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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Zhao, Wenbing Cheng, Jinsai Shen, Tao Luo, Xiong Cleveland State University Department of Electrical Engineering and Computer Science ClevelandOH44115 United States College of Aeronautics & Engineering Kent State University KentOH United States School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781665430654

Musculoskeletal disorders (MSDs) are pervasive in the workforce and constitute the single largest category of work-related illness. The root cause for MSDs is complex. However, there is little dispute that MSD morbidity is primarily due to physical and psychosocial risk factors, and these two domains of risk factors share a common upstream determinant. A work organization influences both the physical load patterns and psychosocial features. In this paper, we propose a technology-facilitated intervention program that could lead to an improved safety culture in workplaces. The program is aimed at addressing one of the physical risk factors, i.e., rest breaks, and a psychosocial risk factor, i.e., social support. First, a wearable soft orthosis is used to detect the types of physical activities and load patterns, and to derive an intelligent rest break schedule for each type of activity and load patterns. The orthosis would also remind the participant to take a rest break at appropriate times. Second, a mobile app is developed to cultivate a learning community where the participants could seek and provide social support and increase their awareness of occupational safety. We collected some preliminary app usage data and developed a methodology of identifying app usage patterns using both supervised and unsupervised learning. The feasibility of the method is validated using synthesized data derived from the collected data. © 2023 IEEE.

关键词： decision tree Gaussian naive Bayes k-means clustering KNN learning community load pattern Musculoskeletal disorders occupational safety orthosis the elbow method

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Towards AI-assisted digital twins for smart railways: preliminary guideline and reference architecture

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Journal of Reliable Intelligent Environments 2023年第3期9卷 303-317页

作者： De Donato, Lorenzo Dirnfeld, Ruth Somma, Alessandra De Benedictis, Alessandra Flammini, Francesco Marrone, Stefano Saman Azari, Mehdi Vittorini, Valeria Department of Electrical Engineering and Information Technology University of Naples Federico II Via Claudio 21 Naples80125 Italy Department of Computer Science and Media Technology Linnaeus University Växjö Sweden School of Innovation Design and Engineering Mälardalen University Eskilstuna Sweden

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow. © 2023, The Author(s).

关键词： Cyber Physical System

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Lower bounds and accelerated algorithms for bilevel optimization

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 795-850页

作者： Kaiyi Ji Yingbin Liang Department of Computer Science and Engineering University at Buffalo Buffalo NY Department of Electrical and Computer Engineering The Ohio State University Columbus OH

Bilevel optimization has recently attracted growing interests due to its wide applications in modern machine learning problems. Although recent studies have characterized the convergence rate for several such popular algorithms, it is still unclear how much further these convergence rates can be improved. In this paper, we address this fundamental question from two perspectives. First, we provide the first-known lower complexity bounds of $\widetilde \Omega\bigg(\sqrt{\frac{L_y\widetilde L_{xy}^2}{\mu_x\mu_y^2}}\bigg)$ and $\widetilde \Omega\big(\frac{1}{\sqrt{\epsilon}}\min\{\kappa_y,\frac{1}{\sqrt{\epsilon^{3}}}\}\big)$ respectively for strongly-convex-strongly-convex and convex-strongly-convex bilevel optimizations. Second, we propose an accelerated bilevel optimizer named AccBiO, for which we provide the first-known complexity bounds without the gradient boundedness assumption (which was made in existing analyses) under the two aforementioned geometries. We also provide significantly tighter upper bounds than the existing complexity when the bounded gradient assumption does hold. We show that AccBiO achieves the optimal results (i.e., the upper and lower bounds match up to logarithmic factors) when the inner-level problem takes a quadratic form with a constant-level condition number. Interestingly, our lower bounds under both geometries are larger than the corresponding optimal complexities of minimax optimization, establishing that bilevel optimization is provably more challenging than minimax optimization. Our theoretical results are validated by numerical experiments.

关键词： bilevel optimization lower bounds accelerated algorithms computational complexity convergence rate optimality

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Bangla Speech-based Emotion Detection using a Hybrid CNN-Transformer Approach 8

Bangla Speech-based Emotion Detection using a Hybrid CNN-Tra...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Ali Shuvo, S.M. Haider Khan, Rahad Bangladesh University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh United International University United City Department of Computer Science and Engineering Madani Ave Dhaka1212 Bangladesh

ISBN: (纸本)9798350305838

Robots are now widely employed in various scenarios to interact with humans. It is vital that the robots understand the speaker's emotion and respond accordingly. Humans possess innate abilities to recognize emotions from speech, but it is challenging to provide machines with the same capabilities to predict emotions from human speech. Recently machine learning and deep learning techniques are employed for emotion recognition tasks. Due to emotions such as angry, surprise etc. being predominant for only few time frames of an example audio of that class, it becomes difficult to detect them and models often have a hard time for these classes. We employ a hybrid CNN-Transformer approach where we attempt to solve this problem by having both locally receptive convolutional features and globally receptive attention features contributing to final classification. We evaluate our approach along with other standard approaches in BanglaSER dataset. We report weighted class accuracy of 92.3% using our proposed approach achieving significant performance gain over current literature. © 2023 IEEE.

