faculty of Computer and Mathematical Sciences, UiTM (Seremban Campus). Data were collected by using questionnaires and distributed to the students through Google forms. This study utilised seven criteria: Price (PR), Battery (BA), Camera (CA), Screen Display (SD), Storage (ST), RAM & Processor (RP) and Operating System (OS). According to the findings, the top three factors influencing consumers' decision to purchase a smartphone are, in order, price (PR), camera (CA), and storage (ST). Additionally, a cause-and-effect diagram was created to assist in comprehending the relationship between all of those criteria. When consumers make smartphone purchase decisions, price (PR) is the most influential cause criterion, while Operating System (OS) is the most influential effect criterion.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"19 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":"126903943","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: 2