2022 13th International Conference on Information and Communication Systems (ICICS)最新文献

GOS: A Genetic OverSampling Algorithm for Classification of Quranic Verses 古兰经经文分类的遗传过采样算法

2022 13th International Conference on Information and Communication Systems (ICICS) Pub Date : 2022-06-21 DOI: 10.1109/ICICS55353.2022.9811224

Bassam Arkok, A. Zeki

引用次数: 0

Comparison Study Of Deep-Learning Architectures For Classification of Thoracic Pathology 胸椎病理分类的深度学习架构比较研究

2022 13th International Conference on Information and Communication Systems (ICICS) Pub Date : 2022-06-21 DOI: 10.1109/ICICS55353.2022.9811150

Nada N.Al Zahrani, R. Hedjar

{"title":"Comparison Study Of Deep-Learning Architectures For Classification of Thoracic Pathology","authors":"Nada N.Al Zahrani, R. Hedjar","doi":"10.1109/ICICS55353.2022.9811150","DOIUrl":"https://doi.org/10.1109/ICICS55353.2022.9811150","url":null,"abstract":"This work aims to study different architectures for the classification of thoracic diseases using pre-trained convolutional neural networks (PCNN) such as VGG-16, ResNet-50, EfficientNetB0, and InceptionV3 which are considered as state-of-the-art deep learning models. Indeed, they are used to detect various thoracic disorders. In this study, the main focus is on COVID-19 and pneumonia to make an optimal diagnosis for these two diseases. Although these diseases are prevalent, the process of detection and diagnosis is challenging. In this work, two unbalanced datasets (COVID-19 and Pneumonia) have been used. After the training phase where hyperparameters of the models have been tuned for best accuracy, a comparison study of these different models is conducted. The EfficientNetB0 model has achieved the highest test accuracy around 96.50% for Pneumonia X-ray images. The same work has been applied to the COVID-19 CT scans dataset, and the highest accuracy is achieved with the ResNet-50 network (99.5%). Therefore, these two models will be used for rapid diagnosis and assist radiologists in the detection process precisely.","PeriodicalId":433803,"journal":{"name":"2022 13th International Conference on Information and Communication Systems (ICICS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127217622","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

Machine Learning for Alzheimer’s Disease Detection Based on Neuroimaging techniques: A Review 基于神经成像技术的机器学习阿尔茨海默病检测:综述

2022 13th International Conference on Information and Communication Systems (ICICS) Pub Date : 2022-06-21 DOI: 10.1109/ICICS55353.2022.9811143

Maha Gharaibeh, Mwaffaq Elhies, Mothanna Almahmoud, Sayel Abualigah, Omar N. Elayan

引用次数: 1

End-to-End Machine Learning Solution for Recognizing Handwritten Arabic Documents 识别手写阿拉伯语文档的端到端机器学习解决方案

2022 13th International Conference on Information and Communication Systems (ICICS) Pub Date : 2022-06-21 DOI: 10.1109/ICICS55353.2022.9811155

Reem E. Shtaiwi, Gheith A. Abandah, Safaa A. Sawalhah

{"title":"End-to-End Machine Learning Solution for Recognizing Handwritten Arabic Documents","authors":"Reem E. Shtaiwi, Gheith A. Abandah, Safaa A. Sawalhah","doi":"10.1109/ICICS55353.2022.9811155","DOIUrl":"https://doi.org/10.1109/ICICS55353.2022.9811155","url":null,"abstract":"The research for offline handwriting recognition (HWR) solutions for various languages has recently gained rising attention, especially in the Arabic language. This is connected to the growing necessity to digitize Arabic documents in several applications such as exploring large documents, automated sorting of express mail, editing of earlier printed documents, and bank control processing. Regrettably, notwithstanding decades of research, there is no satisfactory solution for recognizing cursive handwriting like the Arabic language because of its difficulty. This paper presents end-to-end machine learning solution by applying deep learning techniques like CRNN-BLSTM using the MADCAT dataset to accurately recognize the Arabic handwritten documents after simultaneously learning text detection, segmentation, and finally conversion to editable text. Our integrated method, resulting from integrating several distinct neural networks models, achieved high accuracy in parsing the full page, converting it to lines, and predicting the writing within each line. This approach has been evaluated using a large-scale set of Arabic handwritten documents that contains various problems that need to be addressed, the achieved character error rate is 3.96%.","PeriodicalId":433803,"journal":{"name":"2022 13th International Conference on Information and Communication Systems (ICICS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114069076","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

Stuttering Detection Using Atrous Convolutional Neural Networks 基于卷积神经网络的口吃检测

2022 13th International Conference on Information and Communication Systems (ICICS) Pub Date : 2022-06-21 DOI: 10.1109/ICICS55353.2022.9811183

