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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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A Computational Model for Driver Risk Evaluation and Crash Prediction Using Contextual Data from On-board Telematics

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Recent Advances in computer Science and Communications 2022年第4期15卷 620-635页

作者： Jain, Neetika Mittal, Sangeeta Department of Computer Science & Engineering and Information Technology Faculty of Engineering Jaypee Institute of Information Technology Noida India

Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by the World Health Organization (WHO), approximately 1.35 million people are involved in road traffic crashes resulting in loss of life or physical disability. WHO attributes events like over-speeding, drunken driving, distracted driving, dilapidated road infrastructure and unsafe practices such as non-use of helmets and seatbelts to road traffic accidents. As these driving events negatively affect driving quality and enhance the risk of a vehicle crash, they are termed as negative driving attributes. Methods: A multi-level hierarchical fuzzy rules-based computational model has been designed to capture risky driving by a driver as a driving risk index. Data from the onboard telematics device and vehicle controller area network is used for capturing the required information in a naturalistic way during actual driving conditions. Fuzzy rules-based aggregation and inference mechanisms have been designed to alert about the possibility of a crash due to the onset of risky driving. Results: On-board telematics data of 3213 sub-trips of 19 drivers has been utilized to learn long term risky driving attributes. Furthermore, the current trip assessment of these drivers demonstrates the efficacy of the proposed model in correctly modeling the driving risk index of all of them, including 7 drivers who were involved in a crash after the monitored trip. Conclusion: In this work, risky driving behavior has been associated not just with rash driving but also other contextual data like driver’s long-term risk aptitude and environmental context such as type of roads, traffic volume and weather conditions. Trip-wise risky driving behavior of six out of seven drivers, who had met with a crash during that trip, was correctly predicted during evaluation. Similarly, for the other 12

关键词： Telematics

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Harmful Brain Activity Classification Using Ensemble Deep Learning 66

Harmful Brain Activity Classification Using Ensemble Deep Le...

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66th International Symposium ELMAR, ELMAR 2024

作者： Habijan, Marija Sojo, Robert Tolic, Ivana Hartmann Galic, Irena Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350375411

Electroencephalography (EEG) is a crucial tool for monitoring electrical brain activity and diagnosing neurological conditions. Manual analysis of EEG signals is time-consuming and prone to variability, necessitating automated methods for accurate and efficient interpretation. This paper presents an ensemble learning approach for the automated classification of harmful brain activity from EEG data. The model combines the strengths of ResNet1d GRU, EfficientNetB0, and EfficientNetB1 to predict brain activity from spectrograms and raw EEG signals. By leveraging the unique capabilities of each model and employing a weighted averaging scheme, the ensemble achieves a competitive Kullback-Liebler divergence score of 0.3 on the testing dataset. This approach demonstrates the potential of ensemble learning to improve the accuracy and robustness of automated EEG signal classification, offering a valuable tool for early detection and diagnosis of neurological conditions. © 2024 IEEE.

关键词： Contrastive Learning

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Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

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IEEE Access 2024年 12卷 514-533页

作者： Taherinavid, Siavash Moravvej, Seyed Vahid Chen, Yen-Lin Yang, Jing Ku, Chin Soon Yee, Por Lip Iran University of Science and Technology School of Civil Engineering Tehran13114-16846 Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve

关键词： Smartphones

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Multi-organ segmentation using TransUNet with improved multidimensional Multi-Layer Perceptron 4

Multi-organ segmentation using TransUNet with improved multi...

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4th International Conference on Image Processing and Intelligent Control, IPIC 2024

作者： Liu, Chunlin Huang, Wenxuan Li, Ruiyi Chen, Peizhi Faculty of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China

ISBN: (纸本)9781510682313

Multi-organ segmentation from abdominal images is an important task. Due to the imbalance between different organ and the differences in size, shape, and contrast of different organs, it is a challenging problem in the field of medical image analysis. A powerful feature extraction model is the key to solving this challenge. TransUNet is a popular solution. MultiLayer Perceptron (MLP) is its important part. However, the existing MLP modules only operate on a single feature dimension without interaction across different dimensions. This paper proposes an improved MLP module with multidimensional interaction, named SMLP-Mixer."The SMLP-Mixer achieves multidimensional information interaction while possessing lower computational complexity and fewer parameters in the feature dimension, which can reduce the risk of overfitting. Experiments on the Synapse dataset demonstrate its effectiveness. Compared to TransUNet, the Dice coefficient exhibits a 1.98% improvement, and the HD95 (mm) shows a significant enhancement of 10.83. © 2024 SPIE.

