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714 results on '"Ionescu, Bogdan"'

1. Devices for Prevention of Atrial Tachyarrhythmias

Author

Fernández Lozano, Ignacio, Toquero, Jorge, Fernández Díaz, José Antonio, Ionescu, Bogdan, Moñivas, Vanesa, Ortiz, Pilar, Fuertes, Beatriz, Singh, Balbir, Lokhandwala, Yash, Francis, Johnson, and Gupta, Anup

Subjects
JOURNALS: Indian Pacing and Electrophysiology Journal, Indian Pacing and Electrophysiology Journal
Abstract

Atrial fibrillation (AF) is the most frequent sustained cardiac arrhythmia in clinical practice and, although its importance has been underestimated even in recent years, we are now becoming aware of its clinical transcendence1,2,3. The classical treatment is pharmacological, but its efficacy is limited and it does have side effects4,5. Therefore, in recent years, there has been an increasing interest in other types of non-pharmacological treatments6,7. Physiologic cardiac pacing has proven to be more effective than VVI mode pacing to prevent the occurrence of AF during the follow-up of patients who have had a permanent pacemaker implanted 8,9,10. There are currently different lines of research that use different atrial pacing techniques to prevent and treat episodes of paroxysmal atrial fibrillation11,12. Techniques of multi-site pacing in the right atrium or both atria, new atrial pacing sites, prevention algorithms for paroxysmal atrial fibrillation episodes, and even high-frequency atrial tachyarrhythmia termination algorithms have all been proposed. In this article, we will try to synthesize the grounds for and findings of the different lines of research currently being developed.

Published
2004

2. Deepfake Sentry: Harnessing Ensemble Intelligence for Resilient Detection and Generalisation

Author

Ştefan, Liviu-Daniel, Stanciu, Dan-Cristian, Dogariu, Mihai, Constantin, Mihai Gabriel, Jitaru, Andrei Cosmin, and Ionescu, Bogdan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent advancements in Generative Adversarial Networks (GANs) have enabled photorealistic image generation with high quality. However, the malicious use of such generated media has raised concerns regarding visual misinformation. Although deepfake detection research has demonstrated high accuracy, it is vulnerable to advances in generation techniques and adversarial iterations on detection countermeasures. To address this, we propose a proactive and sustainable deepfake training augmentation solution that introduces artificial fingerprints into models. We achieve this by employing an ensemble learning approach that incorporates a pool of autoencoders that mimic the effect of the artefacts introduced by the deepfake generator models. Experiments on three datasets reveal that our proposed ensemble autoencoder-based data augmentation learning approach offers improvements in terms of generalisation, resistance against basic data perturbations such as noise, blurring, sharpness enhancement, and affine transforms, resilience to commonly used lossy compression algorithms such as JPEG, and enhanced resistance against adversarial attacks., Comment: 16 pages, 1 figure, U.P.B. Sci. Bull., Series C, Vol. 85, Iss. 4, 2023

Published
2024

3. Unveiling challenges and insights in cloud Enterprise Resource Planning systems adoption: A case study of the Oracle Fusion Cloud ERP

Author

Rîndaşu Sînziana-Maria, Ionescu-Feleagă Liliana, Ionescu Bogdan-Ștefan, and Barbu Alexandra Mihaela

Subjects
cloud enterprise resource planning, challenges, user’s reviews, insights, oracle fusion cloud erp, Business, HF5001-6182
Abstract

The migration from legacy systems to cloud Enterprise Resource Planning systems is driven by technological advancements and competitive pressure, prompting companies to seek more agile and scalable solutions. Although developers and vendors aim to ease the adoption process, significant challenges persist and can lead to potential disruptions to business operations and affect the organisations’ reputation. Starting from a qualitative approach, this study aims to explore and uncover the difficulties faced by end users across different roles and industries. An inductive examination of these challenges is undertaken using a thematic approach, focusing on one of the most acclaimed cloud Enterprise Resource Planning solutions, Oracle Fusion Cloud ERP. Continuing with a quantitative examination, the study comparatively analyses the challenges in terms of industry and their evolution from 2020 to 2023. The research sheds light on the breadth and depth of issues faced by direct beneficiaries, demonstrating that even highly regarded solutions exhibit a myriad of challenges. Therefore, our research provides valuable insights for businesses, practitioners, and researchers, enhancing their understanding of the subject and providing insights into strategic decisions related to adopting and using cloud Enterprise Resource Planning systems. Acknowledging and addressing these challenges is crucial for companies navigating the complexities of cloud Enterprise Resource Planning adoption to achieve the full potential of solutions and achieve sustainable competitive advantages in today’s fast-paced business environment.

