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Artykuły w czasopismach na temat „Explicable Machine Learning”

Autor: Grafiati

Data publikacji: 9 listopada 2024

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1

FOMICHEVA, S. G. "INFLUENCE OF ATTACK INDICATOR RANKING ON THE QUALITY OF MACHINE LEARNING MODELS IN AGENT-BASED CONTINUOUS AUTHENTICATION SYSTEMS." T-Comm 17, no. 8 (2023): 45–55. http://dx.doi.org/10.36724/2072-8735-2023-17-8-45-55.

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Security agents of authentication systems function in automatic mode and control the behavior of subjects, analyzing their dynamics using both traditional (statistical) methods and methods based on machine learning. The expansion of the cybersecurity fabric paradigm actualizes the improvement of adaptive explicable methods and machine learning models. Purpose: the purpose of the study was to assess the impact of ranking methods at compromise indicators, attacks indicators and other futures on the quality of detecting network traffic anomalies as part of the security fabric with continuous auth

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Abrahamsen, Nils-Gunnar Birkeland, Emil Nylén-Forthun, Mats Møller, Petter Eilif de Lange, and Morten Risstad. "Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models." Journal of Risk and Financial Management 17, no. 10 (2024): 432. http://dx.doi.org/10.3390/jrfm17100432.

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This paper proposes an explicable early warning machine learning model for predicting financial distress, which generalizes across listed Nordic corporations. We develop a novel dataset, covering the period from Q1 2001 to Q2 2022, in which we combine idiosyncratic quarterly financial statement data, information from financial markets, and indicators of macroeconomic trends. The preferred LightGBM model, whose features are selected by applying explainable artificial intelligence, outperforms the benchmark models by a notable margin across evaluation metrics. We find that features related to li

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Fomicheva, Svetlana, and Sergey Bezzateev. "Modification of the Berlekamp-Massey algorithm for explicable knowledge extraction by SIEM-agents." Journal of Physics: Conference Series 2373, no. 5 (2022): 052033. http://dx.doi.org/10.1088/1742-6596/2373/5/052033.

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Abstract The article discusses the problems of applying self-explanatory machine learning models in Security Information Event Management systems. We prove the possibility of using information processing methods in finite fields for extracting knowledge from security event repositories by mobile agents. Based on the isomorphism of fuzzy production and fuzzy relational knowledge bases, a constructive method for identifying patterns based on the modified Berlekamp-Massey algorithm is proposed. This allows security agents, while solving their typical cryptanalysis tasks, to use the existing built

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Alharbi, Abdulrahman, Ivan Petrunin, and Dimitrios Panagiotakopoulos. "Assuring Safe and Efficient Operation of UAV Using Explainable Machine Learning." Drones 7, no. 5 (2023): 327. http://dx.doi.org/10.3390/drones7050327.

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The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution

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Fujii, Keisuke. "Understanding of social behaviour in human collective motions with non-trivial rule of control." Impact 2019, no. 10 (2019): 84–86. http://dx.doi.org/10.21820/23987073.2019.10.84.

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The coordination and movement of people in large crowds, during sports games or when socialising, seems readily explicable. Sometimes this occurs according to specific rules or instructions such as in a sport or game, at other times the motivations for movement may be more focused around an individual's needs or fears. Over the last decade, the computational ability to identify and track a given individual in video footage has increased. The conventional methods of how data is gathered and interpreted in biology rely on fitting statistical results to particular models or hypotheses. However, d

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Wang, Chen, Lin Liu, Chengcheng Xu, and Weitao Lv. "Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework." International Journal of Environmental Research and Public Health 16, no. 3 (2019): 334. http://dx.doi.org/10.3390/ijerph16030334.

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The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing a reliable and explicable machine learning model. High-risk (HR) and low-risk (LR) drivers were defined by five different scenarios. A number of features were extracted from seven-year crash/violation records. Drivers’ two-year prior crash/violation information was used to predict their driving risk in the subseq

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Valladares-Rodríguez, Sonia, Manuel J. Fernández-Iglesias, Luis E. Anido-Rifón, and Moisés Pacheco-Lorenzo. "Evaluation of the Predictive Ability and User Acceptance of Panoramix 2.0, an AI-Based E-Health Tool for the Detection of Cognitive Impairment." Electronics 11, no. 21 (2022): 3424. http://dx.doi.org/10.3390/electronics11213424.

