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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Chiraag Kaushik Justin Romberg Vidya Muthukumar Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA Electrical and Computer Engineering Industrial & Systems Engineering Atlanta GA

ISBN: (纸本)9798331314385

The classical iteratively reweighted least-squares (IRLS) algorithm aims to recover an unknown signal from linear measurements by performing a sequence of weighted least squares problems, where the weights are recursively updated at each step. Varieties of this algorithm have been shown to achieve favorable empirical performance and theoretical guarantees for sparse recovery and ℓp-norm minimization. Recently, some preliminary connections have also been made between IRLS and certain types of non-convex linear neural network architectures that are observed to exploit low-dimensional structure in high-dimensional linear models. In this work, we provide a unified asymptotic analysis for a family of algorithms that encompasses IRLS, the recently proposed lin-RFM algorithm (which was motivated by feature learning in neural networks), and the alternating minimization algorithm on linear diagonal neural networks. Our analysis operates in a "batched" setting with i.i.d. Gaussian covariates and shows that, with appropriately chosen reweighting policy, the algorithm can achieve favorable performance in only a handful of iterations. We also extend our results to the case of group-sparse recovery and show that leveraging this structure in the reweighting scheme provably improves test error compared to coordinate-wise reweighting.

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Decisioning-Based Approach for Optimising Control engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3212-3221页

作者： Mourad, Nahia Alsattar, Hassan A. Qahtan, Sarah Zaidan, Aws Alaa Deveci, Muhammet Sangaiah, Arun Kumar Pedrycz, Witold British University in Dubai Faculty of Engineering and IT Dubai United Arab Emirates University of Mashreq College of Administrative Science Department of Business Administration Baghdad10067 Iraq Middle Technical University College of Health and Medical Technology-Baghdad Department of Computer Center Baghdad10047 Iraq SP Jain School of Global Management SydneyNSW2141 Australia National Defence University Turkish Naval Academy Department of Industrial Engineering Istanbul34040 Turkey Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin64002 Taiwan University of Alberta Faculty of Engineering Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34010 Turkey

The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue;formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. © 1975-2011 IEEE.

关键词： Internet of things

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Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

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A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance

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Neural Computing and Applications 2024年第30期36卷 18979-18997页

作者： Natarajan, Krishnamoorthy Muthusamy, Suresh Sha, Mizaj Shabil Sadasivuni, Kishor Kumar Sekaran, Sreejith Charles Gnanakkan, Christober Asir Rajan A.Elngar, Ahmed School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Tamil Nadu Perundurai Erode India Centre for Advanced Materials Qatar University Doha Qatar Department of Mechanical and Industrial Engineering Qatar University PO Box 2713 Doha Qatar Department of Electrical Engineering National Institute of Technology Assam Silchar India Department of Electrical and Electronics Engineering Puducherry Technological University Puducherry India Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc., © The Author(s) 2024.

关键词： Diseases

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Electromagnetic Wave Applications for Reliable Fault Detection in Power Transformer Windings 8

Electromagnetic Wave Applications for Reliable Fault Detecti...

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8th International Conference on System Reliability and Safety, ICSRS 2024

作者： Beydaghi, Sina Rahbarimagham, Hesam Gharehpetian, Gevork B. Beydaghi, Sevda Amirkabir University of Technology Electrical Engineering Department Tehran Iran Rafsanjan Branch Islamic Azad University Electrical and computer Engineering Department Rafsanjan Iran Alzahra University Faculty of Art Industrial Design Department Tehran Iran

ISBN: (纸本)9798350354508

This paper presents a novel approach for the localization of radial faults in power transformer windings using electromagnetic waves, emphasizing the critical issues of reliability and noise. The proposed analytical method aims to enhance the reliability of power transformers by effectively identifying and diagnosing radial deformations. The study utilizes matched filtering techniques to analyze received signals, ensuring improved accuracy despite environmental noise interference. By comparing the reference signals from healthy transformers with those from faulty conditions, the methodology demonstrates a robust framework for real-time monitoring. The findings indicate that addressing noise and reliability significantly contributes to the effectiveness of fault detection systems in power transformers. © 2024 IEEE.

