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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Funari, Alessio Conte, Federica Paci, Paola Sapienza University of Rome Institute for Systems Analysis and Computer Science 'Antonio Ruberti' National Research Council Department of Translational and Precision Medicine Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering Rome17177 Italy

ISBN: (纸本)9798350386226

In this article, we propose an updated version of our previously developed model for predicting drug-side effect associations, applied to two case studies: long QT syndrome and asthma. The classifier accepts the name of a specific drug side effect as input and outputs a list of drugs potentially associated with this side effect. By simulating how drug effects propagate within the interactome using the Random Walk with Restart algorithm, the classifier identifies genes potentially associated with the onset of the side effect. Based on the rationale that the more a drug perturbs these genes, the more likely it is to cause the side effect, the model identifies drugs potentially linked to the onset of the side effect. Moreover, the model enables the categorization of drugs into chemical subclasses using the ClassyFire schema, facilitating the analysis of complex side effects, such as asthma, through more specific mechanisms. The results show that the model identifies both drugs known to be associated with certain side effects, as well as drugs not officially reported by the FDA, demonstrating its generalizability and practical relevance. This method is also adaptable for analyzing other side effects. © 2024 IEEE.

关键词： drug side effect network medicine random walk

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An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

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computers, Materials & Continua 2022年第3期70卷 4393-4410页

作者： Abeer D.Algarni Walid El-Shafai Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 84428Saudi Arabia Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia 44519Egypt

COVID-19 remains to proliferate precipitously in the *** has significantly influenced public health,the world economy,and the persons’***,there is a need to speed up the diagnosis and precautions to deal with COVID-19 *** this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical *** paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)*** proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT *** classification stage is firstly applied to detect and classify the input medical CT ***,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT *** classification stage is implemented based on a simple and efficient CNN deep learning *** model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)*** layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection *** the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid *** proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,*** summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis *** the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the *** region of interest can be assesses both automatically and through ***

关键词： COVID-19 segmentation classification CNN SPCNN CT images

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Optimal Strategies to Contain Arboviruses Spread

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SN computer Science 2025年第5期6卷 1-13页

作者： Di Giamberardino, Paolo Iacoviello, Daniela Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy

Globalization, the increase of human travelling and climate changes contribute to the emergency due to arboviruses diffusion. Generally, the infected patients show only mild symptoms, but in some cases, with fragile subjects, this kind of disease can be fatal. In this paper Dengue virus is considered and described by means of a new compartmental model in which the human and the mosquitos populations interact and can infect each other;containment measures, based both on human habits and on the reduction of mosquitoes in different phase of their lives, are determined in the framework of optimal control theory, aiming at decreasing the number of infected patients and limiting the actions to be applied. This latter aspect is particularly important, both for practical and economical aspects, and for ecological reasons, since a too strong actions against mosquitos can be dangerous for the equilibrium of the ecosystem. © The Author(s) 2025.

关键词： Containment strategies Dengue virus Epidemic control Optimal control

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Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems 19

Individually-guided Evolutionary Algorithm for Solving Multi...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Wang, XiaoLing Kang, Qi Zhou, MengChu Tongji University Department of Control Science and Engineering Shanghai China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark United States

ISBN: (纸本)9781665472432

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm that is used for solving multiple optimization tasks concurrently. Most MTO algorithms limit each individual to one task, and thus weaken the performance of information exchange. To address this issue and improve the efficiency of knowledge transfer, this work proposes an efficient MTO framework named individually-guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones. To further improve the efficiency of knowledge transfer, a partial individuals' learning scheme is used to choose suitable individuals to learn from other tasks. Experimental results show its superior advantages over the multifactorial evolutionary algorithm and its variants. © 2022 IEEE.

关键词： Evolutionary algorithms

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Cascaded Design for Finite-Time Trajectory Tracking control of an Underactuated Surface Vehicle 8

Cascaded Design for Finite-Time Trajectory Tracking Control ...

