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The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco

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International Soil and Water Conservation Research 2020年第2期8卷 195-204页

作者： Ayoub Lazaar Abdul Mounem Mouazen Kamal EL Hammouti Michael Fullen Biswajeet Pradhan Muhammad Sohail Memon Karim Andich Abdelilah Monir Laboratory of Applied Geosciences Department of GeologyFaculty of ScienceMohammed First UniversityOujdaMorocco Division of Mechatronics Biostatistics and Sensors(MeBioS)Faculty of Bioscience EngineeringKasteelpark Arenberg 30B-3001 HeverleeBelgium Faculty of Science and Engineering University of WolverhamptonWolverhamptonWV11LYUnited Kingdom Center for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydney2007NSWAustralia Faculty of Agricultural Engineering Sindh Agriculture UniversityTandojam70060Pakistan Laboratory of Applied Geomatics and Soil Science National Institute of Agronomic Research(INRA)OujdaMorocco Department of Mathematics EDP and Scientific Computing TeamMoulay Ismail UniversityMéknesMorocco

Soil organic matter (SOM) is a fundamental soil constituent. The estimation of this parameter in the laboratory using the classical method is complex time-consuming and requires the use of chemical re-agents. The objectives of this study were to assess the accuracy of two laboratory measurement setups of the VIS-NIR spectroscopy in estimating SOM content and determine the important spectral bands in the SOM estimation model. A total of 115 soil samples were collected from the non-root zone (0-20 cm) of soil in the study area of the Triffa Plain and then analysed for SOM in the laboratory by the Walkley-Black method. The reflectance spectra of soil samples were measured by two protocols, Contact Probe (CP) and Pistol Grip (PG)) of the ASD spectroradiometer (350-2500 nm) in the laboratory. Partial least squares regression (PLSR) was used to develop the prediction models. The results of coefficient of determination (R2) and the root mean square error (RMSE) showed that the pistol grip offers reasonable accuracy with an R2=0.93 and RMSE=0.13 compared to the contact probe protocol with an R2=0.85 and RMSE = 0.19. The near-Infrared range were more accurate than those in the visible range for pre-dicting SOM using the both setups (CP and PG). The significant wavelengths contributing to the pre-diction of SOM for (PG) setup were at:424, 597, 1432, 1484, 1830,1920, 2200, 2357 and 2430 nm, while were at 433, 587, 1380, 1431, 1929, 2200 and 2345 nm for (CP) setup.

关键词： Soil organic matter VIS-NIR spectroscopy Reflectance spectra SOM estimation SOM analysis

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Signet Ring Cell Detection from Histological Images Using Deep Learning

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Computers, Materials & Continua 2022年第9期72卷 5985-5997页

作者： Muhammad Faheem Saleem Syed Muhammad Adnan Shah Tahira Nazir Awais Mehmood Marriam Nawaz Muhammad Attique Khan Seifedine Kadry Arnab Majumdar Orawit Thinnukool Department of Computer Science UETTaxila47050Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Civil Engineering Imperial College LondonLondon SW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai 50200Thailand

Signet Ring Cell(SRC)Carcinoma is among the dangerous types of cancers,and has a major contribution towards the death ratio caused by cancerous *** and diagnosis of SRC carcinoma at earlier stages is a challenging,laborious,and costly *** detection of SRCs in a patient’s body through medical imaging by incorporating computing technologies is a hot topic of *** the presented framework,we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning(DL)technique named Mask Region-based Convolutional Neural Network(Mask-RCNN).In the first step,the input image is fed to Resnet-101 for feature *** extracted feature maps are conveyed to Region Proposal Network(RPN)for the generation of the region of interest(RoI)proposals as well as they are directly conveyed to ***,RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected(FC)network and performs classification along with Bounding Box(bb)generation by using FC *** annotations are developed from ground truth(GT)images to perform experimentation on our developed *** introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical *** aim to release the employed database soon to assist the improvement in the SRC recognition research area.

关键词： Mask RCNN deep learning SRC segmentation

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Forecasting the load flow of Engine Driven Pump based on Light Gradient Boosting Machine Model

Forecasting the load flow of Engine Driven Pump based on Lig...

