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science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Akinyemi Omololu Akinrotimi Roseline Oluwaseun Ogundokun Modinat Abolore Mabayoje Rafiat Ajibade Oyekunle Marion Olubunmi Adebiyi Department of Mathematics Sciences(Computer Science Program) Augustine University Ilara-Epe Epe Nigeria Department of Computer Science Landmark University Omu-Aran Nigeria Department of Multimedia Engineering Kaunas University of Technology Lithuania SDG 4. Quality Education Landmark University Omu Aran Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Information and Communication University of Ilorin Ilorin Nigeria Department of Computer Science Landmark University Omu-Aran Omu Aran Nigeria

The financial backbone of every telecommunications company is strictly made up of the number of customers patronizing the organization. Due to the high level of competition amongst existing telecommunication companies, customers sometimes leave, dropping the services of a particular one for the other. Predicting which subscribers may want to leave a telecommunications company and providing solutions to keep them from doing so, are the main objectives of customer churn prediction. Churn prediction helps classify consumers who are likely to move from one business to another. The ability to foresee churners before they leave has been increasingly useful in recent years, especially in light of increased competition among communications carriers. To compute churners for telecommunications providers, the study described in this paper used oversampling techniques to balance churn data and applied the dimensionality reduction technique to discover optimal features with a strong predictive ability for detecting would-be churners and non-churners. The model used Logistic Regression and the Naïve Bayes Classification Algorithm to implement comparative classification strategies. The performance levels of these strategies were compared using a Telecommunications Customer Churn data set, as well as performance metrics such as accuracy, sensitivity, and specificity. The Naïve Bayes Algorithm proves more efficient than its counterpart giving higher positive rate detection and a lower negative rate detection.

关键词： Measurement Companies Sensitivity and specificity Prediction algorithms Data models Telecommunications Numerical models

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MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods

arXiv

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arXiv 2023年

作者： Taghibakhshi, Ali Nytko, Nicolas Zaman, Tareq Uz MacLachlan, Scott Olson, Luke N. West, Matthew Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign United States Department of Computer Science University of Illinois Urbana-Champaign United States Scientific Computing Program Memorial University of Newfoundland Canada Department of Mathematics and Statistics Memorial University of Newfoundland Canada

Domain decomposition methods (DDMs) are popular solvers for discretized systems of partial differential equations (PDEs), with one-level and multilevel variants. These solvers rely on several algorithmic and mathematical parameters, prescribing overlap, subdomain boundary conditions, and other properties of the DDM. While some work has been done on optimizing these parameters, it has mostly focused on the one-level setting or special cases such as structured-grid discretizations with regular subdomain construction. In this paper, we propose multigrid graph neural networks (MG-GNN), a novel GNN architecture for learning optimized parameters in two-level DDMs. We train MG-GNN using a new unsupervised loss function, enabling effective training on small problems that yields robust performance on unstructured grids that are orders of magnitude larger than those in the training set. We show that MG-GNN outperforms popular hierarchical graph network architectures for this optimization and that our proposed loss function is critical to achieving this improved performance. © 2023, CC BY.

关键词： Domain decomposition methods

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Vinod Kumar Isha Jain Durga Sritha Dongla Shivangi Rai Vani Rastogi Eva Kaushik Rohit Kaushik School of Engineering & Applied Science The George Washington University Washington D.C USA Computer Science & Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Computer Science & Engineering Department Gandhi Institute of Technology and Management Visakhapatnam Andhra Pradesh Data Scientist DXC Technology Noida India CS and Mathematics Department Data Analytics Program University of Illinois Springfield USA

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions.

