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Journal articles on the topic 'Machine Interactions'

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1

Yong, Raymond. "Soil Machine Interactions." Journal of Terramechanics 38, no. 1 (2001): 61–62. http://dx.doi.org/10.1016/s0022-4898(99)00021-x.

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Haqqu, Rizca, and Salwa Nur Rohmah. "Interaction Process Between Humans and ChatGPT in the Context of Interpersonal Communication." Jurnal Ilmiah LISKI (Lingkar Studi Komunikasi) 10, no. 1 (2024): 23. http://dx.doi.org/10.25124/liski.v10i1.7216.

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This study examines human interaction with artificial intelligence technology, focusing on the implementation of ChatGPT, a chatbot developed by OpenAI. Through the Human-Machine Communication (HMC) approach, the research describes human-like attributes in ChatGPT, exploring emotional responses and utility in educational, professional, and personal contexts. Qualitative research methods with triangulation techniques were used for a holistic understanding, involving interviews, observations, and document analysis. The results indicate that ChatGPT can provide adaptive responses, adjusting langu
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Jasiulewicz-Kaczmarek, Małgorzata, Katarzyna Antosz, Patryk Żywica, Dariusz Mazurkiewicz, Bo Sun, and Yi Ren. "Framework of machine criticality assessment with criteria interactions." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 2 (2021): 207–20. http://dx.doi.org/10.17531/ein.2021.2.1.

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Criticality is considered as a fundamental category of production planning, maintenance process planning and management. The criticality assessment of machines and devices can be a structured set of activities allowing to identify failures which have the greatest potential impact on the company’s business goals. It can be also used to define maintenance strategies, investment strategies and development plans, assisting the company in prioritizing their allocations of financial resources to those machines and devices that are critical in accordance with the predefined business criteria. In a cr
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Reid, Tahira, and James Gibert. "Inclusion in human–machine interactions." Science 375, no. 6577 (2022): 149–50. http://dx.doi.org/10.1126/science.abf2618.

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Lin, Yingzi. "Toward Intelligent Human Machine Interactions." Mechanical Engineering 139, no. 06 (2017): S4—S8. http://dx.doi.org/10.1115/1.2017-jun-4.

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This article discusses the concept of human assistance systems (HAS) and research works to design the interface of HAS. It also focuses on the issue of how humans and HAS collaborate with each other during such interactions. HAS are expected to detect and compensate for human errors. In a case that a machine is a part of the team to complete an operation, it is highly desired that HAS collaborate with humans effectively. Advances on HAS have been made within application areas including vehicle driving, pilot–flight interfaces, healthcare and rehabilitation, robotics, etc. One important method
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Wathes, C. M. "Interactions between animals and machines." Proceedings of the British Society of Animal Production (1972) 1992 (March 1992): 35. http://dx.doi.org/10.1017/s0308229600021590.

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A new age of mechanisation of animal agriculture is dawning following advances in robotic engineering, coupled with current knowledge of animal physiology, behaviour and disease. The advent of automated machines equipped with novel sensors and controlled by cheap microprocessors will eliminate many of the hazardous, tedious or unpleasant chores currently undertaken by farmers. Automatic attachment of teat cups to dairy cows, robotic sheep shearing and mechanical harvesting of broilers are now feasible and commercial exploitation is likely within a decade. Machines may tackle some tasks which a
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Alonso-García, María, Ana García-Sánchez, Paula Jaén-Moreno, and Manuel Fernández-Rubio. "Performance Analysis of Urban Cleaning Devices Using Human–Machine Interaction Method." Sustainability 13, no. 11 (2021): 5846. http://dx.doi.org/10.3390/su13115846.

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Presently, several jobs require the collaboration of humans and machines to perform different services and tasks. The ease and intuitiveness of the worker when using each machine will not only improve the worker’s experience but also improve the company’s productivity and the satisfaction that all users have. Specifically, electromechanical devices used to provide cleaning services require complex interactions. These interactions determine the usability and performance of devices. Therefore, devices must have appropriate ergonomic arrangements for human–machine interactions. Otherwise, the des
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Hong, Fuxing, Dongbo Huang, and Ge Chen. "Interaction-Aware Factorization Machines for Recommender Systems." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3804–11. http://dx.doi.org/10.1609/aaai.v33i01.33013804.

