2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)最新文献_第2页

Towards the RESPOND-A initiative: Next-generation equipment tools and mission-critical strategies for First Responders 迈向response - a计划:第一响应者的下一代设备工具和关键任务战略

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854967

Elisavet Grigoriou, Manolis Fountoulakis, E. Kafetzakis, I. Giannoulakis, Eleftherios Fountoukidis, P. Karypidis, Dimitrios G. Margounakis, Cleo Varianou Mikelidou, Iasonas Sennekis, G. Boustras

引用次数: 2

Distributed Ensembles of Reinforcement Learning Agents for Electricity Control 电力控制中的分布式强化学习智能体集成

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854987

Pierrick Pochelu, B. Conche, S. Petiton

引用次数: 2

Classification of rain events using directional radio data of commercial microwave links 利用商业微波链路的定向无线电数据对降雨事件进行分类

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9855003

Fabian Kovac, Oliver Eigner, Alexander Adrowitzer, Hubert Schölnast, Alexander Buchelt

引用次数: 0

Management of Decentralized Autonomous Organizations 分散自治组织的管理

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9855004

Richard Marko, Kristian Kost'al

{"title":"Management of Decentralized Autonomous Organizations","authors":"Richard Marko, Kristian Kost'al","doi":"10.1109/COINS54846.2022.9855004","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9855004","url":null,"abstract":"In this paper, we analyze the current management status of decentralized autonomous organizations (DAO) and propose a protocol for managing a DAO. For the management of this form of organization, decentralization and autonomy are particularly critical. This basic premise avoids the potential abuse of power by centralized authority because such a firm is governed by code. In centralized firms, company members have a stronger right to participate in decisions made by high-ranking administrators. Blockchain technology, which provides a decentralized form of indestructible data storage, has gained much attention in recent years. Smart contracts, executable code stored in a blockchain, are based on this concept. We propose an approach to building DAO using smart contracts based on blockchain technology, which provides the optimum conditions for the decentralization and autonomy of DAO. The results show that our proposal outperforms existing solutions in terms of team autonomy within the DAO, the ability of all company members to vote on proposals, and the members elected by the rest of the company for higher positions within the organization. The key benefit of our solution is the universal concept of DAO as a service for any firm with a team-based management approach. This platform may be used by many businesses and also provides extensive access rights control based on predefined rules.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127788198","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

Parkinson’s Disease Severity Estimation using Deep Learning and Cloud Technology 利用深度学习和云技术估计帕金森病的严重程度

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854945

Asma Channa, G. Ruggeri, N. Mammone, R. Ifrim, A. Iera, N. Popescu

{"title":"Parkinson’s Disease Severity Estimation using Deep Learning and Cloud Technology","authors":"Asma Channa, G. Ruggeri, N. Mammone, R. Ifrim, A. Iera, N. Popescu","doi":"10.1109/COINS54846.2022.9854945","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854945","url":null,"abstract":"The management of motor complications in Parkinson’s disease (PD) is an unmet need. This paper proposes an eHealth platform for Parkinson’s disease (PD) severity estimation using a cloud-based and deep learning (DL) approach. The system quantifies the hallmark symptoms of PD using motor signals of patients with PD (PwPD). In this study, the dataset named \"The Michael J. Fox Foundation-funded Levodopa Response Study\" is used for the development and evaluation of computational methods focusing on severity estimation of motor function in response to the levodopa treatment. The data is derived from a wearable inertial device, named Shimmer 3, to collect motion data from a patient’s upper limb which is more affected by the disease during the performance of some standard activities selected by MDS-UPDRS III and at home while performing daily life activities (DLAs). Seventeen PwPD were enrolled from two clinical sites, who have varying degrees of motor impairment. An incorporated cloud-based framework is proposed where patients’ motion data is saved in MS Azure cloud where an automatic evaluation of patients’ motor activities in response to the levodopa dose is performed using continuous wavelet transform and CNN-based transfer learning approach. Experimental results show that the efficiency and the robustness of the proposed procedure are proven by 90.0% accuracy for tremor estimation and 86.4% for bradykinesia, with good performance in terms of sensitivity and specificity in each class.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133064672","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

