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2024 IEEE International Conference on Big Data, BigData 2024

作者： Usai, Marco Loddo, Andrea Putzu, Lorenzo Ruberto, Cecilia Di University of Cagliari Dept. of Mathematics and Computer Science Cagliari Italy University of Cagliari Dept. of Electrical and Electronic Engineering Cagliari Italy

ISBN: (纸本)9798350362480

This study addresses the critical challenge of cell nuclei segmentation in histopathological image analysis, which is essential for cancer diagnosis and prognosis. Traditional segmentation methods often struggle with complexities such as overlapping nuclei and variations in shape and size. Recent advancements in machine learning and deep learning, particularly convolutional neural networks, have improved segmentation accuracy but face limitations due to the need for large annotated datasets, often constrained by privacy regulations in the medical field. This research explores federated learning as a solution, enabling collaborative model training across institutions without sharing sensitive patient data. By leveraging diverse datasets while maintaining privacy, federated learning enhances model robustness and generalization capabilities. The study evaluates various federated learning techniques on in- and out-of-domain test sets, aiming to improve the reliability of segmentation models in clinical settings. The findings suggest that federated learning techniques can effectively address the challenges of data scarcity and domain shift, paving the way for more accurate and widely applicable segmentation algorithms in computational pathology. © 2024 IEEE.

关键词： Federated learning

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Textual Authenticity in the AI Era: Evaluating BERT and RoBERTa with Logistic Regression and Neural Networks for Text Classification 16

Textual Authenticity in the AI Era: Evaluating BERT and RoBE...

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16th International Symposium on Electronics and Telecommunications, ISETC 2024

作者： Hazim, Layth Rafea Ata, Oguz Altinbas University Dept. of Electrical and Computer Engineering Istanbul Turkey College of Computer Science and Mathematics Tikrit University Tikrit Iraq Altinbas University Dept. of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350390865

AI-generated content impersonating human writing is an issue that has gained attention as AI spreads its wings. This particular study serves as a comparison between the existing Logistic Regression and Feedforward Neural Networks (FNNs) by employing sentence-BERT-appended models and RoBERTa in the determination of authenticity in a given text. A dataset that was balanced between human-written and AI-generated texts was utilized. Techniques like tokenization and normalization were first used, followed by feature extraction using transformer-based models. Cross-validation and confusion matrix analysis that used measures such as accuracy, precision, recall, F1 score, and ROC AUC were included to guarantee the models' robustness. The hybrid RoBERTa-FNN model that was deposed challengers was the most outstanding model in respect of precision and recall, and the highest accuracy (99.95%) was obtained as mentioned in the data. The improved performance serves as a proof of how effectively RoBERTa uses its embeddings to represent context on the fine-grained level required for this kind of text classification. This work is a stepping stone to the creation of strong AI text detection systems, besides our advancements to the knowledge of models and embedding performance with respect to text classification. The results lay emphasis on the selection of model configuration and the embedding technique, as they are the key factors in achieving the best results in practical applications. © 2024 IEEE.

关键词： Logistic regression

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Supply Chain Logistics With Hybrid Optimization using ADMM and Vehicle Routing Problem 1

Supply Chain Logistics With Hybrid Optimization using ADMM a...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Reddy, T. Sudeep Dhanush, D. Krithin, T. Jayan, Sarada Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9798350376517

Delivery routing and truck assignment for transportation resources are essential elements in the dynamic world of supply chain management and logistics. This study proposes a framework for maximising the number of trucks assigned and the delivery routes by using OR-Tools to solve the Vehicle Routing Problem (VRP) and the Alternating Direction Method of Multipliers (ADMM) for demand assignment. With consideration for each vehicle's capacity, air conditioning condition, and demand priority, we created a realistic dataset that included warehouse locations, truck specifications, and delivery demand priorities. A time matrix was created by converting a distance matrix and assuming a constant speed of movement between the warehouses. Based on compatibility restrictions including vehicle capacity, air conditioning requirements, demand priority, and overall cost reduction, the ADMM algorithm allocates delivery demands to trucks. To successfully balance these aspects, the objective function incorporates weights for cost, priority, and distance. Following demand assignment, delivery routes are optimised using OR-Tools. Time windows and vehicle capacities are included as limitations to guarantee that deliveries are made within predetermined windows while reducing travel time and distance. In addition, a web application that offers an interactive platform for managing trucks, warehouses, and needs was created utilising the Flask framework. Through an easy-to-use interface, users can examine optimised routes, add new entries, and view extensive informative dashboard. By meeting delivery deadlines and maximising resource utilisation, this integrated approach produces notable operational efficiencies. This illustrates the potential of fusing cutting-edge optimisation techniques with useful application to handle real-world logistics problems. © 2024 IEEE.

关键词： Travel time

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Dual Quaternion Rotational and Translational Equivariance in 3D Rigid Motion Modelling 33

Dual Quaternion Rotational and Translational Equivariance in...

