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Wavelet-Infused Convolution-Transformer for Efficient Segmentation in Medical Images

IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3326-3337页

作者： Dutta, Pallabi Mitra, Sushmita Roy, Swalpa K. Indian Statistical Institute Machine Intelligence Unit Kolkata700108 India Alipurduar Government Engineering and Management College Department of Computer Science and Engineering Alipurduar736206 India

Recent medical image segmentation methods extract the characteristics of anatomical structures only from the spatial domain, ignoring the distinctive patterns present in the spectral representation. This study aims to develop a novel segmentation architecture that leverages both spatial and spectral characteristics for better segmentation outcomes. This research introduces the wavelet-infused convolutional Transformer (WaveCoformer), a computationally effective framework to fuse information from both spatial and spectral domains of medical images. Fine-grained textural features are captured from the wavelet components by the convolution module. A transformer block identifies the relevant activation maps within the volumes, followed by self-attention to effectively learn long-range dependencies to capture the global context of the target regions. A cross-attention mechanism effectively combines the distinctive features acquired by both modules to produce a comprehensive and robust representation of the input data. WaveCoformer outperforms related state-of-the-art networks in publicly available Synapse and Adrenal tumor segmentation datasets, with a mean Dice score of 83.86% and 79%, respectively. The model is feasible for deployment in resource-constrained environments with rapid medical image analysis due to its computationally efficient nature and improved segmentation performance. © 2013 IEEE.

关键词： Convolutional neural networks

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A new multimodal sentiment analysis for images containing textual information

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Multimedia Tools and Applications 2024年 1-30页

作者： Ahuja, Garvit Alaei, Alireza Pal, Umapada School of Computer Science and Engineering Manipal University Jaipur Jaipur India Faculty of Science and Engineering Southern Cross University Gold Coast Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Multimodal sentiment analysis on images with textual content is a research area aiming to understand the sentiment conveyed by visual and textual elements in the images. While multimodal sentiment analysis on images and text (reviews) has its own challenges, the combination of textual and visual content in the form of images presents new challenges as well as opportunities. In this research work, we proposed a multimodal sentiment analysis method that works on images incorporating textual elements. In the textual sentiment analysis model, we initially employed a recognition system to extract textual data from input images. Our proposed multimodal method is based on transfer learning, considering two pre-trained deep learning models, Xception, and RoBERTa, to extract features from both visual and textual content from multimedia images. We then implemented a fusion strategy to combine these two modalities (Visual Sentiment Analysis (VSA) and Textual Sentiment Analysis (TSA)) to enhance the accuracy of the proposed method and to provide a more comprehensive understanding of sentiment in multimedia content. In addition, we curated a custom dataset comprising images with associated text labels and sentiments. To ensure accurate labels, we conducted human evaluations involving thirty annotators. Our dataset includes images labeled with negative, neutral, and positive sentiments. Experimental results demonstrated the effectiveness of combining visual and textual features for sentiment analysis. The findings from this research hold promising implications for real-world applications, such as sentiment analysis in social media, product reviews, and marketing campaigns, where both images and text play a significant role in conveying emotional context. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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A Model-Driven Approach for Consistent Building Automation Control Logic Design and Deployment

A Model-Driven Approach for Consistent Building Automation C...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Knorr, Felix Pannosch, Jürgen Steindl, Gernot Tu Wien Institute of Computer Engineering Research Unit of Automation Systems Vienna Austria

ISBN: (纸本)9798350361025

This paper introduces an innovative model-driven approach for seamlessly integrating control logic into the building planning phase, ensuring consistency throughout deployment. Leveraging an open building information model (BIM), the approach enables model engineers to embed control logic directly into the BIM using an open language. The integrated model facilitates automatic extraction of control logic information, supporting efficient modifications. A proof-of-concept implementation using SIMULTAN, IEC 61131-3 Structured Text, and IDA ICE demonstrates successful deployment in a Heating, Ventilation, and Air Conditioning (HVAC) use case. © 2024 IEEE.

关键词： Air conditioning

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Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques

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Annals of Data Science 2024年第1期11卷 217-242页

作者： Das Guptta, Sumitra Shahriar, Khandaker Tayef Alqahtani, Hamed Alsalman, Dheyaaldin Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & ampTechnology Chittagong4349 Bangladesh Unit of Cybersecurity Department of Computer Science Center of Artificial Intelligence King Khalid University Abha Saudi Arabia School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any third-party systems. In phishing, the attackers typically try to deceive internet users by masking a webpage as an official genuine webpage to steal sensitive information such as usernames, passwords, social security numbers, credit card information, etc. Anti-phishing solutions like blacklist or whitelist, heuristic, and visual similarity based methods cannot detect zero-hour phishing attacks or brand-new websites. Moreover, earlier approaches are complex and unsuitable for real-time environments due to the dependency on third-party sources, such as a search engine. Hence, detecting recently developed phishing websites in a real-time environment is a great challenge in the domain of cybersecurity. To overcome these problems, this paper proposes a hybrid feature based anti-phishing strategy that extracts features from URL and hyperlink information of client-side only. We also develop a new dataset for the purpose of conducting experiments using popular machine learning classification techniques. Our experimental result shows that the proposed phishing detection approach is more effective having higher detection accuracy of 99.17% with the XG Boost technique than traditional approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Search engines

