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2022 IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies, SmartBlock4Health 2022

作者： Al-Asadi, Hanan Hussein Goga, Maria Ali, Haider Abdullah Popa, Ramona Cristina Oludele, Awodele University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania University of Technology-Iraq Baghdad Iraq Technical University of Civil Engineering of Bucharest UTCB Department for Training of Teaching Personnel Bucharest Romania Madenat Alelem University College Baghdad Iraq Babcock University Computer Science Department Ogun State Nigeria

ISBN: (纸本)9781665460149

The use of Agriculture Intelligent Systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy. © 2022 IEEE.

关键词： Intelligent systems

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Handwritten Signature Recognition using Parallel CNNs and Transfer Learning for Forensics

Handwritten Signature Recognition using Parallel CNNs and Tr...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Jaouhar Fattahi Feriel Sghaier Mohamed Mejri Ridha Ghayoula Emil Pricop Baha Eddine Lakdher Department of Computer Science and Software Engineering Laval University Quebec Canada Carthage National Engineering School Carthage University Tunis Tunisia Faculty of Engineering University of Moncton Canada Computers & Electronics Department Automatic Control Petroleum-Gas University of Ploieşti Ploieşti Romania Faculty of Science University of Monastir Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Handwritten signatures hold paramount importance in legal, financial, and administrative domains, necessitating the development of robust signature recognition tools for forensic applications. This paper introduces a handwritten signature recognition (HSR) model employing Parallel Convolutional Neural Networks (CNN) tailored for forensic endeavors. Utilizing the parallel processing capabilities of CNN, our proposed approach adeptly analyzes and extracts discriminative features from handwritten signature images to facilitate precise recognition. In addition, we leverage several transfer learning techniques by parallelizing proven pre-trained CNNs. Extensive experimentation validates the efficacy of our approach on a standard dataset, demonstrating high accuracy and resilience in signature recognition tasks. The proposed approach exhibits substantial promise in augmenting forensic investigations by automating signature verification processes, thereby bolstering fraud detection efforts and upholding the integrity of legal documentation.

关键词： Deep learning Handwriting recognition Forensics Face recognition Transfer learning Feature extraction Fraud Convolutional neural networks Standards Resilience

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GETAE: Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection

arXiv

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arXiv 2024年

作者： Truica, Ciprian-Octavian Apostol, Elena-Simona Marogel, Marius Paschke, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Splaiul Independentei 313 Bucharest060042 Romania Academy of Romanian Scientists Ilfov 3 Bucharest050044 Romania Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Fraunhofer Institute for Open Communication Systems Kaiserin-Augusta-Allee 31 Berlin10589 Germany

In today’s digital age, fake news has become a major problem that has serious consequences, ranging from social unrest to political upheaval. To address this issue, new methods for detecting and mitigating fake news are required. In this work, we propose to incorporate contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch uses Word and Transformer Embeddings and a Deep Neural Network based on feed-forward and bidirectional Recurrent Neural Networks ([Bi]RNN) for learning novel contextual features and creating a novel Text Content Embedding. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE Ensemble combines the two novel embeddings, i.e., Text Content Embedding and Propagation Embedding, to create a novel Propagation-Enhanced Content Embedding which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that using this approach improves fake news detection and outperforms state-of-the-art models. © 2024, CC BY-NC-ND.

关键词： Graph embeddings

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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

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IEEE/CAA Journal of automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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Classification of Skin Lesions from Dermatoscopic Images Using Convolutional Neural Networks

Classification of Skin Lesions from Dermatoscopic Images Usi...

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International Conference on control Systems and computer science (CSCS)

作者： Maria Oniga Alina-Elena Sultana Dan Popescu Daniel-Marian Merezeanu Loretta Ichim Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania Faculty of Electronics Telecommunications and Information Technology University Politehnica of Bucharest Bucharest Romania “St. S. Nicolau” Institute of Virology Bucharest Romania

Skin cancer is one of the most common types of cancer, and it is caused by a variety of dermatological conditions. Identifying abnormalities from skin images is an important pre-diagnostic step to assist physicians in determining the patient’s condition. Thus, to aid dermatologists in the diagnosis process, we proposed five CNN-based classification approaches namely ResNet-101, DenseNet-121, GoogLeNet, VGG16, and MobileNetV2 architectures on which the transfer learning process was applied. The HAM10000-N database consisting of 7,120 images, which was obtained from the original HAM10000 dataset through an augmentation process, was used to train the proposed methods. Moreover, the images from the HAM10000-N were pre-processed by removing hair with the DullRazor algorithm. To evaluate and compare the performance of all networks five metrics were calculated: accuracy, precision, recall, and Fl-score. The best results for the seven-class classification of the HAM10000-N dataset were obtained for DenseNet-121 architecture with 87% accuracy, 0.871 precision, 0.87 recall and 0.872 F1-score.

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Path Planning Algorithm for Multiple UAVs Based on Artificial Potential Field

Path Planning Algorithm for Multiple UAVs Based on Artificia...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Chongde Ren Jinchao Chen Chenglie Du Wenquan Yu School of Software Northwestern Polytechnical University Xi'an China Department of Computer Science Northwestern Polytechnical University Xi'an China Department of Land Unmanned System Research and Development North Automatic Control Technology Research Institute Taiyuan China

Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, aims to determine the optimal routes for multiple UAVs from various starting points to a single destination. However, because of the involvement of complex conditional constraints, path planning becomes a highly challenging problem. The path planning problem involving numerous UAVs is examined in this research, and a SAAPF-MADDPG algorithm based on Artificial Potential Field (APF) is suggested as a solution. First, a SA-greedy algorithm that can change the probability of random exploration by agents based on the number of steps and successful rounds to prevent UAVs from getting trapped in a local optimum. Then, we design complex reward functions based on APF to guide UAVs to destination faster. Finally, SAAPF-MADDPG is evaluated against the MADDPG, DDPG, and MATD3 methods in simulation scenarios to confirm its efficacy.

