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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

引用

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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Optimizing Multi-Class Classification in Educational Data with Ensemble Learning and Data Balancing Techniques 5

Optimizing Multi-Class Classification in Educational Data wi...

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5th International Conference on Intelligent Data science Technologies and Applications, IDSTA 2024

作者： Al-Hammouri, Mohammad F. Hammouri, Ziad Akram Ali Almalkawi, Islam T. Lafee, Ansam The Hashemite University Dept. of Computer Engineering Zarqa Jordan Middle East University Dept. of Computer Science Amman Jordan

ISBN: (纸本)9798350354751

The field of education is increasingly embracing AI tools to improve student outcomes. This work aims to reduce academic failure in higher education by employing machine learning techniques to identify at-risk students early in their educational journey, enabling the implementation of supportive strategies to assist them. This study examines a dataset from a higher education institution and utilizes it to develop a classification model for predicting students' academic performance. The problem is formulated as a multi-class classification task with three categories: Graduate, Enrolled, and Dropout, with a significant imbalance skewed toward the Graduate. To improve prediction accuracy toward the minority class, the data balancing technique SMOTE with Edited Nearest Neighbor (SMOTE-ENN) is applied. Three popular classification models - Random Forest, XGBOOST and CatBoost - are employed. The findings show that SMOTE-ENN significantly improves classification results. Moreover, XGBOOST demonstrated the highest accuracy (94.6%) in correctly identifying all classes, as evidenced by the confusion matrix evaluation, achieving the highest results compared to previous work in the literature. Implementing these models allows for accurate predictions of students' performance and helps reduce dropout rates. © 2024 IEEE.

关键词： Contrastive Learning

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Using IOT and WSNs Technology for Monitoring an Aquaponic Greenhouse System 4

Using IOT and WSNs Technology for Monitoring an Aquaponic Gr...

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Touhami, Achouak Bourouis, Amina Benahmed, Tariq Touhami, Sana Touhami, Nawal Benahmed, Khelifa Ali Kafi university center Dept. Mathematics and Computer Science Tindouf Algeria Tahri Mohammed university Dept. Mathematics and Computer Science Bechar Algeria Ahmed Draria university Dept. Electrical Engineering Adrar Algeria

ISBN: (纸本)9798331519872

The increasing population, climate fluctuations, soil erosion, limited water resources, and concerns about food security highlight the need for effective and sustainable approaches to food production. Aquaponics represents a sustainable method that conserves land and water while minimizing its environmental impact. This technique operates as a closed-loop, soil-less farming system, requiring meticulous monitoring, control, and management. Monitoring an aquaponic greenhouse system involves tracking various parameters to maintain the health and productivity of both plants and fish. Our research introduces a design and algorithms for monitoring an aquaponic greenhouse, utilizing wireless sensor networks (WSN), aquaponic systems, and Internet of Things (IoT) technology. © 2024 IEEE.

关键词： Aquaponic greenhouse Closed-loop Fish Internet of things Plant Wireless sensor networks

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The Effect of Mode of Control on Voltage Stability in DC Micro-grids Operating with Constant Power Loads 33

The Effect of Mode of Control on Voltage Stability in DC Mic...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Zheve, Leon T. Gitau, Michael N. Masike, Lebogang University of Pretoria Dept. of Electrical Electronic and Computer Engineering Pretoria South Africa

ISBN: (纸本)9798331535162

The penetration of renewable energy systems has helped trigger the shift to DC distribution systems. Switching converters are the building blocks of DC distribution systems and these individual blocks have their unique control. Manufacturers of these building blocks tend to overlook the effect of coupling converters together that have closed loop control, and integration engineers face the challenge of voltage instability during integration. Load converters operating with closed-loop control behave as constant power loads and have a destabilizing negative incremental resistance that diminishes the damping of the system at the DC bus they are connected. The mode of control also influences how far the action of constant power loads drives the system into instability. This work compares the average current mode control and voltage mode control applied to small DC network to see which control mode is better to implement for DC distribution grids. Simulations of a small single bus DC distribution system comprising of two constant power loads and a source converter were carried out with the objective of comparing the extent of bus voltage deterioration when using either control modes. The simulation results show that for the same system average current mode control is better at maintaining voltage stability compared voltage mode control. © 2025 IEEE.

