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1st International Conference on Computer, Science, Engineering and Technology, ICComSET 2018

作者： Prabowo, A. Mamat, M. Sukono Ngadiman Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Jenderal Soedirman Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Darul Iman Kuala Terengganu Malaysia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran West-Java Indonesia Physical Health and Recreation Education Study Program Faculty of Health Sciences Universitas Jenderal Soedirman Purwokerto Central-Java Indonesia

People of Cikakak Traditional Village, Wangon District, Banyumas Regency, Central-Java Province are known as the followers of Islam Aboge (Alif Rebo Wage). Their religious holidays such as the beginning of fasting month (Ramadan) and Eid al-Fitr are determined based on the rules they have been using so far, namely Month Code or Sandi Bulan and Year Code or Sandi Tahun. This research is conducted using literature study method and field study in the form of interview with respondents. This research aims at producing a mathematical model which matches the function of these two codes. The results obtained are five mathematical equations to determine the names of year, saptawara and pancawara days for the 1st day of each month, and saptawara and pancawara days for other dates. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Functions

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Prediction of Bankruptcy among Middle 40 per Cent (M40) in Malaysia using Support Vector Machine 22

Prediction of Bankruptcy among Middle 40 per Cent (M40) in M...

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22nd IEEE Student Conference on Research and Development, SCOReD 2024

作者： Sabri, Nurbaity Sahiq, Aqilah Nadiah Md Hamzah, Hazwa Hanim Mohamed Sain, Hartayuni Mangshor, Nur Nabilah Abu Shari, Anis Amilah Universiti Teknologi MARA Cawangan Melaka College of Computing Informatics and Mathematics Kampus Jasin Melaka Malaysia Universiti Teknologi MARA Cawangan Melaka Faculty of Business and Management Kampus Bandaraya Melaka Malaysia Tadulako University Statistics Study Program Mathematics and Natural Science Faculty Palu94118 Indonesia

ISBN: (纸本)9798331510077

The Middle 40 Per Cent (M40) demographic comprises a substantial proportion of Malaysia's consumer base, comprising the median 40% of income earners. This group is a big part of the country's customer base. The rising cost of living, flat wage growth, and rising family debt make this group especially vulnerable to financial instability. Given these economic pressures, predicting the possibility of bankruptcy in this population is becoming increasingly crucial to allow for early intervention and preventive actions. Support Vector Machines (SVM) are recognized for their efficacy in classification tasks, particularly with small or imbalanced datasets. SVM prediction will be utilized using 14694 samples of the Debt Management program (DMP) dataset from 2016 to 2020. Twenty-three (23) features will be implemented on the dataset and Synthetic Minority Oversampling Technique (SMOTE) to improve the imbalanced datasets. The experiment demonstrates that the SVM classifier obtained 91.1% accuracy when implementing PUK kernels. It suggests that SVM is a plausible alternative model for predicting personal bankruptcy in the M40 group. To anticipate personal bankruptcy for M40, additional prediction models will be evaluated in the future. © 2024 IEEE.

关键词： Prediction models

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On the Dominant Local Metric Dimension of Corona Product Graphs

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IAENG International Journal of Applied mathematics 2022年第4期52卷 1页

作者： Umilasari, Reni Susilowati, Liliek Slamin Prabhu, Savari Mathematics Department Airlangga University Indonesia Study Program of Informatics Jember University Indonesia Mathematics Department Rajalakshmi Engineering College Tamil Nadu Chennai602105 India

A nontrivial connected graph T which one of the vertex is v, v is said to distinguish two vertex u;t if the distance between v and u is different from v to t, where u,t (Formula Presented) V (T). Metric dimension is one topic in graph theory that uses the concept of distance. Combining the definition of the local metric dimension and dominating set, there is a new term, we called it dominant local metric dimension and symbolized as Ddiml(T). An ordered subset Wl = {w1,w2,…,wn}(Formula Presented)V (T) is called a dominant local resolving set of T if Wl is a local resolving set as well as a dominating set of T. The goal of this paper’s research is to determine precise values of dominant local metric dimension for the corona product graphs. n copies of the graphs P1,P2,…,Pn of P are made to constructed the corona of any two graph T and P. After that, we link the i-th vertex of T to the vertices of Pi, where n is an order of graph T. T corona P is symbolized by T(Formula Presented)P. © 2022,IAENG International Journal of Applied mathematics. All Rights Reserved.

