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24th International Electronics Symposium, IES 2022

作者： Adamu Islam Mashuri, M. Hadi, Moch Zen Samsono Sudibyo, Rahardhita Widyatra Kristalina, Prima Pratiarso, Aries Department of Informatics and Computer Engineering Electronic Engineering Jawa Timur Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Nowadays, artificial intelligence has been widely implemented in agriculture, animal husbandry, education, and many more. The most widely developed model at this time is the Deep Learning model. To get maximum results, the best Deep Learning architecture is needed. In this paper, we will compare methods for detecting victims of natural disasters based on the Deep Learning model. The models that we will compare are the You Only Look Once (YOLO) algorithm, namely YOLOv5, and Convolutional Neural Network (CNN) with the MobileNet model and the VGG-16 model. We train each algorithm through a natural disaster victim training data set and analyze performance to determine what model is the most optimal. To identify the types of victims of natural disasters. From our results, the MobileNet model is superior to other models with 98% accuracy. © 2022 IEEE.

关键词： Agriculture

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Recommending Public Transit Route: Ant-Colony Optimization or Dijkstra?

Recommending Public Transit Route: Ant-Colony Optimization o...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Jabbar, Abdul Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Informatics Engineering Department Malang Indonesia Universitas Brawijaya Faculty of Computer Science Information System Department Malang Indonesia

ISBN: (纸本)9798350369359

Advances in algorithmic studies have been made recently, especially in the area of finding the shortest path. Despite taking a greedy approach, the Dijkstra algorithm is well recognized for its efficacy and has established itself as the industry standard for identifying the shortest path in a graph. Conversely, scientists have embraced the Ant-Colony Optimization (ACO) approach, which draws inspiration from the behavior of ants. This study examines the two methods and determines if, given equivalent resources, ACO can generate Dijkstra-like recommendations. Research data is subjected to both algorithms, and the outcomes are presented visually. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. Memory consumption, computation time, initial memory allocation, and final memory allocation are evaluation parameters. ACO is more efficient than Dijkstra in terms of execution time and number of pathways traversed, according to the study's repeated evaluation methodology. This iterative assessment procedure is carried out to address the possibility of single measurements resulting from the JavaScript Garbage Collector. The study's findings imply that a larger memory may be necessary for ACO to deliver the desired outcomes. These results could also be impacted by each algorithm's distinct qualities. © 2023 IEEE.

关键词： Ant colony optimization

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Adaptive 360° Video Streaming Over Wireless Communication Channels 9

Adaptive 360° Video Streaming Over Wireless Communication C...

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9th International Conference on Frontiers of Signal Processing, ICFSP 2024

作者： Valiandi, Ioanna Pattichis, Marios S. Kyriacou, Efthyvoulos Panayides, Andreas S. VIDEOMICS Group CYENS Centre of Excellence Nicosia Cyprus University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM United States Cyprus University of Technology Department of Electrical Eng. Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350353235

360° video streaming is one of the prevalent communication technologies for enhancing user experience and has thus seen widespread adoption in virtual and mixed reality applications. However, delivering content at scale while securing the quality of wirelessly communicated 360° videos in real-time poses significant challenges. 360° videos come in ultra-high definition, necessitate unprecedented bitrate demands and involve high encoding complexity. The time-varying nature of underlying wireless channels further introduces a destabilizing factor, calling for video systems to seamlessly adjust to varying bandwidth throughput to maintain adequate quality of service and experience. To address this issue, in this study, we have developed a multi-objective optimization framework for real-time video encoding adaptation. The objective is to optimize both video quality and encoding efficiency while minimizing the required bitrate, subject to real-time application constraints. To achieve this, we relied on generating (offline) precise forward prediction models of video quality, bitrate demands, and encoding time, that can be used to select the optimum encoding configuration in real-time. To validate our methods, we implemented an adaptive video encoding controller, and ran emulations employing actual network traces from 5G mobile video streaming scenarios, using the popular open-source x264 and x265 codecs for video encoding. A dataset of 4K omnidirectional videos at 30 frames per second was used. © 2024 IEEE.

关键词： Video streaming

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Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

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computer Modeling in engineering & Sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani Farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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

作者： Putra, Qori Alfatah Sari, Christy Atika Rachmawanto, Eko Hari Cahyo, Nur Ryan Dwi Mulyanto, Edy Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers. © 2023 IEEE.

关键词： Image segmentation

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Implementation of the SMART Method and Location Based Service Technology in a Sewing Service Search Application 9

Implementation of the SMART Method and Location Based Servic...

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Mumpuni, Retno Santoso, Bagus Jati Wahanani, Henni Endah Rahajoe, Ani Dijah Maulana, Hendra Maghfiro, Feronika Nur Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Department of Informatics Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798350306835

The tailoring business involves services where tailors create or repair clothing. Currently, the number of tailors with various skill sets is on the rise. However, customers often struggle to find tailors who meet their specific needs. They have to spend time visiting tailor shops, just to assess whether a tailor is a suitable match or to place an order, even when the distance and location are uncertain. This inefficiency hampers the process. In this rapidly advancing technological era, many businesses leverage technology to promote their services, including the tailoring industry. To address this issue, the author aims to develop an Android-based Tailor Locator Application that utilizes Location Based Services (LBS) and the SMART Method. This application will utilize LBS technology to display the distance and location between customers and tailors, while continuously updating the location information of both customers and tailors. The application will also implement the SMART method to rank tailors based on ratings and provide tailor recommendations. © 2023 IEEE.

