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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Guerra, Michele Milanese, Roberto Deodato, Michele Ciobanu, Madalina G. Fasano, Fausto University of Molise Mosaic Research Center DiBT Pesche Italy Politecnico di Torino Department of Control and Computer Engineering Torino Italy New York University Division of Science Abu Dhabi United Arab Emirates Fisciano Italy

ISBN: (纸本)9798350386226

Schizophrenia is a psychiatric disorder that presents significant diagnostic challenges due to its complex neurophysiological characteristics. This paper investigates the potential of Large Language Models (LLMs), such as OpenAI's GPT-4 and GPT-o1, in detecting schizophrenia through electroencephalography (EEG) analysis. Using the LMSU public ScZ EEG dataset, we conducted a series of experiments involving different types of input data, including raw EEG signals, frequency band summaries, and graphical representations of brain activity. Our findings demonstrate that LLMs can accurately classify schizophrenic and healthy individuals while offering interpretable, clinically relevant insights aligned with established EEG markers. By integrating these models into the diagnostic workflow, we explore the concept of Symbiotic AI, where LLMs act as cognitive collaborators, enhancing clinicians' ability to analyze complex data efficiently and transparently. This approach not only improves diagnostic accuracy but also facilitates real-time decision-making, paving the way for earlier and more precise detection of schizophrenia in clinical settings. © 2024 IEEE.

关键词： Electroencephalography

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An Abstract Interpretation-Based Framework for WCET Analysis of Parallel Programs

An Abstract Interpretation-Based Framework for WCET Analysis...

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IEEE International Conference on Networking, Sensing and control

作者： Xinbang Hu Guanjun Liu Xiaofeng Li Kaiwen Zhang Jian Guan Bin Gu Department of Computer Science Tongji University Shanghai China Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Estimating the Worst-Case Execution Time (WCET) of programs in an embedded multi-core environment is fundamental for schedulability analysis. In this paper, we propose a framework for calculating the WCET of programs originally designed for single-core systems when migrated to and executed in parallel on multi-core environments. First, we decompose single-core tasks and convert them into parallel programs using OpenMP. Our approach extends the task dependency graph to describe relevant information for parallel tasks (such as execution time estimation, core allocation, and inter-task dependencies). Finally, we present an iterative algorithm based on abstract interpretation. This algorithm takes the parallel task dependency graph as input and uses fixed-point analysis to determine the WCET of parallel programs. We conducted experiments on a physical machine to verify the feasibility of our solution.

关键词： Runtime Program processors Multicore processing Estimation Parallel processing Benchmark testing Real-time systems Sensors Iterative methods Resource management

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Optimal Strategies Trajectory with Multi-Local-Worlds Graph

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computers, Materials & Continua 2023年第4期75卷 2079-2099页

作者： Xiang Yu Chonghua Wang Xiaojing Zheng Chaoyu Zeng Brij BGupta School of Electronics and Information Engineering Taizhou UniversityTaizhouZhejiang318000China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China School of Economics and Management Weifang University of Science and TechnologyShouguangShandong262700China National Computer System Engineering Research Institute of China Beijing102699China Department of Computer Science and Information Engineering Asia UniversityTaiwanTaichung413China Research and Innovation Department Skyline University CollegeSharjahP.O.Box 1797United Arab Emirates Staffordshire University Stoke-on-TrentST42DEUK