Water economy with smart water system in the City of Carouge 卡鲁日市智能水系统的水经济

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854990

E. Kim, R. Rossi, Betty Cortiñas-Lorenzo, B. Magoutas, A. Martinez, Cédric Crettaz, E. Bothos, J. M. F. Montenegro

引用次数: 0

Detecting Pneumonia With TensorFlow and Convolutional Neural Networks 用TensorFlow和卷积神经网络检测肺炎

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854948

Dejan Babic, I. Jovović, Tomo Popović, Stevan Cakic, Luka Filipovic

{"title":"Detecting Pneumonia With TensorFlow and Convolutional Neural Networks","authors":"Dejan Babic, I. Jovović, Tomo Popović, Stevan Cakic, Luka Filipovic","doi":"10.1109/COINS54846.2022.9854948","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854948","url":null,"abstract":"Artificial intelligence is getting more and more involved in our everyday life as a result of enormous amounts of data available for feeding the machine and deep learning algorithms. Deep learning introduced new dimensions and possibilities of applications in medical science. With COVID-19 outbreak in 2020 at global level, the health systems of many countries were overwhelmed. With many patients infected, health system is pressured to correctly diagnose patient’s state of illness. In a lot of occasions, it was almost impossible to correctly diagnose many COVID-19 positive patients that have pneumonia due to many outbreaks in many areas. The intelligent system that could detect pneumonia with certainty could help in easing the pressure on the health system and make doctors focus on more severely ill patients. This paper describes development of pneumonia detection model using TensorFlow to processes the chest X-ray images to determine whether the patient has pneumonia. The model is based on deep learning algorithm supported through convolutional neural network. The model presented in this paper has achieved rather high accuracy (over 95%) in analyzing X-Ray images and could be used to speed up decision process in healthcare.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122069204","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

COINS 2022 Cover Page 硬币2022封面

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/coins54846.2022.9854968

引用次数: 0

GANIBOT: A Network Flow Based Semi Supervised Generative Adversarial Networks Model for IoT Botnets Detection GANIBOT:基于网络流的半监督生成对抗网络模型，用于物联网僵尸网络检测

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854947

K. Saurabh, Ayush Singh, Uphar Singh, O. P. Vyas, M. M. Khondoker

{"title":"GANIBOT: A Network Flow Based Semi Supervised Generative Adversarial Networks Model for IoT Botnets Detection","authors":"K. Saurabh, Ayush Singh, Uphar Singh, O. P. Vyas, M. M. Khondoker","doi":"10.1109/COINS54846.2022.9854947","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854947","url":null,"abstract":"The spread of Internet of Things (IoT) devices in our homes, healthcare, industries etc. are more easily infiltrated than desktop computers have resulted in a surge in botnet attacks based on IoT devices, which may jeopardize the IoT security. Hence, there is a need to detect these attacks and mitigate the damage. Existing systems rely on supervised learning-based intrusion detection methods, which require a large labelled data set to achieve high accuracy. Botnets are onerous to detect because of stealthy command & control protocols and large amount of network traffic and hence obtaining a large labelled data set is also difficult. Due to unlabeled Network traffic, the supervised classification techniques may not be used directly to sort out the botnet that is responsible for the attack. To overcome this limitation, a semi-supervised Deep Learning (DL) approach is proposed which uses Semi-supervised GAN (SGAN) for IoT botnet detection on N-BaIoT dataset which contains \"Bashlite\" and \"Mirai\" attacks along with their sub attacks. The results have been compared with the state-of-the-art supervised solutions and found efficient in terms of better accuracy which is 99.89% in binary classification and 59% in multi classification on larger dataset, faster and reliable model for IoT Botnet detection.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115399860","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: 4

COINS 2022 Cover Page 硬币2022封面

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/coins54846.2022.9854991

引用次数: 0