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2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Gede Sujana Eka Putra, I. Ketut Gede Darma Putra, I. Sudarma, Made Sudana, Anak Agung Kompiang Oka Institute of Business and Technology Indonesia Department of Computer Informatics Denpasar Indonesia Udayana University Department of Information Technology Denpasar Indonesia Udayana University Department of Electrical and Computer Engineering Denpasar Indonesia

ISBN: (纸本)9798350382822

Tuna products are one of Indonesia's leading export commodity products. Accuracy in determining the quality grade of tuna is necessary to ensure food safety and product quality. Several cases of rejection of Indonesian fishery products by the United States and cases of food poisoning show a lack of food safety management in Indonesia. Determination of fish quality grade was done manually by identifying the eyes condition, gills, and meat color, taking a sample of tuna meat using a spike, which is the manual grading process causing human error. Previous studies had been carried out to identify the freshness and quality of fish using odor profiles, and color profiles from color sensors, measuring fish freshness and quality based on fish eye images. According to Robert DiGregorio, five parameters determine the grade quality of tuna, namely freshness, fish size and shape, meat color, texture, and fat content. Fish grades are grouped into grade 1, grade 2+, grade 2, and grade 3. This study aims to determine the grade quality of tuna meat based on color space through image preprocessing, dataset training, and classification. Image preprocessing consists of image cropping, converting images from RGB to HSV, and feature extraction using a wavelet. The training phase uses the k-NN algorithm using k=4 based on the number of classes. The result shows the correlation coefficient between grades of feature extraction using wavelet Symlet better than Haar. Classification of 65 images test dataset using Symlet wavelet and k-NN has a better accuracy of 81.8% compared to the Haar wavelet k-NN with an accuracy of 80.3%. © 2023 IEEE.

关键词： Feature extraction

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Anomaly Detection Employing a 5G Core Data Analytics Framework 13

Anomaly Detection Employing a 5G Core Data Analytics Framewo...

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13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Oliveira, Junia Maisa Almeida, Jonatan De Britto E Silva, Erik Rodrigues Moreira, Larissa Ferreira Moreira, Rodrigo Silva, Flavio O. MacEdo, Daniel F. Nogueira, Jose Marcos Universidade Federal de Minas Computer Science Department Gerais Brazil University of Bologna Department of Electrical Electronic and Information Engineering Italy Universidade Federal de Minas Electrical Engineering Department Gerais Brazil Brazil Department of Informatics Portugal

ISBN: (纸本)9798350376562

The Network Data Analytics Function (NWDAF) within the 5G core is not an inherent feature of open-source 5G cores, making its implementation necessary based on provider demands. However, for those seeking to integrate NWDAF into the core but lacking expertise in core architecture or application programming interface (API) integration, this task can be daunting, potentially resulting in the abandonment of data analytics implementation efforts. While the current version of NWDAF specifies certain use cases, it does not provide alternatives for developing new analytics scenarios. To fill this gap, this paper proposes a framework for 5G network data analytics designed to facilitate algorithmic modifications and the addition of new analytical contexts. The framework was developed considering the 3GPP technical specifications related to data analytics. Its evaluation was performed through a use case focused on cybersecurity anomaly detection applied to a 5G network core. The results demonstrated that the framework simplifies installation, increases flexibility for new algorithms, and integrates seamlessly into the 5G network core, effectively addressing previously identified shortcomings. © 2024 IEEE.

关键词： 3GPP 5G core 5G network anomaly detection data analytics network data analytics NWDAF

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Recognition of Colon Neoplasm using Contrast-Aware Capsule Network 9

Recognition of Colon Neoplasm using Contrast-Aware Capsule N...

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9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

作者： Yeboah, Mavis Serwaa Kwabena Mensah, Patrick Adekoya, Adebayo Felix Abra Ayidzoe, Mighty University of Energy and Natural Resources Department of Computer Science and Informatics Sunyani Ghana Sunyani Technical University Department of Computer Science Sunyani Ghana Catholic University of Ghana Faculty of Computing Engineering and Mathematical Sciences Sunyani Ghana

ISBN: (纸本)9798350385403

Colon neoplasms are the second leading cause of mortality worldwide, and despite advances in endoscopy, even experienced endoscopists miss 22-28% of cases. Intelligent systems can bridge this gap, but current approaches face significant challenges. For instance, Convolutional Neural Networks (CNNs) excel in image recognition but require large datasets, which are often scarce in healthcare. On the other hand, Capsule Networks, suitable for smaller datasets, struggle with complex images. To address these limitations, we propose a novel contrast-aware Capsule Network for colon neoplasm recognition. By incorporating a novel contrast-aware feature transformation algorithm, our proposed model enhances extracted features and focuses on processing relevant features, achieving remarkable recognition accuracies of 96.70%, 93.68%, and 99.99% on PolyGen, ETIS-Larib, and CVC-ClinicDB datasets, respectively. Our approach outperforms state-of-the-art Capsule Network algorithms and offers a promising solution to support endoscopists in accurate colon neoplasm detection, reducing the burden of image analysis and improving patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

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computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique Khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

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Development of Automotive-mobility Training in the TRIREME project 30

Development of Automotive-mobility Training in the TRIREME p...

