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Using NOMA Scheme for the Management of Interference and the Improvement of Performance in 5G Networks 13

Using NOMA Scheme for the Management of Interference and the...

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13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2021

作者： Bouras, Christos Kokkinos, Vasileios Tzioumanis, Georgios University of Patras Computer Technology Institute and Press Diophantus' Computer Engineering and Informatics Dept. Patras Greece

ISBN: (纸本)9781665402194

The new generation of 5G networks, compared to 4G networks, is a very important example of change in achieving very high frequencies in the carrier with huge bandwidth, high densities with a huge number of antennas and improved quality of services, making effective interference management necessary. In this paper, we present the contribution of the Non-Orthogonal Multiple Access (NOMA) scheme to improving the performance of 5G networks. We refer to the technique of sequential interference cancellation applied to some NOMA formats, compare NOMA and Orthogonal Multiple Access (OMA) formats in terms of the capabilities they offer users and NOMA's significant contribution to Multiple-Input Multiple-Output (MIMO) technology and compare three basic NOMA formats in three different resource and user reporting scenarios. Finally, we present the results of the simulations of the above comparisons and analyze the contribution of each scheme to the improvement of the system performance. and copy;2021 IEEE. © 2021 IEEE computer Society. All rights reserved.

关键词： 5G mobile communication systems

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An Implementation of Automatic Dart Code Verification for Mobile Application Programming Learning Assistance System Using Flutter

An Implementation of Automatic Dart Code Verification for Mo...

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2022 International Conference on Electrical and Information technology, IEIT 2022

作者： Syaifudin, Yan Watequlis Hatjrianto, Agus Salim Funabiki, Nobuo Liliana, Dewi Yanti Kaswar, Andi Baso Nurhasan, Usman Dept. of Information Technology State Polytechnic of Malang Malang Indonesia Dept. of Elec. and Comm. Engineering Okayama University Okayama Japan Dept. of Informatics Engineering State Polytechnic of Jakarta Jakarta Indonesia Dept. of Computer Engineering State University of Makassar Makassar Indonesia

ISBN: (数字)9781665453035

ISBN: (纸本)9781665453035

The popularity of smartphone devices has rapidly increased in recent years and many people utilize smartphones for various needs. The development of mobile applications has been aimed at various fields that make the demand for mobile application programmers increase. Recently, Flutter has become a software development kit for cross-platform applications development, including Android and iOS, so many software developers have adopted it. To provide a self-learning system for studying mobile programming with Flutter, we propose a learning assistance system with an automatic Dart code verification feature. Based on our previous studies in Android Programming Learning Assistance System (APLAS), the automatic code verification process can adopt software testing process for Android applications. The learning model provides learning materials for studying and practicing by solving an assignment. A learning topic of developing a simple application is prepared for the proposed system evaluation. 40 university students in IT department have been appointed to study Flutter and solve the assignment. Finally, they can solve the assignment correctly and give positive opinions about using this system. © 2022 IEEE.

关键词： Smartphones

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Muhammad Izzuddin Mahali Jenq-Shiou Leu Nur Achmad Sulistyo Putro Setya Widyawan Prakosa Cries Avian Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronics and Informatics Engineering Education Universitas Negeri Yogyakarta Yogyakarta Indonesia Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems.

关键词： Location awareness Deep learning Training Measurement Accuracy Clustering algorithms Fingerprint recognition Real-time systems Reliability Wireless fidelity

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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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An Iterated Local Search Algorithm for Commuting Bus Routing Problem with Latest Arrival Time Constraint

An Iterated Local Search Algorithm for Commuting Bus Routing...

