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An Attention-Based Deep Network for Plant Disease Classification

Machine Graphics and Vision 2024年第1期33卷 47-67页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Králové Czech Republic Škoda Auto University Mladá Boleslav Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method. © 2024 institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Convolutional neural networks

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Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 257-271页

作者： Christopher Wiedeman Peter Lorraine Ge Wang Richard Do Amber Simpson Jacob Peoples Bruno De Man Department of Electrical and Computer Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA GE Research-Healthcare NiskayunaNY 12309USA Department of Biomedical Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Radiology Memorial Sloan Kettering Cancer CenterNew YorkNY 10065USA Biomedical Computing and Informatics Queen’s UniversityKingstonON K7L 3N6Canada

Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes,especially as the disease progresses into liver *** tomography(CT)is a frontline tool for this task;however,the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction *** hypothesized that image reconstruction with a highfrequency kernel could result in a better characterization of liver metastases features via deep neural *** kernel produces images that appear noisier but preserve more sinogram information.A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver *** pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases,and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using *** of 10,000 liver metastases were generated,scanned,and reconstructed using either standard or high-frequency *** data were used to train and validate deep neural networks to recover crafted metastases characteristics,such as internal heterogeneity,edge sharpness,and edge fractal *** the absence of noise,models scored,on average,12.2%(α=0.012)and 7.5%(α=0.049)lower squared error for characterizing edge sharpness and fractal dimension,respectively,when using high-frequency reconstructions compared to ***,the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical *** results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization,provided that noise is *** work should investigate the informationpreserving kernels in datasets with clinical labels.

关键词： Radiomics Deep learning Computed tomography Colorectal liver metastases Virtual clinical trials Image reconstruction

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Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency - A review and examples of quantitative measurement methods

Evaluating the Inclusiveness of Artificial Intelligence Soft...

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2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Alevizos, Vasileios Georgousis, Ilias Simasiku, Akebu Messinis, Antonis Karypidou, Sotiria Malliarou, Dimitra Karolinska Institute Department of Learning Informatics Managament and Ethics Stockholm Sweden International Hellenic University Mlv Research Group Department of Computer Science Kavala Greece Zambia University Department of Computer Software Engineering Ndola Zambia Greece International Hellenic University Mlv Research Group Department of Computer Science Greece IntelliSolutions Athens Greece

ISBN: (纸本)9798350394528

The escalating integration of Artificial Intelligence (AI) in various domains, especially Project Management (PM), has brought to light the imperative need for inclusivity in AI systems. This paper investigates the role of AI software in augmenting both the inclusiveness and efficiency within the realm of PM. The research pivots around specific criteria that define and measure the inclusiveness of AI in PM, highlighting how AI, when developed with inclusiveness in mind, can significantly enhance project outcomes. However, there are inherent challenges in achieving this inclusiveness, primarily due to biases embedded in AI learning databases and the design and development processes of AI systems. The study offers a comprehensive examination of AI's potential to revolutionize PM by enabling managers to concentrate more on people-centric aspects of their work. This is achieved through AI's ability to perform tasks such as data collection, reporting, and predictive analysis more consistently and efficiently than human counterparts. However, the incorporation of AI in PM extends beyond mere efficiency;it represents a paradigm shift in the epistemology of PM, calling for a deeper understanding of AI's role and impact on society. Despite these advantages, the adoption of AI comes with significant challenges, particularly in terms of bias and inclusiveness. Biased AI learning databases, which use shared and reusable datasets, often perpetuate initially discriminatory algorithms. Moreover, unconscious biases and stereotypes of AI designers, developers, and trainers can inadvertently influence the behavior of the AI systems they create. This necessitates a paradigmatic shift in how AI systems are developed and governed to ensure they do not replicate or exacerbate existing social inequalities. The research proposes a methodological approach involving the development of criteria for inclusion and exclusion, alongside data extraction, to evaluate the inclusiveness and efficienc

关键词： Project management

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Enhancing The Attractiveness of Coloring Books Using Live Texturing Augmented Reality

Enhancing The Attractiveness of Coloring Books Using Live Te...

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2023 International Conference on informatics engineering, Science and Technology, INCITEST 2023

作者： Maulana, Hanhan Anshor, Jajang Saeful Kanai, Hideaki Universitas Komputer Indonesia Department of Informatics Engineering and Computer Science Bandung Indonesia Japan Andvanced Institute of Technology and Science The School of Knowledge Science Ishikawa Japan

ISBN: (纸本)9798350317862

This study aims to build live texturing augmented reality to enhance the attractiveness of coloring books. This research has four main stages, namely data gathering, object preparations, software development and evaluations. This study uses UNITY 3D in building a Live Texturing Augmented Reality System. The AR method used is the Marker method. Users can color the marker. The 3D object will follow the color given to the marker in real time. This system is expected to increase children's attraction to coloring. It is hoped that children's understanding of color can also be improved. Furthermore, this system is expected to help children in deciding on color selection so that children's creativity is getting better. © 2023 IEEE.

