2023 15th International Conference on Developments in eSystems Engineering (DeSE)最新文献_第4页

COVID-LiteNet: A lightweight CNN based network for COVID-19 detection using X-ray images COVID-LiteNet:基于CNN的轻量级网络，用于使用x射线图像检测COVID-19

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099799

Aditya Yadav

{"title":"COVID-LiteNet: A lightweight CNN based network for COVID-19 detection using X-ray images","authors":"Aditya Yadav","doi":"10.1109/DeSE58274.2023.10099799","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099799","url":null,"abstract":"To restrict the virus's transmission in the pandemic and lessen the strain on the healthcare industry, computer-assisted diagnostics for the accurate and speedy diagnosis of coronavirus illness (COVID-19) has become a prerequisite. Compared to other types of imaging and detection, chest X-ray imaging (CXR) provides several advantages. Healthcare practitioners may profit from any technology instrument providing quick and accurate COVID-19 infection detection. COVID-LiteNet is a technique suggested in this paper that combines white balance with Contrast Limited Adaptive Histogram Equalization (CLAHE) and a convolutional neural network (CNN). White balance is employed as an image pre-processing step in this approach, followed by CLAHE, to improve the visibility of CXR images, and CNN is trained using sparse categorical cross-entropy for image classification tasks and gives the smaller parameters file size, i.e., 2.24 MB. The suggested COVID-LiteNet technique produced better results than vanilla CNN with no pre-processing. The proposed approach outperformed several state-of-the-art methods with a binary classification accuracy of 98.44 percent and a multi-class classification accuracy of 97.50 percent. COVID-LiteNet, the suggested technique, outperformed the competition on various performance parameters. COVID-LiteNet may help radiologists discover COVID-19 patients from CXR pictures by providing thorough model interpretations, cutting diagnostic time significantly.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"40 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":"125733989","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

Enhancing TEEN Protocol using the Particle Swarm Optimization and BAT Algorithms in Underwater Wireless Sensor Network 基于粒子群优化和BAT算法的水下无线传感器网络TEEN协议改进

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100062

Ruqaiya D. Jalal, S. Aliesawi

{"title":"Enhancing TEEN Protocol using the Particle Swarm Optimization and BAT Algorithms in Underwater Wireless Sensor Network","authors":"Ruqaiya D. Jalal, S. Aliesawi","doi":"10.1109/DeSE58274.2023.10100062","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100062","url":null,"abstract":"Recently, underwater wireless sensor networks (UWSNs) have been emphasized due to their immense value in monitoring the underwater environment and expanding applications for target recognition and underwater information gathering. Battery power is restricted underwater, and it is also difficult to replace, which limits the power supply. As a result, studies and research seek to extend the life of the network. The proposed Threshold Sensitive Energy Efficiency Sensor Network (TEEN) protocol, along with particle swarming optimization (PSO) and BAT algorithms disclosed in this paper, attempts to improve network lifetime and power consumption via optimal node distribution and cluster header selection. The K-mean technique is used in each algorithm that separates nodes into clusters and selects for each cluster a point to be the central point from which to choose the best node to be the block head (CH). This selection is based on the node with the most energy as well as the node closest to the center point. After this stage, the proposed algorithms continue with Particle Swarm Optimization (PSO), and BAT Apply Cluster Head Update (CH), until the best map is produced. The results revealed that the proposed protocol resulted in a significant reduction in power consumption and network lifetime compared to the original protocol. The results also show that TEEN enhanced with BAT is better than TEEN enhanced with PSO.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"1 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":"131191644","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

BlockChain-based Cooperative UAVs for Secure Data Acquisition and Storage 基于区块链的合作无人机用于安全数据采集和存储