关键词： Mixers (machinery)

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Orbit Weighting Scheme in the Context of Vector Space information Retrieval

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computers, Materials & Continua 2024年第7期80卷 1347-1379页

作者： Ahmad Ababneh Yousef Sanjalawe Salam Fraihat Salam Al-E’mari Hamzah Alqudah Department of Computer Science Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Department of Cybersecurity Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346Ajman13306United Arab Emirates Information Security Department Faculty of Information TechnologyUniversity of PetraAmman11196Jordan Department of Data Science and Artificial Intelligence Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

关键词： information retrieval orbit weighting scheme semantic text analysis Tf-Idf weighting scheme vector space model

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Smart IoT System for Cardiopulmonary Resuscitation Training: Embedded Sensor Integration and Android Application 2

Smart IoT System for Cardiopulmonary Resuscitation Training:...

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2nd International Symposium on information technology and Digital Innovation, ISITDI 2024

作者： Aisuwarya, Ratna Erlina, Tati Mitzi, Richo Attir Universitas Andalas Faculty of Information Technology Computer Engineering Department Padang Indonesia

ISBN: (纸本)9798350389241

Lack of awareness and experience in CPR often discourages individuals from initiating rescue actions of Out-of-hospital cardiac arrest (OHCA). This research presents a new approach to CPR training using embedded system-based devices. This system, which resembles the human body's torso and head sections, monitors and calculates key parameters critical to successful CPR, including compression, airway management, and breathing techniques. Sensor nodes are placed to measure pressure during compression and monitor airway and respiratory processes. The collected data is transmitted and processed using Internet of Things (IoT) technology with a multi-user Android application, facilitating collaborative training sessions. Experimental testing validated the system's functional capabilities with successful network connection, real-time data transmission, and effective execution of CPR procedures. Hardware implementations use microcontrollers and sensors, with Arduino and NodeMcu ESP8266 enabling data processing and transmission. Software components developed within the Arduino IDE and Android Studio ensure efficient data handling and visualization. The results highlight the system's effectiveness in improving the accessibility and affordability of CPR training, potentially improving intervention by witnesses, and improving OHCA survival rates. © 2024 IEEE.

关键词： Resuscitation

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of information technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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Optimization of Transmission Distance in Symmetric Wireless Power Transfer Topology 47

Optimization of Transmission Distance in Symmetric Wireless ...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Vinko, Davor Srnović, Mario Bilandžija, Domagoj Filipović, Luka Faculty Of Electrical Engineering Computer Science And Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350382495

Inductive wireless power transfer (WPT) system uses alternating magnetic field to transmit power from the transmitter to the receiver. To confine the magnetic field, WPT coils are realized with high permeability substrate material that ensures shielding. Aside from shielding, high permeability substrate also increases the coil inductance. When transmitter and receiver coils are placed in close proximity, high permeability substrate of transmitter coil increases the inductance of the receiver coil, and vice versa. This additional inductance increase is a function of the substrate material type and the distance between the coils. To achieve high efficiency and low losses of the WPT system, a capacitor network is added to both transmitter and receiver coil. In a common serial-serial (S-S) WPT system topology, the capacitor networks are asymmetric, i.e. the capacitor network of the transmitter and the receiver are not identical. The efficiency of asymmetric S-S topology is significantly impaired due to variable coil inductance. To attain high efficiency in such operating conditions, this paper proposes symmetric S-S WPT topology with emphasis on transmission distance optimization. © 2024 IEEE.

关键词： Inductive power transmission

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Advanced Construction Materials Based on Concrete to Protect the Living Space from Non-Ionizing Radiation 1

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Mandrić, Vanja Rupčić, Slavko Vinko, Davor Dominković, Ivica Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

This paper presents measurements of the reflection and transmission coefficient of electromagnetic waves through concrete and two concrete-based composites: concrete with steel fibers and concrete with carbon fibers with the aim of enhancing the attenuation of electromagnetic waves passing through concrete and concrete composites. The frequency range for which the measurements were carried out extends from 30 MHz to 18 GHz and includes most of the existing stationary (and mobile) radiation EM sources in the environment in which the general population moves (FM, DVBT2, 2G - 5G mobile radio systems, stationary radar border area control systems). The measurement results show that as the thickness of the concrete increases, the transmission through concrete and concrete composites decreases (parameter S12 decreases). Furthermore, composites with carbon fibers partially reduce, while those with steel fibers significantly reduce, the transmission of EM waves through such composites. The reduction of parameter S12 by composites with steel is up to 52 dB (at a frequency of 2.9 GHz) for a block thickness of 100 mm. It is important to emphasize that in the case of composites with steel fibers, the influence of the fibers on the transmission parameters is more significant than the thickness of the sample. The reason for this is the increase in electrical conductivity of composites with steel fibers due to the increased proportion of conductive components in the concrete. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Steel fibers

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