关键词： Graph theory

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Multi Task Classification Using Deep Learning Approaches for Big Five Personality Traits Prediction: A Review 11

Multi Task Classification Using Deep Learning Approaches for...

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11th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2024

作者： Nudin, Salamun Rohman Gernowo, Rahmat Somantri, Maman Wibowo, Adi Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Diponegoro University Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350389289

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the Science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98%), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67%), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36%) and lowest (59%). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG. © 2024 IEEE.

关键词： Contrastive Learning

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Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype 19

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20th Annual Conference on Neural Information Processing Systems, NIPS 2006

作者： Sing, Tobias Beerenwinkel, Niko Department of Computational Biology Max Planck Institute for Informatics Saarbrücken Germany Department of Mathematics University of California Berkeley CA 94720 United States Program for Evolutionary Dynamics Harvard University Cambridge MA 02138 United States

ISBN: (纸本)9780262195683

Starting with the work of Jaakkola and Haussler, a variety of approaches have been proposed for coupling domain-specific generative models with statistical learning methods. The link is established by a kernel function which provides a similarity measure based inherently on the underlying model. In computational biology, the full promise of this framework has rarely ever been exploited, as most kernels are derived from very generic models, such as sequence profiles or hidden Markov models. Here, we introduce the MTreeMix kernel, which is based on a generative model tailored to the underlying biological mechanism. Specifically, the kernel quantifies the similarity of evolutionary escape from antiviral drug pressure between two viral sequence samples. We compare this novel kernel to a standard, evolution-agnostic amino acid encoding in the prediction of HIV drug resistance from genotype, using support vector regression. The results show significant improvements in predictive performance across 17 anti-HIV drugs. Thus, in our study, the generative-discriminative paradigm is key to bridging the gap between population genetic modeling and clinical decision making.

关键词： Hidden Markov models

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Inferring nonstationary gene networks from temporal gene expression data

Inferring nonstationary gene networks from temporal gene exp...

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Workshop on Signal Processing Systems

作者： Chang, Hsun-Hsien Smith, Jonathan J. Ramoni, Marco F. Children's Hospital Informatics Program Harvard-MIT Division of Health Sciences and Technology Harvard Medical School Boston MA 02115 United States Department of Mathematics Massachusetts Institute of Technology Cambridge MA 02142 United States

ISBN: (纸本)9781424489336

Reverse-engineering transcriptional networks from longitudinal expression profiles is a crucial step towards the study of gene regulatory mechanisms. Genes dynamically orchestrate to each other, the stationarity assumption made by existing methods of transcriptional interaction inference is no longer adequate. As such, we need a new approach to handle the nonstationary behavior in gene expression. On the other hand, microarrays for human studies are equipped with a large number of probe sets, leading the inference of dynamic networks to a computationally intensive task. Hence, there is a need to design the inference algorithm in a tractable manner. This paper develops a Bayesian network approach to inferring the nonstationary transcriptional interactions. The applications of our approach to a clinical study of mechanical periodontal therapy demonstrates a significant improvement over stationary models. Our nonstationary network model also explains the anti-inflammatory effect of mechanical periodontal therapy. ©2010 IEEE.

关键词： Bayesian networks

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Bibliometric Analysis of Trends and Directions in Machine Learning Applied to FinTech 4

Bibliometric Analysis of Trends and Directions in Machine Le...

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4th International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2024

作者： Arif, Erman Suherman, Suherman Widodo, Aris Puji Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350378368

Our article presents a comprehensive analysis of trends and directions in the application of machine learning in the FinTech sector. By utilizing data from academic databases such as Scopus, our objective is to showcase the advancements in this interdisciplinary research area. The study identifies key trends, influential authors, leading journals, and significant collaborative networks that shape the field. The methodology employed involves quantitative bibliometric analysis to evaluate publications from the past five years. The results indicate a substantial increase in the number of studies during this period, highlighting the importance of machine learning in addressing critical financial challenges, including fraud detection, algorithmic trading, credit scoring, and risk management. Additionally, we delve into the main themes, countries, and institutions that dominate the current research agenda. This analysis provides valuable insights into the present state and future direction of machine learning in FinTech, serving as a resource for researchers and practitioners seeking to comprehend and contribute to this dynamic and rapidly evolving field. We also discuss implications for future research, emphasizing the necessity for continuous innovation and interdisciplinary collaboration to maximize the potential of machine learning in transforming financial services. © 2024 IEEE.

