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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Ko, Seonghyeon Kim, Juchan Le, Duc-Tai Bum, Junghyun Han, Jongchul Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of Ophthalmology Samsung Medical Center Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical Center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time. © 2024 IEEE.

关键词： Diagnosis

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IoT-Based Automated Environmental Control System for Oyster Mushroom Cultivation Using ESP8266 and Telegram Bot

IoT-Based Automated Environmental Control System for Oyster ...

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International Conference on informatics and Computing (ICIC)

作者： Evi Triandini M. Kevin Afrianto Bayu Agus Irawan Azman Maricar Padma Nyoman Crisnapati Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Mechatronics Engineering Faculty of Technical Education Rajamangala University of Technology Thanyaburi

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The aim of this study was to create an Internet of Things (IoT) system that can monitor and control temperature and humidity in oyster mushroom production from a distant location. This system was developed with the goal of improving efficiency and the quality of the mushroom yield. The implementation of the system involved the utilization of a NodeMCU ESP8266 microcontroller, which was combined with DS18B20 and DHT22 sensors. The system was then managed by a Telegram bot, enabling continuous operation throughout the day. The findings revealed substantial enhancements in efficiency, as the duration for monitoring and watering decreased from 471.6 seconds to 5 seconds. Additionally, there was a noteworthy reduction in the proportion of suboptimal mushrooms, declining from 12% to 1.72%. The findings indicate that the system offers a dependable and efficient alternative for maintaining ideal environmental conditions, resulting in improved crop quality and decreased labour requirements. The deployment of this technology signifies a significant progression in precision agriculture, providing tangible advantages for small-scale mushroom cultivators and establishing the foundation for future improvements in automated agricultural systems.

关键词： Precision agriculture Temperature sensors Temperature measurement Machine learning algorithms Chatbots Sensor systems Real-time systems Sensors Temperature control Internet of Things

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NCC: Neural concept compression for multilingual document recommendation[Formula presented]

NCC: Neural concept compression for multilingual document re...

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作者： Tashu, Tsegaye Misikir Lenz, Marc Horváth, Tomáš ELTE – Eötvös Loránd University Faculty of Informatics Institute of Academia-Industry Cooperation Department of Data Science and Engineering Pázmány Péter sétány 1/C 1117 Budapest Hungary Pavol Jozef Šafárik University in Košice Faculty of Science Institute of Computer Science Jesenná 5 Košice040 01 Slovakia Department of Artificial Intelligence Bernoulli Institute of Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Wollo University Kombolcha Institute of Technology College of Informatics Kombocha Ethiopia

In this work, we propose a novel method for generating inter-lingual document representations using neural network concept compression. The presented approach is intended to improve the quality of content-based multilingual document recommendation and information retrieval systems by creating a language-independent representation. The main idea is to use mappings to align monolingual representation spaces, using concept compression, to create inter-lingual representations. The proposed approach outperforms traditional cross-lingual retrieval and recommendations methods in experiments conducted on JRC-Acquis and EU bookshop multilingual corpora. Our dataset and code are publicly available at https://***/Tsegaye-misikir/NCC. © 2023

关键词： Information retrieval systems

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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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Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality

Optimizing Magnetic Induction Sensors for Non-Obtrusive Vita...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mahdi Momeni Adrian Radomski Ulkuhan Guler Daniel Teichmann SDU Health Informatics and Technology The Maersk Mc-Kinney Moller Institute University of Southern Denmark Denmark Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper investigates the advancement of magnetic induction-based heart and respiration rate sensing by actively controlling the coil current. This is realized through the implementation of a current-starved inverter mechanism. Experiments show a notable level of accuracy of the proposed circuit in measuring heart and respiration activity when compared to a reference sensor. The direct manipulation of current levels was found to have a direct impact on the signal strength. Incrementing the overall current within the proposed circuit from 60 mA to 100 mA resulted in an augmentation of the output amplitude of the heart rate signal from 8.5 mV to 27 mV, accompanied by a marginal enhancement in beat-to-beat interval accuracy. Moreover, the proposed sensor demonstrates noteworthy precision in monitoring the respiratory rate when compared with the reference sensor under different current values, exhibiting the same trend in signal strength. This finding offers valuable insight for the development of future power-optimized magnetic induction sensors with enhanced robustness.

关键词： Heart rate Current control Accuracy Power demand Inverters Robustness Sensors Biomedical monitoring Monitoring Magnetic circuits

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Self-Reported Learning Strategies and Preferences in Health informatics

Self-Reported Learning Strategies and Preferences in Health ...

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34th Medical informatics Europe Conference, MIE 2024

作者： Rohani, Narjes Gallagher, Michael Gal, Kobi Banas, Kasia Manataki, Areti Usher institute University of Edinburgh United Kingdom Moray House School of Education and Sport University of Edinburgh United Kingdom School of Informatics University of Edinburgh United Kingdom Dept. of Software and Information Systems Engineering Ben-Gurion University Israel School of Computer Science University of St Andrews United Kingdom

ISBN: (纸本)9781643685335

Despite the proliferation of educational programmes in Health informatics (HI) worldwide, there is limited knowledge regarding students' preferences and learning strategies in HI courses. To address this gap, we conducted a study to gather and analyse data from three HI courses. Employing the Motivated Strategies for Learning Questionnaire (MSLQ) and theories of deep and surface learning, we designed a questionnaire to collect data. The analysis of students' responses indicates that machine learning emerges as one of the most interesting topics, while certain topics such as data wrangling of genomics data were more challenging for students. Students expressed a preference for sequential learning. They exhibited multimodal tendencies regarding the type of learning resources, with tendency to prefer learning resources that have more visual contents. In all three courses, learners reported using deep learning strategy rather than surface learning, yet they appear to struggle with employing organisation, elaboration, and peer learning tactics. This study provides valuable insights into HI education, offering recommendations for educators, learners, and researchers to enhance HI education. © 2024 The Authors.

