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IEEE International Conference on Industrial engineering and engineering Management

作者： Daniel Kaluza Marco Seiler Rasha Kashef Electrical computer and Biomedical Engineering Faculty of Engineering and Architectural Science Toronto Metropolitan University Toronto Canada

There has been a sharp rise in the number of fatalities and injuries caused by traffic accidents in urban areas. Cities often have video and image resources that can be analyzed manually using operators to address this problem. This paper introduces an automated collision detection system that utilizes publicly available images captured by Toronto's traffic camera system. The system is based on a Deep Learning model, specifically a DenseNet-161, employed to classify accidents and non-accidents. The results of this classification are then displayed on a graphical user interface. The primary aim of this study is to reduce medical response time and ultimately save lives by issuing automatic alerts. The proposed system has the potential to minimize the severity of accidents and decrease the number of fatalities by notifying emergency services once an accident is detected.

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A Gradient Boosting Algorithm to Predict Energy Consumption for Home Applications 2

A Gradient Boosting Algorithm to Predict Energy Consumption ...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sivakumar, V.G. Arunfred, N. Anusha, N. Balakrishnan, C. Meenakshi, B. Sujatha, S. Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chengalpattu Tamil Nadu Chennai India Vellore Institute of Technology School School of Computer Science and Engineering Department of IoT Tamil Nadu Vellore India S.A.Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Energy-efficient solutions are in high demand in today's fast-developing technological environment, necessitating the use of sophisticated predictive modeling methods. Conventional forecasting systems have problems given the particular aspects related to home demand for energy. As a consequence, new strategies are constantly being created for more accurately foreseeing future occurrences. Weather, user habits, and advancements in technology interfere with precise forecasts even more. This study provides a novel application of the Gradient Boosting approach to solve such obstacles and enhance energy use prediction for residential applications. Through utilising the electrical energy of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), the suggested method takes into account prior use patterns, appliance-specific data, and external factors like as climate. The model's response to changes in the actual world provides exact estimates, providing homeowners with the information they require to make energy-saving decisions. This method also investigates the comprehending of Gradient Boosting models, showing key characteristics that affect energy usage patterns. Stakeholders focus heavily on interpretability to make informed decisions with regard to ways to save energy and resource allocation. © 2024 IEEE.

关键词： Machine learning

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Securing a Generative AI-Powered Healthcare Chatbot

Securing a Generative AI-Powered Healthcare Chatbot

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2024 European Federation for Medical Informatics Special Topic Conference, EFMI STC 2024

作者： Feretzakis, Georgios Anastasiou, Athanasios Pitoglou, Stavros Paxinou, Evgenia Gkoulalas-Divanis, Aris Kalodanis, Konstantinos Tsapelas, Ioannis Kalles, Dimitris Verykios, Vassilios S. School of Science and Technology Hellenic Open University Patras Greece Biomedical Engineering Laboratory National Technical University of Athens Athens Greece Computer Solutions Sa Athens Greece Merative Healthcare Dublin Docklands Dublin 2 Ireland Harokopio University of Athens Kallithea Greece School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9781643685540

In Generative Artificial Intelligence (AI), Large Language Models (LLMs) like GPT-4, Gemini, Claude, and Llama, significantly impact healthcare by aiding in patient care, medical research, and administrative tasks. AI-powered chatbots offer real-time responses and manage chronic diseases, improving patient outcomes and operational efficiency. However, these models pose security and ethical challenges, necessitating robust data privacy, adversarial training, and ethical guidelines. This paper proposes a secure, ethical pipeline for deploying AI healthcare chatbots, integrating advanced privacy-preserving techniques and continuous security assessments to enhance data privacy, resilience, and user trust. © 2024 The Authors.

