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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2023年第3期85卷 3-12页

作者： Jinga, Nicolae Moldoveanu, Florica Moldoveanu, Alin Morar, Anca Mocanu, Irina Butean, Alexandru Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Romania Faculty Engineering "Lucian Blaga" University of Sibiu Romania

In this paper, we introduce new discourse quality metrics and an evaluation method, and furthermore, we provide a pilot implementation and evaluate it in a specific use case – that of public speaking. Voice analysis and discourse quality metrics are important topics as they can solve various problems of modern-day life. In public speaking trainings, it is important to be able to analyze voice and discourse, to offer the speaker valuable feedback and information on how to improve when speaking in public. We identified several papers that study this issue, in which rhythm, tone, fluency and clarity, as well as biological signals, are analyzed, to detect emotions or anxiety. We defined a set of voice quality metrics that can be implemented based on any speech recognition system. Our pilot implementation showcases the difference of using various Speech-to-Text (STT) APIs over the proposed metrics and how these affect the discourse evaluation process. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Speech recognition

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Investigation of the Use of Renewable Energy in Microgrid Applications 9

Investigation of the Use of Renewable Energy in Microgrid Ap...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Keerthi Kumar, N. Selvan, Saravana Ravindra, G. Kallappan, Seeniappan. Natrayan, L. Vinayagam, Nadanakumar Bms Institute of Technology And Management Department of Mechanical Engineering Karnataka Yelahanka560064 India Aimst University Faculty of Engineering And Computer Technology Kedah08100 Malaysia Sree Vidyanikethan Engineering College Department of Electrical And Electronics Engineering Andhra Pradesh Tirupati517102 India Velammal Institute of Technology Department of Mechanical Engineering Tamil Nadu Chennai601204 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602107 India Hindustan Institute of Technology And Science Department of Automobile Engineering Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350365092

This research focuses on the primary aspects that influence the usage of renewable energy sources in microgrids. These aspects include energy demand, the amount of solar irradiation, and the capacity of the batteries. In addition to this, it exemplifies the relevance of these factors and the dynamic relationship between them in the operation of microgrid systems. A number of factors must be considered during the design and implementation of micro grids, as this study shows that renewable energy sources have a major impact on them. The paper highlights the benefits of microgrids, which include lower energy costs, increased energy autonomy, and fewer emissions of greenhouse gases. The findings of this research have the potential to make a contribution toward the overall goal of constructing a resilient and flexible energy infrastructure. This might be accomplished with the help of the findings of this study. This may be done by optimizing the construction of micro grids that display higher performance and effectiveness in capturing renewable energy resources using the insights gathered from the inquiry. These micro grids would be optimized to use the information gained from the investigation. © 2024 IEEE.

关键词： Renewable energy

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Optimised Mission Planning for Heterogeneous Uncrewed Vehicle Teams

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IEEE Access 2024年 1-1页

作者： Hajkarim, Sina M.H. Sujit, P.B. Menon, Prathyush P. Department of Engineering Faculty of Environment Science and Economy University of Exeter Exeter United Kingdom Department of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal Bypass Road Bhopal Madhya Pradesh India

In this paper, we present a new mission planning optimisation method for coverage missions involving Uncrewed Aerial systems (UAS) and Ground Vehicles (GV) to minimize the mission planning time and the UAS and GV route length. Optimal planning of paths for the UAS and GV using the Mixed Integer Linear Program (MILP), often struggles with computational inefficiency and limited scalability in scenarios with a growing number of waypoints and vehicles. To overcome the MILP computational issues, we present a reinforcement learning technique based on rollout policy optimisation called as Multi-Agent Rollout Policy Optimisation (MARPO). Through simulations, we showcase MARPO’s ability to match the precision of conventional MILP formulation in small instances and excel in scalability and computational efficiency in larger cases. Additionally, MARPO is compared with a heuristic method, Multi-Agent Greedy Path-Finding Algorithm (MAGPA), and the superior performance of MARPO in terms of total path length and computational efficiency is demonstrated. Several simulations are presented to showcase the advantages of MARPO. In simulations with 1 UAS and 1 UGV, MARPO achieved path lengths up to 1.56% longer than MILP’s optimum for 9 to 25 waypoints, while significantly reducing computation time by up to 99.88%. In larger scenarios of 36 and 49 waypoints, where MILP was infeasible, MARPO provided convincing solutions with greatly enhanced computational efficiency, demonstrating its robust scalability and effectiveness. Authors

关键词： Heuristic algorithms

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Wideband Four-Element L-Shaped Notch-Patch MIMO Antenna for 5G NR Networks

Wideband Four-Element L-Shaped Notch-Patch MIMO Antenna for ...