关键词： Location based services

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Exploring Energy Culture and Efficiency Awareness: Insights from Private and Public Sectors in Romania 3

Exploring Energy Culture and Efficiency Awareness: Insights ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Muresan, Claudia Stet, Denisa Cirstea, Stefan Czumbil, Levente Polycarpou, Alexis Micu, Dan D. Technical University of Cluj-Napoca Energy Transition Research Center Cluj-Napoca Romania Frederick University Computer Engineering and Informatics Department of Electrical Engineering Nicosia Cyprus

ISBN: (纸本)9798350375923

This paper focuses on the energy transition as a social transition and highlights the importance of understanding individual and collective behaviors, aspirations and perceptions regarding energy consumption. The conceptual framework of energy cultures serves as a basic tool for investigating energy behavior, facilitating a deeper understanding of the factors influencing energy consumption. The study, carried out within the ENERGEIA project, focuses on assessing energy culture and energy efficiency awareness in both private entities and public institutions in Romania. Using a comprehensive questionnaire tool, the research aims to uncover the main challenges and barriers faced by organizations in implementing energy efficiency measures. The paper presents a quantitative analysis of the survey data aiming to gather information about the energy culture of companies and public bodies to identify common opportunities for action to improve energy culture and awareness levels. © 2024 IEEE.

关键词： awareness energy culture energy culture framework energy efficiency sustainability

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Overview of Load Disaggregation Models, Methods and Hardware 59

Overview of Load Disaggregation Models, Methods and Hardware

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59th International Universities Power engineering Conference, UPEC 2024

作者： Lancranjan, Mircea N. Muresan, Alexandru Jurj, Dacian I. Berciu, Alexandru Polycarpou, Alexis Czumbil, Levente Micu, Dan D. Technical University of Cluj-Napoca Department Electrical Engineering Cluj-Napoca Romania Technical University of Cluj-Napoca Faculty of Automation and Computer Science Cluj-Napoca Romania Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (纸本)9798350379730

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated. © 2024 IEEE.

关键词： AI Data Cleaning Energy Efficiency Energy Signature Load Disaggregation Load Monitoring NILM

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Implementation of Convolutional Neural Network for COVID19 Screening using X-Rays Images

Implementation of Convolutional Neural Network for COVID19 S...

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Delimayanti, Mera Kartika Mardiyono, Anggi Warsuta, Bambang Puspitaningrum, Eka Suci Naryanto, Rizqi Fitri Naryaningsih, Agustien Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Indonesia Universitas Negeri Semarang Faculty of Engineering Department of Mechanical Engineering Indonesia Fisheries Research Center Indonesia

ISBN: (纸本)9798350320954

Various unwelcome conditions have existed since the introduction of the novel coronavirus illness (COVID-19). COVID-19 can cause fever, muscle soreness, shortness of breath, cough, headache, and other symptoms. Diagnosis at an early stage is a crucial aspect of successful treatment. Therefore, it is necessary to seek out alternate methods for COVID-19 detection. Among the existing imaging resources, X-ray images are generally accessible and inexpensive. Consequently, an alternate diagnostic tool for detecting COVID-19 instances is provided using available resources. In the first stages of COVID-19, X-rays detected the disease before it spread to the lungs and caused more damage. Machine learning models can help clinicians accomplish jobs more quickly and accurately. In addition to chest X-ray pictures and fundus images, deep learning algorithms have been used to diagnose illnesses. This research was conducted to classify the X-ray chest images in COVID-19 and normal cases based on the public datasets which were used. This analysis uses 5600 images from the accessible resources, and a Convolutional Neural Network (CNN) architecture with the VGG16 algorithm was employed to diagnose COVID19. VGG16 is object identification and classification method that can classify with greater precision than most other deep learning algorithms. Transfer learning and fine-tuning were employed to help for improving the performance. The results showed that the VGG16 network had an accuracy of 98.13%. This research has implications for the early detection of COVID-19 by using X-ray images. The experiment and analysis reveal our suggested method's promising and stable performance compared to the current standard. © 2023 IEEE.

关键词： COVID-19

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Enhancing Energy Efficiency and Sustainability with Hybrid AC/DC Microgrids for Energy Transition 3

Enhancing Energy Efficiency and Sustainability with Hybrid A...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Charalambous, Chrysanthos Polycarpou, Alexis Efthymiou, Venizelos Georghiou, George E. University of Cyprus PV-Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Frederick University Department of Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350375923

The transition to a lower-carbon energy system across Europe brings numerous opportunities and challenges, necessitating advancements in sustainable and eco-friendly energy solutions. This study examines the design and operation of distribution networks and microgrids, particularly emphasizing the integration of hybrid AC/DC distribution systems. The primary goals of this integration are to enhance overall system performance and reduce operating expenses. Utilizing DIgSILENT Power Factory software, the research simulates a microgrid at the FOSS Research Center for Sustainable Energy at the University of Cyprus, incorporating real load and PV generation data. In order to reduce the amount of energy imported from the main grid, this approach evaluates various combinations of AC and DC loads, power production units, and energy storage solutions. The results show that the most optimal structure, with 65% DC loads and 35% AC loads, maximizes efficiency as well as minimizes running expenses. The analysis highlights a notable decrease in imported energy, which dropped from 60.3 kWh/day at full AC loads to 51.6 kWh/day at the optimal DC ratio. Finally, the design and implementation of effective, sustainable energy systems that are in line with changing European energy policy and technology breakthroughs will be significantly impacted by these outcomes. © 2024 IEEE.

关键词： Energy transition

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