Published
2024
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4. Overview of The MediaEval 2022 Predicting Video Memorability Task

Author

Sweeney, Lorin, Constantin, Mihai Gabriel, Demarty, Claire-Hélène, Fosco, Camilo, de Herrera, Alba G. Seco, Halder, Sebastian, Healy, Graham, Ionescu, Bogdan, Matran-Fernandez, Ana, Smeaton, Alan F., and Sultana, Mushfika

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Multimedia
Abstract

This paper describes the 5th edition of the Predicting Video Memorability Task as part of MediaEval2022. This year we have reorganised and simplified the task in order to lubricate a greater depth of inquiry. Similar to last year, two datasets are provided in order to facilitate generalisation, however, this year we have replaced the TRECVid2019 Video-to-Text dataset with the VideoMem dataset in order to remedy underlying data quality issues, and to prioritise short-term memorability prediction by elevating the Memento10k dataset as the primary dataset. Additionally, a fully fledged electroencephalography (EEG)-based prediction sub-task is introduced. In this paper, we outline the core facets of the task and its constituent sub-tasks; describing the datasets, evaluation metrics, and requirements for participant submissions., Comment: 6 pages. In: MediaEval Multimedia Benchmark Workshop Working Notes, 2022

Published
2022

5. Experiences from the MediaEval Predicting Media Memorability Task

Author

de Herrera, Alba García Deco, Constantin, Mihai Gabriel, Demarty, Chaire-Hélène, Fosco, Camilo, Halder, Sebastian, Healy, Graham, Ionescu, Bogdan, Matran-Fernandez, Ana, Smeaton, Alan F., Sultana, Mushfika, and Sweeney, Lorin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

The Predicting Media Memorability task in the MediaEval evaluation campaign has been running annually since 2018 and several different tasks and data sets have been used in this time. This has allowed us to compare the performance of many memorability prediction techniques on the same data and in a reproducible way and to refine and improve on those techniques. The resources created to compute media memorability are now being used by researchers well beyond the actual evaluation campaign. In this paper we present a summary of the task, including the collective lessons we have learned for the research community., Comment: 7 pages, 2 figures, 1 table. Presented at the NeurIPS 2022 Workshop on Memory in Artificial and Real Intelligence (MemARI), 2 December 2022, New Orleans, USA

Published
2022

6. Overview of the ImageCLEF 2024: Multimedia Retrieval in Medical Applications

Author

Ionescu, Bogdan, Müller, Henning, Drăgulinescu, Ana-Maria, Rückert, Johannes, Ben Abacha, Asma, García Seco de Herrera, Alba, Bloch, Louise, Brüngel, Raphael, Idrissi-Yaghir, Ahmad, Schäfer, Henning, Schmidt, Cynthia Sabrina, Pakull, Tabea M. G., Damm, Hendrik, Bracke, Benjamin, Friedrich, Christoph M., Andrei, Alexandra-Georgiana, Prokopchuk, Yuri, Karpenka, Dzmitry, Radzhabov, Ahmedkhan, Kovalev, Vassili, Macaire, Cécile, Schwab, Didier, Lecouteux, Benjamin, Esperança-Rodier, Emmanuelle, Yim, Wen-Wai, Fu, Yujuan, Sun, Zhaoyi, Yetisgen, Meliha, Xia, Fei, Hicks, Steven A., Riegler, Michael A., Thambawita, Vajira, Storås, Andrea, Halvorsen, Pål, Heinrich, Maximilian, Kiesel, Johannes, Potthast, Martin, Stein, Benno, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goeuriot, Lorraine, editor, Mulhem, Philippe, editor, Quénot, Georges, editor, Schwab, Didier, editor, Di Nunzio, Giorgio Maria, editor, Soulier, Laure, editor, Galuščáková, Petra, editor, García Seco de Herrera, Alba, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2024
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8. Advancing Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications with ImageCLEF 2024

Author

Ionescu, Bogdan, Müller, Henning, Drăgulinescu, Ana Maria, Idrissi-Yaghir, Ahmad, Radzhabov, Ahmedkhan, Herrera, Alba Garcia Seco de, Andrei, Alexandra, Stan, Alexandru, Storås, Andrea M., Abacha, Asma Ben, Lecouteux, Benjamin, Stein, Benno, Macaire, Cécile, Friedrich, Christoph M., Schmidt, Cynthia Sabrina, Schwab, Didier, Esperança-Rodier, Emmanuelle, Ioannidis, George, Adams, Griffin, Schäfer, Henning, Manguinhas, Hugo, Coman, Ioan, Schöler, Johanna, Kiesel, Johannes, Rückert, Johannes, Bloch, Louise, Potthast, Martin, Heinrich, Maximilian, Yetisgen, Meliha, Riegler, Michael A., Snider, Neal, Halvorsen, Pål, Brüngel, Raphael, Hicks, Steven A., Thambawita, Vajira, Kovalev, Vassili, Prokopchuk, Yuri, Yim, Wen-Wai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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9. Few-Shot Object Detection as a Service: Facilitating Training and Deployment for Domain Experts

Author

Bailer, Werner, Dogariu, Mihai, Ionescu, Bogdan, Fassold, Hannes, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rudinac, Stevan, editor, Hanjalic, Alan, editor, Liem, Cynthia, editor, Worring, Marcel, editor, Jónsson, Björn Þór, editor, Liu, Bei, editor, and Yamakata, Yoko, editor

Published
2024
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11. Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease

Author

Tautan, Alexandra-Maria, Casula, Elias, Borghi, Ilaria, Maiella, Michele, Bonni, Sonia, Minei, Marilena, Assogna, Martina, Ionescu, Bogdan, Koch, Giacomo, and Santarnecchi, Emiliano

