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Feature Selection and Classification Algorithm Approaches for Parkinson's Disease Detection 5

Feature Selection and Classification Algorithm Approaches fo...

5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Marcos, Hendra Warsito, Budi Wibowo, Adi Diponegoro University Doctoral Program Information System Department Informatics Universitas Amikom Purwokerto Purwokerto Indonesia Diponegoro University Faculty Of Science And Mathematics Department Of Statistics Semarang Indonesia Department Of Computer Science Informatics Diponegoro University Semarang Indonesia

ISBN: (纸本)9781665401968

Millions of people worldwide have developed Parkinson's disease (PD), which is more common by 50. Advances in information technology, especially in the field of machine learning, are being challenged for the development of early detection algorithms. This helps doctors to detect disease early and more accurately based on data. The available data has large dimensions and features so that the feature selection method can be used. This research paper aims to provide insight by comparing several algorithms for feature selection and PD classification. PD classification can help improve treatment efficiency and save time. The problem that arises is how the classification algorithm can show better and more efficient accuracy results. A large number of datasets is one of the challenges in the development of classification algorithms. The voice input dataset was obtained from the UCI machine learning repository. Bagging's algorithm out performed on average 100%, and MLP 96.58%, and RF 92.52%. © 2021 IEEE.

关键词： Classification (of information)

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A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization 7

A Study on Text Feature Selection Using Ant Colony and Grey ...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Widians, Joan Angelina Wardoyo, Retantyo Hartati, Sri Universitas Gadjah Mada Doctoral Program Dept. of Computer Science and Electronics Indonesia Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Faculty of Mathematics and Natural Sciences Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous characteristics and high-element dimensions. Many pattern classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) for FS are the main topics of our thorough assessment of the literature on the Swarm Intelligence (SI) algorithm. Furthermore, it illustrates how the hybrid SI technique is used in FS across various sectors. The hybrid SI technique uses applicable data from various FS methods to find feature subsets with smaller sizes and better classification performance than those found by regular FS algorithms. © 2022 IEEE.

关键词： Ant colony optimization

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Three-Dimensional Wound Reconstruction using Point Descriptors: A Comparative Study

Three-Dimensional Wound Reconstruction using Point Descripto...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： José R. A. Souto Fellipe M. C. Barbosa Bruno M. Carvalho Graduate Program in Systems and Computing Federal University of Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal Brazil

Precise measurements of chronic wound areas are very important for measuring the efficacy of different treatments, since it provides a clear picture of the chosen treatment evolution. The most used techniques still in use for measuring the wound areas involve manual measuring, paper scales and/or acetate tracing, with each health professional possibly measuring the wounds in slightly different ways, specially when dealing with the wound's depth. Thus, it is common to observe inconsistent wound area estimations by different health workers on the same wounds. Moreover, the measuring process requires direct contact with the wound, increasing the contamination and infection risks, besides possibly causing discomfort for the patient. An alternative that has already been proved successful is the use of Structure from Motion (SfM) to recover three-dimensional representations of a wound's surface and estimate that area. In this work, we analyze the effectiveness of several widely used point descriptors (BRIEF, DRINK, FREAK, ORB, SIFT and SURF) in the SfM wound reconstruction task when applied to real images of realistic synthetic wounds made of latex placed on different areas of the body. The results showed that SIFT, SURF and DRINK produced better area estimates. Since the execution time is a factor, we selected DRINK as the standard descriptor for our system, since it is approximately 9.3 and 4.9 times faster than SIFT and SURF, respectively.

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

Multi Task Classification Using Deep Learning Approaches for...

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International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE)

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

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

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.

关键词： Deep learning Accuracy Reviews Psychology Predictive models Brain modeling Multitasking Reliability Information technology Standards

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

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

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), International Conference on

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

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

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.

关键词： Symbiosis Technological innovation Machine learning algorithms Bibliometrics Collaboration Machine learning Market research Dynamic scheduling Fraud Risk management

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Initial Exploration and Understanding of Parameters for Successful Enterprise Architecture Implementation

Initial Exploration and Understanding of Parameters for Succ...

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International Conference on ICT For Smart Society (ICISS)

作者： Dyna Marisa Khairina Purwanto Purwanto Dinar Mutiara Kusumo Nugraheni Doctoral Program in Information Systems School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Diponegoro Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

This study provides an initial exploration and further understanding of the factors that are parameters in evaluating the success of Enterprise Architecture (EA) implementation. An evaluation process is needed to ensure that the EA project implementation provides effectiveness, efficiency, and feasibility of the EA information system as well as assessing previous project experience to avoid future EA project risks. The research aims to present a Systematic Literature Review based on the stages of identification, screening, eligibility, and inclusion so that 14 main articles are selected for review with strong actual relevance. The study results provide insights and references that can be used to further explore key factors in evaluating the success of EA implementation in the future. It was found that there were ten factors that most influenced the evaluation of the success of EA implementation and it was concluded that two potential factors were the most influential and required further exploration in evaluating the success of EA implementation which could be applied in various sectors/dimensions. This study contributes as an initial reference for overcoming problems in investigating and exploring the factors that influence the success of EA implementation as an effort to assess the success of achieving future organizational goals.

