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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Chatbot for the Detection of Depression in University Students in Peru using Sentiment Analysis 4

Chatbot for the Detection of Depression in University Studen...

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4th IEEE International Conference on Advanced Learning Technologies for Education and Research, ICALTER 2024

作者： Saldana Condori, Walther Aaron Mori Leon, Andrea Daniela Dextre Alarcon, Jymmy Stuwart Peruvian University of Applied Science System Engineering Faculty Lima Peru

ISBN: (纸本)9798331510244

Mental disorders, such as depression, usually manifest themselves on average by the age of 24, when many young people are in college. During the COVID-19 pandemic, university students experienced a significant impact on their mental health due to confinement, which highlights the need for and importance of early detection of depression so that it can be treated effectively and in a timely manner. Because of this, a chatbot based on sentiment analysis is proposed for the early detection of depression in university students. The algorithm used in the chatbot makes use of responses to the PHQ-9 questionnaire modified to open-ended questions to predict the level of depression that the student may have. This proposal has 4 stages: Technology Analysis, Algorithm Development, Results and Chatbot Construction. The results obtained showed that the Gradient Boosting machine learning technique obtained an accuracy of 0.81. This result suggests that our chatbot can effectively and early detect depression in university students, providing a valuable contribution to the mental health care of students. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Variable Length Multi-Objective Whale Optimization for Trust Aware Data Gathering in Wireless Sensor Network

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IEEE Access 2025年 13卷 32100-32121页

作者： Saad, Mohammed Ayad Jaafar, Rosmina Chellappan, Kalaivani Faculty of Engineering and Built Environment Department of Electrical Electronics and System Engineering Selangor Bangi43600 Malaysia Al-Kitab University Department of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Universitas Airlangga Biomedical Engineering Study Program Faculty of Science and Technology Surabaya60115 Indonesia

Efficient data collection in wireless sensor networks (WSNs) is crucial. While traditional approaches rely on stationary data sinks, the use of mobile sinks, like unmanned aerial vehicles (UAVs), has shown promise in enhancing data-gathering capabilities. However, existing methods often overlook the importance of trust, leaving the network susceptible to malicious or faulty nodes. To address this issue, we propose a novel trust-aware data-gathering algorithm, Variable Length Multi-Objective Whale Optimization Data Gathering (VLMOWO-DG). Our algorithm simultaneously optimizes energy consumption, delay, and trust while employing mobile sinks to collect data from sensor nodes. By considering trust as a key performance metric, we improve data reliability and security. The VLMOWO-DG algorithm introduces flexibility through variable-length solution representation and application-oriented operators, allowing for efficient exploration of the solution space. Simulation results demonstrate that our proposed algorithm significantly outperforms established benchmarks, NSGA-II and NSGA-III, in terms of domination and hypervolume. This leads to a remarkable improvement of 200% in data gathering performance. © 2015 The Authors.

关键词： Data collection

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A FUNDAMENTAL INVESTIGATION ON THE JUDGEMENT METHOD OF BOND STRENGTH TEST RESULTS FOR POST-INSTALLED ADHESIVE ANCHOR

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AIJ Journal of Technology and Design 2024年第74期30卷 17-22页

作者： Choe, Hongbok Mukai, Tomohisa Ariki, Katsuyoshi Kinugasa, Hideyuki Dept. of Architecture Faculty of Science and Engineering Tokyo Univ. of Science Japan Evaluation System Division Building Dept. National Institute for Land and Infrastructure Management Japan Dept. of Structural Engineering Building Research Institute Urban Renaissance Agency Japan Dept. of Architecture Faculty of Science and Engineering Tokyo Univ. of Science Japan

In this study, a method was developed to assess the results of bond strength tests with the aim of mitigating the degradation of long-term performance in post-installed anchors. This degradation can occur due to the absence of an upper limit on maximum displacement at bond strength and the allowance for significant creep deformation. The study confirmed that utilizing a judgment equation based on linear approximation and tri-linear curves can evaluate the test results that are allowed, necessary to reassess, potentially suitable for use, and requiring disposal. © 2024 Architectural Institute of Japan. All rights reserved.

