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18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Gaitán-Guerrero, Juan F. Martínez-Cruz, Carmen López, José L. Mata-Mata, Francisco Pérez-Cordón, Luis G. Serrano, José-María Morcillo-Martínez, Juana M. Verdejo-Espinosa, Ángeles Cuevas-Martínez, Juan C. Viciana-Abad, Raquel Reche-López, Pedro J. Pérez-Lorenzo, José M. Díaz-Jiménez, David Espinilla, Macarena Department of Computer Science University of Jaén Jaén23071 Spain Department of Languages and Computer Systems University of Granada Granada18071 Spain Psycology Department Faculty of Social Work University of Jaén Jaén23071 Spain Electrical Engineering Department University of Jaén Jaén23071 Spain Telecommunication Engineering Department University of Jaén Jaén23071 Spain

This paper introduces GLOW-ENV, an intelligent Internet of Everything (IoE)-driven mobile application designed with the objective of integrating real-time glucose monitoring data and environmental metrics to enhance diabetes care and management. The proposed IoE ecosystem integrates a continuous glucose monitoring with a personalized Artificial Intelligence model designed to predict glycemic fluctuations in a near-future. Additionally, GLOW-ENV integrates a rule-based recommendation system to dynamically adapt its suggestions based on contextual glucose and environmental data. This framework advances personalized diabetes care, contributing to their progression and well-being offering valuable insights and improving decision-making. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Electronic health record

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Towards Drought Modeling in South Asia: Machine Learning Approaches, Challenges and Opportunities

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IEEE Access 2025年

作者： Reshan, Mana Saleh Al Raza, Muhammad Owais Mahoto, Naeem Ahmed Rajab, Adel Shaikh, Asadullah Elmagzoub, M.A. Rajab, Khairan Najran University College of Computer Science and Information Systems Department of Information Systems Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran61441 Saudi Arabia Istanbul Sabahattin Zaim University Department of Computer Engineering Istanbul34303 Turkey Mehran University of Engineering and Technology Department of Software Engineering Sindh Jamshoro76062 Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Najran61441 Saudi Arabia Najran University College of Computer Science and Information Systems Department of Network and Communication Engineering Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state- of-the-art in ML and DL (Complex Machine learning and deep learning models) for drought modeling, highlighting current challenges such as incomplete and inconsistent datasets, Lack of explainability and interpretability in complex ML/DL models, unavailability of benchmarks. Based on these challenges, this review suggests the following techniques to address these challenges: Data integration (Data fusion), distributed machine learning (Federated Learning) and explainable AI (XAI, SHAP, LIME, etc.). © 2013 IEEE.

关键词： Federated learning

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QSroute : A QoS Aware Routing Scheme for software Defined Networking 8

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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8th IEEE International Conference on software engineering and computer systems, ICSECS 2023

作者： Kamarudin, Imran Edzereiq Ameedeen, Mohamed Ariff Umar Ong, Muhamad Idaham Zabidi, Azlee Universiti Malaysia Pahang AL-Sultan Abdullah Faculty of Computing Department of Computer Systems and Networking Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

ISBN: (纸本)9798350310931

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. software Defined Networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field. © 2023 IEEE.

关键词： Quality of service

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Development of a Query Delay Injection System for the MEC Simulator of the LWMECPS Platform

Development of a Query Delay Injection System for the MEC Si...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Filianin, Ivan Bogatyrev, Vladimir Lesonen, Matvey ITMO University Faculty of Software Engineering and Computer Engineering St. Petersburg Russia Saint-Petersburg State University of Aerospace Instrumentation Department of Information Systems Security St. Petersburg Russia ITMO University Faculty of Infocommunication Technologies St. Petersburg Russia

ISBN: (纸本)9798350395044

In this paper the problem of testing decision making systems for MEC platforms was formulated. Methods and means of organizing the introduction of network delays as part of the emulation system of MEC platforms LWMECPS were analyzed. Test applications mec-test-app and mec-test-client are developed for testing and evaluation of the introduced network delays. Developed mec-orch-app to provide test application management and metrics collection. Experiments have been conducted to estimate the insertion latency and manage the test applications using mec-orch-app. Special attention was paid to the organization of all parts of the system as a future laboratory bench for testing decision systems using reinforcement learning machine learning algorithms. © 2024 IEEE.