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Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named Interaction-aware Factorization Machine (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the feature a
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Kukreja, Aman, James Gopsill, Shuo Su, Aydin Nassehi, and Ben Hicks. "Supporting manufacturing interactions through Artificial Intelligence: An appraisal of the literature." MATEC Web of Conferences 401 (2024): 08005. http://dx.doi.org/10.1051/matecconf/202440108005.

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Artificial Intelligence (AI) is transforming how society works, from real-time classification mechanisms and enhanced patient diagnoses to large language models that can assist workers in real-time. With the increasing interest of the industry in digitising manufacturing, the role of AI will become even more important in promoting meaningful interactions among various stakeholders. This paper appraises AI manufacturing research from the lens of machine/process, human and system interaction. The results show that much of the literature has supported intra-machine/process and system-level intera
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Sagar, Amit, and Bin Xue. "Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions." Protein & Peptide Letters 26, no. 8 (2019): 601–19. http://dx.doi.org/10.2174/0929866526666190619103853.

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The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions
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Komal, Khalid, Rahim Mohammad, Malik Samman, et al. "Human-Machine Interaction in the Age of Artificial Intelligence: Ethical, Technical and Social Implications." Global Scientific and Academic Research Journal of Multidisciplinary Studies 3, no. 11 (2024): 93–101. https://doi.org/10.5281/zenodo.14228435.

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<em>Artificial Intelligence (AI) has significantly advanced the field of Human-Machine Interaction (HMI), enhancing communication, accessibility and responsiveness. Artificial agents have become increasingly prevalent in human social life. However, this rapid development brings complex ethical, technical, and social challenges that impact privacy, bias, and human autonomy. Human-machine interaction (HMI) is the study of how people and machines communicate and work together, and artificial intelligence (AI) is the field of computer science that aims to create machines that can perform tasks tha
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Shamoto, Eiji. "Special Issue on Process Machine Interactions." International Journal of Automation Technology 7, no. 4 (2013): 377. http://dx.doi.org/10.20965/ijat.2013.p0377.

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Machining, such as cutting, grinding and polishing, is involved in the production of many industrial parts as one of manufacturing’s most important processes. Some of the parts are made directly by machining, and many other parts are mass-produced indirectly by machining through dies and molds. The accuracy of these components thus depends strongly on the machining process. Machining is not an easy process, of course, since it generates large force and heat. Although machine tools are controlled to move precisely, the force and heat cause practical problems such as vibration, the displacement
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Katz, Sandra, John Aronis, and Colin Creitz. "Modeling pedagogical interactions with machine learning." Kognitionswissenschaft 9, no. 1 (2000): 45–49. http://dx.doi.org/10.1007/s001970000026.

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14

Demirsoy, Idris, and Adnan Karaibrahimoglu. "Identifying drug interactions using machine learning." Advances in Clinical and Experimental Medicine 32, no. 8 (2023): 0. http://dx.doi.org/10.17219/acem/169852.

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15

Çelik, Rumeysa Hilal, Hacı Aslan Onur İşcil, Ecem Bulut, and Saliha Ece Acuner. "Learning molecular machines by machine learning." Eurasian Journal of Science Engineering and Technology 6, no. 2 (2025): 100–120. https://doi.org/10.55696/ejset.1620495.

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Proteins, often referred to as molecular machines, are essential biomolecules that perform a wide range of cellular functions, typically by forming complexes. Understanding their three-dimendional (3D) structures is key to deciphering their functions. However, a significant gap exists between the vast number of known protein sequences and the relatively limited number of experimentally determined protein structures. Unraveling the mechanisms of protein folding remains a central challenge in understanding the sequence-structure/dynamics-function relationship. In recent years, machine learning (
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16

Wang, Zhibo, Jinxin Ma, Yongquan Zhang, Qian Wang, Ju Ren, and Peng Sun. "Attention-over-Attention Field-Aware Factorization Machine." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6323–30. http://dx.doi.org/10.1609/aaai.v34i04.6101.