关键词： Adversarial machine learning

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Innovative Smart Forest Management

Innovative Smart Forest Management

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2016第三届全球知识经济大会——2016第六届低碳地球峰会、2016第六届国际新能源产业大会、2016第五届国际新兴信息及通讯技术产业大会

作者： Kamen Spassov Mariyana Lyubenova Alexander Chikalanov Yuri Pavlov Master's Degree Program "e-Business and e-Governance"Faculty of Mathematics and Informatics Sofia University

The multifunctional importance of a forest ecosystem as an example of an ecosystem unit is often the reason for contradictions and conflicts between economic,social and environmental *** propose a value based model(VBM) for an integrated assessment of economic,ecological and social forest ecosystems *** model is applicable for monitoring and innovative ecological management of forest areas and allowing the sustainable development of business and used forest *** the modeling purposes,we accept tree ecological indicators(factors),which describe adequately the main *** factors are:timber reserves in ***-l for the assessment of economic effects(material services);species richness,***-1,for the assessment of ecological effect(regulating and supporting services) and percentage of population employed in the forestry sector for the assessment of social effects or *** model represents a multi-attribute utility function,developed on the Decision making theory,Utility theory and stochastic approximation technic used as machine *** calculate utility functions using the preferences of the Decision maker(DM),professional in forest ecology and environmental *** proposed Decision Support System(DSS) integrates few models designated to support an adaptive ecological management of forest areas to achieve their sustainable development and operation as a natural source of ecosystem *** innovative smart system focuses on managing the material services provided by forest *** model relays on key indicators to assess the status of stocks and listed material resources and ball scale through which it provides a synthesized assessment priority and reliable use of business *** propose an innovative approach for estimation of cost efficiency of ecosystem management

关键词： Innovative Smart Forest Management

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Maximizing the business value of software projects a branch & bound approach

Maximizing the business value of software projects a branch ...

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ICEIS 2008 - 10th International Conference on Enterprise Information Systems

作者： Alencar, Antonio Juarez Schmitz, Eber Assis De Abreu, Ênio Pires Fernandes, Marcelo Carvalho Ferreira, Armando Leite Graduate Program in Informatics Institute of Mathematics and Electronic Computer Center Federal University of Rio de Janeiro Rio de Janeiro Brazil COPPEAD School of Business Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9789898111388

This work presents a branch & bound method that allows software managers to determine the optimum order for the development of a network of dependent software parts that have value to customers. In many different circumstances the method allows for the development of complex and otherwise expensive software from a relatively small investment, favoring the use of software development as a means of obtaining competitive advantage.

关键词： Project management

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Scenario-based collaboration: An approach to refinement of plans through Public Engagement

Scenario-based collaboration: An approach to refinement of p...

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International Conference on Computer Supported Cooperative Work in Design

作者： Samuel de Oliveira Apolonio Adriana S. Vivacqua Marcos R. S. Borges PPGI/IM - Graduate Program in Informatics Institute of Mathematics Federal University of Rio de Janeiro (UFRJ) Brazil PPGI/IM — Graduate Program in Informatics Institute of Mathematics Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil

Design is a knowledge intensive activity, which depends on information to be undertaken. Previous designs, recent innovations, new techniques and materials must be taken into account when working on a new project, whatever its nature. Designing a plan is no different from designing a physical artifact, in that several constraints and requirements must be taken into account. Therefore, a collaborative approach that enables the refinement of plans through Public Engagement is of paramount importance to the design process. Many studies have been undertaken regarding the promotion of public participation in the planning process. In this situation, the population can be seen as an important source of information, because they experience the environments addressed by the proposed plan on a daily basis. However, the interaction between government and population presents cultural, political and technological challenges. In this paper, we present the Scenario-Based Collaboration Process (SBCP), which aims to Refine Plans through Public Engagement.

关键词： Planning informatics Lead Collaboration Irrigation

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