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of TMS-evoked EEG responses to classify AD patients from healthy controls (HC). By using a dataset containing 17AD and 17HC, we extract various time domain features from individual TMS responses and average them over a low, medium and high density EEG electrode set. Within a leave-one-subject-out validation scenario, the best classification performance for AD vs. HC was obtained using a high-density electrode with a Random Forest classifier. The accuracy, sensitivity and specificity were of 92.7%, 96.58% and 88.2% respectively., Comment: 4 pages, 4 figures, accepted to the 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 11-15 July 2022, Glasgow, Scotland, UK

Published
2022

12. Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification

Author

Tautan, Alexandra-Maria, Casula, Elias, Borghi, Ilaria, Maiella, Michele, Bonni, Sonia, Minei, Marilena, Assogna, Martina, Ionescu, Bogdan, Koch, Giacomo, and Santarnecchi, Emiliano

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition, I.2.1
Abstract

Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD). In this study, we propose an automatic method of determining the duration of TMS induced perturbation of the EEG signal as a potential metric reflecting the brain's functional alterations. A preliminary study is conducted in patients with Alzheimer's disease (AD). Three metrics for characterizing the strength and duration of TMS evoked EEG (TEP) activity are proposed and their potential in identifying AD patients from healthy controls was investigated. A dataset of TMS-EEG recordings from 17 AD and 17 healthy controls (HC) was used in our analysis. A Random Forest classification algorithm was trained on the extracted TEP metrics and its performance is evaluated in a leave-one-subject-out cross-validation. The created model showed promising results in identifying AD patients from HC with an accuracy, sensitivity and specificity of 69.32%, 72.23% and 66.41%, respectively., Comment: 4 pages, 4 figures, Accepted at 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Glasgow, Scotland, United Kingdome, July 11-15, 2022

Published
2022

13. Overview of The MediaEval 2021 Predicting Media Memorability Task

Author

Kiziltepe, Rukiye Savran, Constantin, Mihai Gabriel, Demarty, Claire-Helene, Healy, Graham, Fosco, Camilo, de Herrera, Alba Garcia Seco, Halder, Sebastian, Ionescu, Bogdan, Matran-Fernandez, Ana, Smeaton, Alan F., and Sweeney, Lorin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Multimedia
Abstract

This paper describes the MediaEval 2021 Predicting Media Memorability}task, which is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task. In 2021, two datasets of videos are used: first, a subset of the TRECVid 2019 Video-to-Text dataset; second, the Memento10K dataset in order to provide opportunities to explore cross-dataset generalisation. In addition, an Electroencephalography (EEG)-based prediction pilot subtask is introduced. In this paper, we outline the main aspects of the task and describe the datasets, evaluation metrics, and requirements for participants' submissions., Comment: 3 pages, to appear in Proceedings of MediaEval 2021, December 13-15 2021, Online

Published
2021

14. An Annotated Video Dataset for Computing Video Memorability

Author

Kiziltepe, Rukiye Savran, Sweeney, Lorin, Constantin, Mihai Gabriel, Doctor, Faiyaz, de Herrera, Alba Garcia Seco, Demarty, Claire-Helene, Healy, Graham, Ionescu, Bogdan, and Smeaton, Alan F.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1,275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020., Comment: 11 pages

Published
2021
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15. Face Verification with Challenging Imposters and Diversified Demographics

Author

Popescu, Adrian, Ştefan, Liviu-Daniel, Deshayes-Chossart, Jérôme, and Ionescu, Bogdan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Face verification aims to distinguish between genuine and imposter pairs of faces, which include the same or different identities, respectively. The performance reported in recent years gives the impression that the task is practically solved. Here, we revisit the problem and argue that existing evaluation datasets were built using two oversimplifying design choices. First, the usual identity selection to form imposter pairs is not challenging enough because, in practice, verification is needed to detect challenging imposters. Second, the underlying demographics of existing datasets are often insufficient to account for the wide diversity of facial characteristics of people from across the world. To mitigate these limitations, we introduce the $FaVCI2D$ dataset. Imposter pairs are challenging because they include visually similar faces selected from a large pool of demographically diversified identities. The dataset also includes metadata related to gender, country and age to facilitate fine-grained analysis of results. $FaVCI2D$ is generated from freely distributable resources. Experiments with state-of-the-art deep models that provide nearly 100\% performance on existing datasets show a significant performance drop for $FaVCI2D$, confirming our starting hypothesis. Equally important, we analyze legal and ethical challenges which appeared in recent years and hindered the development of face analysis research. We introduce a series of design choices which address these challenges and make the dataset constitution and usage more sustainable and fairer. $FaVCI2D$ is available at~\url{https://github.com/AIMultimediaLab/FaVCI2D-Face-Verification-with-Challenging-Imposters-and-Diversified-Demographics}.

Published
2021

16. Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?

Author

De Herrera, Alba García Seco, Kiziltepe, Rukiye Savran, Chamberlain, Jon, Constantin, Mihai Gabriel, Demarty, Claire-Hélène, Doctor, Faiyaz, Ionescu, Bogdan, and Smeaton, Alan F.

