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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Khaderani, Helmi Ahmad Muhammadi, Ishlah Muhammad, Lutfi Razan Sucipto, Adi Kusumastuti, Luthfi Restu Nurhayati, Ade Ahdan, Syaiful Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia Telkom University Faculty of Engineering Jakarta Barat11710 Indonesia

ISBN: (纸本)9798350305029

Pending interest table (PIT) is one of the data structures on each named data network router. The speed of delivery of interest as well as the interest served at PIT are several important parameters for measuring PIT performance. This research was conducted to analyze the performance of PIT by measuring the round trip time (RTT) of sending data and sending interests from consumers to producers on the Palapa Ring Sumatra topology. Testing is carried out using miniNDN by varying the length of the prefix and the number of interests sent. From the simulation and results of the analysis that has been carried out, it is known that the correlation between the length of the prefix and RTT is directly proportional. The longer the prefix of an interest packet, the higher the RTT value. Apart from the simulation results, the number of interest packets sent is the same as those received at each node. © 2023 IEEE.

关键词： Topology

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An Improved Transfer-Learning for Image-Based Species Classification of Protected Indonesians Birds

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computers, Materials & Continua 2022年第12期73卷 4577-4593页

作者： Chao-Lung Yang Yulius Harjoseputro Yu-Chen Hu Yung-Yao Chen Department Industrial Management National Taiwan University of Science and TechnologyTaipei106Taiwan Department Electronics and Computer Engineering National Taiwan University of Science and TechnologyTaipei106Taiwan Department Informatics Universitas Atma Jaya YogyakartaYogyakarta55281Indonesia Department Computer Science and Information Management Providence UniversityTaichung433Taiwan

This research proposed an improved transfer-learning bird classification framework to achieve a more precise classification of Protected Indonesia Birds(PIB)which have been identified as the endangered bird *** framework takes advantage of using the proposed sequence of Batch Normalization Dropout Fully-Connected(BNDFC)layers to enhance the baseline model of transfer *** main contribution of this work is the proposed sequence of BNDFC that can be applied to any Convolutional Neural Network(CNN)based model to improve the classification accuracy,especially for image-based species classification *** experiment results show that the proposed sequence of BNDFC layers outperform other combination of *** addition of BNDFC can improve the model’s performance across ten different CNN-based *** average,BNDFC can improve by approximately 19.88%in Accuracy,24.43%in F-measure,17.93%in G-mean,23.41%in Sensitivity,and 18.76%in ***,applying fine-tuning(FT)is able to enhance the accuracy by 0.85%with a smaller validation loss of 18.33%*** addition,MobileNetV2 was observed to be the best baseline model with the lightest size of 35.9 MB and the highest accuracy of 88.07%in the validation set.

关键词： Transfer learning convolutional neural network(CNN) species classification protected indonesia bird(PIB)

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Delay Tolerant and Multipath Reliability for Energy Efficient Heterogeneous VANETs Network 1

Delay Tolerant and Multipath Reliability for Energy Efficien...

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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Pratama, Zudha Setiarso, Ichwan Mahmood, Sarmad Nozad Almohamadi, Mohammed Alani, Sameer Abbas, Fatima Hashim Abdulhammed, Razan Alkhayyat, Ahmed H. Ali, Rabei Raad University of Dian Nuswantoro Informatics Engineering Kediri Indonesia University of Dian Nuswantoro Visual Communication Design Kediri Indonesia Al-Kitab University Computer Technology Engineering Kirkuk Iraq College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna Iraq University of Mashreq Research Center Baghdad Iraq Al-Mustaqbal University College Medical Laboratories Techniques Babil Iraq College of Technical Engineering The Islamic University Najaf Iraq Northern Technical University Department of Computer Engineering Technology Mosul Iraq

ISBN: (纸本)9798350306484

Vehicular Adhoc Networks (VANETs) provide communication among vehicles to build smart cities using Intelligent Transportation System (ITS). In this paper, we proposed Delay Tolerant and Multipath Reliability (DTMR) in VANETs. The simulation of the DTMR method is performed using OMNet++ network simulator with Simulation of Urban Mobility (SUMO). The parameters which are considered for the performance analysis are end-to-end delay, data failure rate, data success rate and throughput. The results show that the DTMR method produces 85ms lower end-to-end delay, 13% lower data failure rate, 93 % higher data success rate and 614 Kbps higher throughput. The proposed DTMR method has better overall performance than the ISFF and VDPR methods. © 2023 IEEE.