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International Electrical engineering Congress (iEECON)

作者： Chanwit Musika Nathapat Supreeyatitikul Phatsakul Thitimahatthanakusol Jessada Konpang Department of Computer Science Faculty of Science and Technology Rajamangala University of Technology Krungthep Bangkok Thailand Department of Electrical and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Krungthep Bangkok Thailand Department of Telecommunication Engineering Faculty of Engineering and Technology Rajamangala University of Technology Isan Ratchasima Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This work proposes simply wideband four-element L-shaped notch-patch (LNP) MIMO antenna for 5G new radio (NR) networks. The proposed LNP MIMO antenna comprised of four-port antenna elements. The single antenna scheme contained L-shaped with notch microstrip patch antenna and modified ground plane. The proposed LNP MIMO antenna at 4.5 GHz (center frequency) can be provided impedance bandwidth (IBW) of 64% (3.57 – 6.45 GHz). The LNP MIMO antenna has been evaluated the MIMO performance metrics, including transmission coefficient (isolation), envelop correlation coefficient (ECC), diversity gain (DG), and mean effective gain (MEG). The simulated results of the LNP MIMO antenna achieved isolation of 20 dB, ECC of below 0.005, DG of above 9.975 dB, and MEG of approximately -3 dB. All simulated results are in good satisfactory, rendering the proposed LNP MIMO antenna suitable for 5G applications.

关键词： Antenna measurements 5G mobile communication Transmitting antennas Microstrip antennas Rendering (computer graphics) Broadband antennas Microstrip Antennas Wideband Substrates

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Leveraging Retinal Fundus Images with Deep Learning for Diabetic Retinopathy Grading and Classification

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computer systems science & engineering 2023年第8期46卷 1901-1916页

作者： Mohammad Yamin Sarah Basahel Saleh Bajaba Mona Abusurrah ELaxmi Lydia Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz UniversityP.O.Box 80201Jeddah21589Saudi Arabia Department of Business Administration King Abdulaziz UniversityP.O.Box 80201Jeddah21589Saudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

Recently,there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy(DR).DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed *** detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus *** article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy(DLLSHDM-DR)on Retinal Fundus *** proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making *** the DLLSHDM-DR technique,image preprocessing is initially performed to improve the quality of the fundus ***,the DLLSHDM-DR applies HybridNet for producing a collection of feature *** retinal image classification,the DLLSHDM-DR technique exploits the Emperor Penguin Optimizer(EPO)with a Deep Recurrent Neural Network(DRNN).The application of the EPO algorithm assists in the optimal adjustment of the hyperparameters related to the DRNN model for DR detection showing the novelty of our *** assuring the improved performance of the DLLSHDMDR model,a wide range of experiments was tested on the EyePACS *** comparison outcomes assured the better performance of the DLLSHDM-DR approach over other DL models.

关键词： Decision making healthcare sector deep learning diabetic retinopathy emperor penguin optimizer

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Decision Tree based Smart System for Pregnant Women Diagnosis

Decision Tree based Smart System for Pregnant Women Diagnosi...

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2022 International Conference on Intelligent Technology, System and Service for Internet of Everything, ITSS-IoE 2022

作者： Al-Hagree, Salah Alsayadi, Hamzah A. Alsurori, Muneer Mohammed, Marwan Mohsen, Ayedh Abdulaziz Albazel, Mohammad Gawbah, Hezam Alndary, Ebtisam Ibb University Faculty of Sciences Computer Science Department Yemen Alqalam University Faculty of Information Technology and Engineering Information Technology Department Yemen University of Science and Technology Information Technology Department Yemen

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

one of the reasons of women and newborns death in developing countries is the complications during pregnancy and childbirth. An early diagnosis of pregnancy problems would help to alleviate some of these risks. In this work, a suggested solution to help the physicians for fast pregnant diagnosis is proposed. A smart system is designed for easing women diagnosis during pregnancy. The core purpose of this research work is to build up an Android application which gives a deliberate approach in providing the best diagnosis of pregnancy so that women will not be caught in the is the complications during pregnancy. Android application uses Machine Learning algorithms such as decision tree algorithm. In this paper, decision tree algorithm (DT) is used for predicting pregnancy states, which performs better and more robust than the other models. The best diagnosis of pregnancy is predicted using C4.5 and CART algorithms. Proposed system had used decision tree algorithms that employed a database of the most diseases associated with women during pregnancy. Pregnant women is interacted with proposed system via an android application mobile interface, they had provided the algorithm with the case conditions, which processed to guide both physician and pregnant women on the level of risk. Response of the system is based on input data that processed according to predetermined variables and decision tree outcomes. Implemented experiments had proofed the efficiency of the system to facilitate diagnosis for better pregnant mothers' health care. The CART algorithm has gained a better level of evaluation comparing to other C4.5 algorithm. CART algorithm has achieved the highest correctly classified instances and incorrectly classified instances, which were 89.65% in correctly classified instances, 90.77 % in incorrectly classified instances, 9.23% evaluation measure. © 2022 IEEE.