关键词： Deep learning Pathology Accuracy Melanoma computer architecture Benchmark testing Skin Convolutional neural networks Lesions Medical diagnosis

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Bayesian Parameter Inference for Partially Observed Diffusions using Multilevel Stochastic Runge-Kutta Methods

arXiv

引用

arXiv 2023年

作者： Moral, Pierre Del Hu, Shulan Jasra, Ajay Ruzayqat, Hamza Wang, Xinyu Institut de Mathematiques de Bordeaux FR Bordeaux33405 France School of Mathematics and Statistics Wenlan School of Business China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Zhongnan University of Economics and Law CN China

We consider the problem of Bayesian estimation of static parameters associated to a partially and discretely observed diffusion process. We assume that the exact transition dynamics of the diffusion process are unavailable, even up-to an unbiased estimator and that one must time-discretize the diffusion process. In such scenarios it has been shown how one can introduce the multilevel Monte Carlo method to reduce the cost to compute posterior expected values of the parameters for a pre-specified mean square error (MSE);see [12]. These afore-mentioned methods rely on upon the Euler-Maruyama discretization scheme which is well-known in numerical analysis to have slow convergence properties. We adapt stochastic Runge-Kutta (SRK) methods for Bayesian parameter estimation of static parameters for diffusions. This can be implemented in high-dimensions of the diffusion and seemingly under-appreciated in the uncertainty quantification and statistics fields. For a class of diffusions and SRK methods, we consider the estimation of the posterior expectation of the parameters. We prove that to achieve a MSE of O(ϵ2), for ϵ > 0 given, the associated work is O(ϵ−2). Whilst the latter is achievable for the Milstein scheme, this method is often not applicable for diffusions in dimension larger than two. We also illustrate our methodology in several numerical examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Wire Before You Walk

Wire Before You Walk

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Asilomar Conference on Signals, Systems & computers

作者： Tesfa Asmara Dhananjay Bhaskar Ian Adelstein Smita Krishnaswamy Michael Perlmutter Department of Computer Science Pomona College Claremont CA USA Mathematics and Statistics Pomona College Claremont CA USA Department of Genetics Yale School of Medicine New Haven CT USA Department of Mathematics Yale University New Haven CT USA Department of Computer Science Yale University New Haven CT USA Program for Applied Mathematics Yale University New Haven CT USA Computational Biology and Bioinformatics Program Yale University New Haven CT USA Wu Tsai Institute Yale University New Haven CT USA Department of Mathematics Boise State University Boise ID USA

Node embeddings aim to associate a vector to every vertex of a graph which can then be used for downstream tasks such as clustering, classification, or link prediction. Many popular node embeddings such as no $d$ e2vec and DeepWalk are based upon counting which nodes frequently co-occur in random walks of the graph. In this paper, we show that the performance of such algorithms can be improved by rewiring the edges of the graph through a variety of network indices before running DeepWalk. These rewirings effectively give the random walker an inductive bias and increase the accuracy of a logistic regression classifier applied to the node embedding on several benchmark data sets.

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Topological Learning for Motion Data via Mixed Coordinates

arXiv

引用

arXiv 2023年

作者： Luo, Hengrui Kim, Jisu Patania, Alice Vejdemo-Johansson, Mikael Lawrence Berkeley National Laboratory United States DataShape team Inria Saclay LMO Université Paris-Saclay France Department of Mathematics and Statistics University of Vermont United States Department of Mathematics CUNY College of Staten Island and Computer Science Program CUNY Graduate Center United States

Topology can extract the structural information in a dataset efficiently. In this paper, we attempt to incorporate topological information into a multiple output Gaussian process model for transfer learning purposes. To achieve this goal, we extend the framework of circular coordinates into a novel framework of mixed valued coordinates to take linear trends in the time series into consideration. One of the major challenges to learn from multiple time series effectively via a multiple output Gaussian process model is constructing a functional kernel. We propose to use topologically induced clustering to construct a cluster based kernel in a multiple output Gaussian process model. This kernel not only incorporates the topological structural information, but also allows us to put forward a unified framework using topological information in time and motion series. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

引用

International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Investigating Students’ Perception towards the Use of Social Media for Computing Education in Nigeria

