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11th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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An Accurate and Wide-Range AFC for Modified Walsh-Hadamard Code Division Multiplexing 16

An Accurate and Wide-Range AFC for Modified Walsh-Hadamard C...

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16th International Conference on Advanced Technologies for Communications, ATC 2023

作者： Kojima, Toshiharu Iwabuchi, Koki The University of Electro-Communications Graduate School of Informatics and Engineering Department of Computer and Network Engineering 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan

ISBN: (纸本)9798350301328

This paper proposes a novel automatic frequency control (AFC) for modified Walsh-Hadamard code division multiplexing (MWHCDM). While MWHCDM is suitable for helicopter satellite communications, where rotor blades periodically block the transmission channel, it is affected by a time-varying Doppler shift due to helicopter maneuvers. The conventional AFC has a problem where its pull-in range is significantly narrower than the fluctuation range of the Doppler shift. The proposed AFC utilizes the structure of MWHCDM signals, and its operating range covers the fluctuation range with enough margin. computer simulation shows that the proposed AFC outperforms the conventional one on both operating/pull-in range and frequency estimation accuracy. © 2023 IEEE.

关键词： Frequency estimation

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Leveraging LLMs for Translating and Classifying Mental Health Data 4

Leveraging LLMs for Translating and Classifying Mental Healt...

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4th Workshop on Multilingual Representation Learning, MRL 2024

作者： Skianis, Konstantinos Seza Dogruöz, A. Pavlopoulos, John Department of Computer Science and Engineering University of Ioannina Greece LT3 IDLab Universiteit Gent Belgium Department of Informatics Athens University of Economics and Business Greece Archimedes AthenaRC Greece

ISBN: (纸本)9798891761841

Large language models (LLMs) are increasingly used in medical fields. In mental health support, the early identification of linguistic markers associated with mental health conditions can provide valuable support to mental health professionals, and reduce long waiting times for patients. Despite the benefits of LLMs for mental health support, there is limited research on their application in mental health systems for languages other than English. Our study addresses this gap by focusing on the detection of depression severity in Greek through user-generated posts which are automatically translated from English. Our results show that GPT3.5-turbo is not very successful in identifying the severity of depression in English, and it has a varying performance in Greek as well. Our study underscores the necessity for further research, especially in languages with less resources. Also, careful implementation is necessary to ensure that LLMs are used effectively in mental health platforms, and human supervision remains crucial to avoid misdiagnosis. ©2024 Association for Computational Linguistics.

关键词： Electronic health record

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Customized toolbox in VR Design 26

Customized toolbox in VR Design

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Drampalou, Georgia Kourniatis, Nikolaos Voyiatzis, Ioannis Department of Informatics and Computer Engineering University of West Attica Greece Department of Civil Engineering University of West Attica Greece

ISBN: (纸本)9781450398541

Designing in an environment of Virtual Reality is a field that gains ground. Although more and more applications are constantly released, the majority of designers have not yet been introduced into Virtual Reality. A widely used tool for designing is Rhino which has been released by McNeel and Rhino Developer encourages the users to create plug-ins for custom use of the software. In parallel, Meta's developing tools offer their users the chance to customize their own applications. Towards the direction of Virtual Reality integration, both have already released new products for their users. RhinoVR is an open source plug-in for Rhino, while at the same time Meta provides Oculus hardware and platform solutions for users to turn their concept into reality. What is yet to be developed is a model that encourages the customization of a VR design platform, focusing mainly on developing a personalized toolbox for designing. © 2022 ACM.

关键词： Virtual reality

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Pulmonary Diseases Decision Support System Using Deep Learning Approach

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computers, Materials & Continua 2022年第10期73卷 311-326页

作者： Yazan Al-Issa Ali Mohammad Alqudah Hiam Alquran Ahmed Al Issa Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk UniversityIrbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid 22110Jordan Pediatrician Mediclinic HospitalAl Ain14444UAE

