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Segmentation and Classification of Gastric Cancer from Endoscopic Image Dataset with the Aid of Artificial Intelligence

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Segmentation and Classification of Gastric Cancer from Endos...

International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Cuddapah Anitha Vemu Srinivas Rao Ramyadevi. R Maya. R Subhasish Debroy Mahaveerakannan R School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh Department of Computer Science & Engineering Saveetha Engineering College Department of Electrical & Electronics Engineering Chennai Institute of Technology & Applied Research Dr. B.C. Roy Engineering College Durgapur West Bengal Department of Computer Science and Engineering Saveetha School of Engineering SIMETs Chennai Tamil Nadu India

In order to detect early stomach malignancies, upper GI endoscopy is commonly used. An object detection model, a form of deep learning, was projected as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. Yet there were difficulties in reducing false positives in the results that were detected. Thus, this research proposes an automatic classification method for stomach cancer classification using a (CNN) that has been pre-trained. To classify cancer in endoscopic pictures automatically, our method is superior to those that rely on traditional, manual features. Thirteen convolutional layers with small 33 size kernels and three fully linked layers make up the suggested model. We use the learning approach, which involves pre-training the weight of fine-tuning the weight of layers with the Slime Mould Algorithm, to deal with the data scarcity (SMA). Experiments employing 1208 photos from healthy subjects and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross validation approach. These findings suggest the projected strategy will be effective for automating the diagnosis of early stomach cancer in endoscopic pictures.

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Holes in Convex and Simple Drawings

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arXiv 2024年

作者： Bergold, Helena Orthaber, Joachim Scheucher, Manfred Schröder, Felix Institute of Computer Science Freie Universität Berlin Germany TUM School of Computation Information and Technology Technische Universität München Germany Institute of Software Technology Graz University of Technology Austria Institut für Mathematik Technische Universität Berlin Germany Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic Institute of Mathematics Technische Universität Berlin Germany

Gons and holes in point sets have been extensively studied in the literature. For simple drawings of the complete graph a generalization of the Erdős–Szekeres theorem is known and empty triangles have been investigated. We introduce a notion of k-holes for simple drawings and study their existence with respect to the convexity hierarchy. We present a family of simple drawings without 4-holes and prove a generalization of Gerken’s empty hexagon theorem for convex drawings. A crucial intermediate step will be the structural investigation of pseudolinear subdrawings in convex drawings. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

KNAC: an approach for enhancing cluster analysis with background knowledge and explanations

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arXiv 2021年

作者： Bobek, Szymon Kuk, Michal Brzegowski, Jakub Brzychczy, Edyta Nalepa, Grzegorz J. Institute of Applied Computer Science Jagiellonian University Kraków31-007 Poland AGH University of Science and Technology Kraków Poland

Pattern discovery in multidimensional data sets has been the subject of research for decades. There exists a wide spectrum of clustering algorithms that can be used for this purpose. However, their practical applications share a common post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be the bottleneck in the process, especially in cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters but also conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (KNAC). Its main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution is not restricted to any existing clustering algorithm. Instead, KNAC can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and a model-agnostic improvement of any state-of-the-art clustering method. We demonstrate the feasibility of our method on artificially, reproducible examples and in a real life use case scenario. In both cases, we achieved better results than classic clustering algorithms without augmentation. Copyright © 2021, The Authors. All rights reserved.

关键词： Clustering algorithms

Towards the Heider balance - A cellular automaton with a global neighborhood

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arXiv 2021年

作者： Krawczyk, Malgorzata J. Kulakowski, Krzysztof Burda, Zdzislaw AGH University of Science and Technology Faculty of Physics and Applied Computer Science al. Mickiewicza 30 Kraków30-059 Poland

We study a simple deterministic map that leads a fully connected network to Heider balance. The map is realized by an algorithm that updates all links synchronously in a way depending on the state of the entire network. We observe that the probability of reaching a balanced state increases with the system size N. Jammed states become less frequent for larger N. The algorithm generates also limit cycles, mostly of length 2, but also of length 3, 4, 6, 12 or 14. We give a simple argument to estimate the mean size of basins of attraction of balanced states and discuss symmetries of the system including the automorphism group as well as gauge invariance of triad configurations. We argue that both the symmetries play an essential role in the occurrence of cycles observed in the synchronous dynamics realized by the algorithm. Copyright © 2021, The Authors. All rights reserved.

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Generating datasets for software requirements prioritization research

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Generating datasets for software requirements prioritization...

2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020

作者： Nazim, Mohd. Mohammad, Chaudhary Wali Sadiq, Mohd. Faculty of Engineering and Technology Department of Applied Sciences and Humanities New Delhi110025 India Indian Institute of Science Department of Computer Science and Automation Bangalore560012 India

ISBN: (纸本)9781728150703

Software requirements are the need of the stakeholders which are identified during requirements elicitation process. There are different types of the software requirements like functional requirements, non-functional requirements, testing requirements, etc. After requirements elicitation process, a system may have hundreds or thousands of requirements;and during the development process it is not possible to implement all the software requirements because of the several limitations of an organization as well as the stakeholders. To develop the successful software product, the elicited software requirements must be prioritized before the development of software so that high ranked software requirements can be identified. Computation of the ranking values of the software requirements is a difficult task because it involves lot of computational steps like evaluation of software requirements, computation of group preferences from the individual preferences, computation of the ranking values after normalizing the preference matrices, etc. Among these steps, in this paper, we mainly focus on the evaluation of the software requirements using linguistic variables because in real life applications people use linguistic variables to specify their preferences on software requirements. The objective of this paper is to generate a dataset of 32 functional requirements after evaluating it on the basis of 3 non-functional requirements so that the generated dataset can be used as an input into the software requirements prioritization methods. The institute examination system is used as case study to explain the steps for the generation of the dataset. © 2020 IEEE.