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2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022

作者： Hashimoto, H. Hu, Y. Sugiura, T. Takada, Y. Yagiura, M. Tokyo University of Marine Science and Technology Dept. Logistics and Information Engineering Tokyo Japan Tokyo University of Science Dept. Applied Mathematics Tokyo Japan Nagoya University Graduate School of Information Science Dept. Computer Science and Mathematical Informatics Nagoya Japan

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of the bus stops at which he/she wants to get off. Each employee walks to his/her destination after getting off the bus and has a desired time when he/she wants to arrive there, which is called the latest arrival time. The objective is to find a set of routes with the minimum total traveling cost that covers all employees' destinations under bus capacity and latest arrival time constraints. We propose an iterated local search algorithm in which we use two kinds of neighborhood operations called set-del/1-ins and VRP-OPT∗. The set-del/1ins operation consists of three neighborhood operations, setdel, 1-ins and set-del-ins to remove or assign employees from or to a bus route. The VRP-OPT∗ is a local search algorithm to improve the distances of routes based on four neighborhood operations, 2-opt, 2-opt∗, relocate and cross-exchange. Computational results show the effectiveness of the two neighborhood operations. © 2022 IEEE.

关键词： Personnel

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Enhancing Cabbage Plant Protection: CNN Algorithm for Insect Identification

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect...

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Information technology Research and Innovation (ICITRI), International Conference on

作者： Remi Senjaya Purnawarman Musa Puji Sularsih Novrina Novrina Witari Aryunani Suhartini Suhartini dept. of Informatics Engineering Gunadarma University Depok Indonesia dept. of Electrical Engineering Gunadarma University Depok Indonesia dept. of Information Technology Gunadarma University Depok Indonesia dept. of Computer Science Gunadarma University Depok Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

People frequently find it straightforward to identify insects that feed on cabbage plants based on the insects’ characteristics. However, challenges arise when employing a computer for precise bug categorization and identification. The implementation of Deep Learning (DL) utilizes the Convolutional Neural Network (CNN) technique, renowned for its effectiveness in categorizing and recognizing objects in digital photo data. The CNN algorithm delivers notable results in photo identification, simulating the human visual brain’s image recognition through artificial neural networks in the Python programming language. To enrich the user experience, frameworks like Keras and TensorFlow are employed in neural network development. Our research will use the CNN algorithm to determine insect species in cabbage images. Achieving high accuracy involves coupling the CNN algorithm with the VGGNet architectural model. Notably, utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique; however, the validation accuracy was slightly lower at 94.25%. The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively. Moreover, based on the model (150 epochs), the testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%.

关键词： Training Visualization Technological innovation Accuracy Insects Prediction algorithms User experience Classification algorithms Convolutional neural networks Testing

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A Web and Mobile Application to Identify the Availability of Drinking Water Information During a Water Crisis

A Web and Mobile Application to Identify the Availability of...

引用

2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Razeeth, Suhail Shihara, Fathima Pavithra, Kasuni Kaliyamoorthy, Maran Nawaz, Samsudeen Sabraz Kariyapper, R.K.A.R. South Eastern University of Sri Lanka Dept. of Information and Communication Technology Sri Lanka Uva Wellassa University of Sri Lanka Dept. of Computer Science and Informatics Sri Lanka Sri Sai Ram Institute of Management Studies Sri Sai Ram Engineering College India South Eastern University of Sri Lanka Department of Management and Information Technology Sri Lanka

ISBN: (纸本)9798350394580

Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such instances, people find difficult to gather information regarding consumable drinking water availability. In order to facilitate the meeting of commercial water sellers and customers and to engage the general public and social services in addressing the drinking water problem, this study is focused on building both web and mobile applications. The web application was implemented with a selected set of functionalities such as social login, social sharing, geocoding, reverse geocoding, viewing, and filtering posts using Angular 6, *** web framework, and Leaflet. The mobile application was developed to direct the customers to the water availability location with a shortest path using the Dijkstra's algorithm. Case evaluation was conducted with the Rotaract Club members in Badulla and thinking aloud method was used to pick up the user's ideas while interacting with the system. It can be concluded that the created web and mobile applications would offer the public a clever way to draw attention to an issue with water availability, and the commercial water service providers are given a venue to market their services and locate potential customers. © 2023 IEEE.