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8th International Conference on control Science and Systems engineering, ICCSSE 2022

作者： Chen, Ziyin Ge, Shuzhi Sam Beijing Institute of Space Mechanics & Electricity Department of Control Engineering Beijing China National University of Singapore Department of Electrical & Computer Engineering Singapore Singapore

ISBN: (纸本)9781665484138

This paper discusses the cascaded design for finitetime trajectory tracking control of an underactuated unmanned surface vehicle (USV). The tracking error dynamics is established with the guidance of a virtual reference vehicle, and transformed into a new cascaded structure, so the control of the overall system can be equivalent to stabilization of two interconnected subsystems separately, which can facilitate the control design by verifying the properties of the interconnection instead of iterative constructing Lyapunov functions for the whole system. The nonsingular fast terminal sliding mode (NFTSM) method is applied to stabilize the kinematic and dynamic subsystems in the present of model uncertainties, which can drive the states to the origin in finite time and avoid the possible singularity during the control phase. The stability of the cascaded system is guaranteed by verifying the satisfaction of the sufficient conditions. Numerical simulation results are provided to demonstrate the effectiveness of the proposed cascaded design. © 2022 IEEE.

关键词： Unmanned surface vehicles

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Volt-Var control for Utility-scale Solar PV Plants to Replace SVCs 4

Volt-Var Control for Utility-scale Solar PV Plants to Replac...

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4th International Conference on High Voltage engineering and Power Systems, ICHVEPS 2023

作者： Shimomukai, Ken Maeda, Haruka Fujita, Goro Shibaura Institute of Technology Functional Control Systems Graduate School of Engineering and Science Tokyo Japan Shibaura Institute of Technology Graduate School of Engineering and Science Department of Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350318678

One way to increase solar photovoltaic penetration in the grid is management of voltage fluctuations. This is because a photovoltaic plant cannot be interconnected to the grid if it causes voltage violations. Voltage violation is where voltage exceeds the acceptable range. Often, grid operators request photovoltaic plant owners to regulate voltage sufficiently with expensive and space-consuming static Var compensators. Unfortunately, this sometimes makes the project less feasible. This paper argues that there are better ways to regulate voltage. We ran a simulation with a 70 MWp photovoltaic plant as an addition to the grid. Without voltage regulation, voltage violations were found to be significant. This paper found that oversizing the inverter sufficiently would remove all voltage violations without deploying a static Var compensator. This is often a cheaper and space-saving solution for voltage management. This paper argues that economics and spatial efficiency of reactive power compensation devices is key to increasing photovoltaic penetration. © 2023 IEEE.

关键词： Reactive power

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ShadowNets: Efficient and Accurate Face Recognition for Resource-Constrained Devices 25

ShadowNets: Efficient and Accurate Face Recognition for Reso...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Mirza, Inzela Iqbal, Shahzaib Alhaqbani, Bandar Alshammari, Bandar Khan, Tariq Razzak, Imran Department of Computing Islamabad Pakistan Tcc Research and Development Labs Technology Control Company Riyadh Saudi Arabia Computer and Information Sciences College Jouf University Saudi Arabia School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798350379037

Over recent years, deep convolutional neural networks have advanced the field of face recognition in both verification and identification applications. In this paper, we present ShadowNets, a highly efficient and compact face recognition network. We employed multiple shadow blocks in a cascading manner where the output of each shadow block is given as the input to the next shadow block. By integrating the advantages of Focal Modulation Blocks, Shadow Block and Ghost Modules, our proposed ShadowNets achieves superior face recognition performance, optimized specifically for edge devices. This network architecture not only minimizes computational costs and storage needs but also delivers high accuracy, making it suitable for deployment in edge environments. Our extensive experiments on challenging benchmark face datasets showcase the outstanding performance by achieving accuracy 98.85% on LFW, 84.37% on CFP-FP, and 91.23% on AgeDB-30 which validates the effectiveness of ShadowNets over state-of-the-art lightweight and deep face recognition models. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient data-driven occupancy detection in office environments and feature impact analysis

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International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Fouzi, Harrou Ramakrishna, Kini K. Madakyaru, Muddu Ying, Sun Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Computer Thuwal 23955-6900 Saudi Arabia Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Chemical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India