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The high prevalence of Alzheimer-type dementia and the limitations of traditional neuropsychological tests motivate the introduction of new cognitive assessment methods. We discuss the validation of an all-digital, ecological and non-intrusive e-health application for the early detection of cognitive impairment, based on artificial intelligence for patient classification, and more specifically on machine learning algorithms. To evaluate the discrimination power of this application, a cross-sectional pilot study was carried out involving 30 subjects: 10 health control subjects (mean age: 75.62

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Hermitaño Castro, Juler Anderson. "Aplicación de Machine Learning en la Gestión de Riesgo de Crédito Financiero: Una revisión sistemática." Interfases, no. 015 (August 11, 2022): e5898. http://dx.doi.org/10.26439/interfases2022.n015.5898.

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La gestión de riesgos bancarios puede ser dividida en las siguientes tipologías: riesgo crediticio, riesgo de mercado, riesgo operativo y riesgo de liquidez, siendo el primero el tipo de riesgo más importante para el sector financiero. El presente artículo tiene como objetivo mostrar las ventajas y desventajas que posee la implementación de los algoritmos de machine learning en la gestión de riesgos de crédito y, a partir de esto, mostrar cuál tiene mejor rendimiento, mostrando también las desventajas que puedan presentar. Para lograr el objetivo se realizó una revisión sistemática de la liter

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Umar, Muhammad, Ashish Shiwlani, Fiza Saeed, Ahsan Ahmad, Masoomi Hifazat Ali Shah, and Anoosha Tahir. "Role of Deep Learning in Diagnosis, Treatment, and Prognosis of Oncological Conditions." International Journal of Membrane Science and Technology 10, no. 5 (2023): 1059–71. http://dx.doi.org/10.15379/ijmst.v10i5.3695.

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Deep learning, a branch of artificial intelligence, excavates massive data sets for patterns and predictions using a machine learning method known as artificial neural networks. Research on the potential applications of deep learning in understanding the intricate biology of cancer has intensified due to its increasing applications among healthcare domains and the accessibility of extensively characterized cancer datasets. Although preliminary findings are encouraging, this is a fast-moving sector where novel insights into deep learning and cancer biology are being discovered. We give a framew

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Valdivieso-Ros, Carmen, Francisco Alonso-Sarria, and Francisco Gomariz-Castillo. "Effect of the Synergetic Use of Sentinel-1, Sentinel-2, LiDAR and Derived Data in Land Cover Classification of a Semiarid Mediterranean Area Using Machine Learning Algorithms." Remote Sensing 15, no. 2 (2023): 312. http://dx.doi.org/10.3390/rs15020312.

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Land cover classification in semiarid areas is a difficult task that has been tackled using different strategies, such as the use of normalized indices, texture metrics, and the combination of images from different dates or different sensors. In this paper we present the results of an experiment using three sensors (Sentinel-1 SAR, Sentinel-2 MSI and LiDAR), four dates and different normalized indices and texture metrics to classify a semiarid area. Three machine learning algorithms were used: Random Forest, Support Vector Machines and Multilayer Perceptron; Maximum Likelihood was used as a ba

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Pai, Kai-Chih, Wen-Cheng Chao, Yu-Len Huang, et al. "Artificial intelligence–aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs." DIGITAL HEALTH 8 (January 2022): 205520762211203. http://dx.doi.org/10.1177/20552076221120317.

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Objective The aim of this study was to develop an artificial intelligence–based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data. Method The transfer learning method was used to train a convolutional neural network (CNN) model with an external image dataset to extract the image features. Then, the last layer of the model was fine-tuned to determine the probability of ARDS. The clinical data were trained using three machine learning algorithms—eXtreme Gradient Boosting (XGB), random forest (RF), and logistic regression (LR

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Zhao, Ziting, Tong Liu, and Xudong Zhao. "Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids." Computational Intelligence and Neuroscience 2021 (March 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/5538573.

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Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for gett

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Rudas, Imre J. "Intelligent Engineering Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 4 (2000): 237–39. http://dx.doi.org/10.20965/jaciii.2000.p0237.

