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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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Automatic Heliostat Directing System Based On Microcontroller ESP32

Automatic Heliostat Directing System Based On Microcontrolle...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Medyanto, Richard Sanjaya, Nicholas Liawatimena, Suryadiputra Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350381290

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers. © 2023 IEEE.

关键词： Solar energy

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Design and Development of an Internet of Things (IoT)-Based Application and System to Detect Stunting in Infants and Toddlers 10

Design and Development of an Internet of Things (IoT)-Based ...

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10th International HCI and UX Conference in Indonesia, CHIuXiD 2024

作者： Tiffany, Theresa Jeremia, James Lie, Zener Sukra Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Engineering Program Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350380439

Currently, the process of monitoring children's growth in Indonesia relies on manual methods for collecting anthropometric data. These methods pose a risk of data recording errors. Additionally, the ratio of healthcare workers to children in Indonesia is 1:90, a significant disparity that hinders efforts to reduce the prevalence of stunting in the country. To support the third Sustainable Development Goal (SDG), which emphasizes 'Good Health and Well-being,' it is crucial to address issues in the stunting management process, from data collection to monitoring. The design and development of this iOS-based mobile application aim to assist in monitoring child growth and provide guidance and steps for prevention and treatment. Besides application development, it also involves the design and development of an Internet of Things (IoT) system to aid in the collection of children's anthropometric data. The research methodology includes studying stunting cases in Indonesia, anthropometric data calculation methods, stunting prevention and management steps, application design and development, automatic anthropometric data collection, and online data storage and transmission using IoT technology. The developed application and system have been tested and successfully integrated. The IoT device has an accuracy rate of 99.83% for weight measurement calibration and 98.68% for height or length measurement calibration. From the testing results of the child condition calculation method in the application, an accuracy rate of 100% was achieved. © 2024 IEEE.

关键词： Anthropometry

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Traffic Signs Detection System Using YOLO (You Only Look Once) That Provides Notification 1

Traffic Signs Detection System Using YOLO (You Only Look Onc...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Setiawan, Yosua Puji, Muhammad Nurul Astuti, Winda Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368918

Road accidents have become a problem for many road users in many countries. It may affect human daily activity and tasks, potentially influencing productivity and safety. Therefore, reducing road accidents as swiftly and effectively as possible is critical. For this reason, a Traffic signs detection system has been developed in this work to improve road safety. The detection system can detect traffic signs on the road such as traffic lights, speed limits, and no U-turn signs, and then it will provide users with the corresponding text on an LCD and vibration feedback using a motor. Throughout this work, the You Only Look Once (YOLO) algorithm is proposed for detecting traffic signs on the road. Moreover, a camera is used to extract live videos which serve as an input for the Raspberry Pi 4 model B containing custom YOLOv5 and YOLOv8 models. The system then sends the output to an LCD, a vibration motor, and a radio frequency (RF) module as notifications for users. In the end, the performance of the proposed method has been collected and it is found to produce good accuracy within an inconsiderable amount of 1.677 images and 2.263 annotations. Therefore, the accuracy of 0.69 for YOLOv5 and 0.7 for YOLOv8 were obtained from the training and validation process. Furthermore, the system accurately detects the traffic signs on the road. © 2024 IEEE.

关键词： Traffic signs

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i-Glove: An Intelligent Glove System Based on Deep Learning to Support Deaf-blind Individuals in Recognizing Banknotes 3

i-Glove: An Intelligent Glove System Based on Deep Learning ...

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3rd IEEE International Conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain, Fahad Mohammad Alavi, Rafit Safat, Yeamin Sarker, Gobinda Chandra Noor, Nafisa Hossain, Ekram Mymensingh Engineering College Department of Computer Science and Engineering Mymensingh Bangladesh University of Dhaka Department of Robotics and Mechatronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The deaf-blind community faces significant challenges in identifying banknotes due to simultaneous hearing and vision impairments, which complicate their ability to conduct daily financial transactions. Recent advances in Artificial Intelligence (AI)-based assistive technologies offer promising potential to address these challenges. This study presents the "i-Glove,"a wearable device specifically developed to assist the deaf-blind community in Bangladesh with accurate banknote identification. The i-Glove is designed to recognize nine common Bangladeshi banknotes (e.g., BDT 20, 50, 100) through a camera module integrated on the palmar side of the glove, which captures real-time images of the banknotes. These images are then processed by a convolutional neural network (CNN) model to accurately classify each banknote. Identified notes are translated into unique vibration patterns that are delivered to the user through a shaftless vibration motor embedded in the glove, providing intuitive, non-visual communication. Compared to previous approaches, our model demonstrates superior performance even with a compact dataset, achieving an overall F1-score accuracy of 96.55%, with class-specific precision, recall, and F1-score metrics ranging from 90% to 100%. Additionally, a dedicated application is developed which supports real-time banknote detection and interfaces seamlessly with the glove. The proposed system represents a significant step forward in assistive technology for the deaf-blind community, empowering the users with greater autonomy in financial transactions. © 2024 IEEE.

