585 research outputs found
Tramp Ship Scheduling Problem with Berth Allocation Considerations and Time-dependent Constraints
This work presents a model for the Tramp Ship Scheduling problem including
berth allocation considerations, motivated by a real case of a shipping
company. The aim is to determine the travel schedule for each vessel
considering multiple docking and multiple time windows at the berths. This work
is innovative due to the consideration of both spatial and temporal attributes
during the scheduling process. The resulting model is formulated as a
mixed-integer linear programming problem, and a heuristic method to deal with
multiple vessel schedules is also presented. Numerical experimentation is
performed to highlight the benefits of the proposed approach and the
applicability of the heuristic. Conclusions and recommendations for further
research are provided.Comment: 16 pages, 3 figures, 5 tables, proceedings paper of Mexican
International Conference on Artificial Intelligence (MICAI) 201
FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
This contribution presents a deep learning method for the segmentation of
prostate zones in MRI images based on U-Net using additive and feature pyramid
attention modules, which can improve the workflow of prostate cancer detection
and diagnosis. The proposed model is compared to seven different U-Net-based
architectures. The automatic segmentation performance of each model of the
central zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor were
evaluated using Dice Score (DSC), and the Intersection over Union (IoU)
metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of
76.9% in the test set, outperforming most of the studied models in this work
except from R2U-Net and attention R2U-Net architectures.Comment: This paper has been accepted at the 22nd Mexican International
Conference on Artificial Intelligence (MICAI 2023
A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems
Domain experts should provide relevant domain knowledge to an Intelligent
Tutoring System (ITS) so that it can guide a learner during problemsolving
learning activities. However, for many ill-defined domains, the domain
knowledge is hard to define explicitly. In previous works, we showed how
sequential pattern mining can be used to extract a partial problem space from
logged user interactions, and how it can support tutoring services during
problem-solving exercises. This article describes an extension of this approach
to extract a problem space that is richer and more adapted for supporting
tutoring services. We combined sequential pattern mining with (1) dimensional
pattern mining (2) time intervals, (3) the automatic clustering of valued
actions and (4) closed sequences mining. Some tutoring services have been
implemented and an experiment has been conducted in a tutoring system.Comment: Proceedings of the 7th Mexican International Conference on Artificial
Intelligence (MICAI 2008), Springer, pp. 765-77
WiSeBE: Window-based Sentence Boundary Evaluation
Sentence Boundary Detection (SBD) has been a major research topic since
Automatic Speech Recognition transcripts have been used for further Natural
Language Processing tasks like Part of Speech Tagging, Question Answering or
Automatic Summarization. But what about evaluation? Do standard evaluation
metrics like precision, recall, F-score or classification error; and more
important, evaluating an automatic system against a unique reference is enough
to conclude how well a SBD system is performing given the final application of
the transcript? In this paper we propose Window-based Sentence Boundary
Evaluation (WiSeBE), a semi-supervised metric for evaluating Sentence Boundary
Detection systems based on multi-reference (dis)agreement. We evaluate and
compare the performance of different SBD systems over a set of Youtube
transcripts using WiSeBE and standard metrics. This double evaluation gives an
understanding of how WiSeBE is a more reliable metric for the SBD task.Comment: In proceedings of the 17th Mexican International Conference on
Artificial Intelligence (MICAI), 201
A Dedicated Genetic Algorithm for Two-Dimensional Non-Guillotine Strip Packing
This paper introduces DGA, a new dedicated genetic algorithm for a two-dimensional (2D) non-guillotine strip packing problem (2D-SPP). DGA integrates two key features: a hierarchical fitness function and a problem-specific crossover operator (WAX for "wasted area based crossover"). The fitness function takes into account not only the final height of the strip (to be minimized), but also the wasted areas. The goal of the meaningful (and "visual”) WAX crossover operator is to preserve the good property of parent packing configurations. To assess the proposed DGA, experimental results are shown on a set of well-known zero-waste benchmark instances and compared with previously reported genetic algorithms as well as the best performing meta-heuristic algorithms
Design of optimal search engine using text summarization through artificial intelligence techniques
Natural language processing is the trending topic in the latest research areas, which allows the developers to create the human-computer interactions to come into existence. The natural language processing is an integration of artificial intelligence, computer science and computer linguistics. The research towards natural Language Processing is focused on creating innovations towards creating the devices or machines which operates basing on the single command of a human. It allows various Bot creations to innovate the instructions from the mobile devices to control the physical devices by allowing the speech-tagging. In our paper, we design a search engine which not only displays the data according to user query but also performs the detailed display of the content or topic user is interested for using the summarization concept. We find the designed search engine is having optimal response time for the user queries by analyzing with number of transactions as inputs. Also, the result findings in the performance analysis show that the text summarization method has been an efficient way for improving the response time in the search engine optimizations
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