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The "information revolution" of our time affects our entire generation. While a vision of the "Information Society," with its financial, legal, business, privacy, and other aspects has emerged in the past few years, the "traditional scene" of information technology, that is, industrial automation, maintained its significance as a field of unceasing development. Since the old-fashioned concept of "Hard Automation" applicable only to industrial processes of fixed, repetitive nature and manufacturing large batches of the same product1)was thrust to the background by keen market competition, the k

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Fazelpour, Sina, and Maria De-Arteaga. "Diversity in sociotechnical machine learning systems." Big Data & Society 9, no. 1 (2022): 205395172210820. http://dx.doi.org/10.1177/20539517221082027.

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There has been a surge of recent interest in sociocultural diversity in machine learning research. Currently, however, there is a gap between discussions of measures and benefits of diversity in machine learning, on the one hand, and the broader research on the underlying concepts of diversity and the precise mechanisms of its functional benefits, on the other. This gap is problematic because diversity is not a monolithic concept. Rather, different concepts of diversity are based on distinct rationales that should inform how we measure diversity in a given context. Similarly, the lack of speci

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Saladi, Saritha, Yepuganti Karuna, Srinivas Koppu, et al. "Segmentation and Analysis Emphasizing Neonatal MRI Brain Images Using Machine Learning Techniques." Mathematics 11, no. 2 (2023): 285. http://dx.doi.org/10.3390/math11020285.

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MRI scanning has shown significant growth in the detection of brain tumors in the recent decade among various methods such as MRA, X-ray, CT, PET, SPECT, etc. Brain tumor identification requires high exactness because a minor error can be life-threatening. Brain tumor disclosure remains a challenging job in medical image processing. This paper targets to explicate a method that is more precise and accurate in brain tumor detection and focuses on tumors in neonatal brains. The infant brain varies from the adult brain in some aspects, and proper preprocessing technique proves to be fruitful to a

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Munk, Anders Kristian, Asger Gehrt Olesen, and Mathieu Jacomy. "The Thick Machine: Anthropological AI between explanation and explication." Big Data & Society 9, no. 1 (2022): 205395172110698. http://dx.doi.org/10.1177/20539517211069891.

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According to Clifford Geertz, the purpose of anthropology is not to explain culture but to explicate it. That should cause us to rethink our relationship with machine learning. It is, we contend, perfectly possible that machine learning algorithms, which are unable to explain, and could even be unexplainable themselves, can still be of critical use in a process of explication. Thus, we report on an experiment with anthropological AI. From a dataset of 175K Facebook comments, we trained a neural network to predict the emoji reaction associated with a comment and asked a group of human players t

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Parker, J. Clint. "Below the Surface of Clinical Ethics." Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 48, no. 1 (2023): 1–11. http://dx.doi.org/10.1093/jmp/jhac041.

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Abstract Often lurking below the surface of many clinical ethical issues are questions regarding background metaphysical, epistemological, meta-ethical, and political beliefs. In this issue, authors critically examine the effects of background beliefs on conscientious objection, explore ethical issues through the lenses of particular theoretical approaches like pragmatism and intersectional theory, rigorously explore the basic concepts at play within the patient safety movement, offer new theoretical approaches to old problems involving decision making for patients with dementia, explicate and

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Tay, Louis, Sang Eun Woo, Louis Hickman, and Rachel M. Saef. "Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining." European Journal of Personality 34, no. 5 (2020): 826–44. http://dx.doi.org/10.1002/per.2290.

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In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground trut

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Hussain, Iqram, Rafsan Jany, Richard Boyer, et al. "An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME." Sensors 23, no. 17 (2023): 7452. http://dx.doi.org/10.3390/s23177452.

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Electroencephalography (EEG) is a non-invasive method employed to discern human behaviors by monitoring the neurological responses during cognitive and motor tasks. Machine learning (ML) represents a promising tool for the recognition of human activities (HAR), and eXplainable artificial intelligence (XAI) can elucidate the role of EEG features in ML-based HAR models. The primary objective of this investigation is to investigate the feasibility of an EEG-based ML model for categorizing everyday activities, such as resting, motor, and cognitive tasks, and interpreting models clinically through

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Mucha, Tomasz, Sijia Ma, and Kaveh Abhari. "Riding a bicycle while building its wheels: the process of machine learning-based capability development and IT-business alignment practices." Internet Research 33, no. 7 (2023): 168–205. http://dx.doi.org/10.1108/intr-10-2022-0769.

