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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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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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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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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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University department Recommendations Using Subject-Score-Based Skyline Queries

University Department Recommendations Using Subject-Score-Ba...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Damarjati, Cahya Wicaksana, Galang Suripto, Slamet Wijayanto, Heri Setyawan, Haris Chen, Hsing-Chung Universitas Muhammadiyah Yogyakarta Department of Information Technology Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Yogyakarta Indonesia Universitas Mataram Department of Informatics Engineering Mataram Indonesia Asia University China Medical University Department of Computer Science and Information Engineering Department of Medical Research Taichung Taiwan

ISBN: (纸本)9798350379839

Selecting the right university department is a critical decision-making problem for high school graduates, significantly influenced by limited guidance and personal preferences. Traditional counseling methods often do not fully utilize available academic data, resulting in less effective recommendations. This study proposed a Subject-Score-Based Skyline Queries Algorithm to enhance decision-making by recommending university departments based on candidates' high school subject scores. It used the score differences and minimum score rule in addition to the native Skyline Queries. The Algorithm identifies departments where a candidate's grades meet or exceed minimum requirements across multiple academic subjects, thereby suggesting optimal matches. This approach aims to mitigate uncertainty and enhance decision confidence among prospective students, potentially reducing dropout rates and improving academic outcomes. Experimental results demonstrate the method's effectiveness in providing personalized recommendations, underscoring its applicability beyond university admissions to various decision-making contexts. Future research could extend this approach by incorporating non-academic factors for more comprehensive guidance. © 2024 IEEE.

关键词： Students

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Performance Analysis of RouteSegmentation Algorithm in Identifying Perimeter Area Around Travel Route 16

Performance Analysis of RouteSegmentation Algorithm in Ident...

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16th International Conference on Information Technology and Electrical engineering, ICITEE 2024

作者： Bachmid, Muhammad Avied Anshari Haq, Muhammad Yasir Mumtaz, Muhammad Rafly Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Informatics Engineering Malang Indonesia Universitas Brawijaya Faculty of Computer Science Malang Indonesia Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Information System Malang Indonesia

ISBN: (纸本)9798350375817

Prior study has developed the RouteSegmentation algorithm to identify the perimeter area surrounding a route. In this study, a comparative experiment was carried out to investigate the performance of the RouteSegmentation algorithm implementation in contrast to the RouteBoxer algorithm. Both algorithms play a crucial role in geographical information systems (GIS), particularly in enhancing navigational tools by identifying points of interest along specified routes. Through systematic experimentation using various perimeter distance values and types of route, the trade-offs between processing efficiency and the granularity of the area identified by these algorithms were evaluated. The results demonstrate that RouteSegmentation maintained relatively consistent performance across different perimeter distance values and statistically significantly faster processing time than RouteBoxer. Furthermore, while the RouteBoxer algorithm benefits from larger perimeter distance values by reducing processing time, it compromises the coverage results of the algorithm and potentially undermines its usefulness in practical scenarios. This study not only provides insights into the optimal use of perimeter distance values for each algorithm but also guides users in selecting the appropriate algorithm based on their specific application needs, balancing detailed geographical analysis and runtime efficiency. © 2024 IEEE.

关键词： Transportation routes

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A Niching Regression Adaptive Memetic Algorithm for Multimodal Optimization of the Euclidean Traveling Salesman Problem

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IEEE Transactions on Evolutionary Computation 2023年第5期27卷 1413-1426页

作者： Jian, Shi-Jie Hsieh, Sun-Yuan National Cheng Kung University Department of Computer Science and Information Engineering Tainan City70101 Taiwan Institute of Manufacturing Information and Systems The Center for Innovative FinTech Business Models The International Center for the Scientific Development of Shrimp Aquaculture National Cheng Kung University Department of Computer Science and Information Engineering Institute of Medical Informatics Tainan City70101 Taiwan