This paper constructs a non-cooperative/cooperative stochasticdifferential game model to prove that the optimal strategies trajectory ofagents in a system with a topological configuration of a Multi-Local-Worldgraph would converge into a certain attractor if the system’s configuration isfixed. Due to the economics and management property, almost all systems aredivided into several independent Local-Worlds, and the interaction betweenagents in the system is more complex. The interaction between agents inthe same Local-World is defined as a stochastic differential cooperativegame;conversely, the interaction between agents in different Local-Worldsis defined as a stochastic differential non-cooperative game. We construct anon-cooperative/cooperative stochastic differential game model to describethe interaction between agents. The solutions of the cooperative and noncooperativegames are obtained by invoking corresponding theories, and thena nonlinear operator is constructed to couple these two solutions *** last, the optimal strategies trajectory of agents in the system is proven toconverge into a certain attractor, which means that strategies trajectory arecertainty as time tends to infinity or a large positive integer. It is concluded thatthe optimal strategy trajectory with a nonlinear operator of cooperative/noncooperativestochastic differential game between agents can make agentsin a certain Local-World coordinate and make the Local-World paymentmaximize, and can make the all Local-Worlds equilibrated;furthermore, theoptimal strategy of the coupled game can converge into a particular attractorthat decides the optimal property.

关键词： Multi-Local-world graph stochastic differential game optimal strategy attractor

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Exploiting Machine Learning and LSTM for Human Activity Recognition: Using Physiological and Biological Sensor Data from Actigraph

Exploiting Machine Learning and LSTM for Human Activity Reco...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Matthew Oyeleye Tianhua Chen Pan Su Grigoris Antoniou Department of Computer Science School of Computing and Engineering University of Huddersfield UK School of Control and Computer Engineering North China Electric Power University China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Human activity recognition involves identifying the daily living activities of an individual through the utilization of sensor attributes and intelligent learning algorithms. The identification of intricate human activities proves to be a labo-rious task, given the inherent difficulty of capturing long-term dependencies and extracting efficient features from unprocessed sensor data. For this purpose, this study aims at recognizing and classifying human activities using physiological and biological sensor data generated by Actigraph, as they can accurately measure moderate-to-vigorous intensity physical which is mostly affected by body composition and also better suited for self-monitoring. We examined the effectiveness of these features by applying prevalent machine learning classifiers and long short-term memory (LSTM) networks on recently publicly available data, which includes accelerometer and heart rate recordings. The results from our experiments showed that LSTM models performed better than conventional ML classifiers with the best result achieving an accuracy of 86.5%. The findings also confirms the significance of the heart rate in accurately classifying and identification of human activity more.

关键词： Heart rate Machine learning Feature extraction Recording Human activity recognition Biosensors Biomedical monitoring

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Speech Recognition Using Spectrogram Images and Formant Data with Neural Networks

Speech Recognition Using Spectrogram Images and Formant Dat...

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20th International Conference on Dependability of computer Systems, DepCoS-RELCOMEX 2025

作者： Kucharski, Mateusz Skubalska-Rafajlowicz, Ewa Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Wroclaw Poland Department of Computer Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031927331

This paper proposes a novel approach to improving speech recognition performance by combining spectrogram images with extracted formant frequency data within a dual input neural network framework. While spectrograms are widely used in speech recognition due to their ability to capture time, frequency, and amplitude information, they may not always effectively highlight the subtle characteristics of formant patterns, especially in low resolution images. To address this, we introduce a hybrid model that integrates spectrogram images with formant parameters derived using the linear predictive coding method. The model utilizes parallel convolutional neural network processing spectrogram images and the other handling time series formant data. Outputs are then concatenated and further processed through fully connected layers. This double channel design enables the network to take advantage of complementary information from both spectral and parametric speech features, leading to improved recognition accuracy. The model is trained and evaluated using the Google Speech Commands dataset, achieving superior results compared to traditional KNN classifiers, formant-only models, and standard spectrogram-based CNNs. The proposed method achieves a 93% recognition accuracy, outperforming conventional approaches and demonstrating the potential of formant-augmented neural networks for more robust speech recognition tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Solar Wind Density Forecasting with U-Net and LSTM-based Neural Networks 8

Solar Wind Density Forecasting with U-Net and LSTM-based Neu...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Iacobelli, Emanuele Fiani, Francesca Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Forecasting changes in solar wind properties accurately is crucial for predicting space weather, as it significantly impacts the majority of space operations and the telecommunication system. To meet this challenge, we introduce an architecture that combines U-Net’s capabilities for segmenting coronal holes from high-resolution sun images with the predictive abilities of Long Short-Term Memory (LSTM) and ConvLSTM models. This architecture predicts solar wind density using sun surface images obtained from the AIA 193 Å dataset (provided by NASA) and historical electron and proton density data from the OMNI and ELM2 datasets (also provided by NASA), covering the entire year 2012. Our findings demonstrate the system’s ability to generate reliable coronal hole segmentation maps and achieve good accuracy in forecasting solar wind density. © 2023 Copyright for this paper by its authors.