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PurposeRecent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.Des

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Calabuig, J. M., L. M. Garcia-Raffi, and E. A. Sánchez-Pérez. "Aprender como una máquina: introduciendo la Inteligencia Artificial en la enseñanza secundaria." Modelling in Science Education and Learning 14, no. 1 (2021): 5. http://dx.doi.org/10.4995/msel.2021.15022.

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<p class="p1">La inteligencia artificial está presente en el entorno habitual de todos los estudiantes de secundaria. Sin embargo, la población general -y los alumnos en particular- no conocen cómo funcionan estas técnicas algorítmicas, que muchas veces tienen mecanismos muy sencillos y que pueden explicarse a nivel elemental en las clases de matemáticas o de tecnología en los Institutos de Enseñanza Secundaria (IES). Posiblemente estos contenidos tardarán muchos años en formar parte de los currículos de estas asignaturas, pero se pueden introducir como parte de los contenidos de álgebra

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Ahn, Yongsu, Muheng Yan, Yu-Ru Lin, Wen-Ting Chung, and Rebecca Hwa. "Tribe or Not? Critical Inspection of Group Differences Using TribalGram." ACM Transactions on Interactive Intelligent Systems 12, no. 1 (2022): 1–34. http://dx.doi.org/10.1145/3484509.

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With the rise of AI and data mining techniques, group profiling and group-level analysis have been increasingly used in many domains, including policy making and direct marketing. In some cases, the statistics extracted from data may provide insights to a group’s shared characteristics; in others, the group-level analysis can lead to problems, including stereotyping and systematic oppression. How can analytic tools facilitate a more conscientious process in group analysis? In this work, we identify a set of accountable group analytics design guidelines to explicate the needs for group differen

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Topper, Noah, George Atia, Ashutosh Trivedi, and Alvaro Velasquez. "Active Grammatical Inference for Non-Markovian Planning." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 647–51. http://dx.doi.org/10.1609/icaps.v32i1.19853.

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Planning in finite stochastic environments is canonically posed as a Markov decision process where the transition and reward structures are explicitly known. Reinforcement learning (RL) lifts the explicitness assumption by working with sampling models instead. Further, with the advent of reward machines, we can relax the Markovian assumption on the reward. Angluin's active grammatical inference algorithm L* has found novel application in explicating reward machines for non-Markovian RL. We propose maintaining the assumption of explicit transition dynamics, but with an implicit non-Markovian re

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Cantillo Romero, Janer Rafael, Javier Javier Estrada Romero, and Carlos Henríquez Miranda. "APLICACIÓN DE ALGORITMOS DE APRENDIZAJE AUTOMÁTICO EN GEOCIENCIA: REVISIÓN INTEGRAL Y DESAFÍO FUTURO." REVISTA AMBIENTAL AGUA, AIRE Y SUELO 14, no. 2 (2023): 9–18. http://dx.doi.org/10.24054/raaas.v14i2.2783.

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Este artículo aborda la aplicación de técnicas de Aprendizaje Automático o Machine Learning en la geoingeniería y geociencia, destacando su relevancia en la predicción y comprensión de fenómenos naturales. A pesar de prescindir de leyes físicas explícitas, los modelos de ML ofrecen flexibilidad para adaptarse y descubrir patrones complejos. En particular, se resalta la capacidad del aprendizaje automático para mejorar la precisión y eficiencia en la predicción de la susceptibilidad a deslizamientos de tierra, con enfoques como el aprendizaje supervisado y no supervisado. Se menciona la importa

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Bhattacharyya, Som Sekhar, and Srikant Nair. "Explicating the future of work: perspectives from India." Journal of Management Development 38, no. 3 (2019): 175–94. http://dx.doi.org/10.1108/jmd-01-2019-0032.

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PurposeThe world is witnessing the advent of a wide range of technologies like machine learning, big data analytics, artificial intelligence, blockchain technology, robotics, additive manufacturing, augmented and virtual reality, cloud computing, Internet of Things and such others. Amidst this concoction of diverse technologies, the future of work is getting redefined. Thus, the purpose of this paper is to understand the future of work in the context of an emerging economy like India.Design/methodology/approachThe authors undertook a qualitative research with a positivist approach. The authors

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Ge, Hanwen, Yuekun Bai, Rui Zhou, et al. "Explicable Machine Learning for Predicting High-Efficiency Lignocellulose Pretreatment Solvents Based on Kamlet–Taft and Polarity Parameters." ACS Sustainable Chemistry & Engineering, April 29, 2024. http://dx.doi.org/10.1021/acssuschemeng.4c01563.