关键词： Differential privacy

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NeuroSparse: An Unsupervised Framework for Inferring Brain Connectivity in Autism Diagnosis

NeuroSparse: An Unsupervised Framework for Inferring Brain C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Shuqi Zhang, Lijuan Lan, Qing Ma, Qiujie Huang, Huan Miao, Jiaqing Mou, Shiyu Luo, Cheng Yao, Dezhong Tan, Ying Southwest Minzu University Key Laboratory for Computer Systems of State Ethnic Affairs Commission Chengdu China Moe Key Lab for Neuroinformation University of Electronic Science and Technology of China Clinical Hospital Chengdu Brain Science Institute Chengdu China University of Malaya Faculty of Engineering Department of Biomedical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350386226

Autism spectrum disorder (ASD) is a mental disorder that severely affects social interaction and communication skills. A timely and accurate diagnosis is crucial for effective intervention. However, as an objective diagnostic tool, the existence of spurious connections and noise within the functional connectivity (FC) matrix in the brain contribute to the complexity of ASD diagnosis. In this study, we propose an unsupervised sparse graph structure learning framework, called NeuroSparse, for reasoning regarding the topological connectivity relationships between brain regions. First, the framework fuses spatiotemporal information from static and dynamic FC to provide a comprehensive representation of complex brain networks. Next, the context and neighborhood information of nodes are captured as their local and global embeddings, and the latent representations of node embeddings are learned, which are then decoded to infer the connectivity relationships among the regions of interest. Finally, we introduce three innovative loss functions to regularize and optimize the sparse representation generation process. The experimental results showed that, after learning using NeuroSparse, the FC matrix achieved state-of-the-art performance in ASD diagnosis using only a simple multilayer perceptron, indicating its wide-ranging application potential. The code is available at https://***/yangshuqigit/NeuroSparse. © 2024 IEEE.

关键词： Unsupervised learning

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Improved Bat Algorithm with Deep Learning-Based biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9899-9903页

作者： Zhang, Yichi Zhang, Yuchen Zhu, Lipeng Xiao, Sa Tang, Wanbin Eldar, Yonina C. Zhang, Rui University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu611731 China Kash Institute of Electronics and Information Industry Kash844000 China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Chinese University of Hon g Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this correspondence, we propose a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. To maximize the system sum rate, we jointly optimize the digital beamformer, analog beamformer, and positions of sub-arrays, under the constraints of unit modulus, finite movable regions, and power budget. Due to the non-concave/non-convex objective function/constraints, as well as the highly coupled variables, the formulated problem is challenging to solve. By employing fractional programming, we develop an alternating optimization framework to solve the problem via a combination of Lagrange multipliers, penalty method, and gradient descent. Numerical results reveal that the proposed MA-aided hybrid beamforming scheme significantly improves the sum rate compared to its fixed-position antenna (FPA) counterpart. Moreover, with sufficiently large movable regions, the proposed scheme with sub-connected MA arrays even outperforms the fully-connected FPA array. © 1967-2012 IEEE.

关键词： Budget control

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Ultra-compact and broadband polarization-insensitive mode-order converting power splitter

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Chinese Optics Letters 2024年第3期22卷 36-41页

作者：陈浩琪姚润葵汪鹏君符强陈伟伟戴世勋孔德军林健金涛李军戴庭舸杨建义 Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou 325035China College of Science and Technology Ningbo UniversityNingbo 315211China Laboratory of Infrared Materials and Devices The Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211China Department of Information Science and Electronics Engineering Zhejiang UniversityHangzhou 310027China

A polarization-insensitive mode-order converting power splitter using a pixelated region is presented and investigated in this *** TE_(0)and TM_(0)modes are injected into the input port,they are converted into TE_(1)and TM_(1)modes,which evenly come out from the two output *** finite-difference time-domain method and direct-binary-search optimization algorithm are utilized to optimize structural parameters of the pixelated region to attain small insertion loss,low crosstalk,wide bandwidth,excellent power uniformity,polarization-insensitive property,and compact *** results reveal that the insertion loss,crosstalk,and power uniformity of the fabricated device at 1550 nm are 0.57,-19.67,and 0.094 d B in the case of TE polarization,while in the TM polarization,the relevant insertion loss,crosstalk,and power uniformity are 0.57,-19.40,and 0.11 d *** a wavelength range from 1520 to 1600 nm,for the fabricated device working at TE polarization,the insertion loss,crosstalk,and power uniformity are lower than 1.39,-17.64,and 0.14 *** the case of TM polarization,we achieved an insertion loss,crosstalk,and power uniformity less than 1.23,-17.62,and 0.14 dB.