关键词： DC distribution systems

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An Interpretable Framework for Predicting Type 2 Diabetes using ML and Explainable AI 26

An Interpretable Framework for Predicting Type 2 Diabetes us...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahmed, Kazi Fardeen Uz Zaman, Md Shahid Peyal, Hasibul Islam Hossain, Anika Rahman Ratul, Md. Toyaha Abdal, Md. Nazmul Islam, Md. Injamul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Uttara University Dept. of Computer Science & Engineering Bangladesh Khulna University Dept. of Computer Science & Engineering Discipline Bangladesh

ISBN: (纸本)9798350359015

Diabetes Mellitus is an incurable disease and stands as a major universe cause of destruction. With the universal prevalence of diabetes increasing rapidly, accurate detection and identification of the disease have become crucial. In this paper, we present an explainable AI-based diabetes indicative methodology that is carefully constructed, effective, and, most importantly, interpretable. Using two real-world diabetes datasets, the three most well-known machine learning classifiers in the literature - Random Forest (RF), Decision Tree (DT), and XGBoost - were subjected with quantitative assessments. After training and assessment of all classification models, the suggested method achieved the best results in the XGBoost classifier for the Sylhet dataset with 99.4% accuracy and the Pima Indian dataset with 92.59% accuracy. The datasets were normalized across observations to provide standardized values for improved analytical applicability and comparability. Explainable AI has been implemented for the XGBoost machine learning model by generating both global and local explanations using Shapley additive explanations (SHAP) and Local interpretable model-agnostic explanations (LIME). The elements that contribute to diabetes are explained and shown in graphs to help medical professionals make decisions about clinical diagnosis and treatment options. © 2023 IEEE.

关键词： Decision Tree (DT) Diabetes diabetes diagnosis explainable AI interpretable LIME Pima Indian dataset Random Forest (RF) SHAP Sylhet dataset XGBoost

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Detection of Primary Open Angle Glaucoma Based on Deep CNN Using Fundus Images 26

Detection of Primary Open Angle Glaucoma Based on Deep CNN U...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ratul, Md. Toyaha Rahman Afroge, Shyla Peyal, Hasibul Islam Zafrin, Dia Ahmed, Kazi Fardeen Abdal, Md. Nazmul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Uttara University Dept. of Computer Science & Engineering Bangladesh Khulna University Dept. of Computer Science & Engineering Discipline Bangladesh

ISBN: (纸本)9798350359015

The early and accurate diagnosis of glaucoma, a primary cause of permanent blindness, is critical for efficient treatment and prevention of vision loss. Although the exact causes of glaucoma are not yet fully understood, it is thought to be a result of several factors, including raised pressure inside the eye and decreased blood supply to the optic nerve. We have developed a convolutional neural network model for accurate detection of glaucoma. Methods based on deep learning have been effective at classifying diseases in retinal fundus images, facilitating in the evaluation of the growing number of images. The goal of this work is to create and train a unique deep CNN model that makes use of the connections between related eye-fundus tasks and metrics used to identify glaucoma. We have meticulously selected two distinct datasets to underpin this research endeavor: the ACRIMA dataset and the LAG dataset. Notably, our model attains a remarkable accuracy score of 99.29% on the ACRIMA dataset and an equally commendable accuracy score of 97.22% on the LAG dataset. This performance eclipses that of the majority of contemporary deep CNN models, underscoring the prowess and sophistication of our approach. © 2023 IEEE.

关键词： CNN deep learning fundus image glaucoma image classification

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MOSA: Matrix Optimized Self-Attention Hardware Accelerator for Mobile Device

MOSA: Matrix Optimized Self-Attention Hardware Accelerator f...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Chang, Yang-Rwei Chen, Hsuan-Fu Shih, Horng-Yuan Tamkang University Dept. Electrical and Computer Engineering New Taipei City Taiwan

ISBN: (纸本)9798331510756

The Self-Attention mechanism, which lies at the core of Transformer architectures, plays a vital role in capturing long-range dependencies. However, its high computational complexity and significant memory requirements pose major challenges for resource-constrained hardware such as mobile devices. In particular, frequent memory accesses and inefficient matrix multiplication operations often result in performance bottlenecks. Therefore, the development of dedicated hardware accelerators for Self-Attention, focusing on optimizing matrix operations and reducing memory usage, is essential for improving AI processing efficiency on mobile devices. This paper presents a Self-Attention hardware accelerator designed specifically for mobile devices. By optimizing the matrix multiplication process, the accelerator effectively reduces data transmission and memory access frequency, thereby lowering the overall computational complexity. It also reuses intermediate computation results, minimizing frequent memory read and write operations, which significantly reduces memory bandwidth requirements. This data reuse strategy not only cuts down on redundant computations but also greatly enhances computational efficiency. In addition, the accelerator eliminates the need for Key Transpose operations, further simplifying certain computational steps. Compared to traditional algorithms, the idle time is reduced by 66.5%., and signal line transmission costs are reduced by approximately 80%. With an optimized hardware architecture, mobile devices can efficiently support complex deep learning applications, such as speech processing and image recognition. © 2025 IEEE.