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2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2021

作者： Niu, Wei Song, Xiaogang Wang, Hao Chen, Juan Xi'an Aeronautics Computing Technique Research Institute AVIC Xi'an China Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an China Xi'an Institute of Applied Optics Xi'an China

ISBN: (纸本)9780738146492

The flow rate of aircraft hydraulic system is one of the important parameters to measure the performance of aircraft. The stability of flow rate of hydraulic pipeline is related to the safety of hydraulic system and even aircraft flight. Aiming at the problem that it is difficult to measure the load flow of aircraft hydraulic system, a prediction method based on ensemble learning for the load flow of Engine Driven Pump (EDP) is proposed. According to the different working conditions of EDP, the Light Gradient Boosting Machine (LightGBM) model is established. Through Particle Swarm Optimization algorithm optimizes the super parameters of LightGBM algorithm, and realizes the regression prediction mode. Finally, compared with the multiple linear regression, random forest, experimental results indicate that the proposed model obviously can improve the prediction accuracy. © 2021 IEEE.

关键词： Forecasting

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Travel Mode Choice Modeling: Predictive Efficacy between Machine Learning Models and Discrete Choice Model

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Open Transportation Journal 2021年第1期15卷 241-255页

作者： Ali, Nur Fahriza Mohd. Sadullah, Ahmad Farhan Mohd. Majeed, Anwar PP Abdul Razman, Mohd Azraai Mohd. Zakaria, Muhammad Aizzat Nasir, Ahmad Fakhri Ab. School of Civil Engineering Universiti Sains Malaysia Engineering Campus Pulau Pinang Nibong Tebal 14300 Malaysia Innovative Manufacturing Mechatronics and Sports Laboratory Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Pahang Pekan 26600 Malaysia Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan 26600 Malaysia

Background: A complex travel behaviour among users is intertwined with many factors. Traditionally, the exploration in travel mode choice modeling has been dominated by the Discrete Choice model, nonetheless, owing to the advancement in computational techniques, machine learning has gained traction in understanding travel behavior. Aim: This study aims at predicting users’ travel model choice by means of machine learning models against a conventional Discrete Choice Model, i.e., Binary Logistic Regression. Objective: To investigate the comparison between machine learning models, namely Neural Network, Random Forest, Decision Tree, and Support Vector Machine against the Discrete Choice Model (Binary Logistic Regression) in the prediction of travel mode choice amongst Kuantan City. Methodology: The dataset was collected in Kuantan City, Malaysia, through the Revealed/Stated Preferences (RP/SP) Survey. The data collected was split into a ratio of 80:20 for training and testing before evaluating them between the aforesaid models. The hyperparameters of the models were set to default. The performance of the models is evaluated based on classification accuracy. Results: It was shown in the present study that the Neural Network Model is able to attain a higher prediction accuracy as compared to Binary Logistic Regression (Discrete Choice Model) in classifying mode choice of Kuantan users either to choose public transport or private vehicles as daily transportation. Feature importance technique is crucial for identifying the significant features in modelling travel mode choice. It is demonstrated that the Neural Network Model can yield exceptional classification of mode choice up to 73.4% and 72.4% of training and testing data, respectively, by considering the features identified via the feature importance technique, suggesting the viability of the proposed technique in supporting an informed decision. Conclusion: The findings highlight the strengths and limitations of the

关键词： Discrete choice model Machine learning technique Private vehicles Public transport Revealed/Stated preference survey Travel mode choice

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We are "not" too (young/old) to collaborate: Prominent key Barriers to intergenerational innovation 2

We are "not" too (young/old) to collaborate: Prominent key B...

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23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019

作者： Nurhas, Irawan Aditya, Bayu R. Geisler, Stefan Ojala, Arto Pawlowski, Jan M. Institute of Positive Computing Hochschule Ruhr West Bottrop Germany University of Jyväskylä Jyväskylä Finland School of Applied Sciences Telkom University Bandung Indonesia Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Institute of Positive Computing Hochschule Ruhr West University of Applied Sciences Bottrop Germany

In this study, we analyzed the barriers to technology-supported intergenerational innovation to understand better how young and old can collaborate towards global innovations. Researchers in different disciplines have already identified various barriers to intergenerational collaboration. However, barriers are changing depending on the context of collaboration, and difficulties still exist to support intergenerational innovation in global settings. Therefore, we investigated the barriers that emerge when people work with someone decades older or younger. The results of our study have shown what barriers are influenced by age, what barriers exist only for senior and younger adults. The study theoretically contributes to deepening the Information Systems (IS) community's understanding of the barriers to intergenerational innovation that need to be considered when developing systems for global innovation. © Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019.