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30th IEEE International Symposium for Design and Technology in Electronic Packaging, SIITME 2024

作者： Medgyes, Balint Farkas, Csaba Spanyik, Marek Danieli, Stefano Cantiani, Elena Stolfa, Jakub Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Electronics Technology Budapest Hungary VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Computer Science Ostrava Czech Republic Mylia-Adecco Formazione Milan Italy

ISBN: (纸本)9798331539511

To achieve climate neutrality by 2050, the consistent transformation of the mobility sector is one of the most important pillars of the European Green deal. As a result of green and sustainable approaches, novel automotive concepts and massive digitalisation in the mobility ecosystem is undergoing a continuous development towards eco-friendly solutions and novel business models, and the changing landscape of automotive industry comes with a significant social impact as well. The overall horizontal collaboration on skills agenda in the Automotive-Mobility Ecosystem is assured throughout the Automotive Skills Alliance (ASA) [1], which is the Large-scale Pact for Skills Partnership. The latest strategic project of the partnership to support its activities is the Digital & Green Skills Towards Future of the Mobility Ecosystem (TRIREME) which promotes a collaboration between industry stakeholders and training providers including Vocational education Trainings (VETs), Higher Educations (HEs) and universities), moreover, also emphasizing the widespread public representation of different project segments and evaluating the social impacts and regional impact of the changes in the Automotive-Mobility Ecosystem. In parallel to the development of sectoral skills strategy, our department (department of Electronics Technology-ETT) is contributing to deliver specific training materials and courses within Work Package 4 (WP4), which is based on the gathered and processed intelligence from previous desk research, survey activity and participation in professional events. The methodology of course development relies on the concept of modularity, our professional competences in the field of Automotive-mobility, and the fulfillment of requirements to cover the needed skills, the competence and the occupational profile subsets. © 2024 IEEE.

关键词： Automotive Mobility Modular Courses Skills Training Development

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Assessing the Maturity of Blockchain-based Supply Chain Management and Logistics: Comprehensive Examination of the Current Situation 6

Assessing the Maturity of Blockchain-based Supply Chain Mana...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Sener, Umut Çaldag, Murat Tahir Gökalp, Ebru Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Technology and Knowledge Management Baskent University Ankara Turkey Department of Computer Engineering Hacettepe University Ankara Turkey

Blockchain technology, a transformative force in digital transactions, has emerged as a significant enabler of secure, transparent, and traceable operations within supply chain management and logistics (SCML). Its immutable nature facilitates enhanced management of SCML activities by seamlessly integrating information flow throughout the value chain, and substantially increasing data integrity and operational reliability. While organizations are attracted to these capabilities, they still lack comprehensive guidelines to fully benefit from blockchain in SCML. There is a fundamental need to assist organizations in assessing and enhancing their blockchain-based SCML capabilities. Maturity models (MMs) are tools that help organizations assess and improve their processes, providing step-by-step guidelines from basic to advanced levels of maturity. Therefore, this study aims to identify and analyze MMs focused on blockchain-based SCML. An extensive systematic literature review (SLR) was conducted, and thirteen relevant studies were identified and evaluated against a comprehensive set of quality criteria. The analysis revealed that none of the existing MMs fully satisfied all criteria, highlighting a significant gap in the field. This research provides a thorough systematic analysis of MMs for blockchain-based SCML, offering an in-depth view of their levels, dimensions, research focuses, domains, industry targets, and their quality evaluation. The significance of this study lies in identifying the necessity for an MM specifically designed for blockchain-based SCML, addressing a critical gap in the current literature. These findings contribute to the growing body of knowledge on blockchain integration in SCML and provide a foundation for future research. © 2024 The Authors. Published by Elsevier B.V.

