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International Conference of computer and informatics engineering (IC2IE)

作者： Asep Kurniawan Prihatin Oktivasari Nur Fauzi Muhammad Noviandri Muhammad Iqbal Computer and Informatics Engineering Politeknik Negeri Jakarta Jakarta Indonesia

In the business process of a payment system using web applications, it requires a container as a place to run a program or what is commonly referred to as a server with services such as a web server, database server, and security system. This research aims to be able to provide cloud computing infrastructure for payment systems. With the aim of creating efficient, stable and secure server services. This research uses Google Cloud and Amazon Web Service as Load Balance providers for cloud computing, as well as using NginX for web server needs and cloudflare as a domain distribution and security system. The results of this study are information for designing a server based on cloud computing and can provide or prepare infrastructure needs for payment system needs, servers built with load balance are efficient and stable because they have a low failure value, and can accommodate up to 10,000 users with a user per connection ratio of 33.3 % and 100% can divert attacks sending Internet Control Message Protocol packets that lead to the Internet Protocol Server to the Internet Protocol Content Delivery Network service provider cloudflare.

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Dual-Band Planar Microstrip Monopole Antenna Design Using Multi-Objective Hybrid Optimization Algorithm

Dual-Band Planar Microstrip Monopole Antenna Design Using Mu...

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2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Hosseini, V. Shapour, F. Pinho, P. Farhang, Y. Majidzadeh, K. Ghobadi, Ch. Nourinia, J. Barshandeh, S. Shokri, M. Amiri, Zh. Jalilirad, M. Kaboutari, K. Payame Noor University Department of Computer Engineering and Information Technology Tehran Iran Islamic Azad University Urmia Branch Computer Engineering Department Urmia Iran Instituto de Telecomunicações Aveiro Portugal University of Aveiro Department of Electronics Telecommunications and Informatics Aveiro Portugal Islamic Azad University Khoy Branch Department of Computer Engineering Khoy Iran Urmia University Department of Electrical Engineering Urmia Iran School of Engineering Department of Computer Science Afagh Higher Education Institute Urmia Iran

ISBN: (纸本)9798350312843

This research uses a new proposed multi-objective optimization algorithm that utilizes an untested combined chaotic map to integrate Customized Mutated PSO (CM-PSO) with a Modified Genetic Algorithm (MGA) to design an antenna with specific electromagnetic characteristics. The hybrid approach achieves preferred outcomes faster than PSO, CM-PSO, GA, and MGA by avoiding being trapped in local minimums. The proposed Metaheuristic Algorithm (MA) functionality has been authenticated successfully using Benchmark Functions (BFs) like Rastrigin Function (RaF), Ackley Function (AF), Rosenbrock Function (RoF), and Booth Function (BoF). Finally, the validity of the offered approach for electromagnetic applications is demonstrated by optimizing a dual-band planar microstrip monopole antenna with a simple structure, such that its optimized S11 be less than -10 dB at two frequency bands encompasses 2.4 to 2.484 and 5.15 to 5.825 GHz for Wireless Local Area Network (WLAN) with IEEE 802.11 standards. The proposed algorithm allows the optimization criteria to be customized. The optimization algorithm developed in MATLAB is used to determine the necessary parameter adjustments in order to achieve expected frequency bands using CM-PSO or an innovative MGA, while high-frequency and electromagnetic simulations are performed using computer Simulation Technology (CST) Studio Suite. The dimensions of the proposed antenna's elements are critical input parameters of the algorithm, named decision variables. © 2023 IEEE.

关键词： Monopole antennas

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An Effective Deep Learning Technique for Analyzing COVID - 19 Using X-Ray Images 15th

An Effective Deep Learning Technique for Analyzing COVID - 1...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Uma Maheswari, V. Aluvalu, Rajanikanth Guduri, Manisha Kantipudi, M. V. V. Prasad Department of Computer Science and Engineering Chaitanya Bharati Institute of Technology Telangana Hyderabad India Pune India School of Computing and Informatics University of Louisiana at Lafayette LafayetteLA70503 United States University Pune412115 India