关键词： Emotion Recognition

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DMWM: Dual-Mind World Model with Long-Term Imagination

arXiv

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arXiv 2025年

作者： Wang, Lingyi Shelim, Rashed Saad, Walid Ramakrishnan, Naren Department of Electrical and Computer Engineering Virginia Tech United States Department of Computer Science Virginia Tech United States

Imagination in world models is crucial for enabling agents to learn long-horizon policy in a sample-efficient manner. Existing recurrent state-space model (RSSM)-based world models depend on single-step statistical inference to capture the environment dynamics, and, hence, they are unable to perform long-term imagination tasks due to the accumulation of prediction errors. Inspired by the dual-process theory of human cognition, we propose a novel dual-mind world model (DMWM) framework that integrates logical reasoning to enable imagination with logical consistency. DMWM is composed of two components: an RSSM-based System 1 (RSSM-S1) component that handles state transitions in an intuitive manner and a logic-integrated neural network-based System 2 (LINN-S2) component that guides the imagination process through hierarchical deep logical reasoning. The inter-system feedback mechanism is designed to ensure that the imagination process follows the logical rules of the real environment. The proposed framework is evaluated on benchmark tasks that require long-term planning from the DMControl suite. Extensive experimental results demonstrate that the proposed framework yields significant improvements in terms of logical coherence, trial efficiency, data efficiency and long-term imagination over the state-of-the-art world models. © 2025, CC BY.

关键词： Efficiency

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Analysis of integration of IoMT with blockchain: issues, challenges and solutions

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Discover Internet of Things 2024年第1期4卷 21页

作者： Mazhar, Tehseen Shah, Syed Faisal Abbas Inam, Syed Azeem Awotunde, Joseph Bamidele Saeed, Mamoon M. Hamam, Habib Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Science University of Johannesburg Auckland Park Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The incorporation of Artificial Intelligence (AI) into the fields of Neurosurgery and Neurology has transformed the landscape of the healthcare industry. The present study describes seven dimensions of AI that have transformed the way of providing care, diagnosing, and treating patients. It has exhibited unparalleled accuracy in analyzing complex medical imaging data and expediting precise diagnoses of neurological conditions. It has also enabled personalized treatment plans by harnessing patient-specific data and genetic information, promising more effective therapies. For instance, AI-powered surgical robots have brought precision and remote capabilities to neurosurgical procedures, reducing human error. In AI, machine learning models predict disease progression, optimizing resource allocation and patient care, whereas wearable devices with AI provide continuous neurological monitoring, and enable early intervention for chronic conditions. It has also accelerated drug discovery by analyzing vast datasets, potentially leading to breakthrough therapies. Chatbots and virtual assistants powered by AI, enhance patient engagement and adherence to treatment plans. It holds promise in further personalization of care, augmented decision-making, earlier intervention, and the development of groundbreaking treatments. The present study mainly focuses on the incorporation of blockchain technology and provides a reasonable understanding of the associated issues and challenges along with its solutions. It will allow AI and healthcare professionals to advance the field and contribute towards the improvement of an individual's well-being when facing neurological challenges. © The Author(s) 2024.

关键词： Neurosurgery

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Efficient Deep CNN Model for COVID-19 Classification

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computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

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Modeling the Learning and Use of Probability Distributions in Chimpanzees and Humans 44

Modeling the Learning and Use of Probability Distributions i...

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44th Annual Meeting of the Cognitive science Society: Cognitive Diversity, CogSci 2022

作者： Shultz, Thomas R. Nobandegani, Ardavan S. Department of Electrical & Computer Engineering McGill University Canada School of Computer Science McGill University Canada Department of Psychology McGill University Canada

We present a neural-network computational model of a recent experiment revealing that chimpanzees show some ability to reason probabilistically. Specifically, we show that the neural probability learner and sampler (NPLS) system can account for both success by chimpanzees and better performance by human controls. NPLS effectively combines learning probability distributions with sampling from those learned distributions to guide action choices. Because NPLS also simulates learning and use of probability distributions by human infants, this brings us closer to a unifying model of probabilistic reasoning, across various age groups and species. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Probability distributions

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Enhancing Kriging with Inductive Spatio-Temporal GraphODE

Enhancing Kriging with Inductive Spatio-Temporal GraphODE

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Iranian Conference on electrical engineering (ICEE)

作者： Amin Sheykhzadeh Behzad Moshiri Ebrahim Ghafar-Zadeh School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

Sensory networks in environmental monitoring provide real-time data on critical parameters, but the costs of installation and maintenance limit high-resolution data acquisition. Researchers aim to estimate values at specific locations without prior data samples, considering two approaches: virtual sensors and kriging. While virtual sensors face challenges in dynamic sensor networks where for every sensor added or disconnected the whole network should be retrained, kriging, especially spatio-temporal kriging using Graph Neural Networks, overcomes traditional kriging drawbacks and allows adaptability in dynamic sensor networks without frequent retraining. Despite their success, existing spatio-temporal kriging methods face challenges, notably the over-smoothing problem, restricting their ability to utilize deeper graph structures for a more comprehensive latent representation. In this paper, we propose a two-part method based on neural differential equations. The first part estimates values using spatial adjacency, while the second part refines these estimates considering temporal dependencies. Our approach explicitly addresses the over-smoothing problem, leading to a 2-8% improvement over state-of-the-art baseline methods. The results hold promise for enhancing the accuracy and effectiveness of environmental monitoring applications.

关键词： Backpropagation Adaptation models Soft sensors Face recognition Data integration Predictive models Mathematical models

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Gamma Sampling for Intrusion Detection with Imbalanced Data 10th

Gamma Sampling for Intrusion Detection with Imbalanced Dat...

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10th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2024

作者： Kamalov, Firuz Mitra, Rohan Sulieman, Hana Department of Electrical Engineering Canadian University Dubai Dubai United Arab Emirates Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates Department of Mathematics and Statistics American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9789819637737

Recent advances in artificial intelligence have prompted the use of machine learning methods in network security. In this paper, we address the issue of imbalanced data that is often present in network security datasets used in machine learning. We propose an oversampling method based on the Gamma distribution to balance the data prior to training. The results on several imbalanced datasets show the potential of the proposed method as a viable tool to build intrusion detection systems based on artificial intelligence. The accompanying code for the study is available on Github. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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