Subjects
Computer Science - Multimedia, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper describes the MediaEval 2020 \textit{Predicting Media Memorability} task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video-to-Text dataset, containing more action rich video content as compared with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for participants' run submissions., Comment: 3 pages, 1 Figure

Published
2020

18. Overview of the ImageCLEF 2023: Multimedia Retrieval in Medical, Social Media and Internet Applications

Author

Ionescu, Bogdan, Müller, Henning, Drăgulinescu, Ana-Maria, Yim, Wen-Wai, Ben Abacha, Asma, Snider, Neal, Adams, Griffin, Yetisgen, Meliha, Rückert, Johannes, García Seco de Herrera, Alba, Friedrich, Christoph M., Bloch, Louise, Brüngel, Raphael, Idrissi-Yaghir, Ahmad, Schäfer, Henning, Hicks, Steven A., Riegler, Michael A., Thambawita, Vajira, Storås, Andrea M., Halvorsen, Pål, Papachrysos, Nikolaos, Schöler, Johanna, Jha, Debesh, Andrei, Alexandra-Georgiana, Coman, Ioan, Kovalev, Vassili, Radzhabov, Ahmedkhan, Prokopchuk, Yuri, Ştefan, Liviu-Daniel, Constantin, Mihai-Gabriel, Dogariu, Mihai, Deshayes, Jérôme, Popescu, Adrian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Giachanou, Anastasia, editor, Li, Dan, editor, Aliannejadi, Mohammad, editor, Vlachos, Michalis, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2023
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19. ImageCLEF 2023 Highlight: Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications

Author

Ionescu, Bogdan, Müller, Henning, Drăgulinescu, Ana Maria, Popescu, Adrian, Idrissi-Yaghir, Ahmad, García Seco de Herrera, Alba, Andrei, Alexandra, Stan, Alexandru, Storås, Andrea M., Abacha, Asma Ben, Friedrich, Christoph M., Ioannidis, George, Adams, Griffin, Schäfer, Henning, Manguinhas, Hugo, Filipovich, Ihar, Coman, Ioan, Deshayes, Jérôme, Schöler, Johanna, Rückert, Johannes, Ştefan, Liviu-Daniel, Bloch, Louise, Yetisgen, Meliha, Riegler, Michael A., Dogariu, Mihai, Constantin, Mihai Gabriel, Snider, Neal, Papachrysos, Nikolaos, Halvorsen, Pål, Brüngel, Raphael, Kozlovski, Serge, Hicks, Steven, de Lange, Thomas, Thambawita, Vajira, Kovalev, Vassili, Yim, Wen-Wai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kamps, Jaap, editor, Goeuriot, Lorraine, editor, Crestani, Fabio, editor, Maistro, Maria, editor, Joho, Hideo, editor, Davis, Brian, editor, Gurrin, Cathal, editor, Kruschwitz, Udo, editor, and Caputo, Annalina, editor

Published
2023
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23. Overview of the ImageCLEF 2022: Multimedia Retrieval in Medical, Social Media and Nature Applications

Author

Ionescu, Bogdan, Müller, Henning, Péteri, Renaud, Rückert, Johannes, Abacha, Asma Ben, de Herrera, Alba G. Seco, Friedrich, Christoph M., Bloch, Louise, Brüngel, Raphael, Idrissi-Yaghir, Ahmad, Schäfer, Henning, Kozlovski, Serge, Cid, Yashin Dicente, Kovalev, Vassili, Ştefan, Liviu-Daniel, Constantin, Mihai Gabriel, Dogariu, Mihai, Popescu, Adrian, Deshayes-Chossart, Jérôme, Schindler, Hugo, Chamberlain, Jon, Campello, Antonio, Clark, Adrian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Barrón-Cedeño, Alberto, editor, Da San Martino, Giovanni, editor, Degli Esposti, Mirko, editor, Sebastiani, Fabrizio, editor, Macdonald, Craig, editor, Pasi, Gabriella, editor, Hanbury, Allan, editor, Potthast, Martin, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2022
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24. ImageCLEF 2022: Multimedia Retrieval in Medical, Nature, Fusion, and Internet Applications

Author

de Herrera, Alba G. Seco, Ionescu, Bogdan, Müller, Henning, Péteri, Renaud, Abacha, Asma Ben, Friedrich, Christoph M., Rückert, Johannes, Bloch, Louise, Brüngel, Raphael, Idrissi-Yaghir, Ahmad, Schäfer, Henning, Kozlovski, Serge, Cid, Yashin Dicente, Kovalev, Vassili, Chamberlain, Jon, Clark, Adrian, Campello, Antonio, Schindler, Hugo, Deshayes, Jérôme, Popescu, Adrian, Ştefan, Liviu-Daniel, Constantin, Mihai Gabriel, Dogariu, Mihai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hagen, Matthias, editor, Verberne, Suzan, editor, Macdonald, Craig, editor, Seifert, Christin, editor, Balog, Krisztian, editor, Nørvåg, Kjetil, editor, and Setty, Vinay, editor

Published
2022
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25. MediaEval 2018: Predicting Media Memorability Task

Author

Cohendet, Romain, Demarty, Claire-Hélène, Duong, Ngoc, Sjöberg, Mats, Ionescu, Bogdan, Do, Thanh-Toan, and Rennes, France

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we present the Predicting Media Memorability task, which is proposed as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation. Participants are expected to design systems that automatically predict memorability scores for videos, which reflect the probability of a video being remembered. In contrast to previous work in image memorability prediction, where memorability was measured a few minutes after memorization, the proposed dataset comes with short-term and long-term memorability annotations. All task characteristics are described, namely: the task's challenges and breakthrough, the released data set and ground truth, the required participant runs and the evaluation metrics.