关键词： Distribution transformers

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Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification

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Iran Journal of computer Science 2024年第1期8卷 185-215页

作者： Kia, Marjan Sadeghi, Soroush Safarpour, Homayoun Kamsari, Mohammadreza Jafarzadeh Ghoushchi, Saeid Ranjbarzadeh, Ramin Department of Business Information System Business Analysis (ISBA) Park University Parkville United States School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Software Engineering University of Szeged Szeged Hungary Faculty of Electrical Engineering Malek-Ashtar University of Technology(MUT) Esfahan Iran Faculty of Industrial Engineering Urmia University of Technology Urmia Iran School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland

This study presents a novel approach for brain MRI classification by integrating multiple state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet, AlexNet, and ResNet50, with an attention mechanism. The primary objective was to accurately detect the presence or absence of brain tumors—a critical challenge in medical diagnosis. The methodology was validated on a comprehensive dataset comprising 3264 brain MRI scans, systematically divided into training, validation, and testing subsets. Preprocessing techniques tailored for MRI data were employed to enhance input quality. Each model was initially developed and evaluated individually to establish a performance baseline before their integration. The proposed ensemble technique capitalized on the complementary strengths of these models to enhance classification accuracy and robustness. The inclusion of an attention mechanism further refined the models by emphasizing critical regions in the MRI scans, thereby boosting predictive performance. In addition, data augmentation techniques were applied to mitigate overfitting and increase dataset resilience. Performance metrics, including precision, accuracy, and recall, demonstrated significant improvements across all evaluations, particularly for the models incorporating the attention mechanism. These findings highlight the potential of combining advanced DL architectures with attention mechanisms as a promising strategy for precise and reliable brain tumor classification in MRI scans. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AlexNet Attention mechanism Brain tumor classification MobileNet ResNet50

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Machine Learning Techniques in Additive Manufacturing: A Review

Machine Learning Techniques in Additive Manufacturing: A Rev...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Alsalem, Ahmed Zohdy, Mohamed Oakland University Department of Industrial and Systems Engineering RochesterMI United States Oakland University Department of Electrical and Computer Engineering RochesterMI United States

ISBN: (纸本)9798350322347

Additive manufacturing (AM) is a 3D printing process widely used in industries such as automotive, healthcare, aerospace, and consumer goods. It creates different 3D objects by adding different layers over one another. AM manufacturing processes are capable enough to produce complex parts, consisting of intricate designs and geometries, characterized by well-established grain structure and properties without expensive tooling in reduced lead time and at low cost. It is a multi-physical process, including various parameters that impact the final product's quality. Moreover, Machine Learning (ML) techniques are suitable approaches aimed at understanding and predicting the complex processes of AM. In this study, an overview of AM and the challenges in the AM is presented, as well as the ML approaches to address the challenges in AM processes are also discussed. The recently introduced ML techniques in AM processes are evaluated explicitly. To be more precise, limited training data, material choice, and standardization regarding data pose major challenges that need to be addressed when applying ML techniques to AM methods. © 2023 IEEE.

关键词： 3D printing

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Sensitivity, criticality, and optimization in bipartite networks: application in urban transit infrastructure

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Environment systems and Decisions 2025年第2期45卷 1-22页

作者： Rocco, Claudio M. Barker, Kash Radhakrishnan, Sridhar Moronta, Jose Faculty of Engineering Universidad Central de Venezuela Caracas Venezuela School of Industrial and Systems Engineering University of Oklahoma Norman OK United States School of Computer Science University of Oklahoma Norman OK United States Departamento de Tecnología Industrial Universidad Simon Bolivar Caracas Venezuela

This study presents a comprehensive analysis of weighted bipartite networks, investigating their sensitivity to random weight variations, identifying critical links/nodes through zeroing-out experiments, and proposing multi-objective optimization models for network enhancement. By systematically examining the effects of random weight variations on three bipartite indexes (nestedness, modularity, and robustness), we assess the stability and resilience of the network under stochastic perturbations. Through targeted experiments, we identify critical links/nodes that significantly influence network topology and functionality. Lastly, we propose tri-objective optimization models that integrate objectives to guide network design and management decisions. Our findings provide valuable insights into the dynamics and optimization of weighted bipartite networks, with implications for network resilience, efficiency, and performance optimization. An example related to the Athens metro system illustrates the proposed approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Bipartite networks Modularity Nestedness Robustness Transportation network Tri-objective