Pulmonary diseases are common throughout the world,especially in developing *** diseases include chronic obstructive pulmonary diseases,pneumonia,asthma,tuberculosis,fibrosis,and recently *** general,pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert *** recent years,many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung *** this paper,the performance of four popular pretrained models(namely VGG16,DenseNet201,DarkNet19,and XceptionNet)in distinguishing between different pulmonary diseases was *** the best of our knowledge,this is the first published study to ever attempt to distinguish all four cases normal,pneumonia,COVID-19 and lung opacity from ChestX-Ray(CXR)*** models were trained using Chest-X-Ray(CXR)images,and statistically tested using 5-fold cross *** individual models,XceptionNet outperformed all other models with a 94.775%accuracy and Area Under the Curve(AUC)of Receiver Operating Characteristic(ROC)of 99.84%.On the other hand,DarkNet19 represents a good compromise between accuracy,fast convergence,resource utilization,and near real time detection(0.33 s).Using a collection of models,the 97.79%accuracy achieved by Ensemble Features was the highest among all surveyed methods,but it takes the longest time to predict an image(5.68 s).An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time,such a dependable system can be used in rural areas and various healthcare sectors.

关键词： Pulmonary diseases deep learning lung opacity classification majority voting ensemble features

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Comparison of CNN-Based Design for Shrimp Seed Counting Machine 25

Comparison of CNN-Based Design for Shrimp Seed Counting Mach...

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25th International Electronics Symposium, IES 2023

作者： Nurmaida, Firnanda Pristiana Dewantara, Bima Sena Bayu Gunawan, Agus Indra Mahendra, Alfany Riza Prayanata, Julian Widya Fasya, Zulfikar Davbi Mahendra Trianto, Setyo Wahyu Politeknik Elektronika Negeri Surabaya Department of Electrical Engineering Indonesia Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Indonesia PT. Pakoakuina Department of 4.0 Indonesia PT. Sandana Department of Engineering Indonesia

ISBN: (纸本)9798350314731

Vannamei shrimp is the main commodity for shrimp exports in Indonesia. One of the important processes in shrimp cultivation is the process of sowing the seeds. Among the crucial stages of shrimp farming, the initial step involves purchasing shrimp seeds from nurseries before commencing the seeding process. Currently, the most process of calculating shrimp seeds is still done manually, leading to reduced efficiency and inadequate accuracy. In this research, the authors propose an implementation of a shrimp seed counting machine to enhance the manual counting method. By utilizing a camera, the machine captured images of shrimp seeds to facilitate calculation. Therefore, the obtained image from the camera was processed in the Raspberry PI 4 processor using various CNN methods, i.e., Faster R-CNN ResNet50, Faster R-CNN ResNet101, and SSD MobileNetV2. The primary objective of this research to compare these three models. Based on the F1 score, it is found that ResNet101 achieved the best performance in detection compared to the other two methods. However, this method results in the longest executing time compared to Faster R-CNN ResNet50 and SSD MobileNetV2. © 2023 IEEE.

关键词： Convolutional neural networks

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eduARM: Web Platform to Support the Teaching and Learning of the ARM Architecture 15

eduARM: Web Platform to Support the Teaching and Learning of...

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15th International Conference on computer Supported Education, CSEDU 2023

作者： Alves, Maria Inês Araújo, António Duarte Lima, Bruno Faculty of Engineering University of Porto Porto Portugal INESC TEC Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal INESC TEC Department of Informatics Engineering Faculty of Engineering University of Porto Porto Portugal

ISBN: (纸本)9789897586415

computer architecture is a prevalent topic of study in informatics and Electrical engineering courses, though students’ overall grasp of this subject’s concepts is many times hampered, mainly due to the lack of educational tools that can intuitively represent the internal behaviour of a CPU. With the evolution of the ARM architecture and its adoption in higher education institutions, the demand for this sort of tool has increased. Educational tools, specifically developed for the ARMv8 processor, are scarce and inadequate for what is necessary in an academic context. In order to contribute towards solving this problem, eduARM, a practical and interactive web platform that simulates how a ARMv8 CPU functions, was developed and is presented through this paper. Since this tool’s main purpose is to aid computer architecture students, contributing to an improvement in their learning experience, it comprises varied concepts of computer architecture and organization in a simple and intuitive manner, such as the internal structure of a CPU, in both its unicycle and pipelined versions, and the effects of executing a set of instructions. As to better understand its value, the developed tool was then validated through a case study with the participation of computer architecture students. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Students

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Plugged-in electric vehicle-assisted demand response strategy for residential energy management

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Energy informatics 2023年第1期6卷 6页

作者： Alfaverh, Khaldoon Alfaverh, Fayiz Szamel, Laszlo Department of Electric Power Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics P.O.B. 91 Budapest1521 Hungary School of Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom

Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip. © 2023, The Author(s).