引用

Journal of Information Systems Education 2021年第3期32卷 212-228页

作者： Agbo, Friday J. Olawumi, Olayemi Balogun, Oluwafemi S. Sanusi, Ismaila T. Olaleye, Sunday A. Sunday, Kissinger Kolog, Emmanuel A. Atsa’am, Donald D. Adusei-Mensah, Frank Adegbite, Ayobami Ipeayeda, Funmilola W. School of Computing University of Eastern Finland 80110 Joensuu 70211 Kuopio Finland Department of Marketing Management and International Business University of Oulu Oulu90570 Finland Department of Computer Science National Chengchi University Taipei Taiwan Department of Operations and MIS University of Ghana Business School Accra Ghana Department of Mathematics Statistics and Computer Science University of Agriculture Makurdi Nigeria Institute of Public Health and Clinical Nutrition University of Eastern Finland Kuopio Finland Bio Environmental Science Program Morgan State University BaltimoreMD21251 United States Department of Computer Science Ajayi Crowther University Oyo Nigeria

This study examined the use of a social media platform – WhatsApp – by computer science students for learning computing education in the context of a Nigerian education institution. Nowadays, a large community of students in higher education institutions has embraced the WhatsApp platform for social interactions which makes it a useful tool in education. In this study, students formed three closed groups, and each group had a specific computing topic they discussed. Their discussions were in the form of posting questions, providing answers to questions, or expressing knowledge on the group topic. A questionnaire was used to collect data from the participants regarding their experiences. We conducted a descriptive analysis of the students’ learning outcomes. The results show that the use of social media contributes positively to students’ learning achievement, and they are motivated to acquire more knowledge about different computing topics © 2021, Journal of Information Systems Education. All Rights Reserved.

关键词： Students

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Optimizing the Identification of Suitable Congregations for Preachers Using a GMM-PCA-BIC Hybrid Clustering Approach

Optimizing the Identification of Suitable Congregations for ...

引用

International Conference of computer and Informatics Engineering (IC2IE)

作者： Rahmad Kurniawan Ibnu Daqiqil I D Fatayat Sukamto Aidil Fitriansyah Fitra Lestari Ilyas Husti Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Department of Industrial Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

The assignment of preachers to mosques in Riau Province faces significant challenges due to the geographic dispersion of mosques and inconsistent scheduling practices. Preachers often travel long distances, and mismatched assignments lead to inefficiencies and reduced effectiveness in delivering sermons. This study addresses these issues by leveraging advanced clustering techniques to optimize preacher assignments. Specifically, we applied Gaussian Mixture Models (GMM) to cluster mosque and preacher data based on geolocation, activity, and profiles. We integrated Principal Component Analysis (PCA) for dimensionality reduction to enhance the clustering performance, forming a GMM-PCA hybrid approach. Data was obtained from the Indonesian Ulema Council Riau Province Chapter, which consists of 185 mosques and 250 preachers, totaling 435 data points. The results indicate that using GMM alone, the optimal clustering was achieved with 10 clusters, yielding a Silhouette Score of 0.31. The GMM-PCABIC hybrid approach improved clustering quality. It achieved optimal results with 9 clusters, a significantly higher Silhouette Score of 0.48, and a lower BIC Score of 2696.18. These findings demonstrate the novelty of the GMM-PCA-BIC hybrid method in effectively capturing the complex characteristics of mosque and preacher data, ensuring efficient and relevant preacher assignments. Implementing this optimized clustering approach allows mosque administrators to make data-driven decisions, enhancing the effectiveness of religious activities.

关键词： Measurement Instruments Geology Education Real-time systems Geospatial analysis Informatics Gaussian mixture model Faces Principal component analysis

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

引用

International Electrical Engineering Congress (iEECON)

作者： Nutyada Nantapanich Pisit Mahatthanajatuphat Norakamon Wongsin Wilaiporn Lee Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis.

关键词： Analytical models Adaptation models Correlation Liquids Predictive models Microwave sensors Polynomials Fractals Glucose Predictive analytics

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