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Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

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Fast Quaternion Product Units for Learning Disentangled Representations in SO(3)

TechRxiv

引用

TechRxiv 2022年

作者： Qin, Shaofei Zhang, Xuan Xu, Hongteng Xu, Yi MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Computer Science & Engineering Texas A&M University College StationTX77843 United States Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Real-world 3D structured data like point clouds and skeletons often can be represented as data in a 3D rotation group (denoted as SO(3)). However, most existing neural networks are tailored for the data in the Euclidean space, which makes the 3D rotation data not closed under their algebraic operations and leads to sub-optimal performance in 3D-related learning tasks. To resolve the issues caused by the above mismatching between data and model, we propose a novel non-real neuron model called quaternion product unit (QPU) to represent data on 3D rotation groups. The proposed QPU leverages quaternion algebra and the law of the 3D rotation group, representing 3D rotation data as quaternions and merging them via a weighted chain of Hamilton products. We demonstrate that the QPU mathematically maintains the SO(3) structure of the 3D rotation data during the inference process and disentangles the 3D representations into "rotation-invariant" features and "rotation-equivariant" features, respectively. Moreover, we design a fast QPU to accelerate the computation of QPU. The fast QPU applies a tree-structured data indexing process, and accordingly, leverages the power of parallel computing, which reduces the computational complexity of QPU in a single thread from O(N) to O(log N). Taking the fast QPU as a basic module, we develop a series of quaternion neural networks (QNNs), including quaternion multi-layer perceptron (QMLP), quaternion message passing (QMP), and so on. In addition, we make the QNNs compatible with conventional real-valued neural networks and applicable for both skeletons and point clouds. Experiments on synthetic and real-world 3D tasks show that the QNNs based on our fast QPUs are superior to state-of-the-art real-valued models, especially in the scenarios requiring the robustness to random rotations. © 2022, CC BY.

关键词： 3D modeling

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Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

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engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

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Tipping Prediction of a Class of Large-Scale Radial-Ring Neural Networks

SSRN

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SSRN 2024年

作者： Lu, Yunxiang Xiao, Min Wu, Xiaoqun Karimi, Hamid Reza Xie, Xiangpeng Cao, Jinde Zheng, Wei Xing College of Automation College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China College of Computer Science and Software Engineering Shen Zhen University Shen Zhen518060 China Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China School of Mathematics Southeast University Nanjing210096 China School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW2751 Australia

Understanding the emergence of collective dynamics in large-scale neural networks remains a challenging endeavor. This paper aims to address this gap by utilizing dynamic systems theory, particularly emphasizing tipping mechanisms. First of all, we introduce a novel $(n+mn)$-scale radial-ring neural network and utilize the topological approach of Coates' flow graph to determine the characteristic equation of the linearized network. Secondly, by deriving stability conditions and predicting the tipping point using the algebraic approach based on the integral element idea, we identify significant factors such as synaptic transmission delay, self-feedback coefficient, and network topology. Finally, we validate the effectiveness of the methodology in predicting the tipping point through numerical simulations. The simulations provide a thorough portrayal of the dynamics exhibited by large-scale neural networks, while also integrating robustness tests. This research contributes to a deeper understanding of the mechanisms underlying collective dynamics in large-scale neural networks, offering valuable insights for both theoretical frameworks and practical applications. © 2024, The Authors. All rights reserved.

关键词： Flow graphs

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A Reverse Mamba Attention Network for Pathological Liver Segmentation

arXiv

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arXiv 2025年

作者： Zeng, Jun Jha, Debesh Aktas, Ertugrul Keles, Elif Medetalibeyoglu, Alpay Antalek, Matthew Lewandowski, Robert Ladner, Daniela Borhani, Amir A. Durak, Gorkem Bagci, Ulas School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Department of Computer Science University of South Dakota VermillionSD United States Machine & Hybrid Intelligence Lab. Northwestern University ChicagoIL United States Department of Interventional Radiology Northwestern University ChicagoIL United States Department of Surgery Northwestern University ChicagoIL United States Department of Radiology Northwestern University ChicagoIL United States

We present RMA-Mamba, a novel architecture that advances the capabilities of vision state space models through a specialized reverse mamba attention module (RMA). The key innovation lies in RMA-Mamba’s ability to capture long-range dependencies while maintaining precise local feature representation through its hierarchical processing pipeline. By integrating Vision Mamba (VMamba)’s efficient sequence modeling with RMA’s targeted feature refinement, our architecture achieves superior feature learning across multiple scales. This dual-mechanism approach enables robust handling of complex morphological patterns while maintaining computational efficiency. We demonstrate RMA-Mamba’s effectiveness in the challenging domain of pathological liver segmentation (from both CT and MRI), where traditional segmentation approaches often fail due to tissue variations. When evaluated on a newly introduced cirrhotic liver dataset (CirrMRI600+) of T2-weighted MRI scans, RMA-Mamba achieves the state-of-the-art performance with a Dice coefficient of 92.08%, mean IoU of 87.36%, and recall of 92.96%. The architecture’s generalizability is further validated on the cancerous liver segmentation from CT scans (LiTS: Liver Tumor Segmentation dataset), yielding a Dice score of 92.9% and mIoU of 88.99%. Our code is available for public: © 2025, CC BY.

关键词： Magnetic resonance imaging

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Deep and handcrafted feature supported diabetic retinopathy ...

引用

2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes;(i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

引用

2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： computerized tomography

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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond

arXiv

引用

arXiv 2022年

作者： Hedström, Anna Weber, Leander Bareeva, Dilyara Krakowczyk, Daniel Motzkus, Franz Samek, Wojciech Lapuschkin, Sebastian Höhne, Marina M.-C. Understandable Machine Intelligence Lab TU Berlin Berlin10587 Germany Department of Electrical Engineering and Computer Science TU Berlin Berlin10587 Germany Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin10587 Germany Department of Computer Science University of Potsdam Potsdam14476 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus-a comprehensive, evaluation toolkit in Python that includes a growing, wellorganised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://***/understandable-machine-intelligence-lab/Quantus/). © 2022, CC BY-NC-SA.

关键词： Python

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Unlocking the Secrets Behind Advanced Artificial intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

引用

Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Connecting the Dots in Trustworthy Artificial intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation

arXiv

引用

arXiv 2023年

作者： Díaz-Rodríguez, Natalia Ser, Javier Del Coeckelbergh, Mark de Prado, Marcos López Herrera-Viedma, Enrique Herrera, Francisco Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain Derio48160 Spain Bilbao48013 Spain Department of Philosophy University of Vienna Vienna1010 Austria School of Engineering Cornell University IthacaNY14850 United States ADIA Lab Al Maryah Island Abu Dhabi United Arab Emirates Department of Mathematics Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

MSC Codes 68T01Trustworthy Artificial intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system’s life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight;robustness and safety;privacy and data governance;transparency;diversity, non-discrimination and fairness;societal and environmental wellbeing;and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society. © 2023, CC BY-NC-ND.

关键词： Artificial intelligence

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