关键词： Energy efficiency

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Development of Mutal Recognition of Education Approach in Advanced computer-Oriented engineering Technologies in the Baltic Region 64

Development of Mutal Recognition of Education Approach in Ad...

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64th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical university, RTUCON 2023

作者： Kunicina, Nadezhda Rassolkin, Anton Bruzgiene, Rasa Plonis, Darius Caiko, Jelena Riga Technical University Institute of Electrical Engineering and Electronics Feee Riga Latvia Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia Kaunas University of Technology Faculty of Informatics Department of Computer Sciences Kaunas Lithuania Vilnius Gediminas Technical University Department of Electronic Systems Vilnius Lithuania

ISBN: (纸本)9798350317732

The continued improvement and development of study programmes content for mutual recognition of study programmes among the largest technical universities in the Baltic region provides a very good opportunity for regional development as it brings a good advancement in academics and research at the involved universities, allows for more flexible and reliable collaboration with industrial partners, and provides an opportunity to develop new technologies and implement them for regional support. The goal of the development is to promote the involvement of students in the design of adaptive systems by providing them with the necessary set of skills that will allow them to immediately engage in the design of innovative adaptive systems and successfully solve technological challenges that have been identified through the cooperation of educational and research institutions. The adoption of an innovative educational strategy will aid in the creation of new technology and creative solutions even before students begin their studies. Collaboration between academic personnel and industry will help students build their abilities. © 2023 IEEE.

关键词： Students

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Rendering Delay Minimization in RIS-Assisted Edge Computing for IIoT with VR Streaming

Rendering Delay Minimization in RIS-Assisted Edge Computing ...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Wang, Xuan Mukherjee, Mithun Kumar, V. Mavromoustakis, C.X. Shoufan, A. Guo, M. Zhang, Qi Nanjing University of Information Science and Technology Nanjing China Bharat Sanchar Nigam Limited India Cyprus Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Guangdong Polytechnic Normal University Guangzhou China Aarhus University Digit Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350370218

The end-users who are subjected to inadequate wireless network coverage sometimes experience a lack of ef-ficient utilization of available bandwidth due to low channel gain. The circumstance mentioned above has a negative impact on the overall virtual reality (VR) streaming efficiency in the industrial Internet of Things (IIoT) with VR add-ons. This study focuses on examining a wireless network that utilizes a reconfigurable intelligent surface (RIS) to assist in improving the channel efficiency for end-user devices located at weak signal reception zones. The optimization problem formulation related to the allocation of rendering resources in edge servers and the distribution of downlink bandwidth for VR content from the edge server can be expressed as a quadratically constrained quadratic problem. The non-convex optimization problem has been solved by dividing the problem into three sub-problems and solved using the block coordinate descent (BCD) method. Based on our comprehensive simulation findings, we demonstrate a significant enhancement in reducing delays, including rendering and transmission delays, when employing RIS compared to other reference scenarios. This improvement is observed through an increase in the number of end-users, including those suffering from suboptimal signal reception conditions. © 2023 IEEE.

关键词： AR/VR Edge computing IIoT reconfigurable intelligent surfaces rendering delay

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On the Optimization of User Allocation in Heterogeneous 5G Networks Using DUDe Techniques

On the Optimization of User Allocation in Heterogeneous 5G N...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Konstantinos Tsachrelias Apostolos Gkamas Chrysostomos-Athanasios Katsigiannis Christos Bouras Vasileios Kokkinos Philippos Pouyioutas Computer Engineering and Informatics Department University of Patras Patras Greece University Ecclesiastical Academy of Athens Athens Greece Computer Science Department University of Nicosia Nicosia Cyprus

In previous years, 3G and 4G cellular homogeneous networks configured with macro Base Stations (BSs) relied only on the downlink signal, even though transmission power and interference levels differ significantly between uplink and downlink. In 5G Heterogeneous Networks (HetNet), a new generation which have multiple BSs of different types such as Femto BS and Macro BS, there is the possibility of choosing to receive the data from one BS and transmit them to a different BS, and therefore decoupling the uplink and downlink. Especially, the increasing demand for faster, more reliable, and efficient connectivity has made the optimization of User Equipment (UE) allocation in 5G networks a crucial task. To tackle the challenges posed by heterogeneous 5G networks, this study compares and evaluates the performance of Downlink/Uplink Decoupling (DUDe) and traditional Downlink/Uplink Coupled (DUCo) user allocation approaches. Simulation results show that DUDe UE allocation outperforms DUCo by providing improved network performance and more efficient utilization of network resources in diverse network conditions. These findings have important implications for the design and optimization of 5G networks and provide valuable insights for researchers and practitioners in the field.

