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Informatica (Slovenia) 2023年第8期47卷 77-88页

作者： Jasim, Hend Muslim Ghrabat, Mudhafar Jalil Jassim Abdulrahman, Luqman Qader Nyangaresi, Vincent Omollo Ma, Junchao Abduljabbar, Zaid Ameen Abduljaleel, Iman Qays Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Iraqi Commission for Computers and Informatics The Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Science Department Al-Turath University College Baghdad10013 Iraq College of Health Science Hawler Medical University Erbil Iraq Jaramogi Oginga Odinga University of Science and Technology Bondo40601 Kenya College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Technical Computer Engineering Department AL-Kunooze University College Basrah6100 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah61004 Iraq

One of the segmentation techniques with the greatest degree of success used in numerous recent applications is multi-level thresholding. The selection of appropriate threshold values presents difficulties for traditional methods, however, and, as a result, techniques have been developed to address these difficulties multidimensionally. Such approaches have been shown to be an efficient way of identifying the areas affected in multi-cancer cases in order to define the treatment area. Multi-cancer methods that facilitate a certain degree of competence are thus required. This study tested storing MRI brain scans in a multidimensional image database, which is a significant departure from past studies, as a way to improve the efficacy, efficiency, and sensitivity of cancer detection. The evaluation findings offered success rates for cancer diagnoses of 99.08%, 99.87%, 94%;97.08%, 98.3%, and 93.38% sensitivity;the success rates of in particular were 99.99%;98.23%, 99.53%, and 99.98%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Image segmentation

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Procedural Modeling Method for Automatic Generation of Multi-Layout Virtual Content with Existing Elements

Procedural Modeling Method for Automatic Generation of Multi...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Emir Cogo Ehlimana Cogo Damir Pozderac Selma Rizvić Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

Procedural modeling is used to generate virtual content in organized layouts of exterior and interior elements. There is a large number of existing layout generation methods, and newer approaches propose the generation of multiple layout types within the same generation session. This introduces additional constraints when manually created layout elements need to be combined with the automatically generated content. Existing approaches are either designed to work with existing elements for a single layout type, or require a high amount of manual work for adding existing elements within multiple layouts. This paper presents a method that enables the application of existing subdivision methods on multiple layout types by inserting existing content into the generation result. This method can generate test cases by creating variations of partially generated layouts for procedural modeling methods that can work with existing content.

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Topic Relation Graph Construction to Identify Challenges in Introducing Programming in Indonesian K-12 Education 1

Topic Relation Graph Construction to Identify Challenges in ...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Ayub, Mewati Karnalim, Oscar Toba, Hapnes Wijanto, Maresha Caroline Dulimarta, Hans Saputra, Williyandie Kotambunan, Tabitha Emanuella Master Program of Computer Science Maranatha Christian University Bandung Indonesia Ewha Womans University Dept. of Computer Science and Engineering Seoul Korea Republic of School of Computing Grand Valley State University MI United States Informatics Engineering Maranatha Christian University Bandung Indonesia

ISBN: (纸本)9798350306156

Programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential challenges in introducing programming to K-12 students. We developed a questionnaire survey covering four identified dimensions of challenges: administrative, facilities, teachers, and students. We also asked about common programming assessments and their preferred software features for teaching programming. Forty K-12 teachers were invited to complete the survey. The responses were analyzed with thematic analysis using a bigram-based Latent Dirichlet Allocation topic modeling and descriptive statistics. Our study shows that the challenges include limited learning modules, an insufficient number of computers, limited programming skills, and limited computational thinking skills. Scratch was the most common programming language used and many programming assessments were about debugging a program or writing a small program. Visualization and animation can be helpful in teaching programming. © 2023 IEEE.

关键词： Students

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Sarcasm detection in product reviews using textual entailment approach 23

Sarcasm detection in product reviews using textual entailmen...

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Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval

作者： Shubham Sinha Tarun Vijeta Pratik Kishor Kubde Ayush Pradeep Gajbhiye Mansi Anup Radke Christopher Jones Department of Computer Science and Engineering Visvesvaraya National Institute of Technology India School of Computer Science and Informatics Cardiff University UK

