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Integrative Multi-Source Mapping of Coastal Environments Using Hy-perspectral Data Fusion Platforms

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Han, Huimin Por, Lip Yee Talpur, Bandeh Ali Hashim, Muhammad Yang, Jing Saidani, Taoufik Djumaniyazova, Mukhayya Xusinovna Madrakhimovich, Khudoynazarov Egambergan Talpur, Kazim Raza Hainan Vocational University of Science and Technology Mechanical and Electrical Engineering Col-lege Haikou571126 China Uni-versiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Trinity Col-lege Dublin School of Computer Science and Statistics Ireland National Textile Uni-versity Faisalabad Faisalabad Business School Pakistan Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur50603 Malaysia Northern Border University Center for Scientific Research and Entrepreneurship Arar73213 Saudi Arabia Urganch State University Pedagogy and Psychology Department Uzbekistan Mamun University Uzbekistan Jamshoro Institute of Health Management and Re-search Sciences Sindh Pakistan

Hyperspectral images (HSIs) not only have a large number of spectral features, but also show a comprehensive spa-tial distribution of land cover and offer substantial advantages in the fine classification of ground materials. However, due to the high dimensionality and redundancy of HSIs and class imbalance in hyperspectral datasets, improving classification performance is still a huge challenge. Recently, graph-based learning algorithms are proven to be better classifiers for high dimensional data. Learning graph data and modeling spatial topological links be-tween features are significantly more accessible and more effective with graph-based deep learning, which has been demonstrated to be a promising approach. In the past few years, convolutional neu-ral networks (CNNs) and graph convolutional networks (GCNs) have achieved good results in HSI classification, but CNNs strug-gle to achieve good accuracy in low samples, while GCNs have a substantial computational cost. To resolve these issues, this paper proposes a feature fusion model using a 3D-CNN and hybrid-GAT named FFCHG. The algorithm consists of two elements: the 3D-CNN, which produces good classification for 3D HSI cube data, and GAT-based encoder and decoder modules that help in im-proving the classification accuracy of the 3D-CNN. Finally, the multiple features are merged with the help of two neural network models. Experiments on three public HSI datasets show that the proposed methods perform better than other state-of-the-art methods using the limited training samples and in low classifica-tion time with accuracy of more than 90% in all datasets and kappa with more than 91% for each dataset. © 2025 IEEE.

关键词： Convolutional neural networks

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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent system (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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IoT Integration in Agriculture: Advantages, Challenges, and Future Perspectives: Short survey

IoT Integration in Agriculture: Advantages, Challenges, and ...

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International Conference on Wireless Networks and Mobile Communications (WINCOM)

作者： Abdulaziz Aborujilah Abdulaziz Alashbi Ibrahim Shayea Abdul Hakim Mohamed Abdulraqeb Alhammadi Ayman A. El-Saleh Abdul Ahad Department of Management Information System College of Commerce & Business Administration Dhofar University Salalah Sultanate of Oman Department of Information Systems A’Sharqiyah University (ASU) Sultanate of Oman Electronics and Communication Engineering Department Faculty of Electrical and Electronics Engineering Technical University (ITU) Istanbul Turkey Communication Systems and Networks Research Lab Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia (UTM) Kuala Lumpur Malaysia

The era of the Internet of Things (IoT) has commenced, captivating us with the extensive application of futuristic technologies in the realm of smart farming. Various methods for enhancing agricultural tasks are actively under development to meet the burgeoning demand for food production. The maintenance and monitoring of farms have advanced significantly, the adoption of IoT-based alternatives to traditional human-driven tasks is rapidly getting more importance. Io T integrates a diverse range of technologies to elevate production and management within the agricultural sector. This survey delves into numerous advantages and challenges associated to integrating IoT technology in agricultural process. We present a collection of applications, an IoT architecture elucidating all its components, and a roster of protocols that enhance network performance. Furthermore, we discuss future prospective directions, placing emphasis on 5G, robotics, UAVs, and Big Data.