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Factorization Machine (FM) has been a popular approach in supervised predictive tasks, such as click-through rate prediction and recommender systems, due to its great performance and efficiency. Recently, several variants of FM have been proposed to improve its performance. However, most of the state-of-the-art prediction algorithms neglected the field information of features, and they also failed to discriminate the importance of feature interactions due to the problem of redundant features. In this paper, we present a novel algorithm called Attention-over-Attention Field-aware Factorization
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17

Stanley, Jeff, Ozgur Eris, and Monika Lohani. "A Conceptual Framework for Machine Self-Presentation and Trust." International Journal of Humanized Computing and Communication 2, no. 1 (2021): 20–45. http://dx.doi.org/10.35708/hcc1869-148366.

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Increasingly, researchers are creating machines with humanlike social behaviors to elicit desired human responses such as trust and engagement, but a systematic characterization and categorization of such behaviors and their demonstrated effects is missing. This paper proposes a taxonomy of machine behavior based on what has been experimented with and documented in the literature to date. We argue that self-presentation theory, a psychosocial model of human interaction, provides a principled framework to structure existing knowledge in this domain and guide future research and development. We
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McBride, John M., and Tsvi Tlusty. "The physical logic of protein machines." Journal of Statistical Mechanics: Theory and Experiment 2024, no. 2 (2024): 024001. http://dx.doi.org/10.1088/1742-5468/ad1be7.

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Abstract Proteins are intricate molecular machines whose complexity arises from the heterogeneity of the amino acid building blocks and their dynamic network of many-body interactions. These nanomachines gain function when put in the context of a whole organism through interaction with other inhabitants of the biological realm. And this functionality shapes their evolutionary histories through intertwined paths of selection and adaptation. Recent advances in machine learning have solved the decades-old problem of how protein sequence determines their structure. However, the ultimate question r
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Wong, Pak-Hang. "Rituals and Machines: A Confucian Response to Technology-Driven Moral Deskilling." Philosophies 4, no. 4 (2019): 59. http://dx.doi.org/10.3390/philosophies4040059.

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Robots and other smart machines are increasingly interwoven into the social fabric of our society, with the area and scope of their application continuing to expand. As we become accustomed to interacting through and with robots, we also begin to supplement or replace existing human–human interactions with human–machine interactions. This article aims to discuss the impacts of the shift from human–human interactions to human–machine interactions in one facet of our self-constitution, i.e., morality. More specifically, it sets out to explore whether and how the shift to human–machine interactio
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Mageed Elamin, Khalid Abd El, Bakri Altyeb Musa, Nada Elnasry, and Sawsan Al Mekawi. "Predicting Student Achievement: Exploring Non-Cognitive Feature Interactions Using Machine Learning Models." Journal of Educational & Psychological Research 6, no. 3 (2024): 01–14. https://doi.org/10.33140/jepr.06.03.03.

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This research investigates how non-cognitive skills can predict student achievement, as measured by GPA. Non-cognitive traits like self-control, goal attainment, interpersonal connections, and leadership skills develop in students at various stages and are influenced, whether positively or negatively, by their environment and social circle. Because non-cognitive features alone are complex and intertwined, feature engineering is needed to create new features that combine these non-cognitive traits with each other or with cognitive features, in order to better predict student success by Analyzin
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21

Spillard, Samuel, Christopher J. Turner, and Konstantinos Meichanetzidis. "Machine learning entanglement freedom." International Journal of Quantum Information 16, no. 08 (2018): 1840002. http://dx.doi.org/10.1142/s0219749918400026.

