Article
Computer Science, Artificial Intelligence
Yu Su, Zeyu Cheng, Jinze Wu, Yanmin Dong, Zhenya Huang, Le Wu, Enhong Chen, Shijin Wang, Fei Xie
Summary: This paper proposes a graph-based Cognitive Diagnosis model that directly discovers the interactions among students, skills, and questions through a heterogeneous cognitive graph. The model designs a performance-relative propagator and an attentive knowledge aggregator to handle this task. Extensive experimental results demonstrate the effectiveness and extendibility of the model.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hongtian Chen, Zhigang Liu, Cesare Alippi, Biao Huang, Derong Liu
Summary: This study presents a data-driven intelligent fault diagnosis method for nonlinear dynamic systems. By parameterizing nonlinear systems through a generalized kernel representation and analyzing the theoretical relationship between supervised and unsupervised learning, the designed method achieves the same performance with the use of a bridge between them.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
K. U. Jaseena, Binsu C. Kovoor
Summary: Weather forecasting is the practice of predicting the state of the atmosphere based on different weather parameters. Accurate weather forecasts are crucial in various fields. With the advancement of atmospheric observing systems and the increasing volume of weather data, deep learning techniques are being used to improve weather prediction. This paper provides a comprehensive review of weather forecasting approaches and discusses potential future research directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
P. Zicari, G. Folino, M. Guarascio, L. Pontieri
Summary: The study proposes a comprehensive ticket classification framework that leverages deep learning methods and AI-based interpretation to address overfitting and black-box model interpretation challenges in ticket classifications. Tests on real data demonstrated the accuracy of classifications and the practical value of associated explanations.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
P. Zicari, G. Folino, M. Guarascio, L. Pontieri
Summary: Intelligent Ticket Management Systems use deep learning methods for ticket classification and provide interpretation methods for operators and analysts.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Chemistry, Multidisciplinary
Karolina Kudelina, Toomas Vaimann, Bilal Asad, Anton Rassolkin, Ants Kallaste, Galina Demidova
Summary: This paper reviews the fault diagnostic techniques based on machine learning, highlighting the increasing capability of using cloud computation for processing faulty data and the potential of utilizing mathematical models of electrical machines for training AI algorithms in the era of industry 4.0.
APPLIED SCIENCES-BASEL
(2021)
Article
Environmental Sciences
Tohren C. G. Kibbey, Rafal Jabrzemski, Denis M. O'Carroll
Summary: This study explores the use of supervised machine learning to identify the source of PFAS in water samples, focusing on distinguishing between PFAS from AFFF fire suppression foam and other sources. The results show that although PFAS composition can vary significantly at a site, machine learning can be used to recognize compositional patterns in the environment for source allocation.
Review
Computer Science, Hardware & Architecture
Chandni, Monika Sachdeva, Alok Kumar Singh Kushwaha
Summary: A tumor is a life-threatening disease characterized by abnormal cell growth in any part of the human body. Early detection is crucial for treatment and increasing life expectancy. Machine Learning and Deep Learning techniques offer reliable and effective methods for intelligent data-driven systems and assist in the intelligent diagnosis of fatal diseases like tumors.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Engineering, Civil
Saeed Rahmani, Asiye Baghbani, Nizar Bouguila, Zachary Patterson
Summary: Graph neural networks (GNNs) have gained popularity in the field of intelligent transportation systems (ITS) due to their ability to analyze graph-structured data. However, there is currently no comprehensive review of recent advancements and future research directions in all transportation areas. This survey provides an overview of GNN studies in ITS, exploring various applications such as traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. It also identifies domain-specific research directions, opportunities, challenges, and previously overlooked research opportunities in edge and graph learning, multi-modal models, and unsupervised and reinforcement learning methods for developing more powerful GNNs. The survey also highlights popular baseline models and datasets for each transportation domain.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Xiaobo Zhu, Jiale Gao, Yijie Dai, Jianguo Zhang, Weidong Zhang, Daying Sun, Wenhua Gu
Summary: A novel human gait recognition method is proposed in this study, which effectively combines grid-less planar flexible pressure sensors and multilayer heterogeneous machine learning algorithms to accurately characterize the dynamic gait features of the human body. This method not only has high accuracy and low cost, but also has advantages such as multifunctionality, portability, and real-time monitoring. It provides important technical support for early intervention and rehabilitation treatments.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Mathematics, Applied
Iftikhar Ahmad, Hira Ilyas, Muhammad Asif Zahoor Raja, Tahir Nawaz Cheema, Hasnain Sajid, Kottakkaran Sooppy Nisar, Muhammad Shoaib, Mohammed S. Alqahtani, C. Ahamed Saleel, Mohamed Abbas
Summary: This article investigates the effects of recurring malaria re-infection on the spread dynamics of the disease using a supervised learning based neural networks model. The study aims to discuss the dynamics of malaria spread and improve prediction and analysis through the use of Levenberg-Marquardt artificial neural networks (LMANNs) and numerical treatment of the malaria model. The results show the reliable performance and efficacy of the LMANNs model.