The traveling salesman problem (TSP) has been studied for many years. In particular, the multimodal optimization of the TSP is important for practical applications, because decision-makers can select the best candidate based on current conditions and requirements. In the Euclidean TSP, there are n points in R^d space with Euclidean distance between any two points, that is, d(x, y) =||x-y||_2. The goal is to find a tour of minimum length visiting each point. In this article, we only focus on the case that d=2. Recently, in order to efficiently handle the multimodal optimization of the TSP, some methods have been developed to deal with it. Nevertheless, these methods usually perform poorly for large-scale cases. In this article, we propose a niching regression adaptive memetic algorithm (MA) to handle the multimodal optimization of the Euclidean TSP. We use the MA as the baseline algorithm and incorporate the neighborhood strategy to maintain the population diversity. In addition, we design a novel regression partition initialization and adaptive parameter control to enhance our algorithm, and propose the concept of the redundant individual to improve the search efficiency. To validate the performance of the proposed algorithm, we comprehensively conduct experiments on the multimodal optimization of TSP benchmark and the well-known TSPLIB library. The experimental results reveal that the proposed method outperforms other methods, especially for large-scale cases. © 1997-2012 IEEE.

关键词： Approximation algorithms

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Real Time Prediction Model for Air Pollution and Air Quality Index based on Machine Learning 1

Real Time Prediction Model for Air Pollution and Air Quality...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Kumar, Rakesh Sharma, Bhisham Shekhar, Shashi Dhaou, Imed Ben Singhal, Saurabh GLA University Department of Computer Engineering & Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals' wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city. © 2023 IEEE.

关键词： Forecasting

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Hybrid Encryption Based on Fernet and Rivest Shamir Adleman (RSA)

Hybrid Encryption Based on Fernet and Rivest Shamir Adleman ...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Hisbullah, Muhammad Dafi Sari, Christy Atika Rachmawanto, Eko Hari Handoko, Lekso Budi Umam, Chaerul Isinkaye, Folasade University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia Ekiti State University Department of Computer Science Ekiti Nigeria

ISBN: (纸本)9798350339215

RSA is an asymmetric encryption algorithm that uses two different keys, a public key to encrypt the plain text and a private key to decrypt the cipher text. Fernet is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. This study uses Fernet and RSA which is the combination of symmetric and asymmetric encryption called hybrid encryption. In addition, the cipher text will be hidden inside an image using Stepic. Hybrid Encryption uses asymmetric encryption to encrypt the symmetric encryption secret key, it will secure the symmetric encryption. The result of this study is the lowest error that we got as the MSE is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche (AVA) with 79.00% and 42.34% in order. Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 46.48% for UACI and 99.86% for the NPCR. © 2023 IEEE.

关键词： Signal to noise ratio

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Enhancing Collaborative Learning through Virtual Escape Rooms: Impact on Participants’ Perceptions

Enhancing Collaborative Learning through Virtual Escape Room...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Akrivi Krouska Christos Troussas Panagiotis Karkazis Phivos Mylonas Cleo Sgouropoulou Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (数字)9798331504502

ISBN: (纸本)9798331504519

Digital revolution has transformed education, allowing students to interact, communicate, and collaborate beyond traditional classroom settings. computer-Supported Collaborative Learning (CSCL) provides opportunities for teamwork and knowledge sharing regardless of geographic location. Recently, Virtual Reality (VR) has emerged as a promising tool to address these challenges, offering immersive, three-dimensional environments that enhance collaborative learning experiences. Collaborative Virtual Environments (CVEs) extend VR’s potential by enabling users to interact and work together in shared virtual spaces. Moreover, virtual escape rooms have gained traction as an innovative approach to game-based learning, combining the benefits of collaboration, problem-solving, and engagement to create an immersive and interactive educational experience. In view of the above, this paper explores the innovative use of virtual reality (VR) and educational digital escape room games as tools to promote collaborative learning in synchronous distance education and lifelong learning. By designing a cooperative game-based learning environment, the study seeks to leverage the problem-solving and teamwork skills inherent in such games to enhance the learning experience. The impact of these technologies on participants’ perceptions was assessed comparing the information emerged from two surveys conducted before and after the intervention. The findings show that the incorporation of virtual escape room into collaborative learning environments effects positively participants’ perceptions.

关键词： Surveys Federated learning Education Virtual environments Games Real-time systems Teamwork Problem-solving

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