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Sun, Kun, and Jiayi Pan. "Model of Storm Surge Maximum Water Level Increase in a Coastal Area Using Ensemble Machine Learning and Explicable Algorithm." Earth and Space Science 10, no. 12 (2023). http://dx.doi.org/10.1029/2023ea003243.

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AbstractThis study proposes a novel, new ensemble model (NEM) designed to simulate the maximum water level increases caused by storm surges in a frequently cyclone‐affected coastal water of Hong Kong, China. The model relies on storm and water level data spanning 1978–2022. The NEM amalgamates three machine learning algorithms: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and XGBoost (XGB), employing a stacking technique for integration. Six parameters, determined using the Random Forest and Recursive Feature Elimination algorithms (RF‐RFE), are used as input features for the NE

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Kim, Ho Heon, Dong-Wook Kim, Junwoo Woo, and Kyoungyeul Lee. "Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders." Human Genomics 18, no. 1 (2024). http://dx.doi.org/10.1186/s40246-024-00595-8.

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Abstract Background In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the

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Clarke, Gerald P., and Adam Kapelner. "The Bayesian Additive Regression Trees Formula for Safe Machine Learning-Based Intraocular Lens Predictions." Frontiers in Big Data 3 (December 18, 2020). http://dx.doi.org/10.3389/fdata.2020.572134.

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Purpose: Our work introduces a highly accurate, safe, and sufficiently explicable machine-learning (artificial intelligence) model of intraocular lens power (IOL) translating into better post-surgical outcomes for patients with cataracts. We also demonstrate its improved predictive accuracy over previous formulas.Methods: We collected retrospective eye measurement data on 5,331 eyes from 3,276 patients across multiple centers who received a lens implantation during cataract surgery. The dependent measure is the post-operative manifest spherical equivalent error from intended and the independen

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Khan, Ijaz, Abdul Rahim Ahmad, Nafaa Jabeur, and Mohammed Najah Mahdi. "An artificial intelligence approach to monitor student performance and devise preventive measures." Smart Learning Environments 8, no. 1 (2021). http://dx.doi.org/10.1186/s40561-021-00161-y.

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AbstractA major problem an instructor experiences is the systematic monitoring of students’ academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to collect enormous amount of data concerning their students from various sources, however, the institutes are craving novel procedures to utilize the data to magnify their prestige and improve the education quality. This research evaluates the effe

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Siddique, Abu Bokkar, Eliyas Rayhan, Faisal Sobhan, et al. "Spatio-temporal analysis of land use and land cover changes in a wetland ecosystem of Bangladesh using a machine-learning approach." Frontiers in Water 6 (July 10, 2024). http://dx.doi.org/10.3389/frwa.2024.1394863.

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This study investigates quantifiable and explicable changes in Land Use and Land Cover (LULC) within the context of a freshwater wetland, Hakaluki Haor, in Bangladesh. The haor is a vital RAMSAR site and Ecologically Critical Area (ECA), which needs to be monitored to investigate LULC change patterns for future management interventions. Leveraging Landsat satellite data, the Google Earth Engine Database, CART algorithm, ArcGIS 10.8 and the R programming language, this study analyses LULC dynamics from 2000 to 2023. It focuses explicitly on seasonal transitions between the rainy and dry seasons

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Funer, Florian. "Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship." Philosophy & Technology 35, no. 1 (2022). http://dx.doi.org/10.1007/s13347-022-00505-7.

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AbstractThe initial successes in recent years in harnessing machine learning (ML) technologies to improve medical practice and benefit patients have attracted attention in a wide range of healthcare fields. Particularly, it should be achieved by providing automated decision recommendations to the treating clinician. Some hopes placed in such ML-based systems for healthcare, however, seem to be unwarranted, at least partially because of their inherent lack of transparency, although their results seem convincing in accuracy and reliability. Skepticism arises when the physician as the agent respo

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Zhang, Jiahui, Wenjie Du, Xiaoting Yang, et al. "SMG-BERT: integrating stereoscopic information and chemical representation for molecular property prediction." Frontiers in Molecular Biosciences 10 (June 30, 2023). http://dx.doi.org/10.3389/fmolb.2023.1216765.