关键词： Decision trees

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Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model

Improving Aspect-Based Sentiment with End-to-End Semantic Ro...

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Přibáň, Pavel Pražák, Ondrvej Department of Computer Science and Engineering University of West Bohemia Faculty of Applied Sciences Czech Republic NTIS - New Technologies for the Information Society University of West Bohemia Faculty of Applied Sciences Czech Republic

ISBN: (纸本)9789544520922

This paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic information from a Semantic Role Labeling (SRL) model. We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state. We believe that this end-to-end model is well-suited for our newly proposed models that incorporate semantic information. We evaluate the proposed models in two languages, English and Czech, employing ELECTRA-small models. Our combined models improve ABSA performance in both languages. Moreover, we achieved new stateof-the-art results on the Czech ABSA. © 2023 Incoma Ltd. All rights reserved.

关键词： Sentiment analysis

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A Squeeze-Excitation ResNet Approach for Effective Classification of Parasitic Eggs

A Squeeze-Excitation ResNet Approach for Effective Classific...

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2024 International Visualization, Informatics and Technology Conference, IVIT 2024

作者： Muthulakshm, M. Venkatesan, K. Rahayu, Syarifah Bahiyah Elangovan, Karthickeien Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Communication Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India National Defence University Malaysia Faculty of Defense Science and Technology Kuala Lumpur Malaysia National Defence University Malaysia Cyber Security and Digital Industrial Revolution Centre Kuala Lumpur Malaysia

ISBN: (纸本)9798350354447

The identification and classification of different kinds of parasite eggs in microscopic samples represent a critical challenge in the field of Soil-transmitted helminth infection diagnosis. Traditional methods are often labor-intensive and time-consuming. The emergence of deep learning models has shown promising results in automating this process by extracting intricate features from complex images. This study aims to develop an automated system for accurately classifying parasite egg types in microscopic images by leveraging the ability of squeeze excitation layers to learn the global information from the input. The proposed system employs features extracted by ResNet50 and ResNet101 with Squeeze Excitation (SE) layers for analysis. The extracted features are then input into a Support Vector Classifier. The study systematically evaluates the features extracted from ResNet50+SE and ResNet101+SE. Results from the evaluation demonstrate the efficacy of the ResNet50+SE in accurately classifying parasite egg types in microscopic images with an accuracy of 0.94. The study provides valuable insights into the choice of squeeze-excitation block added Resnet in the context of parasitology, contributing to the advancement of automated medical image analysis. The findings hold great potential for improving diagnostic processes and supporting epidemiological studies through efficient and accurate parasite detection. © 2024 IEEE.

关键词： Deep learning

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Utilizing Image Recognition Methods for the Identification and Counting of Live Nematodes in Petri Dishes

Utilizing Image Recognition Methods for the Identification a...

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International Carpathian control Conference (ICCC)

作者： Martin Radvansky Alena Kadlecova Milos Kudelka Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB-TU Ostrava Ostrava-Poruba Czech Republic Department of Experimental Biology Faculty of Science Palacky University Olomouc Czech Republic

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

This article addresses a system designed for identifying and quantifying several species of nematodes, free-living and parasitic, placed on Petri dishes using modern image recognition methods. Traditional methods of counting nematodes are often time-consuming and prone to errors, leading to inaccurate biological and ecological study results. Our method employs advanced machine learning algorithms and computer vision to detect and count live nematodes automatically. The process utilizes algorithms to recognize specific larval characteristics, such as size and shape. This allows for high accuracy and efficiency while minimizing human intervention. The results demonstrate that our approach is robust and precise and can be widely utilized in biological research and applications.

关键词： computer vision Image recognition Machine learning algorithms Accuracy Shape Pipelines Machine learning

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification 4

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Fitrianah, Devi Suryaningrum, Kristien Margi Sagala, Noviyanti Tri Maretta Ayumi, Vina Lim, Siew Mooi School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Mathematics Statistics Department Jakarta Indonesia Universitas Mercu Buana Informatics Engineering Program Faculty of Computer Science Jakarta Indonesia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Computer Science Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665473279

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%. © 2022 IEEE.

关键词： Fish

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