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Automated Warehouse 5G Infrastructure Modeling Using Variational Autoencoders

Automated Warehouse 5G Infrastructure Modeling Using Variati...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Gulia, Rahul Ganguly, Amlan Kwasinski, Andres Kuhl, Michael E. Rashedi, Ehsan Hochgraf, Clark Rochester Institute of Technology Department of Computer Engineering RochesterNY United States Rochester Institute of Technology Department of Industrial and Systems Engineering RochesterNY United States Rochester Institute of Technology Department of Electrical and Computer Engineering Technology RochesterNY United States

ISBN: (纸本)9798350364910

The next decade is poised for a transformative shift in wireless communication technologies, driven by the increasing demand for data-intensive applications. Innovations in signal processing, network architecture estimation and design, and spectrum utilization will be critical in realizing the potential of 5G and beyond. These advancements will enable the seamless integration of emerging technologies and empower the digital transformation of industries and society. In this paper, we introduce a Variational Autoencoder (VAE) based model for indoor radio propagation modeling within an automated Industry4.0 warehouse or factory floor in the 5G wireless bands. We detail the creation of training data tensors, the architecture of our proposed VAE model, and its training using tensors that capture the impact of various interacting objects within a 5G-enabled smart warehouse infrastructure. The model is trained on multiple warehouse configurations to predict the signal-to-interference-plus-noise ratio (SINR) for both the validation dataset and unknown warehouse configurations. We present reconstruction error heatmaps to demonstrate the accuracy of our model and analyze its performance in different complex warehouse environments. © 2024 IEEE.

关键词： Signal to noise ratio

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ASSESSMENT OF MUSCULOSKELETAL DISORDER RISKS AMONG DENTISTS DURING DENTAL TREATMENT IN JORDAN: A COMPARATIVE ERGONOMIC STUDY 50

ASSESSMENT OF MUSCULOSKELETAL DISORDER RISKS AMONG DENTISTS ...

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50th International Conference on computers and industrial engineering: Sustainable Digital Transformation, CIE 2023

作者： Al Meanazel, Osama T. Obeidat, Mazin H. Almomani, Hesham A. Alomosh, Roua N. Ui Islam, Qamar Department of Industrial Engineering Faculty of Engineering Hashemite University Az Zarqa Jordan Ministry of Health Amman Jordan Department of Electrical and Computer Engineering College of Engineering Dhofar University Dhofar Oman

ISBN: (纸本)9781713886952

This research manuscript examines the risk level of musculoskeletal disorders (MSDs) among dentists in Jordan. Fifty participating dentists were involved in the study, and three ergonomic assessment tools, Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and Nordic Musculoskeletal Questionnaire (N.M.Q.), were employed to assess the risk level. Factors such as gender, posture, and body mass index (BMI) were also investigated for their influence on the risk level. Dentists faced moderate risk (RULA: mean score 5.38 ± 1.24). Gender and posture showed no significant differences (RULA: P-values .121, .458). REBA indicated medium risk (6 ± 1.84). No significant gender or position differences (REBA: P-values .852, .963). N.M.Q. responses: 82% neck pain, 64% lower back pain. Also, 44% shoulder, 42% upper back, and 32% wrist/hand pain. Elbow, hips/thighs, knees, ankles/feet were reported less frequently (16%, 16%, 14%, 18%). The results of this study shed light on the risks dentists face concerning MSDs during their dental practice in Jordan. It emphasizes the importance of implementing ergonomic interventions to minimize such risks and promote the well-being of dental professionals. Further research and interventions are recommended to address specific areas of concern, ensuring dentists' longterm health and productivity in the field. © 2023 computers and industrial engineering. All rights reserved.

关键词： Diseases

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