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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Nesrine Bnouni Hadil Ben Amor Islem Rekik Mohamed Salah Rhim Basel Solaiman Najoua Essoukri Ben Amara Université de Sousse École Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisia Faculty of Computer and Informatics Engineering Brain And Signal Research and Analysis Laboratory Istanbul Technical University Istanbul Turkey Faculty of Medicine of Monastir Tunisia IMT Atlantique Bretagne-Pays de la Loire Ecoles Mines-Telecom France

ISBN: (纸本)9781665414937;9781665430579

Cervical cancer is the fourth most common gynecological malignant cancer in the world. It presents one of the principal causes of cancer death in women. Treatment planning depends on the cancer stage (tumor size, nodal status, and local extension), which can be identified using magnetic resonance imaging. For effective cancer diagnosis and prognosis, automated segmentation methods of cervical tumor are highly desired as they can alleviate the burden of manual segmentation. However, typical automatic segmentation methods, including deep learning methods (e.g., Convolutional Neural Networks - CNN), might fail due to intensity inhomogeneity, poor contrast, and noise present in medical images. As a solution, the performance of such methods can be boosted by using an automated image preprocessing framework. This paper first proposes an ensemble preprocessing method to improve the performance of a CNN for cervical cancer segmentation. Specifically, we propose a histogram-based, smoothing and sharpening-based, and morphological image processing methods. Then, we devise three CNNs with the same architecture. Each CNN is trained independently using each of the three proposed preprocessed datasets. For evaluation, we used leave-one-out cross-validation, where a left-out testing image sample passes through each CNN to output a probability segmentation map. Ultimately, through applying majority voting to the three outputted segmentation maps, we get the final label map. Our method significantly ( ) outperformed benchmark methods with a classification accuracy increasing from 74.1% by single CNN with no-processing to 76.8% by applying the majority voting to the three CNNs with different preprocessing methods.

关键词： Deep learning Image segmentation Smoothing methods Manuals Boosting Nonhomogeneous media Planning

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Microscopic Traffic Simulation using SUMO 21

Microscopic Traffic Simulation using SUMO

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21st IEEE International Conference on Intelligent Transportation Systems (ITSC)

作者： Lopez, Pablo Alvarez Behrisch, Michael Bieker-Walz, Laura Erdmann, Jakob Flotterod, Yun-Pang Hilbrich, Robert Lucken, Leonhard Rummel, Johannes Wagner, Peter Wiessner, Evamarie Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781728103235

Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.

关键词： Tools Microscopy Roads Data models Urban areas Vehicle dynamics Mathematical model

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Diagnophone: An Electronic Stethoscope For Respiratory Audio Analysis

Diagnophone: An Electronic Stethoscope For Respiratory Audio...

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International Conference on computer Science and engineering (UBMK)

作者： Ege Yağ Çakır Gökhan İnce Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Today, pulmonary diseases are one of the major causes of mortality in the world. Even though there are different diagnostic tests available such as X-ray and tomography, the stethoscope is still the first, cheapest and the most frequently used diagnostic device for the physicians. In this paper, a smart electronic stethoscope has been designed to help physicians with the diagnosis of the disease using Machine Learning. In order to create a design that satisfies all the needs of the physicians, 15 doctors and medical students from several hospitals have been contacted and interviewed. The developed system has been tested with different machine learning techniques and its efficiency has been shown by obtaining 84% accuracy while classifying respiratory audio.

