2022 25th International Conference on Computer and Information Technology (ICCIT)最新文献_第2页

Rate Insight: A Comparative Study on Different Machine Learning and Deep Learning Approaches for Product Review Rating Prediction in Bengali Language 率洞察:不同机器学习和深度学习方法在孟加拉语产品评论评级预测中的比较研究

2022 25th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2022-12-17 DOI: 10.1109/ICCIT57492.2022.10055515

R. Chowdhury, Farhad Uz Zaman, Arman Sharker, Mashfiq Rahman, F. Shah

{"title":"Rate Insight: A Comparative Study on Different Machine Learning and Deep Learning Approaches for Product Review Rating Prediction in Bengali Language","authors":"R. Chowdhury, Farhad Uz Zaman, Arman Sharker, Mashfiq Rahman, F. Shah","doi":"10.1109/ICCIT57492.2022.10055515","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055515","url":null,"abstract":"In this contemporary era of digital marketing, ecommerce has emerged as one of the most preferred methods for day-to-day shopping. Ever since the COVID-19 pandemic, online shopping behavior has forever changed to less or no human-to-human interaction. As a result, it is getting more difficult for e-commerce enterprises to observe and evaluate market trends, particularly when done through consumer behavior analysis. To identify behavioral patterns and customer review-rating discrepancies, extensive analysis of product reviews is a substantial research field. Lack of benchmark corpora and language processing techniques, predicting review ratings in Bengali has become increasingly problematic. This paper thoroughly analyzes the approach to product review rating prediction for Bengali text reviews exploiting our own constructed dataset that was collected from an e-commerce website called DarazBD1. We acquired product reviews with labels known as ratings of five sentiment classes, from \"1\" to \"5\". It is noteworthy that we established a well-balanced dataset using our automated scraping system and a significant amount of time and effort is spent to maintain quality standards through the human annotation process. Exploration of multiple approaches to machine learning models such as logistic regression, random forest, multinomial naïve Bayes, and support vector machine, the best classification accuracy score of 78.63% is achieved by SVM. Subsequently, using Word2Vec, FastText, and GloVe embeddings with three deep neural network(DNN) architectures: CNN, Bi-LSTM, and a combination of CNN and Bi-LSTM, CNN+Bi-LSTM gave the highest accuracy score of 75.25% among the DNN architectures.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132033898","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

Designing a Bangla Parser using TRIE Based on Deterministic Finite Automata 基于确定性有限自动机的TRIE孟加拉语解析器设计

2022 25th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2022-12-17 DOI: 10.1109/ICCIT57492.2022.10056008

K. Hasan, Md. Sakhawat Hossain, Md. Abdulla Al-Sun, Md. Mostafizur Rahman

{"title":"Designing a Bangla Parser using TRIE Based on Deterministic Finite Automata","authors":"K. Hasan, Md. Sakhawat Hossain, Md. Abdulla Al-Sun, Md. Mostafizur Rahman","doi":"10.1109/ICCIT57492.2022.10056008","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10056008","url":null,"abstract":"We describe a new method of parsing Bangla language based on Deterministic Finite Automata (DFA) and implement the parser using a TRIE data structure. Hence we call the parser as TRIE parser. TRIE parser successfully parses sentences faster than other important parsing schemes as it needs no formal rules, no parameters and no Context Free Grammars (CFG). the scheme stores the Bangla grammar symbols or Pasts Of Speech (POS) as a state of the DFA and process a sentence following the operations of a DFA. If the set of POS symbols reaches to final state, then parsing is successful otherwise unsuccessful. The parser uses the grammar rules in compressed form hence it becomes very less space consuming. Therefore, it can be implemented in light weight fashion in main memory. The TRIE parser is compared with two other parsers and it shows that the proposed TRIE parser outperforms others in terms of processing time with an increasing number of sentences in the input paragraph. Necessary figures and examples are used to properly explain the proposed TRIE parser.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132214725","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

Aspect-Based Sentiment Analysis of Bangla Comments on Entertainment Domain 基于方面的孟加拉语娱乐评论情感分析

2022 25th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2022-12-17 DOI: 10.1109/ICCIT57492.2022.10055705