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A comprehensive evaluation of machine learning and deep learning algorithms for wind speed and power prediction

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Decision Analytics Journal 2024年 13卷

作者： Elmousalami, Haytham Elmesalami, Hadi Hesham Maxi, Mina Farid, Ahmed Abdel Kader Mohamed Elshaboury, Nehal Department of Infrastructure Engineering Faculty of Engineering and Information Technology The University of Melbourne Vic 3010 Australia Projects Section Head and PMP at General Petroleum Company (GPC) Egypt Planning Engineer at Riyadh Factory Aluminum System Riyadh Saudi Arabia Civil engineering department at the British university in Egypt Cairo Egypt Civil Engineering Department Faculty of engineering Benha university Benha 13518 Egypt Construction and Project Management Research Institute Housing and Building National Research Centre Giza Egypt Industrial Ecology Energy and Process Engineering Norwegian University of Science and Technology (NTNU) Norway

Accurate wind speed and power predictions are crucial for renewable wind energy applications. This study compares and evaluates twelve machine learning (ML) and deep learning (DL) algorithms, including single and ensemble models across various time scales from 10 min to a day and a half ahead with a particular focus on ensemble prediction algorithms. Moreover, the study proposes a wind speed and power prediction system where the outcome of the wind speed prediction (WSP) model serves as input for the wind power prediction (WPP) model. Several evaluation metrics, such as mean absolute percentage error (MAPE) and mean square error (MSE) were calculated to benchmark different model accuracies. For WSP, the extremely randomized trees, decision tree, and bagging ensemble algorithms demonstrated high accuracy across different time scales where the MAPE ranged from 3.4% to 9.2%, the MSE ranged from 0.17 to 1.15, and the adjusted coefficient of determination ranged from 94% to 99%. For WPP, bagging ensemble algorithms and extremely randomized trees were also effective for predicting different time scales where the MAPE ranged from 4.12% to 11.7% and the MSE ranged from 10945 to 2.4. Ensemble ML algorithms provide better and more accurate results than single ML algorithms. The extreme gradient boosting model showed relatively small computational time and memory according to computational cost. Moreover, this study conducted a sensitivity analysis where air pressure, wind vane, and humidity were the key predictors for WSP and WPP. © 2024 The Author(s)

关键词： Artificial intelligence Decision analytics Deep learning Ensemble machine learning Sustainability Wind speed and power prediction

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Analysis of Perceptron Quantum Artificial Neural Networks to Classify the Feasibility of Prospective Debtors 1

Analysis of Perceptron Quantum Artificial Neural Networks to...

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1st International Conference on Advanced information Scientific Development, ICAISD 2020

作者： Pujiastuti, Lise Wahyudi, Mochamad Solikhun STMIK Antar Bangsa Information System Study Program Banten Indonesia Universitas Bina Sarana Informatika Faculty of Technical and Informatics Information Technology Study Program DKI Jakarta Indonesia AMIK Tunas Bangsa Pematangsiantar Informatics Management Study Program North Sumatra Indonesia

Bank is a business entity whose activities are collecting funds from the public in the form ofdeposits and channeling them to the public in the form of loans or other forms to improve the livesof many people. This study aims to classify the feasibility data of prospective borrowers with the perceptron algorithm using quantum computing to facilitate the bank in determining the prospective debtor. The results of this study are an analysis of the feasibility of prospective borrowers usingthe perceptron algorithm with quantum computing. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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Method for modelling the number of HIV and AIDS cases using least square spline Biresponse nonparametric negative binomial regression

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MethodsX 2025年 14卷 103336页

作者： Ramadan, Arip Chamidah, Nur Budiantara, I Nyoman Lestari, Budi Aydin, Dursun Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Statistics Department Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences The University of Jember Jember 68121 Indonesia Department of Statistics Faculty of Science Muğla Sıtkı Koçman University Muğla 48000 Turkey Research Scholar at Department of Mathematics University of Wisconsin Oshkosh Algoma Blvd Oshkosh 54901 WI United States

