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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： El-Fiky, Azza Shouman, Marwa Ahmed Hamada, Salwa El-Sayed, Ayman Karar, Mohamed Esmail Electronics Research Institute Department of Informatics Cairo Egypt Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Minuf Egypt Menoufia University Faculty of Electronic Engineering Dept. Industrial Electronics and Control Engineering Minuf Egypt

ISBN: (纸本)9781665418423

Chest radiography presents one of the main medical imaging modalities for diagnosing lung diseases. To assist radiologists during interventional procedures, this paper aims at proposing a transfer learning-based classifier to automatically identify 14 different thoracic diseases in Chest X-ray (CXR) images. The proposed method is relied on deep residual neural networks with 50 layers (ResNet-50) to accomplish the diagnostic task of many chest diseases. In this study, a public dataset of 112, 120 frontal radiograph images for Chest X-ray has been used for validating the proposed deep learning classifier. It achieved the best performance of multi-label classification of normal and 14 different lung diseases with an average area under curve (AUC) of 0.911 and F1-score of 0.66. This study demonstrated that the proposed ResNet-50 classifier as a transfer learning model outperforms other relevant methods in the previous studies for automatic multi-label classification of chest X-rays. © 2021 IEEE.

关键词： computer aided diagnosis

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Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems

arXiv

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arXiv 2024年

作者： Keren, Sarah Essayeh, Chaimaa Albrecht, Stefano V. Morstyn, Thomas Taub Faculty of Computer Science Technion - Israel Institute of Technology Israel Departement of Engineering Nottingham Trent University United Kingdom School of Informatics University of Edinburgh United Kingdom Department of Engineering Science University of Oxford United Kingdom

The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges. These have rendered traditional centralized energy-market paradigms insufficient due to their inability to support the dynamic and evolving nature of the network. This survey explores how multi-agent reinforcement learning (MARL) can support the decentralization and decarbonization of energy networks and mitigate the associated challenges. This is achieved by specifying key computational challenges in managing energy networks, reviewing recent research progress on addressing them, and highlighting open challenges that may be addressed using MARL. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Prioritizing TWAS Genetic Biomarkers for Melanoma Metastasis via Gene Expression Profiling*

Prioritizing TWAS Genetic Biomarkers for Melanoma Metastasis...

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Mohamed N. Saad Belal Medhat Mohamed Hamed Biomedical Engineering Department Faculty of Engineering Minia University Egypt Institute for Biostatistics and Informatics in Medicine and Ageing Research (IBIMA) Rostock University Medical Center Rostock Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Egypt Faculty of Media Engineering and Technology German University in Cairo Egypt

The development of novel therapeutic approaches that target gene alterations in melanoma cells remains an area of active research. This study prioritized ten genes out of 76 genes - associated with melanoma through a transcriptome-wide association study (TWAS). We investigated the potential of the identified ten genes in prognosis of metastasis in melanoma *** Relevance: Our findings have clinical implications for melanoma treatment strategies. Targeted therapies that specifically address the dysregulation of our 10 genes may hold promise in preventing or slowing melanoma metastasis.

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Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

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Information Technology International Seminar (ITIS)

作者： Iwan Purnama Rahmadani Pane Adri Gabriel Sooai Firman Edi Ahmad Abdul Chamid Puput Dani Prasetyo Adi Department of Information Technology Faculty of Science and Technology Universitas Labuhanbatu Labuhanbatu Indonesia Department of Computer Engineering Faculty of Computer Science Universitas Katolik Widya Mandira Kupang NTT Indonesia Department of Engineering Management Faculty of Industrial Technology Batam Institute of Technology Batam City Indonesia Department of Informatics Engineering Muria Kudus University Kudus Indonesia Department of Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

This article presents a simulation study on the use of LoRaWAN technology in autonomous vehicles. The research focuses on developing an Auto-Handover Gateway for Autonomous Vehicles, utilizing LoRaWAN parameters such as bit rates, spreading factor (SF), packet loss, and received signal strength. These parameters are tested in simulations of autonomous vehicle movements, which include both regular and random motion patterns. The aim is to assess the performance of LoRaWAN in different scenarios and its impact on communication in autonomous vehicle systems. The simulation results show several key trends. As the vehicle moves further, the spreading factor (SF) increases, and bit rates tend to decrease, indicating a drop in communication performance over longer distances. The research also identifies that the gateway with the closest proximity to the vehicle is selected, both in random and non-random movement scenarios. Additionally, packet loss increases as the distance between the vehicle and the gateway grows, highlighting the challenges of maintaining reliable communication over long distances. These findings provide insights into optimizing LoRaWAN parameters for real-time communication in autonomous vehicles, contributing to the development of intelligent vehicle networks.