关键词： Adhesives

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Development of a machine learning model for predicting abnormalities of commercial airplanes

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Data science and Management 2024年第3期7卷 256-265页

作者： Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah Harumi Veny Fara Nissya Nur Hafidzoh Department of Computer Engineering Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Intelligent System Research Group(ISysRG) Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Department of Informatics Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia College of Engineering Studies Universiti Teknologi MARA(UiTM)Shah Alam40450Malaysia

Airplanes are a social necessity for movement of humans,goods,and *** are generally safe modes of transportation;however,incidents and accidents occasionally *** prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast *** study combined data-quality detection,anomaly detection,and abnormality-classification-model *** research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and *** data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial *** results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.

关键词： Automatic dependent surveillance-broadcast data Commercial airplanes accident Data-labeling Machine learning Prediction model

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Battery Monitoring system on IoT-Based Smart Micro Grid 4

Battery Monitoring System on IoT-Based Smart Micro Grid

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4th International Conference on Electronic and Electrical engineering and Intelligent system, ICE3IS 2024

作者： Iksan, Nur Widodo, Djoko Adi Djuniadi, Djuniadi Putri, Riana Defi Mahadji Suryanto, Agus Udayanti, Erika Devi Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Department of Information System Indonesia

ISBN: (纸本)9798350378368

The use of batteries as an energy storage medium has a very important role in the installation of renewable energy power plants, such as photovoltaics to overcome intermittency in photovoltaics and to maintain stable power quality. In this study, the installation of solar plants that was used as the research object was a smart micro grid that integrates photovoltaic electrical energy sources with the Utility electricity network for residential scale. The battery monitoring system is used to manage the performance of existing batteries on the electricity grid through analysis of the battery's electrical values such as current, voltage, power and energy, so that it can function optimally in storing electrical energy and supplying electricity to users. Internet of things (IoT) technology is used to provide easy transfer of data obtained through the process of data acquisition to data visualization provided to be accessed by users in real time. This research aims to develop an IoT-based battery monitoring system. The development carried out focuses on the data acquisition process, the data transfer process using IoT technology, the process of analyzing and visualizing data on the IoT cloud and remote access to users. © 2024 IEEE.

关键词： Battery IoT Monitoring Smart Micro Grid

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STUDY ON A SIMPLIFIED MODEL OF A HIGH-RISE SHEAR-TYPE BUILDING FOR EVALUATION OF Q–Δ RESONANCE

Journal of Structural and Construction Engineering

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Journal of Structural and Construction engineering 2023年第803期88卷 59-70页

作者： Anamizu, Ryosuke Kohiyama, Masayuki School of Science for Open and Environmental Systems Graduate School of Science and Technology Keio University Japan Dept. of System Design Engineering Faculty of Science and Technology Keio University Japan

To evaluate Q–Δ resonance, which is a torsional resonance phenomenon caused by geometric non-linearity, we proposed a simplified model for an n-story non-eccentric building model with a shear-type structure. We designed and fabricated a four-story small-scale specimen assuming a non-eccentric steel structure with a height of 324 m, and conducted shaking table tests. As a result, the occurrence of Q–Δ resonance was confirmed as predicted by the simplified model. In addition, the simplified model reproduced the resonance amplitude in a finite element model of the specimen with the out-of-plane rotation of slabs constrained and its validity was confirmed. © 2023 Authors. All rights reserved.

关键词： Tall buildings

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Development of a quantitative evaluation system for static electricity induced luminescence using image proccesing 9

Development of a quantitative evaluation system for static e...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Eguchi, Taiga Ishizu, Nanami Lee, Jin Yien Fujio, Yuki Fukuda, Osamu Kikunaga, Kazuya Saga University Faculty of science and engineering Saga Japan Sensing System Research Center Aist Tosu Japan

ISBN: (纸本)9798350363043

Static Electricity induced Luminescence (SEL) material (SrAl2O4: Eu2+) was recently discovered, which emits luminescence due to external energy from static electric. Static electric that could not be visualized can now be recognized through luminescence and SEL is expected to be a new image-based static electricity sensing technology. In this study, we developed a system to quantitatively evaluate SEL material using image analysis. The experimental results confirm correlation between the applied voltage and the maximum intensity of the SEL. © 2024 IEEE.

关键词： Strontium compounds

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