2023 15th International Conference on Developments in eSystems Engineering (DeSE)最新文献_第9页

A Transfer Learning Based Intrusion Detection System for Internet of Vehicles 基于迁移学习的车联网入侵检测系统

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099623

Achref Haddaji, S. Ayed, L. Chaari

{"title":"A Transfer Learning Based Intrusion Detection System for Internet of Vehicles","authors":"Achref Haddaji, S. Ayed, L. Chaari","doi":"10.1109/DeSE58274.2023.10099623","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099623","url":null,"abstract":"With the fast expansion of the internet of vehicles (IoV) and the emergence of new types of threats, the traditional machine learning-based intrusion detection systems must be updated to meet the security requirements of the current environment. Recently, deep learning has shown exceptional performance in IoV intrusion detection. However, deep learning-based intrusion detection system (DL-IDS) models are more fixated and dependent on the training dataset. In addition, the behavior changes with the occurrence of attacks. They pose a real problem for the DL-IDS and make their detection more complicate. In this paper, we present a deep transfer learning based intrusion detection in-vehicle (TRLID) model for IoV using the CAN bus protocol. In our proposed model, a data preparation approach is proposed to clean up bus data and convert it to an image for usage as input to the deep learning model. Indeed, we used transfer learning characteristics because they enable us to transfer the source task's knowledge to the target task. Therefore, we trained our model using different dataset including different attacks. The experimental results show that our proposed TRLID achieved good results where the intelligence integration of transfer learning was efficient for attacks detection.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122149753","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

Transformer Based Approach for Depression Detection 基于变压器的凹陷检测方法

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099629

Anagha Anil Khaparde, Rik Das, Rupal Bhargava

引用次数: 0

Optimal Sensor Placement Strategy for Structural Health Monitoring with Application of the Aqueduct El Hnaya of Carthage 结构健康监测传感器优化布置策略&以迦太基El Hnaya渡槽为例

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100124

Wael Doghri, A. Saddoud, L. Chaari

引用次数: 0

Dezvent - Digitalizing Attendance System with 2FA and Face Recognition Implementation 数字化考勤系统与2FA和人脸识别的实现

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100308

Zoe Lim Mei Yi, Julia Juremi

引用次数: 0

Sign Language Recognition using Deep Learning 使用深度学习的手语识别

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100055

M. Mahyoub, F. Natalia, S. Sudirman, J. Mustafina

{"title":"Sign Language Recognition using Deep Learning","authors":"M. Mahyoub, F. Natalia, S. Sudirman, J. Mustafina","doi":"10.1109/DeSE58274.2023.10100055","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100055","url":null,"abstract":"Sign Language Recognition is a form of action recognition problem. The purpose of such a system is to automatically translate sign words from one language to another. While much work has been done in the SLR domain, it is a broad area of study and numerous areas still need research attention. The work that we present in this paper aims to investigate the suitability of deep learning approaches in recognizing and classifying words from video frames in different sign languages. We consider three sign languages, namely Indian Sign Language, American Sign Language, and Turkish Sign Language. Our methodology employs five different deep learning models with increasing complexities. They are a shallow four-layer Convolutional Neural Network, a basic VGG16 model, a VGG16 model with Attention Mechanism, a VGG16 model with Transformer Encoder and Gated Recurrent Units-based Decoder, and an Inflated 3D model with the same. We trained and tested the models to recognize and classify words from videos in three different sign language datasets. From our experiment, we found that the performance of the models relates quite closely to the model's complexity with the Inflated 3D model performing the best. Furthermore, we also found that all models find it more difficult to recognize words in the American Sign Language dataset than the others.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123819804","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

An Efficient Approach for Resilience and Reliability Against Cascading Failure 针对级联故障的弹性和可靠性的有效方法

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100283

D. M. Vistro, A. Rehman, Zufishan Hameed

引用次数: 0

Intracranial hemorrhage detection and classification from CT images based on multiple features and machine learning approaches 基于多特征和机器学习方法的CT图像颅内出血检测与分类

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099988

Mohammad A. Abdul Majeed, Omar Munthir Al Okashi, Azmi Tawfeq Alrawi

{"title":"Intracranial hemorrhage detection and classification from CT images based on multiple features and machine learning approaches","authors":"Mohammad A. Abdul Majeed, Omar Munthir Al Okashi, Azmi Tawfeq Alrawi","doi":"10.1109/DeSE58274.2023.10099988","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099988","url":null,"abstract":"The regulating organ of the body is the brain. Early diagnosis of brain disorders can have a significant impact on efforts to treat them. A brain hemorrhage is a form of stroke caused by a bursting artery in the brain, resulting in bleeding in the surrounding tissues. Through a brain Computed Tomography (CT) scan, brain hemorrhage can be identified. CT is the most extensively used diagnostic imaging technology for identifying brain illnesses due to its speed, low cost, and wide variety of uses. During a CT scan, a small X-ray beam revolves around the body to capture a sequence of images from different angles. The computer then produces a cross-sectional representation of the body. Intracranial hemorrhage (ICH) is a medical condition that requires prompt identification and treatment. Since ICH early detection and therapy can improve health outcomes, there is a need for a triage system that can immediately identify and speed up the treatment process. In this paper, we will use standard machine learning (Support Vector Machine, Random Forest and Decision Tree) methodologies to present a method for automatically detecting the ICH in a two-dimensional reduced form of a CT scan of the brain. Four main steps make up the method. First, a preprocessing pipeline that can successfully remove the bone from the skull is put into place. The following step is applying a feature extraction method. Then, a suitable feature-selection (PCA) model is proposed, which will enhance the model's performance by minimizing any redundancy produced by the selected feature extraction. The data set from the CT scans is classified into normal and abnormal in the last stage, which involves training and testing a machine learning model. The accuracy for our proposed model using Random Forest (RF), is 92.5%. RF achieves higher performance than other used ML methods.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115639621","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

