2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)最新文献_第4页

Diabetic Retinopathy: New Perspectives with Artificial Intelligence 糖尿病视网膜病变:人工智能的新视角

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626762

Zineb Farahat, N. Souissi, M. Belmekki, Kawtar Megdiche, Safia Benamar, Yasmine Bennani, Soufiane Bencherif, Nabil Ngote

{"title":"Diabetic Retinopathy: New Perspectives with Artificial Intelligence","authors":"Zineb Farahat, N. Souissi, M. Belmekki, Kawtar Megdiche, Safia Benamar, Yasmine Bennani, Soufiane Bencherif, Nabil Ngote","doi":"10.1109/ICDS53782.2021.9626762","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626762","url":null,"abstract":"Artificial Intelligence, machine learning, and Deep Learning are gradually changing medical practice and are poised to influence nearly every aspect of the human condition. With recent advances in digital data acquisition, these techniques have the potential to be applied in almost every area of medicine, and are expanding into areas previously thought to be only the province of human experts, and ophthalmology is no an exception to this trend.However, appropriately designed clinical trials are needed before these emerging techniques can be applied in a real-world clinical setting. In this work, we outline recent breakthroughs in Artificial Intelligence technologies and their applications in ophthalmology, specifically in the screening of Diabetic Retinopathy which is a microvascular disorder occurring due to long term effects of diabetes leading to blindness. After a comparative study of image processing solutions, we chose image segmentation to analyze the images provided by the Ophthalmic Center of the Cheikh Zaïd Foundation in Rabat.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116211678","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

Text sentiment analysis with CNN & GRU model using GloVe 使用GloVe进行CNN和GRU模型的文本情感分析

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626715

Abdelhaq Zouzou, I. E. Azami

引用次数: 4

Distributed intrusion detection system in the cloud environment based on Apache Kafka and Apache Spark 基于Apache Kafka和Apache Spark的云环境下分布式入侵检测系统

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626721

Mohamed Ouhssini, K. Afdel, Mohamed Idhammad, Elhafed Agherrabi

{"title":"Distributed intrusion detection system in the cloud environment based on Apache Kafka and Apache Spark","authors":"Mohamed Ouhssini, K. Afdel, Mohamed Idhammad, Elhafed Agherrabi","doi":"10.1109/ICDS53782.2021.9626721","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626721","url":null,"abstract":"After, the emergence of cloud computing (CC), it’s gained more attraction to be used for organizations and users. CC allows to migrate the computing power to the internet services. That makes cloud system target of attackers to disrupt services or data breaching. Many existing works try to deal with security issues in cloud computing systems, but it is still suffering against new updated attacks. Therefore, it’s necessary to develop new IDS able to detect attacks with high performance. In this paper, we present a distributed IDS based on big data tools and machine learing algorithms to detect attacks in the cloud systems. This proposed system designed to be installed in the front of cloud network architecture. The network traffic is collected from edge routers and streamed with Kafka component to Spark component for prepressing, anomaly detection and attack classification. In preprocessing stage, data cleaning, formatting and feature selection based on K-means clustering are performed. In the anomaly detection and attack classification, we compared different machine learning algorithms optimized with hypermeters tuning based on grid search. Various experiments are conducted on Google cloud platform to evaluated the system using CIDDS-001 dataset. The Decision tree classifier outperform all in term of accuracy and F1-score in anomaly detection stage the same for attacks classification stage, Random Forest yielded Decision tree in term of accuracy and F1-score. Duo to lower detection time, we choose DT to build our system.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129536211","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

Interoperability model between heterogeneous MAS platforms based on mobile agent and reinforcement learning 基于移动代理和强化学习的异构MAS平台互操作模型

