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Breast cancer risk prediction in African women using Random Forest Classifier

Cancer Treatment and Research Communications 2021年 28卷 100396-100396页

作者： Macaulay, Babafemi Oluropo Aribisala, Benjamin Segun Akande, Soji Alabi Akinnuwesi, Boluwaji Ade Olabanjo, Olusola Aanu Department of Computer Science Lagos State University Nigeria Department of Surgery Lagos State University Teaching Hospital Nigeria

Introduction: One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. Proper risk assessment is hence very important in reducing mortality. Some computational techniques have been developed for breast cancer risk assessment in the developed world, but such techniques do not work well in Africa because of the difference in risk profiles of African women e.g. later menarche, low drug abuse and low smoking rate. Aim: In this work, we propose a bespoke risk prediction model for African women using Random Forest Classifier (RFC) machine learning technique. methods: A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Twenty-five risk factors were included, for example, smoking, alcohol intake, occupational hazards and age at menopause. Four approaches were empirically used in the feature selection, these are the use of Chi-Square, mutual information gain, Spearman correlation and the entire features. RFC algorithm was used to develop the prediction model. Results: We found that family history of breast cancer, dense breast, deliberate abortion, age at first child, fruit intake and regular exercise are predictors of breast cancer. The RFC model gave an accuracy of 91.67%, sensitivity of 87.10%, specificity of 96.55% and Area under curve (AUC) of 92% when all the risk factors were included in the model while an accuracy of 96.67%, sensitivity of 93.75%, specificity of 100% and AUC of 97% were obtained when correlation-selected features were included in the model. The Chi-Square selected features gave the best performance with 98.33% accuracy, 100% sensitivity, 96.55 specificity and 98% AUC. Mutual information gain selected feature gave the same results as Ch

关键词： African women Breast cancer Feature selection Machine learning Random forest Risk prediction

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When to update your model: constrained model-based reinforcement learning 22

When to update your model: constrained model-based reinforce...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Tianying Ji Yu Luo Fuchun Sun Mingxuan Jing Fengxiang He Wenbing Huang Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences JD Explore Academy *** Inc Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9781713871088

Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to degrade performance by drastic model updating. In this work, we first propose a novel and general theoretical scheme for a non-decreasing performance guarantee of MBRL. Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. These discoveries encourage us to formulate a constrained lower-bound optimization problem to permit the monotonicity of MBRL. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns. Motivated by these analyses, we design a simple but effective algorithm CMLO (Constrained Model-shift Lower-bound Optimization), by introducing an event-triggered mechanism that flexibly determines when to update the model. Experiments show that CMLO surpasses other state-of-the-art methods and produces a boost when various policy optimization methods are employed.

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"Prompt-Gamma Neutron Activation Analysis (PGNAA)" Metal Spectral Classification using Deep Learning Method

arXiv

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arXiv 2022年

作者： Cheng, Ka Yung Shayan, Helmand Krycki, Kai Lange-Hegermann, Markus Campus Kiel Kaistraße 101 Kiel24114 Germany Department of Electrical Engineering & Computer Science OWL University of Applied Sciences and Arts Campusallee 12 Lemgo32657 Germany Department of Mathematical Methods & Software Development AiNT GmbH Stolberg52222 Germany

There is a pressing market demand to minimize the test time of Prompt Gamma Neutron Activation Analysis (PGNAA) spectra measurement machine, so that it could function as an instant material analyzer [1]–[3], e.g. to classify waste samples instantaneously and determine the best recycling method based on the detected compositions of the testing sample. This article introduces a new development of the deep learning classification and contrive to reduce the test time for PGNAA machine. We propose both Random Sampling methods and Class Activation Map (CAM) to generate "downsized" samples and train the CNN model continuously. Random Sampling methods (RSM) aims to reduce the measuring time within a sample, and Class Activation Map (CAM) is for filtering out the less important energy range of the downsized samples. We shorten the overall PGNAA measuring time down to 2.5 seconds while ensuring the accuracy is around 96.88% for our dataset with 12 different species of substances. Compared with classifying different species of materials, it requires more test time (sample count rate) for substances having the same elements to archive good accuracy. For example, the classification of copper alloys requires nearly 24 seconds test time to reach 98% *** Codes 68T07, 82D35 Copyright © 2022, The Authors. All rights reserved.

关键词： Chemical activation

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Study for the improvement of the moroccan public higher education system based on a strategic plan for learning technologies 22

Study for the improvement of the moroccan public higher educ...