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100072

Bilel Najeh, Aicha Idriss Hentati, M. Fourati, L. Chaari, A. Alanezi

引用次数: 0

Deep Learning-Based Speech Enhancement Algorithm Using Charlier Transform

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099854

S. A. Jerjees, Hala Jassim Mohammed, Hayder S. Radeaf, Basheera M. Mahmmod, S. Abdulhussain

{"title":"Deep Learning-Based Speech Enhancement Algorithm Using Charlier Transform","authors":"S. A. Jerjees, Hala Jassim Mohammed, Hayder S. Radeaf, Basheera M. Mahmmod, S. Abdulhussain","doi":"10.1109/DeSE58274.2023.10099854","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099854","url":null,"abstract":"Machine learning, a part of artificial intelligence, is recently used in speech enhancement algorithms (SE). The primary focus of SE is finding the original speech signal from the distorted one. Specifically, deep learning is used in SE because it handles nonlinear mapping problems for complicated features. In this paper, Charlier polynomials-based discrete transform, simply discrete Charlier transform (DCHT), has been used to get the spectra of the noisy signal using a fully connected neural network. Deep learning effectively acquires the context information of speech signal and gets enhanced speech with good quality and intelligibility properties. The proposed algorithm is tested experimentally through self-comparison to obtain the best speech enhancement models corresponding to the DCHT parameter. The experiment is performed with different values of the DCHT parameter. In addition, the well-known TIMIT database is used for evaluation purposes. Different speech measures are used in the experiment. The realized results show the ability of the trained model based on DCHT to enhance the speech signal and provide good results on specific conditions.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"19 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":"129819690","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

Camel Detection and Monitoring Using Image Processing and IoT 使用图像处理和物联网的骆驼检测和监控

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100123

Mahmoud Madi, Y. Basha, Yazan Albadersawi, Fayadh S. Alenezi, S. Mahmoud, D. Abd, D. Al-Jumeily, Wasiq Khan, Abir Jaafar Hussien

引用次数: 0

Inverse Kinematics Optimization for Humanoid Robotic Legs Based on Particle Swarm Optimization 基于粒子群算法的仿人机器人腿逆运动学优化

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100167

Hayder S. Radeaf, M. Z. Al-Faiz

{"title":"Inverse Kinematics Optimization for Humanoid Robotic Legs Based on Particle Swarm Optimization","authors":"Hayder S. Radeaf, M. Z. Al-Faiz","doi":"10.1109/DeSE58274.2023.10100167","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100167","url":null,"abstract":"Calculating the Inverse Kinematic (IK) equations is a complex problem due to the nonlinearity of these equations. Choosing the end effector orientation affects the reach of the target location. The Forward Kinematics (FK) of Humanoid Robotic Legs (HRL) is determined by using Denavit-Hartenberg (DH) method. The HRL has two legs with five Degrees of Freedom (DoF) each. The paper proposes using a Particle Swarm Optimization (PSO) algorithm to optimize the best orientation angle of the end effector of HRL. The selected orientation angle is used to solve the IK equations to reach the target location with minimum error. The performance of the proposed method is measured by six scenarios with different simulated positions of the legs. The proposed method is compared with procedures that used different optimization algorithms such as Genetic Algorithm (GA), Differential Evolution (DE), and Invasive Weed Optimization (IWO). The Root Mean Square Error (RMSE) and computation time are used as comparison measures. The proposed method gives the best results among others, and it reaches the target location with an average RMSE of 10−12 with 2.5 seconds average computation time.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"485 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":"127568978","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

Deep Audio Embeddings and Attention Based Music Emotion Recognition 深度音频嵌入和基于注意的音乐情感识别