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626723

N. E. A. Amrani, Sidi Mohamed Snineh, M. Youssfi, O. Abra, O. Bouattane

{"title":"Interoperability model between heterogeneous MAS platforms based on mobile agent and reinforcement learning","authors":"N. E. A. Amrani, Sidi Mohamed Snineh, M. Youssfi, O. Abra, O. Bouattane","doi":"10.1109/ICDS53782.2021.9626723","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626723","url":null,"abstract":"A model of interoperability between heterogeneous multi-agent systems (MAS) platforms is presented in this article. This model is based on a mobile agent and reinforcement learning (RL) to provide semantic interoperability and message-oriented middleware for technology interoperability. The latter concerns the technical problems of linking systems, the definition of interfaces, the data format, and the communication protocols needed to exchange messages. In this context, we propose an architecture based on the AMQP protocol. On the other hand, semantic interoperability depends on the application domain of each system and refers to the ability of two agents to exchange data while preserving their semantics, and to the ability of the receiving agent to translate or convert the information received to ensure efficient collaborative exchanges. In this context, our technique is as follows: when an agent of MAS1 receives a message from another agent of MAS2 and needs information to interpret this message, it requests the help of the mobile agent. The latter must migrate to the MAS2 equipped with a technique allowing it to extract the information necessary for the interpretation of the message. After this step, the mobile agent offers information to the first agent. If this information helps the agent to interpret the message, the mobile agent receives a positive reward from MAS1, otherwise, it receives a negative reward. The goal of the mobile agent is to maximize their rewards. The experimental results have shown the effectiveness of our approach. Indeed, according to the results, the experience of the mobile agent who has already explored the environment makes it possible to accelerate the learning process of other agents.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129677322","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

A Discrete Adapted Dragonfly Algorithm For Solving The Traveling Salesman Problem 求解旅行商问题的离散自适应蜻蜓算法

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626738

Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, A. Amphawan

{"title":"A Discrete Adapted Dragonfly Algorithm For Solving The Traveling Salesman Problem","authors":"Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, A. Amphawan","doi":"10.1109/ICDS53782.2021.9626738","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626738","url":null,"abstract":"The Traveling Salesman Problem (TSP) is a combinatorial optimization problem which has a plethora of real-world applications in various domains. Since large scale TSP problems are difficult to be solved by deterministic algorithms in a reasonable amount of time, heuristic and meta-heuristic algorithms such as swarm intelligence algorithms are usually used for solving TSP. These algorithms provide near-optimal solutions in a feasible amount of time. The Dragonfly Algorithm (DA) is a recent swarm intelligence algorithm and it has shown to have a higher performance as compared to other swarm intelligence algorithms in various applications. Since the original DA algorithm is proposed for solving continuous optimization problems, it cannot be used for solving TSP and although a binary version of the algorithm, called BDA, is also proposed, it is not suitable for solving TSP. Hence, in this paper, a variant of the DA algorithm is proposed. DA is adapted for solving TSP by adapting its equations and making use of the method of swap sequences to update the position of the artificial dragonflies in the search space. The algorithm is employed to solve a TSP problem which consists of four cities and the results show that the proposed algorithm is able to provide the optimal TSP path.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115937833","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

Data Mining Tool To Help The Scientific Community Develop Answers To Covid-19 Queries 数据挖掘工具，帮助科学界开发Covid-19查询的答案

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626771

S. Rajath, Amit Kumar, Mayank Agarwal, Sanjana C. Shekar, V. B. Prasad

{"title":"Data Mining Tool To Help The Scientific Community Develop Answers To Covid-19 Queries","authors":"S. Rajath, Amit Kumar, Mayank Agarwal, Sanjana C. Shekar, V. B. Prasad","doi":"10.1109/ICDS53782.2021.9626771","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626771","url":null,"abstract":"Science has time and again proven to be one of the most powerful tools in finding solutions to the problems faced by the world. Let it be natural or man-made challenges, hard work put into finding efficient answers to tackle them has proven to safeguard the ecosystem. Sometimes the research community is put under pressure when humanity faces the challenge of survival like the Covid-19 pandemic. A great extent of published works needs to be studied to find an optimal solution to existing or new queries related to the virus. In this research work, we build an efficient data mining tool using the CORD-19 Dataset to help the community come up with answers to Covid-19 related questions. We use a combination of semantic and keyword search to reduce the solution space of our model. Our model makes use of parallelism, paraphrasing, and state-of-the-art natural language processing techniques which will serve as a time and energy-saving tool for the information need of all doctors and researchers who are trying to put an end to the pandemic and avoid future possible outbreaks.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123150564","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

Modeling and Implementation of a Lunate Implant Based on 3D Reconstruction of CT Scan Images 基于CT扫描图像三维重建的月骨种植体建模与实现

