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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Munia, Afsana Ahmed Hossain, Ibrahim Jalali, Seyed Mohammad Tabarisaadi, Pegah Rahman, Ashikur Nahavandi, Saeid Deakin University Institute for Intelligent Systems Research and Innovation VIC3216 Australia Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350337020

Precise image segmentation is one of the dominant factors in disease diagnosis. A typical application is the segmentation of breast ultrasound images, allowing radiologists to suggest what to do next. After emerging deep learning technology especially convolutional neural networks (CNNs), the image segmentation model achieved state-of-the-art performance in various medical applications such as cancer detection and classification, lung node segmentation, cell segmentation and so on. However, despite these successes, a big question arises: to what extent is the model certain about the predicted result? Generally, most deep learning models focus on high accuracy but not on uncertainty of predicted results, which is not enough to make a critical real-life decision such as a disease diagnosis, where a wrong decision can be life-threatening. Hence for making a crucial decision, it is essential that the predicted result will provide not only accuracy but also estimate model uncertainty. Our contribution to this research is to build a system that predicts pixel-wise semantic segmentation and provides uncertainty estimation of the predicted results. It is achieved by adding a dropout layer during training and using Monte Carlo dropout in inference. We evaluate our model with the breast ultrasound image dataset (BUSI) and compare the results with a few other state-of-the-art methods where our method outperforms others in terms of IoU. © 2023 IEEE.

关键词： Breast cancer Deep learning Semantic segmentation Ultrasound images Uncertainty quantification

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Applying LSTM for River's Water Level Forecasting as Potential Flood Indication: A Case Study in Greece

Applying LSTM for River's Water Level Forecasting as Potenti...

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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Theodoros Tziolas Elpiniki Papageorgiou Konstantinos Papageorgiou Georgios Zardas Alexios Lekidis Papazoglou Panagiotis Dept. of Energy Systems University of Thessaly Gaiopolis Campus Larisa Greece Dept. of Electrical and Computer Engineering University of Thessaly Volos Greece Dept. of Architecture and Computer and Communication Simulation Systems National and Kapodistrian University of Athens Chalkida Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Floods can have significant life-threatening and economic impacts on human societies. Flood forecasting with data-driven methods is a crucial aspect of disaster risk management, providing valuable insights into potential flood events and allowing for timely mitigation measures. This work illustrates the application of a Long Short-Term Memory (LSTM) neural network for short-term forecasting of a river's water level. The water treatment data used for training the LSTM models were gathered from Sperchios river, located in central Greece. Historical time-series data in a half-hour interval of the river's water level, water velocity, and area precipitation were utilized for the development of the proposed scheme. The developed model is deployed in the backend of a web application for half-hour ahead predictions of the river's water level that are visualized in the frontend. The post-analysis of the deployed forecasting model indicates that it consistently delivers forecasts with minimal error and demonstrates high accuracy in recent flood events.

关键词： Training Precipitation Biological system modeling Prevention and mitigation Neural networks Predictive models Rivers Floods Forecasting Long short term memory

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Affine-NoC: Multi-ring NoCs exploiting long physical links

Affine-NoC: Multi-ring NoCs exploiting long physical links

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International Workshop on Network on Chip architectures, NoCArc

作者： Enrique Vallejo Evangelos Mageiropoulos Nikolaos Chrysos Manolis G.H. Katevenis Computer Science Dept. Universidad de Cantabria Spain Santander Spain Computer Architecture and VLSI Systems (CARV) Laboratory Foundation for Research and Technology – Hellas (FORTH) Heraklion Greece

ISBN: (数字)9798331506421

ISBN: (纸本)9798331506438

Routerless multi-ring proposals are low-cost NoCs that employ multiple independent rings, avoiding any crossbars or arbitration mechanisms. Their complexity lies on the selection of the set of rings to connect all the nodes. Previous proposals result in unbalanced designs with high hop *** work introduces Affine-NoC, a balanced routerless NoC based on a novel arrangement of rings derived from the Affine Plane. Affine-NoC exploits express channels to connect distant processing elements, allowing for a completely balanced layout of the set of rings and a reduction in average distance and diameter without sacrificing bisection *** shows that Affine-NoC presents a balanced design with a low number of rings per node and reduced complexity, similar cost to previous proposals in terms of aggregated link length, while it simplifies the multiplex units and reduces the hop count. Simulation results show that Affine-NoC reduces hop count and average latency by 76% and 20.5% respectively compared to previous designs, it reduces deflections by 27% and avoids unfairness, making it a feasible alternative for multi-ring routerless NoCs.

关键词： Multiplexing Network topology Flow production systems Simulation Layout computer architecture Complexity theory Topology Proposals Logic

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Mathematical Model of Asset and Liability Management in the Presence of Normative and Internal Constraints 5

Mathematical Model of Asset and Liability Management in the ...