ISBN: (纸本)9798400709227

Sarcasm is a form of sentiment characterized by the use of words that express the opposite of what is meant. Sarcasm detection has applications in multiple domains ranging from sentiment analysis in product reviews to user feedback, and online forums. Sarcasm detection is important to understand user opinions and intentions in areas such as sentiment-based classification and opinion mining. This can result in better product development and customer service. Sarcasm detection can be a challenging task because sarcastic sentences may use positive expressions to convey negative meanings or may use negative sentences to convey positive meanings. Also, sarcastic sentences form a very small component of the entire communication. The increasing use of sarcasm in various social media such as Twitter, Reddit, Amazon product reviews, etc. has highlighted the importance of detecting and understanding sarcasm in various contexts. Sarcasm detection is a challenging problem for NLP systems that often rely on statistical models for performing sentiment analysis. In this research, the focus is on the use of a textual entailment approach for detecting sarcasm. Textual entailment is a natural language inference task that involves determining whether one text (hypothesis) can be derived from another text (premise). The underlying assumption behind this approach is that - if there is a contradiction between the premise and hypothesis, we can say that the hypothesis is sarcastic. To test our approach, an annotated corpus of 3000 product reviews was developed methodically from the Amazon Reviews dataset and tested using the textual entailment approach. The proposed approach achieved an F1 score of 0.76 on this dataset. The result is better than the baseline considered which is the BERT binary classifier which gives an F1 score of 0.48 on the same dataset.

关键词： Hypothesis Natural Language Inference Premise Sarcasm Detection Textual Entailment

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Exploring the influence of waste glass granular replacement on compressive strength in concrete mixtures: a normalization and modeling study

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Journal of Building Pathology and Rehabilitation 2024年第1期9卷 1-15页

作者： Ahmad, Soran Abdrahman Ahmed, Hemn Unis Rafiq, Serwan Khwrshid Gul-Mohammed, Jaza Faiq Ahmed, Dler Ali Rostam, Khalan Jalil Fqi, Kawa Omar Civil Engineering Department Collage of Engineering University of Sulaimani Kurdistan Region Sulaimani Iraq Civil Engineering Department University of Halabja Kurdistan Region Halabja Iraq Computer Engineering Deptartment Collage of Engineering University of Sulaimani Kurdistan Region Sulaimani Iraq Civil Engineering Department University of Garmian Kurdistan Region Kalar Iraq Statistics and Informatics Department College of Administration and Economies University of Sulaimani Kurdistan Region Sulaimani Iraq Qaiwan Company Kurdistan Region Sulaimani Iraq

In many third-world countries, effective waste material management has become a crucial concern due to the escalating quantity of waste materials. Among these materials, waste glass holds significance due to its widespread usage in various daily human functions. Numerous researchers have explored the feasibility of using waste glass as a substitute for cement or sand in concrete. This paper focuses on employing waste glass granules as a replacement for coarse aggregate in concrete, as coarse aggregate constitutes a significant portion of the concrete mix. A comprehensive review of prior studies is conducted to elucidate the impact of utilizing waste glass granules as a substitute for coarse aggregate on both the fresh and mechanical properties of concrete. To achieve this objective, experimental data from previous research are gathered, and statistical models are developed using Gaussian progress regression (GPR), Support vector machine (SVM), Ensemble boosting tree (EBT), artificial neural network (ANN), along with multi-linear regression (MLR). These models are employed to predict the compressive strength of concrete. Various statistical parameters are utilized to evaluate and compare the efficiency of these models, with the aim of identifying the most effective one. Among the models considered, the artificial neural network is deemed the most efficient, as it demonstrates a higher R² and lower values of RMSE, MAE, and SI. Specifically, the SI value of the ANN model is higher by 265%, 118%, 333%, and 113% compared to MLR, GPR, SVM, and EBT models, respectively. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Coarse aggregate Concrete Modeling Normalization Waste glass granular

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Guaranteed Coverage Prediction Intervals with Gaussian Process Regression

arXiv

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arXiv 2023年

作者： Papadopoulos, Harris The Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia1036 Cyprus

Gaussian Process Regression (GPR) is a popular regression method, which unlike most Machine Learning techniques, provides estimates of uncertainty for its predictions. These uncertainty estimates however, are based on the assumption that the model is well-specified, an assumption that is violated in most practical applications, since the required knowledge is rarely available. As a result, the produced uncertainty estimates can become very misleading;for example the prediction intervals (PIs) produced for the 95% confidence level may cover much less than 95% of the true labels. To address this issue, this paper introduces an extension of GPR based on a Machine Learning framework called, Conformal Prediction (CP). This extension guarantees the production of PIs with the required coverage even when the model is completely misspecified. The proposed approach combines the advantages of GPR with the valid coverage guarantee of CP, while the performed experimental results demonstrate its superiority over existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Prediction models

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Application of ensemble models approach in anemia detection using images of the palpable palm

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Medicine in Novel Technology and Devices 2023年第4期20卷 76-86页

作者： Peter Appiahene Samuel Segun Dzifa Dogbe Emmanuel Edem Yaw Kobina Philip Sackey Dartey Stephen Afrifa Emmanuel Timmy Donkoh Justice Williams Asare Department of Computer Science and Informatics University of Energy and Natural ResourcesGhana Department of Information and Communication Engineering Tianjin UniversityChina c Department of Basic and Applied Biology University of Energy and Natural ResourcesGhana