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22nd International Symposium on computers in Education, SIIE 2020

作者： García-Peñalvo, Francisco José Sánchez-Prieto, José Carlos Rodríguez-Conde, M. José Gamazo, Adriana García-Holgado, Alicia Abad, Fernando Martínez Grial Research Group Iuce Computer Science Department University of Salamanca Salamanca Spain Grial Research Group Iuce Didactics Organization and Research Methods Department University of Salamanca Salamanca Spain

Within the framework of the comprehensive educational reform of Moroccan universities, the twinning project financed by the European Union has the general objective of accompanying, assisting and promoting the reform of the Moroccan higher education system within the framework of its strategic vision 2015-2030 and its approach to the European Higher Education Area, which has a twofold aim: To enhance the employability of graduates and to improve the governance of the university system. The purpose of this paper is to present the work carried out in the component 3 of this project, focused on the diversification of teaching methods (elearning strategy). This component has aimed to provide guidelines and recommendations for addressing the main problems of Higher Education in Morocco and improving its quality. © 2020 CEUR-WS. All rights reserved.

关键词： E-learning

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Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

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Advances in Manufacturing 2021年第3期9卷 388-402页

作者： Long-Hua Xu Chuan-Zhen Huang Jia-Hui Niu Jun Wang Han-Lian Liu Xiao-Dan Wang Center for Advanced Jet Engineering Technologies Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education)National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University)School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China School of Mechanical and Manufacturing Engineering The University of New South Wales(UNSW)SydneyNSW2052Australia Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China

During the actual high-speed machining process,it is necessary to reduce the energy consumption and improve the machined surface ***,the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining ***,a novel intelligent system is proposed for prediction and optimization.A novel adaptive neuro-fuzzy inference system(NANFIS)is proposed to predict the energy consumption and surface *** the NANFIS model,the membership functions of the inputs are expanded into:membership superior and membership *** membership functions are varied based on the machining *** inputs of the NANFIS model are cutting parameters,and the outputs are the machining *** optimization,the optimal cutting parameters are obtained using the improved particle swarm optimization(IPSO)algorithm and NANFIS ***,the IPSO algorithm as a learning algorithm is used to train the NANFIS *** proposed intelligent system is applied to the high-speed milling process of compacted graphite *** experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2%and 93.4%,*** NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent *** on the IPSO algorithm and NANFIS models,the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling *** is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes,thereby enabling sustainable and intelligent manufacturing.

关键词： Novel adaptive neuro-fuzzy inference system(NANFIS)model Improved particle swarm optimization(IPSO)algorithm Energy consumption Surface roughness Multiobjective optimization

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Phase transitions in swarm optimization algorithms

arXiv

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arXiv 2025年

作者： Vantuch, Tomáš Zelinka, Ivan Adamatzky, Andrew Marwan, Norbert Department of Computer science Technical University of Ostrava Czech Republic Unconventional Computing Lab UWE Bristol United Kingdom Transdisciplinary Concepts and Methods Potsdam Germany

Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks. We demonstrate that swarms optimisation algorithms also exhibit transitions from chaos, analogous to motion of gas molecules, when particles explore solution space disorderly, to order, when particles follow a leader, similar to molecules propagating along diffusion gradients in liquid solutions of reagents. We analyse these ‘phase-like’ transitions in swarm optimization algorithms using recurrence quantification analysis and Lempel-Ziv complexity estimation. We demonstrate that converging and non-converging iterations of the optimization algorithms are statistically different in a view of applied chaos, complexity and predictability estimating indicators. © 2025, CC BY-NC-ND.

关键词： Cellular automata

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Recent developments in the thermal radiative flow of dusty Ellis trihybrid nanofluid with activation energy using Hamilton-Crosser thermal conductivity model

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International Journal of Thermofluids 2025年 27卷

作者： Okasha, Mostafa Mohamed Abbas, Munawar Akgül, Ali Formanova, Shoira Ibrahim, Talib K. Hassani, Murad Khan Department of Mechanical Engineering College of Engineering Northern Border University Arar Saudi Arabia Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Topkapı Istanbul34010 Turkey Department of Chemistry and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Ghazni University Department of Mathematics Afghanistan Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia /Mersin 10 Turkey

This study scrutinizes the characteristics of activation energy on Darcy Forchheimer radiative flow of dusty Ellis trihybrid nanofluid over a Riga plate when dust and nanoparticles are present. The goal of the present work is to use the Hamilton-Crosser thermal conductivity model to scrutinize the heat transmission for the Darcy Forchheimer flow of dusty Ellis trihybrid nanofluid. The flow is impacted by heat source with the properties of Marangoni convection. The base fluid, propylene glycol (C3H8O2), is mixed with Ag, TiO2 and Al2O3 nanoparticles. The model is applicable to sophisticated heat transfer systems, including solar energy harvesting and electronic device cooling technologies. Additionally, it finds application in thermal management of industrial processes using nanofluids and aerospace engineering. Using the shooting technique, the numerical results of the governing equations are obtained (RKF-45th). The impacts on dimensionless physical quantities of interest of geometrical and physical properties relevant to this study are analysed using the required tables and figures. The results demonstrated that the Ellis fluid parameter raised the heat transmission, mass transmission rate, and velocity profiles. As the chemical reaction parameter upsurges, the concentration distributions decrease. © 2025 The Author(s)

关键词： Activation energy

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Designing Problem-based Learning at a department Level in Resources and Environment: An Interdisciplinary Study 22

Designing Problem-based Learning at a Department Level in Re...