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5th International Conference on Control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Zhukova, Aleksandra Flerova, Anna Chernov, Aleksey Pilnik, Nikolay FRC CSC of RAS Dept. of Mathematical Modelling of Economic Systems MIPT Moscow Russia FRC CSC RAS Dept. of Mathematical Modelling of Economic Systems Moscow Russia Moscow Institute of Physics and Technology MIPT-School of Applied Mathematics and Computer Science Moscow Russia

ISBN: (纸本)9798350309126

In this paper we present the mathematical formulation of the problem of planning the Asset Liability Management if the Presence of Normative and Internal Constraints. As a nonlinear optimal control problem, it has specifics due to the state constraints. The maximum principle involves verification of regularity property in order to apply computational methods. We reformulate the problem as the problem with convex objective function and linear, but mixed system of constraints. This enables application of optimization methods to compute the optimal controls, and study the dependence of controls on the parameters that describe the external environments and the management objective. © 2023 IEEE.

关键词： Optimal control systems

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Impact of Rain Attenuation on Short-Range Frequency Operations in 5G Networks

Impact of Rain Attenuation on Short-Range Frequency Operatio...

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International Conference on Microwaves, Radar and Wireless Communications

作者： Sandra Bazebo Matondo Pius Adewale Owolawi Dept. Computer Systems engineering Tshwane University of Technology Pretoria South Africa

ISBN: (数字)9788396972613

ISBN: (纸本)9798350371611

Attenuation caused by rain poses significant challenges for millimeter wave communication systems, especially in regions with heavy rainfall. Accurate attenuation prediction is required to set realistic performance expectations and ensure robust millimeter-wave communications links for 5G networks even in challenging environments. The aim of this work is to model and optimize rain attenuation in terrestrial 5G using evolutionary algorithms. The attenuation is modelled using symbolic regression that takes rain rate, path length, and frequencies between 24.25 GHz and 86 GHz as input. Next, differential evolution is used to determine the optimal parameters that best fit the model. The model is found to predict the attenuation with minimal errors. The proposed model’s performance is evaluated against the ITU-R, Crane, Da Silva and Melo models. The proposed model’s accuracy and performance are further validated based on the obtained MSE, MAE and R-squared values.

关键词： Rain Accuracy Cranes 5G mobile communication Evolutionary computation Predictive models Millimeter wave radar

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model 3

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Elshabrawy, Mahmoud Eid, Marwa M. Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed Delta Higher Institute of Engineering and Technology Dept. of Communications & Electronics Mansoura Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Ain Shams University Faculty of Computer and Information Sciences Dep. of Computer Science Cairo Egypt University Mansoura Faculty of Engineering Computer Engineering and Control Systems Dept. Egypt

ISBN: (纸本)9798350323511

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models. © 2023 IEEE.

关键词： Ant colony optimization

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Enhancing Train Transportation in Sri Lanka: A Smart IOT based Multi-Subsystem Approach using MQTT

arXiv

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arXiv 2025年

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Bandara, Anuththara Arachchige, Gayantha Godakanda Gamage, Narmada Wijayaraja, Jaliya L. Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka

This research proposes a system as a solution for the challenges faced by Sri Lanka’s historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study’s data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka’s railway infrastructure. © 2025, CC BY.

关键词： Global positioning system

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Intelligent Doctor Chatbot

Intelligent Doctor Chatbot

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International Multidisciplinary Information technology and Engineering Conference (IMITEC)

作者： Mahlori Khoza Moses Olaifa Pius Owolawi Vusi Malele Dept. of Computer Systems Engineering Tshwane University of Technology Tshwane South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

This study proposes a cutting edge Intelligent Doctor Chatbot that improves healthcare delivery and provides patients with easily accessible and customized health related information through the use of Natural language processing (NLP) and artificial intelligence (AI) technologies. These technologies are used by the chatbot to provide accurate medical advice based on symptom analysis and general health guidance in an interactive manner. The doctor chatbot addresses the growing demand for reliable healthcare information and assistance, particularly in situations where access to medical professionals is limited. By leveraging its advanced algorithms and medical knowledge base, the chatbot provides users with quick and accurate answers to their health-related questions. A key feature of the doctor chatbot is that users describe their symptoms and receive preliminary assessments on potential causes and suggested courses of action. It allows people to take an active role in their healthcare management and preventive measures.

关键词： Accuracy System performance Knowledge based systems Medical services Oral communication Chatbots Multilingual History Reliability Medical diagnostic imaging

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Assessing Interaction Mechanics in Extended Reality for Control Engineering Applications

Assessing Interaction Mechanics in Extended Reality for Cont...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Armin Remenyi Saleh Alsaleh Aleksei Tepljakov Dept. of Software Science Tallinn University of Technology Tallinn Estonia Dept. of Computer Systems Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

This research paper delves into the evaluation of human-machine interactions within the realm of Extended Reality (XR) applications, focusing on the integration of Digital Twins (DT) for control systems. The research explores multiple XR interaction methods and assesses their efficacy in enhancing user experience and operational efficiency in industrial settings, specifically in control systems. The study employs advanced tools such as the Microsoft HoloLens 2 headset, Unreal Engine 5, and the Mixed Reality Toolkit (MRTK) to develop and test XR interaction methods. Data collection and analysis involves usability testing using the System Usability Scale (SUS) and the NASA Task Load Index (NASA-TLX) to measure cognitive, physical and temporal demands on users. The findings highlight the most effective XR interaction strategies, providing valuable insight for future research and practical implementations in intelligent manufacturing environments.

关键词： Technological innovation Control engineering Extended reality NASA User experience Software Digital twins Telecommunications Usability Testing

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency 5

Intelligent Operational Decision Support System for Sugar Fa...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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