关键词： Contrastive Learning

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RLA-DDTC: A Reinforcement Learning Agent-based approach for Decision-making for Dynamic Traffic Control in C-ITS systems

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Procedia computer Science 2024年 246卷 3475-3484页

作者： Ameni Aloui Hela Hachicha Ezzeddine Zagrouba University of Tunis El Manar Higher Institute of Computer Science Laboratory of Informatics Modeling and Information and Knowledge Processing (LIMTIC) 2 Rue Abou Rayhane Bayrouni 2080 Ariana Tunisia College of Computer Science and Engineering - University of Jeddah Jeddah Saudi Arabia

The Cooperative Intelligent Transport System (C-ITS) aims to address traffic efficiency, road safety, and environmental sustain-ability by facilitating communication among autonomous entities with complex behaviors. Traffic congestion is a significant issue in modern urban areas, influenced by factors like accidents, road works, weather and peak hours, leading to wasted time, fuel, and increased pollution. To address these challenges, multi-agent systems have proven effective in managing dynamic traffic in C-ITS environments. In this paper, we propose a novel Reinforcement Learning Agent (RLA) method for decision-making in C-ITS systems to optimize traffic flow and control. Our method utilizes the Q-learning algorithm, enabling effective problem-solving. We conducted experiments to demonstrate the versatility and effectiveness of our approach in complex C-ITS environments. Our method surpasses traditional metrics like mean travel time and mean speed, validated through comparisons with Actor-Critic methods and against the Original Traffic Trace (OTT). This research contributes to advancing decision-making capabilities within C-ITS systems by leveraging RLA for traffic optimization and efficiency.

关键词： Reinforcement learning Q-learning Decision-making C-its system multi-agent-system

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Cognitive Radio Technique for Blockchain-based Vehicles with IoT 5

Cognitive Radio Technique for Blockchain-based Vehicles with...

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5th International Conference on Contemporary Computing and informatics, IC3I 2022

作者： Veerraju, Sampenga Ghantasala, G S Pradeep Verma, Ekta Kumar, Ajay Akram, Shaik Vaseem Pant, Kumud Dadi Institute of Engineering and Technology Department of Electronics and Communication Engineering Andhra Pradesh Anakapalle India Chitkara Institute of Engineering and Technology Department of Computer Science and Engineering Chitkara University Punjab India Scientific Officer National Informatics Center New Delhi India JECRC University Computer Science and Engineering Rajasthan Jaipur India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

In the In the Internet of Vehicles (IoV), intelligent decision-making is performed by objects with cognition. by comprehending both the social and physical worlds, Cognition Radio Network (CRN) is seen as a workable option. The existence of dangerous devices (MD) further impairs network performance, although For services to be improved and safety metrics to be driven, spectrum availability and information exchange among vehicles are crucial. Recently, a blockchain approach has been added to CRN-based IoV to stop data modification from these Entities and enable the cars to monitor both legal and illegitimate network activity. In this paper, we provide security to IoV throughout spectrum sensors and details transfer using CRN by sensing the paths through with a decision-making technique called Method for Order Personal taste by Similarity to a Solution (topsis (TOPSIS), a technique that inspires the confidence of its Behavioral Users (CU) by analysing certain predefined attributes. The network also maintains a blockchain to track every action involving information that has been saved. Using different frequency sensing and security settings in comparison to an IoV baseline solution, the suggested method is thoroughly evaluated against a number of security criteria. Numerous simulations indicate that when compared to the baseline system, our suggested approach is around 70% more effective at identifying malicious nodes and DoS threats. © 2022 IEEE.

关键词： Internet of things

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DERAINIZER for Unpaired Single Image De-raining

DERAINIZER for Unpaired Single Image De-raining

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2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

作者： Fernando, Hasal Ayoob, Mohamed Poravi, Guhanathan University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computer Science Colombo 06 Sri Lanka

ISBN: (纸本)9781665425032

Rain streaks are known to cause many performance or accuracy reductions in many computer vision systems. The problem of obstructing rain streaks is addressed within the domain of 'De-raining'. De-raining is the process of removing rain streaks. It has two main sub-categories which are video de-raining and single image de-raining. Many works on single image de-raining have taken place using both traditional approaches as well as deep learning approaches. This paper does a critical analysis of credible, novel, and best performing single image de-raining systems. Also, the paper addresses an unpaired training gap which exists within the domain with the novel de-raining system 'DERAINIZER', enabling all future researchers to employ unpaired training data to train their models. It is followed by results, research limitations and possible future works. © 2021 IEEE.

关键词： computer vision

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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

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Neural Computing and Applications 2024年 1-20页

作者： Zhang, Ying Xu, Shimin Zhang, Linghui Jiang, Weiwei Alam, Sameer Xue, Dabin College of General Aviation and Flight Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Air Traffic Management Research Institute School of Mechanical & ampAerospace Engineering Nanyang Technological University Singapore Singapore Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong

Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors. © The Author(s) 2024.

关键词： Forecasting

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