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Quantum many-body systems realize many different phases of matter characterized by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies that the physics is dictated by an interacting Hamiltonian. The interaction distance has been successfully used to quantify the effect of interactions in a variety of states of matter via the entanglement spectrum [C. J. Turner, K. Meichanetzidis, Z. Papic and J. K. Pachos, Nat. Commun. 8 (2017) 14926, Phys. Rev. B 97 (2018) 125104]. The computation of the inte
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22

LAKTIONOVA, Anna. "HUMAN-MACHINE INTERACTIONS: ALIGNING, ADAPTING, BEING AN AGENT." Filosofska dumka (Philosophical Thought), no. 4 (December 12, 2024): 130–42. https://doi.org/10.15407/fd2024.04.130.

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In the paper, the touchstone points of the project “Towards an agency-based philosophy of (advanced) technology” are outlined. The main plot of this elaboration concerns human-machine interactions and appropriate interpretation of reciprocal aligning, adapting within involved into such interactions agents; as well as the status as such of being an agent. Into the theoretical and historical background of the project such spheres as Philosophy of Science, Philosophy of Technology, Philosophy of Engineering and Design Technological Actions, STS (Science and Technology Studies), Applied Ethics etc
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23

Chen, Jia-An, and Sheng D. Chao. "Intermolecular Non-Bonded Interactions from Machine Learning Datasets." Molecules 28, no. 23 (2023): 7900. http://dx.doi.org/10.3390/molecules28237900.

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Accurate determination of intermolecular non-covalent-bonded or non-bonded interactions is the key to potentially useful molecular dynamics simulations of polymer systems. However, it is challenging to balance both the accuracy and computational cost in force field modelling. One of the main difficulties is properly representing the calculated energy data as a continuous force function. In this paper, we employ well-developed machine learning techniques to construct a general purpose intermolecular non-bonded interaction force field for organic polymers. The original ab initio dataset SOFG-31
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Xu, Jiandong, Jiong Pan, Tianrui Cui, Sheng Zhang, Yi Yang, and Tian-Ling Ren. "Recent Progress of Tactile and Force Sensors for Human–Machine Interaction." Sensors 23, no. 4 (2023): 1868. http://dx.doi.org/10.3390/s23041868.

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Human–Machine Interface (HMI) plays a key role in the interaction between people and machines, which allows people to easily and intuitively control the machine and immersively experience the virtual world of the meta-universe by virtual reality/augmented reality (VR/AR) technology. Currently, wearable skin-integrated tactile and force sensors are widely used in immersive human–machine interactions due to their ultra-thin, ultra-soft, conformal characteristics. In this paper, the recent progress of tactile and force sensors used in HMI are reviewed, including piezoresistive, capacitive, piezoe
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Gambino, Andrew, Jesse Fox, and Rabindra Ratan. "Building a Stronger CASA: Extending the Computers Are Social Actors Paradigm." Human-Machine Communication 1 (February 1, 2020): 71–86. http://dx.doi.org/10.30658/hmc.1.5.

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The computers are social actors framework (CASA), derived from the media equation, explains how people communicate with media and machines demonstrating social potential. Many studies have challenged CASA, yet it has not been revised. We argue that CASA needs to be expanded because people have changed, technologies have changed, and the way people interact with technologies has changed. We discuss the implications of these changes and propose an extension of CASA. Whereas CASA suggests humans mindlessly apply human-human social scripts to interactions with media agents, we argue that humans ma
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Amershi, Saleema, James Fogarty, Ashish Kapoor, and Desney Tan. "Effective End-User Interaction with Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1529–32. http://dx.doi.org/10.1609/aaai.v25i1.7964.

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End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate th
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B. Kalpana, P. Krishnamoorthy, S. Kanageswari, and Anitha J. Albert. "Machine learning approaches for predicting species interactions in dynamic ecosystems." Scientific Temper 15, no. 03 (2024): 2961–67. https://doi.org/10.58414/scientifictemper.2024.15.3.69.