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EU COST Action On Future Generation Optical Wireless Communication Technologies, 2nd White Paper

arXiv

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arXiv 2023年

作者： Ghassemlooy, Zabih Khalighi, Mohammad-Ali Zvanovec, Stanislav Shrestha, Amita Ortega, Beatriz Petkovic, Milica Pang, Xiaodan Sirtori, Carlo Orsucci, Davide Moll, Florian Cossu, Giulio Spirito, Veronica Ninos, Michail P. Ciaramella, Ernesto Bas, Joan Amay, Marc Huang, Shenjie Safari, Majid Gutema, Tilahun Popoola, Wasiu Matus, Vicente Rabadán, José Perez-Jimenez, Rafael Panayirci, Erdal Diamantoulakis, Panagiotis D. Haas, Harald Ijeh, Ikenna Chinazaekpere Optical Communications Research Group Faculty of Engineering and Environment Northumbria University United Kingdom Aix-Marseille University CNRS Centrale Marseille Institut Fresnel Marseille France Dept. of Electromagnetic Field Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic German Aerospace Center Germany Institute of Telecommunications and Multimedia Applications Universitat Politècnica de València Spain University of Novi Sad Faculty of Technical Sciences Serbia KTH Royal Institute of Technology Sweden Laboratoire de Physique de l'École normale supérieure France Scuola Superiore Sant'Anna TeCIP Institute Italy Spain The University of Edinburgh United Kingdom Méditerranée France School of Engineering University of Edinburgh United Kingdom University of Las Palmas de Gran Canaria Spain Department of Electrical and Electronics Engineering Kadir Has University Istanbul Turkey Department of Applied Informatics University of Macedonia Thessaloniki54636 Greece Department of Electronics and Electrical Engineering The University of Strathclyde GlasgowG1 1XQ United Kingdom Department of Electrical & Electronic Engineering Alex Ekwueme Federal University Ebonyi State Ndufu-Alike Ikwo Nigeria

NEWFOCUS is an EU COST Action targeted at exploring radical solutions thatcould influence the design of future wireless networks. The project aims toaddress some of the challenges associated with optical wireless communication(OWC) and to establish it as a complementary technology to the radio frequency(RF)-based wireless systems in order to meet the demanding requirements of thefifth generation (5G) and the future sixth generation (6G) backhaul and accessnetworks. Only 6G will be able to widely serve the exponential growth inconnected devices (i.e., more than 500 billion) in 2030, real-time holographiccommunication, future virtual reality, etc. Space is emerging as the newfrontier in 5 and 6G and beyond communication networks, where it offershigh-speed wireless coverage to remote areas both in lands and sees. Thisactivity is supported by the recent development of low-altitude Earth orbitsatellite mega-constellations. The focus of this 2nd White Paper is on the useof OWC as an enabling technology for medium- and long-range links fordeployment in (i) smart-cities and intelligent transportation systems;(ii)first- and last-mile access and backhaul/fronthaul wireless networks;(iii)hybrid free-space optics/RF adaptive wireless connections;(iv)space-to-ground, inter-satellite, ground-to-air, and air-to-air communications;and (v) underwater communications. © 2023, CC BY-NC-ND.

关键词： Orbits

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Impact of artificial intelligence on employees performance in information technology sector

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AIP Conference Proceedings 2023年第1期2587卷

作者： P. Lalitha Kumari Jay Kumar Pandey Karunendra Verma G. Taviti Naidu Ankur Gupta Malik Bader Alazzam Dept. of Computer Science and Engineering Visakha Institute of Engineering & Technology 57th Division Narava Visakhapatnam Andhra Pradesh India Dept. of Electronics and Communication Engineering Shri Ramswaroop Memorial University Dewa Road Barabanki Uttar Pradesh India Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India Dept. of Management Dr. L. Bullayya College Visakhapatnam Andhra Pradesh India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

The focus of research is to propose smart approach to evaluate the performance of employees in IT sector. The research made use of artificial intelligence in order to fulfill the objectives. The different attributes that are influencing performance of employees are technical skill, salary, training, working environment, supervision, time schedule. A survey has been conducted where the ranking has been made considering votes of different employees in IT Company. Their person views are obtained considering influencing factors. The data set has been filtered using optimization mechanism and machine learning mechanism has been applied to train the network in order to predict the employee performance considering influencing factors. However there have been several existing researches but those research works have considered limited influencing factor. Moreover there is lack of machine learning approach that could provide suitable solution to build a mechanism that would be capable to design smart model considering influencing factors.

