Article
Multidisciplinary Sciences
Li-Xian Chen, Shih-Wen Su, Chia-Hung Liao, Kai-Sin Wong, Shyan-Ming Yuan
Summary: The increasing number of online open-access journals facilitates academic exchanges, but the prevalence of predatory journals poses a threat to the integrity of scholarly reporting. In response, the authors propose the use of machine learning methods in their Academic Journal Predatory Checking (AJPC) system. This system utilizes data collection, feature extraction, and model prediction to distinguish between legitimate and predatory journals, yielding results comparable or superior to existing identification methods.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Mohammadreza Tavakoli, Abdolali Faraji, Jarno Vrolijk, Mohammadreza Molavi, Stefan T. Mol, Gabor Kismihok
Summary: This paper presents an Artificial Intelligence (AI) driven learning recommender system called eDoer, which analyzes online job vacancy announcements, collects open online educational resources, and provides personalized learning pathways and content to learners. An initial validation through a randomized experiment supports the effectiveness of eDoer in helping learners acquire basic statistics knowledge.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Chenxia Jin, Jusheng Mi, Fachao Li, Jiahuan Zhang
Summary: Recommender system is crucial for personalized recommendation. The paper introduces a model-based collaborative filtering method and its challenges, proposes a method of integrating content features described by fuzzy sets into similarity computation, and establishes a matrix factorization model based on core users. Experiments show that the proposed method achieves better prediction performance.
Article
Computer Science, Cybernetics
Dina Nawara, Rasha Kashef
Summary: This article proposes a multi-CARS based on consensus clustering (MCARS-CC) to address the challenges of data sparsity, real-time scalability, and personalization in existing recommendation systems. Experimental results show that the proposed model outperforms other baseline techniques in terms of accuracy and error-based metrics, and incorporating hypergraph partitioning algorithm (HGPA) further improves the performance of the model.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Computer Science, Information Systems
Manish Budhathoki, Abeer Alsadoon, Ahmed Dawoud, Nizar Al Bassam, Oday D. Jerew, P. W. C. Prasad
Summary: This study proposes a solution to improve the accuracy of recommendation systems by using a knowledge graph and an enhanced relation reliability and prediction probability algorithm. The algorithm considers semantic relations between entities and improves prediction through an attention mechanism and sigmoid function. Experimental results demonstrate that the proposed solution achieves better recommendation accuracy and shorter processing time in various stages and datasets.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Kevin Naik, Supriya Patel
Summary: The IoT-based smart home management system proposed in this paper enables efficient energy utilization and reliable connectivity. It utilizes sensors, actuators, smartphones, web services, and microcontrollers to achieve intercommunication and provides a deterministic control of security features. The system is completely open-source, offering adaptability to changing needs.
Article
Computer Science, Artificial Intelligence
Anna Gatzioura, Joao Vinagre, Alipio Mario Jorge, Miquel Sanchez-Marre
Summary: This study introduces a hybrid recommender system HybA, which focuses on improving the quality of music playlist recommendations by considering semantic similarity at the recommendation moment, and providing support for dimensions beyond accuracy, such as coherence and diversity. Experiments have shown that this system outperforms other state of the art techniques in terms of accuracy, while balancing between diversity and coherence.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Information Systems
Hyeseong Park, Jaeik Jeong, Kyung-Whan Oh, Hongseok Kim
Summary: This paper proposes an autoencoder-based recommendation system that can detect and remove natural noise in rating data, enhancing the accuracy of recommendations.
Article
Computer Science, Information Systems
Oumaima Stitini, Soulaimane Kaloun, Omar Bencharef
Summary: Nowadays, recommendation systems play an important role in facilitating user's desires by offering diverse item recommendations. However, the issues of over-specialization and lack of diversity still persist. This study introduces a Revolutionary Recommender System using a Genetic Algorithm (RRSGA) to address these issues and improve the accuracy of predictions. The results demonstrate the effectiveness of RRSGA in making recommendations that users are likely to appreciate.
Article
Computer Science, Information Systems
Vidal Alonso-Secades, Alfonso-Jose Lopez-Rivero, Manuel Martin-Merino-Acera, Manuel-Jose Ruiz-Garcia, Olga Arranz-Garcia
Summary: Incorporating technology into virtual education encourages educational institutions to migrate from the current learning management system to an intelligent virtual educational system, utilizing students' data to enhance efficiency. This paper presents the design of an intelligent virtual educational system for primary education in developing countries, utilizing technologies such as artificial intelligence, big data, educational data mining, and web analytics. The system consists of four subsystems that provide personalized learning and evaluation processes for teaching.
Article
Computer Science, Software Engineering
Oumayma Ouedrhiri, Oumayma Banouar, Salah El Hadaj, Said Raghay
Summary: Quantum algorithms, benefiting from the superposition property of quantum information, offer significant speedup compared to classical algorithms. In this article, a new recommender system combining an adapted quantum K-means algorithm and the singular value decomposition algorithm is proposed, achieving better performance than previous systems tested.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Elvira G. Rincon-Flores, Eunice Lopez-Camacho, Juanjo Mena, Omar Olmos
Summary: This study aims to determine the usefulness of K-nearest neighbor and random forest algorithms in improving the teaching-learning process and reducing academic failure. The results show that the predictions became more accurate with larger datasets.