关键词： Augmented reality

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Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns

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engineering 2024年第8期39卷 74-83页

作者： Usman L.Abbas Yuxuan Zhang Joseph Tapia Selim Md Jin Chen Jian Shi Qing Shao Department of Chemical and Materials Engineering University of KentuckyLexingtonKY 40506USA Department of Biosystems and Agricultural Engineering University of KentuckyLexingtonKY 40506USA Institute for Biomedical Informatics Department of Computer ScienceUniversity of KentuckyLexingtonKY 40506USA

Non-ionic deep eutectic solvents(DESs)are non-ionic designer solvents with various applications in catalysis,extraction,carbon capture,and ***,discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES *** search for DES relies heavily on intuition or trial-and-error processes,leading to low success rates or missed *** that hydrogen bonds(HBs)play a central role in DES formation,we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning(ML)models to discover new DES *** first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics(MD)simulation *** analysis reveals that DES systems have two unique features compared to non-DES systems:The DESs have①more imbalance between the numbers of the two intra-component HBs and②more and stronger inter-component *** on these results,we develop 30 ML models using ten algorithms and three types of HB-based *** model performance is first benchmarked using the average and minimal receiver operating characteristic(ROC)-area under the curve(AUC)*** also analyze the importance of individual features in the models,and the results are consistent with the simulation-based statistical ***,we validate the models using the experimental data of 34 *** extra trees forest model outperforms the other models in the validation,with an ROC-AUC of *** work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

关键词： Machine learning Deep eutectic solvents Molecular dynamics simulations Hydrogen bond Molecular design

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Don't Forget the Exception! : Considering Robustness Changes to Identify Design Problems 20

Don't Forget the Exception! : Considering Robustness Changes...

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20th IEEE/ACM International Conference on Mining Software Repositories, MSR 2023

作者： Oliveira, Anderson Correia, Joao Sousa, Leonardo Assuncao, Wesley K. G. Coutinho, Daniel Garcia, Alessandro Oizumi, Willian Barbosa, Caio Uchoa, Anderson Pereira, Juliana Alves Informatics Department - Pontifical Catholic Rio de Janeiro Brazil Carnegie Mellon University Department of Electrical and Computer Engineering Pittsburgh United States Institute of Software Systems Engineering Linz Austria Itapajé Brazil

ISBN: (纸本)9798350311846

Modern programming languages, such as Java, use exception-handling mechanisms to guarantee the robustness of software systems. Although important, the quality of exception code is usually poor and neglected by developers. Indiscriminate robustness changes (e.g., the addition of empty catch blocks) can indicate design decisions that negatively impact the internal quality of software systems. As it is known in the literature, multiple occurrences of poor code structures, namely code smells, are strong indicators of design problems. Still, existing studies focus mainly on the correlation of maintainability smells with design problems. However, using only these smells may not be enough since developers need more context (e.g., system domain) to identify the problems in certain scenarios. Moreover, these studies do not explore how changes in the exceptional code of the methods combined with maintainability smells can give complementary evidence of design problems. By covering both regular and exception codes, the developer can have more context about the system and find complementary code smells that reinforce the presence of design problems. This work aims to leverage the identification of design problems by tracking poor robustness changes combined with maintainability smells. We investigated the correlation between robustness changes and maintainability smells on the commit history of more than 160k methods from different releases of 10 open-source software systems. We observed that maintainability smells can be worsened or even introduced when robustness changes are performed. This scenario mainly happened for the smells Feature Envy, Long Method, and Dispersed Coupling. We also analyzed the co-occurrence between robustness and maintainability smells. We identified that the empty catch block and catch throwable robustness smells were the ones that co-occurred the most with maintainability smells related to the Concern Overload and Misplaced Concern design problems. T

关键词： Maintainability

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Genera: A Serious Game Leveraging Blockchain Technology for Renewable Energy Education 14

Genera: A Serious Game Leveraging Blockchain Technology for ...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Stamatakis, Dimitris Kogias, Dimitrios G. Leligou, Helen C. Karkazis, Panagiotis A. Penalvo, Elisa Department of Industrial Design and Production Engineering University of West Attica Petrou Ralli Athens12243 Greece Department of Informatics and Computer Engineering University of West Attica Ag. Spyridonos Athens12210 Greece Institute of Energy Engineering U. Politecnica de Valencia Spain

ISBN: (纸本)9781837242689

The GENERA Card Game combines serious gaming and blockchain technology to raise awareness on renewable energy. As part of the GENERA project, this multiplayer game engages players by assigning them the role of a town's mayor tasked with expanding their town using sustainable energy sources. The game integrates educational content with interactive gameplay, utilizing NFTs for the in-game assets and ERC-20 tokens as rewards. Developed using web technologies, the game benefits from extensive accessibility while maintaining high-quality features. A survey of 45 players indicated high satisfaction with the game's educational impact and accessibility, though it highlighted areas for improvement in user experience and mechanics. The study underscores the potential of serious games in promoting environmental awareness and the benefits of blockchain technology in enhancing educational tools. Future updates will focus on adding new features, improving user interface and experience, and integrating realistic data on renewable energy sources to further enrich the educational content. The findings suggest that serious games like GENERA can effectively raise awareness and foster a deeper understanding of renewable energy, thereby supporting global sustainability efforts. © The Institution of engineering & Technology 2024.