关键词： Reinforcement learning

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Evaluation of the Energy Efficiency of software Calculations in the Design of Microcontroller Devices 17

Evaluation of the Energy Efficiency of Software Calculations...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Topalov, Andrii Styslo, Taras Tkach, Vitalii Peschanenko, Volodymyr Styslo, Oksana Skrypyuk, Rostyslav Admiral Makarov National University of Shipbuilding Department of computerized control systems Mykolaiv Ukraine King Danylo University Department of IT Ivano-Frankivsk Ukraine Kherson State University Department of computer science and software engineering Ivano-Frankivsk Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of automation and computer-integrated technologies Ivano-Frankivsk Ukraine

ISBN: (纸本)9798331520564

The paper presents a study of the effectiveness of software from the point of view of minimizing the energy consumption of microprocessor devices. In this case, the programming of the microcontroller in various programming languages for arithmetic operations, as well as data output through the port, is considered. In the course of the tests, the peculiarities of the application of this or that program and their operation, taking into account the energy saving of the microcontroller, are noted. © 2024 IEEE.

关键词： Microcontrollers

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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

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computer systems Science & engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

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Integration of Smart Traffic Fog Nodes and Homes through a Model Driven Approach 2

Integration of Smart Traffic Fog Nodes and Homes through a M...

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2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Alotaibi, Saud S. Rashid, Muhammad Azam, Farooque Abbas, Waseem Rasheed, Yawar Sinky, Mohammed H Anwar, Muhammad Waseem Umm Al-Qura University Department of Information Systems Makkah Saudi Arabia Umm Al-Qura University Computer Engineering Department Makkah Saudi Arabia H-12 Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665436052

Evolving concepts of smart cities include smart traffic lights and smart homes. These devices keep on observing the activities of users and share it with the cloud for further processing and decisions accordingly. This makes these systems complex ones. Therefore, a model-driven framework is needed which can integrate smart traffic lights with smart homes for the minimization of inherent development complexity. This article proposes a model driven framework, which integrates the smart traffic Fog nodes and smart homes. The basic objective is to share the mutual data related to traffic using cloud computing technology. To achieve this, a meta model is proposed. Subsequently, a Sirius based graphical modeling tool is developed which may be used to model the complex scenarios of smart lights and smart homes. A model to text transformation code is also written to automatically generate the target java code from the developed model. The validity of the proposed model has been demonstrated through a real world case study. In future, the proposed framework can be extended by incorporating the concepts of security and integrity of data. © 2022 IEEE.

关键词： Fog

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract) 39

A Survey on Modern Deep Neural Network for Traffic Predictio...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Tedjopurnomo, David Alexander Bao, Zhifeng Zheng, Baihua Choudhury, Farhana Murtaza Qin, A.K. Rmit University School of Computing Technologies Australia Singapore Management University School of Computing and Information Systems Singapore The University of Melbourne School of Computing and Information Systems Australia Swinburne University of Technology Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350322279

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare;making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field. © 2023 IEEE.

关键词： Deep neural networks

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SimulateIoT-Federations: Domain Specific Language for designing and executing IoT simulation environments with Fog and Fog-Cloud federations (Poster) 10

SimulateIoT-Federations: Domain Specific Language for design...

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STAF 2022 Workshop: 10th International Workshop on Bidirectional Transformations, BX 2022, 2nd International Workshop on Foundations and Practice of Visual Modeling, FPVM 2022 and 2nd International Workshop on MDE for Smart IoT systems, MeSS 2022

作者： Barriga, José A. Clemente, Pedro J. Quercus Software Engineering Group Department of Computer and Telematic Systems Engineering University of Extremadura Av. Universidad S/n Cáceres10003 Spain

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software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

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computers, Materials & Continua 2022年第4期71卷 1521-1546页

作者： Zuhaira Muhammad Zain Sapiah Sakri Nurul Halimatul Asmak Ismail Reza M.Parizi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Information Technology College of CommunityPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia College of Computing and Software Engineering Kennesaw State UniversityMariettaGA30060USA

Developing successful software with no defects is one of the main goals of software *** order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital *** learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated *** this paper,we aim to address such issues in predicting software *** propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects *** design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA *** experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software *** to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software ***,in turn,could lead to saving software development resources and producing more reliable software.

关键词： Defects software defect prediction deep learning convolutional neural network machine learning

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