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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： AlShurbaji, Mohammad Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350393118

The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0 &90 to 60 &150, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections. © 2023 IEEE.

关键词： Generative adversarial networks

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Design and fabrication of photovoltaics based on MFS (Ag/BaTiO3/silicon p-type) structure

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Materials Science for Energy Technologies 2024年第1期7卷 29-34页

作者： Irzaman Dahrul, M. Rahmani, M. Rukyati, A.M. Samsidar Nurhidayah Deswardani, F. Peslinof, M. Jenie, R.P. Iskandar, J. Wahyuni, Y. Priandana, K. Siskandar, R. Department of Physics Faculty of Mathematics and Natural Sciences IPB University Bogor16680 Indonesia Department of Physics Faculty of Sciences and Technology Jambi University Jambi36361 Indonesia Department of Nutrition Faculty of Health Science and Technology Binawan University East Jakarta13630 Indonesia Department of Computer Engineering Pakuan University Bogor16142 Indonesia Department of Computer Science IPB University Bogor16680 Indonesia Computer Engineering Technology Study Program College of Vocational Studies IPB University West Java Bogor16151 Indonesia

The experiment was carried out by growing BaTiO3 (Undoped or Li-doped) on p-type Si(1 0 0) substrates using the Chemical Solution Deposition (CSD) method and spin coating at a rotational speed of 3000 rpm for 60 s, followed by heating at 850 °C. The characterization results show that the bandgap energy value of the thin film due to lithium doping reduces the bandgap energy value. This is presumably because the donor atom added to a semiconductor causes the allowable energy level to be slightly below the conduction band. The presence of this new band causes the thin film bandgap energy to decrease with a five-valent tantalum dip. The morphological properties showed that the BaTiO3/Si(1 0 0) thin film particles in the deposited lithium had a fairly homogeneous grain. With the addition of lithium acetate as a binder into barium titanate, the grain size is getting smaller because it is suspected that the lithium-ion radius is smaller than the barium-ion radius. Measurement of I-V on the thin film shows that the output voltage value increases with more light intensity hitting the surface of the thin film. The greater the light intensity, the greater the energy of the photons, so the electrons are easier to jump. The three things above (both electrical and morphological properties) conclude that the thin films grown have the potential for photovoltaics. © 2023

关键词： Thin films

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device 8

Objective Sleep Quality Measurement based on Fuzzy Logic and...

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8th International Conference on computer Science and Computational Intelligence, ICCSCI 2023

作者： Wijaya, Jason A. Andersen, Pauw D. Surantha, Nico Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method. © 2023 The Authors. Published by Elsevier B.V.

关键词： actigraphy artificial intelligences fuzzy logic sleep sleep quality

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Domain Adaptation in Color-Shifted Datasets Through Adversarial Learning Balancing 4

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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4th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Hidayaturrahman Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo School of Computer Science Bina Nusatara University Mathematics Department Jakarta11480 Indonesia Bina Nusatara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Computer Engineering Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation. © 2023 IEEE.

关键词： Color

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A Q-Learning Hybrid BRKGA Applied to the Knapsack Problem with Forfeits 13

A Q-Learning Hybrid BRKGA Applied to the Knapsack Problem wi...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Souto, Gabriel Aritsugi, Masayoshi Mendonca, Israel Simonetti, Luidi González, Pedro Henrique Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350308365

This paper presents a hybrid BRKGA (Q-HBRKGA) that combines BRKGA with Q-learning and a Local Branching technique to solve the Knapsack Problem with Forfeits(KPF). The aim is to tackle this problem, a vari-ant of the 0-1 Knapsack Problem, where a penalty cost is imposed when both items of a forfeit pair are included in the solution. Notably, Q-HBRKGA achieves superior results compared to existing approaches, establishing a valid strategy for KPF problem-solving. Computational experiments, utilizing benchmark instances, demonstrate the efficacy of Q-HBRKGA by outperforming state-of-the-art methods documented in the literature. © 2024 IEEE.

