A capable multimedia content discovery platform based on visual content analysis and intelligent data enrichment

Baran, Remigiusz; Dziech, Andrzej; Zeja, Andrzej

doi:10.1007/s11042-017-5014-1

Cited by 10 publications

(3 citation statements)

References 15 publications

(15 reference statements)

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“…Be it literature, scientific reports, product sources, videos, images or web pages, the number of digitally accessible items in all these areas is now so large that it can no longer be surveyed by a single person without a recommendation system. There are various functions for recommendation systems (from the points of view of the user and the operator) (Ricci et al, 2011;Baran, 2017): "find some good items; find all good items; annotation in context; recommend a sequence; recommend a bundle; platform-independent; scalable; compliance with copyright rules; increase the number of items sold; sell more diverse items; increase user satisfaction; increase user fidelity; better understand what the user wants." The methods and algorithms of the recommendation systems that dominate the Internet are highly optimized and strongly adapted to the respective tasks and object types.…”

Section: Recommendation Systemsmentioning

confidence: 99%

An interface to view collections of visual art

Pflüger

Viehhauser²,

Tumanov

2023

Int J Digit Humanities

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Art experts prefer being able to look at the individual images they are working on in the course of their research. However, if one were to look at digitally accessible images in the field of visual art, one would be dealing with billions of images; no one can handle visually examining such huge numbers of images one at a time. Therefore, art experts need special tools to examine and describe artworks in the context of other artworks. We used our experience from previous projects and interviews with members of the target group (art historians, curators, art dealers, and artists) to identify the central issues these experts encounter when working with large image collections and to determine the functionality and properties a system must offer to support their work. The results led to the customized interface LadeCA.View, which is now used in several projects. LadeCA.View enables experts to describe an exhibition or a collection of visual art in such a way that a user can obtain an overview of the intention, content, and structures of the exhibition or collection within a short period of time without looking at each image individually. LadeCA.View can also be used as an interface to probe more deeply into a collection or exhibition. In this paper we show the functions and visualizations of the interface and explain the design decisions. Furthermore, we outline LadeCA.View’s scope of applicability using three case studies

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Section: Recommendation Systemsmentioning

confidence: 99%

An interface to view collections of visual art

Pflüger

Viehhauser²,

Tumanov

2023

Int J Digit Humanities

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“…We meet it in e-commerce, administration, education, lifestyle, industry, agriculture, gaming, social media, finance, astronomy, traffic management, the military, etc. [10][11][12][13][14][15][16]…”

Section: Introductionmentioning

confidence: 99%

Ethical Dilemmas of Using Artificial Intelligence in Medicine

Astărăstoae,

Rogozea,

Leaşu

et al. 2024

American Journal of Therapeutics

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Background: Artificial intelligence (AI) is considered the fourth industrial revolution that will change the evolution of humanity technically and relationally. Although the term has been around since 1956, it has only recently become apparent that AI can revolutionize technologies and has many applications in the medical field. Areas of Uncertainty: The ethical dilemmas posed by the use of AI in medicine revolve around issues related to informed consent, respect for confidentiality, protection of personal data, and last but not least the accuracy of the information it uses. Data Sources: A literature search was conducted through PubMed, MEDLINE, Plus, Scopus, and Web of Science (2015–2022) using combinations of keywords, including: AI, future in medicine, and machine learning plus ethical dilemma. Ethics and Therapeutic Advances: The ethical analysis of the issues raised by AI used in medicine must mainly address nonmaleficence and beneficence, both in correlation with patient safety risks, ability versus inability to detect correct information from inadequate or even incorrect information. The development of AI tools that can support medical practice can increase people's access to medical information, to obtain a second opinion, for example, but it is also a source of concern among health care professionals and especially bioethicists about how confidentiality is maintained and how to maintain cybersecurity. Another major risk may be related to the dehumanization of the medical act, given that, at least for now, empathy and compassion are accessible only to human beings. Conclusions: AI has not yet managed to overcome certain limits, lacking moral subjectivity, empathy, the level of critical thinking is still insufficient, but no matter who will practice preventive or curative medicine in the next period, they will not be able to ignore AI, which under human control can be an important tool in medical practice.

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“…People navigate over 1 billion kilometers a day using mobile routing services, and many of these services provide travelers with information on transit networks. In most of such applications, given an origin-destination pair and a desired departure or arrival time, the route associated with the minimum expected trip time is provided to the user (Pankaj et al, 2018;Baran, Dziech, & Zeja, 2018;Townsend, 2017). However, the uncertainty associated with these recommendations, either due to variability in travel time or the transit service headway, is rarely accounted for.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering Recommender System for Timely Arrival Problem in Road Transport Networks Using Viterbi and the Hidden Markov Algorithms

Ofem

Agana

Felix

2022

International Journal of Software Innovation

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In this study, a timely arrival recommender system (TARS) using Viterbi and hidden Markov Model (HMM) was developed. Ratings from current road users were used as inputs and trained to provide recommendations to prospective road users on the best routes to follow to get to their destinations from any source in time. The system was deployed on Android devices and iPhones with Google map. Real time data on current road conditions were collected from twenty-one (21) bolt drivers in Calabar Metropolis traversing various routes from Unical to Watt Market. The system could calculate arrival time in km/h, generate nearest nodes on each route, detect routes with free or congested traffic flow, and then recommend the best route in real time to users for timely arrival. The application, if adopted, can aid road users to save time, cost, and reduce stress on both humans and the vehicles used.

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A capable multimedia content discovery platform based on visual content analysis and intelligent data enrichment

Cited by 10 publications

References 15 publications

An interface to view collections of visual art

An interface to view collections of visual art

Ethical Dilemmas of Using Artificial Intelligence in Medicine

Collaborative Filtering Recommender System for Timely Arrival Problem in Road Transport Networks Using Viterbi and the Hidden Markov Algorithms

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