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This paper explores the application of machine learning (ML) techniques in predicting species interactions within dynamic ecosystems. Using a multi-faceted approach, we investigate the effectiveness of various ML algorithms in analyzing species interaction strengths through an example dataset. Visualizations, including bar, line, and pie charts, depict the distribution and patterns of species interactions, providing valuable insights into ecological dynamics. Additionally, a comparative analysis examines the data requirements and characteristics of four ML approaches: Generalized Linear Models
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İpekesen, Sibel, Süreyya Betül Rufaioğlu, Murat Tunç, and Behiye Tuba Bicer. "Machine Learning in Legume Breeding: Modeling Genotype and Environment Interactions." EJONS International Journal on Mathematic, Engineering and Natural Sciences 8, no. 4 (2024): 493–503. https://doi.org/10.5281/zenodo.14253789.

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In this review, addresses the importance of machine learning techniques in modelling genotype-environment (G&times;E) interactions in legume breeding. Agricultural production is greatly affected by climate change and environmental stressors, and a better understanding of these interactions is critical for the development of environmentally adaptive and high-yielding varieties. Genotype-environmental interactions are valuable for understanding how genetic and environmental factors affect plant performance. The study reported that machine learning algorithms such as support vector machines (SVM)
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V., Dr Suma. "COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW." Journal of Trends in Computer Science and Smart Technology 2019, no. 02 (2019): 131–39. http://dx.doi.org/10.36548/jtcsst.2019.2.006.

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The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and
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Zhu, Chaoyang. "Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 01–13. http://dx.doi.org/10.17762/ijritcc.v11i7.7736.

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Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with Grey Relational Analysis (GRA) integrated with Salp Swarm Algori
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Xu, Canran, and Ming Wu. "Learning Feature Interactions with Lorentzian Factorization Machine." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6470–77. http://dx.doi.org/10.1609/aaai.v34i04.6119.

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Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions. Recent advances in this area are empowered by deep learning methods which could learn sophisticated feature interactions and achieve the state-of-the-art result in an end-to-end manner. These approaches require large number of training parameters integrated with the low-level representations, and thus are memory and computational inefficient. In this paper, we propose a new model named “LorentzFM” that can learn feature interactions embedded i
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McKinney, Brett A., David M. Reif, Marylyn D. Ritchie, and Jason H. Moore. "Machine Learning for Detecting Gene-Gene Interactions." Applied Bioinformatics 5, no. 2 (2006): 77–88. http://dx.doi.org/10.2165/00822942-200605020-00002.

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33

Landry, H., C. Laguë, and M. Roberge. "Discrete element modeling of machine–manure interactions." Computers and Electronics in Agriculture 52, no. 1-2 (2006): 90–106. http://dx.doi.org/10.1016/j.compag.2006.02.002.

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Ali, O. S., M. S. Aggour, and R. H. McCuen. "Dynamic soil–pile interactions for machine foundations." International Journal of Geotechnical Engineering 11, no. 3 (2016): 236–47. http://dx.doi.org/10.1080/19386362.2016.1213479.

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Hamp, Tobias, and Burkhard Rost. "More challenges for machine-learning protein interactions." Bioinformatics 31, no. 10 (2015): 1521–25. http://dx.doi.org/10.1093/bioinformatics/btu857.

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Robie, Alice A., Kelly M. Seagraves, S. E. Roian Egnor, and Kristin Branson. "Machine vision methods for analyzing social interactions." Journal of Experimental Biology 220, no. 1 (2017): 25–34. http://dx.doi.org/10.1242/jeb.142281.

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Starke, Sebastian, He Zhang, Taku Komura, and Jun Saito. "Neural state machine for character-scene interactions." ACM Transactions on Graphics 38, no. 6 (2019): 1–14. http://dx.doi.org/10.1145/3355089.3356505.

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Kosuge, Kazuhiro, Yoshio Fujisawa, and Toshio Fukuda. "Control of Man-Machine-Environment Mechanical Interactions." Transactions of the Japan Society of Mechanical Engineers Series C 59, no. 562 (1993): 1751–56. http://dx.doi.org/10.1299/kikaic.59.1751.