关键词： Matrix algebra

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Design of a Compact 2-Bit PIN Diode Reflection Type Phase Shifter using Radial Stub

Design of a Compact 2-Bit PIN Diode Reflection Type Phase Sh...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Bilawal, Farah Babaeian, Fatemeh Karmakar, Nemai Chandra Dept. of Electrical and Computer Systems Engineering Monash University Melbourne Australia

ISBN: (纸本)9798350379693

The goal of this research is to create an optimized hybrid coupler structure for a PIN diode reflection type phase shifter (RTPS) operating at microwave frequencies. To date, no phase shifter has been reported with the proposed technique, compactness, minimum phase error and balanced amplitude. Another limitation of designing such structures at such high frequency range, is that the 50Ω impedance lines' width approaches its length for given substrate thickness, that is undesirable. This study introduces a new method of developing a module at 100 Ω and translating it back to 50 Ω at the input and output terminals. Moreover, the proposed design is highly resilient to manufacturing constraints owing to the addition of radial stubs instead of lumped components, as reflective loads. Its performance with respect to its bandwidth coverage is better with 15% bandwidth enhancement, insertion loss around 3dB, and reflection co-efficient at all ports is better than -15dB over the whole operating bandwidth. The simulated and measured results validate the proposed design effectively. © 2024 IEEE.

关键词： Bandwidth

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Delay Minimization in UAV-Enabled MEC Networks Using Q-Learning 10

Delay Minimization in UAV-Enabled MEC Networks Using Q-Learn...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Moiz, Syed Khwaja, Ahmed S. Alnoman, Ali Anpalagan, Alagan Jaseemuddin, Muhammad Toronto Metropolitan University Dept. of Electrical and Computer Engineering Toronto Canada American University of Ras Al Khaimah Dept. of Computer Science and Engineering Ras al Khaimah United Arab Emirates

ISBN: (纸本)9798350373011

Mobile edge computing (MEC) is a promising solution for the ever-increasing computational demands of the ubiquitous Internet of things (IoT) applications. However, efficient resource management algorithms are needed to satisfy the quality-of-service (QoS) requirements of delay-sensitive IoT systems. In this paper, we propose a new Q-learning-based load-balancing resource management (QLLB) algorithm to reduce the computational delay in a MEC system. Unlike existing algorithms, the proposed algorithm optimally assigns tasks to unmanned aerial vehicles (UAVs) considering load balancing, and employs the Q-learning algorithm to achieve efficient CPU usage management at the MEC server level. Results show that the QLLB algorithm exhibits a considerable improvement in the overall performance compared to the baseline and round-robin algorithms. Specifically, under heavy load conditions, we observe a 68% reduction in the total delay and a 75% reduction in the overall algorithm execution time with respect to the baseline algorithm. Furthermore, we observe a 59% improvement in the combined UAV selection and task data transmission delay. © 2024 IEEE.

关键词： Quality of service

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Load-Aware Container Orchestration on Kubernetes Clusters 14

Load-Aware Container Orchestration on Kubernetes Clusters

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14th International Conference on Cloud Computing and Services science, CLOSER 2024

作者： Marchese, Angelo Tomarchio, Orazio Dept. of Electrical Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789897587016

Microservice Architecture is quickly becoming popular for building extensive applications designed for deployment in dispersed and resource-constrained cloud-to-edge computing settings. Being a cloud-native technology, the real strength of microservices lies in their loosely connected, autonomously deployable, and scalable features, facilitating distributed deployment and flexible integration across powerful cloud data centers to heterogeneous and often constrained edge nodes. Hence, there is a need to devise innovative placement algorithms that leverage these microservice features to enhance application performance. To address these issues, we propose extending Kubernetes with a load-aware orchestration strategy, enhancing its capability to deploy microservice applications within shared clusters characterized by dynamic resource usage patterns. Our approach dynamically orchestrates applications based on runtime resource usage, continuously adjusting their placement. The results, obtained by evaluating a prototype of our system in a testbed environment, show significant advantages over the vanilla Kubernetes scheduler. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Containers

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