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11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Yang, Haoyu Tourani, Roshan Li, Jia Caraballo, Pedro Steinbach, Michael Kumar, Vipin Simon, Gyorgy University of Minnesota Dept. of Computer Science and Engineering Minneapolis United States University of Minnesota Institute for Health Informatics Minneapolis United States Mayo Clinic Dept. of Internal Medicine Rochester United States

ISBN: (纸本)9798350302639

Causal Structure Discovery (CSD) is the task of learning the set of underlying causal relationships from observational data. Due to their computational scalability and flexibility, a recently developed class of CSD methods, NOTEARS, based on a formulation allowing for continuous optimization, is gaining popularity. However, this formulation can lead to incorrect edge orientations when little/no likelihood advantage is conferred upon any edge orientation, e.g., a → b → c versus c → b → a. In longitudinal data, like electronic health records (EHRs), temporal relationships are observable among many pairs of variables. Such temporal relationship is imperfect but still suggest an orientation since causal effects cannot travel backwards in time. Following this idea, we propose methods to incorporate precedence constraints into continuous optimization-based CSD methods. Experiments on both a synthetic and two real-world datasets validate the effectiveness of the proposed precedence constraints. © 2023 IEEE.

关键词： Machine learning

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Image-Based Path-Planning for Navigation of Soft-Growing Robots in the Spinal Sub-Arachnoid Space

Image-Based Path-Planning for Navigation of Soft-Growing Rob...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： George Dimas Panagiotis G. Kalozoumis Panagiotis Vartholomeos Dimitris K. Iakovidis Dept. of Computer Science and Biomedical Informatics School of Science University of Thessaly Lamia Greece

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Recently, soft robotic procedures are being developed aiming at achieving targeted minimally invasive operations or drug administration in specific locations in the human body. Accordingly, specialized path-planning algorithms need to be investigated for the estimation of efficient paths that would protect the delicate tissue structures and simultaneously satisfy a set of requirements for the efficient navigation of the robot. In this context, we propose an image-based 3D path-planning algorithm, which is an extension of the A* algorithm, for the navigation of a soft-growing robot inside the spinal sub-arachnoid space (SSS). The proposed algorithm is capable of estimating a safe pathway towards the goal location, while ensuring the establishment of anchor points that facilitate the efficient steering of the soft robot. The algorithm is evaluated using a highly detailed model of the SSS with respect to its capacity to satisfy the robot movement requirements and path efficiency.

关键词： Minimally invasive surgery Three-dimensional displays Targeted drug delivery Navigation Magnetic resonance imaging Refining Phantoms Kinematics Soft robotics Cost function

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Towards the Selection of Distance Metrics for k-NN Classifier in Students' Performance Prediction Modeling

Towards the Selection of Distance Metrics for k-NN Classifie...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Khan, Ijaz Mohamed Zabil, Mohammed Hazli Ahmad, Abdul Rahim Jabeur, Nafaa College of Computing and Informatics' Univsiti Tenaga Nasional Department of Computing Kajang Malaysia Buraimi University College Information Technology Dept. Al-Buraimi Oman Deparmtne of Computing and Informatics' Kajang Malaysia German University of Technology Computer Science Dept. Muscat Oman

ISBN: (纸本)9798350302684

This paper investigates the impact of changing distance metrics on the performance of the k-NN classifier. The study investigates the variation in models performance with changing distance metric and value of k in the context of students' performance prediction models. The research utilizes datasets specifically designed for students' performance prediction modeling. Starting with a I-NN model, the experiments increment the value of k by 2 until the size of the dataset is reached. The experiments are repeated with different distance metrics derived from Minkowski derivation, including Euclidean, Manhattan, and Chebyshev. The findings indicate that there is no unanimously dominant distance metric for every dataset. However, the Euclidean and Manhattan distance metrics emerge effective, while Chebyshev exhibits lower performance. The research concludes Euclidean and Manhattan distance metrics as appropriate metrics for students' performance prediction modeling using the k-NN classifier. © 2023 IEEE.

关键词： Forecasting

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Sports Analytics: A Comparison of Machine Learning Perfomance for Profiling Badminton Athlete 1

Sports Analytics: A Comparison of Machine Learning Perfomanc...

