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Vocational Education and Electrical engineering, International Conference on

作者： Komang Candra Brata Nobuo Funabiki Sritrusta Sukaridhoto Evianita Dewi Fajrianti Mustika Mentari Dept. of Information and Communication Systems Okayama University Okayama Japan Dept. of Informatics Engineering Universitas Brawijaya Malang Indonesia Dept. of Informatic and Computer Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Dept. of Information Technology State Polytechnic of Malang Malang Indonesia

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements.

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Constructing a Cryptocurrency-Price Prediction Model Using Deep Learning 8

Constructing a Cryptocurrency-Price Prediction Model Using D...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Tran, Toai Kim Le, Thanh Thi Tuyet Bui, Thinh Tien Dang, Vang Quang Senkerik, Roman Ho Chi Minh City University of Technology and Education Faculty of Economics No 1 Vo Van Ngan Street Linh Chieu Ward Thu Duc City Viet Nam VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Dept. of Computer Science 17. Listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Tomas Bata University in Zlin Faculty of Applied Informatics T. G. Masaryka 5555 Zlin76001 Czech Republic

ISBN: (纸本)9781665491068

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies. © 2022 IEEE.

关键词： Forecasting

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

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

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Konstantina-Helen Tsarapatsani Vasilios D. Tsakanikas Antonis Sakellarios Hans J. Trampisch Efterpi Karapintzou Henrik Rudolf George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina 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 Greeece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina and Biomedical Research Institute-FORTH Ioannina Greece

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

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.

关键词： Accuracy Sensitivity Explainable AI Biological system modeling Peptides Receivers Predictive models Indexes Cardiovascular diseases Random forests

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Clustering Based User Allocation in 5G Networks*

Clustering Based User Allocation in 5G Networks*

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Christos Bouras Rafail Kalogeropoulos Ioannis Sina Computer Technology Institute & Press “Diophantus201D Univ. of Patras Greece Patras Greece Computer Engineering and Informatics Dept.

Machine learning is an extremely efficient technique for solving complex problems without the use of traditional programming but rather enabling machines to learn from an input of data and train them to cope with various problems. The rapid growth in the number of active mobile devices, mobile applications and services dictates an efficient utilization of mobile and wireless networking infrastructure. Communication networks need to evolve and valorize machine learning methods in order to process large volumes of data without introducing excessive time delay in these computations. Upcoming 5G systems are expected to be the first network infrastructure to support exploding mobile traffic volumes and machine learning techniques can be used in order to help manage the rise in data volumes. We present a mechanism for resource allocation in mobile and wireless networks, that effectively utilizes machine learning techniques.

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Intelligent Horizontal Autoscaling in Edge Computing Using a Double Tower Neural Network

SSRN

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SSRN 2022年

作者： Violos, John Tsanakas, Stylianos Theodoropoulos, Theodoros Leivadeas, Aris Tserpes, Konstantinos Varvarigou, Theodora School of Electrical and Computer Engineering National Technical University of Athens Zografou Greece Dept. of Informatics and Telematics Harokopio University of Athens Tavros Greece Dept. of Software and Information Technology Engineering École de Technology Supérieure Montreal Canada

Edge computing is characterised by a varying workload intensity that has a strong effect in the applications performance and their resource requirements. Thus, in order to maintain a sustainable performance a resource autoscaling mechanism is needed that will automatically add or remove computational nodes. This autoscaling mechanism must ensure that the user’s requirements in terms of Quality of Service (QoS) are satisfied, while respecting the Service Level Agreements (SLAs) and delivering always-on services with affordable ondemand computing solutions. Additionally, the autoscaling could take place proactively or reactively and the adjustability of the resources could be static or dynamic. To this end, the research goal of this paper is to design a novel autoscaling mechanism that minimizes the tasks execution and maximizes the throughput with the minimum number of resources. In this direction, we propose an Intelligent Horizontal Proactive Autoscaling (IHPA) mechanism that leverages resource usage metrics of processing Edge nodes such as CPU, RAM and Bandwidth in order to provide timely scale up and scale down decisions. The IHPA is based on a double tower Deep Learning (DL) architecture. In order to find a close to optimal DL architecture and guarantee the generality of our approach we also propose the innovative hybrid Bayesian Evolution Strategy method. We conduct an extensive simulation of the IHPA with three baseline task offloading mechanisms keeping the same statistical task generation distribution. We also compare the performance of our proposed IHPA against the reactive autoscaling and the Kubernetes Horizontal autoscaling mechanisms. The results show that our framework shows significant improvements in terms of latency and throughput, while making an optimal use of the resources. © 2022, The Authors. All rights reserved.