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Molecular property prediction is a crucial task in various fields and has recently garnered significant attention. To achieve accurate and fast prediction of molecular properties, machine learning (ML) models have been widely employed due to their superior performance compared to traditional methods by trial and error. However, most of the existing ML models that do not incorporate 3D molecular information are still in need of improvement, as they are mostly poor at differentiating stereoisomers of certain types, particularly chiral ones. Also,routine featurization methods using only incomplet

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Hu, Chang, Chao Gao, Tianlong Li, Chang Liu, and Zhiyong Peng. "Explainable artificial intelligence model for mortality risk prediction in the intensive care unit: a derivation and validation study." Postgraduate Medical Journal, January 19, 2024. http://dx.doi.org/10.1093/postmj/qgad144.

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Abstract Background The lack of transparency is a prevalent issue among the current machine-learning (ML) algorithms utilized for predicting mortality risk. Herein, we aimed to improve transparency by utilizing the latest ML explicable technology, SHapley Additive exPlanation (SHAP), to develop a predictive model for critically ill patients. Methods We extracted data from the Medical Information Mart for Intensive Care IV database, encompassing all intensive care unit admissions. We employed nine different methods to develop the models. The most accurate model, with the highest area under the

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Marey, Ahmed, Parisa Arjmand, Ameerh Dana Sabe Alerab, et al. "Explainability, transparency and black box challenges of AI in radiology: impact on patient care in cardiovascular radiology." Egyptian Journal of Radiology and Nuclear Medicine 55, no. 1 (2024). http://dx.doi.org/10.1186/s43055-024-01356-2.

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AbstractThe integration of artificial intelligence (AI) in cardiovascular imaging has revolutionized the field, offering significant advancements in diagnostic accuracy and clinical efficiency. However, the complexity and opacity of AI models, particularly those involving machine learning (ML) and deep learning (DL), raise critical legal and ethical concerns due to their "black box" nature. This manuscript addresses these concerns by providing a comprehensive review of AI technologies in cardiovascular imaging, focusing on the challenges and implications of the black box phenomenon. We begin b

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Smith, Matthew G., Jack Radford, Eky Febrianto, et al. "Machine learning opens a doorway for microrheology with optical tweezers in living systems." AIP Advances 13, no. 7 (2023). http://dx.doi.org/10.1063/5.0161014.

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It has been argued that linear microrheology with optical tweezers (MOT) of living systems “is not an option” because of the wide gap between the observation time required to collect statistically valid data and the mutational times of the organisms under study. Here, we have explored modern machine learning (ML) methods to reduce the duration of MOT measurements from tens of minutes down to one second by focusing on the analysis of computer simulated experiments. For the first time in the literature, we explicate the relationship between the required duration of MOT measurements (Tm) and the

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Sun, Deliang, Yuekai Ding, Haijia Wen, and Fengtai Zhang. "A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping." Earth Surface Processes and Landforms, August 6, 2023. http://dx.doi.org/10.1002/esp.5675.

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AbstractLandslide susceptibility mapping (LSM) enables the prediction of landslide occurrences, thereby offering a scientific foundation for disaster prevention and control. In recent years, numerous studies have been conducted on LSM using machine learning techniques. However, the majority of machine learning models is considered “black box” models due to their lack of transparent explanations. In contrast, the QLattice model serves as a white box model, as it can elucidate the decision‐making mechanism while representing a novel approach to whitening machine learning models. QLattice possess

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Ahn, Sungyong. "On That <em>Toy-Being</em> of Generative Art Toys." M/C Journal 26, no. 2 (2023). http://dx.doi.org/10.5204/mcj.2947.

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Exhibiting Procedural Generation Generative art toys are software applications that create aesthetically pleasing visual patterns in response to the users toying with various input devices, from keyboard and mouse to more intuitive and tactile devices for motion tracking. The “art” part of these toy objects might relate to the fact that they are often installed in art galleries or festivals as a spectacle for non-players that exhibits the unlimited generation of new patterns from a limited source code. However, the features that used to characterise generative arts as a new meditative genre, s

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Luo, Hong, Jisong Yan, Dingyu Zhang, and Xia Zhou. "Identification of cuproptosis-related molecular subtypes and a novel predictive model of COVID-19 based on machine learning." Frontiers in Immunology 14 (July 17, 2023). http://dx.doi.org/10.3389/fimmu.2023.1152223.