关键词： integrated optics optical waveguide polarization-insensitive property mode-order converting power splitter

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An Optimized Algorithm for CR-MIMO Wireless Networks

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computers, Materials & Continua 2022年第4期71卷 697-715页

作者： Imran Khan Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah Fahad Ahmed Al-Zahrani Hirofumi Amano Samih M.Mostafa Department of Electrical Engineering University of Engineering&TechnologyPeshawarPakistan Department of Computer Science King Khalid UniversityMuhayel AseerKSA Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Computer Engineering Department Umm Al-Qura UniversityMecca24381Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

With the rapid development of wireless communication technology,the spectrum resources are increasingly strained which needs optimal *** radio(CR)is one of the key technologies to solve this *** sensing not only includes the precise detection of the communication signal of the primary user(PU),but also the precise identification of its modulation type,which can then determine the a priori information such as the PU’service category,so as to use this information to make the cognitive user(CU)aware to discover and use the idle spectrum more effectively,and improve the spectrum *** sensing is the primary feature and core part of *** sensing algorithms includes energy detection,cyclostationary feature detection,matched filter detection,and so *** energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal,but it is easily affected by noise and the threshold is not easy to *** combination of multiple-input multiple-output(MIMO)with CR improves the spectral efficiency and multipath fading *** best utilize the PU spectrum while minimizing the overall transmit power,an iterative technique based on semidefinite programming(SDP)and minimum mean squared error(MMSE)is ***,this article proposed a new method for max-min fairness *** compared to existing algorithms,the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-tointerference plus noise ratio(SINR).Furthermore,the proposed algorithm effectively improved the system performance in terms of number of iterations,interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.

关键词： Cognitive radio spectrum utilization bandwidth primary user

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DeLashNet: A Deep Network for Eyelash Artifact Removal in Ultra-Wide-Field Fundus Images 22

DeLashNet: A Deep Network for Eyelash Artifact Removal in Ul...

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5th International Conference on Control and computer Vision, ICCCV 2022

作者： Sha, Dengfeng Ma, Yuhui Zhang, Dan Zhang, Jiong Zhao, Yitian Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Science Ningbo China School of Cyber Science and Engineering Ningbo University of Technology Ningbo China

ISBN: (纸本)9781450397315

The interference of eyelash artifacts in ultra-wide-field fundus (UWF) images has always been a serious problem in preventing precise clinical observations of pathology. Currently, the automatic removal of eyelash artifacts in UWF images remains unsolved and thus will eventually affect the diagnosis accuracy. In this paper, we propose a deep learning architecture called DeLashNet to eliminate eyelash artifacts from UWF images. Our DeLashNet consists of two stages: the first stage is the eyelash artifact removal stage based on a conditional generative adversarial network, and the second stage is the background refinement stage using an encoder-decoder structure. To solve the issue of lacking training samples with eyelashes, we design a novel eyelash growing model to generate synthetic eyelashes with labels and finally established a paired synthetic eyelashes (PSE) dataset. Experiments are conducted to verify the effectiveness of our proposed DeLashNet on eyelash artifact removal. The comparative and ablation studies demonstrate that the proposed DeLashNet achieved satisfactory removal performance on eyelash artifacts of UWF images. © 2022 ACM.

关键词： Generative adversarial networks

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Development of a distributed embedded apogee prediction and control system with sensor fusion for sounding rockets

Development of a distributed embedded apogee prediction and ...

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AIAA SciTech Forum and Exposition, 2024

作者： Slawęcki, Piotr Slonka, Piotr Żebrowski, Wojciech Wesolowska, Zuzanna AGH University of Kraków AGH Space Technology Centre Kraków30-059 Poland AGH University of Kraków Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland AGH University of Kraków Faculty of Applied Mathematics Kraków30-059 Poland AGH University of Kraków Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Kraków30-059 Poland

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper presents the development process of a distributed embedded system, designed by the members of AGH Space Systems student team with the aim of predicting the optimal engine cut-off time mid-flight to precisely reach a target apogee, with the use of a Pitot tube, inertial measurement units and barometric data. The authors evaluate various analytical approaches to determine the predicted apogee based on the vehicle’s momentary altitude and vertical velocity. © 2024 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Embedded systems

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