Published
2018

26. Overview of the ImageCLEF 2021: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications

Author

Ionescu, Bogdan, Müller, Henning, Péteri, Renaud, Abacha, Asma Ben, Sarrouti, Mourad, Demner-Fushman, Dina, Hasan, Sadid A., Kozlovski, Serge, Liauchuk, Vitali, Cid, Yashin Dicente, Kovalev, Vassili, Pelka, Obioma, de Herrera, Alba García Seco, Jacutprakart, Janadhip, Friedrich, Christoph M., Berari, Raul, Tauteanu, Andrei, Fichou, Dimitri, Brie, Paul, Dogariu, Mihai, Ştefan, Liviu Daniel, Constantin, Mihai Gabriel, Chamberlain, Jon, Campello, Antonio, Clark, Adrian, Oliver, Thomas A., Moustahfid, Hassan, Popescu, Adrian, Deshayes-Chossart, Jérôme, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Candan, K. Selçuk, editor, Ionescu, Bogdan, editor, Goeuriot, Lorraine, editor, Larsen, Birger, editor, Müller, Henning, editor, Joly, Alexis, editor, Maistro, Maria, editor, Piroi, Florina, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2021
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27. Introduction to the Special Issue on Realistic Synthetic Data: Generation, Learning, Evaluation.

Author

Ionescu, Bogdan, Patras, Ioannis, Müller, Henning, and Del Bimbo, Alberto

Subjects
MACHINE learning, APPLIED sciences, CONTROL (Psychology), HUMAN facial recognition software, GENERATIVE adversarial networks, HUMAN activity recognition
Abstract

The article introduces a special issue on Realistic Synthetic Data, focusing on the generation, learning, and evaluation of synthetic data for various modalities. It discusses the importance of synthetic data in Machine Learning and Deep Learning, highlighting the challenges of annotating real data and the potential of generative models like GANs. The special issue features articles on topics such as object detection, fake news detection, facial expression generation, and underwater image synthesis, showcasing innovative approaches and solutions in the field of synthetic data generation. [Extracted from the article]

Published
2025
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28. Exploring Generative Adversarial Networks for Augmenting Network Intrusion Detection Tasks.

Author

Constantin, Mihai Gabriel, Stanciu, Dan-Cristian, Ştefan, Liviu-Daniel, Dogariu, Mihai, Mihăilescu, Dan, Ciobanu, George, Bergeron, Matt, Liu, Winston, Belov, Konstantin, Radu, Octavian, and Ionescu, Bogdan

Subjects
GENERATIVE adversarial networks, COMPUTER vision, COMPUTER network traffic, DATA augmentation, MACHINE learning
Abstract

The advent of generative networks and their adoption in numerous domains and communities have led to a wave of innovation and breakthroughs in AI and machine learning. Generative Adversarial Networks (GANs) have expanded the scope of what is possible with machine learning, allowing for new applications in areas such as computer vision, natural language processing, and creative AI. GANs, in particular, have been used for a wide range of tasks, including image and video generation, data augmentation, style transfer, and anomaly detection. They have also been used for medical imaging and drug discovery, where they can generate synthetic data to augment small datasets, reduce the need for expensive experiments, and lower the number of real patients that must be included in medical trials. Given these developments, we propose using the power of GANs to create and augment flow-based network traffic datasets. We evaluate a series of GAN architectures, including Wasserstein, conditional, energy-based, gradient penalty, and LSTM-GANs. We evaluate their performance on a set of flow-based network traffic data collected from 16 subjects who used their computers for home, work, and study purposes. The performance of these GAN architectures is described according to metrics that involve networking principles, data distribution among a collection of flows, and temporal data distribution. Given the tendency of network intrusion detection datasets to have a very imbalanced data distribution, i.e., a large number of samples in the "normal traffic" category and a comparatively low number of samples assigned to the "intrusion" categories, we test our GANs by augmenting the intrusion data and checking whether this helps intrusion detection neural networks in their task. We publish the resulting UPBFlow dataset and code on GitHub.1 [ABSTRACT FROM AUTHOR]

Published
2025
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29. Unveiling challenges and insights in cloud Enterprise Resource Planning systems adoption: A case study of the Oracle Fusion Cloud ERP.

Author

Rîndaşu, Sînziana-Maria, Ionescu-Feleagă, Liliana, Ionescu, Bogdan-Ștefan, and Barbu, Alexandra Mihaela

Subjects
ENTERPRISE resource planning, TECHNOLOGICAL innovations, LEGACY systems, CONSUMERS' reviews, RESEARCH personnel
Abstract

The migration from legacy systems to cloud Enterprise Resource Planning systems is driven by technological advancements and competitive pressure, prompting companies to seek more agile and scalable solutions. Although developers and vendors aim to ease the adoption process, significant challenges persist and can lead to potential disruptions to business operations and affect the organisations' reputation. Starting from a qualitative approach, this study aims to explore and uncover the difficulties faced by end users across different roles and industries. An inductive examination of these challenges is undertaken using a thematic approach, focusing on one of the most acclaimed cloud Enterprise Resource Planning solutions, Oracle Fusion Cloud ERP. Continuing with a quantitative examination, the study comparatively analyses the challenges in terms of industry and their evolution from 2020 to 2023. The research sheds light on the breadth and depth of issues faced by direct beneficiaries, demonstrating that even highly regarded solutions exhibit a myriad of challenges. Therefore, our research provides valuable insights for businesses, practitioners, and researchers, enhancing their understanding of the subject and providing insights into strategic decisions related to adopting and using cloud Enterprise Resource Planning systems. Acknowledging and addressing these challenges is crucial for companies navigating the complexities of cloud Enterprise Resource Planning adoption to achieve the full potential of solutions and achieve sustainable competitive advantages in today's fast-paced business environment. [ABSTRACT FROM AUTHOR]

