2022 18th International Computer Engineering Conference (ICENCO)最新文献_第2页

ICT Governance Performance Implications in Higher Education: A Systematic Review of the Literature 信息通信技术治理绩效对高等教育的影响:文献系统综述

2022 18th International Computer Engineering Conference (ICENCO) Pub Date : 2022-12-29 DOI: 10.1109/ICENCO55801.2022.10032465

Ali Etkkali, Poba-Nzou Placide, Carlos Denner dos Santos

引用次数: 0

Kinship Verification Analysis Based on Color Features 基于颜色特征的亲属关系验证分析

2022 18th International Computer Engineering Conference (ICENCO) Pub Date : 2022-12-29 DOI: 10.1109/ICENCO55801.2022.10032517

Nermeen Nader, F. E. El-Gamal, M. Elmogy

引用次数: 0

Distributed Sensors, Computation and AI for Automation, Protection and Maintenance of Power Grid 分布式传感器、计算和人工智能在电网自动化、保护和维护中的应用

2022 18th International Computer Engineering Conference (ICENCO) Pub Date : 2022-12-29 DOI: 10.1109/ICENCO55801.2022.10032522

Shahriar Khan

{"title":"Distributed Sensors, Computation and AI for Automation, Protection and Maintenance of Power Grid","authors":"Shahriar Khan","doi":"10.1109/ICENCO55801.2022.10032522","DOIUrl":"https://doi.org/10.1109/ICENCO55801.2022.10032522","url":null,"abstract":"The advances in computers and communications in the ongoing Fourth industrial revolution have not been matched by corresponding advances in the power grid. Fifty-year-old problems of the grid remain, such as uncontrolled blackouts, lack of automation, and uncertainty about replacing aging equipment. In the Ukraine war, attacks on the power grid have drawn attention to limiting spreading blackouts. CTs and PTs for monitoring high voltage lines remain large and expensive structures. Human operators are required at substations, and much of their decisions are taken over the phone. New developments in sensors, communications, smart electronics are today allowing a smarter more automated grid. Aging transformers and generators nearing end-of-life can be better monitored and replaced. Load shedding can be smart and pre-planned, so that low-priority feeders, neighborhoods, households and equipment are disconnected first. A blackout from cascaded tripping of generators can be arrested by remote disconnection of loads equivalent to the lost generation. The new smart grid may be based on the structure of the internet, with distributed and non-centralized sensors and computations. The multiple inputs from sensors and various operation conditions are an excellent candidate for AI. Methods have been proposed here for prioritizing and shedding loads automatically and remotely before overstressed generators are tripped. A disconnection of a generator is simulated in the IEEE 36 bus, following shedding of an equivalent amount of load, so as to arrest the blackout. According to existing trends, improved smart grids will become a major growth area in the coming decades.","PeriodicalId":307952,"journal":{"name":"2022 18th International Computer Engineering Conference (ICENCO)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115783894","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

Online Reconfigurable Convolutional Neural Network for Real-Time Applications 实时应用的在线可重构卷积神经网络

2022 18th International Computer Engineering Conference (ICENCO) Pub Date : 2022-12-29 DOI: 10.1109/ICENCO55801.2022.10032512