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A few-shot learning graph multi-trajectory evolution network for forecasting multimodal baby connectivity development from a baseline timepoint

arXiv

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arXiv 2021年

作者： Bessadok, Alaa Nebli, Ahmed Mahjoub, Mohamed Ali Li, Gang Lin, Weili Shen, Dinggang Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse 4011 Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Mannouba Tunisia Department of Radiology and BRIC University of North Carolina Chapel HillNC United States School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development United Imaging Intelligence Co. Ltd. Shanghai China

Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analysis requires acquisition of longitudinal connectomic datasets. However, both neonatal and postnatal scans are rarely acquired due to various difficulties. A small body of works has focused on predicting baby brain evolution trajectory from a neonatal brain connectome derived from a single modality. Although promising, large training datasets are essential to boost model learning and to generalize to a multi-trajectory prediction from different modalities (i.e., functional and morphological connectomes). Here, we unprecedentedly explore the question: "Can we design a few-shot learning-based framework for predicting brain graph trajectories across different modalities?" To this aim, we propose a Graph Multi-Trajectory Evolution Network (GmTE-Net), which adopts a teacher-student paradigm where the teacher network learns on pure neonatal brain graphs and the student network learns on simulated brain graphs given a set of different timepoints. To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs). To boost the performance of the student network, we introduce a local topology-aware distillation loss that forces the predicted graph topology of the student network to be consistent with the teacher network. Experimental results demonstrate substantial performance gains over benchmark methods. Hence, our GmTE-Net can be leveraged to predict atypical brain connectivity trajectory evolution across various modalities. Our code is available at https://***/basiralab/GmTE-Net. © 2021, CC BY-NC-ND.

关键词： Trajectories

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Vehicular Volunteer Computing (VVC): A Novel Paradigm for the Volunteering of Vehicular Resources;Opportunities and Challenges

Vehicular Volunteer Computing (VVC): A Novel Paradigm for th...

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International Conference on Computing, Electronics & Communications engineering (iCCECE)

作者： Wiseborn Manfe Danquah D. Turgay Altilar Faculty of Computer Engineering & Informatics Istanbul Technical University Istabul Turkey

ISBN: (纸本)9781728121390

Modern applications and scientific research involve resource demanding tasks such as storage, retrieval and analysis of large and complex data. To meet the huge resource requirement, vehicular volunteer computing is proposed in this paper. With the advancements in vehicular communication technologies and embedment of computing resources in vehicles, a large number of powerful computing resources would be available on roads and at parking lots which can be employed to make the execution of projects possible without relying on dedicated servers and supercomputers. In our proposed vehicular volunteer computing, compute intensive task hosted on a server is divided into parts and scheduled to volunteered vehicles for execution. This study examines the characteristics as well as opportunities presented by vehicular volunteer computing for the accomplishment of important projects such as scientific research and other computation intensive applications. Research challenges that need to be overcome for vehicular volunteering computing to be more efficient are also presented in this study.

关键词： computer applications Distributed processing Cloud computing Servers Task analysis Sensors Computational modeling

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State of the Art of Audio- and Video-Based Solutions for AAL