关键词： Seminars Simulation Bit rate LoRaWAN Packet loss Logic gates Market research Real-time systems Reliability Autonomous vehicles

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Normal Vector Direction-based 3D LiDAR Point Cloud Planar Surface Removal for Object Cluster Minimization in Human Activity Monitoring System

Normal Vector Direction-based 3D LiDAR Point Cloud Planar Su...

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IEEE Instrumentation and Measurement Technology Conference

作者： Nova Eka Budiyanta Eko Mulyanto Yuniarno Tsuyoshi Usagawa Mauridhi Hery Purnomo Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Science and Electrical Engineering Graduate School of Science and Technology Kumamoto University Kumamoto Japan Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS) Surabaya Indonesia

The use of 3-Dimensional Light Detection and Ranging (3D LiDAR) point cloud as the alternative data to reduce privacy exposure in monitoring systems has been carried out in several studies. Unfortunately, various challenges in using point clouds intersect with the amount of data and computational costs. Several studies attempted to optimize the point cloud processing approach by segmenting the ground plane to get the object clusters separated. However, many unnecessary points can still burden the computation process. Since the ground plane mainly represents the horizontal planar plane on the x, y axis, this study tried to reduce the points on the vertical planar plane on the x, z and y, z axes with the x, y horizontal planar plane as well based on the surface normal vector direction of each point. The proposed approach has successfully reduced the raw point cloud by 79.29% removing the point cloud that indicates the planar surface of the three axes while maintaining the essential object of the monitoring system on the KITTI raw dataset. Therefore, the object cluster can be minimized, supporting the computational costs for further research in human activity monitoring systems.

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Effect of Interest Variation and Prefix Length on Pending Interest Table (PIT) Performance in Named Data Networking (NDN)

Effect of Interest Variation and Prefix Length on Pending In...

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International Conference on Wireless and Telematics (ICWT)

作者： Helmi Ahmad Khaderani Ishlah Muhammadi Lutfi Razan Muhammad Adi Sucipto Luthfi Restu Kusumastuti Ade Nurhayati Syaiful Ahdan Nana Rachmana Syambas Dept. of Telecommunication Engineering Bandung Institute of Technology Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Faculty of Engineering Telkom University Jakarta Barat Indonesia

Pending interest table (PIT) is one of the data structures on each named data network router. The speed of delivery of interest as well as the interest served at PIT are several important parameters for measuring PIT performance. This research was conducted to analyze the performance of PIT by measuring the round trip time (RTT) of sending data and sending interests from consumers to producers on the Palapa Ring Sumatra topology. Testing is carried out using miniNDN by varying the length of the prefix and the number of interests sent. From the simulation and results of the analysis that has been carried out, it is known that the correlation between the length of the prefix and RTT is directly proportional. The longer the prefix of an interest packet, the higher the RTT value. Apart from the simulation results, the number of interest packets sent is the same as those received at each node.

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Deep Learning Based Application To Detect Face Masks For Health Protocol Surveillance During The Covid-19 Pandemic 7

Deep Learning Based Application To Detect Face Masks For Hea...

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7th IEEE Information Technology International Seminar, ITIS 2021

作者： Via, Yisti Vita Puspaningrum, Eva Yulia Arwindo Kurniawan, Dicky Giancini Nudin, Salamun Rohman Nurfiana Munir, Mochammad Syahrul Universitas Pembangunan Nasional Veteran Jawa Timur Faculty of Computer Science Department of Informatics Surabaya Indonesia Universitas Negeri Surabaya Faculty of Engineering Departement of Informatics Surabaya Indonesia Institut Informatika Dan Bisnis Darmajaya Faculty of Computer Science Department of Computer System Lampung Indonesia Akademi Komunitas Negeri Pacitan Departement of Tata Laksana Studio Produksi Pacitan Indonesia

ISBN: (纸本)9781665408073

As part of preventing the rapid spread of the COVID-19 virus, masks should be used besides physical distancing, avoiding crowds, setting good ventilation, cleaning hands, and more. In fact, there are still many people who do not use masks in public places. Security officers usually carry out the supervision system for the use of masks in public places. Direct interaction between security officers and many visitors in the long term will trigger a high risk of being infected. It is better if there is a system that can monitor the use of masks automatically and continuously. In this study, a deep learning algorithm with YOLOv3 architecture is applied to a web-based mask detection system. YOLOv3 is one of the best algorithms that has been widely applied and optimized by researchers for object detection. This system has been tested in two scenarios using different data sets, namely without augmentation and with augmentation. For each scenario, three experiments were carried out using varying amounts of training data, namely 10, 100, and 800. The best mAP and confidence values were obtained from experiments using 800 training data with augmentation. This mAP value is 0.99 with a confidence value range between 0.9 to 1. © 2021 IEEE.

