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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Olanloye, Odunayo Dauda Adeniyi, Abidemi Emmanuel Aworinde, Halleluyah Oluwatobi Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Mejdoub, Youssef Department of Computer Science Bowen University Osun Iwo Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Lagos Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

A prominent application in healthcare is the classification and detection of breast cancer using convolutional neural networks (CNNs). It entails creating a system that can precisely pinpoint malignant spots or gauge the tendency of breast cancer through the examination of diagnostic pictures like mammograms. This study focuses on the use of Convolutional Neural Networks for the detection and classification of breast cancer. The proposed model analyzes mammogram images to distinguish cancerous and non-cancerous instances automatically. Preparing the dataset developing the CNNs model’s architecture, training the model, assessing its performance, and producing predictions based on hypothetical data are all the steps in the process. The CNN model learns to extract relevant features and patterns from a large dataset, increasing classification accuracy. Tests show that the CNN model is effective at correctly identifying breast cancer. This method will help medical professionals diagnose patients quickly and correctly, improving patient outcomes in the fight against breast cancer. The prediction can be improved using post-processing techniques and the trained model can be used in practical applications. The model accuracy was 0.98 and the model loss was 0.14. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Averaging-Based Energy Management Strategy for Hybrid Battery-Ultracapacitor Energy Storage System

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IEEE Transactions on Vehicular Technology 2025年

作者： Rezaei, H. Abdollahi, S.E. Abdollahi, Seyedreza Filizadeh, S. Babol Noshirvani University of Technology Energy Conversion Research Laboratory Department of Power Engineering Faculty of Electrical and Computer Engineering Babol*** Iran University of Science and Technology of Mazandaran Electrical and Computer Engineering Department Behshahr48518-78195 Iran University of Manitoba Department of Electrical and Computer Engineering WinnipegMBR3T 5V6 Canada

Considering a battery as the energy source for steady average current and an ultracapacitor as the dynamic energy buffer, this paper proposes a novel control strategy based on average current demand. The methodology averages the demand current over a time window to determine battery current, combined with rules to maintain the ultracapacitor's state of charge above 50%. However, the battery current is significantly affected by the averaging time window. A small window causes fluctuations, while a large one imposes unwanted currents to control the ultracapacitor's state of charge. As so, by optimizing the averaging window, this strategy minimizes battery current stress and power loss. Simulations under an urban driving cycle compare the proposed strategy with a sole-battery configuration, dynamic programming (benchmark), and rule-based approaches (original strategy). The simulation results indicate that the proposed strategy smooths battery currents, achieves over 75% of dynamic programming's effectiveness in extending battery life, and reduces computational burden and memory usage by over 85%. The strategy's low computational burden, and causal nature enable real-time control, which is demonstrated practically on an experimental setup. © 2025 IEEE.

关键词： State of charge

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Impact of exposure time and power intensity on cell viability in 3D bioprinting 12

Impact of exposure time and power intensity on cell viabilit...

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Laser 3D Manufacturing XII 2025

作者： Tavares-Negrete, Jorge A. Babayigit, Ceren Boyraz, Ozdal Esfandyarpour, Rahim Laboratory for Integrated Nano Bio Electronics Innovation The Henry Samueli School of Engineering University of California IrvineCA92697 United States Department of Biomedical Engineering The University of California IrvineCA92697 United States Department of Electrical Engineering and Computer Science The University of California IrvineCA92697 United States Department of Mechanical and Aerospace Engineering The University of California IrvineCA92697 United States Henry Samueli School of Engineering University of California IrvineCA92697 United States

ISBN: (纸本)9781510684560

In 3D bioprinting, the photopolymerization of bioinks is a critical step in building desired structures with high precision. The chosen parameters, such as exposure time and light intensity during photopolymerization of the bioinks, significantly influence the resolution, cell viability, and proliferation of the bioprinted constructs. This study investigates how varying exposure times (10-30 s) and light intensities (0.04-0.11 mW/mm^2) in a laser-based bioprinter affect the performance of 3T3 fibroblasts embedded in GelMA bioinks. The metabolic activity and cell morphology of the bioprinted constructs were assessed over a week-long culture period. The findings reveal a clear relationship between longer exposure times and reduced cell viability. On day 7, samples exposed for extended periods exhibited the lowest metabolic activity, with differences of around 40% between treatments. In contrast, samples exposed to different light intensities resulted in variations below 15% in metabolic activity. GelMA bioinks exhibited less favorable outcomes under prolonged exposure, with cells displaying more rounded and less proliferative morphologies. In contrast, lower exposure times promoted more elongated cell shapes and network formation. These results highlight the importance of optimizing exposure time and light intensity to preserve cell wellness in 3D bioprinted constructs. © 2025 SPIE.