关键词： Clean energy

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Progressive Motion Boosting for Video Frame Interpolation

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IEEE Transactions on Multimedia 2023年 25卷 8076-8090页

作者： Xiao, Jing Xu, Kangmin Hu, Mengshun Liao, Liang Wang, Zheng Lin, Chia-Wen Wang, Mi Satoh, Shin'Ichi The School of Computer Science Wuhan University Wuhan430072 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430072 China The Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan

Video frame interpolation has made great progress in estimating advanced optical flow and synthesizing in-between frames sequentially. However, frame interpolation involving various resolutions and motions remains challenging due to limited or fixed pre-trained networks. Inspired by the success of the coarse-to-fine scheme for video frame interpolation, i.e., gradually interpolating frames of different resolutions, we propose a progressive boosting network (ProBoost-Net) based on a multi-scale framework to achieve flexible recurrent scales and then gradually optimize optical flow estimation and frame interpolation. Specifically, we designed a dense motion boosting (DMB) module to transfer features close to real motion to the decoded features from the later scales, which provides complementary information to refine the motion further. Furthermore, to ensure the accuracy of the estimated motion features at each scale, we propose a motion adaptive fusion (MAF) module that adaptively deals with motions with different receptive fields according to the motion conditions. Thanks to the framework’s flexible recurrent scales, we can customize the number of scales and make trade-offs between computation and quality depending on the application scenario. Extensive experiments with various datasets demonstrated the superiority of our proposed method over state-of-the-art approaches in various scenarios. © 2023 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Motion estimation

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Research on the Design Method of Prefabricated Concrete Structure Based on BIM

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computer-Aided Design and Applications 2023年 20卷 148-164页

作者： Yang, Fan Hussain, Saddam Fadhel, Mustafa Abbas Ghafoor, Kayhan Zrar Gannan University of Science and Technology Jiangxi Ganzhou China Department of Civil and Architecture Engineering Kyushu Institute of Technology Fukuoka Kitakyushu8040015 Japan University of Al Muthanna Samawa66001 Iraq Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq

With the gradual development in industrialization, the advent of computer and information technology has evolved in the field of engineering and construction industry. BIM technology has become a valuable part of prefabricated concrete engineering with the advancement of modern technology based on 3D modeling simulation. In order to realize the industrialization and automation of prefabricated concrete structures, this paper analyzes the characteristics of fabricated concrete structure and BIM, combining the two techniques. This work applies BIM to fabricated concrete design and chooses finite element analysis and calculation software ETABS to analyze the structure model. The results show that the mass participation ratio of the model studied in this paper reaches more than 90%, the interlayer displacement angle is less than 1/550, and the modal and shell internal force analysis results meet the requirements. In this paper, modal analysis, shell internal force analysis under live load and structural dynamic analysis under response spectrum load conditions were carried out on the ETABS model converted from the Revit model and the model directly established by ETABS. The ETABS model converted from the Revit model by the data interface plug-in can be used for structural analysis. © 2023, CAD Solutions, LLC. All rights reserved.

关键词： Finite element method

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Improving Power Transformer Lifetime Prediction using Hyperparameter Optimization 24

Improving Power Transformer Lifetime Prediction using Hyperp...

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24th International Electronics Symposium, IES 2022

作者： Ningrum, Ayu Ahadi Maulida, Ihdalhubbi Ansari, Rudy Syarif, Iwan Universitas Muhammadiyah Banjarmasin Department of Informatics Banjarmasin Indonesia Electronic Engineering Polytechnic Institute of Surabaya Department of Informatic and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Since the global pandemic has significantly impacted human life, technology has become a vital role in various sectors. The more technology used, the more we need the electricity supply. The stability of the electricity supply is an absolute thing to customers. Power Transformer is essential equipment for delivering electricity to customers. So the condition of the power transformers should be an essential thing that must be considered. Deep Learning is part of artificial intelligence that is widely applied to facilitate the human need. Based on its role, accuracy in the prediction results will be absolute. Hyperparameter optimization is the essential methods in the machine learning process. Errors in assigning hypermeter values can harm producing predictive values. This study discusses how to optimize the prediction results of the lifetime prediction on a power transformer. With optimal prediction results, it can help electricity management companies monitor conditions. Thereby minimizing the risk of disruption of electricity supply to customers. Models are tested and verified using a real dataset from a power transformer in several locations. The best hyper-parameter for this dataset is Bayesian Search, producing 0,001343 for Mean Absolute Error (MAE). © 2022 IEEE.

关键词： Power transformers

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