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International Conference on Telecommunications and Signal Processing (TSP)

作者： Mervenur Cakir Murat Ekinci Elif Baykal Kablan Mursel Sahin Department of Software Engineering Karadeniz Technical University Trabzon Turkey Department of Computer Engineering Karadeniz Technical University Trabzon Turkey Department of Cardiology Faculty of Medicine Karadeniz Technical University Trabzon Turkey

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Aortic valve calcium scoring is extensively utilized for diagnosing, treating, monitoring, and assessing the risk of aortic stenosis and coronary artery disease. The gold standard method for determining aortic valve calcium score is computed tomography (CT). However, CT is expensive and exposes patients to radiation. Alternatively, echocardiography, which is a cheaper and radiation-free method, may not provide an objective result due to inter- and intra-observer variability among cardiologists. Therefore, automated measurement of aortic valve calcium score is essential. In this paper, a fully convolutional neural network approach is proposed for the segmentation of aortic valve calcified regions from echocardiography images, as the first step in predicting aortic valve calcium score. This aims to achieve a more objective, faster, easier, less costly, and radiation-free automatic calculation of aortic valve calcium score. The proposed method achieves a precision of 86.3%, recall of 80.27%, a dice coefficient of 76.27%, and a Jaccard index of 61.9% on the new dataset including 118 echocardiography images across 82 individual patients. These results show the feasibility and potential efficacy of fully automating aortic valve calcium scoring from echocardiography images.

关键词： Training Visualization Image segmentation Echocardiography Calcium Computed tomography Valves

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Compact NSGA-II for Multi-objective Feature Selection*

arXiv

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arXiv 2024年

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada

Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other post-processing task conducted after feature selection. In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features. In order to select optimal features, we have proposed a binary Compact NSGA-II (CNSGA-II) algorithm. Compactness represents the population as a probability distribution to enhance evolutionary algorithms not only to be more memory-efficient but also to reduce the number of fitness evaluations. Instead of holding two populations during the optimization process, our proposed method uses several Probability Vectors (PVs) to generate new individuals. Each PV efficiently explores a region of the search space to find non-dominated solutions instead of generating candidate solutions from a small population as is the common approach in most evolutionary algorithms. To the best of our knowledge, this is the first compact multiobjective algorithm proposed for feature selection. The reported results for expensive optimization cases with a limited budget on five datasets show that the CNSGA-II performs more efficiently than the well-known NSGA-II method in terms of the hypervolume (HV) performance metric requiring less memory. The proposed method and experimental results are explained and analyzed in detail. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

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computers, Materials & Continua 2023年第3期74卷 5267-5281页

作者： Muhammad Javaid Iqbal Muhammad Waseem Iqbal Muhammad Anwar Muhammad Murad Khan Abd Jabar Nazimi Mohammad Nazir Ahmad Department of Information Technology The Superior UniversityLahorePakistan Department of Software Engineering The Superior UniversityLahorePakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Department of Computer Science Government College UniversityFaisalabadPakistan Deptment of Oral and Maxillofacial Surgery Faculty of DentistryUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangorMalaysia

The brain tumour is the mass where some tissues become old or damaged,but they do not die or not leave their *** brain tumour masses occur due to malignant *** tissues must die so that new tissues are allowed to be born and take their *** segmentation is a complex and time-taking problem due to the tumour’s size,shape,and appearance *** finding such masses in the brain by analyzing Magnetic Resonance Images(MRI)is a crucial task for experts and *** could not work for large volume images simultaneously,and many errors occurred due to overwhelming image *** main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning *** research study proposed an automatic model for tumor segmentation in MRI *** proposed model has a few significant steps,which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative(NIFTI)volumes into the 3D NumPy *** the second step,the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated *** the third step,the proposed model uses state-of-the-art Medical Image Computing and computer-Assisted Intervention(MICCAI)BRATS 2018 dataset withMRI modalities such as T1,T1Gd,T2,and Fluidattenuated inversion recovery(FLAIR).Tumour types in MRI images are classified according to the tumour *** of these masses carried by state-of-the-art approaches such that the first is enhancing tumour(label 4),edema(label 2),necrotic and non-enhancing tumour core(label 1),and the remaining region is label 0 such that edema(whole tumour),necrosis and *** proposed model is evaluated and gets the Dice Coefficient(DSC)value for High-grade glioma(HGG)volumes for their test set-a,test set-b,and test set-c 0.9795, 0.9855 and 0.9793, respectively.