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BackgroundTo explicate the pathogenic mechanisms of cuproptosis, a newly observed copper induced cell death pattern, in Coronavirus disease 2019 (COVID-19).MethodsCuproptosis-related subtypes were distinguished in COVID-19 patients and associations between subtypes and immune microenvironment were probed. Three machine algorithms, including LASSO, random forest, and support vector machine, were employed to identify differentially expressed genes between subtypes, which were subsequently used for constructing cuproptosis-related risk score model in the GSE157103 cohort to predict the occurrence

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Mitchell, Shira, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum. "Algorithmic Fairness: Choices, Assumptions, and Definitions." Annual Review of Statistics and Its Application 8, no. 1 (2020). http://dx.doi.org/10.1146/annurev-statistics-042720-125902.

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A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of statistical and machine learning models. The rapid growth of this new field has led to wildly inconsistent motivations, terminology, and notation, presenting a serious challenge for cataloging and comparing definitions. This article attempts to bring much-needed order. First, we explicate the various choices and assumptions made—often implicitly—to justify the use of prediction-based decision-making. Ne

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Tobing, Margaret BR, Fizri Ismaliana SNA, Nadya Risky Hayrunnisa, Nur Indah Tika Haswuri, Cucu Sutarsyah, and Feni Munifatullah. "An Exploration of Artificial Intelligence in English Language Teaching As a Foreign Language." International Journal of Social Science and Human Research 06, no. 06 (2023). http://dx.doi.org/10.47191/ijsshr/v6-i6-78.

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The aim of this study is to analyze the technologies which are currently being used in foreign language teaching and learning English as an applied language at the university level based on the findings of the detected experimental studies. The PRISMA criteria for systematic reviews and meta-analyses were compiled in the methodology. The findings of the experimental studies shown the lack of innovative technologies, such as chatbots or virtual reality (VR) devices, which are commonly utilized in foreign language (FL) education. Furthermore, mobile apps are primarily concerned with the acquisit

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Guest, Olivia. "What Makes a Good Theory, and How Do We Make a Theory Good?" Computational Brain & Behavior, January 24, 2024. http://dx.doi.org/10.1007/s42113-023-00193-2.

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AbstractI present an ontology of criteria for evaluating theory to answer the titular question from the perspective of a scientist practitioner. Set inside a formal account of our adjudication over theories, a metatheoretical calculus, this ontology comprises the following: (a) metaphysical commitment, the need to highlight what parts of theory are not under investigation, but are assumed, asserted, or essential; (b) discursive survival, the ability to be understood by interested non-bad actors, to withstand scrutiny within the intended (sub)field(s), and to negotiate the dialectical landscape

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Maity, Sourav, and Karan Veer. "An Approach for Evaluation and Recognition of Facial Emotions Using EMG Signal." International Journal of Sensors, Wireless Communications and Control 14 (January 5, 2024). http://dx.doi.org/10.2174/0122103279260571231213053403.

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Background: Facial electromyography (fEMG) records muscular activities from the facial muscles, which provides details regarding facial muscle stimulation patterns in experimentation. Objectives: The Principal Component Analysis (PCA) is mostly implemented, whereas the actual or unprocessed initial fEMG data are rendered into low-spatial units with minimizing the level of data repetition. Methods: Facial EMG signal was acquired by using the instrument BIOPAC MP150. Four electrodes were fixed on the face of each participant for capturing the four different emotions like happiness, anger, sad an

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P., Naachimuthu K. "Sustainable Agriculture - The Indian Way." Journal of Rural and Industrial Development 3, no. 1 (2015). http://dx.doi.org/10.21863/jrid/2015.3.1.002.

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The five natural elements (earth, water, fire, air, and sky), the sun and the moon, plants, trees, birds, and animals, came into existence much ahead of the human beings. In fact, man, as a part of nature, was the last creation in the universe. Though, we (human beings) have been created with the superlative degree of intellect, there is so much that can be learnt from nature, traditions of wisdom from the world teach us that a divine essence flows through all creations. Together with nature, man can co-create groundbreaking ideas that would help create wealth and well-being, for nature offers

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