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Cybersecurity Threat Detection in the Behavior of IoT Devices: Analysis of Data Mining Competition Results 18

Cybersecurity Threat Detection in the Behavior of IoT Device...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Czerwinski, Michal Michalak, Marcin Biczyk, Piotr Adamczyk, Blazej Iwanicki, Daniel Kostorz, Iwona Brzeczek, Maksym Janusz, Andrzej Hermansa, Marek Wawrowski, Lukasz Kozlowski, Artur Qed Software Mazowiecka 11/49 Warsaw0-052 Poland Ukasiewicz Research Network - Institute of Innovative Technologies Emag Leopolda 31 Katowice40-189 Poland Efigo Sp. Z O.o. M.Kopernika 8/6 Katowice40-064 Poland Faculty of Mathematics University of Warsaw Banacha 2 Warsaw02-097 Poland Department of Computer Networks and Systems Faculty of Automatic Control University of Technology Electronics and Computer Science Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9788396744784

The paper discusses a data science competition centered around the development of an anomaly detection system for IoT devices. The competition utilized a unique environment that allowed for the operation and monitoring of real IoT devices, including scheduling of attacks on these devices. The environment was used to collect the data, which included both normal and attack-induced behavior of IoT devices. The paper presents the background of the competition, the top models submitted, and the competition results. The paper also includes a discussion about restrictions related to the use of synthetic attack data as input for constructing anomaly detection systems. © 2023 Polish Information Processing Society.

关键词： Quality control

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Game-based Resource Allocation for Secure UAV Communication in Wireless Networks

Game-based Resource Allocation for Secure UAV Communication ...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Riya Kakkar Rajesh Gupta Smita Agrawal Sudeep Tanwar Emil Pricop Dept. of Computer Science and Engg. Inst. of Tech. Nirma University Ahmedabad India Automatic Control Comp. & Elect. Dept. Petroleum-Gas University of Ploiesti Ploieşti Romania

Unmanned aerial vehicles (UAVs) are one of the applications of device-to-device (D2D) technology, where two UAVs can communicate with or without the existence of a base station. It facilitates high content delivery, low latency, low cost, and high data rate communication in wireless networks. Despite the aforementioned benefits, an eavesdropping attack is one of the critical challenges that cause interference in UAV communication in the presence of an eavesdropper. To mitigate the aforementioned challenges, we proposed a secure resource allocation for UAV communication in wireless networks. The applied single carrier orthogonal frequency division multiple access (SC-OFDMA) technique utilize the orthogonality feature to provide secure UAV communication in wireless networks. Moreover, a zero-sum game approach is considered to enable the secure and optimal resource allocation for UAV communication. The main aim of the zero-sum game theory is to maximize the communication channel's data rate and secrecy capacity to achieve a secure and optimal UAV communication scenario in wireless networks. Finally, the performance and simulation of the proposed approach are evaluated against the existing random approach in terms of various metrics such as data rate and secrecy capacity of the communication channel.

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Home Care Assistance Solution based on a Software Multi-Agents System

Home Care Assistance Solution based on a Software Multi-Agen...

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International Conference on control Systems and computer science (CSCS)

作者： Mohammad Rasras Iuliana Marin Şerban Radu Doctoral School of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest ROMANIA Dept. of Engineering in Foreign Languages University POLITEHNICA of Bucharest Bucharest ROMANIA Dept. of Computer Science University POLITEHNICA of Bucharest Bucharest ROMANIA

A smart home allows automated technology to control systems and appliances to assist daily life activities. In the current work, a prototype of a designed smart home health-aware system is presented. Three predefined ruled agents have been used to gather data, communicate between themselves, interpret the data based on several machine learning algorithms to predict abnormality for cardiac conditions. The paper also compares results obtained by running five machine learning algorithms. The agents function in accordance with predefined dataset describing residents ’ age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, and glucose levels, smoking and alcohol consumption habits, and lastly, physical activity. Our plan is to simplify ubiquitous health monitoring for smart home’s residents. The proposed solution aims to monitor parametric measurements to alert health specialists in case of emergencies.

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Research on Data Communication Monitoring System of Master and Slave Equipment in Avionics System Based on 1553B Bus

Research on Data Communication Monitoring System of Master a...

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Networking Systems of AI (INSAI), International Conference on

作者： Zhitian Zhu Yanfang Fu Chengtao Lu Yongxing Qiao Zhipeng Cai School of Weapon Science and Technological Xi'an Technological University Xi'an China School of Computer Science and Engineering Xi'an Technological University Xi'an China North-Automatic Control Technology Institute Taiyuan China

When the terminal sub-device sends data to the main device of the peer avionics system, it faces the related problems of data loss, blurred transmission, and format parsing errors. In response to these problems, this paper combs the basic concept and basic topology structure of the airborne bus 1553B applied to the master-slave equipment of the avionics system, analyzes the application method of the 1553B bus in the avionics system for high-efficiency and high-precision data transmission, and proposes an industrial control system. The scheme of optimizing data transmission between master and slave devices through 1553B bus access to the avionics system provides a new research idea for the development of data communication between master and slave devices in the real avionics system. The data communication monitoring system of the master-slave equipment of the electrical system will avoid risks for future real scenarios in terms of data analysis, data flow, and whether the transmission and reception are successful on the ground.

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