关键词： Brain tumour segmentation magnetic resonance images modalities dice coefficient low-grade glioma U-Net

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Semantic, Technical and Legal Interoperability of European Company Open Data in Practice: The STIRData Approach

Semantic, Technical and Legal Interoperability of European C...

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12th International Conference on Data Science, Technology and Applications, DATA 2023

作者： Klımek, Jakub Chortaras, Alexandros Mısek, Jakub Yang, Jim J. Skagemo, Steinar Tzouvaras, Vassilis Charles University Faculty of Mathematics and Physics Department of Software Engineering Czech Republic School of Electrical and Computer Engineering National Technical University of Athens Greece Institute of Law and Technology Masaryk University Czech Republic Norwegian Digitalisation Agency Norway Brønnøysund Register Centre Norway

ISBN: (纸本)9789897586644

As part of the Open Data Directive, the European Commission has published a list of high-value datasets (HVDs) that public sector bodies must make available as open data. The list also contains specific data items that must be included in these datasets. However, it does not prescribe any technical means of how the data should be published, severely hindering the interoperability of the datasets once they are published. One of the HVD topics is company data. In this practice report paper, we present results of STIRData, a project co-financed by the Connecting Europe Facility Programme of the European Union, focusing on technical, semantic, and legal interoperability of open data from business registries, covering the company data HVDs topic. The results include a data architecture and a data specification to make the published data technically and semantically interoperable, and legal interoperability guidelines to ensure legal interoperability of the published data. Moreover, proof-of-concept transformations of data from selected European business registries are shown using open source tools and according to the specification. Finally, a user-orientated platform for browsing and analysing the data is presented as an example of the possibilities of using the data published in an interoperable way. © 2023 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Data consistency

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Discrete RKZD(Reciprocal-Kind Zhang Dynamics) Algorithm Solving Time-Independent Linear Matrix-Vector Equation(TILMVE) Simplified from Time-Dependent Situation

Discrete RKZD(Reciprocal-Kind Zhang Dynamics) Algorithm Solv...

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第42届中国控制会议

作者： Yunong Zhang Ji Lu Mingzhi Mao Shuai Li School of Computer Science and Engineering Sun Yat-sen University School of Software Engineering Sun Yat-sen University Faculty of Information Technology and Electrical Engineering University of Oulu

We present a continuous reciprocal-kind Zhang dynamics(RKZD) model for solving the time-dependent linear matrixvector equation. On the basis of the model, we deduce its simplified form for solving the time-independent linear matrix-vector equation(TILMVE). Subsequently, for more efficient computation and easier implementation in digital hardware, we utilize Euler forward difference formula(EFDF) to discretize the continuous RKZD model, resulting in a discrete RKZD ***, numerical experimental results attest to the feasibility and high effectiveness of the discrete RKZD algorithm for solving TILMVE. Comparisons with the discrete gradient neural network(or termed discrete gradient dynamics), Jacobi iteration, as well as Gauss-Seidel iteration highlight the superior convergence properties of the discrete RKZD algorithm.

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Smart Home User Interface: Overview

Smart Home User Interface: Overview

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： Renat Faizrakhmanov Alexey Platunov Mohammad Reza Bahrami Cyberphysical Systems Laboratory Innopolis University Kazan Russian Faculty of Software Engineering and Computer Systems ITMO University Saint-Petesburg Russia

This paper overviews the Smart Home User Interfaces (UI) in the following aspects: general definition of Human-Machine Interface (HMI), types of common Smart Home UI, market analysis of HMI, voice assistants and chatbots. This study was conducted to determine existed Smart Home UI and evaluate them in the future works. This comparing and evaluation needed to choose appropriate and convenient Smart Home UI for certain application. The overview leaded to the following findings: Smart Home market is growing; certain global trends influenced on this market and on HMI in general; certain groups of people are more interested in Smart Home technologies; each type of Smart Home User Interface has its leaders. This study can help researchers learn about the main types of Smart Home User Interfaces and trends in this market.

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Smart Home User Interface: Development and Comparison

Smart Home User Interface: Development and Comparison

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： R. Faizrakhmanov A. Platunov M. Bahrami Cyberphysical Systems Laboratory Innopolis University Kazan Russian Faculty of Software Engineering and Computer Systems ITMO University Saint-Petesburg Russia

The active development of the Internet of Things (IoT) in recent years has increased people’s need for control smart devices for home. At the same time, the complexity of these devices is growing. Therefore, Smart Home systems require a simple and convenient interface for control. However, methods for evaluating and comparing interfaces are not clear. In this paper, we described a method to comparing and evaluating different types of Smart Home interfaces. In addition, we evaluate results experimentally. For this comparison, we used a popular Smart Home control software on the market. The results showed that Smart Home graphical user interfaces are more convenient than a messenger or voice. Voice interface is suitable for controlling a Smart Home but is not informative enough in feedback. On the other hand, the familiar interface of the messenger turned out to be the least convenient. This study will help both researchers and practitioners evaluate and choose the right Smart Home interface.

