2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)最新文献_第2页

An Efficient Computer System for Alzheimer Diseases Classification Using Fast Finite Shearlet Transform Domain and Support Vector Machine Classifier 基于快速有限Shearlet变换域和支持向量机分类器的阿尔茨海默病分类系统

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) Pub Date : 2022-05-28 DOI: 10.1109/SETIT54465.2022.9875815

Meriem Saim, A. Feroui

{"title":"An Efficient Computer System for Alzheimer Diseases Classification Using Fast Finite Shearlet Transform Domain and Support Vector Machine Classifier","authors":"Meriem Saim, A. Feroui","doi":"10.1109/SETIT54465.2022.9875815","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875815","url":null,"abstract":"Alzheimer’s disease (AD) is the most common cause of neurodegenerative dementia in the elderly population. Several researchers have developed numerous methods for AD stage classification based on machine learning and deep learning over the last few decades. In this field, the main challenge is to design an algorithm that enables the acquisition of a good classification with better performance to achieve a certain diagnosis. Furthermore, capturing the brain atrophy information spatially distributed in magnetic resonance imaging (MRI) to distinguish between Alzheimer’s disease stages is a challenging task. In this work, we proposed a method for AD disease stage classification to classify four categories: three phases of AD compared to non-demented cases using the Fast Finite Shearlet Transform (FFST), the gray level co-occurrence matrix (GLCM), and the SVM algorithm classifier. Our proposed method is established on a set of 400 MRI images and investigates the impact of the diverse directions of the FFST on the classification results. The proposed algorithm obtained good performance compared to the state of the art and shows that the use of the shearlet domain improve the classification accuracy which led to better detection.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123322811","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

Adapting the LodView RDF Browser for Navigation over the Multilingual Linguistic Linked Open Data Cloud 调整LodView RDF浏览器以在多语言语言链接的开放数据云上导航

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) Pub Date : 2022-05-28 DOI: 10.1109/SETIT54465.2022.9875628

A. Kirillovich, K. Nikolaev

引用次数: 0

A Comparison of Machine Learning Methods for best Accuracy COVID-19 Diagnosis Using Chest X-Ray Images 胸部x线图像诊断COVID-19最佳准确性的机器学习方法比较

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) Pub Date : 2022-05-28 DOI: 10.1109/SETIT54465.2022.9875477

Hussein Ahmed Ali, Walid Hariri, Nadia Smaoui Zghal, Dalenda Ben Aissa

{"title":"A Comparison of Machine Learning Methods for best Accuracy COVID-19 Diagnosis Using Chest X-Ray Images","authors":"Hussein Ahmed Ali, Walid Hariri, Nadia Smaoui Zghal, Dalenda Ben Aissa","doi":"10.1109/SETIT54465.2022.9875477","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875477","url":null,"abstract":"Coronavirus (COVID-19) changed the view of people towards life in all the countries of the world in December 2019. The virus has made chaos that cannot be predicted. This problem requires using a variety of technologies to aid in the identification of COVID-19 patients and to control the disease spread. For suspected instances of COVID-19 disease, chest X-ray (CXR) imaging is a standard with fewer costs, but it does not need a COVID-19 examination approach without using technology to help for a suitable diagnosis. In response to this issue, a big dataset of CXR images was divided into four classes found on the website Kaggle. Dealing with large data of the images needs dataset reprocessing through choosing the optimal method for getting speed and best accuracy. Dataset reprocessing converts into gray level then adjust image intensity, resize and extract the best features then apply Machine Learning ML models. The use of different prediction models, ML algorithms, and their performances are calculated with evaluation on the dataset after reprocessing. Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and K-Nearest Neighbors (KNN) are models used to foretell the specialized who would be diagnosed with COVID-19 quickly by using CXR images classification. The KNN has revealed the best accuracy compared with the others such as GNB, DT, SGD, LR, and RF. Also, KNN has the best-weighted average for all parameters, which are precision, sensitivity, and F1-score compared with the other models.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114075622","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

Design and Simulation of a PV System Controlled through a Hybrid INC-PSO Algorithm using XSG Tool 基于XSG工具的混合inco - pso算法控制的光伏系统设计与仿真

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) Pub Date : 2022-05-28 DOI: 10.1109/SETIT54465.2022.9875738

Akram Amri, Intissar Moussa, A. Khedher

引用次数: 0

Adaptive Diffusion Based Restoration for Noisy Facial Image Recognition 基于自适应扩散的人脸图像识别复原方法

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) Pub Date : 2022-05-28 DOI: 10.1109/SETIT54465.2022.9875579

