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KDTL: knowledge-distilled transfer learning framework for diagnosing mental disorders using EEG spectrograms

Neural Computing and Applications 2024年第30期36卷 18919-18934页

作者： Singh, Shreyash Jadli, Harshit Padma Priya, R. Surya Prasath, V.B. Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH45257 United States Department of Biomedical Informatics College of Medicine University of Cincinnati CincinnatiOH45267 United States Department of Computer Science University of Cincinnati CincinnatiOH45221 United States

Electroencephalography (EEG) is a well-known modality in neuroscience and is widely used in identifying and classifying neurological disorders. This paper investigates how EEG data can be used along with knowledge distillation-based deep learning models to detect mental disorders like epilepsy and sleep disorders. The EEG signals are converted into time–frequency plots using short-time Fourier transforms. Further, we propose a novel methodology for using knowledge distillation-based transfer learning (KDTL). Knowledge distillation is becoming quite prevalent in the machine learning field and is associated with various applications in our work;we propose its use in the detection of mental disorders from EEG spectrograms. We convert the EEGs using short-term Fourier transform to obtain time–frequency representation and apply teacher-student by first training a large teacher model and use knowledge distillation to train a student model. In our experiments, we found that ConvNext teacher and MobileNet student combination obtained better results. Our proposed KDTL approach is tested on two datasets with multiple cases, namely the Bonn and ISRUC datasets and obtain 98% and 95% accuracies, respectively. Further experimental results show that the overall KDTL methodology can obtain high classification accuracy across both datasets in binary and multiclass classifications and proves to be better than multiple prior works. Further, our KDTL approach provides a way to train lightweight models which have a smaller number of trainable parameters and thereby constitute lower training time overall. Our proposed KDTL-based approach obtained accurate results in diagnosing mental disorders from EEG spectrograms. Compared to other related methods, KDTL outperformed across tasks with obtained good results in both binary and multiclass classifications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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Mobility-Aware Workload Distribution and Task Allocation for Mobile Edge Computing Networks 1st

Mobility-Aware Workload Distribution and Task Allocation fo...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Karras, Aristeidis Karras, Christos Giannaros, Anastasios Tsolis, Dimitrios Sioutas, Spyros Computer Engineering and Informatics Department University of Patras Patras Greece Department of History and Archaeology University of Patras Patras Greece

ISBN: (纸本)9783031337420

Mobile Edge Computing (MEC) is a promising computing paradigm that provides computing and storage services for mobile and big data applications. MEC servers are deployed at base stations to establish a mobile edge network (MEN) where mobile users can offload tasks to nearby servers to speed up their mobile applications. However, challenges such as the quality of workload distribution in edge computing environments still need to be tackled. Most studies and offloading strategies assume mobile users are stationary, but in reality, users move and this affects workload distribution and response time of mobile applications. In this work, we propose a solution that addresses these challenges by reducing energy consumption and enhancing QoS by assigning mobile tasks to MEC servers in the users’ predicted trajectories using a Random waypoint Model. We propose OptiMEC for scheduling mobile tasks in a Mobile Edge Computing (MEC) environment in 5G networks that utilize central-base stations. We consider the task properties, user mobility, and delay constraints in our proposed algorithmic scheme and we also propose an energy-load balancing heuristic. The problem is formulated as an optimization and constraint satisfaction problem and we propose a near-optimal solution for scheduling mobile tasks to MEC servers. The results of our simulation experiments show that our proposed solution can significantly reduce energy consumption in Mobile Edge Networks (MENs) and improve QoS by executing tasks under constrained time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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Assessing the Viability of AC to DC Microgrid Conversion: A Comprehensive Analysis of Technical and Economic Factors 59

Assessing the Viability of AC to DC Microgrid Conversion: A ...

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59th International Universities Power engineering Conference, UPEC 2024

作者： Charalambous, Chrysanthos Polycarpou, Alexis Efthymiou, Venizelos Georghiou, George E. University of Cyprus Pv Technology Laboratory Department Of Electrical and Computer Engineering Cyprus Phaethon Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Frederick University Department of Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350379730

In the face of global warming and the acceleration of climate changes, urgent and decisive measures must be taken to mitigate these phenomena. This study addresses this urgent need by exploring the transition from traditional AC distribution systems to more efficient DC and hybrid AC/DC systems, recognizing the utmost importance of both technical developments and economic considerations. Through a thorough overview of existing literature and empirical knowledge gathered from the nanogrid of FOSS Research Centre for Sustainable Energy of University of Cyprus, this research clarifies the inherent advantages of DC and AC/DC hybrid systems. In addition, it presents a detailed case study for the comprehensive assessment of the feasibility and effectiveness of these systems. The preliminary economic analysis carried out in this study demonstrates the cost-effectiveness and performance improvements offered by hybrid AC/DC microgrids compared to conventional AC configurations. According to the findings of this work it is obvious that the ROI increases with the increase of DC loads over AC loads until the case where DC loads reach the 75% of the total load (ROI=13.9%). However, even in the case of Full DC microgrid, the ROI is higher by 1% compared to the case of Full AC microgrid thus significantly reducing the payback period. These findings not only underline the urgency of the transition to more efficient and sustainable energy distribution systems, but also provide valuable information for policymakers, industry stakeholders and researchers. By embracing these innovative technologies, a more resilient and environmentally responsive energy infrastructure can be promoted that is in line with the demands of a rapidly changing global climate. © 2024 IEEE.