Occupancy detection is crucial in optimizing building energy efficiency and enhancing occupant comfort. This study introduces an innovative data-driven approach for accurate occupancy detection in an office room environment. Specifically, the methodology combines the advantages of Independent Component Analysis (ICA) to extract essential features from multivariate data and Kantorovitch distance (KD)-based schemes for detection sensitivity. The KD scheme’s detection threshold is computed nonparametrically using kernel density estimation to enhance the sensitivity of occupancy detection. The efficacy of this strategy is evaluated utilizing publicly available data recorded during winter in Mons, Belgium, capturing vital environmental parameters such as temperature, humidity, light, and CO2 levels through specialized sensors. Results demonstrate that the ICA-KD approach achieves an averaged accuracy of 98.355%, surpassing conventional approaches like Principal Component Analysis (PCA)-based, ICA-based, and other state-of-the-art methods. Additionally, the study uses Shapley Additive exPlanations (SHAP) with XGBoost to explore the impact of input variables on occupancy detection, highlighting the influence of various factors under different testing conditions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Building occupancy Data-driven Environmental data Occupancy detection Semi-supervised detection

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On-Device Learning with Raspberry Pi for GCN-Based Epilepsy EEG Classification

On-Device Learning with Raspberry Pi for GCN-Based Epilepsy ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, Zhuoli Wang, Chuansheng Huang, Jiayan Grau, Antoni Guerra, Edmundo Yan, Jiaquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University College of New Engineering Industry Putian China Minjiang University School of Computer and Big Data Fuzhou China

ISBN: (纸本)9798350386226

Epilepsy is a chronic brain disease characterized by recurrent and transient seizures, which is accompanied by super-synchronous abnormal discharge of electroencephalogram (EEG) signals. As a non-invasive auxiliary diagnostic technique, EEG is currently important means of seizure detection. However, due to ignoring the spatial topological relationship between electrodes, existing data-driver methods fail to fully reflect the interaction between signals. Meanwhile, their models usually be designed with a large number of redundant parameters, making it difficult to deploy to micro-embedded devices with limited-resources. In this paper, we propose a on-device learning with edge device for epilepsy EEG classification network based on GCN (oDLGCN-EEG). Specifically, to analyze the spatial relationships between various electrodes and their temporal dependencies, we design a Brain Topology Network (BTN) for the spatiotemporal dependency feature map construction. To capture the internal activity during epilepsy seizures, we design a Neural Feature Extraction Module (NFEM) for the neural activity feature map construction. Besides, we propose a pruning scheme to optimize the model, which successfully deploys the optimized oDLGCN-EEG on the embedded Raspberry Pi device for efficient and low-power consumption intelligent epilepsy classification. Experiments in comparison with state-of-the-art methods show that oDLGCN-EEG achieves the best classification accuracy and with the smallest parameter number on baseline EEG dataset. The code is available at https://***/cathnat/Epileptic-Classification. © 2024 IEEE.

关键词： Topology

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V-EduGram: Voice-based control Technique of Hologram Technology in Education 2

V-EduGram: Voice-based Control Technique of Hologram Technol...

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2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Abdlfatah, Reem F. Alomaier, Alaa T. Ahmad, Asma S. Elmahal, Doaa M. Ibrahim, Dina M. College of Computer Qassim University Department of Information Technology Buraydah Saudi Arabia Computers and Control Engineering Department Faculty of Engineering Tanta University Egypt

ISBN: (纸本)9781665436052

Education is one of the most critical fields in people's life that largely affects the development and progress of societies, and different methods are used to transfer the knowledge and information to the students, usually under the guidance of some educators. The use of images and visual displaying methods in education is of actual worth;visual means help us learn, explain tough concepts, and attract attention. The visual content is either displayed in a two-dimensional '2D' way which may include some problems, or in a three-dimensional '3D' way that is considered the proper way to display the visual information because it corresponds to human vision. Hologram technology is the one of the technologies that display 3D visual content, usually, depending on the reflections of light or laser beams so that people are able to see the 3D content directly with their eyes. The usage of Hologram technology in education as a tool to display visual information in a three-dimensional way outperforms other used ways and is considered to positively affect educational outcomes. Given the situation in which information is transferred from the teacher to students in education and using Hologram's technology to display visual educational content to them, teachers face a difficulty in the process of controlling the display of content on the Hologram projector while teaching students. This research studies the development of the process of controlling the display of the Holograms on the Hologram projector and gives teachers the ability to control the device using the voice commands. © 2022 IEEE.

关键词： Augmented reality

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