关键词： Cell culture

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A CT-based Prediction System for Determining Respiratory Support Level in COVID-19 Patients

A CT-based Prediction System for Determining Respiratory Sup...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sharafeldeen, Ahmed Balaha, Hossam Magdy Farahat, Ibrahim Shawky Ghazal, Mohammed Connelly, James van Bogaert, Eric El-Baz, Ayman Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Department of Computer Science Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Department of Radiology Faculty of Medicine University of Louisville LouisvilleKY United States

ISBN: (纸本)9798350368741

A novel diagnostic system is introduced for assessing the required level of respiratory support for COVID-19 patients. It bases its assessments on the correlation between detected COVID-19 lesions and the respiratory support levels administered to the patients. The correlation with computed tomography (CT) scans will focus on three respiratory support levels, including minimal support (Level 0), non-invasive support (Level 1, such as soft oxygen), and invasive support (Level 3, e.g., mechanical ventilation). The system initially outlines the pulmonary regions from CT scans, then identifies COVID-19 lesions within these segmented lung regions. Subsequently, three second-order texture features are extracted from the identified COVID-19 lesion regions to detect differences and abnormalities across varying severity levels in COVID-19 cases. To obtain the suitable level of respiratory support for each patient, a fusion mechanism based on backpropagation neural network is utilized to integrate the diagnosis of these three features, individually generated by the support vector machine (SVM) classifier. The system's performance is evaluated on 307 COVID-19 patients using a hold-out validation approach. This evaluation included various metrics, such as sensitivity, specificity, F1-score, Cohen's kappa, and accuracy, demonstrating impressive results. Specifically, it achieved a score of 97.25%, 98.56%, 97.26%, 95.79%, and 97.25%, respectively. The results demonstrate the effectiveness of the integrated system, which uses various second-order features, in predicting respiratory support needs for COVID-19 patients, outperforming both its individual components and other machine learning-based classification systems. © 2025 IEEE.

关键词： computer-Aided Diagnosis (CAD) GLCM GLRLM Machine Learning (ML) Neural Network (NN) SFM

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Towards New Frontiers in Mobile Manipulation: Team CTU-UPenn-NYU at MBZIRC 2020

Field Robotics

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Field Robotics 2025年 2卷 75-106页

作者： Petr Štibinger George Broughton Filip Majer Zdeněk Rozsypálek Anthony Wang Kshitij Jindal Alex Zhou Dinesh Thakur Giuseppe Loianno Tomáš Krajník Martin Saska Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Department of MAE Tandon School of Engineering New York University GRASP Laboratory University of Pennsylvania Philadelphia PA USA Department of ECE and MAE Tandon School of Engineering New York University

In this paper we present an autonomous robotic system for picking, transporting, and precisely placing magnetically graspable objects. Such a system would be especially beneficial for construction tasks where human presence is not possible, e.g. due to chemical or radioactive pollution. The system comprises of two primary components — a wheeled, mobile platform and a manipulator arm. Both are interconnected through an onboard computer and utilize various onboard sensors for estimating the state of the robot and its surroundings. By using efficient processing algorithms, data from the onboard sensors can be used in a feedback loop during all critical operational sections, resulting in a robust system capable of operating on uneven terrain and in environments without access to satellite navigation. System functionality has been proven in Challenge II of the MBZIRC 2020 competition. The Challenge required a ground robot to build an L-shaped structure of colored bricks laid in a predefined pattern. Such a mission incorporates several demanding subchallenges, spanning multiple branches of computer science, cybernetics, and robotics. Moreover, all the subchallenges had to be performed flawlessly in rapid succession, in order to complete the Challenge successfully. The extreme difficulty of the task was highlighted in the MBZIRC 2020 finals, where our system was among the only two competitors (out of 32) that managed to complete the task in autonomous mode.