Abedal-Kareem Al-Banna, E. Edirisinghe, H. Fang

{"title":"Stuttering Detection Using Atrous Convolutional Neural Networks","authors":"Abedal-Kareem Al-Banna, E. Edirisinghe, H. Fang","doi":"10.1109/ICICS55353.2022.9811183","DOIUrl":"https://doi.org/10.1109/ICICS55353.2022.9811183","url":null,"abstract":"Stuttering is a neurodevelopmental speech disorder that affects 70 million people worldwide, approximately 1% of the whole population. People who stutter (PWS) have common speech symptoms such as block, interjection, repetition, and prolongation. The speech-language pathologists (SLPs) commonly observe these four groups of symptoms to evaluate stuttering severity. The evaluation process is tedious and time-consuming for (SLP) and (PWS). Therefore, this paper proposes a new model for stuttering events detection that may help (SLP) to evaluate stuttering severity. Our model is based on a log mel spectrogram and 2D atrous convolutional network designed to learn spectral and temporal features. We rigorously evaluate the performance of our model on two stuttering datasets (UCLASS and FluencyBank) using common speech metrics, i.e. F1-score, recall, and the area under the curve (AUC). Our experimental results indicate that our model outperforms state-of-the-art methods in prolongation with an F1 of 52% and 44.5% on the UCLASS and FluencyBank datasets, respectively. Also, we gain 5% and 3% margins on the UCLASS and FluencyBank datasets for fluent class.","PeriodicalId":433803,"journal":{"name":"2022 13th International Conference on Information and Communication Systems (ICICS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131202040","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

Analysis of Jordanian University Students Problems Using Data Mining System 利用数据挖掘系统分析约旦大学生问题

2022 13th International Conference on Information and Communication Systems (ICICS) Pub Date : 2022-06-21 DOI: 10.1109/ICICS55353.2022.9811199

M. Issa, Omar A. Darwish, Doaa Habeeb Allah, Farah Shatnawi, Dirar A. Darweesh, Yahya M. Tashtoush

{"title":"Analysis of Jordanian University Students Problems Using Data Mining System","authors":"M. Issa, Omar A. Darwish, Doaa Habeeb Allah, Farah Shatnawi, Dirar A. Darweesh, Yahya M. Tashtoush","doi":"10.1109/ICICS55353.2022.9811199","DOIUrl":"https://doi.org/10.1109/ICICS55353.2022.9811199","url":null,"abstract":"Many social media users, especially university students, suffer from some problems that occur in their daily lives, such as anger, stress, depression, sleep problems, physical and psychological fatigue. Most of the students do not express their feelings to their parents and friends. Therefore, social media such as Facebook and Twitter are the best way to express their feelings and opinions because they feel safe and more comfortable there. This paper attempted to collect posts from Facebook and Twitter tweets. In this research, we focused on Twitter tweets and Facebook posts, which were mainly in the Arabic language and belonged to the Jordanian University Students. The huge amount of tweets and posts were not clean, so tweets and posts needed to be analyzed by labeling them to study load, sleep issues, negative feelings and positive feelings. Then, we classified them using many algorithms in data mining, such as a Naive Bayes Multi-Label Classification Algorithm (NB), K-Nearest Neighbor Classification Algorithm (K-NN) and Decision Tree Classification Algorithm. The results were compared based on three parameters which are: accuracy, precision and recall. The NB Algorithm achieved the best performance in terms of accuracy, precision and recall value, in comparison with the K-NN and Decision Tree Algorithm.","PeriodicalId":433803,"journal":{"name":"2022 13th International Conference on Information and Communication Systems (ICICS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114498431","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 scheduling algorithm for Adaptive C-RAN Architecture 一种自适应C-RAN架构的调度算法

2022 13th International Conference on Information and Communication Systems (ICICS) Pub Date : 2022-06-21 DOI: 10.1109/ICICS55353.2022.9811223

Ala' A. Samarneh, A. Alma'aitah

引用次数: 0

The Use of SCRUM Methodology for Final Year Undergraduate Project During Corona Pandemic 在冠状病毒大流行期间使用SCRUM方法进行最后一年的本科项目

2022 13th International Conference on Information and Communication Systems (ICICS) Pub Date : 2022-06-21 DOI: 10.1109/ICICS55353.2022.9811142

Hamed Fawareh, Eman F. Al-Qbelat, Mohammed N. Al-Refai

引用次数: 0

Using Deep Learning for Positive Reviews Prediction in Explainable Recommendation Systems 在可解释推荐系统中使用深度学习进行正面评论预测

2022 13th International Conference on Information and Communication Systems (ICICS) Pub Date : 2022-06-21 DOI: 10.1109/ICICS55353.2022.9811151

Hafed Zarzour, M. Alsmirat, Y. Jararweh

引用次数: 1

A Two-Step Method for Multi-GNSS Positioning 多gnss定位的两步法

2022 13th International Conference on Information and Communication Systems (ICICS) Pub Date : 2022-06-21 DOI: 10.1109/ICICS55353.2022.9811161

Y. Teng, Shuocheng Yuan, Zhi Zheng

引用次数: 1