Article
Mathematics, Interdisciplinary Applications
Salim Lahmiri, Chakib Tadj, Christian Gargour, Stelios Bekiros
Summary: In this study, various deep learning systems were designed and validated to enhance the diagnosis of infant cry records using signal processing techniques and cepstrum analysis-based coefficients as inputs. The results showed that convolutional neural networks achieved the highest accuracy and sensitivity, while deep feedforward neural networks had the highest specificity.
CHAOS SOLITONS & FRACTALS
(2022)
Review
Computer Science, Information Systems
Diana Susan Joseph, Pranav M. Pawar, Rahul Pramanik
Summary: The use of various technologies for intelligent crop production is increasing, with deep learning using convolutional neural networks (CNN) proving to be more efficient for plant disease diagnosis. This review focuses on the identification of plant diseases from leaf images using CNN based deep learning models and provides an overview of state-of-the-art studies using visualization techniques and hyperspectral images for disease diagnosis. The challenges and areas for further research in developing a plant disease diagnostic system are also discussed.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Niccolo Guiducci, Claudio Tortorici, Claudio Ferrari, Stefano Berretti
Summary: This paper proposes a novel approach based on Graph Neural Networks for 3D mesh relief pattern classification. The approach captures local and global features of the relief patterns by constructing local and global mesh structures. Experimental results on SHREC'17 and SHREC'20 relief patterns track datasets demonstrate the superior performance of the proposed approach.
COMPUTERS & GRAPHICS-UK
(2023)
Article
Computer Science, Theory & Methods
William C. Sleeman, Rishabh Kapoor, Preetam Ghosh
Summary: This study proposes a new taxonomy for describing multimodal classification models, aiming to address challenges in the field such as inconsistent terminologies and architectural descriptions, as well as unresolved issues like big data, class imbalance, and instance-level difficulty. The paper presents examples of applying this taxonomy to existing models and offers a checklist for the clear and complete presentation of future models.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Manuel Roveri, Francesco Trovo
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2017)
Article
Computer Science, Artificial Intelligence
Cesare Alippi, Giacomo Boracchi, Manuel Roveri
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2017)
Article
Automation & Control Systems
Giacomo Boracchi, Michalis Michaelides, Manuel Roveri
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2018)
Article
Computer Science, Information Systems
Antonio Giuzio, Giansalvatore Mecca, Elisa Quintarelli, Manuel Roveri, Donatello Santoro, Letizia Tanca
INFORMATION SYSTEMS
(2019)
Article
Chemistry, Analytical
Romano Fantacci, Francesca Nizzi, Tommaso Pecorella, Laura Pierucci, Manuel Roveri
Article
Computer Science, Artificial Intelligence
Manuel Roveri
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)
Editorial Material
Computer Science, Artificial Intelligence
Jing-Hao Xue, Zhanyu Ma, Manuel Roveri, Nathalie Japkowicz
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2020)
Article
Computer Science, Hardware & Architecture
Simone Disabato, Manuel Roveri, Cesare Alippi
Summary: The article presents a design methodology for allocating the execution of Convolutional Neural Networks in distributed IoT applications, aiming to minimize latency between data collection and decision-making under constraints on memory and processing load at the unit level.
IEEE TRANSACTIONS ON COMPUTERS
(2021)
Article
Computer Science, Artificial Intelligence
Simone Disabato, Manuel Roveri
Summary: Tiny machine learning (TML) is a new research area focused on designing machine and deep learning techniques for embedded systems and IoT devices. This article introduces a TML for concept drift (TML-CD) solution, which utilizes deep learning feature extractors and a k-nearest neighbors (k-NNs) classifier to adapt to changes in the data-generating process. Experimental results demonstrate the effectiveness of the proposed solution.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Giuseppe Canonaco, Marcello Restelli, Manuel Roveri
ECAI 2020: 24TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Julio J. Valdes, Ljubomir Nikolic, Simone Disabato, Manual Roveri
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Dario Cogliati, Mirko Falchetto, Danilo Pau, Manuel Roveri, Gabriele Viscardi
2018 FIRST IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE FOR INDUSTRIES (AI4I 2018)
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Cesare Alippi, Simone Disabato, Manuel Roveri
2018 17TH ACM/IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Cesare Alippi, Viviana D'Alto, Mirko Falchetto, Danilo Pau, Manuel Roveri
2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2017)
Article
Computer Science, Artificial Intelligence
Cesare Alippi, Stavros Ntalampiras, Manuel Roveri
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2017)