关键词： Robots Manipulators Robot sensing systems Reliability Autonomous aerial vehicles Service robots Multi-robot systems Assembly Valves Safety

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Application of Explainable Ai in Predicting Symptomatic Influenza a Infection

SSRN

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SSRN 2024年

作者： Lin, Tsung-Hsien Cheng, Zhi-Jian Xie, Zhong-Ru Chang, Kuan Y. Biomedical AI Laboratory Department of Computer Science & Engineering National Taiwan Ocean University Keelung Taiwan Computational Drug Discovery Laboratory School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA United States

DeepFlu predicts the onset of symptomatic influenza infection from pre-exposure gene expression, yet the role of individual genes in the model is unclear. This study uses SHapley Additive exPlanations (SHAP), an explainable artificial intelligence method, to assess individual gene impacts on Deep Flu for H1N1 and H3N2 influenza virus infections. The analysis is based on two gene expression datasets: GSE52428 and GSE73072. Five common key genes across different datasets are identified: HLA-DQA1, HLA-DQB1, XIST, RPS4Y1, and KDM5D. These genes are linked to immunity and sex, with higher expression of XIST and lower expression of RPS4Y1 or KDM5D suggesting increased fe male susceptibility to influenza. This integration of SHAP with this deep learning model offers early predictive insights and interpretability for influenza susceptibility. © 2024, The Authors. All rights reserved.

关键词： Gene expression

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A Robust Framework for Epileptic Seizure Diagnosis: Utilizing GRU-CNN Architectures in EEG Signal Analysis

A Robust Framework for Epileptic Seizure Diagnosis: Utilizin...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Maryam Nikoupour Mohammad Reza Keyvanpour Seyed Vahab Shojaedini Department of Computer Engineering Faculty of Engineering Data Mining Laboratory Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository’s epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods.

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MLFusion: Multilevel Data Fusion using CNNS for atrial fibrillation detection

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computers in Biology and Medicine 2025年 189卷 109901-109901页

作者： John, Arlene Parhi, Keshab K. Cardiff, Barry John, Deepu Biomedical Signals and Systems Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7522 NB Netherlands Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States School of Electrical and Electronic Engineering University College Dublin DublinD04 FX62 Ireland

Data fusion, involving the simultaneous integration of signals from multiple sensors, is an emerging field that facilitates more accurate inferences in instrumentation applications. This paper presents a novel fusion methodology for multi-sensor multimodal data using convolutional neural networks (CNNs), which introduces a data-driven approach to automatically select the optimal level of information abstraction for fusion. Unlike traditional methods, the proposed model allows for a self-learning fusion process, eliminating the need for manual selection of the fusion level by the designer. The model uniquely integrates feature extraction and fusion into a unified framework, enhancing efficiency and performance. Additionally, the fusion network incorporates signal quality indicators (SQIs) as input streams, enabling the model to account for the quality of each input signal during the fusion process. The methodology is evaluated on the task of atrial fibrillation detection through the fusion of two multimodal signal inputs taken from a portion of the MIMIC III database. The fusion network, using electrocardiogram (ECG) and photoplethsymogram (PPG) signals as inputs, and trained with an average loss function, achieved an accuracy of 99.33% and a sensitivity of 99.74%. The model's robustness in the presence of noisy inputs is also analyzed, demonstrating the effectiveness of the SQI-based multi-level fusion approach. This novel methodology represents a significant advancement in data fusion by offering a fully automated, quality-aware approach to multi-sensor multimodal signal fusion. © 2025

关键词： Convolutional neural networks

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Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1308-1322页

作者： Erxin Xie Na Chen Jiangtao Peng Weiwei Sun Qian Du Xinge You Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhanChina Department of Geography and Spatial Information Techniques Ningbo UniversityNingboChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanChina

Recently,transformer‐based networks have been introduced for the classification of hyperspectral image(HSI).Although transformer‐based methods can well capture spectral sequence information,their ability to fuse different types of information contained in HSI is still *** exploit rich spectral,spatial and semantic information in HSI,a novel semantic and spatial‐spectral feature fusion transformer(S3FFT)network is proposed in this *** the proposed S3FFT method,spatial attention and efficient channel attention(ECA)modules are employed for the extraction of shallow spatialspectral ***,a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature ***,the semantic,spatial and spectral features are combined by the transformer for *** with traditional deep learning methods and recently transformer‐based methods,the proposed S3FFT shows relatively better results on three HSI datasets.

关键词： image classification machine learning

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