关键词： Requirements engineering

Simulation of CO2 Storage using a Parameterization Method for Essential Trapping Physics: FluidFlower Benchmark Study

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arXiv 2023年

作者： Wang, Yuhang Zhang, Ziliang Vuik, Cornelis Hajibeygi, Hadi School of Environmental Studies China University of Geosciences Wuhan430078 China Faculty of Electrical Engineering Mathematics and Computer Science Department of Applied Mathematics Delft University of Technology Delft2628XE Netherlands Faculty of Civil Engineering and Geosciences Department of Geoscience and Engineering Delft University of Technology Delft2628CV Netherlands

An efficient compositional framework is developed for simulation of CO2 storage in saline aquifers during a full-cycle injection, migration and post-migration processes. Essential trapping mechanisms, including structural, dissolution, and residual trapping, which operate at different time scales are accurately captured in the presented unified framework. In particular, a parameterization method is proposed to efficiently describe the relevant physical processes. The proposed framework is validated by comparing the dynamics of gravity-induced convective transport with that reported in the literature. Results show good agreement for both the characteristics of descending fingers and the associated dissolution rate. The developed simulator is then applied to study the FluidFlower benchmark model. An experimental setup with heterogeneous geological layers is discretized into a two-dimensional computational domain where numerical simulation is performed. Impacts of hysteresis and the diffusion of CO2 in liquid phase on the migration and trapping of CO2 plume are investigated. Inclusion of the hysteresis effect does not affect plume migration in this benchmark model, whereas diffusion plays an important role in promoting convective mixing. This work casts a promising approach to predict the migration of the CO2 plume, and to assess the amount of trapping from different mechanisms for long-term CO2 storage. © 2023, CC BY-NC-ND.

关键词： Porous materials

A Feature Subset Selection Method Based on the Combination of PCA and Improved GA 2

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A Feature Subset Selection Method Based on the Combination o...

2nd International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2020

作者： Li, Yinghao Shi, Kai Qiao, Fuqiang Luo, Hongyu Tianjin University of Technology School of Computer Science and Engineering China Tianjin Sino-German University of Applied Sciences College of Software and Communication China

ISBN: (纸本)9781728196381

Nowadays, intrusion detection data has redundant features, which leads to unsatisfactory detection results. This paper proposes a feature selection method based on the combination of principal component analysis (PCA) and improved Genetic algorithm(GA). First, PCA is used to reduce the dimensionality of the intrusion data, and the cosine similarity is used to calculate angles between the principal component and the original feature;the feature with high cosine similarity to the principal component is selected and the genetic algorithm is input, and the improved inter-group crossover strategy is adopted to reduce the probability of entering the local optimum. The fitness function of the genetic algorithm is the detection rate, and the optimal feature subset is selected as the intrusion detection data feature. Experimental results show that this method can improve the efficiency of intrusion detection and improve the classification ability of the classifier. © 2020 IEEE.

关键词： Principal component analysis

Percolation thresholds on triangular lattice for neighbourhoods containing sites up-to the fifth coordination zone

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arXiv 2021年

作者： Malarz, Krzysztof Agh University of Science and Technology Faculty of Physics and Applied Computer Science al. Mickiewicza 30 Kraków30-059 Poland

We determine thresholds pc for random-site percolation on a triangular lattice for all available neighborhoods containing sites from the first to the fifth coordination zones, including their complex combinations. There are 31 distinct neighbourhoods. The dependence of the value of the percolation thresholds pc on the coordination number z are tested against various theoretical predictions. The newly proposed single scalar index ξ = Σizir2i=i (depending on the coordination zone number i, the neighbourhood coordination number z and the square-distance r2to sites in i-th coordination zone from the central site) allows to differentiate among various neighbourhoods and relate pcto ξ. The thresholds roughly follow a power law pc∝ ξ-γwith γ ≈ 0.710(19). © 2021, CC BY.

关键词： Percolation (fluids)

Building an Italian-Chinese Parallel Corpus for Machine Translation from the Web 20

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Building an Italian-Chinese Parallel Corpus for Machine Tran...

6th EAI International Conference on Smart Objects and Technologies for Social Good, GOODTECHS 2020

作者： Tse, Rita Mirri, Silvia Tang, Su-Kit Pau, Giovanni Salomoni, Paola School of Applied Sciences - Macao Polytechnic Institute Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education - Macao China Department of Computer Science and Engineering University of Bologna Bologna Italy Computer Science Department - Ucla Los AngelesCA United States

ISBN: (纸本)9781450375597

In an increasingly globalized world, being able to understand texts in different languages (even more so in different alphabets and charsets) has become a necessity. This can be strategic even while moving and travelling across different countries, characterized by different languages. With this in mind, bilingual corpora become critical resources since they are the basis of every state-of-the-art automatic translation system;moreover, building a parallel corpus is usually a complex and very expensive operation. This paper describes an innovative approach we have defined and adopted to automatically build an Italian-Chinese parallel corpus, with the aim of using it for training an Italian-Chinese Neural Machine Translation. Our main idea is to scrape parallel texts from the Web: we defined a general pipeline, describing each specific step from the selection of the appropriate data sources to the sentence alignment method. A final evaluation was conducted to evaluate the goodness of our approach and its results show that 90% of the sentences were correctly aligned. The corpus we have obtained consists of more than 6,000 sentence pairs (Italian and Chinese), which are the basis for building a Machine Translation system. © 2020 ACM.

关键词： Neural machine translation