Anemia is a public health issue with serious ramifications for human health *** particularly affects pregnant women and children from 6 to 59 months old even though every individual is at *** occurs when the Hb level is below its normal threshold or when the red blood cells are weakened or *** discover medical remedies on time,early detection or diagnosis of anemia assist patients to understand their *** invasive approach for anemia detection is costive and time-consuming as compared to the non-invasive approach which is reliable and suitable for developing communities where medical resources and personnel are *** study uses palpable palm images(dataset)collected from 710 participants in selected hospitals in *** images were extracted,segmented and converted into RGB percentile to train,validate and tested the machine learning models.A hybrid model was developed with the application of ensemble learning models using the R programming language on the R Studio ***,voting,boosting and bagging ensemble model techniques were used to build the hybrid models,the stacking ensemble model achieved an accuracy of 99.73%.The study justifies that ensemble models are efficient for medical disease diagnosis or detection such as anemia.

关键词： Machine learning Anemia detection Ensemble learning Algorithms

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Testing DOM-utilizing functionalities through mutation testing

Testing DOM-utilizing functionalities through mutation testi...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Damir Pozderac Mujo Hadžić Irfan Prazina Vensada Okanovic Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

Software development is implemented in several key phases, one of which is software testing. Software testing consists of selecting techniques for the purpose of finding software defects and bugs in the process of writing code. There are several ways and approaches that lead us to that purpose, with the goal of selecting the most adequate method in terms of cost, complexity, and efficiency. In this paper, we will take a deeper dive into mutation testing techniques. Mutation testing techniques are fault-based and focus more on test structures than the input data, which is considered the testing start point. The basic concept of mutation testing consists of a few steps, which will be covered in this paper, and metrics that measure how effective the tests really are. With a few code examples, we will show why code coverage, which is mostly taken as a measure while testing, is sometimes not the most reliable source and does not give a full picture when talking about the quality of written tests.

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Semantic Segmentation of Brain Tumors: A Performance Evaluation Using DeepLabV3+, UNet, and Intel’s OpenVINO Toolkit

Semantic Segmentation of Brain Tumors: A Performance Evaluat...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Medina Kapo Amila Akagic Emir Buza Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Artificial intelligence, Machine Learning, and Deep Learning are increasingly making significant contributions to the field of medicine. Individual patient conditions, disease localization, and various influencing factors underscore the complexity of disease diagnosis and treatment planning. Introducing new technologies can revolutionize medical diagnostics, facilitating swift and accurate assessments. Among the noninvasive diagnostic methods, Magnetic Resonance Imaging (MRI) stands out, particularly in tumor diagnosis. UNet, renowned for its effectiveness in medical image analysis, serves as a robust model for semantic segmentation, as does DeepLabV3+. However, these models are inherently complex, and their inference process can be time-consuming. By leveraging the OpenVINO toolkit, the inference process is significantly reduced. In this study, nearly a 2-fold acceleration is achieved in inference time with the DeepLabV3+ model and a roughly 1.2-fold improvement with the UNet model on CPU. Moreover, when employing GPU with FP16 precision, the acceleration reached almost 2.5fold for UNet and nearly 3-fold for DeepLabV3+, showcasing the substantial performance enhancements attainable through optimized hardware utilization.

关键词： Deep learning Adaptation models Semantic segmentation Magnetic resonance imaging Graphics processing units Brain modeling Hardware Medical diagnosis Medical diagnostic imaging Tumors

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Training Data Debugging Through a Lens of Outliers in Neuron Coverage

Training Data Debugging Through a Lens of Outliers in Neuron...

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AI, Science, engineering, and Technology (AIxSET), Conference on

作者： Kozo Okano Daichi Ofuchi Shinpei Ogata Shin Nakajima Electrical and Computer Engineering Shinshu University Nagano Japan National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350390995

ISBN: (纸本)9798350391008

As the behavior of neural networks is dependent on the characteristics of training data, choosing appropriate data is mandatory to achieve expected levels of their prediction performance such as the accuracy or robustness. Data aug-mentation, or adding data, to improve accuracy can negatively affect robustness, because these two performance indicators are known to be incompatible. Training data debugging may involve deleting some data points as well as adding new ones; a question here is under what guiding principle the addition or deletion is conducted. Such a debugging method, debugging through a lens of outliers in neuron coverage, was proposed for cases of training classical fully-connected neural networks, but its applicability to CNN was unclear. This paper confirms, through experiments, that the debugging method is effective for cases of training CNN models as well, and furthermore demonstrates a concrete debugging process of discarding outliers and augmenting new data points so that the CNN exhibits balanced accuracy and robustness, without sacrificing neither performance indicator.

关键词： Training Accuracy Neurons Training data Debugging Machine learning Robustness Data models Biological neural networks Lenses

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