Published
2024
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31. The 2021 ImageCLEF Benchmark: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications

Author

Ionescu, Bogdan, Müller, Henning, Péteri, Renaud, Abacha, Asma Ben, Demner-Fushman, Dina, Hasan, Sadid A., Sarrouti, Mourad, Pelka, Obioma, Friedrich, Christoph M., de Herrera, Alba G. Seco, Jacutprakart, Janadhip, Kovalev, Vassili, Kozlovski, Serge, Liauchuk, Vitali, Cid, Yashin Dicente, Chamberlain, Jon, Clark, Adrian, Campello, Antonio, Moustahfid, Hassan, Oliver, Thomas, Schulz, Abigail, Brie, Paul, Berari, Raul, Fichou, Dimitri, Tauteanu, Andrei, Dogariu, Mihai, Stefan, Liviu Daniel, Constantin, Mihai Gabriel, Deshayes, Jérôme, Popescu, Adrian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hiemstra, Djoerd, editor, Moens, Marie-Francine, editor, Mothe, Josiane, editor, Perego, Raffaele, editor, Potthast, Martin, editor, and Sebastiani, Fabrizio, editor

Published
2021
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32. DeepFusion: Deep Ensembles for Domain Independent System Fusion

Author

Constantin, Mihai Gabriel, Ştefan, Liviu-Daniel, Ionescu, Bogdan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lokoč, Jakub, editor, Skopal, Tomáš, editor, Schoeffmann, Klaus, editor, Mezaris, Vasileios, editor, Li, Xirong, editor, Vrochidis, Stefanos, editor, and Patras, Ioannis, editor

Published
2021
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34. DynFace: A Multi-label, Dynamic-Margin-Softmax Face Recognition Model

Author

Cordea, Marius, Ionescu, Bogdan, Gadea, Cristian, Ionescu, Dan, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Arai, Kohei, editor, and Kapoor, Supriya, editor

Published
2020
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35. Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications

Author

Ionescu, Bogdan, Müller, Henning, Péteri, Renaud, Abacha, Asma Ben, Datla, Vivek, Hasan, Sadid A., Demner-Fushman, Dina, Kozlovski, Serge, Liauchuk, Vitali, Cid, Yashin Dicente, Kovalev, Vassili, Pelka, Obioma, Friedrich, Christoph M., García Seco de Herrera, Alba, Ninh, Van-Tu, Le, Tu-Khiem, Zhou, Liting, Piras, Luca, Riegler, Michael, Halvorsen, Pål, Tran, Minh-Triet, Lux, Mathias, Gurrin, Cathal, Dang-Nguyen, Duc-Tien, Chamberlain, Jon, Clark, Adrian, Campello, Antonio, Fichou, Dimitri, Berari, Raul, Brie, Paul, Dogariu, Mihai, Ştefan, Liviu Daniel, Constantin, Mihai Gabriel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Joho, Hideo, editor, Lioma, Christina, editor, Eickhoff, Carsten, editor, Névéol, Aurélie, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2020
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36. ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications

Author

Ionescu, Bogdan, Müller, Henning, Péteri, Renaud, Dang-Nguyen, Duc-Tien, Zhou, Liting, Piras, Luca, Riegler, Michael, Halvorsen, Pål, Tran, Minh-Triet, Lux, Mathias, Gurrin, Cathal, Chamberlain, Jon, Clark, Adrian, Campello, Antonio, Seco de Herrera, Alba G., Ben Abacha, Asma, Datla, Vivek, Hasan, Sadid A., Liu, Joey, Demner-Fushman, Dina, Pelka, Obioma, Friedrich, Christoph M., Dicente Cid, Yashin, Kozlovski, Serge, Liauchuk, Vitali, Kovalev, Vassili, Berari, Raul, Brie, Paul, Fichou, Dimitri, Dogariu, Mihai, Stefan, Liviu Daniel, Constantin, Mihai Gabriel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jose, Joemon M., editor, Yilmaz, Emine, editor, Magalhães, João, editor, Castells, Pablo, editor, Ferro, Nicola, editor, Silva, Mário J., editor, and Martins, Flávio, editor

Published
2020
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37. Financial Strategies for Sustainability: Examining the Circular Economy Perspective.