关键词： Bibliographies Testing Information systems

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Principal Curvatures Estimation with Applications to Single Cell Data

Principal Curvatures Estimation with Applications to Single ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yanlei Mezrag, Lydia Sun, Xingzhi Xu, Charles Macdonald, Kincaid Bhaskar, Dhananjay Krishnaswamy, Smita Wolf, Guy Rieck, Bastian Université de Montréal Dept. of Math. & Stat Canada Mila - Quebec AI Institute MontréalQC Canada Yale University Dept. of Comp. Sci. United States Dept. of Genetics United States Applied Mathematics Program New HavenCT United States Université de Fribourg Department of Informatics FR Fribourg Switzerland Helmholtz Zentrum München Institute of AI for Health BY Munich Germany

ISBN: (纸本)9798350368741

The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learning consists in hypothesizing that datasets lie on a lower dimensional manifold. This allows to study the geometry of point clouds by extracting meaningful descriptors like curvature. In this work, we will present Adaptive Local PCA (AdaL-PCA), a data-driven method for accurately estimating various notions of intrinsic curvature on data manifolds, in particular principal curvatures for surfaces. The model relies on local PCA to estimate the tangent spaces. The evaluation of AdaL-PCA on sampled surfaces shows state-of-the-art results. Combined with a PHATE embedding, the model applied to single-cell RNA sequencing data allows us to identify key variations in the cellular differentiation. © 2025 IEEE.

关键词： Gaussian curvature Principal curvature principal directions single-cell

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An event-driven approach for resources planning in distributed power generation systems

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International Journal of Electrical Power and Energy Systems 2022年 137卷 107768-107768页

作者： Maschio, Dierli M.R. Duarte, Bruno Lazzaretti, André E. Lafay, Jean-Marc S. Adzkiya, Dieky da Costa, Jean Patric Teixeira, Marcelo Graduate Program in Electrical Engineering Federal University of Technology Paraná Brazil Graduate Program in Electrical Engineering and Industrial Informatics Federal University of Technology Paraná Brazil Department of Mathematics Institut Teknologi Sepuluh Nopember Indonesia

Renewable Energy Sources (RESs) stand as a straightforward alternative to complement the energy grid and have been recurrently integrated into the generation and distribution matrices. Physically, generation components have distributed nature and they are expected to work coordinately, complementing each other to supply centralised microgrids, which interface them with the main power grid and consumers. As RESs have intermittent nature, engineers are limited to plan resources consumption, fulfil unit commitments, stratify time, cost, environmental impact, and quality of energy. This paper exploits the event-driven behaviour of microgrids to provide ways for them to recognise their context and cooperate towards common goals. Our main result is a Petri net simulation model that estimates customisation policies to be applied back over the microgridvia event-based control. Results show that in some cases it is possible to save effort (and resources) of power generation by replacing reactiveness for a more balanced, context-aware, energy chain. A real photovoltaic plant is modelled by our approach with 86% accuracy. © 2021 Elsevier Ltd

关键词： Renewable energy resources

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Automatic Aircraft Shadow Removal from Remote Sensing Images Using Mask-ShadowGAN 8

Automatic Aircraft Shadow Removal from Remote Sensing Images...

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8th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2021

作者： Ganyaporngul, Sirapavee Cooharojananone, Nagul Kruachottikul, Pravee Trakulwaranont, Donnaphat Satoh, Shin'ichi Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Chulalongkorn University Technopreneurship and Innovation Management Program Bangkok Thailand National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781665428958

Objects with shadow may cause a problem for image classification. For example, it can separate one object into many objects. It can also alter the size or shape of the object resulting in misclassification. In this paper, we focus on removing aircraft shadow from remote sensing images where the shadows occur on wings, bodies, and tails. Since it is very difficult to get shadow-free aircraft images and a shadow aircraft image of the same type for the training part, we adopted Mask-ShadowGAN for solving this issue. The benefit of the Mask-ShadowGAN algorithm is that, in the training part, the technique does not require the same images that have both shadow and shadow-free. In the experiment, we evaluated our proposed technique using RMSE and Jaccard similarity index for measurement. The experimental result shows that our technique shows promising results. We present both best and worst result based on sorted similarity index. © 2021 IEEE.

关键词： Remote sensing

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The Application of Conjugate Gradient Method to Motion Control of Robotic Manipulators

The Application of Conjugate Gradient Method to Motion Contr...

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Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2021

作者： Sulaiman, Ibrahim M. Malik, Maulana Giyarti, Wed Mamat, Mustafa Ibrahim, Mohd Asrul Hery Ahmad, Muhammad Zaini School of Quantitative Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Kuala Terennganu21300 Malaysia Department of Mathematics Universitas Indonesia Depok Indonesia Mathematics Education Study Program Universitas Islam Negeri Sunan Kalijaga Depok Indonesia Faculty of Entrepreneurship and Business Universiti Malaysia Kelantan Kota Bhar Malaysia Institute of Engineering Mathematics Universiti Malaysia Perlis Kangar Malaysia

ISBN: (纸本)9789811920943

Many industrial and engineering problems are transformed into optimization problems and solved using various numerical based methods. One of the frequently used method is the Steepest descent algorithm which converge to the solution in only one iteration, given the current point and provided the quadratic function is positive definite. However, this method is not suitable for large scale functions because of lack of gradient information and high computational cost. This study aims to suggest a new conjugate gradient algorithm for motion control of robotic manipulators and unconstrained optimization models. The convergence result of the new algorithm would be discussed under some suitable conditions. Computational simulations are carried out on the discrete-time kinematics equation of a two-joint planar robot manipulator to illustrates the efficiency of the algorithm. The algorithm was further extended to unconstrained optimization problems in addition to motion control of robotic manipulators. Preliminary results prove that the new algorithm is efficient compared to the existing CG algorithm. The comparisons are made using the set of 50 standard benchmark functions including number of iterations and CPU time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Flexible manipulators

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