A. Shams, M. Sabry, Hassan Soubra, Mohammed Abdel-Megeed Salem

{"title":"Online Reconfigurable Convolutional Neural Network for Real-Time Applications","authors":"A. Shams, M. Sabry, Hassan Soubra, Mohammed Abdel-Megeed Salem","doi":"10.1109/ICENCO55801.2022.10032512","DOIUrl":"https://doi.org/10.1109/ICENCO55801.2022.10032512","url":null,"abstract":"The omnipresence of real-time embedded systems enabled the smart paradigm, with smart devices being adopted by a larger portion of people worldwide. Such devices are capable of assisting people through the means of Human-Computer Interaction (HCI). Many features of these smart devices are based on Artificial Intelligence (AI) that uses neural networks (NN). Nevertheless, the computational power of embedded systems in smart devices is limited. Hence, adding more AI-powered modules becomes a challenge. Especially if the system is prone to an overload of tasks or can experience a failure that could lead to a sudden decrease in performance. To ensure that these cases are handled correctly without a total system failure, neural networks need to be able to be reconfigured quickly and effectively to cope with the available situation. This paper proposes a convolutional neural network (CNN) that can be reconfigured online at runtime to reduce its computational needs. The proposed approach changes the size of any NNs online without needing to save a whole new network to disk. This is done through offline training a network in its full size, then removing some layers and replacing them with a smaller number of layers while making sure only the new layers are being trained. After the new configuration has been trained offline, only the new layers are saved. This allows the network to be re-configurable while occupying very little disk space, simply by swapping the old layers and plugging in the new ones during runtime. This proposal has been tested on CIFAR 10 and was able to decrease the model size by up to 73.81% in only 0.18 seconds for a trade-off with accuracy.","PeriodicalId":307952,"journal":{"name":"2022 18th International Computer Engineering Conference (ICENCO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134480278","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

PID Control of Dual Axis Inertially Stabilized Platform Simscape Multibody Model 双轴惯性稳定平台Simscape多体模型的PID控制

2022 18th International Computer Engineering Conference (ICENCO) Pub Date : 2022-12-29 DOI: 10.1109/ICENCO55801.2022.10032521

K. Ahmed, A. Roshdy, W. G. Ata, A. M. Salem

引用次数: 0

Emotional Speech Synthesis using End-to-End neural TTS models 基于端到端神经TTS模型的情绪语音合成

2022 18th International Computer Engineering Conference (ICENCO) Pub Date : 2022-12-29 DOI: 10.1109/ICENCO55801.2022.10032463

S. Nithin, J. Prakash

{"title":"Emotional Speech Synthesis using End-to-End neural TTS models","authors":"S. Nithin, J. Prakash","doi":"10.1109/ICENCO55801.2022.10032463","DOIUrl":"https://doi.org/10.1109/ICENCO55801.2022.10032463","url":null,"abstract":"Speech synthesis aims at artificially generating human like speech. The advancements in Artificial Intelligence and Natural Language Processing (NLP), particularly deep learning networks, have significantly improved such attempts. Text to Speech Synthesis (TTS) models based on deep neural networks are now able to generate speech samples with high naturality and intelligibilty. Improving the paralinguistic characteristics of speech, like emotion, is also desirable for effective speech synthesis. But availability of suitable emotional speech dataset for neural TTS may be limited. Transfer Learning offers a viable solution for such scenarios of limited resources. In this paper, we present an overview of emotional speech synthesis using end-to-end neural TTS models and compare the performance of Tacotron 2 and FastSpeech 2 for transfer learning based emotional speech synthesis. To generate speech waveform from generated mel spectrogram, HiFi-GAN vocder is used. The synthesized speech samples are evaluated using ScSer speech emotion recognition model. The Tacotron 2 and FastSpeech 2 models reported classification accuracies of 90% and 79% respectively.","PeriodicalId":307952,"journal":{"name":"2022 18th International Computer Engineering Conference (ICENCO)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116792506","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

Brain Cancer Diagnosis Based on Histopathological Images Using Handcrafted Features 基于手工特征的组织病理学图像的脑癌诊断

2022 18th International Computer Engineering Conference (ICENCO) Pub Date : 2022-12-29 DOI: 10.1109/ICENCO55801.2022.10032507