We propose a new statistical modeling method to analyze functional relationship between predictor variables and correlated response variables, and apply it for modeling the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users. The proposed method is LS-Spline BNNBR that is a Biresponse Nonparametric Negative Binomial Regression (BNNBR) based on least squares spline (LS-Spline). To validate the proposed method, we compare it with classical method, namely Ordinary Least Square Biresponse Parametric Negative Binomial Regression (OLS-BPNBR). The results show that the LS-Spline BNNBR method provided deviance value of 7.542334 which is smaller than that of OLS-BPNBR method, namely 7.993935. Thus, the proposed method has better prediction fit than the classical method. • This study discusses new statistical modeling method for the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users where the response variables are correlated with each other. • The model estimation results show that the number of HIV and AIDS cases in Indonesia will decrease if the early-age marriages percentage increases and the contraceptive users percentage decreases. • The results are useful to provide a scientific basis for making policies to control the number of HIV and AIDS cases in Indonesia. © 2025 The Authors

关键词： AIDS BLSSNNBR model Contraceptive user Early-age marriage HIV Least square spline

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Two-Layer SVM, Towards Deep Statistical Learning

Two-Layer SVM, Towards Deep Statistical Learning

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Alireza Kazemi Reza Boostani Mahmoud Odeh Mohammad Rasmi AL-Mousa Mathematics Department Faculty of Science Shiraz University Shiraz Fars Iran CSE&IT Dept. Electrical and Computer Engineering School Shiraz University Shiraz Fars Iran Department of Management Information System Zarqa University Jordan Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan

Support Vector Machine (SVM) is originally a binary large-margin classifier emerged from the concept of structural risk minimization. Multiple solutions such as one-versus-one and one-versus-all have been proposed for creating multi-class SVM using elementary binary SVMs. Also multiple solutions have been proposed for SVM model selection, adjusting margin-parameter C and the Gaussian kernel variance. Here, an improved classifier named SVM-SVM is proposed for multi-class problems which increases accuracy and decreases dependency to margin-parameter selection. SVM-SVM adopts two K-class one-vs-one SVMs in a cascaded two-layer structure. In the first layer, input features are fed to one-vs-one SVM with non-linear kernels. We introduce this layer as a large-margin non-linear feature transform that maps input feature space to a discriminative K*(K-1)/2 dimensional space. To assess our hierarchical classifier, some datasets from the UCI repository are evaluated. Standard one-vs-one SVM and one-vs-one fuzzy SVM are used as reference classifiers in experiments. Results show significant improvements of our proposed method in terms of test accuracy and robustness to the model (margin and kernel) parameters in comparison with the reference classifiers. Our observations suggest that a multi-layer (deep) SVM structures can gain the same benefits as is seen in the deep neural nets (DNNs).

关键词： Support vector machines Electric potential Statistical learning Neural networks Transforms Nonhomogeneous media Robustness

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Performance LoRa technology for Autonomous Vehicles

Performance LoRa Technology for Autonomous Vehicles

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International Seminar on Intelligent technology and Its Applications (ISITIA)

作者： Puput Dani Prasetyo Adi Iwan Purnama Arfanda Anugrah Siregar Angga Putra Juledi Firman Edi Abdul Karim Yuyu Wahyu Fairuz Iqbal Maulana Suhardi Atmoko Budi Susilo Toni Ilham Maliki Harahap Lintang Patria National Research and Innovation Agency (BRIN) Bandung Indonesia Faculty of Science and Technology Labuhanbatu University Labuhanbatu North Sumatra Indonesia Telecommunication Engineering Study Program Politeknik Negeri Medan Medan City North Sumatra Indonesia Department of Engineering Management Faculty of Industrial Technology Batam Institute of Technology Batam City Riau Islands Indonesia Department of Information Technology Budi Darma University Medan North Sumatra Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Teacher Training and Education Labuhanbatu University Labuhanbatu North Sumatra Indonesia Faculty of Engineering Universitas Terbuka Jakarta Indonesia

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss.