关键词： Data integrity

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Efficiency and fairness improvement for elastic optical networks using reinforcement learning-based traffic prediction

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Journal of Optical Communications and Networking 2022年第3期14卷 25-42页

作者： Valkanis, Anastasios Papadimitriou, Georgios Beletsioti, Georgia Varvarigos, Emmanouel Nicopolitidis, Petros Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

The characteristics of traffic in core networks have changed in recent years, with the data flows among network nodes now showing large volumes but also large fluctuations during the day. In such a dynamic environment, the static allocation of network resources by making reservations at the peak rates is wasteful, both in terms of energy consumption and resource efficiency. Periodic reconfiguration of network resources is necessary for the full exploitation of an elastic optical network's potential. In this paper, we present a resource reconfiguration scheme for an IP over elastic optical core network. The cornerstone of the proposed scheme is a novel traffic prediction mechanism based on reinforcement learning. Using the forecasting mechanism, a heuristic routing and spectrum assignment algorithm was designed to efficiently and fairly allocate network resources periodically. Simulation results show both a reduction of the bandwidth blocking probability and an increase of the fairness index regarding the network resources allocation over corresponding algorithms in the literature. © 2009-2012 Optica Publishing Group.

关键词： Reinforcement learning

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Social Media Insights into Climate Change: Sentiment Analysis Using VADER and RoBERTa

Social Media Insights into Climate Change: Sentiment Analysi...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Gerasimos Vonitsanos Andreas Kanavos Georgios Bardis Phivos Mylonas Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (数字)9798331504502

ISBN: (纸本)9798331504519

Sentiment analysis, a critical branch of Natural Language Processing (NLP), is pivotal for uncovering the emotional undertones within textual data, thereby revealing public sentiments on diverse topics. This study conducts a comparative analysis of two prominent sentiment analysis tools—VADER, a lexicon and rule-based approach, and RoBERTa, a transformer-based deep learning model. It focuses on their efficacy in analyzing tweets related to climate change, a topic of global significance that elicits a wide range of public opinions. The nuanced and dynamic nature of social media language poses unique challenges, particularly in contexts such as climate change discussions. We assess how effectively each model discerns positive, neutral, and negative sentiments across different categories of climate change-related tweets, delineated into Pro, Neutral, News, and Anti stances. Our findings indicate that RoBERTa outperforms VADER in capturing contextual nuances and providing detailed sentiment classifications. This detailed capability allows RoBERTa to better reflect the complex public opinions on climate change, offering invaluable insights to policymakers, researchers, and environmental advocates. This study not only aids in better understanding and engaging with public discourse on social media but also highlights the potential of advanced NLP tools in shaping environmental communication and policy formulation.

关键词： Climate change Sentiment analysis Natural language processing Social networking (online) Machine learning Text mining Computational linguistics Public policy Environmental monitoring

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Automobile Spare Part Classification via AI Image Processing Techniques, a Comparison Study 12

Automobile Spare Part Classification via AI Image Processing...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Lee, Boon Hoe Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Majeed, Anwar Abdul P.P. University of Aleppo Faculty of Informatics Engineering Syria Sunway University Bandar Sunway School of Engineering and Technology Selangor Darul Ehsan47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350391398

Artificial intelligence (AI) and image recognition technologies are developing rapidly, creating new opportunities for process improvement in industry, especially in spare parts classification. Spare parts classification using images has not been thoroughly researched and utilized in the car repair industry, especially in Malaysia. This project seeks to create a reliable and effective system that uses AI to identify and classify spare parts from user-uploaded images precisely. The main goals are to simplify inventory management and improve the precision and speed of part identification in diverse industrial environments. In the end, an accuracy of 98.70% was achieved for spare part type classification and 89.70% for both type and car brand classification with the use of transfer learning, finetuning, and object detection techniques. © 2024 IEEE.

关键词： Inventory control

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Towards Robust Federated Learning: Investigating Poisoning Attacks Under Clients Data Heterogeneity 19

Towards Robust Federated Learning: Investigating Poisoning A...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Soubih, Abdenour Lahmer, Seyyid Ahmed Abuhamad, Mohammed Abuhmed, Tamer College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of Information Engineering University of Padova Padua Italy Department of Computer Science Loyola University Chicago Chicago United States

ISBN: (纸本)9798331507817

Federated Learning (FL) offers a privacy-preserving solution by enabling multiple clients to train a shared model collaboratively without centralizing data. However, the decentralized nature of FL presents challenges, particularly regarding security and performance under adversarial conditions. This paper investigates the effects of poisoning attacks under data heterogeneity. Our experiments evaluate the impact of varying malicious client fractions and poison concentration levels on the accuracy of the model. We explore the effects of poisoning attacks on FedAvg and FedNova models using medical imaging tasks. Our findings reveal that increasing data heterogeneity exacerbates the effects of poisoning, with FedNova demonstrating greater resilience compared to FedAvg. We found that the number of malicious clients plays a more significant role in degrading performance than the ratio of poisoning samples shared by each malicious client, suggesting that even modest levels of poisoning can be tolerated by most algorithms. The study highlights the importance of developing robust defense mechanisms to maintain model performance under adversarial conditions. © 2025 IEEE.

关键词： Federated learning

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