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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Jalalzadeh, Amir Hossein Talebi, Seyed Saeid Kamangar, Mehrdad Hashemi Shahed University Engineering Faculty Department of Biomedical Engineering Tehran Iran Noshirvani University Department of Electrical and Computer Engineering Babol Iran Shomal University Engineering Faculty Department of Electrical and Electronic Engineering Amol Iran

ISBN: (纸本)9798350383942

Accurate removal of brain tumors is always one of the most important challenges for surgeons, as the continuous change of the brain state after opening the skull and releasing the resulting pressure causes the tumor state to change. By registration of preoperative MR images on intraoperative ultrasound images, the extent of this change is estimated and a new image of the brain is created. The result shows the changes and shifts in the brain, and the surgeon removes the tumor based on this image. Image registration using new deep learning methods has attracted the attention of many researchers due to its high efficiency and accuracy. In this paper, the images of 22 male and female patients with grade 2 glioma tumors were used to evaluate the proposed method. MR images of the patients were taken before surgery, while ultrasound images were taken during surgery and after cranial incision. The deep network used in this paper to compensate for non-rigid changes is voxel morph. All images were fed to the pre-trained network in pairs, and the results are reported for each individual. The average error of all images for the proposed method is 3.56 ± 1.72. This shows the improvement in performance compared to the previous methods since in this work the landmarks were not used in training phase. © 2024 IEEE.

关键词： Tumors

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A Unified Multi-Domain Face Normalization Framework for Cross-domain Prototype Learning and Heterogeneous Face Recognition

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5282-5295页

作者： Pang, Meng Zhang, Wenjun Lu, Yang Cheung, Yiu-Ming Zhou, Nanrun Nanchang University School of Mathematics and Computer Sciences China Xiamen University Department of Computer Science and Technology School of Informatics China Hong Kong Baptist University Department of Computer Science Hong Kong Hong Kong Shanghai University of Engineering Science School of Electronic and Electrical Engineering China

Face normalization is a critical technique for improving the robustness and generalizability of face recognition systems by reducing intra-personal variations arising from expressions, poses, occlusions, illuminations, and domain shifts. Existing normalization methods, however, often lack the flexibility to handle multi-factorial variations and exhibit limited cross-domain adaptability. To address these challenges, we propose a Unified Multi-Domain Face Normalization Network (UMFN), which is designed to process facial images with diverse variations from various domains and reconstruct frontal, neutralized facial prototypes in the target domain. As an unsupervised domain adaptation model, the UMFN facilitates concurrent training across multiple cross-domain datasets and demonstrates robust prototype reconstruction capabilities. Notably, the UMFN functions as a joint prototype and feature learning framework, extracting domain-agnostic identity features through a decoupling mapping network and adversarial training with a feature domain classifier. Furthermore, we design an efficient Heterogeneous Face Recognition (HFR) network that integrates these domain-agnostic features and the identity-discriminative features extracted from normalized prototypes, enhanced by contrastive learning to improve identity recognition accuracy. Empirical evaluation on multiple cross-domain benchmark datasets validate the effectiveness of the UMFN for face normalization and the superiority of the HFR network for heterogeneous face recognition. © 2005-2012 IEEE.

关键词： Adversarial machine learning

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Development of Novel-Epoxy-Based Transformer Bushing Insulator for High Breakdown Voltage (BDV) 3

Development of Novel-Epoxy-Based Transformer Bushing Insulat...

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3rd International Conference on Emerging Trends in electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Siddique, Abubakar Akram, Laraib Aslam, Waseem Mishal, Areeba Shahid, Muhammad Usama Ahmed, Haseeb Department of Electrical and Biomedical Engineering Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Electrical Electronics & Computer Engineering University of Sargodha Sargodha Pakistan