ISBN: (纸本)9783031810794

The COVID - 19 disease is a extremely contagious viral infection with significant global health insinuations. COVID has a negative impact on the world economy. If positive patients are identified early, the proliferation of this pandemic infection can be slackened. COVID-19 disease prophecy is useful for finding the people with health issues who are at risk of COVID. The Deep Learning and machine learning approaches for COVID prediction using X-ray images have the possibility to be tremendously helpful in addressing the shortage of clinicians and doctors in rural areas. This research presents a deep layered Convolutional Neural Network (CNN) for recognizing the COVID-19 patients by training the system with the datasets of around 6000 chest X-ray images collected from various sources. We are categorized them as normal or COVID - impacted. Our model trained the X-ray images of chest to find these patients. And it also predicted well with the help of image analysis and deep learning by showcasing the better results comparing with the state-of-the-art methods in terms of the average accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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Efficient Power Control for UAV Based on Trajectory and Game Theory

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computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim Norafida Ithnin Roobaea Alroobaea Majed Alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network *** Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this *** the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless *** quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility *** energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless *** has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a *** proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas *** is taken into consideration to agree with the objective of 5G *** order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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Distributed Active Partial Label Learning

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Intelligent Automation & Soft Computing 2023年第9期37卷 2627-2650页

作者： Zhen Xu Weibin Chen Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhou325006China College of Computer Science and Artificial Intelligence Engineering Wenzhou UniversityWenzhou325006China

Active learning(AL)trains a high-precision predictor model from small numbers of labeled data by iteratively annotating the most valuable data sample from an unlabeled data pool with a class label throughout the learning ***,most current AL methods start with the premise that the labels queried at AL rounds must be free of ambiguity,which may be unrealistic in some real-world applications where only a set of candidate labels can be obtained for selected ***,most of the existing AL algorithms only consider the case of centralized processing,which necessitates gathering together all the unlabeled data in one fusion center for *** that data are collected/stored at different nodes over a network in many real-world scenarios,distributed processing is chosen *** this paper,the issue of distributed classification of partially labeled(PL)data obtained by a fully decentralized AL method is focused on,and a distributed active partial label learning(dAPLL)algorithm is *** proposed algorithm is composed of a fully decentralized sample selection strategy and a distributed partial label learning(PLL)*** the sample selection process,both the uncertainty and representativeness of the data are measured based on the global cluster centers obtained by a distributed clustering method,and the valuable samples are chosen in ***,using the disambiguation-free strategy,a series of binary classification problems can be constructed,and the corresponding cost-sensitive classifiers can be cooperatively trained in a distributed *** experiment results conducted on several datasets demonstrate that the performance of the dAPLL algorithm is comparable to that of the corresponding centralized method and is superior to the existing active PLL(APLL)method in different parameter ***,our proposed algorithm outperforms several current PLL methods using the random selection strategy,especially when only s

关键词： Active learning partial label learning distributed processing disambiguation-free strategy

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Gendering Robots in Human-Robot Interaction: An Interdisciplinary Approach 20

Gendering Robots in Human-Robot Interaction: An Interdiscipl...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Akalin, Neziha Arnelid, Maria Harrison, Katherine School of Engineering Jönköping University Dept. of Computer Science and Informatics Jönköping Sweden Linköping University Unit of Gender Studies Dept. of Thematic Studies Linköping Sweden

ISBN: (纸本)9798350378931

This paper emphasizes the importance of interdisciplinary collaboration in exploring the concept of robot gender within Human-Robot Interaction (HRI). It draws on a case study of the authors' own collaboration, where interdisciplinary discussions on the nature of gender informed the design of a multimethod study. This approach allowed us to avoid assigning a binary gender to Pepper robot, while still encouraging participants to reflect on their own gendering practices during interactions with robots. Additionally, we invite readers to consider alternative ways of conceptualizing robot gender in HRI - specifically, as fluid and performative rather than binary, which relies on stereotypical cues. After describing how our discussions on gender influenced and reshaped the study design, we offer practical advice on fostering interdisciplinary collaborations. These suggestions focus on communication strategies, mindset, and the practical setup of collaborative studies. We hope that these recommendations will inspire other researchers to continue exploring new and interdisciplinary ways of approaching robot gender in HRI. © 2025 IEEE.

