2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)最新文献

Deep Learning-Based Transfer Learning for the Detection of Leukemia 基于深度学习的白血病检测迁移学习

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084138

Joydeb Sheet, Chanchal Ghosh, Biplab Kanti Das

{"title":"Deep Learning-Based Transfer Learning for the Detection of Leukemia","authors":"Joydeb Sheet, Chanchal Ghosh, Biplab Kanti Das","doi":"10.1109/ISACC56298.2023.10084138","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10084138","url":null,"abstract":"Leukemia is typically diagnosed by using a microscope to examine blood and bone marrow smears as well as extremely challenging procedures like the Cytochemical test, which can be used to identify and categorize leukemia. However, these techniques are incredibly pricy, dependent, and time-consuming on the skill and cunning of the experts involved. In contrast to conventional methods, which have several drawbacks, this research suggests a classification model depends upon blood microscopic images for detecting leukemia by utilizing transfer learning. The suggested study demonstrates a computer-aided diagnosis method that uses Mobilenet V2 and Convolutional Neural Networks (CNNs) to identify leukemia images over healthy images. When it comes to using deep learning for image analysis and the comparison outcomes of the current study for diagnostic tasks, the implementation of Mobilenet V2 is relatively simple. Results demonstrate that the suggested model, having a general accuracy of 96.58%, a sensitivity of 95.17%, and a specificity of 98.58%, gives better predictions than separate models for classifying leukemia.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124400490","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

Mitigating Cold Start Problem in Recommendation Systems via Transfer Learning Approach 利用迁移学习方法缓解推荐系统中的冷启动问题

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084296

Manisha Jangid, Rakesh Kumar

{"title":"Mitigating Cold Start Problem in Recommendation Systems via Transfer Learning Approach","authors":"Manisha Jangid, Rakesh Kumar","doi":"10.1109/ISACC56298.2023.10084296","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10084296","url":null,"abstract":"Recommender system provides personalized services to its customers from a huge amount of choices available to them in different domains i.e. E-commerce, Music streaming apps, E- Learning platforms. Understanding the preferences of new users is a challenging task in recommendation systems, commonly known as Cold Start problem, since no prior knowledge about the users interests is provided. In such scenarios, data from social networking sites could be utilized to determine user preferences. In this work, we have proposed a novel Dove Regression based Recommendation System (DRbRS) which employs a Transfer learning approach that utilizes user interactions from social media sites and applies learnt information to suggest pertinent products to new users. The user profile is built using information directly provided by the user and predictions are made based on this classification using machine learning methods like classification trees. The results and comparative analysis of the proposed model indicate better performance and various performance measurements, including accuracy, precision, Recall, and F1-score were evaluated.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116129720","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

Estimation of Position of Lathe Tool Using Edge Detection Technique 基于边缘检测技术的车床刀具位置估计

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084225

S. Kumari, Charu Pathak, Shruti Vashist, P. Mahapatra

引用次数: 1

Exploring Stationarity and Fractality in Stock Market Time-series 股票市场时间序列的平稳性和分形性探讨

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084056

Debabrata Batabyal, Dwaipayan Bandopadhyay, Bikash Sadhukhan, Nabanita Das, S. Mukherjee

引用次数: 0

Modeling a Hybrid Stemmer for Kokborok Kokborok混合型茎的建模

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084267

Pankaj Debbarma, Pusparwng Hrangkhawl, S. Debbarma

引用次数: 0

Processing IoT Sensor Fire Dataset Using Machine Learning Techniques 使用机器学习技术处理物联网传感器火灾数据集

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084317

Sudip Suklabaidya, Indrani Das

引用次数: 0

COVID-19 detection using lightweight CNN architecture on chest X-ray images 基于胸部x线图像的轻量级CNN架构的COVID-19检测

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084139

Sanjan Mahjabin Farin, Md. Sazzadul Islam Islam Prottasha, S. Reza

引用次数: 1

Cyberbullying Detection using Deep Learning Techniques on Bangla Facebook Comments 利用深度学习技术对孟加拉语Facebook评论进行网络欺凌检测

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083690

Srabon Bhowmik Shanto, Mohammed J. Islam, Md. Abdus Samad

{"title":"Cyberbullying Detection using Deep Learning Techniques on Bangla Facebook Comments","authors":"Srabon Bhowmik Shanto, Mohammed J. Islam, Md. Abdus Samad","doi":"10.1109/ISACC56298.2023.10083690","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10083690","url":null,"abstract":"Bullying someone online is known as Cy-berbullying. As social media and the internet have grown in popularity, it has taken a very negative turn. People use the Internet's services to aggressively attack others while hiding behind a screen. It appears in various ways and is typically presented as text on most social networks. Cyberbullying frequently causes severe mental and physical distress, especially for women and children, and it even sometimes causes victims to attempt suicide. Because of the serious social consequences, online harassment draws attention. Therefore, researchers have begun to focus on identifying cyberbullies on social networks, particularly in the Bangla language. However, for accuracy and implementation, there is always room for improvement. This paper conducts an analysis using two deep learning algorithms, namely, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), to identify cyberbullies in Facebook comments written in Bangla. A total of 8736 comments were collected, and 7072 of them were retained for the final analysis after being filtered out due to irrelevancy. In the study, we used data preprocessing steps such as text cleaning, the removal of punctuation and special characters, tok-enization, the removal of stop words, and later joining the stemmed words. Cleaned textual data was fed to the deep learning models for prediction. Our research showed that, with an accuracy of 83.55 %, the GRU over LSTM model produced the best results on the current dataset.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127007884","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

Traffic Sign Recognition System using Ensemble based Deep Learning Models 基于集成深度学习模型的交通标志识别系统

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083886

Deep Dey, Sukanta Majumder, A. Halder

引用次数: 0

Pre-trained Word Embeddings In Deep Multi-label Personality Classification Of YouTube Transliterations YouTube音译的深度多标签人格分类中的预训练词嵌入

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084047

Mohmad Azhar Teli, M. Chachoo

{"title":"Pre-trained Word Embeddings In Deep Multi-label Personality Classification Of YouTube Transliterations","authors":"Mohmad Azhar Teli, M. Chachoo","doi":"10.1109/ISACC56298.2023.10084047","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10084047","url":null,"abstract":"The study of personality computation involves util-ising algorithms to analyse differences in behaviour and person-ality. Numerous applications, including human resource management, criminal profiling, and human-computer interaction, could receive help from automatic personality analysis. Psychology enables us to analyse personality in terms of traits, which are adjectival indicators. A MLC problem arises because it is possible for a single person to display several traits and not just one. Therefore, traditional machine learning methods might not be effective. In this study, transliterations from the YouTube Personality dataset were utilised to classify personalities using multi-label semi-supervised learning algorithms. The supervised algorithms that we used are; Neural network, CNN, RNN, LSTM, Bi-LSTM, GRU, and Bi-GRU. In our experiments, we trained all the algorithms with Keras, GloVe, FastText and Word2Vec embeddings. The results are very promising with F-score of up to 88 percent which suggests inter label-correlations could aid in creating better models for APRT.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121662996","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