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Narjes Vara, Mahdieh Mirzabeigi, Hajar Sotudeh, Seyed Mostafa Fakhrahmad
Summary: This study investigates the feasibility of using the k-means clustering algorithm to enhance the effectiveness of the RICEST Journal Finder System. The results show that the application of the k-means clustering algorithm improves the accuracy of the recommended journals.
Article
Computer Science, Information Systems
Seyyed Hamid Ghafouri, Seyyed Mohsen Hashemi, Patrick C. K. Hung
Summary: This article discusses methods for predicting QoS values of web services and recommending the best services based on these values. The methods are categorized into memory-based, model-based, and collaborative filtering methods combined with other techniques. The article introduces famous studies in each category, reviews the problems and benefits of each category, and proposes suggestions for future work.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Green & Sustainable Science & Technology
Miguel Torres-Ruiz, Rolando Quintero, Giovanni Guzman, Kwok Tai Chui
Summary: This paper proposes a patient-centered recommender system architecture to address the capacity and vulnerability issues in healthcare systems due to COVID-19. The system provides recommendations based on user profiles and a ranked list of medical facilities, incorporating semantic and geospatial processing. The approach was tested in diverse districts of Mexico City, and the results were visualized in a Web-GIS application.
Article
Computer Science, Artificial Intelligence
Andre M. Carrington, Douglas G. Manuel, Paul W. Fieguth, Tim Ramsay, Venet Osmani, Bernhard Wernly, Carol Bennett, Steven Hawken, Olivia Magwood, Yusuf Sheikh, Matthew McInnes, Andreas Holzinger
Summary: This paper proposes a new method called deep ROC analysis to evaluate the performance of binary classifiers and diagnostic tests. It provides more detailed information compared to traditional performance measures. The method measures the performance in multiple groups and allows comparisons between groups. The paper also offers a new interpretation of AUC as balanced average accuracy, relevant to individuals.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Carlo Combi, Beatrice Amico, Riccardo Bellazzi, Andreas Holzinger, Jason H. Moore, Marinka Zitnik, John H. Holmes
Summary: This paper focuses on the importance of explainable artificial intelligence (XAI) in the field of biomedicine. By bringing together researchers with different roles and perspectives, it explores XAI in depth and presents a series of requirements for achieving explainability in AI.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Biochemical Research Methods
Andreas Holzinger, Katharina Keiblingera, Petr Holub, Kurt Zatloukal, Heimo Mueller
Summary: Due to the successes of AI, such as ChatGPT, and the combination of biotechnology, new potential solutions have emerged to address global problems and contribute to sustainability.
Article
Radiology, Nuclear Medicine & Medical Imaging
Gabriel Adelsmayr, Michael Janisch, Heimo Mueller, Andreas Holzinger, Emina Talakic, Elmar Janek, Simon Streit, Michael Fuchsjaeger, Helmut Schoellnast
Summary: This study aimed to investigate whether CT texture analysis can differentiate between different types of lung cancers and tumors. The study included 133 patients who underwent CT-guided biopsy of the lung. The results showed significant differences in texture features between different entities, and the use of a HU threshold affected the results of the analysis. Rating: 7/10
EUROPEAN JOURNAL OF RADIOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Federico Cabitza, Andrea Campagner, Gianclaudio Malgieri, Chiara Natali, David Schneeberger, Karl Stoeger, Andreas Holzinger
Summary: This paper presents a framework for defining different types of explanations of AI systems and criteria for evaluating their quality. It proposes a structural view of constructing explanations and suggests a typology based on the explanandum, explanantia, and their relationship. The paper highlights the importance of epistemological and psychological perspectives in defining quality criteria and aims to support clear inventories, verification criteria, and validation methods for AI explainability.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biophysics
Theresa Letz, Carina Hoerandtner, Matthias C. Braunisch, Peter Gundel, Julia Matschkal, Martin Bachler, Georg Lorenz, Andrea Koerner, Carolin Schaller, Moritz Lattermann, Andreas Holzinger, Uwe Heemann, Siegfried Wassertheurer, Christoph Schmaderer, Christopher C. Mayer
Summary: The aim of this study is to compare automatic to manual measurements of left ventricular hypertrophy (LVH) parameters and investigate their predictive value for cardiovascular and all-cause mortality in patients with end-stage kidney disease (ESKD). The study found that automatic algorithms can be as reliable as manual measurements in assessing LVH parameters and predicting risk in ESKD patients.