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Oborski, P. "Man-machine interactions in advanced manufacturing systems." International Journal of Advanced Manufacturing Technology 23, no. 3-4 (2004): 227–32. http://dx.doi.org/10.1007/s00170-003-1574-5.

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Hadas, A., W. E. Larson, and R. R. Allmaras. "Advances in modeling machine-soil-plant interactions." Soil and Tillage Research 11, no. 3-4 (1988): 349–72. http://dx.doi.org/10.1016/0167-1987(88)90006-2.

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Addessi, Anna Rita, and François Pachet. "Experiments with a musical machine: musical style replication in 3 to 5 year old children." British Journal of Music Education 22, no. 1 (2005): 21–46. http://dx.doi.org/10.1017/s0265051704005972.

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The relationship between new technology and learning is gaining increasing relevance in the field of music education (Webster, 2002; Folkestad et al., 1998). However, only a few studies have considered the nature of the interaction between children and musical machines. This article describes an observation study of children aged 3–5 years confronting a particular interactive musical system, the Continuator, which is able to produce music in the same style as a human playing the keyboard (Pachet, 2003). The analysis of two case studies suggests that the Continuator is able to develop interesti
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Degani, Asaf, Claudia V. Goldman, Omer Deutsch, and Omer Tsimhoni. "On Sensitivity and Holding in Automotive Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 1906–10. http://dx.doi.org/10.1177/1541931213601435.

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We propose an approach to human-machine interactions that emphasizes sensitivity to the user’s needs and consecrates caretaking, or holding, of the user so as to fulfill these needs. We borrow the concepts of sensitivity and holding from psychoanalysis and then operationalize them in the context of human-machine interaction. A pilot study of drivers’ interactions with a climate control system was conducted to understand drivers’ needs, wants, and manner of interaction. Based on these results we built an AI-based system that is sensitive to the users’ needs and attempts to fulfill them in a ded
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Idhaya, T., A. Suruliandi, and S. P. Raja. "Drug-Protein Interactions Prediction Models Using Feature Selection and Classification Techniques." Current Drug Metabolism 24, no. 12 (2023): 817–34. http://dx.doi.org/10.2174/0113892002268739231211063718.

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Background:: Drug-Protein Interaction (DPI) identification is crucial in drug discovery. The high dimensionality of drug and protein features poses challenges for accurate interaction prediction, necessitating the use of computational techniques. Docking-based methods rely on 3D structures, while ligand-based methods have limitations such as reliance on known ligands and neglecting protein structure. Therefore, the preferred approach is the chemogenomics-based approach using machine learning, which considers both drug and protein characteristics for DPI prediction. Methods:: In machine learnin
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Carrubbo, Luca, Francesco Polese, Monica Drăgoicea, Leonard Walletzký, and Antonietta Megaro. "Value co-creation ‘gradients’: enabling human-machine interactions through AI-based DSS." ITM Web of Conferences 41 (2022): 01002. http://dx.doi.org/10.1051/itmconf/20224101002.

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Artificial Intelligence-based Decision Support Systems (AI-based DSS) are becoming increasingly important in many contexts. This work aims to define a type of human-machine interactions for new value co-creation processes' ranks, to help identify factors that can stimulate value co-creation in human-machine interactions. To understand if the outcome of a man-machine interaction can contribute to the co-creation of value, and in what way, the work carried out is epistemological and typological, also based on System Thinking. A matrix of novel gradients of the relationships between humans and no
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Cirilo-Piñon, Oscar I., Agustín Barrera-Sánchez, Cesar H. Guzmán-Valdivia, et al. "Impedance Controller Analysis for a Two-Degrees-Of-Freedom Ankle Rehabilitation Machine with Serious Game Interactions." Computation 13, no. 1 (2024): 7. https://doi.org/10.3390/computation13010007.