Berrimi Fella, Hedli Riadh, Kara-Mohammed Chafia

{"title":"Adaptive Diffusion Based Restoration for Noisy Facial Image Recognition","authors":"Berrimi Fella, Hedli Riadh, Kara-Mohammed Chafia","doi":"10.1109/SETIT54465.2022.9875579","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875579","url":null,"abstract":"Identity recognition from corrupted face image remains difficult, since noise can seriously affect the image quality. Thus, it is necessary to enhance facial image before starting the recognition process. In this paper, we extract relevant features from the noisy facial image using PCA decomposition that seperates the small features from the large ones. For restoring these features, we apply an adaptive diffusion method based on eigenratio of the vectors that represent each feature. Therefore, the denoising process is adapted according to region caracteristics where the small features are enhanced by shock of backward diffusion filter and the large features are smoothed with isotropic diffusion.We have used the ORL database and three different types of noise: Gaussian, uniform and salt-pepper. The proposed method tests six classifiers to find the best one. Numerical experiments show that the proposed method gives the best results for the tested noise types in terms of objective metrics, PSNR and SSIM. They show that SVM classifier provides good performance and outperforms other classifiers with the highest accuracy of 97.85%.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125251773","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

DeepRetino: Ophthalmic Disease Classification from Retinal Images using Deep Learning Deep pretino:使用深度学习从视网膜图像中分类眼部疾病

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) Pub Date : 2022-05-28 DOI: 10.1109/SETIT54465.2022.9875570

Fatima Zahra Belharar, Nabila Zrira

引用次数: 1

Technology Skills in Medical Education During Corona Pandemic: Special Case Study 冠状病毒大流行期间医学教育中的技术技能:特殊案例研究

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) Pub Date : 2022-05-28 DOI: 10.1109/SETIT54465.2022.9875496

Rajaa Jassim Mohammed, J. K. Abbas, Sura Khalil Ibrahim

引用次数: 0

Forensic Handwritten Signature Identification Using Deep Learning 使用深度学习的法医手写签名识别

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) Pub Date : 2022-05-28 DOI: 10.1109/SETIT54465.2022.9875697

Omar Tarek, Ayman Atia

{"title":"Forensic Handwritten Signature Identification Using Deep Learning","authors":"Omar Tarek, Ayman Atia","doi":"10.1109/SETIT54465.2022.9875697","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875697","url":null,"abstract":"Forgery is a type of fraud defined as the act of forging a copy or an imitation of a document, signature, or banknote which is considered a form of illegal criminal activity. In this paper, we are focusing on the identification and detection of handwritten signature forgeries inside documents. The proposed system uses contemporary methods that utilize a deep learning approach of CNNs (Convolutional Neural Networks) for binary image classification and aims to help forensic examiners measure the genuineness of handwritten signatures. We considered using a number of five different classification models of CNN which are, VGG-16, ResNet50, Inception-v3, Xception, and Our CNN model. The purpose for using these different CNN models is to determine and study which model is best at identifying images containing text data containing similar resemblances. Upon comparing these CNN models, we concluded that the ResNet50 model was able to reach the highest score at identifying handwritten signatures with an accuracy of 82.3% and 86% when tested on datasets of 300 images and 140 images respectively. Regarding future work, this is a required step that determines what model to focus on for more in-depth analysis and classification of the characteristics of handwritten signatures.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134151250","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

Medical decision-Making based on Combined CRISP-DM Approach and CNN Classification for Cardiac MRI 基于CRISP-DM方法和CNN分类的心脏MRI医疗决策

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) Pub Date : 2022-05-28 DOI: 10.1109/SETIT54465.2022.9875820

Houneida Sakly, Mohamed Bjaoui, Mourad Said, N. Kraiem, M. Bouhlel

引用次数: 0

An Aid Decision Tool for Real Time Application Systems 实时应用系统的辅助决策工具

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) Pub Date : 2022-05-28 DOI: 10.1109/SETIT54465.2022.9875745

Ines Ben Hlima, Halim Kacem, A. Gharsallah

{"title":"An Aid Decision Tool for Real Time Application Systems","authors":"Ines Ben Hlima, Halim Kacem, A. Gharsallah","doi":"10.1109/SETIT54465.2022.9875745","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875745","url":null,"abstract":"In the last decades, the Internet of Things (IoT) has become ubiquitous as its architectures, based on microcontroller, comprise many devices. Currently, many Integrated Development Environments (IDEs), simulators and code generators help developers build up a program without supporting the code aid decision features of microcontroller systems and their peripherals. Thus, developer should have good programming skills to write optimized performance and efficiency code for microcontroller due to the complexity of this modern device, application constraint, etc.We present, in this paper, an aid decision microcontroller framework (ADM) that can be used to help the developer design their application quickly and efficiency, while respecting application constraint specified by the developer as an input. The components of ADM include Graphical User Interface (GUI), tools used to develop the configuration of processor and its peripherals.AMD is developed using Qt/C++ framework. It is interfaced with microcontrollers intensively used in IoT, such as STM32F405 from STMicroelectronics, to implement specific applications with optimized configuration and usage of the MCU resources.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122942083","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