关键词： Edge computing

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Mental Health Professionals' Insights on Digital Health Solutions for Anxiety in the UAE

Mental Health Professionals' Insights on Digital Health Solu...

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IEEE International Requirements engineering Conference Workshops (REW)

作者： Mariam Al Meqbaali Sofia Ouhbi Reem K Jan Leena Amiri Fadwa Al Mugaddam Mohamed Adel Serhani Nazar Zaki Dept. Computer Science & Software Engineering CIT UAEU Al Ain UAE Dept. Information Technology Uppsala University Uppsala Sweden Mohammed Bin Rashid University of Medicine and Health Sciences Dubai UAE Dept. Psychiatry and Behavioral Science CMHS UAEU Al Ain UAE College of Computing and Informatics University of Sharjah Sharjah UAE

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

In recent years, anxiety disorders have become more prevalent worldwide, leading to an increased demand for mental healthcare services. Barriers such as stigma and cost, limit access to these services, prompting the exploration of alternative solutions. Digital technology shows significant promise in improving access to mental healthcare services. This study aims to explore the preferences, and concerns of Mental Health Professionals (MHPs) regarding digital health solutions for self-management of anxiety disorders in the UAE via an online survey. Participation was voluntary and anonymous, with 18 MHPs taking part. The survey included 12 questions covering demographics and key design elements. Most respondents displayed positive attitudes toward integrating digital anxiety solutions in the UAE, providing feedback and highlighting considerations. 61% endorsed the use of digital technology in anxiety disorder treatment, while 94% favored mindfulness exercises for anxiety management. 72% expressed interest in utilizing technology for therapy. Notable concerns raised included privacy and dependency. Guided by MHPs insights, we propose a conceptual framework for the design of digital anxiety solutions in the UAE. This framework prioritizes cultural appropriateness and language accessibility, offering solutions in Arabic and English. It encompasses diverse features, including a blended approach, treatment approaches, and relaxation techniques, to address anxiety disorders and ensures user safety through risk assessment and management. This framework can assist developers of digital anxiety solutions by highlighting the features and elements that MHPs deem most important.

关键词： Surveys Privacy Anxiety disorders Medical treatment Mental health Safety Electronic healthcare

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FoodX, a System to Reduce Food Waste 3

FoodX, a System to Reduce Food Waste

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3rd International Conference on computer and informatics engineering, IC2IE 2020

作者： Shinta Oktaviana, R. Febriani, Diana Ambarwati Yoshana, Intan Payanta, L.R. Politeknik Negeri Jakarta Dept. Computer and Informatics Engineering Indonesia Instituto Politecnico de Braganca Dept. Technology and Management Portugal

ISBN: (纸本)9781728182476

Food waste is a serious problem that occurs in various countries. Indonesia is a country that produces food waste, the second largest after Saudi Arabia. Currently, there are several communities who care about the issue of food waste and hunger in Indonesia. The Community collects excess food from eligible donor consumption to be distributed to people in need. They have the aim to reduce the problem of food waste and numbers starving in Indonesia. However, the process of channeling food to donors and the community is still practically a manual where the community contacts the donors one by one, so it is considered less effective. This research aims to create a system to connect the community with individuals or organizations that want to donate excess food. In order for users to give faster feedback, this system was made using the prototype methodology. At the final stage of the development, testing was carried out by involving several volunteers and 3 communities to see the completeness of the features system. FoodX system made already accommodate the needs of 2 types of food communities (with and without volunteer). © 2020 IEEE.