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Exploring ERP User Behaviour in Developing Countries: Integrating Self-efficacy and UTAUT Perspectives

Exploring ERP User Behaviour in Developing Countries: Integr...

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Advancement in Data Science, E-learning and information systems (ICADEIS), International Conference on

作者： Dedy Syamsuar Muhammad Angga Oktaharisetia Deden Witarsyah Ahmad Luthfi Peter Dell Yudistira Sira Permana Darius Antoni Information Systems Department School of Information Systems Bina Nusantara University Indonesia Program Pasca Sarjana Universitas Bina Darma Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia School of Management and Marketing Faculty of Business and Law Curtin University Australia Teknik Informatika Politeknik Sekayu Sekayu Indonesia Faculty of Computer Science Universitas Indo Global Mandiri Palembang Indonesia

ERP stands for enterprise resource planning. It is an information system that is all rolled into one, is very flexible and adaptable, and optimizes business operations while also centralizing all of the company's data. This study aims to empirically investigate the elements that affect users' intention and usage of enterprise resource planning (ERP). This research incorporates self-efficacy into the unified theory of acceptance and use of technology (UTAUT2). A quantitative technique was taken, and as a result, 143 replies that might be used were effectively collected. After that, the information was put through a structural equation modelling partial least square (SEM-PLS) analysis. According to the findings of this research, self-efficacy appears to have a significant influence on all endogenous variables (EE, BI, and UB), as indicated by t-values of 3.951, 7.573, and 5.492, respectively. In addition, the analysis also indicated that PE (4.317), EE (2.397), and HM (3.084) are likewise statistically and substantively significant to BI. The result shows that the R2 for BI and UB indicate a moderate impact of 0.603 and 0.521, respectively. It is suggested that the model has valuable applications in academic and real-world contexts.

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Affecting factors on perceived usefulness of area-business continuity management: A perspective from employees in industrial areas in Thailand 12

Affecting factors on perceived usefulness of area-business c...

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12th ACEH International Workshop on Sustainable Tsunami Disaster Recovery: Sharing Experience, Knowledge and Culture 2019, AIWEST-DR 2019

作者： Meechang, K. Leelawat, N. Tang, J. Ino, E. Kodaka, A. Chintanapakdee, C. Watanabe, K. Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Disaster and Risk Management Information Systems Research Group Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand International School of Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Advanced Disaster Prevention Engineering Center Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya466-8555 Japan Graduate School of System Design and Management Keio University Kanagawa223-8526 Japan Department of Civil Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Department of Architecture Civil Engineering and Industrial Management Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya466-8555 Japan

The 2011 Thailand Floods heavily impacted 7 industrial complexes, in which 56.7% were Japanese companies. Many notable companies received severe damage until they had to cease their production. Area Business Continuity management (Area-BCM) implemented in Thailand stems from this disaster which causes both private and public sectors to think about their business sustainability. The Area-BCM project is an on-going implementation in Thailand aiming to enhance collaboration among stakeholders in industrial areas for coping with upcoming threats. One of the most significant factors before launching a plan is to understand individual attitudes and perceptions pertaining to the Area-BCM project for the best practice, effective and continuous outcomes. This study aims to investigate various factors that affect the perceived usefulness (PU) about implementing Area-BCM. Our proposed research model is developed aligning with the behavioral model and factors influencing flood mitigation consisting of Subjective norms, Experience, Worry about flooding, and Flood hazard knowledge. Questionnaires were distributed to employees in the industrial areas which were flooded in 2011. The developed model was tested by Partial Least Square Structural Equation Modeling (PLS-SEM). The results consequently show that subjective norms and flood hazard knowledge significantly influence perceived usefulness. This can be implied that, in an organization, major thoughts of related people could shape individual perceptions about using a disaster management plan. Moreover, the governmental and local authorities should be a significant force, that helps support plan implementation and educate people about disaster knowledge. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Disasters

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