ISBN: (纸本)9798331504861

In today’s trends, nanocomposites have garnered significant interest due to their potential to enhance dielectric properties and be used as insulators. Incorporating nanoparticles into the base epoxy makes achieving highly efficient dielectric materials feasible. However, the low electric field stress management capability and insufficient relative permittivity of typical epoxy provide a hindrance. Efforts have been made to overcome these limitations by adding nanosized fillers with high relative permittivity to the epoxy to enhance the dielectric properties. This research study focuses on the development of Novel-Epoxy-Based Transformer Bushing Insulator using nanomaterial Al2O3 and SiO2. The epoxy resin served as the base material, with nano-fillers added to improve its dielectric properties. The hardener was added to initiate the curing process and create a highly crosslinked structure. Surfactants were introduced to improve the interaction between the epoxy resin and nanoparticles. The nano-silica and nano-alumina particles were combined in different ratio configurations to create the nanocomposites by using the preparation method;Sol-Gel and Green Synthesis, which were subsequently tested for their dielectric properties. The proposed sample is the nanocomposite made by the green synthesis method and with the combination of 0.3 g + 0.3 g of the nanoparticle. Results show improved dielectric strength of the proposed nanocomposites compared to conventional porcelain materials. This paper presents an environment friendly approach for developing epoxy-based transformer bushing insulators, offering enhanced performance. ©2024 IEEE.

关键词： Permittivity

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Mental Confusion Prediction in E-Learning Contexts with EEG and Machine Learning 3rd

Mental Confusion Prediction in E-Learning Contexts with EE...

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3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Trigka, Maria Dritsas, Elias Mylonas, Phivos Department of Informatics and Computer Engineering University of West Attica Athens Greece Department of Electrical and Computer Engineering University of Patras Patras Greece

ISBN: (纸本)9783031440960

In cognitive science, the term confusion is used to capture the decline in learners’ cognitive ability, which affects their ability to think, solve a problem, learn and understand. Unlike classroom education, in online e-learning contexts (e.g., in Massive Open Online Courses (MOOC)), confusion hinders the smooth evolution of the learning process from the learners’ side, as the educator can’t immediately interact with the students to restore cognitive equilibrium. This paper presents a Machine Learning (ML) based approach by comparing several classifiers that were trained and tested exploiting Electroencephalogram (EEG) data (namely, band power, attention and mediation features) acquired by the MindSet device to efficiently distinguish "Confused" from "Not-Confused" subjects. In particular, J48 was the dominant model reaching an optimal performance with accuracy, precision and recall equal to 99.9% and an Area Under the Curve (AUC) of 100%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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Smart Textiles to Enable In-Home Health Care: State of the Art, Challenges, and Future Perspectives

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第4期3卷 120-150页

作者： Majumder, Sumit Roy, Arup Kumer Jiang, Wei Mondal, Tapas Deen, M. Jamal Chittagong University of Engineering and Technology Department of Biomedical Engineering Chattogram4349 Bangladesh Chittagong University of Engineering and Technology Department of Chemistry Chattogram4349 Bangladesh McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada McMaster University Department of Pediatrics HamiltonONL8S 4K1 Canada McMaster University Department of Electrical and Computer Engineering The School of Biomedical Engineering HamiltonONL8S 4K1 Canada

The rapid advances in high-speed and low-power computing, communication, and sensor technologies have propelled the growth of Internet-of-Things (IoT) technology. IoT enables sensors, actuators, and devices to work together in applications such as a smart home to track the health, activity, comfort, and well-being of residents, thus potentially revolutionizing the healthcare system. Textile-based wearables offer a promising solution to monitor health and activity in a smart home setting while ensuring comfort and unobtrusiveness. In fact, there has been a growing trend among researchers to develop textile-based sensors for measuring and monitoring vital signs, such as heart rate, body temperature, respiratory rate, biochemicals, humidity, and activities of daily living (ADLs). Researchers are also actively pursuing the development of electronic components, which are based on textiles, thus creating a new generation of wearable technology. Therefore, it is no wonder that the smart-textile market is expected to grow to over 4 billion by 2030. However, factors such as cost-effectiveness, manufacturing scalability, device performance, environmental and safety concerns, and privacy issues may impede the market penetration of smart textiles. In this article, we present a comprehensive overview of the current state of research and development in the manufacturing of smart textiles and textile-based sensors with a focus on their application in monitoring and management of health in a smart home environment. © 2022 IEEE.

关键词： Smart textiles

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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Neural Collaborative Filtering for Improved Tourism Destination Recommendation 8

Neural Collaborative Filtering for Improved Tourism Destinat...