Implement of Intelligent Controller for 6DOF Robot Based on a Virtual Reality Model 基于虚拟现实模型的六自由度机器人智能控制器的实现

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099597

Y. A. Mashhadany, A. Alrawi, Zeyid T. Ibraheem, Sameer Algburi

{"title":"Implement of Intelligent Controller for 6DOF Robot Based on a Virtual Reality Model","authors":"Y. A. Mashhadany, A. Alrawi, Zeyid T. Ibraheem, Sameer Algburi","doi":"10.1109/DeSE58274.2023.10099597","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099597","url":null,"abstract":"Every designer aspires to produce designs that are superior to those of their rivals in terms of quality, speed, or efficiency. Using an ANFIS (Adaptive Neural Inference System) controller and a proportional, integrated, derived (2DO-PID) 2-degree of freedom controller, this study suggests a high-performance design for a 6-DOF manipulator. Finding the best value for the controller settings that smoothly regulate the robot's movements to the desired aim is the primary objective of this exercise. The first step in the design process is to naturally determine the best values for the parameters of a traditional PID controller. The creation of a high-resolution 2DOF-PID controller is the next phase. It performs better than the conventional correct order using a mysterious physics control technique. The parameters of the 2DOF-PID controller are estimated based on the undeniably significant nature of the control effect. The final stage in achieving the high performance of the control system under consideration is the hybrid 2DOF-PID and ANFIS controller, which uses the prior output as a predictive point. The use of both modern and vintage consoles. Six-degree-of-freedom elbow curves are supported. Because the manipulator's trajectory exceeded the settling time and affected the movement, it was possible to minimize. MATLAB 2021b and Robotics Toolbox 9 were used to design and simulate the entire remote-control system. The controller's optimal design is built using a 3-dimensional model of a 6-DOF manipulator created with MATLAB/virtual Simulink's reality (VR) technology. MATLAB generates the manipulator instructions, which are then used to generate a real trajectory with a virtual reality model.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124623268","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 System Implementation: Point-of-Sales (POS) System Integrated with Business Intelligence (BI) Capability Focused on SME in Indonesia 一个系统实现:与商业智能(BI)能力集成的销售点(POS)系统，专注于印尼的中小企业

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099802

Faiz Maruf Al Kautsaf, Mohammad Namazee Bin Mohd Nizam, Khalida Shajaratuddur Harun

引用次数: 0

Identification of authentic and counterfeit Viagra tablets using near-infrared spectroscopic methods and machine learning algorithms 使用近红外光谱方法和机器学习算法识别真假伟哥片剂

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100015

Sarah Rowlands, D. Al-Jumeily, S. Assi

{"title":"Identification of authentic and counterfeit Viagra tablets using near-infrared spectroscopic methods and machine learning algorithms","authors":"Sarah Rowlands, D. Al-Jumeily, S. Assi","doi":"10.1109/DeSE58274.2023.10100015","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100015","url":null,"abstract":"Counterfeit medicinal and lifestyles products are a global issue that impacts public health. Counterfeit products are often made in unsafe and unsanitary conditions before their release to the public without testing by regulatory bodies. One product that is particularly susceptible to online counterfeiting is Viagra, which is one of the highest selling medicines worldwide. A total of 57 Viagra tablets were used for the study; this included 27 authentic and 30 counterfeit tablets which were measured using near-infrared spectroscopy (NIRS). Spectra obtained using the NIR spectrometer non-destructively were exported into a multi-paradigm numerical computing environment where machine learning algorithms (MLAs) were applied using Matlab 2007a. Four algorithms were used related to correlation in wavelength space (CWS), K-nearest neighbour (KNN), principal component analysis (PCA) and PCA combined with fuzzy C-mean clustering (PCA-FCM). The algorithms were applied unsupervised to the authentic and counterfeit tables with no prior labelling to any of the tablets. The results showed two clear groups/clusters between the authentic and counterfeit tablets. In particular, PCA and PCA-FCM showed further subgroups among the counterfeit tablets that corresponded to their varying manufacturing sources. In summary, the use of NIRS and MLAs proved an effective method for identifying counterfeit Viagra medicines rapidly and non-destructively.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127394632","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