Naira Elazab, Wael A. Gab-Allah, M. Elmogy

{"title":"Brain Cancer Diagnosis Based on Histopathological Images Using Handcrafted Features","authors":"Naira Elazab, Wael A. Gab-Allah, M. Elmogy","doi":"10.1109/ICENCO55801.2022.10032507","DOIUrl":"https://doi.org/10.1109/ICENCO55801.2022.10032507","url":null,"abstract":"Traditionally, pathologists have used a light microscope to examine tissue sections mounted on glass slides to diagnose and categorize brain tumors. The outcomes of this method may not always be perfectly correct and are labor- and time-intensive. The procedure of classifying tumors could be made better using the computer-aided method. In this paper, we present an automated procedure that combines the following three feature extraction techniques to classify histopathological images of brain tumors: color histogram, Hu invariant moments, and scale-invariant hybrid image descriptor (RSHD). Color normalization and augmentation techniques are used for preprocessing the histopathological images before the feature extraction phase. In order to classify brain tumor images, three feature sets were extracted. Next, the generated features were fused together to form a combined feature set fed into the machine learning classifier. For differentiating between different sub-types of brain cancer, the XGBoost model performs best in terms of classification accuracy, precision, F1 score, and recall. XGBoost obtained 97.5% for recall, 94.1% for F1 score, 93.1% for precision, and 92.7% for accuracy. The proposed method produced effective results, outperforming multiple state-of-the-art investigations.","PeriodicalId":307952,"journal":{"name":"2022 18th International Computer Engineering Conference (ICENCO)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127409559","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

Performance Analysis for Fractional Order Fuzzy PID Controller: A Comparative Study with Various PID Controller Structures 分数阶模糊PID控制器的性能分析:与各种PID控制器结构的比较研究

2022 18th International Computer Engineering Conference (ICENCO) Pub Date : 2022-12-29 DOI: 10.1109/ICENCO55801.2022.10032464

Emad A. Elsheikh, Mohamed I. Abdo

引用次数: 0

Performance Evaluation for RIS-Assisted Communications with Power Allocation and Phase Shift Optimization 基于功率分配和相移优化的ris辅助通信性能评估

2022 18th International Computer Engineering Conference (ICENCO) Pub Date : 2022-12-29 DOI: 10.1109/ICENCO55801.2022.10032516

O. Wasif, E. Maher, A. El-Mahdy

引用次数: 0

DDoS Attack Detection in IoT Using Machine Learning Based Intrusion Detection System (IDS) 使用基于机器学习的入侵检测系统(IDS)检测物联网中的DDoS攻击

2022 18th International Computer Engineering Conference (ICENCO) Pub Date : 2022-12-29 DOI: 10.1109/ICENCO55801.2022.10032515

Mohamed Elkashlan, H. Aslan, Marianne A. Azer

{"title":"DDoS Attack Detection in IoT Using Machine Learning Based Intrusion Detection System (IDS)","authors":"Mohamed Elkashlan, H. Aslan, Marianne A. Azer","doi":"10.1109/ICENCO55801.2022.10032515","DOIUrl":"https://doi.org/10.1109/ICENCO55801.2022.10032515","url":null,"abstract":"The market for Internet of things (IoT) has expanded tremendously. This needs the creation of an ecosystem that is user-friendly and secure. Internet connected IoT devices provide rich user experience and add-on services. Eventually, the IoT devices are interconnected to a management system. Attacking the IoT ecosystem is rising at the same rate as those of the physical attacks and vandalism happening in the IT world. The cyberattack is more severe than the physical attack as it may affect thousands of the IoT devices at the same time. Intrusion Detection is vital in defending diverse types of attacks and unauthorized activities. Fundamentally, the Intrusion Detection System’s (IDS) problem is a classification problem. The IDS tries to find out each traffic stream is legitimate or malicious. Furthermore, the IDS can identify the type of the malicious traffic. This study is a benchmark of different machine learning classifier algorithms for finding fraudulent traffic specifically in the IoT network environment. A typical IoT dataset obtained from actual IoT traffic is used in the study. We compare various classification algorithms that is placed inline to the traffic and contain different types of attacks targeting the IoT network. The obtained results from the different algorithms would improve the stability and significantly reduce the amount of cyberattacks that could disrupt daily living activities in the IoT Environment.","PeriodicalId":307952,"journal":{"name":"2022 18th International Computer Engineering Conference (ICENCO)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128932058","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