关键词： NASA

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Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

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computer Systems science & engineering 2022年第12期43卷 1219-1230页

作者： Yanman Li Jun Zhang Yang Hu Yingnan Zhao Yi Cao NARI Nanjing Control System Ltd. Nanjing210032China School of Computer Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and computer Engineering University of WindsorWindsorN9B 3P4Canada

Safety helmet-wearing detection is an essential part of the intelligentmonitoring system. To improve the speed and accuracy of detection, especiallysmall targets and occluded objects, it presents a novel and efficient detectormodel. The underlying core algorithm of this model adopts the YOLOv5 (YouOnly Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (CompleteIntersection Over Union) Loss function, and the Mish activation function. First,it applies the attention mechanism in the feature extraction. The network can learnthe weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accuratebounding box regression. Third, it utilizes Mish activation function to improvedetection accuracy and generalization ability. It builds a safety helmet-wearingdetection data set containing more than 10,000 images collected from the Internetfor preprocessing. On the self-made helmet wearing test data set, the averageaccuracy of the helmet detection of the proposed algorithm is 96.7%, which is1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.

关键词： Safety helmet wearing detection object detection deep learning YOLOv5 Attention Mechanism

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Complex Network Pinning control based on Adjacency Degree and Resistance Distance Algorithm 3

Complex Network Pinning Control based on Adjacency Degree an...

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3rd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2023

作者： Zhong, Hao Jia, Haoran Jia, Yanting Sun, Haiyi School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Office of Academic Affairs Shenyang Jianzhu University Shenyang China Department of Science Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9781510668867

In this paper, a new strategy for pinning control node selection is proposed, namely: pinning control strategy based on ADRD (Adjacency Degree and Resistance Distance) algorithm. Firstly, the model of the undirected network and the sufficient conditions to achieve synchronization are given. Secondly, the steps of the pinning control strategy based on the ADRD algorithm are specifically given and applied to dolphins network, and by comparing the pinning control strategy based on the resistance distance, it is concluded that: the pinning control strategy based on the ADRD algorithm is better than the pinning control strategy based on the resistance distance. Finally, the correctness and effectiveness of the method are verified by numerical simulation. © 2023 SPIE.

关键词： Complex networks

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Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1176-1193页

作者： Xiaoling Wang Qi Kang MengChu Zhou Qi Deng Zheng Fan Haoyue Liu Department of Control Science and Engineering Tongji University Shanghai China College of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

To realize Industry 5.0, manufacturers face various optimization problems that seldom appear in isolation. Evolutionary MultiTasking (EMT) is an effective method to solve multiple related problems by extracting and utilizing common knowledge. Knowledge transfer is the key to the effectiveness of EMT. Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance. There is plentiful knowledge in assistant tasks, and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks. EMT is thus confronted with a challenge to find appropriate knowledge. This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks. During the evolution process, better-performing candidates are supposed to have advantages in exploitation. Therefore, assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge. Specifically, the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population. Considering that target and assistant sub-populations have different characteristics, we use domain adaptation to reduce their distribution discrepancies. In this way, the trained classifier can classify assistant individuals more accurately, and truly useful knowledge can be selected for target tasks. The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems.

关键词： Learning systems Diversity reception Machine learning Learning (artificial intelligence) Multitasking Fifth Industrial Revolution Optimization Knowledge transfer Faces Convergence

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Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

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23rd International Conference on Computational science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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