关键词： food waste hunger rate social entrepreneurship. sustainable development goals

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Optimization of Building Construction Control System Based on Nonlinear Image Enhancement

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computer-Aided Design and Applications 2023年 20卷 213-224页

作者： Liu, Congru Lin, Mingsen Ghafoor, Kayhan Zrar Al-Dabagh, Mustafa Zuhaer Nayef Karras, Dimitrios A. School of Urban Construction Wuhan University of Science and Technology Wuhan China Changjiang Polytechnic Wuhan China Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq School of Science Dept. General Athens Greece

To reflect the nonlinear characteristics of the building structural adjustment system, an active vibration control strategy based on the nonlinear is proposed. In this method, the size of the structural control force is determined by comparing the allowable energy with the actual energy, and its direction is determined by taking the derivative of the energy. Because there is no need to solve the linear matrix inequality, the calculation is simple and the operation is convenient. Moreover, when multiple actuators with the same capacity limit are used, the control output force is consistent, thus avoiding the design of multiple control systems and simplifying the controller design. The simulation results show that the maximum controlled displacements of floor 6 to floor 8 are 1.16cm, 1.12cm, and 0.97cm, respectively. The proposed nonlinear energy control method has the advantages of simple control process and avoiding actuator saturation. © 2023 CAD Solutions, LLC.

关键词： Floors

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OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics

arXiv

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arXiv 2022年

作者： Passalis, Nikolaos Pedrazzi, S. Babuska, R. Burgard, W. Dias, D. Ferro, F. Gabbouj, M. Green, O. Iosifidis, A. Kayacan, E. Kober, J. Michel, O. Nikolaidis, N. Nousi, P. Pieters, R. Tzelepi, M. Valada, A. Tefas, A. Dept. of Informatics Aristotle University of Thessaloniki Greece Cyberbotics Switzerland Dept. of Cognitive Robotics Delft University of Technology Netherlands Dept. of Computer Science University of Freiburg Germany PAL Robotics Spain The units of Computing Sciences and Automation Technology and Mechanical Engineering Tampere University Finland Agrointelli Denmark The Department of Electrical and Computer Engineering Aarhus University Denmark

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles. Copyright © 2022, The Authors. All rights reserved.

关键词： Robotics

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Exploring Hot/Cold Data Separation for Garbage Collection Efficiency Enhancement on OCSSDs

Exploring Hot/Cold Data Separation for Garbage Collection Ef...

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International SoC Design Conference, ISOCC

作者： Yu-Shiang Tsai Shuo-Han Chen Yong-Cheng Liaw Cheng-Yueh Wu Dept. of Computer Science & Information Engineering National Taipei Uni.of Technology Taipei Taiwan Dept. of Computer Science Inst. of Artificial Intelligence Innovation National Yang Ming Chiao Tung Uni. Hsinchu Taiwan Inst. of Artificial Intelligence Innovation National Yang Ming Chiao Tung Uni. Hsinchu Taiwan

Solid State Drives (SSDs) are widely used in various electronic devices and have gained a significant market share because SSDs do not have mechanical components, have fast access speeds and are less likely to be damaged by external forces. To fulfill the functionalities of conventional storage devices, the limitations of SSDs become prominent and require the Flash Translation Layer (FTL) to manage the NAND flash memory. Nevertheless, the conventional FTL functions without the assistance of the host system, which leads to an information gap and decreased efficiency. Recently, the concept of open-channel SSD (OCSSD) has emerged to allow direct host control over SSD internals. Leveraging the concept of OCSSD, this paper proposes a host-assisted garbage collection enhancement mechanism to facilitate the efficiency of garbage collection through hot/cold data separation, and the proposed mechanism can effectively reduce the number of garbage collections by an average of 60.8%.

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