Author

Munteanu, Ionela, Ionescu-Feleagă, Liliana, and Ionescu, Bogdan Ștefan

Abstract

Circular economy (CE) research has advanced significantly in recent years in areas like environmental science, engineering, and technology. Governance presents CE strategies as imperative and compelling, often linked to efforts to achieve sustainable business development. However, the financial quantification of a CE is still underexplored and heterogeneous, with multiple research streams exploring the financial dimension. The aims of this study are twofold: (i) to analyze the productivity of the scientific literature and the conceptual evolution of circularity as it relates to financial strategies; and (ii) to formulate future research directions addressing a CE in close connection with financial sustainability knowledge. Bilbiometrix R-package 2023.12.1 and VOSviewer version 1.6.20 were used to analyze a sample of 279 articles retrieved from WoS and Scopus using the PRISMA protocol. The analysis covered the period 2010–2023, with the diachronic thematic analysis revealing four key themes: (1) CE and green financial performance; (2) environmental impact of CE; (3) performance approach of CE and metrics; (4) CE and sustainable development. Building on previous knowledge, we developed an integrated and multidisciplinary framework of CE embedded in financial performance. Future research agenda shaped by the emerging themes and research gaps can support managers, shareholders, or governance in incorporating CE policies into operational activities and strengthening sustainable circularity knowledge. [ABSTRACT FROM AUTHOR]

Published
2024
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40. The Observation of Actors’ Vocal Emotion Exercises with Deep Learning and Spectral Analysis

Author

Bratan, Costin Andrei, primary, Tocila-Matasel, Claudia, additional, Andrei, Alexandra-Georgiana, additional, Tebeanu, Ana Voichita, additional, Franti, Eduard, additional, Dascalu, Monica, additional, Ionescu, Bogdan, additional, Iana, Gheorghe, additional, Bobeș, Gabriela, additional, Morosanu, Bogdan, additional, Oproiu, Ana-Maria, additional, and Iorgulescu, Gabriela, additional

Published
2024
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43. ImageCLEF 2019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature

Author

Ionescu, Bogdan, Müller, Henning, Péteri, Renaud, Cid, Yashin Dicente, Liauchuk, Vitali, Kovalev, Vassili, Klimuk, Dzmitri, Tarasau, Aleh, Abacha, Asma Ben, Hasan, Sadid A., Datla, Vivek, Liu, Joey, Demner-Fushman, Dina, Dang-Nguyen, Duc-Tien, Piras, Luca, Riegler, Michael, Tran, Minh-Triet, Lux, Mathias, Gurrin, Cathal, Pelka, Obioma, Friedrich, Christoph M., Garcìa Seco de Herrera, Alba, Garcia, Narciso, Kavallieratou, Ergina, del Blanco, Carlos Roberto, Cuevas, Carlos, Vasillopoulos, Nikos, Karampidis, Konstantinos, Chamberlain, Jon, Clark, Adrian, Campello, Antonio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crestani, Fabio, editor, Braschler, Martin, editor, Savoy, Jacques, editor, Rauber, Andreas, editor, Müller, Henning, editor, Losada, David E., editor, Heinatz Bürki, Gundula, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2019
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44. ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

Author

Ionescu, Bogdan, Müller, Henning, Péteri, Renaud, Dang-Nguyen, Duc-Tien, Piras, Luca, Riegler, Michael, Tran, Minh-Triet, Lux, Mathias, Gurrin, Cathal, Cid, Yashin Dicente, Liauchuk, Vitali, Kovalev, Vassili, Ben Abacha, Asma, Hasan, Sadid A., Datla, Vivek, Liu, Joey, Demner-Fushman, Dina, Pelka, Obioma, Friedrich, Christoph M., Chamberlain, Jon, Clark, Adrian, de Herrera, Alba García Seco, Garcia, Narciso, Kavallieratou, Ergina, del Blanco, Carlos Roberto, Rodríguez, Carlos Cuevas, Vasillopoulos, Nikos, Karampidis, Konstantinos, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Azzopardi, Leif, editor, Stein, Benno, editor, Fuhr, Norbert, editor, Mayr, Philipp, editor, Hauff, Claudia, editor, and Hiemstra, Djoerd, editor

Published
2019
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45. Overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contest

Author

Ramírez, Gabriela, Villatoro, Esaú, Ionescu, Bogdan, Escalante, Hugo Jair, Escalera, Sergio, Larson, Martha, Müller, Henning, Guyon, Isabelle, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Zhang, Zhaoxiang, editor, Suter, David, editor, Tian, Yingli, editor, Branzan Albu, Alexandra, editor, Sidère, Nicolas, editor, and Jair Escalante, Hugo, editor

Published
2019
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46. Assessment of Total Antioxidant Capacity, 8-Hydroxy-2′-deoxy-guanosine, the Genetic Landscape, and Their Associations in BCR::ABL-1 -Negative Chronic and Blast Phase Myeloproliferative Neoplasms.

Author

Găman, Mihnea-Alexandru, Mambet, Cristina, Neagu, Ana Iulia, Bleotu, Coralia, Gurban, Petruta, Necula, Laura, Botezatu, Anca, Ataman, Marius, Diaconu, Camelia Cristina, Ionescu, Bogdan Octavian, Ghiaur, Alexandra Elena, Tatic, Aurelia, Coriu, Daniel, Găman, Amelia Maria, and Diaconu, Carmen Cristina

Subjects
OXIDANT status, MYELOPROLIFERATIVE neoplasms, POLYCYTHEMIA vera, ACUTE myeloid leukemia, REACTIVE oxygen species
Abstract