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8th International Conference on Information Technology, Information Systems and electrical engineering, ICITISEE 2024

作者： Marzuki, Imam Hariadi, Mochamad Rachmadi, Reza Fuad Arif, Yunifa Miftahul Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Indonesia Universitas Islam Negeri Maulana Malik Ibrahim Department of Informatics Engineering Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350368970

This study evaluates the effectiveness of Neural Collaborative Filtering (NCF) in enhancing tourism destination recommendation. The researchers employed a methodology encompassing a comprehensive literature review, integration of user preference dataset, and the application of the NCF algorithm. The evaluation utilized MAE and RMSE metrics, demonstrating significant improvement compared to the Neural Network method. The results highlight the superior accuracy and reliability of the NCF-based recommendation system in providing tourism destination recommendation. This study emphasizes the practical significance of implementing NCF, offering a more sophisticated and efficient approach to enhancing user experience. The implication of this research contribute to academic needs and are expected to provide practical benefits for recommendation system in the tourism sector. The researchers elucidate critical differences in the NCF algorithm compared to benchmarks, and the introduction explores gaps in NCF applications for tourism, reinforcing the importance of this study. Experimental results show that the NCF model achieved an MAE of 0.306 and an RMSE of 0.371, outperforming the Neural Network's MAE of 0.794 and RMSE of 0.960, confirming superior predictive accuracy. © 2024 IEEE.

关键词： Collaborative filtering

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Pharos: A Transparent and Steerable Visualization Recommendation System

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Journal of Computing Science and engineering 2023年第1期17卷 1-12页

作者： Chang, Youli L’Yi, Sehi Kim, Young Taek Song, Hyunjoo Kim, Bohyoung Seo, Jinwook Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Department of Biomedical Informatics Harvard Medical School BostonMA United States School of Computer Science and Engineering Soongsil University Seoul Korea Republic of Division of Biomedical Engineering Hankuk University of Foreign Studies - Global Campus Yongin Korea Republic of

We propose Pharos, a novel visualization recommendation system that makes use of several provenance data sources and multi-perspective overviews to boost the transparency and steerability of the recommendation engine. Pharos helps a user understand recommendation contexts along with the user’s analysis progress in three complementary overviews. Pharos also serves categorized and scalable recommendations, either expanding or narrowing a user’s analysis scope. Based on provenance data and explicit user annotations (i.e., bookmarked or excluded visualizations), Pharos dynamically updates a recommendation list. According to the provided context, a user can steer the recommendation direction by filtering recommended candidates and rearranging them via the weight controller of the similarity measure on the recommendation engine. We showed how Pharos helped users understand and steer visualization recommendations through two comparative user studies. © 2023. The Korean Institute of Information Scientists and Engineers

关键词： Recommender systems

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A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

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Multimedia Tools and Applications 2025年第8期84卷 5349-5375页

作者： Shafique, Arslan Mehmood, Abid Alawida, Moatsum Elhadef, Mourad Rehman, Mujeeb Ur School of Biomedical Engineering University of Glasgow Glasgow United Kingdom Department of Computer Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Cyber Technology Institute School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications. © The Author(s) 2024.

关键词： Cryptography

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Assessment of Neuromuscular Function in Patients with Long-Term Symptoms of COVID-19 9th

Assessment of Neuromuscular Function in Patients with Lon...

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9th Latin American Congress on biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on biomedical engineering, CBEB 2022

作者： Cremasco, Emilia F. Zambalde, Ellen P. Germer, Carina M. Elias, Leonardo A. Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas Campinas Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas Campinas Brazil Department of Biomedical Engineering Federal University of Pernambuco Recife Brazil

ISBN: (纸本)9783031494062

Part of individuals infected by SARS-CoV-2 experiences long-lasting effects that include a wide range of symptoms, with fatigue being one of the most common complaints. Self-reported fatigue can have different manifestations, and although chronic fatigue has been reported as a long-term effect of other viral infections, the etiology of this complaint is still unclear. In this work, we employed an experimental protocol to assess the neuromuscular function in participants who had COVID-19 (12 women, 5–30 weeks after COVID-19 diagnosis) and age-matched controls (9 women). The COVID participants presented a high degree of variability in their symptoms, and the majority (91%) had mild to moderate manifestations. Participants performed an abduction force of the index finger in three different motor tasks, namely isometric force steadiness, reaction time, and time-to-task failure (fatigue task). The experiment was performed in three stages: pre-fatigue, post-fatigue, and after a resting interval of 5 min. We found significant differences between groups for the maximum voluntary contraction, indicating the COVID group had a lower force generation capacity. Nonetheless, no significant difference was found in measures related to force control;however, a larger and more homogeneous number of participants is needed to confirm these observations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Coronavirus

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