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International Symposium on applied Computational Intelligence and Informatics ( SACI)

作者： Tünde Kaufman Jozsef Katona National Media and Infocommunications Authority Budapest Hungary Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary

ISBN: (数字)9798331515478

ISBN: (纸本)9798331515485

The increasing demand for IoT devices in industries has generated a high level of cybersecurity threats, with botnets including Mozi exploiting poor passwords and unpatched vulnerabilities to target networked infrastructure. Peer-to-peer (P2P) topology of Mozi, employing Distributed Hash Tables (DHTs) for Command-and-Control (C2) communications, makes it resistant to takedown operations and capable of high-scale Distributed Denial-of-Service (DDoS) attack, data exfiltration, and remote command execution. Traditional one-class, machine learning techniques such as OneClass Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) have been widely used for discovering malwarerelated anomalies. However, such techniques require high labelled training datasets, whose availability in dynamically changing environments of malwares proves challenging. In this work, an AutoRegressive Integrated Moving Average (ARIMA)-based mechanism for discovering time-series anomalies in IoT-infected devices with Mozi infection is proposed. In an experimental setup using a Raspberry Pi running Pi-hole, baseline profiles of CPU consumption, RAM usage, and network activity are first established under normal conditions. Next, a system is subjected to a simulation of infection with Mozi malware and monitored for deviation with ARIMA forecasting and Local Outlier Factor (LOF) for discovering anomalies. By comparing predicted values with actual values, effective intrusion detection for malware-related anomalies is attained, providing a real-time, adaptive mechanism for early intrusion detection. In conclusion, with its effectiveness in discovering stealthy behaviour of malwares, employing ARIMA for discovering time-series anomalies in IoTinfected devices proves effective, and its employment in IoT cybersecurity aids in providing real-time mechanism for discovering threats for system admins and security professionals. As future work, a combination of ARIMA with deep neural networks such as Long

关键词： Training Botnet Intrusion detection Malware Real-time systems Peer-to-peer computing Topology Internet of Things Long short term memory Anomaly detection

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Exploring different Actor Roles in Orchestrations of System of Systems

Exploring different Actor Roles in Orchestrations of System ...

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IEEE International Conference on System of Systems engineering (SoSE)

作者： Tomas Nordstrom Leon Rene Sutfeld Terese Besker RISE Research Institutes of Sweden AB and Department of Applied Physics and Electronics Umeå University Sweden Computer Science RISE Research Institutes of Sweden AB Gothenburg Sweden Systems Engineering RISE Research Institutes of Sweden AB Gothenburg Sweden

ISBN: (数字)9798350365917

ISBN: (纸本)9798350365924

The rising complexity and interconnectedness of contemporary systems engineering solutions have spurred the development of Systems-of-Systems (SoS). Within a SoS, we find interconnected constituent systems (CSs), each possessing individual capabilities and managerial autonomy. For effective management of a SoS, a dedicated orchestration mechanism is crucial. However, currently, there is a lack of a well-defined internal structure or an understanding of roles and interactions between sub-parts of the Orchestrator. This paper proposes a framework for SoS orchestration, identifying distinct actors and their functional roles for an effective SoS operation. Our framework consists of two main groups: SoS Governors (handling legal and financial aspects) and the Orchestrator (responsible for the overall SoS operation). Key actors within the Orchestrator are identified and described, including the Goal Transformer, Registrar, Composer, Executor, Cartographer, Data Curator, and Translator. The paper concludes by discussing the dynamic collaboration between orchestration actors and the significance of clearly defining these roles for complex SoS management. This study offers a foundation for future research and practice in SoS orchestration, potentially enhancing these complex systems’ resilience, effectiveness, and overall management.

关键词： Law Collaboration Transformers Complexity theory System of systems Resilience

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Offshore renewable energies: exploring floating modular energy islands—materials, construction technologies, and life cycle assessment

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Journal of Ocean engineering and Marine Energy 2025年 1-26页