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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Vieira, Guilherme Grassucci, Eleonora Valle, Marcos Eduardo Comminiello, Danilo Dept. of Applied Mathematics Brazil Sapienza University of Rome Dept. of Information Engineering Electronics and Telecomm Italy

ISBN: (纸本)9798350324112

Objects' rigid motions in 3D space are described by rotations and translations of a highly-correlated set of points, each with associated x, y, z coordinates that real-valued networks consider as separate entities, losing information. Previous works exploit quaternion algebra and their ability to model rotations in 3D space. However, these algebras do not properly encode translations, leading to sub-optimal performance in 3D learning tasks. To overcome these limitations, we employ a dual quaternion representation of rigid motions in the 3D space that jointly describes rotations and translations of point sets, processing each of the points as a single entity. Our approach is translation and rotation equivariant, so it does not suffer from shifts in the data and better learns object trajectories, as we validate in the experimental evaluations. Models endowed with this formulation outperform previous approaches in a human pose forecasting application, attesting to the effectiveness of the proposed dual quaternion formulation for rigid motions in 3D space. © 2023 IEEE.

关键词： Rotation

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Multilingual Sentiment Analysis using DistilBert

Multilingual Sentiment Analysis using DistilBert

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Gupta, Nishi Bhattarai, Mandip Chavan, Abhishek Pimpri Chinchwad College of Engineering Dept. of Applied Science and Humanities Maharastra Pune India Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Maharastra Pune India

ISBN: (纸本)9798331517953

In today's virtual age, studying feelings and sentiments through textual content and emojis is essential. This research article explores a way to use natural language processing (NLP) techniques to interpret feelings from textual content and emoji content. We used the distilbert-base-uncased-finetuned-sst2english version, a small variant model of BERT, to categorize feelings as positive or negative. Our experiments confirmed that the version executed extraordinarily properly throughout more than one language, with especially great effects on Indonesian textual content. Our assessment confirmed that the version has an excellent accuracy of91.50% and an F1 rating of 91.44% for Indonesian, highlighting its wonderful overall performance in this language. It additionally plays properly in English, French, German, Arabic, and Italian, even though overall performance may also range through language. These effects reveal the version's effectiveness for duties, social media monitoring, client comments evaluation, and inventory marketplace fashion assessment. We finish with a conclusion about the results of those findings. © 2024 IEEE.

关键词： Natural language processing systems

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The metric-aware kernel-width choice for LIME 1

The metric-aware kernel-width choice for LIME

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Joint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Barrera-Vicen, Aurelio Paluzo-Hidalg, Eduardo Gutiérrez-Naranjo, Miguel A. Dept. Computer Science and Artificial Intelligence University of Seville Spain Dept. Applied Mathematics I University of Seville Spain

Local Interpretable Model-Agnostic Explanations (LIME) are a well-known approach to provide local interpretability to Machine Learning models. LIME uses an exponential smoothing kernel based on the kernel width value, which defines the width of the local neighbourhood. In this paper, we study the influence of the distances for these local explanations, and we explore the choice of kernel width to guarantee a fair performance comparison between the distances. © 2023 CEUR-WS. All rights reserved.

关键词： Lime

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Measurements and analysis of 5G mobile communications signals in outdoor environments

Measurements and analysis of 5G mobile communications signal...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Fernandez, Marta Guerra, David Gil, Unai Pena, Ivan Dept. of Electronic Technology Bilbao Spain Dept. of Communications Engineering Bilbao Spain Dept. of Applied Mathematics Bilbao Spain

ISBN: (纸本)9798350321524

Mobile communications services are under continuous enhancement, resulting in the introduction of new transmission and reception techniques, new equipment designs and the use of new frequency bands. However, with the development of technology, and especially, with the rapid advancement of 5G services, social concern about the potential harmful effects of wireless communication networks has also increased. In order to guarantee health safety in wireless networks, human exposure to electromagnetic fields generated by radio communication systems is usually tested to check compliance with regulations. This can be done by measuring received signal strengths and comparing them with exposure limits. This paper presents an evaluation of electromagnetic exposure due to 5G mobile communications in outdoor environments. A measurement campaign was carried out in several locations of the city of Bilbao using professional equipment in order to acquire 5G signal data in different environments. Then, recorded data was processed and analyzed in order to characterize electromagnetic exposure according to the scenario and frequency band. Finally, a comparison with exposure limits and with the results derived from a previous measurement campaign carried out a year before is presented. © 2023 IEEE.

关键词： Base stations

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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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A New Projective Point in Convex Polygon Test with O(log N) Complexity 17

A New Projective Point in Convex Polygon Test with O(log N) ...

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17th IEEE International Scientific Conference on Informatics, INFORMATICS 2024

作者： Skala, Vaclav University of West Bohemia Faculty of Applied Sciences Dept. of Computer Science and Engineering Pilsen Czech Republic

ISBN: (纸本)9798350387674

This contribution describes a novel and fully projective algorithm for a point-in-convex polygon test with computational complexity of O(log N) in E2. The polygon vertices and tested points can be given in projective space, i.e., with the homogeneous coordinate w ≠ 0, without conversion to Euclidean space. It is independent of the polygon orientation. It uses vector-vector operations and is therefore aimed for implementation using SSE or AVX instructions. The algorithm is simple and robust, easy to implement. © 2024 IEEE.

关键词： Vectors

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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