关键词： Social robots

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CrazyKhoreia: A Robotic Perception System for MAV Path Planning From Digital Images

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SN computer Science 2025年第5期6卷 1-20页

作者： Restrepo-Garcia, Santiago Romero-Cano, Victor Faculty of Engineering Universidad Autónoma de Occidente Cll 25 115-85 Valle del Cauca Cali 760030 Colombia School of Computer Science and Informatics Cardiff University Wales Cardiff CF24 4 AG United Kingdom

Micro Air Vehicles (MAVs) can be used for a wide range of applications, such as drone-based light shows for cultural or marketing purposes, or as a replacement for fireworks. However, more widespread usage of this technology is precluded by the usability gap between choreography design and deployment on target drones. Seamless deployment of MAV choreographies requires the building of computational interfaces that can obtain drone trajectories from conventional media such as digital images. In this paper, we propose CrazyKhoreia, a low-cost, computationally efficient approach to MAV choreography design. CrazyKhoreia is a robotic perception system that obtains a safe, traversable, and accurate waypoint matrix from a digital image. We validate our trajectory generation system through two distinct modes of operation: light painting, where an MAV flies through all waypoints, and multi-drone formation, in which multiple MAVs are arranged to emulate a given image. The utility of each mode is evaluated differently, using the full resolution CrazyKhoreia output as a reference for comparison. For the light painting mode, we logged the MAV’s pose at a frequency of 10 Hz and compared it with the reference for different levels of detail in the reproduced path. The Root Mean Squared Error (RMSE) ranges from 0.1287 to 0.3190 m. For multi-drone formation, where the swarm remains stationary, we computed the RMSE by comparing observed positions with the reference across multiple tests, resulting in a mean RMSE value of 0.1360 m. © The Author(s) 2025.

关键词： Boundary tracing computer vision Drone swarm MAV Path planning Robotic perception

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Enhancing Cryptocurrency Value Prediction: A Comparative Study of Novel Random Forest and K-Nearest Neighbor Algorithms for Improved Accuracy 5

Enhancing Cryptocurrency Value Prediction: A Comparative Stu...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kiruthiga, S. Balamanigandan, R. Mahaveerakannan, R. Jenifer, A. Mary Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Saveetha University Department of Spatial Informatics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350379945

The effectiveness of the Novel Random Forest (RF) Algorithm for predicting cryptocurrency prices was evaluated and compared to the K-Nearest Neighbor (KNN) Algorithm. Machine learning methods were used to develop the two algorithms, and a pretest power analysis was conducted using two groups with the iteration of 10 at 85% of G-power and the setup parameters are alpha = 0.05 and beta = 0.85. Hence the P value is less than 0.005 (P © 2024 IEEE.

关键词： k-nearest neighbors

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Normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification 3rd

Normative Modeling with Focal Loss and Adversarial Autoenc...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Zhao, Songlin Zhou, Rong Zhang, Yu Chen, Yong He, Lifang Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Bioengineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9783031820069

In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer’s Disease (AD) diagnosis and biomarker identification. Our method is an end-to-end approach that embeds an adversarial focal loss discriminator within the autoencoder structure, specifically designed to effectively target and capture more complex and challenging cases. We first use the enhanced autoencoder to create a normative model based on data from healthy control (HC) individuals. We then apply this model to estimate total and regional neuroanatomical deviation in AD patients. Through extensive experiments on the OASIS-3 and ADNI datasets, our approach significantly outperforms previous state-of-the-art methods. This advancement not only streamlines the detection process but also provides a greater insight into the biomarker potential for AD. Our code can be found at https://***/soz223/FAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Lockstep Vs Microarchitecture: A Comparison

Lockstep Vs Microarchitecture: A Comparison

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IEEE International SOC Conference

作者： Ján Mach Lukáš Kohútka Pavel Čičák Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Bratislava Slovakia

ISBN: (数字)9798350377569

ISBN: (纸本)9798350377576

Safety/mission-critical applications require high dependability of the control systems. Their state-of-the-art protection approach is a system-level lockstep. This paper compares the system-level dual and triple lockstep technique to the microarchitecture-level protection of the Hardisc RISC-V core. For a fair comparison, each system is based on the same core(s) and integrates protection against bit-flips in memory and transient faults in the bus. We propose a fault injection methodology, combining pre-synthesis simulation with synthesis data to analyse the vulnerability of a system to faults. The fault injection campaigns show that the Hardisc can withstand fault rates orders of magnitude higher than the dual-core lockstep system while preserving the same area and power consumption. It comes with a 5% frequency penalty. We have shown that system failures are more frequent once the fault rate reaches an application-specific threshold (one fault in 100 clock cycles).

关键词： Fault tolerance Microarchitecture Power demand Event detection Fault tolerant systems Registers System-on-chip Protection Transient analysis Clocks

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