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Sinadia, Herbie Ewaldo Murwantara, I Made Universitas Pelita Harapan Faculty Of Computer Science Graduate Program Informatics Dept. Tangerang Banten Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

Sports analytics have become an important part in the improvement of athlete performances in the last decade. Badminton is one such sport where sports analytics can prove beneficial for its athletes. By incorporating data analysis and machine learning techniques, we can learn more about an athlete's performance and profile them via their results in official badminton matches, and coaches as well as athletes may use the results to identify possible improvements for the athlete. This work presents a model to profile the performances of two Indonesian badminton athletes through machine learning using official score data of matches played from 2018 to 2021. We have built a model that performs athlete profiling through regression, classification, and clustering methods. Our result shows that Random Forest outperformed SVM method on regression and the two methods performed similarly on classification, while K-means and hierarchical clustering models displayed similar results in data clustering. © 2022 IEEE.

关键词： Sports

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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest 8

Borneo Smart Forest Information System for Management of Dip...

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8th IEEE Information Technology International Seminar, ITIS 2022

作者： Wati, Masna Puspitasari, Novianti Hairah, Ummul Widians, Joan Angelina Septiarini, Anindita Tjikoa, Ade Fiqri Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350398199

Kalimantan's tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan's rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation. © 2022 IEEE.

关键词： Information systems

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Evaluation of Pseudorandom Pixel Placement in Enhancing Face Recognition using VGG-Face 5

Evaluation of Pseudorandom Pixel Placement in Enhancing Face...

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5th International Conference on Image, Video Processing, and Artificial Intelligence, IVPAI 2023

作者： Hadis, Muhammad Sabirin Akita, Junichi Toda, Masashi Dept. of Electrical Engineering and Computer Science Kanazawa University Kakuma-machi Ishikawa Kanazawa920-1192 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University Kumamoto860-8555 Japan

ISBN: (纸本)9781510674660

In efforts to enhance face recognition performance, techniques ranging from super-resolution methods to the use of Local Binary Pattern (LBP) and deep learning have been explored. Among these, the pseudorandom pixel placement (PSE) technique has demonstrated potential in face recognition. Nevertheless, its testing was previously limited to just 8 subjects. This study undertakes a comprehensive evaluation of the PSE technique with a larger sample, utilizing 2000 subjects from the DigiFACE1M dataset and leveraging the state-of-the-art VGG-Face deep learning model. Through experiments involving 10 different PSE patterns on 144.000 face images, our findings indicate that, compared to Regular Pixel Placement (REG), PSE achieved an improvement in average accuracy by 1.05%, reduced the standard deviation by 1.47%, and resulted in 31 additional subjects achieving 100% accuracy. We conclude that PSE consistently outperforms REG in face recognition tasks using the VGG-Face model across the majority of tested scenarios. © 2024 SPIE. All rights reserved.

关键词： Pixels

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Universally Wheeler Languages

arXiv

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arXiv 2025年

作者： Becker, Ruben Castiglione, Giuseppa D’Agostino, Giovanna Policriti, Alberto Prezza, Nicola Restivo, Antonio Riccardi, Brian Dept. of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy Dept. of Mathematics and Computer Science University of Palermo Italy Dept. of Mathematics Computer Science and Physics University of Udine Italy Dept. of Informatics Systems and Communication University of Milano-Bicocca Italy

The notion of Wheeler languages is rooted in the Burrows-Wheeler transform (BWT), one of the most central concepts in data compression and indexing. The BWT has been generalized to finite automata, the so-called Wheeler automata, by Gagie et al. [Theor. Comput. Sci. 2017]. Wheeler languages have subsequently been defined as the class of regular languages for which there exists a Wheeler automaton accepting them. Besides their advantages in data indexing, these Wheeler languages also satisfy many interesting properties from a language theoretic point of view [Alanko et al., Inf. Comput. 2021]. A characteristic yet unsatisfying feature of Wheeler languages however is that their definition depends on a fixed order of the alphabet. In this paper we introduce the Universally Wheeler languages UW, i.e., the regular languages that are Wheeler with respect to all orders of a given alphabet. Our first main contribution is to relate UW to some very well known regular language classes. We first show that the Striclty Locally Testable languages are strictly included in UW. After noticing that UW is not closed under taking the complement, we prove that the class of languages for which both the language and its complement are in UW exactly coincides with those languages that are Definite or Reverse Definite Secondly, we prove that deciding if a regular language given by a DFA is in UW can be done in quadratic time. We also show that this is optimal unless the Strong Exponential Time Hypothesis (SETH) fails. © 2025, CC BY.