N. Sultana, R. Sultana, Risul Islam Rasel, M. M. Hoque

{"title":"Aspect-Based Sentiment Analysis of Bangla Comments on Entertainment Domain","authors":"N. Sultana, R. Sultana, Risul Islam Rasel, M. M. Hoque","doi":"10.1109/ICCIT57492.2022.10055705","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055705","url":null,"abstract":"Low-resource natural language processing is getting more attention nowadays. Aspect-Based Sentiment Analysis (ABSA) from a high-resource language such as English becomes unchallenging because of sufficient datasets and experimentation tools. However, Aspect-Based Sentiment Analysis from low-resource languages such as Bangla is quite hard. So, many researchers are investing their time and knowledge in low-resource natural language processing. In this paper, we are proposing a Bangla Aspect-Based Sentiment Analysis model using Bangla natural language processing. We have collected 4012 Bangla text comments related to cricket, drama, movie, and music from YouTube. We have applied some very prominent supervised machine learning techniques such as Support Vector Classifier (SVC), Random Forest (RF), and Linear Regression (LR). We have achieved more than 75% accuracy in classifying positive, negative, and neutral sentiments and 80% accuracy in extracting aspects from Bangla texts. Finally, we used publicly available datasets to test our proposed model's generalizability. Furthermore, we find that our proposed approach surpasses earlier related research.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131022975","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

BloodComm: A Peer-to-Peer Blockchain-based Community for Blood Donation Network BloodComm:一个基于点对点区块链的献血网络社区

2022 25th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2022-12-17 DOI: 10.1109/ICCIT57492.2022.10055757

Chowdhury Mohammad Abdullah, M. Kamal, Fairuz Shaiara, A. Kamal, Md. Azam Hossain

引用次数: 0

Bangla Fake News Detection using Machine Learning, Deep Learning and Transformer Models 使用机器学习、深度学习和变压器模型检测孟加拉假新闻

2022 25th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2022-12-17 DOI: 10.1109/ICCIT57492.2022.10055592

Risul Islam Rasel, Anower Hossen Zihad, N. Sultana, M. M. Hoque

{"title":"Bangla Fake News Detection using Machine Learning, Deep Learning and Transformer Models","authors":"Risul Islam Rasel, Anower Hossen Zihad, N. Sultana, M. M. Hoque","doi":"10.1109/ICCIT57492.2022.10055592","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055592","url":null,"abstract":"News Categorization is one of the primary applications of Text Classification, especially, Fake news classification. In recent days, many researchers have done plenty of work on Fake news detection in rich resource languages like English. But, due to a lack of resources and language processing tools, research on low-resource languages like Bangla is still insignificant. In this study, we try to build a Bangla Fake news dataset combining newly collected fake news data and available secondary datasets. Previously available datasets contained redundant data, which we reduced in our experiment. Finally, we build a Fake news dataset that contains 4678 distinct news data. We experimented with our data with multiple Machine Learning (LR, SVM, KNN, MNB, Adaboost, and DT), Deep Neural Networks (LSTM, BiLSTM, CNN, LSTM-CNN, BiLSTM-CNN), and Transformer (Bangla-BERT, m-BERT) models to attain some state of the art results. The best performing models are CNN, CNN-LSTM, and BiLSTM, with the accuracy of 95.9%, 95.5%, and 95.3%, respectively. We also tested our models by applying the previously existing datasets, and we got a 1.4% to 3.4% improvement in accuracy from previous results. Besides accuracy improvement, our models show a significant increase in recall of fake news data compared to the prior studies.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133158581","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

Fusion of Shallow and Deep Features for Classifying Skin Lesions 融合浅、深特征分类皮肤病变

2022 25th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2022-12-17 DOI: 10.1109/ICCIT57492.2022.10055219