arXiv

引用

arXiv 2022年

作者： Aleksic, Slavisa Atanasov, Michael Agius, Jean Calleja Camilleri, Kenneth Čartolovni, Anto Climent-Pérez, Pau Colantonio, Sara Cristina, Stefania Despotovic, Vladimir Ekenel, Hazım Kemal Erakin, Ekrem Florez-Revuelta, Francisco Germanese, Danila Grech, Nicole Sigurðardóttir, Steinunn Gróa Emirzeoğlu, Murat Iliev, Ivo Jovanovic, Mladjan Kampel, Martin Kearns, William Klimczuk, Andrzej Lambrinos, Lambros Lumetzberger, Jennifer Mucha, Wiktor Noiret, Sophie Pajalic, Zada Pérez, Rodrigo Rodriguez Petrova, Galidiya Petrovica, Sintija Pocta, Peter Poli, Angelica Pudane, Mara Spinsante, Susanna Salah, Albert Ali Santofimia, Maria Jose Islind, Anna Sigríður Stoicu-Tivadar, Lacramioara Tellioğlu, Hilda Zgank, Andrej Leipzig University of Applied Sciences Germany TU Wien Austria Department of Anatomy University of Malta Malta Centre for Biomedical Cybernetics Faculty of Engineering Department of Systems and Control Engineering Malta Catholic University of Croatia Croatia Universidad de Alicante Spain Institute of Information Science and Technologies The National Research Council of Italy Italy Department of Systems and Control Engineering University of Malta Malta University of Luxembourg Luxembourg Dept. of Computer Engineering Istanbul Technical University Turkey Reykjavik University School of Computer Science Iceland Sağlık Bilimleri Fakültesi Fizyoterapi ve Rehabilitasyon Karadeniz Technical University Research Information System Turkey TU Sofia Bulgaria Singidunum University Belgrade Serbia University of South Florida United States SGH Warsaw School of Economics Poland Department of Communication and Internet Studies Cyprus University of Technologies Cyprus Technical University of Vienna Austria VID Specialized University Oslo Norway Getafe University Hospital Spain Riga Technical University Latvia University of Zilina Slovakia Università Politecnica Marche Italy Utrecht University Netherlands Universidad de Castilla-La Mancha Spain Reykjavik University Iceland University Politehnica Timisoara Romania University of Maribor Slovenia

It is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may

关键词： computer vision

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FINO-Net: A deep multimodal sensor fusion framework for manipulation failure detection

arXiv

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arXiv 2020年

作者： Inceoglu, Arda Aksoy, Eren Erdal Ak, Abdullah Cihan Sariel, Sanem Artificial Intelligence and Robotics Laboratory Faculty of Computer and Informatics Engineering Istanbul Technical University Maslak Turkey School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Halmstad Sweden

We need robots more aware of the unintended outcomes of their actions for ensuring safety. This can be achieved by an onboard failure detection system to monitor and detect such cases. Onboard failure detection is challenging with a limited set of onboard sensor setup due to the limitations of sensing capabilities of each sensor. To alleviate these challenges, we propose FINO-Net, a novel multimodal sensor fusion based deep neural network to detect and identify manipulation failures. We also introduce FAILURE, a multimodal dataset, containing 229 real-world manipulation data recorded with a Baxter robot. Our network combines RGB, dept. and audio readings to effectively detect failures. Results indicate that fusing RGB with dept. and audio modalities significantly improves the performance. FINO-Net achieves %98.60 detection accuracy on our novel dataset. Code and data are publicly available at https://***/ardai/fino-net. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Predicting Defects with Latent and Semantic Features from Commit Logs in an Industrial Setting

Predicting Defects with Latent and Semantic Features from Co...

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IEEE/ACM International Conference on Automated Software engineering - Workshops (ASE Workshops)

作者： Beyza Eken RiFat Atar Sahra Sertalp Ayşe Tosun Faculty of Computer and Informatics Engineering Istanbul Technical University Turkey Ericsson Turkey Istanbul Technical University Istanbul Turkey

Software defect prediction is still a challenging task in industrial settings. Noisy data and/or lack of data make it hard to build successful prediction models. In this study, we aim to build a change-level defect prediction model for a software project in an industrial setting. We combine various probabilistic models, namely matrix factorization and topic modeling, with the expectation of overcoming the noisy and limited nature of industrial settings by extracting hidden features from multiple resources. Commit level process metrics, latent features from commits, and semantic features from commit messages are combined to build the defect predictors with the use of Log Filtering and feature selection techniques, and two machine learning algorithms Naive Bayes and Extreme Gradient Boosting (XGBoost). Collecting data from various sources and applying data pre-processing techniques show a statistically significant improvement in terms of probability of detection by up to 24% when compared to a base model with process metrics only.

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