Myeloproliferative neoplasms (MPNs), namely, polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF), are clonal stem cell disorders defined by an excessive production of functionally mature and terminally differentiated myeloid cells. MPNs can transform into secondary acute myeloid leukemia (sAML/blast phase MPN) and are linked to alterations in the redox balance, i.e., elevated concentrations of reactive oxygen species and markers of oxidative stress (OS), and changes in antioxidant systems. We evaluated OS in 117 chronic phase MPNs and 21 sAML cases versus controls by measuring total antioxidant capacity (TAC) and 8-hydroxy-2′-deoxy-guanosine (8-OHdG) concentrations. TAC was higher in MPNs than controls (p = 0.03), particularly in ET (p = 0.04) and PMF (p = 0.01). MPL W515L-positive MPNs had higher TAC than controls (p = 0.002) and triple-negative MPNs (p = 0.01). PMF patients who had treatment expressed lower TAC than therapy-free subjects (p = 0.03). 8-OHdG concentrations were similar between controls and MPNs, controls and sAML, and MPNs and sAML. We noted associations between TAC and MPNs (OR = 1.82; p = 0.05), i.e., ET (OR = 2.36; p = 0.03) and PMF (OR = 2.11; p = 0.03), but not sAML. 8-OHdG concentrations were not associated with MPNs (OR = 1.73; p = 0.62) or sAML (OR = 1.89; p = 0.49). In conclusion, we detected redox imbalances in MPNs based on disease subtype, driver mutations, and treatment history. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Translation, Cultural Adaptation, and Validation into Romanian of the Myeloproliferative Neoplasm Symptom Assessment Form—Total Symptom Score (MPN-SAF TSS or MPN-10) Questionnaire.

Author

Găman, Mihnea-Alexandru, Scherber, Robyn Marie, Ursuleac, Iulia, Crişan, Ana Manuela, Bădeliţă, Sorina Nicoleta, Ionescu, Bogdan Octavian, Ghiaur, Alexandra Elena, Brînză, Melen, Pîrciulescu, Nicoleta, Lascăr, Toma Octavian, Diaconu, Camelia Cristina, Găman, Amelia Maria, and Coriu, Daniel

Subjects
MYELOPROLIFERATIVE neoplasms, SYMPTOM burden, POLYCYTHEMIA vera, EXPLORATORY factor analysis, SYMPTOMS
Abstract

Background: Patients with myeloproliferative neoplasms (MPNs) experience a high disease-related symptom burden. A specific instrument to evaluate quality of life (QoL), i.e., the MPN Symptom Assessment Form Total Symptom Score (MPN-SAF TSS; MPN-10), was developed. We conducted the translation, cultural adaptation, and validation into Romanian of the MPN-10. Methods: We translated the MPN-10 and tested its psychometric properties. Results: We recruited 180 MPN patients: 66 polycythemia vera (36.67%), 61 essential thrombocythemia (33.89%), 51 primary and secondary myelofibrosis (SMF) (28.33%), and 2 MPN-unclassifiable (1.11%). The mean TSS was 19.51 ± 16.51 points. Fatigue, inactivity, and concentration problems were the most cumbersome symptoms. We detected scoring differences between MPN subtypes regarding weight loss (p < 0.001), fatigue (p = 0.006), early satiety (p = 0.007), night sweats (p = 0.047), pruritus (p = 0.05), and TSS (p = 0.021). There were strong positive associations between TSS and inactivity, fatigue, and concentration problems, and moderate negative correlations between QoL scores and all MPN-10 items. Cronbach's α internal consistency coefficient was 0.855. The Kaiser–Meyer–Olkin construct validity test result was 0.870 and the Bartlett Sphericity Test was significant (p < 0.001). Symptom scores were loaded into one single factor according to the exploratory factor analysis. Conclusions: The Romanian MPN-10 version displayed excellent psychometric properties and is a reliable instrument for assessing symptom burden and QoL in Romanian MPN patients. [ABSTRACT FROM AUTHOR]

Published
2024
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50. INFORMATION TECHNOLOGY AND ITS IMPLICATIONS FOR INTERNAL AUDITING: AN EMPIRICAL STUDY ON LEBANESE ORGANIZATIONS

Author

Ali KASSEM and IONESCU BOGDAN STEFAN

Subjects
information technology, internal auditing, risk evaluation, empirical study, and lebanon., Commercial geography. Economic geography, HF1021-1027, Economics as a science, HB71-74
Abstract

The use of Information Technology in organizations and in a business context has notably been associated with the manner in which transactions are carried out through recording, processing, and reporting of information. This often happens to organizations that have automated information systems to perform the aforesaid activities. In this regard, the study, therefore, aims to investigate the impact of the rapid growth of the Information technology to internal auditing activities and examine if there are changes that are identified in information technology evaluations that are undertaken in Lebanese Organizations. In carrying out this research, a quantitative methodology approach was deployed where a surveying technique was used to facilitate the study by use of a selfadministered questionnaire that aided in achieving these objectives. Five hundred copies of the questionnaire were randomly distributed to Lebanese organizations located across Beirut specifically to accountants and auditors. The survey results were analyzed using SPSS software to show the impact of information technology in auditing activities and the changes that have been experienced in Information technology and their implications on the accounting sector. The survey findings have been used to conclude if the internal auditors need to enhance their knowledge and skills of computerized information system (CIS) which can help improve planning, directing, supervising and reviewing the work performed. Factors that affect Auditors performance on the Information Technology evaluations were identified and their impacts assessed. The research conclusively highlighted the impact of the study on managers and auditors.

Published
2019

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