关键词： Convolutional neural networks

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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

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computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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Classification of Jackfruit Species Using Deep Learning Model 2

Classification of Jackfruit Species Using Deep Learning Mode...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Veeresha, R.K. Karegoudra, Shilpa Kumar, Bhavin Pavan, Pavan Department of Robotics and Artificial Intelligence Udupi574110 India Department of Computer Science and Engineering Udupi574110 India

ISBN: (纸本)9798350354461

Identifying Jackfruit spices using a deep learning model involves leveraging advanced neural network architectures to classify spices accurately. The model is trained on a dataset containing images of various spices commonly used with Jackfruit, such as deng surya, manmohan, prakashchandra, vietnamearly, and others. Through multiple layers of convolutional neural networks (CNNs) and possibly recurrent neural networks (RNNs), the model learns intricate patterns and features within the images to differentiate between different species. Transfer learning techniques might also be employed to adapt pre-trained models to the specific task of spice identification. The model's performance is evaluated on a separate test dataset to measure its accuracy, precision, recall, and F1 score. The paper provides a comprehensive overview of the deep learning approach to identifying Jackfruit spices, highlighting its potential applications in culinary research, food industry automation, and dietary analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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CredChain: Academic and Professional Certificate Verification System using Blockchain

CredChain: Academic and Professional Certificate Verificatio...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Al Ahmed, Saniyat Mamun Rudro, Rifat Al Prity, Afrina Jannat Saha, Suman Mansoor, Nafees Nur, Kamruddin Department of Computer Science Dhaka Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh Department of IoT and Robotics Engineering Gazipur Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350350289

The integrity of academic and professional creden-tials is a foundation in education and the professional fields. Traditional certificate verification methods are flawed by in-efficiencies, susceptibility to fraud, and a reliance on manual processes that compromise security and expedience. To address these issues, we present CredChain, a robust Academic and Professional Certificate Verification System that harnesses the power of blockchain technology. Our system utilizes Ethereum-based Decentralized Applications (DApps) and Smart Contracts integrated with the InterPlanetary File System (IPFS) to ensure immutable, secure, and transparent verification processes. We outline the novel algorithms for essential system functions such as certificate uploading, verification, and retrieval of certificates. The CredChain system has been subjected to rigorous testing and validation, demonstrating its effectiveness in mitigating the risk of fraudulent certificates and streamlining the verification process accurately. © 2024 IEEE.

关键词： Smart contract

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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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Continuous Query-based Data Trading

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Performance Evaluation Review 2024年第1期52卷 73-74页

作者： Cheng, Jin Ding, Ningning Lui, John C.S. Huang, Jianwei School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Electrical and Computer Engineering Northwestern University Evanston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

In the era of big data, traditional data trading methods designed for one-time queries on static databases fail to meet the demands of continuous query-based trading on streaming data, often resulting in repeated and inaccurate charges due to neglecting computation sharing in continuous query execution. To address this, we introduce CQTrade, the first trading framework tailored for continuous queries, which incorporates computation sharing across different time windows and queries, enhancing integration with existing trading systems. Our contributions include a theoretical analysis of computation-sharing techniques, the development of a general optimization problem to maximize seller revenue adaptable to various techniques, and the proposal of a branch-and-price algorithm to handle the problem's complexity. Our evaluation shows that the proposed framework improves the success rate of data trading by 12.8% and boosts the seller's revenue by an average of 28.7%, compared to the one-time query-based data trading methods used in the current data market. © 2024 Owner/Author.

关键词： Integer programming

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