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Optimization of Residential Net Load using Demand Response and Battery Storage

Optimization of Residential Net Load using Demand Response a...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Jorge Bracho Neofytos Ilia George Makrides Alex R. Tseng Panagiotis Herodotou George E. Georgiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus (UCY) PHAETHON Centre of Excellence (CoE) for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Electrical and Computer Engineering Princeton University New Jersey U.S

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Demand side management (DSM) and solar-plus-storage systems are enabling technologies that facilitate consumers to increase self-consumption (SC), while reducing electricity costs through efficient and intelligent electricity usage. Even though many demand response (DR) strategies have been presented for the residential sector, a major challenge remains the joint optimal utilization and synergies of DR and the battery systems flexibility scheduling. This paper explores how the optimization of DR and battery energy storage systems (BESS) scheduling can notably improve the net load profile of residential users. The obtained results indicate that the optimized DR model and battery storage effectively support load shifting and peak energy utilization. Specifically, the analysis suggests that the combination of DR and an 8kWh battery storage system increased the SC from 55.04% to 95.84% and the SS from 37.50% to 66.88%, compared to a baseline scenario (without battery storage and DR actions). Ultimately, the proposed optimization achieved a higher degree of energy autonomy for the yearly profile of a median household by minimizing the sum of absolute values of the net load with load shifting and BESS charging/discharging. Finally, the optimization model can be used by aggregators and system operators for scheduling and optimizing flexibility actions.

关键词： Renewable energy sources Costs Electricity Optimization models Solar energy Power system stability Demand response Stability analysis Batteries Sustainable development

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Hybrid Classical-Quantum Optimization for Ensemble Learning 9

Hybrid Classical-Quantum Optimization for Ensemble Learning

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9th International Conference on Advanced informatics: Concepts, Theory and Applications, ICAICTA 2022

作者： Yulianti, Lenny Putri Santoso, Judhi Trisetyarso, Agung Surendro, Kridanto Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Bina Nusantara University Program - Computer Science Bandung Indonesia

ISBN: (数字)9781665456685

ISBN: (纸本)9781665456685

Ensemble learning has gained attention due to its ability to improve predictive performance in classification and regression problems. However, there are several issues related to optimizing accuracy, diversity, and efficiency simultaneously in the ensemble learning process. Existing studies mostly focus on one or two of them, or focus on all three but only on the specific process of ensemble learning. Besides, the quantum approach has been expected to accelerate the optimization process while maintaining accuracy. The contribution of this study is to design a hybrid classical-quantum methodology for optimizing ensemble learning in a holistic way. This methodology combines classical (i.e. clustering, improved evaluation measures, and generalized weighted ensemble with internally tuned hyperparameters) and quantum (i.e. quantum annealing) approaches, and this methodology is analyzed to provide more optimal results. © 2022 IEEE.

关键词： Learning systems

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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A Mamdani Fuzzy Inference System for Comprehensive Diabetes Risk Evaluation

A Mamdani Fuzzy Inference System for Comprehensive Diabetes ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Tanjina Ahmed Tuly Tanjid Ahammed Shafin Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shamsul Arefin Mohammad Shorif Uddin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetes mellitus is a chronic condition characterized by insufficient insulin production and the inability to effectively utilize the insulin hormone, leading to elevated blood glucose levels. As diabetes is incurable and persists throughout a patient's lifetime, effective management requires early identification of risk factors to ensure continuous and feasible treatment. This study proposes a unique decision support system based on the Mamdani Fuzzy Inference System (MFIS) to predict diabetes risk. This study utilizes a publicly available diabetes dataset containing 442 data that includes nine key health metrics such as BMI, blood glucose, cholesterol levels, and blood pressure to predict the risk of diabetes. The dataset aims to support early diagnosis and better diabetes management by providing insights into individual risk factors. This resource contributes to the development of more accurate decision-support systems in healthcare. We identify key variables and establish a membership function to assess individual risk. The Fuzzy Inference System (FIS) is well-suited for handling complex and uncertain conditions, making it an effective tool for this application. The advantages of the MFIS include its ability to incorporate expert knowledge through linguistic variables and fuzzy rules. This system demonstrates the practical benefits of MFIS in developing intelligent decision-making tools, particularly in the domain of diabetes risk prediction.

关键词： Fuzzy logic Decision support systems Accuracy Uncertainty Predictive models Blood pressure Diabetes Glucose Insulin Blood

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