作者： Marino, Enzo Gkantou, Michaela Malekjafarian, Abdollah Bali, Seevani Baniotopoulos, Charalampos van Beeck, Jeroen Borg, Ruben Paul Bruschi, Niccolo Cardiff, Philip Chatzi, Eleni Čudina, Ivan Dinu, Florea Efthymiou, Evangelos Ferri, Giulio Gervásio, Helena Heng, Junlin Jiang, Zhiyu Lenci, Stefano Lukačević, Ivan Manuel, Lance Meyer, Angela Méndez-Morales, Mariela Osmanović, Adnan Pakrashi, Vikram Pandit, Amiya Rega, Giuseppe Skejić, Davor Tesch, Luana Ungureanu, Viorel Uzunović, Tarik Verma, Amrit Shankar Department of Civil and Environmental Engineering University of Florence Florence Italy School of Civil Engineering and Built Environment Liverpool John Moores University Liverpool UK School of Civil Engineering University College Dublin Dublin Ireland School of Mechanical and Materials Engineering University College Dublin Dublin Ireland Department of Civil Engineering University of Birmingham Birmingham UK Von Karman Institute for Fluid Dynamics Sint-Genesius-Rode Belgium Faculty for the Built Environment University of Malta Msida Malta Department of Civil Environmental and Geomatic Engineering ETH Zürich Zurich Switzerland Faculty of Civil Engineering University of Zagreb Zagreb Croatia Politehnica University Timişoara Timisoara Romania Romanian Academy Timisoara Romania Department Civil Engineering Aristotle University of Thessaloniki Thessaloníki Greece Department of Civil Engineering University of Coimbra Coimbra Portugal Department of Engineering Sciences University of Agder Kristiansand Norway Department of Civil and Building Engineering and Architecture Polytechnic University of Marche Ancona Italy Department of Civil Architectural and Environmental Engineering The University of Texas at Austin Austin USA Department of Geoscience and Remote Sensing Delft University of Technology Delft The Netherlands School of Engineering and Computer Science Bern University of Applied Sciences Bern Switzerland Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina Department of Structural and Geotechnical Engineering Sapienza University of Rome Rome Italy Department of Mechanical Engineering University of Maine Orono USA

Floating modular energy islands (FMEIs) are modular, interconnected floating structures designed to collectively produce, store, convert, and transport renewable energy. This review aims to establish a foundation for developing innovative approaches to sustainably harness multi-energy sources in offshore environments. It leverages existing technological expertise while exploring new solutions to address specific challenges associated with FMEIs. The review initially presents existing technologies for floating energy structures and assesses their applicability to FMEI. The structural materials that could be utilised for the construction of a floating energy island are subsequently reviewed. Next, the offshore construction technologies suitable for FMEI are reviewed. Finally, studies on the life cycle assessment of hybrid energy systems are examined, highlighting the environmental advantages of integrating multiple renewable energy sources, thereby underscoring the potential of FMEIs.

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Olive Disease Classification Based on VGGNET and Fine-Tuning Process

Olive Disease Classification Based on VGGNET and Fine-Tuning...

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

作者： N. Dhaffouli S. Hidouri N. Nasri Telecommunications Departement Higher Institute Applied Science and Technology Gafsa University Gafsa Tunisia SETIT Laboratory on Smart Systems for Engineering & E-Health Sfax Tunisia Department of Computer Sciences Campus Universitaire Sidi Ahmed Zarrouk Gafsa Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Agriculture serves as a fundamental pillar of Tunisia’s economy, providing employment and livelihood for a large segment of the population, thereby making it an essential sector for the country. Nevertheless, the sector faces challenges such as reduced agricultural output and quality due to factors like pesticide usage, water scarcity, climate change, and notably, plant diseases. These plant diseases represent one of the most significant threats to global agricultural productivity and quality. Traditional methods of plant disease detection heavily rely on expert identification, which can quickly cause delays in field management and crop disease control. The best strategy to manage these diseases and reduce crop loss is to put protective measures in place as soon as possible. To this purpose, machine learning and deep learning models-based method for the detection and classification of olive leaf disease is developed in order to improve the speed and precision of disease classification. In this study, we explore how machine learning and deep learning techniques may be used to improve agricultural practices and solve these issues. We specifically review the state-of-the-art literature on the use of deep learning and machine learning algorithms in diverse agricultural applications. We also presented a novel method for categorizing olive leaf diseases, by integrating deep learning architectures, VGG16, and fine tuning. Also, in this approach, we simulated a total of two distinct deep models utilizing pre-trained convolutional neural network architectures (VGG19 and VGG16) and along with four machine learning classifiers (RF, SVM, KNN and DT). In addition, we simulated a novel method for categorizing olive leaf diseases, by integrating deep learning architectures, VGG16, and fine tuning, our results findings showed that VGG16 with fine-tuning classifier is the best method for classification of healthy and diseased plant leaves. It achieves a testing accuracy score of 98%

关键词： Deep learning Support vector machines Plant diseases Accuracy Crops Nearest neighbor methods Loss measurement Tuning Testing Classification tree analysis