关键词： Information systems

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FusionMamba: dynamic feature enhancement for multimodal image fusion with Mamba

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Visual Intelligence 2024年第1期2卷 1-18页

作者： Xie, Xinyu Cui, Yawen Tan, Tao Zheng, Xubin Yu, Zitong School of Computing and Information Technology Great Bay University Guangdong Dongguan 523000 China Faculty of Applied Sciences Macao Polytechnic University Macao Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong

Multimodal image fusion aims to integrate information from different imaging techniques to produce a comprehensive, detail-rich single image for downstream vision tasks. Existing methods based on local convolutional neural networks (CNNs) struggle to capture global features efficiently, while Transformer-based models are computationally expensive, although they excel at global modeling. Mamba addresses these limitations by leveraging selective structured state space models (S4) to effectively handle long-range dependencies while maintaining linear complexity. In this paper, we propose FusionMamba, a novel dynamic feature enhancement framework that aims to overcome the challenges faced by CNNs and Vision Transformers (ViTs) in computer vision tasks. The framework improves the visual state-space model Mamba by integrating dynamic convolution and channel attention mechanisms, which not only retains its powerful global feature modeling capability, but also greatly reduces redundancy and enhances the expressiveness of local features. In addition, we have developed a new module called the dynamic feature fusion module (DFFM). It combines the dynamic feature enhancement module (DFEM) for texture enhancement and disparity perception with the cross-modal fusion Mamba module (CMFM), which focuses on enhancing the inter-modal correlation while suppressing redundant information. Experiments show that FusionMamba achieves state-of-the-art performance in a variety of multimodal image fusion tasks as well as downstream experiments, demonstrating its broad applicability and superiority. © The Author(s) 2024.

关键词： Feature enhancement Image fusion Mamba Multimodal

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Breast-Cancer Detection using Thermal Images with Marine-Predators-Algorithm Selected Features 7

Breast-Cancer Detection using Thermal Images with Marine-Pre...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Rajinikanth, Venkatesan Kadry, Seifedine Taniar, David Damasevicius, Robertas Rauf, Hafiz Tayyab St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Monash University Faculty of Information Technology ClaytonVIC3800 Australia Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Staffordshire University Centre for Smart Systems Ai and Cybersecurity Stoke-on-Trent United Kingdom

ISBN: (纸本)9781665441261

Breast-cancer (BC) is one of the major diseases in women group and the early diagnosis and treatment is necessary to cure the disease. Early detection of BC is very essential to implement appropriate treatment and the proposed research aims to develop an automated BC detection system using Breast-Thermal-Images (BTI). The executed approach is as follows;(i) Recording the image for various breast orientation, (ii) Extracting the healthy/DCIS image patches, (iii) Treating the patches with image processing scheme, (iv) Feature extraction, (v) Feature optimization with Marine-Predators-Algorithm (MPA), and (vi) Two-class classification and validation. In this work, the essential image features, such as GLCM and LBP with varied weights are considered to classify the clinically collected BTI into healthy/DCIS class using a chosen two-class classifier. The result of this study confirms that the Decision-Tree (DT) classifier helps to achieve enhanced accuracy (>92%) compared to other methods adopted in this research. © 2021 IEEE.

关键词： Diseases

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Machine-Learning-Scheme to Detect Choroidal-Neovascularization in Retinal OCT Image 7

Machine-Learning-Scheme to Detect Choroidal-Neovascularizati...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Taniar, David Rauf, Hafiz Tayyab St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Monash University Faculty of Information Technology ClaytonVIC3800 Australia Staffordshire University Centre for Smart Systems Ai and Cybersecurity Stoke-on-Trent United Kingdom

ISBN: (纸本)9781665441261

Eye is a fundamental sensory organ and any disease in eye will severely affect the sensory signal evaluation and conclusion making capability of the brain. The Choroidal-Neovascularization (CNV) is one of the harsh eye diseases in which a new blood-vessel grow from the choroid. Usually, the major cause of CNV is due to wet Age-Related-Macular-Degeneration (ARMD) and the formed new vessel will cause a leak in fluid which makes the retinal wet. The untreated CNV will lead to vision loss. In this research, detection of CNV using Optical-Coherence-Tomography (OCT) is presented using 484 images (242 Healthy and 242 CNV). In this work, a Machine-Learning-Scheme (MLS) is developed to examine the resized OCT of 256x256 pixels and the stages of this MLS includes;pre-processing, feature extraction, Mayfly-Optimization-Algorithm (MFA) based feature reduction, and two-class classification. The experimental outcome of this technique confirmed that the Fine-Gaussian-SVM (SVM-FG) classifier helped to accomplish an improved classification accuracy (>92%) compared to the alternative classifiers of this study. © 2021 IEEE.