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An ankle sprain can be caused by daily activities such as running, walking, or playing sports. In many cases, the patient’s ankle suffers severe or permanent damage that requires rehabilitation to return to its initial state. Thanks to technological advances, robotics has allowed for the development of machines that generate precise, efficient, and safe movements. In addition, these machines are manipulated by a specific control depending on the rehabilitation objective. Impedance control is used in ankle rehabilitation machines for active–resistive-type rehabilitation, where the patient parti
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Steil, Jochen, Dominique Finas, Susanne Beck, Arne Manzeschke, and Reinhold Haux. "Robotic Systems in Operating Theaters: New Forms of Team–Machine Interaction in Health Care." Methods of Information in Medicine 58, S 01 (2019): e14-e25. http://dx.doi.org/10.1055/s-0039-1692465.

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Background Health information systems have developed rapidly and considerably during the last decades, taking advantage of many new technologies. Robots used in operating theaters represent an exceptional example of this trend. Yet, the more these systems are designed to act autonomously and intelligently, the more complex and ethical questions arise about serious implications of how future hybrid clinical team–machine interactions ought to be envisioned, in situations where actions and their decision-making are continuously shared between humans and machines. Objectives To discuss the many di
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Ikhsan, Gunadi, Budi Wiyono Bambang, Arifin Imron, and Tri Djatmika Rudijanto W.W. Ery. "Effectiveness Analysis of Teamwork Management Based on Machine Intelligence." INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS 06, no. 09 (2023): 4239–49. https://doi.org/10.5281/zenodo.8348127.

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Teamwork in an organization often experiences ups and downs in interpersonal relationships due to ineffective communication-interaction problems. Whereas in addition to leadership issues, organizational effectiveness is also largely determined by the interactions within the organization. Poor interaction-communication is the result of interpersonal ignorance of one another. This study tries to analyze the effectiveness of cooperation based on the machine intelligence owned by each personal organization, where methods like this are relatively not widely known. This study uses an approach that i
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Wachtler, Josef, and Martin Ebner. "Scheduling Interactions in Learning Videos: A State Machine Based Algorithm." International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI) 1, no. 1 (2019): 58. http://dx.doi.org/10.3991/ijai.v1i1.10995.

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Based on the currently developing trend of so called Massive Open Online Courses it is obvious that learning videos are more in use nowadays. This is some kind of comeback because due to the maxim “TV is easy, book is hard” [1][2] videos were used rarely for teaching. A further reason for this rare usage is that it is widely known that a key factor for human learning is a mechanism called selective attention [3][4]. This suggests that managing this attention is from high importance. Such a management could be achieved by providing different forms of interaction and communication in all directi
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P SONAJI, L SUBRAMANIAN, and M RAJESH. "Artificial intelligence-driven drug interaction prediction." World Journal of Biology Pharmacy and Health Sciences 17, no. 2 (2024): 297–305. http://dx.doi.org/10.30574/wjbphs.2024.17.2.0070.

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Artificial intelligence (AI) is developing at a rapid pace and this has led to revolutionary changes in many fields, including healthcare. Drug interaction prediction, which evaluates possible interactions between various medications to guarantee patient safety and maximize therapeutic outcomes is a crucial component of healthcare. This work investigates the use of artificial intelligence (AI) methods for predicting drug interactions, with a particular emphasis on the combination of natural language processing, knowledge graphs, and machine learning algorithms. The manual curation and experime
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P, SONAJI, SUBRAMANIAN L, and RAJESH M. "Artificial intelligence-driven drug interaction prediction." World Journal of Biology Pharmacy and Health Sciences 17, no. 2 (2024): 297–305. https://doi.org/10.5281/zenodo.11296167.

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Artificial intelligence (AI) is developing at a rapid pace and this has led to revolutionary changes in many fields, including healthcare. Drug interaction prediction, which evaluates possible interactions between various medications to guarantee patient safety and maximize therapeutic outcomes is a crucial component of healthcare. This work investigates the use of artificial intelligence (AI) methods for predicting drug interactions, with a particular emphasis on the combination of natural language processing, knowledge graphs, and machine learning algorithms. The manual curation and experime
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