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Process Flow and Design Optimization of a Microbolometer

Process Flow and Design Optimization of a Microbolometer

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International Conference on Electrical Information and Communication Technology (EICT)

作者： Mukti Rana Abdulaziz T. Almaktoom Division of Physics Engineering Mathematics and Computer Sciences and Optical Science Center for Applied Research Delaware State University Dover USA Department of Operations and Supply Chain Management Jeddah Kingdom of Saudi Arabia

Microbolometers using a-Si 0.15 Ge 0.85 O 0.0236 thin films were fabricated using radio frequency magnetron sputtering and lift of technique. When a 200 nm-thick and 40 × 40 μm 2 Si 0.15 Ge 0.85 O 0.0236 pixels on top of the electrode arm was used, it was observed that the electrode arms and the pixels were wrapped after they were subjected to the surface micromachining process. In order to fix this problem, the design and process flow of the microbolometer array were optimized to obtain mechanically stable microbolometers. We used Ge x Si 1-x O y , silicon nitride, NiCr and silicon nitride to provide mechanical strength and stability to the microbolometer pixels. With the modified structure and process flow optimization it was found that the detectors of bridge structure were suspended on top of the substrate. We performed forming gas passivation at 250 o C for seven hours to minimize the 1/f-noise. A temperature coefficient of resistance (TCR) of 4.8%/K was obtained at 295 K. We obtained the highest detectivity and responsivity values 1 × 10 4 V/W and 8.3 × 10 6 cm-Hz 1/2 /W.

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Decentralized Optimal Control for Linear Stochastic Systems with Control Signals subject to Unknown Noises

Decentralized Optimal Control for Linear Stochastic Systems ...

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International Conference on Control and Automation (ICCA)

作者： Zhaorong Zhang Juanjuan Xu Minyue Fu Xun Li School of Computer Science and Technology Shandong University Qingdao China School of Control Science and Engineering Shandong University Jinan China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Applied Mathematics the Hong Kong Polytechnic University Hong Kong SAR China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Decentralized strategies have been extensively applied to LQ optimal control problems, whereas, stochastic systems with unknown random parameters have not been comprehensively studied. In this paper, we consider a class of stochastic systems with a decentralized configuration consisting of multiple controllers which have access to Gaussian noises with unknown statistical information. The stabilizing and optimal control strategies are acquired by designing a novel stochastic approximation algorithm recursively evaluating the zero points of certain matrix equations, which is confirmed to be equivalent with solving the corresponding Riccati equations. The proof of convergence and boundness of the proposed algorithm is presented.

关键词： Q-learning Automation Stochastic systems Gaussian noise Riccati equations Optimal control Approximation algorithms

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IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks

arXiv

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arXiv 2024年

作者： Lin, Junchao Ling, Zenan Feng, Zhanbo Xu, Jingwen Liao, Minxuan Zhou, Feng Hou, Tianqi Liao, Zhenyu Qiu, Robert C. School of EIC Huazhong University of Science and Technology China Department of Computer Science and Engineering Shanghai Jiao Tong University China Center for Applied Statistics School of Statistics Renmin University of China China Lagrange Mathematics and Computation Research Center of Huawei China School of Science Wuhan University of Technology China

Implicit graph neural networks (IGNNs), which exhibit strong expressive power with a single layer, have recently demonstrated remarkable performance in capturing long-range dependencies (LRD) in underlying graphs while effectively mitigating the over-smoothing problem. However, IGNNs rely on computationally expensive fixed-point iterations, which lead to significant speed and scalability limitations, hindering their application to large-scale graphs. To achieve fast fixed-point solving for IGNNs, we propose a novel graph neural solver, IGNN-Solver, which leverages the generalized Anderson Acceleration method, parameterized by a tiny GNN, and learns iterative updates as a graph-dependent temporal process. To improve effectiveness on large-scale graph tasks, we further integrate sparsification and storage compression methods, specifically tailored for the IGNN-Solver, into its design. Extensive experiments demonstrate that the IGNN-Solver significantly accelerates inference on both small- and large-scale tasks, achieving a 1.5× to 8× speedup without sacrificing accuracy. This advantage becomes more pronounced as the graph scale grows, facilitating its large-scale deployment in real-world applications. The code to reproduce our results is available at https://***/landrarwolf/IGNN-Solver. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Reflections from the Workshop on AI-Assisted Decision Making for Conservation