关键词： Finite automata

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Mining Road Traffic Accident Data for Prediction of Accident Severity

Mining Road Traffic Accident Data for Prediction of Accident...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Bahiru, Tadesse Kebede Manjula, V.S. Akele, Tadesse Birara Tesfaw, Engdaw Ayalew Belay, Tadesse Destaw Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information Technology Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Pg Dept of Computer Science Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information System Amhara Kombolcha Ethiopia Wachamo University School of Computing and Informatics College of Engineering and Technology Dept. of Information Technology SSNPR Hosaena Ethiopia

ISBN: (纸本)9781665474511

As World Health Organization reports show more than 1.3 million people die each year from traffic accidents and more than twenty to fifty million people are harmed by non-fatal accidents. Understanding and identifying the major factors of road traffic accidents can help the stakeholders to take appropriate measures to minimize the accidents. Support Vector Machine (SVM), J48, and Naïve Bayes (NB) classification techniques are implemented to build a model that predicts accident severities and to identify road traffic accident factors. As experimental findings showed weather conditions, number of lanes, road lighting conditions, and speed limit are the determinant factors that cause more accident severity. Finally, the classification and prediction performance of the models are compared using various performance evaluation techniques. J48 classifier has better classification performance than Support Vector Machine and Naïve Bayes classifiers. Naïve Bayes is the classifier that showed the lowest performance than others, but it has good performance in classifying fatal accident severities rather than serious and slight accident severities. © 2023 IEEE.

关键词： Support vector machines

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Maintenance of Model Resilience in Distributed Edge Learning Environments 19

Maintenance of Model Resilience in Distributed Edge Learning...

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19th International Conference on Intelligent Environments, IE 2023

作者： Wang, Qiyuan Anagnostopoulos, Christos Fornes, Jordi Mateo Kolomvatsos, Kostas Vrachimis, Andreas University of Glasgow School of Computing Science United Kingdom University of Lleida Department of Computer Science Spain University of Thessaly Dept. of Informatics and Telecommunications Greece Glasgow University School of Computing Science United Kingdom

ISBN: (纸本)9798350312225

Distributed Machine Learning (DML) at the edge of the network involves model learning and inference across networking nodes over distributed data. One type of model learning could be the delivery of predictive analytics services to formulate intelligent environments, however, those environments heavily rely on real-time inference and are significantly influenced by changes in the underlying data (concept drifts). Moreover, the quality of service and availability in DML environments are directly tied to each node's reliability, since such environments are highly susceptible to the impact of node failures. Even if such challenges can be tackled with distributed resilience mechanisms, their effectiveness and efficiency, due to concept drifts, should be maintained to ensure continuous and sustained quality of service. DML systems operate in dynamic environments, thus, they require their models to be updated according to the novel trends embedded in the new data they encounter. We, therefore, introduce several model maintenance mechanisms to ensure resilient DML systems in the long term when concept drifts emerge. We provide a comprehensive experimental evaluation of our resilience maintenance mechanisms over synthetic and real data showcasing their importance and applicability in edge learning environments. © 2023 IEEE.

关键词： Quality of service

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Instance-Level Safety-Aware Fidelity of Synthetic Data and its Calibration 27

Instance-Level Safety-Aware Fidelity of Synthetic Data and i...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Cheng, Chih-Hong Stockel, Paul Zhao, Xingyu Chalmers & Univ. of Gothenburg Dept. of Computer Science & Engineering Sweden University of Hildesheim Department of Informatics Germany University of Warwick WMG CV4 7AL United Kingdom

ISBN: (纸本)9798331505929

Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable alternative to real-world data collection. We focus on its role in safety-critical applications, introducing four types of instance-level fidelity that go beyond mere visual input characteristics. The aim is to ensure that applying testing on synthetic data can reveal real-world safety issues, and the absence of safety-critical issues when testing under synthetic data can provide a strong safety guarantee in real-world behavior. We suggest an optimization method to refine the synthetic data generator, reducing fidelity gaps identified by deep learning components. Experiments show this tuning enhances the correlation between safety-critical errors in synthetic and real data. © 2024 IEEE.

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