关键词： Optical tomography

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Exploring diabetes through the lens of AI and computer vision: Methods and future prospects

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Computers in Biology and Medicine 2025年 185卷 109537-109537页

作者： Chundi, Ramesh G., Sasikala Basivi, Praveen Kumar Tippana, Anitha Hulipalled, Vishwanath R. N., Prabakaran Simha, Jay B. Kim, Chang Woo Kakani, Vijay Pasupuleti, Visweswara Rao School of Computer Applications Dayananda Sagar University Bangalore India School of Computer Science and Applications REVA University Rukmini Knowledge Park Bangalore560064 India Pukyong National University Industry-University Cooperation Foundation Pukyong National University Busan48513 Korea Republic of Department of Biotechnology School of Applied Sciences REVA University Rukmini Knowledge Park Bangalore560064 India School of Computing and Information Technology REVA University Rukmini Knowledge Park Bangalore560064 India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India Abiba Systems CTO and RACE Labs REVA University Rukmini Knowledge Park Bangalore560064 India Department of Nanotechnology Engineering College of Engineering Pukyong National University Busan48513 Korea Republic of Integrated System Engineering Inha University 100 Inha-ro Nam-gu Incheon22212 Korea Republic of School of Biosciences Taylor's University Lakeside Campus Selangor Subang Jaya47500 Malaysia Faculty of Earth Sciences Universiti Malaysia Kelantan Campus Jeli Jeli Kelantan17600 Malaysia

Early diagnosis and timely initiation of treatment plans for diabetes are crucial for ensuring individuals’ well-being. Emerging technologies like artificial intelligence (AI) and computer vision are highly regarded for their ability to enhance the accessibility of large datasets for dynamic training and deliver efficient real-time intelligent technologies and predictable models. The application of AI and computer vision techniques to enhance the analysis of clinical data is referred to as eHealth solutions that employ advanced approaches to aid medical applications. This study examines several advancements and applications of machine learning, deep learning, and machine vision in global perception, with a focus on sustainability. This article discusses the significance of utilizing artificial intelligence and computer vision to detect diabetes, as it has the potential to significantly mitigate harm to human life. This paper provides several comments addressing challenges and recommendations for the use of this technology in the field of diabetes. This study explores the potential of employing Industry 4.0 technologies, including machine learning, deep learning, and computer vision robotics, as effective tools for effectively dealing with diabetes related aspects. © 2024 Elsevier Ltd

关键词： Artificial intelligence Computer vision Diabetes Deep learning Machine learning

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A Collaborative Consumption Digital Platform for Government Organizations using Design Science

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Digital Government: Research and Practice 2022年第1期3卷 1–14页

作者： Gebeyehu, Seffi Twinomurinzi, Hossana Faculty of Computing Bahirdar Technology Institute Bahirdar University P.O.Box 26 Bahirdar Ethiopia Applied Information Systems Department School of Consumer Intelligence and Information Systems College of Business and Economics Office 17 Auckland Park Bunting Road Campus Johannesburg South Africa

The growth of digital technologies that enable the sharing of resources, underutilized assets, and services, is bringing new ways of shared consumption commonly called the "sharing economy"or "collaborative consumption". This study used a design science paradigm and the elaborated action design method (eADR) to design and develop a "collaborative consumption"(CC) digital artifact under the government to government (G2G) domain towards improving resource limitations in developing country contexts. The Ethiopian WoredaNet government project was used as a case study. The new artifact was evaluated using the post-study system usability framework (n = 35) to reveal its practical usefulness in improving resource limitations. A new design principle was also elicited: intentionally elicit user responses for unfamiliar digital innovations. The study recommends for policymakers of low-income countries (LICs) to create new sharing-capable policies in the G2G domain, and consider extending it towards non-government organizations and citizens. Overall, the findings contribute to Information Systems discourse and digital government practice by identifying the importance of novel CC digital platforms to optimize government resources in LICs. The study also contributes to the eADR method by identifying that the action research cycle is not a strict requirement but helpful as an entry point into design science efforts. © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Developing countries

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