关键词： Energy utilization

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Logo Detection Plagiarism Using Generative Adversarial Network 9

Logo Detection Plagiarism Using Generative Adversarial Netwo...

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9th International Conference on informatics and Computing, ICIC 2024

作者： Rivanni, Rizky Zamzami, Elviawaty Muisa Sembiring, Rahmat Widia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia

ISBN: (纸本)9798331517601

Logo, as a graphic form and visual representation of a company, organization, or brand, has been widely utilized and disseminated across the internet. This accessibility makes logo easily replicable, allowing for the imitation and reuse of concepts or forms for creating new logo. Therefore, it is required to have a system that is able to analyze and identify the similarity between logos. In this study, logo plagiarism detection is proposed by using Generative Adversarial Network based on CycleGAN approach. The logo training process involves generator and discriminator networks as part of the GAN process to recognize the logos characteristics. The trained GAN model is tested by generating logo images using the model weights and inputting comparison logos to test their similarity through Template Matching method for plagiarism detection. From the generated test logo results, the logo produced by the generator closely resemble the original logo. The training results using GAN based on CycleGAN approach yielded average loss value of Generator A 6.3572, Generator B 6.1541, Discriminator A 0.6378 and Discriminator B 0.7186. The lowest logo plagiarism analysis value was obtained with an accuracy of 20.11% for logos with gradual changes in shape and different perspectives. The highest accuracy of 93.40% was achieved for logos with no changes at all. © 2024 IEEE.

关键词： CycleGAN Generative Adversarial Network Logo plagiarism Logo similarity Template Matching

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Brain Tumour Segmentation in MRI Data Using Gray Level Co-Occurrence Matrix

Brain Tumour Segmentation in MRI Data Using Gray Level Co-Oc...

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2024 International Conference on Electrical and Information Technology, IEIT 2024

作者： Al Ghifari, Muhammad Zidni Ferriastuti, Widiana Harsono, Tri Sigit, Riyanto Hayati, Fierly Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Dr. Soetomo General Academic Hospital Airlangga University Faculty of Medicine Department of Radiology Surabaya Indonesia

ISBN: (纸本)9798331516888

In the medical field, the image segmentation process is very important as the first step to diagnose a disease. Currently, brain tumors are one of the most life-threatening diseases. To detect brain tumors more accurately in less time, many techniques have been proposed using image segmentation. This research will present a brain tumour segmentation method on brain MRI data using GLCM (Grey Level Co-Occurrence Matrix). The input data in this system is the planned MRI data obtained from a hospital and Open Source MRI data in the form of grey level which is calculated using GLCM. Calculation using GLCM which is to find the maximum probability among the grey values of surrounding cells. The result of the GLCM calculation produces texture values (Texture Features). The combination of the intensity value in the MRI data and its texture value as an input parameter in the segmentation process. The method used to segment the tumour is thresholding by identifying contours on the MRI image. Both intensity-based and texture-based images are subjected to the same segmentation method. The segmentation result in this study is the categorization of cell areas affected by tumours and normal cells. To see the performance of the system built in this study using DSC (Dice Similarity Coefficient) and JC (Jaccard Coefficient) presented in the form of a percentage. Tests were conducted with 25 MRI data with two types of tumours namely glioblastoma and meningioma. Evaluation using Dice Similarity and Jaccard methods showed a small difference in accuracy between texture and intensity images. The texture image has an average Dice Similarity of 82.81% and Jaccard of 88.40%. While the intensity image has an average Dice Similarity of 80.35% and Jaccard 87.22%. In conclusion, the GLCMCA method provides slightly higher and more precise segmentation accuracy. © 2024 IEEE.

关键词： Magnetic resonance imaging

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