关键词： Reinforcement learning

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Smart Energy Management with Integrated WSN and WMN: Advancing Efficiency and Sustainability

Smart Energy Management with Integrated WSN and WMN: Advanci...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Ali Alrasheedi Mousa Al-Abdulhadi Raha Almutairi Abdullah AlRuwaiei Almas Khlaif Abdullah AlAlati Omar R. Daoud Electrical and Computer Engineering Program School of Engineering and Computing American International University Aljahra Kuwait

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

The aim of this paper is to provide an advanced WSN solution for power-sustainable energy management and efficient communication, integrating the latest developments from the SEMS. This work will focus on the research effort directed at improving M2M communication and integrating WSNs with wireless mobile networks, as well as addressing PAPR, energy consumption, and scalability. It now applies to real-time monitoring of power usage, room temperature, and lighting by integrating renewable resources of energy with sophisticated algorithms and sensor networks. Key results include a 16.6% saving in energy consumptions, 78% in system delay improvements, and the implementation of solar energy systems that have realized significant cost savings while assuring environmental sustainability. This work proves the ability of the approach through wide simulations and real-world deployments for scalable and eco-friendly energy management solutions. Besides, this work is closely related to worldwide goals for reduction of carbon footprints and efficient resource utilization.

关键词： Wireless sensor networks Renewable energy sources Energy consumption Scalability Peak to average power ratio Real-time systems Parity check codes Energy efficiency Delays Sustainable development

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A Secure Cloud-Based Infrastructure for Virtual Sensors in IoT Environments 6

A Secure Cloud-Based Infrastructure for Virtual Sensors in I...

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6th International Conference of Women in Data Science at Prince Sultan University, WiDS-PSU 2023

作者： Makda, Tahira Jehan Luiza Barros, Ana Dilek, Selma University of Doha for Science and Technology College of Business Management Doha Qatar University of Ceará Computer Science Program State Fortaleza Brazil Hacettepe University Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665477239

Recent work has proven the effort of researchers to integrate small sensors and a cloud environment, delivering the Internet of Things (IoT). Sensors as a service are one of the leading research concerns in this context. Nevertheless, security is becoming one of the most significant attributes of the IoT as sensors become more human-independent and are being extensively used to monitor human lives. That way, IoT brings many key security challenges that need attention, some of which we address in this position paper. We present a cloud-based infrastructure that can deliver sensors and actuators as a service, providing secure communication between them and the control nodes on which IoT applications rely while implementing Big Data algorithms. For mapping our proposal, two scenarios related to Health Assistance are discussed, considering secure communications in a sensor network. In conclusion, we propose a scope for future research in this field considering digital twin concepts. Since domains exploiting IoT technologies can benefit from adopting Digital Twin, our goal is to evolve a virtual sensor and actuator system into this technology. © 2023 IEEE.

关键词： Actuators

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Optimized Instruction Set Architecture for programmable Memory Test Pattern Generation 21

Optimized Instruction Set Architecture for Programmable Memo...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Park, Seokmin Park, Jewoo Lee, Young-Woo Inha University School of Electrical and Computer Engineering Incheon Korea Republic of Inha University Program in Semiconductor and Device Korea Republic of Hyundai Motor Company Hwaseong Korea Republic of

ISBN: (纸本)9798350377088

Continuous progress in memory semiconductor manufacturing technology has significantly increased capacities, densities, and operating frequencies. However, these developments have also increased the probability of memory defects, consequently diminishing yield rates. To overcome this issue, various memory test pattern algorithms have been devised to identify the type of memory defect, primarily categorized into linear and nonlinear patterns. This paper presents an instruction-based approach for efficiently generating diverse nonlinear patterns. The proposed instruction set architecture (ISA) provides flexibility in generating both linear and intricate nonlinear patterns, while also ensuring a compact memory test setup cycle, regardless of the memory cell size. © 2024 IEEE.

关键词： Cost reduction

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Electricity Consumption Prediction using Generative Models

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Transactions of the Korean Institute of Electrical Engineers 2022年第1期71卷 218-224页

作者： Kim, Heesang Im, Hyeonseung Moon, Yang-Sae Dept. of Computer Science and Engineering Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of

Recently, advanced metering infrastructure (AMI) has been deployed for power demand distribution and energy saving, and correspondingly traditional watt-hour meters installed in apartments and industrial sites are also being replaced with AMIs. Accordingly, power demand prediction using AMIs will become increasingly important to save electrical energy consumption. In this paper, we develop various deep learning-based electricity consumption prediction models using simple neural networks, convolutional neural networks, recurrent neural networks, and encoder-decoder-based generative models. To build prediction models, we use average power demand data collected from various home smart meters. Experimental results show that the generative model outperforms other deep learning-based models in terms of mean squared error, and we roughly explain why the generative model is better than other models by examining the activation layer output distributions. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Watt hour meters

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Prediction of Internet Broadband Connectivity Classification in West Java Indonesia using Machine Learning

Prediction of Internet Broadband Connectivity Classification...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Kusuma Atmaja, Wahyu Haris Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department West Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Engineering Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy. © 2023 IEEE.

关键词： Prediction models

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