PHYSIOLOGICAL MEASUREMENT
(2023)
Review
Pathology
Markus Plass, Michaela Kargl, Tim-Rasmus Kiehl, Peter Regitnig, Christian Geissler, Theodore Evans, Norman Zerbe, Rita Carvalho, Andreas Holzinger, Heimo Mueller
Summary: The development of digital pathology allows pathologists to utilize AI-based computer programs for advanced analysis of whole slide images. However, the best-performing AI algorithms for image analysis are considered black boxes, making it unclear why they produce specific results. This article addresses the issue of explainability in digital pathology and discusses the necessity of explainable AI (XAI) techniques to enhance transparency and causal understanding. The authors argue for the development of user interfaces that enable contextual understanding and interactive questioning to bridge the gap between AI processes and medical experts' knowledge.
JOURNAL OF PATHOLOGY CLINICAL RESEARCH
(2023)
Article
Mathematics, Interdisciplinary Applications
Claire Jean-Quartier, Katharina Bein, Lukas Hejny, Edith Hofer, Andreas Holzinger, Fleur Jeanquartier
Summary: In response to socioeconomic development, this study focuses on the transparency and sustainability aspects of artificial intelligence in terms of energy consumption. The research measures carbon emissions and energy consumption of Python algorithms and tests the impact of explainability on algorithmic energy consumption. The results can guide the selection of tools to measure algorithmic energy consumption and raise awareness of emission-based model optimization by highlighting the sustainability of explainable artificial intelligence.
Article
Health Care Sciences & Services
Julian Matschinske, Julian Spaeth, Mohammad Bakhtiari, Niklas Probul, Mohammad Mahdi Kazemi Majdabadi, Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Anne Hartebrodt, Balazs-Attila Orban, Sandor-Jozsef Fejer, Olga Zolotareva, Supratim Das, Linda Baumbach, Josch K. Pauling, Olivera Tomasevic, Bela Bihari, Marcus Bloice, Nina C. Donner, Walid Fdhila, Tobias Frisch, Anne-Christin Hauschild, Dominik Heider, Andreas Holzinger, Walter Hoetzendorfer, Jan Hospes, Tim Kacprowski, Markus Kastelitz, Markus List, Rudolf Mayer, Monika Moga, Heimo Mueller, Anastasia Pustozerova, Richard Roettger, Christina C. Saak, Anna Saranti, Herald H. H. W. Schmidt, Christof Tschohl, Nina K. Wenke, Jan Baumbach
Summary: Machine learning and artificial intelligence have achieved promising results in various fields, driven by the increasing availability of data. However, these data are often distributed across different institutions and cannot be easily shared due to strict privacy regulations. Federated learning (FL) enables the training of distributed machine learning models without sharing sensitive data. However, implementing FL is time-consuming and requires advanced programming skills and complex technical infrastructures.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Review
Computer Science, Artificial Intelligence
Andreas Holzinger, Anna Saranti, Alessa Angerschmid, Bettina Finzel, Ute Schmid, Heimo Mueller
Summary: Artificial intelligence has made significant progress in standard pattern recognition tasks, but there is still a significant gap between AI and human-level concept learning. To analyze current approaches and drive progress, experimental environments and diagnostic/benchmark datasets are needed for explainable machine intelligence. This paper provides an overview of current AI solutions for benchmarking concept learning, reasoning, and generalization, discusses state-of-the-art diagnostic/benchmark datasets, and explores future research directions in this exciting field.
Article
Computer Science, Interdisciplinary Applications
Johann Steszgal, Peter Kieseberg, Andreas Holzinger
Summary: Reducing food waste is crucial for minimizing the human impact on the environment and improving the efficient use of natural resources. The healthcare sector, in particular, has significant potential for adopting sustainable practices in food management. This article highlights the key challenges and proposes a solution for reducing food waste in real-world healthcare settings.
Article
Radiology, Nuclear Medicine & Medical Imaging
Gabriel Adelsmayr, Michael Janisch, Ann-Katrin Kaufmann-Buehler, Magdalena Holter, Emina Talakic, Elmar Janek, Andreas Holzinger, Michael Fuchsjaeger, Helmut Schoellnast
Summary: The reproducibility problems in radiomics are well known, and the segmentation of target lesions greatly affects texture analysis variability. This study aimed to compare the interobserver reliability of manual 2D vs. 3D lung lesion segmentation with and without pre-definition of volume using a HU threshold.
EUROPEAN RADIOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Edgar R. Weippl, Andreas Holzinger, Peter Kieseberg
MACHINE LEARNING AND KNOWLEDGE EXTRACTION
(2023)
Article
Computer Science, Artificial Intelligence
Iztok Fister Jr, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz, Andreas Holzinger
Summary: This article introduces a novel visualization method developed based on the principles of explainable artificial intelligence to present association rules for time series. The experiments conducted in the context of smart agriculture demonstrate the great potential of the proposed visualization method.
Proceedings Paper
Computer Science, Artificial Intelligence
Jianlong Zhou, Fang Chen, Andreas Holzinger
Summary: This article provides an overview of the relationship between explanation and AI fairness, demonstrating that AI explanations help identify potential variables that drive unfair outcomes and influence human's fairness judgment. Ensuring the trustworthiness of AI decision-making from the perspective of explainability and fairness presents different challenges.
XXAI - BEYOND EXPLAINABLE AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers
(2022)