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal) 17

Smart Farmer Education System for Disease Identification, Se...

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17th IEEE International Conference on Industrial and Information Systems, ICIIS 2023

作者： Bandara, Keshara Vidhanaarachchi, Samitha Nanayakkara, A.A.E. Kulasinghe, Kavindu Rajapakshe, Sanka Wickramasinghe, Asela Yapa, Kanishka Sri Lanka Institute of Information Technology Department of Information Technology New Kandy Road Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka University of Ruhuna Faculty of Agriculture Department of Soil Science Mapalana Kamburupitiya Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350323634

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed 'AI-based mobile application system for Brinjal Diseases,' which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections. © 2023 IEEE.

关键词： Crowdsourcing

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Interactive and Explainable Optimized Learning for DDoS Detection in Consumer IoT Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Saiyed, Makhduma F Al-Anbagi, Irfan Hossain, M. Shamim University of Regina Faculty of Engineering and Applied Science ReginaSKS4S 0A2 Canada King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

The integration of Internet of Things (IoT) in consumer environments enhances convenience and security while increasing Human-computer Interaction (HCI). However,this increased interactivity has also increased the vulnerability of Consumer IoT (CIoT) networks to cyber threats,mainly Distributed Denial of Service (DDoS) attacks. The DDoS attacks,which vary in volume,present substantial challenges to these networks' operational integrity and customer trust. This paper introduces the Artificial Intelligence (AI)-driven (ADEPT) system that utilizes explainable and optimized deep-ensemble learning with pruning for DDoS detection. The system uses attention-based ensemble DL for DDoS detection,combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. To address the resource constraints of edge devices in CIoT networks,the system uses Differential Evolution (DE)-based pruning and quantization techniques,optimizing the model for efficient deployment on edge nodes while preserving high performance. An HCI interface is designed to allow network administrators and researchers to engage with the system through dynamic visualizations,facilitating complex data interpretation and empowering administrators to refine detection strategies. The interface,integrating SHapley Additive exPlanations (SHAP) and risk assessment,enhances model transparency and interpretability,highlighting the synergy of HCI and AI. The ADEPT system is evaluated using an experimental testbed and CIoT datasets and has demonstrated over 90% accuracy in detecting high-and low-volume DDoS attacks. © 1975-2011 IEEE.

关键词： Risk assessment

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A Statistical Model to Assess the Team's Productivity in Agile software Teams 4

A Statistical Model to Assess the Team's Productivity in Agi...

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4th IEEE International Conference and Workshop in Obuda on Electrical and Power engineering, CANDO-EPE 2021

作者： Mashmool, Amir Khosravi, Samiyeh Joloudari, Javad Hassannataj Inayat, Irum Gandomani, Taghi Javdani Mosavi, Amir University of Birjand Faculty of Engineering Computer Engineering Department Birjand Iran National University of Computer and Emerging Sciences School of Computing Department of Software Engineering Islamabad Pakistan Shahrekord University Department of Computer Science Shahrekord*** Iran Obuda University Institute of Software Design and Development Budapest1034 Hungary

ISBN: (纸本)9781665420235

Agile methods promise to achieve high productivity and provide high-quality software. Agile software development is the most important approach that has spread through the world of software development over the past decade. software team productivity measurement is essential in agile teams to increase the performance of software development. Due to the prevalence of agile methodologies and increasing competition of software development companies, software team productivity has become one of the crucial challenges for agile software companies and teams. Awareness of the level of team productivity can help them to achieve better estimation results on the time and cost of the projects. However, to measure software productivity, there is no definitive solution or approach whether in traditional and agile software development teams that lead to the occurrence of many problems in achieving a reliable definition of software productivity. Hence, this study aims to propose a statistical model to assess the team's productivity in agile teams. A survey was conducted with forty software companies and measured the impact of six factors of the team on productivity in these companies. The results show that team effectiveness factors including inter-team relationship, quality conformance by the team, team vision, team leader, and requirements handled by the team had a significant impact on the team's productivity. Moreover, the results also state that inter-team relations affect the most on software teams' productivity. Finally, the model fit test showed that 80% of productivity depends on team effectiveness factors. © 2021 IEEE.

关键词： Productivity

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