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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Blais, Marc-Andre Akhloufi, Moulay A. Dept. of Computer Science MonctonNB Canada

ISBN: (纸本)9798350374766

Unmanned vehicles, such as drones, have surged in popularity in recent years. Swarms of these vehicles offer new opportunities in applications such as agriculture, weather monitoring and natural events management. However, efficiently controlling a large swarm of unmanned vehicles poses a significant challenge. Intelligent solutions, particularly reinforcement learning, have been proposed to address this challenge. We introduce a proximity-based reward system for multi-agent reinforcement learning to handle the issue of reward sparsity. Our goal is to develop an approach for controlling a swarm towards a common objective while maintaining robust swarm cohesion. In this paper, we compare various distance-based functions to build a comprehensive reward system. Specifically, we explore the Euclidean, Manhattan, Chebyshev and Minkowski distances in our experiments. We evaluate the impact of these proximity-based reward systems on four reinforcement learning algorithms. We conduct a comparison of our reward systems using various metrics during validation and test episodes. Our goal is to highlight the importance of comparing different algorithms and distance functions in the development of multiagent reinforcement learning systems. © 2024 IEEE.

关键词： Reinforcement learning

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Analysis on Short-Term Degradation of Oxide Thin-Film Transistors and Degradation Modeling for Prediction

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Transactions of the Korean Institute of electrical Engineers 2023年第3期72卷 402-405页

作者： Park, Ji-Hwan Kang, Kyeong-Soo Park, Junhyeong Lee, Soo-Yeon Dept. of Electrical and Computer Engineering SOFT Foundry Institute Seoul National University Seoul Korea Republic of

Gate bias stress can change the threshold voltage of amorphous indium gallium zinc oxide (a-IGZO) thin-film transistor (TFT). Especially, short-term degradation of threshold voltage should be compensated since it causes motion artifacts in the display. Therefore, a new short-term degradation model was proposed based on the stretched-exponential model. Moreover, the accuracy of the proposed model was confirmed by calculating the coefficient of determination between the measured data and the fitted data. Copyright © The Korean Institute of electrical Engineers.

关键词： Thin film transistors

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Near-end Intelligibility Improvement Through Voice Transformation in Transfer Learning Framework 31

Near-end Intelligibility Improvement Through Voice Transform...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Biswas, Ritujoy Nathwani, Karan Abrol, Vinayak Dept. of Electrical Engineering IIT Jammu India Infosys Centre for AI Dept. of Computer Science and Engineering IIIT Delhi India

ISBN: (纸本)9789464593600

In recent works, using voice transformation functions (VTF) in optimal shifting of formants has improved near-end speech intelligibility. Though these VTFs are promising, they are computationally expensive to optimize and generate unwanted artifacts during voice modification. Additionally, they were specific to the environmental condition they were optimized for. For the applicability of this approach to different languages without re-optimization, transfer learning (TL) was used to shape the parameters of VTF to accommodate the target language [1]. However, TL across noises and TL across languages and noises (simultaneously) was not viable due to the dependency on pitch information of source and target noises. Hence in this work, a statistical Gaussian Transformation Function (GTF) is developed with parameters optimized for specific environmental conditions. Defined by just three parameters, the optimization time came down, and the intelligibility surpassed the previously used VTF. Additionally, GTF allows TL across both noises and languages simultaneously, with fewer artifacts while shifting the formants. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Speech intelligibility

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Movie Genre Prediction Using AI-Generated Data and Natural Language Processing 26

Movie Genre Prediction Using AI-Generated Data and Natural L...

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

作者： Tanvir, Sifat Abontee, Ayesha Parpia Rayhan, Md. Abu Rahman, Md. Fuadur Sultana, Tasnim Ahmed, Ajwa Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350359015

Every movie belongs to one or more specific genre. In this work, a dataset was prepared consisting of AI-generated movie titles and plots of 10 different genres. Text vectorization was used to convert every single word of the titles and plots into vectors. These vectors were used to predict which genre each movie belongs to. For training, a sequential neural network model was built and used, and an accuracy of almost 80% was achieved. © 2023 IEEE.