关键词： Deep learning

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Assessment of natural radionuclides and radiological health hazards of the soil of the general company for Sulphur Al-Mishraq in Ninevah Governorate, Iraq

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International Journal of Nuclear Energy Science and Technology 2024年第2-3期17卷 197-216页

作者： Ghazal, Abdulwahab Ibrahim Jasim, Shihab Ahmed Al-Muhee, Nidhal Ali Mohammed Ali Wais, Taha Yaseen Najam, Laith Ahmed Namq, Berivan F. Mansour, Howaida Department of Physiology and Medical Physics College of Medicine Ninevah University Mosul Iraq Department of Computer and Informatics Engineering Ninevah University Mosul Iraq Department of Medical Physics College of Science University of Mosul Mosul Iraq Chemical Biological and Radiological Safety and Security Division University of Mosul Mosul Iraq Department of Physics College of Science University of Mosul Mosul Iraq Department of Basic Science College of Dentistry Kirkuk University Kirkuk Iraq Department of Physics Faculty of Women for Arts Science and Education Ain Shams University Cairo Egypt

The current research assessed the radiological hazards associated with radionuclide activity in soil samples collected from the General Company for Sulphur Al-Mishraq in Nineveh Governorate, Iraq. A gamma-ray spectrometer (HPGe) was used. The average activities of 40K, 232Th, and 226Ra in the soil samples were 307.62 Bq kg-1, 18.66 Bq kg-1 and 26.19 Bq kg-1, respectively. The radium equivalent activity, absorbed gamma dose rate, annual effective dose equivalent, annual gonadal dose equivalent, excess lifetime cancer risk, external and internal hazard indices and gamma radiation hazard index ranged from 48.20 to 105.49 Bq kg-1, 22.91 to 49.76 nGy h-1, 28.09 to 61.02 μSv y-1, 162.33 to 350.97 μSv y-1, 98.33 to 213.58 ×10-4, 0.13 to 0.28, 0.17 to 0.38 and 0.36 to 0.78, respectively. These values are below the global averages report. Statistical analysis was used to examine and understand the distribution of natural radionuclides and radiation hazard characteristics in the study area. Although the soil in the study area poses no immediate radiological risk to humans, regular monitoring is recommended. © 2024 Inderscience Enterprises Ltd.

关键词： Radiation hazards

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A Framework for Automated Cardiovascular Magnetic Resonance Image Quality Scoring based on EuroCMR Registry Criteria

A Framework for Automated Cardiovascular Magnetic Resonance ...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Shahabedin Nabavi Mohsen Ebrahimi Moghaddam Ahmad Ali Abin Alejandro F. Frangi Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Division of Informatics Imaging and Data Sciences Schools of Computer Science and Health Sciences The University of Manchester Manchester U.K.

Cardiovascular magnetic resonance (CMR) imaging is a radiation-free modality widely used for functional and structural evaluation of the cardiovascular system. Achieving an accurate diagnosis requires having good-quality images. Subjective CMR image quality assessment is a tedious, time-consuming and costly process. This paper presents an automated scoring framework for CMR image quality assessment that uses deep learning models to evaluate left ventricular coverage and CMR imaging artefacts. The quality scoring in the proposed framework is an attempt to automate some of the subjective quality control criteria of the EuroCMR registry for the short-axis cine steady-state free precession (SSFP) CMR images. The scores given by a radiologist and a cardiologist with experience in CMR imaging for the images of 50 subjects from the UK Biobank were used to validate the proposed framework. The Pearson correlation coefficient (PCC) and the Spearman rank-order correlation coefficient (SRCC) calculated for the experts’ quality scores versus ones obtained from the proposed framework are 0.908 and 0.806 on average. The results show that the quality scoring by the proposed framework is highly correlated with the experts’ opinions. The proposed framework can be used for post-imaging quality assessment of short-axis cine SSFP CMR images and quality control of large population studies such as the UK Biobank.

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