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10100058

S. Gupta

{"title":"Deep Audio Embeddings and Attention Based Music Emotion Recognition","authors":"S. Gupta","doi":"10.1109/DeSE58274.2023.10100058","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100058","url":null,"abstract":"The emotion is an intricated impression present in the music that is extremely hard to capture even using refined feature engineering techniques. The emotion of a song is an important feature that can be used for various MIR tasks like recommendation systems, music therapy, and automatic playlist generation. In this research, we investigate the application of L3- Net deep audio embeddings with the attention-based deep neural network model using positional encoding for recognizing musical emotions. In addition, we have constructed a master dataset using the 4Q audio emotion dataset and Bi-modal emotion dataset which is used in this research as the main dataset. The L3-Net deep audio embeddings are being used as features for the neural network model that does not require any feature engineering and other audio-based features. We have proposed two attention-based neural network models with and without recurrent layers. The positional encoding mechanism has helped the ACNN model to learn the recurrent information in the audio embeddings without any recurrent layers. Therefore, we conclude that the ACNN model has performed better than other models with the F1-score of 0.79 using the AdamP optimizer.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"11 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":"124480439","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

Enhancing Intrusion Prevention in Snort System 增强Snort系统的入侵防御

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099757

Sarah Abdulrezzak, Firas A. Sabir

{"title":"Enhancing Intrusion Prevention in Snort System","authors":"Sarah Abdulrezzak, Firas A. Sabir","doi":"10.1109/DeSE58274.2023.10099757","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099757","url":null,"abstract":"Information systems in businesses, organizations live through continuous evolution, including Centralized data centers, local area networks, and Internet access. Although Internet access offers myriad resources, it also enables the outside world to connect to and engage with local network resources. This generates a vulnerability to organizational information systems, which require security measures. To keep the network secure from unauthorized access and survive an attack without affecting the availability of services to legitimate users; various security measures were forged to provide protected network connection, including intrusion Detection and Prevention System (IDPS). IDPS aims to secure the network from both internal and external Intrusions; acting like a safety net and an additional layer of defense. Network IDPS can identify and mitigate numerous attacks by alerting security administrators, dropping malign packets and blocking offending IPs and potential attacks. Snort is a rule based IDPS. In this paper Snort is used to prevent probing, DoS and brute force attacks by utilizing inline mode and iptable and net filter library. New snort rules are proposed to block the three attacks by dropping their packets. The experimental results of this study show that all the attacks are halted and the prevention rate is about 99 percent for malicious packets.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"8 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":"132058399","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

Modify Multiple Object Detection and Tracking to Improve the Execution Time 修改多目标检测和跟踪，提高执行时间

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099782

Rashad N. Razak, Hadeel N. Abdullah

{"title":"Modify Multiple Object Detection and Tracking to Improve the Execution Time","authors":"Rashad N. Razak, Hadeel N. Abdullah","doi":"10.1109/DeSE58274.2023.10099782","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099782","url":null,"abstract":"Multi-Object Detection and Tracking (MODT) is crucial in various contexts. Despite this, significant advancements in detection and tracking speed were needed to meet the challenge during the implementation phase. Introduce a revised algorithmic framework for (MODT) to speed up processing and make it more stable for use in real-time settings. The object's position and velocity were predicted using a Kalman filter and a background subtraction detection approach. When the detector isn't actively searching for a new object, it can be beneficial to discard the successive two frames and replace them with the Kalman filter's prediction and estimated value for the monitored object to speed up the process. Adding some image filter-like aspect ratio object and motion which help to reduce the effectiveness of the shadow and variations of the lighting conditions in the scene, which improve the proposed algorithm detection and tracking, This is useful for daytime preprocessing in an automated traffic surveillance system and inside pedestrian monitoring, and it can be shown with the help of a video camera. The results of these preliminary tests indicate that the proposed algorithm for this vehicle monitoring system works. It demonstrates that when applied to a single camera, the proposed method can monitor, detect, and track many vehicles or human being simultaneous, with improved execution time by 22% over the standard background subtraction and tolerable complexity.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"70 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":"132258411","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

Study on Communicative Robots Assisting Elderly Persons 交流机器人辅助老年人的研究

2023 15th International Conference on Developments in eSystems Engineering (DeSE) Pub Date : 2023-01-09 DOI: 10.1109/DeSE58274.2023.10099833

Samar Taleb, L. Chaari, M. Fourati

引用次数: 0