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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Salas, Werner H. Postgraduate Unit of the Faculty of Systems Engineering and Computer Science National University of San Marcos Lima Peru

Enterprise Resource Planning (ERP) is a business system that supports most of the critical processes of a company. It helps maintain a unified and reliable repository of information for decision-making. Implementing an ERP is a complex project, which implies a high level of effort and investment. Although the methodologies provided by the leading ERP providers are beneficial, there is still a high failure rate in the implementation. Many authors have analyzed the factors and causes of these implementation failures. There is also research to propose new implementation models that replace the existing ones. However, there is little research on existing methodologies incorporating new concepts from other disciplines to improve them. In this research, we propose a model that complements the current methodologies and uses the best practices in project management and software engineering directly related to the issues found in the literature. We submitted it to a group of experts on the subject to validate the model based on the Delphi method. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Project management

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Integrating the Technologies of Image Processing and Virtual Reality for the Digital Preservation of Disappeared Archaeological Sites

Integrating the Technologies of Image Processing and Virtual...

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2024 Lock-in or log out? Wie digitale Souveranitat gelingt, INFORMATIK 2024

作者： Al-Ameen, Zohair Mahmood, Basim Al-Sarraf, Abdullah Alchalabi, Oday University of Mosul ICT Research Unit Computer Center Mosul41002 Iraq University of Mosul College of Engineering Mosul41002 Iraq

ISBN: (纸本)9783885797463

Tangible historical and cultural assets refer to physical artifacts that can be touched and intergenerationally transferred. This kind of asset is considered the past that the next generations should memorize. One of the issues that archaeologists frequently face is the methods used in digitally restoring disappeared archaeological assets. This is due to their low quality, noisy texture, or uncolored images. On the other hand, the advent of new technologies such as digital image processing (DIP) and virtual reality (VR) makes it palatable to digitalize and sustain tangible historical assets. These technologies are recognized as efficient tools insofar as they can contribute to preserving the heritage of civilizations. Hence, this study suggests an approach that integrates DIP and VR techniques to restore historical assets that have disappeared from low-quality images. The findings show that the proposed methodology is successful in restoring archaeological sites or objects, as demonstrated by the results obtained. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Virtual environments

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An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5033-5040页

作者： William, P. Patil, Jaikumar M. Panda, Sunita Venugopal, Anita Vidyullatha, Pellakuri Kumar, Nellore Manoj Jandwani, Aman Department of Information Technology Sanjivani College of Engineering SPPU Department of Computer Science and Engineering Shri Sant Gajanan Maharaj College of Engineering SGBAU Department of Electrical Electronics and Communication Engineering GITAM School of Technology IT Unit Dhofar University Department: Computer Science and Engineering Koneru Lakshmaiah Education Foundation Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Research Task

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (%) and power consumption (%). The results of the study reveal that the proposed approach is impactful in the smart collection and management of waste. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： And hygienic environment Deep learning Internet of things (IoT) Smart cities Smart waste

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Image Classification Method to Identify Mature Coconut 3

Image Classification Method to Identify Mature Coconut

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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Adikaram, K.K.L.B. Senevirathna, A.M.N.N. Wijesinghe, W.P.P.M. Premachandra, Chinthaka University of Ruhuna Computer Unit Faculty of Agriculture Sri Lanka Shibaura Institute of Technology Department of Electronics Engineering Japan University of Wayamba Department of Biosystems Engineering Sri Lanka

ISBN: (纸本)9798350374766

This study focuses on developing an automated image classification method to identify mature coconuts, a critical phase in coconut harvesting. By emulating the technique of expert coconut pickers who assess color and texture, our goal is to revolutionize the industry, enhancing efficiency, reducing labor costs, and mitigating environmental impact. The preliminary model developed using Teachable Machine an established image classification software tool showed highly promising results in the identification of mature coconuts. © 2024 IEEE.

关键词： Image classification

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Statistical inference on multicomponent stress–strength reliability with non-identical component strengths using progressively censored data from Kumaraswamy distribution

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Soft Computing 2024年第17-18期28卷 9317-9339页

作者： Saini, Shubham Patel, Jyoti Garg, Renu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India Machine Intelligence Unit Indian Statistical Institute Kolkata700108 India Department of Statistics Ramanujan College Delhi110019 India

In this article, we draw inferences on stress–strength reliability in a multicomponent system with non-identical strength components based on the progressively censored data from the Kumaraswamy distribution (KuD). When one shape parameter of KuD is known, the uniformly minimum variance unbiased estimator is produced. To evaluate the reliability of such systems when all the parameters are unknown, the maximum likelihood and Bayes estimators are developed. Along with coverage probabilities, the asymptotic confidence and highest posterior credible (HPD) intervals are also obtained. Tierney–Kadane’s approximation and Markov chain Monte Carlo methods are used for Bayesian computations. To compare the performance of estimators, a Monte Carlo simulation study is performed. Also, one real-life example is analyzed for illustrative purposes. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Maximum likelihood estimation

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Sustainable AI: inside the deep, alongside the green

Sustainable AI: inside the deep, alongside the green

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Conte, Francesco Cordelli, Ermanno Guarrasi, Valerio Iannello, Giulio Sicilia, Rosa Soda, Paolo Tortora, Matteo Tronchin, Lorenzo Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Italy Unit of Innovation Entrepreneurship Sustainability Department of Engineering University Campus Bio-Medico of Rome Italy

The increasingly inordinate production of productivity-enhancing technologies in today's society has led to an increased risk of damaging the environment from which key resources are derived, if conducted in an uncontrolled manner. It is therefore necessary to optimise strategies to make technological progress more sustainable. Artificial Intelligence (AI) plays a crucial role in this scenario and it is therefore crucial to consider sustainable AI as an integral step in the whole process. In this work we present the results and the topics under investigations in our laboratory focused on the responsible use of AI to meet technological improvement. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Artificial intelligence

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When You Doubt, Abstain: A Study of Automated Fact-checking in Italian Under Domain Shift 9

When You Doubt, Abstain: A Study of Automated Fact-checking ...