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626756

B. E. Ghali, Omar El Midaoui, Mohamed Ayman El Ghali, Zineb Farahat, Marzak Imad, Nabil Ngote, Kawtar Megdiche

{"title":"Modeling and Implementation of a Lunate Implant Based on 3D Reconstruction of CT Scan Images","authors":"B. E. Ghali, Omar El Midaoui, Mohamed Ayman El Ghali, Zineb Farahat, Marzak Imad, Nabil Ngote, Kawtar Megdiche","doi":"10.1109/ICDS53782.2021.9626756","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626756","url":null,"abstract":"Kienbock’s disease is an avascular necrosis of the lunate bone of the hand, which manifests itself by pain in the wrist associated with a certain stiffness and above all a loss of clenching strength. If left untreated, the natural course of this disease is towards the progressive aggravation and destruction of the lunate and then the whole wrist. In the advanced stages of the disease, the lunate becomes too damaged to be preserved. Several surgical techniques can be considered, including the placement of an implant. In this perspective, the present paper deals with the modelling and the realization of a lunate implant. To do this, we first used scanner images provided by the Cheikh Zaid International University Hospital in Rabat (Morocco) in order to obtain a 3D reconstruction of the semilunar bone. The measurements carried out on this reconstruction allowed us to determine the parameters necessary for its modelling. In a second step, we proceeded to the choice of the material taking into account several criteria such as biocompatibility, elastic limit, cost, … Finally, we proceeded to the 3D printing of a prototype. The results obtained are satisfactory and could contribute to a better management of patients suffering from Kienbock disease.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125261016","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

Social Arabic Emotion Analysis: A Comparative Study of Multiclass Classification Techniques 阿拉伯社会情感分析:多类分类技术的比较研究

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626753

Hanane Elfaik, E. Nfaoui

{"title":"Social Arabic Emotion Analysis: A Comparative Study of Multiclass Classification Techniques","authors":"Hanane Elfaik, E. Nfaoui","doi":"10.1109/ICDS53782.2021.9626753","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626753","url":null,"abstract":"The advent of social media platforms (Twitter, Facebook, etc.) has facilitated the development of user generated content, and users tend to express and share their opinions and emotions about political movements, social events, products, and services. Thus, the emotion detection task has emerged and has received significant attention from academia. It is recognized as a part of affective computing to understand and extract the human affective state expressed in a piece of writing (tweet, post, etc.). In this work, we have measured and compared the performance of different traditional machine learning models namely Linear SVC, Logistic Regression, Multinomial and Complement Naive Bayes with the accuracy produced by two of the top performing deep learning techniques namely Convolution Neural Network and Long-Short-Term Memory networks. Two types of social media datasets were utilized in this study. One is the Arabic emotions Twitter dataset (AETD) and the other one is the Iraqi Arabic emotion dataset (IAEDS). The experimental results demonstrate that Convolution Neural Network deep based technique performs better than machine learning-based algorithms on both datasets used in this study. Additionally, the results establish that proposed models outperform the current state-of-the-art Arabic emotion recognition methods, achieving a 2.4% improvement in accuracy on the AETD dataset. Furthermore, the proposed models can significantly distinguish between the different emotion labels.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128379983","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

Automatic Generation of Chest X-ray Reports Using a Transformer-based Deep Learning Model 使用基于变压器的深度学习模型自动生成胸部x射线报告

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626725

Ayoub Benali Amjoud, M. AMROUCH

{"title":"Automatic Generation of Chest X-ray Reports Using a Transformer-based Deep Learning Model","authors":"Ayoub Benali Amjoud, M. AMROUCH","doi":"10.1109/ICDS53782.2021.9626725","DOIUrl":"https://doi.org/10.1109/ICDS53782.2021.9626725","url":null,"abstract":"Medical images and chest X-rays, in particular, are primarily the most widely used radiological tests in clinical practice for diagnosis and treatment. Reading and interpreting a chest x-ray can be time-consuming for an experienced radiologist, more difficult for the less experienced, and almost impossible for an average person. An X-ray computer-assisted reporting system can ease the physician’s reporting task and provide decision support for radiologists. Also, it will accelerate the deployment of computer-assisted medical decision-making systems. This paper proposes an automatic chest x-ray generating report framework based on combining the transfer learning technique and the transformer approach to generate reports. We apply a pretrained convolutional neural network model from the ImageNet database for feature extraction to exploit deep neural networks’ power. Then, we use a modified transformer encoder-decoder. We test our network on Open I, Indiana University’s Chest X-ray Collection (IU X-Ray), one of the most comprehensive public data sets currently used for chest X-ray captioning. We show that our model produced promising results. Our model performed better than the state-of-the-art results in terms of performance.","PeriodicalId":351746,"journal":{"name":"2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128624147","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: 12

Parallel Algorithm on GPU for Wireless Sensor Data Collection using Multiple UAVs 基于GPU的多无人机无线传感器数据采集并行算法

2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) Pub Date : 2021-10-20 DOI: 10.1109/ICDS53782.2021.9626714

Vincent Roberge, M. Tarbouchi

引用次数: 0