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Proceedings of the 14th International Conference on Education Technology and computers

作者： Fenghua Li Chong Han Chunfang Zhou Mei Wang Dianyao Gong Che Yang Tao Du PBL Teaching Innovation Research Center of Northeastern University Department of Resources and Environment School of metallurgy Northeastern University China Department of Resources and Environment School of metallurgy Northeastern University China Department of Mathematics and Computer Science University of Southern Denmark Denmark The State Ley Laboratory of Rolling and Automation Northeastern University China School of metallurgy Northeastern University China

ISBN: (纸本)9781450397766

Interdisciplinary research, which aims to solve practical issues in the realm of industrial applications, has emerged as a key trend in the current transformation of knowledge production mode, creating a new type of educational strategy to match. Problem-based Learning (PBL) is a teaching paradigm that emphasizes the practical orientation and application qualities of a college education. Northeastern University's Resources and Environment department arose from the need to address complicated environmental challenges. It has inherent interdisciplinary characteristics and bears the social duty of sustainable development. The discipline seeks to link "Practical Cognition" with "Disciplinary Logic," as well as realign the "target-content-method" of education to meet current demands. The teaching-learning practice tries to correspond to the professional training goals and the student's capability output through the reform and practice of a divisional level with the PBL method, integration of multidisciplinary and industrial resources, carrying out teaching-learning activities based on interdisciplinary theory and PBL principles, improving students' transformative skills, and rebuilding sustainable social responsibilities.

关键词： Transformative skills teaching alignment Problem-based learning Interdisciplinary

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ASYMPTOTIC EXPECTED T-FUNCTIONALS OF RANDOM POLYTOPES WITH APPLICATIONS TO Lp SURFACE AREAS

arXiv

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arXiv 2022年

作者： Hoehner, Steven Li, Ben Roysdon, Michael Thäle, Christoph Department of Mathematics & Computer Science Longwood University United States Department of Mathematical Methods in Physics Warsaw University Poland Institute for Computational and Experimental Research in Mathematics United States Department of Pure Mathematics Tel Aviv University Israel Faculty of Mathematics Ruhr University Bochum Germany

An asymptotic formula is proved for the expected T -functional of the convex hull of independent and identically distributed random points sampled from the Euclidean unit sphere in n according to an arbitrary positive continuous density. As an application, the approximation of the sphere by random polytopes in terms of Lp surface area differences is *** Codes 52A22, 52A27, 60D05 (Primary) 52A20, 52B11 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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Integrating computational thinking practices into early childhood education in culturally responsive ways: Insights from research–practice partnership

Future in Educational Research

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Future in Educational Research 2024年第4期2卷 359-381页

作者： Jeanne Hubelbank Mia Dubosarsky Shakhnoza Kayumova Tiffany Davis Nea Sann Shawn Fortin Gillian Smith Independent Evaluator Sudbury Massachusetts USA The STEM Education Center Worcester Polytechnic Institute Worcester Massachusetts USA Department of STEM Education and Teacher Development University of Massachusetts Dartmouth Massachusetts USA Ashburnham Westminster Regional School District Ashburnham Massachusetts USA Worcester Child Development Head Start Worcester Massachusetts USA Teaching & Learning Belchertown Public Schools Belchertown Massachusetts USA Interactive Media & Game Development Department of Computer Science Worcester Polytechnic Institute Worcester Massachusetts USA

The study examines the research–practice partnership (RPP) model aimed at co-constructing and integrating computational thinking (CT) in culturally responsive (CR) ways within early childhood and elementary (PreK-5) classrooms, focusing on teachers' RPP experiences. Recent research underlines the importance of integrating CT in early childhood and elementary education, demonstrating that culturally, linguistically, and developmentally responsive computational activities in PreK-5 settings are key to such efforts. However, there is a gap in research regarding effective professional development models that would prepare early childhood teachers in terms of both disciplinary ideas and practices of CT, as well as attending to cultural, linguistic, and developmental differences in young children in asset-based ways. Our research contributes to this body of knowledge by examining how RPP can be a model for teacher learning in which teachers, administrators, and researchers can co-develop knowledge and confidence in integrating CT into the PreK-5 teaching curriculum and practices in CR ways. The study outlines the collaborative development of processes and frameworks co-constructed by the RPP educators, as well as teacher-developed curricular materials and lesson plans integrating CT in CR ways. Findings include teachers' experience of these lessons and teacher insights about the support needed to incorporate CT and CR practices into their existing curricula. The study concludes by highlighting the potential challenges and opportunities inherent in such endeavors, thereby contributing to the broader discourse on supporting CT integration into early childhood classrooms in CR ways.

关键词： computational thinking cultural responsive practices early childhood research practice partnership teacher professional development

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