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9th Italian Conference on Computational Linguistics, CLiC-it 2023

作者： Valer, Giovanni Ramponi, Alan Tonelli, Sara University of Trento Department of Information Engineering and Computer Science Trento Italy Digital Humanities Unit Trento Italy

Data for building fact-checking models for Italian is scarce, often contains ambiguous claims, and lacks textual diversity. This makes it hard to reliably apply such tools in the real world to support fact-checkers' work. In this paper, we propose a categorization of claim ambiguity and label the largest Italian test set based on it. Moreover, we create challenge sets across two axes of variation: genres and fact-checking sources. Our experiments using transformer-based semantic search show a large drop in performance under domain shift, and indicate the benefit of models' abstention in case of lacking evidence. Italiano. I dati per la creazione di modelli di fact-checking per l'italiano sono esigui, contengono spesso affermazioni ambigue e presentano una limitata diversità stilistica. Questo rende l'uso dei modelli risultanti da parte dei fact-checkers poco affidabile. In questo lavoro classifichiamo l'ambiguità delle affermazioni contenute nel più grande test set di fact-checking per l'italiano e creiamo dei nuovi challenge test set che riflettono stili e fonti differenti. I nostri esperimenti basati sulla ricerca semantica mostrano un notevole calo delle prestazioni in caso di cambio di dominio e indicano l'utilità dell'astensione da parte dei modelli in caso di limitate evidenze. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Semantic Web

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Combining Event Segment Classification and Graph Self-Encoder for Event Prediction 2

Combining Event Segment Classification and Graph Self-Encode...

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2nd International Conference on Big Data, Information and computer Network, BDICN 2023

作者： Yuan, Yewen Wang, Yongli Gong, Xiaoze Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Unit Baicheng63850 China

ISBN: (纸本)9798350321364

Scripted event prediction is the automatic learning of knowledge from unstructured text to predict subsequent events based on a given sequence of events. Existing work has mainly been based on event pairs or entire event chains for learning, which may ignore much structural and semantic information. In order to learn global dependencies between nodes in event evolution graphs, this paper proposes an event prediction method combining event segment classification and graph self-encoder, which uses a variational self-encoder of the graph to obtain the global structure of the event graph, learn and represent potential evolution patterns between events, and construct a global representation of the event graph. The method combines different rules to classify event segments, learns the features of different event segments, and predicts events by determining the scores of candidate events based on the correlation between the event segments and the candidate events. Experiments on a standard dataset demonstrate the effectiveness of the method. © 2023 IEEE.

关键词： Forecasting

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Efficient Automatic Annotation of Binary Masks for Enhanced Training of computer Vision Models

Efficient Automatic Annotation of Binary Masks for Enhanced ...

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2023 IEEE International Symposium on Multimedia, ISM 2023

作者： Seychell, Dylan Kenely, Matthew Bartolo, Matthias Debono, Carl James Bugeja, Mark Sacco, Matthew University of Malta Dept. of Artificial Intelligence Msida Malta University of Malta Dept. of Communications and Computer Engineering Msida Malta Research & Development Unit SeyTravel Ltd. Birkirkara Malta

ISBN: (纸本)9798350395761

In modern computer vision models, the quality and quantity of training data have become crucial. Datasets deemed sufficient a few years ago now require data augmentation to increase their size. This presents a challenge, especially when these supplementary datasets lack annotations in standard formats like COCO, VGG, or YOLO. One solution to this problem is to learn semantic boundaries from binary images of unannotated datasets, thereby increasing the data available for training and evaluating models. However, choosing an efficient annotation method can be both time-consuming and effort-intensive. This research paper explores three approaches, ranging from traditional image processing algorithms to the recently introduced Segment Anything Model (SAM). The study demonstrates how these different algorithms perform on various datasets and concludes that the proposed image processing method strikes the best balance between performance and efficiency. © 2023 IEEE.

关键词： computer vision

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Combining Feature Fusion Link Prediction with Graph Neural Networks 2

Combining Feature Fusion Link Prediction with Graph Neural N...

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2022 2nd Conference on High Performance Computing and Communication engineering, HPCCE 2022

作者： Zheng, Yamin Wang, Yongli Gong, Xiaoze School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Unit Baicheng63850 China

ISBN: (纸本)9781510663374

At present, Graph Neural Network (GNN) methods usually follow the node centered message passing process, and rely heavily on smooth node characteristics rather than graph structure. In view of this limitation, based on the heuristic method and graph attention mechanism, a feature fusion link prediction model (SAFL) combined with graph neural network is proposed. This model extracts the enclosing subgraphs around the target, combines the attention mechanism to assign neighbor weights to learn useful structural features, considers the impact of different nodes on the link, and fuses the graph neural network with the characteristics of input nodes to predict the link. The experiment on OGB dataset shows that the proposed link prediction model based on heuristic method enhances the graph structure characteristics, effectively represents the connectivity of enclosing subgraphs, and achieves better performance in link prediction. © 2023 SPIE.

关键词： Graph neural networks

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