关键词： Clean energy

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EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images 3

EfficientNetB0-based Automated Diabetic Retinopathy Classifi...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Majaw, Ezekiel Alaric Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Venkatesan Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy. © 2024 IEEE.

关键词： Contrastive Learning

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-CommunicationsChofuTokyo 182-8585Japan Information Technology Center Nagoya UniversityNagoya 464-8601Japan National Institute of Informatics(NII) Tokyo 101-8430Japan Hohhot Minzu College Hohhot 010051China Toyota Motor Corporation Tokyo 112-8701Japan Department of Computer Science and Engineering Shanghai Jiaotong UniversityShanghai 200240China

Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central *** requirement outpaces the capacity of traditional communication *** tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise *** solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge *** system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient ***,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain *** results show a great reduction in latency by sharding and an increase in model accuracy,confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Urban Safety Improvement 22

Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Ur...

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22nd IEEE International Symposium on Intelligent Systems and informatics, SISY 2024

作者： Glucina, Matko Zigulic, Nikola Frank, Domagoj Lorencin, Ivan Matika, Dario Istrian University of Applied Sciences Department of Engineering Pula Croatia University of Rijeka Faculty of Engineering Rijeka Croatia University North Koprivnica Department of Computer Science and Informatics Croatia Juraj Dobrila University of Pula Faculty of Informatics Pula Croatia University North Department of Electrical Engineering Varazdin Croatia

ISBN: (纸本)9798350385601

Individual safety in urban and city centers is one of the fundamental rights of every person. Today's innovations open up various possibilities for improving the quality of life and safety, but if they are not applied, they carry the danger of an additional increase in crime or greater insecurity in urban centers. This investigation aims to apply insights into the anticipation of illegal actions by various criminal groups that use illegal means, such as weapons, to get property that does not belong to them. In this research, computer vision and advanced strategies such as YOLOv10 and YOLOv9 are used to compare their properties and performance for improving security in urban and populated areas. The best results were achieved by YOLOv9m with a high percentage of mean average precision in the amount of 0.923, while the worst performance was shown by YOLOv10n with a mean average precision of 0.902. Precision and recall for the YOLOv9 and YOLOv10 models were also taken into account and the results are as follows: 0.984.. 0.993.. 0.979.. and 0.996. © 2024 IEEE.

关键词： Neural networks

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Using Web Technologies to Implement a Modular Integrated System for Augmented Tourism Destinations 3rd

Using Web Technologies to Implement a Modular Integrated Sys...

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

作者： Palamas, Stergios Voutos, Yorghos Zigoura, Paraskevi Mylonas, Phivos Chasanis, Vasileios Department of Informatics Ionian University Corfu Greece iED - Institute of Entrepreneurship Development Larissa Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031440960

Identifying and promoting attractions and points of interest to visitors, especially through multimedia and – lately - AR/VR technologies, has been shown to enhance the pre-traveling and on-site overall experience. Throughout this paper, the development of a modular, web-based, generally applicable platform, consisting of interconnected modules and based on open-source technologies is presented. Not long ago, the deployment of engaging, rich-media user experiences required special equipment and native development tools. The present work demonstrates, how recent advancements in web technologies and modern web browsers (e.g., the WebGL), enable the deployment of such cutting-edge services, on mainstream mobile devices, using exclusively well-established web technologies like HTML, CSS and JavaScript. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Web browsers

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Relevance Vector Machine for Code Smell Detection 22

Relevance Vector Machine for Code Smell Detection

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22nd IEEE International Conference on Industrial informatics, INDIN 2024

作者： Putro, Hanson Prihantoro Yuhana, Umi Laili Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Engineering Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798331527471

Various approaches have been proposed to detect code smells, including machine learning models. However, there are still challenges to improving detection accuracy and selecting appropriate quality metrics. This research introduces the use of Relevance Vector Machine (RVM) for code smell detection, especially Magic Value smells, which often affect source code readability and maintainability in a software development industry. This approach utilizes source code analysis using two source code representations (raw source code and token stream) and feature extraction using two text mining techniques (Bag of Words/BoW and Term Frequency-Inverse Document Frequency/TF-IDF). The experiment involves analyzing 983 records of labeled source code datasets. The results show that RVM can detect code smells with a very high level of accuracy, reaching 99.9% on some kernel configurations, also nearly perfect precision, and F1 scores. This paper contributes to introducing the use of RVM for detecting code smells in source code, explores source code representation and feature extraction techniques, and shows that RVM can achieve high detection accuracy. © 2024 IEEE.

关键词： Software design

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Analyzing the Impact of Context Representation and Scope in Code Infilling 24

Analyzing the Impact of Context Representation and Scope in ...

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46th International Conference on Software engineering: Companion, ICSE-Companion 2024

作者： Heo, Jinseok Lee, Eunseok Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400705021

Existing studies solve software engineering tasks using code infilling through LLMC. They utilize context information, which refers to data near the target code of infilling, as input prompts. Although prompts are essential for infilling the target code, current studies use them without analyzing the impact of the representation and scope of context on code infilling. In this study, we analyzed how context representation and scope affect the performance of code infilling. We used XLCost, which contains code, comments, and a function comment for various programming languages. The combination of code and a function comment for context representation yielded the best code infilling performance. Furthermore, we found that the context scope is proportional to performance. Our analysis results can be applied in various tasks that involve code infilling in the future. © 2024 IEEE computer Society. All rights reserved.

关键词： Software engineering

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