arXiv

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arXiv 2023年

作者： Xu, Lily Rolf, Esther Beery, Sara Bennett, Joseph R. Berger-Wolf, Tanya Birch, Tanya Bondi-Kelly, Elizabeth Brashares, Justin Chapman, Melissa Corso, Anthony Davies, Andrew Garg, Nikhil Gaylard, Angela Heilmayr, Robert Kerner, Hannah Klemmer, Konstantin Kumar, Vipin MacKey, Lester Monteleoni, Claire Moorcroft, Paul Palmer, Jonathan Perrault, Andrew Thau, David Tambe, Milind School of Engineering and Applied Sciences Harvard University United States Harvard University United States United States Department of Computer Science University of Colorado Boulder United States Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Biology Carleton University Canada Department of Computer Science and Engineering The Ohio State University United States Department of Electrical and Computer Engineering The Ohio State University United States Department of Evolution Ecology and Organismal Biology The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Google United States Electrical Engineering and Computer Science University of Michigan United States Department of Environmental Science Policy and Management University of California Berkeley United States Department of Aeronautics and Astronautics Stanford University United States Department of Organismic and Evolutionary Biology Harvard University United States Operations Research and Information Engineering Cornell Tech United States African Parks South Africa Bren School of Environmental Science and Management University of California Santa Barbara United States School of Computing and Augmented Intelligence Arizona State University United States Microsoft Research United States Department of Computer Science and Engineering University of Minnesota United States Inria Paris France United States Wwf Global Science United States

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Conservation

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Blister Package Classification Using ResNet-101 for Identification of Medication 25

Blister Package Classification Using ResNet-101 for Identifi...

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25th International computer science and engineering Conference, ICSEC 2021

作者： Hnoohom, Narit Maitrichit, Nagorn Chotivatunyu, Pitchaya Sornlertlamvanich, Virach Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand Musashino University Faculty of Data Science Tokyo Japan University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

This research aimed to fine-Tune image classification with deep learning techniques to verify the dispensing of prescriptions in hospitals. The proposed approach will be able to help pharmacies reduce the errors that lead to patients receiving the wrong medications. The image classification model uses a double-side transformed image dataset with download from Highlighted Deep Learning (HDL) paper. The dataset collected two-hundred seventy-Two images for types of medicine blister packs, including 72 images of front-side and backside merged with a horizontal cropped background, which were used for training the model. The blister package image dataset uses a deep learning model with a ResNet-101 pre-Trained model from the TensorFlow framework. The experimental results indicated that the TensorFlow framework achieved higher precision, recall, and F1-score than the Caffe framework. A ResNet-101 model with histogram equalization in the front and backside has the highest accuracy at 100 percent. © 2021 IEEE.

关键词： Image classification

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Synthesizing realistic images for improved early detection of novel pandemics: A GAN enabled approach

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Heliyon 2025年第6期11卷

作者： Rounaq, Sumera Shah, Shahid Munir Aljawarneh, Mahmoud Mohammad Khan, Sarah Muhammad, Ghulam Department of Computer Science DHA Suffa University Karachi Pakistan Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman Jordan System Architect Low Code Solutions London United Kingdom Department of Computer Science Bahria University Karachi Pakistan

Infectious diseases like the novel Coronavirus (COVID-19) affect millions of individuals if not managed well in time. Thus, to reduce the transmission rate, effective diagnostic techniques must be identified. Early detection and diagnosis may be aided by Artificial Intelligence (AI) based automated diagnostics;however, these algorithms need a lot of pertinent data to be trained. In the early stages of a pandemic, gathering a lot of data is never an easy task. This presents challenges for accurate automatic diagnosis of such infectious diseases. To address this issue, a customized Generative Adversarial Networks (GAN) based architecture i. e., CoroWGAN-GP is proposed to synthesize images and to expand datasets. A dataset of 588 images with COVID-19, Normal, and Viral Pneumonia categories (taken from Cohen's repository [1]) was expanded using the proposed CoroWGAN-GP approach and used to train a proposed customized deep CNN architecture i. e., DeepCNN-CovNet19. Experimental results demonstrate that the proposed model achieved a considerable detection accuracy. i. e., 98.0 % in detecting the disease. The effectiveness of the proposed method was further evaluated by comparing its results with the state-of-the-art methods in the literature. Comparative analyses indicate that the proposed approach offers a promising solution for the accurate diagnosis of COVID-19-like pandemics. © 2025 The Authors

关键词： Chest X-ray Convolutional neural networks COVID-19 CXR GAN Pandemic Wasserstein GAN

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