Ishmamur Rahman, M. K. Islam, Abu Nowshed Chy, Muhammad Anwarul Azim

{"title":"Fusion of Shallow and Deep Features for Classifying Skin Lesions","authors":"Ishmamur Rahman, M. K. Islam, Abu Nowshed Chy, Muhammad Anwarul Azim","doi":"10.1109/ICCIT57492.2022.10055219","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055219","url":null,"abstract":"A skin lesion is an unusual change of skin tissues. While this can be caused by harmless skin diseases, there is also the chance of the lesion being cancerous. Skin cancer is one of the most common and deadly cancers in the world, which is caused by exposure to the ultraviolet radiation emitted by the sun. Due to the difficulty in visually differentiating between harmless and cancerous skin lesions, people are less likely to get medical attention straight away. Early diagnosis is crucial to ensure an effective treatment. Clinical and dermoscopy based diagnosis of cancerous skin lesions is costly, painful and sometimes inaccurate. Various researches report performing the classification of skin lesions using image processing techniques. Previous works in this domain are plenty, which reported fairly good results, where image processing and the use of both machine learning and deep learning models are seen. In this research, we propose a novel method which focused on important feature extraction, and fusing multiple features to improve the classification of malignant skin cells using traditional machine learning models, despite having imbalanced data distribution. The ISIC 2018 challenge dataset HAM10000 was used in our work. After preprocessing, we extracted shallow and deep features from the images. Shallow features consisted of position-wise color features and Scale Invariant Feature Transform (SIFT) features. Deep features were extracted by a transfer learning model MobileNetV3, which is pre-trained on Imagenet. These features were combined to form a more representative feature for the data. We parameter tuned five machine learning classifiers to do a binary classification on the processed data. The best accuracy, 81%, was obtained by using Support Vector Machine with an f1-score of 68%. Second best results were achieved by Random Forest Classifier, with an accuracy and F1-score of 80% and 67% respectively.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132565505","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

Semantic Clustering of Bangla Natural Word using Different Word Embedding Techniques 基于不同词嵌入技术的孟加拉语自然词语义聚类

2022 25th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2022-12-17 DOI: 10.1109/ICCIT57492.2022.10054703

Aroni Saha Prapty, K. Hasan

引用次数: 0

Progressive Recommendation by Incremental Tensor Factorization 增量张量分解的渐进式推荐

2022 25th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2022-12-17 DOI: 10.1109/ICCIT57492.2022.10054697

Dipannita Biswas, K. M. Azharul Hasan, Zaima Zarnaz

引用次数: 0

Largest Shift String Matching Algorithm: Blend of Berry Ravindran, Zhu-Takaoka and Back & Forth Matching Algorithm 最大移位字符串匹配算法:混合Berry Ravindran, Zhu-Takaoka和来回匹配算法

2022 25th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2022-12-17 DOI: 10.1109/ICCIT57492.2022.10055575

M. Hasan, Prince Mahmud, Mst. Merina Khatun, Md. Hasibur Rahman, Md. Tarequl Islam

{"title":"Largest Shift String Matching Algorithm: Blend of Berry Ravindran, Zhu-Takaoka and Back & Forth Matching Algorithm","authors":"M. Hasan, Prince Mahmud, Mst. Merina Khatun, Md. Hasibur Rahman, Md. Tarequl Islam","doi":"10.1109/ICCIT57492.2022.10055575","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055575","url":null,"abstract":"With the exponential growth of biological databases, it is a highly challenging and essential task to find the exact string from these databases. Various string pattern searching strategies played an essential role in solving string search problems. Mainly, the string pattern matching algorithm aims to reduce the running time which depends on attempts and character comparisons. Our paper proposed a new hybrid strategy, the Largest Shift Algorithm (LSA), to solve these string pattern matching problems more effectively. Our suggested new hybrid algorithm combines the most advantageous characteristics of Berry Ravindran, Zhu-Takaoka, and a customized Back & Forth Matching (BFM) algorithm. These three algorithms were chosen as they perform better in the tests for counting attempts and character comparisons. Three distinct types of algorithms were tested to analyze the performance of our proposed LSA algorithm, discussed in the literature which are BRR, maximum-shift, and quick-search algorithms. We have used English text, DNA, and protein sequences as data for their different nature. The proposed algorithm outperforms the previous algorithms in terms of performance such as runtime, total attempts, and comparisons of character.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115728271","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

Estimating Aerodynamic Data via Supervised Learning 通过监督学习估计空气动力学数据

2022 25th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2022-12-17 DOI: 10.1109/ICCIT57492.2022.10054896

Azizul Haque, Tanzim Hossain, M. N. Murshed, K. I. B. Iqbal, Mohammad Monir Uddin

引用次数: 0