关键词： crowdsourcing GIS mobile application water availability web application

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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis 6

Development of a Calibration-Free Brain-Computer Interface U...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Cengiz, Korhan Shreyas, J. Udayaprasad, P.K. Gururaj, H.L. Kumar, Dilip S.M. College of Information Technology University of Fujairah United Arab Emirates Faculty of Organization and Informatics University of Zagreb Croatia Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (纸本)9798350394634

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology. © 2024 IEEE.

关键词： Electroencephalography

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Using clinical entity recognition for curating an interface terminology to aid fast skimming of EHRs

Using clinical entity recognition for curating an interface ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kollapally, Navya Martin Dehkordi, Mahshad Koohi H. Perl, Yehoshua Geller, James Deek, Fadi P. Liu, Hao Keloth, Vipina K. Elhanan, Gai Einstein, Andrew J. Zhou, Shuxin Kean University Department of Computer Science Union United States New Jersey Institue of Technology Department of Computer Science Newark United States New Jersey Institue of Technology Department of DataScience Newark United States New Jeresy Institue of Technology Department of Informatics Newark United States Montclair State University School of Computing Montclair United States Yale University Section of Biomedical Informatics and DataScience New Haven United States University of Nevada School of Medicine Nevada United States Columbia Medical Center Dept. of Medicine New York United States

ISBN: (纸本)9798350386226

Highlighting of Electronic Health Records (EHRs) involves marking essential content of EHR notes, corresponding to concepts of a clinical terminology. However, employing the best clinical terminology (SNOMED CT) for highlighting EHRs, captures only a portion of their crucial content. In this paper, we describe the curation of a Cardiology Interface Terminology (CIT) dedicated to the application of highlighting EHRs of cardiology patients. We utilize a Clinical-Named Entity Recognition (Clinical NER) approach for extracting phrases, of higher granularity than SNOMED CT concepts, from EHRs, for enriching CIT. For this purpose, we train a neural network model with BIOE-tagged (Beginning, Inside, End, and Outside) cardiology entities. Transfer Learning can be used to facilitate the curation of an interface terminology for highlighting EHRs for other specialties e.g. Nephrology. Large-scale highlighting enables overworked physicians and other healthcare providers to fast skim the dense volume of EHRs they regularly read. Secondary research and EHRs interoperability are other applications that can be supported by highlighting. © 2024 IEEE.

关键词： Electronic health record

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Non-Invasive Human Body Temperature Detection for Online Event Data Recorder

Non-Invasive Human Body Temperature Detection for Online Eve...

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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Muhammad Ikhsan Sani Prajna Deshanta Ibnugraha Marlindia Ike Sari Moch. Fahru Rizal Fanni Husnul Hanifa Salsabila Anza Salasa Anggi Angga Reksa Aditya Firmansyah Hartono Nur Cahya Utama Dept. of Computer Technology Telkom University Bandung Indonesia Dept. of Digital Marketing Telkom University Bandung Indonesia Dept. of Informatics Telkom University Bandung Indonesia

The concept of black-box or Event Data Recorder (EDR) implementation on ground vehicle provides an alternative method for preemptive or prescriptive safety procedure. Utilizing all accessible technologies for accident prevention is crucial for ensuring transportation safety and regulation. Much effort has been devoted to developing thermal imaging as an additional sensor for EDR. However, several factors must be considered when using thermal imaging device on Event Data Recorder. Given that fever is a prevalent indication of several contagious illnesses, it is crucial to emphasize the monitoring of human body temperature for the purpose of fever screening. The goal is to reduce the number of individuals with elevated body temperatures from utilizing public transportation. This work aims to introduce a thermal camera application as a supplementary data source for the Event Data Recorder (EDR) in public transportation. The paper also proposed an alternative solution based on the use of a portable Forward-looking Infrared (FLIR) thermal imaging camera. The FLIR thermal camera is incorporated into the online EDR system. The system has been evaluated and can collect data for future analysis, as demonstrated by both indoor and outdoor tests. It may function in a somewhat limited range within small space area up to 3-meter detection range. Further studies will have to clarify on increasing the accuracy with another algorithm or methodology.

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