关键词： Embedding GPT-3.5 Neural Network NLP Text Vectorization

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Mobile Gateways for LoRa End-Devices: Support Connectivity on the Move 29

Mobile Gateways for LoRa End-Devices: Support Connectivity o...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Moysiadis, Vasileios Skoutas, Dimitrios N. Skianis, Charalabos Lagkas, Thomas Argyriou, Vasileios Sarigiannidis, Panagiotis University of the Aegean Dept. of Information and Communication Systems Engineering Samos83200 Greece International Hellenic University Dept. of Computer Science Greece University of Western Macedonia Dept. of Electrical and Computer Engineering Kozani Greece Kingston University Dept. of Networks and Digital Media Kingston upon Thames United Kingdom

ISBN: (纸本)9798350377644

This paper proposes a method enabling mobile LoRa gateways to support mobile end devices. The approach utilizes trilateration and similar approaches based on RSSI to estimate the position of end devices and specify the movement of the gateways to maintain contact. The evaluation of the proposed method tests various parameters such as speed and number of end devices, and it shows that it is able to retain contact even with speeds up to 12 mps. Moreover, the evaluation shows that the ADR-MIN algorithm performs better in this scenario than the default ADR. © 2024 IEEE.

关键词： Gateways (computer networks)

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Adaptive Passive Beamforming in RIS-Aided Communications with Q-Learning

Adaptive Passive Beamforming in RIS-Aided Communications wit...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Chene, Thomas Bounhar, Oumaima Othman, Ghaya Rekaya-Ben Damen, Oussama Télécom Paris Communications and Electronics dept. Palaiseau France University of Waterloo Electrical and Computer Engineering dept. Waterloo Canada

ISBN: (纸本)9798350368369

Reconfigurable Intelligent Surfaces (RIS) appear as a promising solution to combat wireless channel fading and interferences. However, the elements of the RIS need to be properly oriented to boost the data transmission rate. In this work, we propose a new strategy to adaptively configure the RIS without Channel State Information (CSI). Our goal is to minimize the number of RIS configurations to be tested to find the optimal one. We formulate the problem as a stochastic shortest path problem, and use Q-Learning to solve it. © 2025 IEEE.

关键词： Fading channels

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Enhancing Academic Feedback Systems with Deep Learning and Ensemble Methods 1

Enhancing Academic Feedback Systems with Deep Learning and E...

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1st International Conference on Advances in computer science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sharma, Hemant Joshi, Hiren Gujarat University Dept. Computer Science Ahmedabad India

ISBN: (纸本)9798331518578

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems. © 2025 IEEE.

关键词： Contrastive Learning

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Construction and Enhancement of an RNA-Based Knowledge Graph for Discovering New RNA Drugs 40

Construction and Enhancement of an RNA-Based Knowledge Graph...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Cavalleri, Emanuele Mesiti, Marco AnacletoLab Dept. of Computer Science Milan Italy

ISBN: (纸本)9798350317152

Cutting-edge technologies in RNA biology are pushing the study of fundamental biological processes and human diseases and accelerate the development of new drugs tailored to the patient's biomolecular characteristics. Even if many structured and unstructured data sources report the interaction among different RNA molecules and some other biomedical entities (e.g., drugs, diseases, genes), we still lack a comprehensive and well-described RNA-centered Knowledge Graph (KG) that contains such information and sophisticated services that support the user in its creation, maintenance, and enhancement. This PhD project aims to create a biomedical KG (named RNA-KG) to represent, and eventually infer, biological, experimentally validated interactions between different RNA molecules. We also wish to enhance the KG content and develop sophisticated services designed ad-hoc to support the user in predicting uncovered relationships and identifying new RNA-based drugs. Services will rely on deep learning methods that consider the heterogeneity of the graph and the presence of an ontology that describes the possible relationships existing among the involved entities. Moreover, we will consider Large Language Models (LLMs) in combination with RNA-KG for interacting with the user with the ground truth information contained in our KG for extracting relationships from unstructured data sources. © 2024 IEEE.

关键词： RNA

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An Overview of Machine Learning Applications in Blockchain for Green Energy Exchanges 33

An Overview of Machine Learning Applications in Blockchain f...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

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Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

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