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Skill-Based engineering for the Description and Mapping of Smart Grid Applications 29

Skill-Based Engineering for the Description and Mapping of S...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Knorr, Felix Frühwirth, Thomas Institute of Computer Engineering Research Unit of Automation Systems Tu Wien Vienna Austria

ISBN: (纸本)9798350361230

This paper introduces an innovative approach for the description and mapping of applications in the smart grid domain. By utilizing a skill-based engineering approach, not only flexibility and changeability will increase, but also the need for describing the same application multiple times for different grids decays. As a first use-case, Fault Location, Isolation, and Service Restauration (FLISR) as an example application and a real-world grid in Vietnam have been chosen. By showing how this novel approach could be applied to these two entities, its potential is demonstrated. The IEC 61850-based approach uses a two-step procedure for the mapping. After defining the application's requirements, a mapping algorithm can check if the application can be deployed to a specific grid. In the second step, the actual mapping is described to deploy the application to the devices in the field automatically. Having the requirement description, a recommender system can suggest possible adaptions to the grid in case the check fails. © 2024 IEEE.

关键词： Power grids

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A Novel Approach to Improving Distributed Deep Neural Networks over Cloud Computing

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第17期17卷 94-107页

作者： Ali, Muhtada Zuhair Muttair, Karrar Shakir Department of Computer Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Nanotechnology and Advanced Materials Research Unit Faculty of Engineering University of Kufa Najaf Iraq

In recent years, deep distributed neural networks (DDNNs) and neural networks (NN) have excelled in an extensive list of applications. For example, deep convolutional neural networks (DCNNs) are constantly gaining new features in various tasks in computer vision. At the same time, the number of end devices, including Internet of Things (IoT) devices has increased prominently. These devices are attractive targets for machine learning applications because they are often directly connected to sensors. For example (cameras, microphones, and gyroscopes) that record large amounts of input data in a stream mode. This study presents the design of a DDNN with end devices, edges, and clouds that spans computer hierarchies. The idea presented is considered one of the new ideas because it depends on two layers to distinguish, namely the convolutional layer and the pooling layer. The main objective behind using these two layers in one proposal is to provide and obtain the best results. Finally, we discovered that the proposed technique produced the best results in terms of accuracy and cost, with the precision of the definition reaching 99 % and the cost being quite affordable at 25. As a result, we conclude that these results are far superior to those achieved by the researchers in their ideas provided in previous recent literature. © 2023 by the authors of this article. Published under CC-BY.

关键词： Cloud computing

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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computer Modeling in engineering & Sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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Towards a Uniform Exchange Format for Home and Building Automation using VDI 3814 28

Towards a Uniform Exchange Format for Home and Building Auto...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Knorr, Felix Kastner, Wolfgang Research Unit of Automation Systems Institute of Computer Engineering Vienna Austria

ISBN: (纸本)9798350339918

Exchanging technical documents in the building automation domain is a complicated process. Files are distributed either as drawings, spreadsheets, or text documents. Each stakeholder has to re-enter the data into their own system, and changes are revised manually, often even without revision control. This paper presents a uniform exchange format based on the 'graphical' standard VDI 3814. To increase acceptance, the industry standards XSD and XML were chosen. As a result of this work, a model is provided that covers the concepts and exchange files provided in the VDI 3814 standard. Given a supporting tool, data can be entered, revised, and exchanged automatically. Based on this unified representation, it is subsequently possible to transfer the data into one of the already existing ontologies in this domain by using model transformations. Some of these ontologies are also referred to in this paper. © 2023 IEEE.

关键词： Ontology

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Enhancing Statistical Analysis with Markov Chain Models Using a Shiny R Interface 6th

Enhancing Statistical Analysis with Markov Chain Models Usin...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Torres-Cruz, Fred Coaquira-Flores, Evelyn Eliana Canqui-Flores, Bernabé Ibañez-Quispe, Vladimiro Coyla-Idme, Leonel Postgraduate Unit of Statistics and Computer Engineering Faculty of Statistics and Computer Engineering Universidad Nacional del Altiplano de Puno P.O. Box 291 Puno Peru

ISBN: (纸本)9789819774227

This study demonstrates the expansive utility of Markov chains in statistical modeling, emphasizing their role in simulating complex systems within diverse fields, including engineering, economics, biology, and computer science. We present an innovative integration of Markov chain theory to predict the future states of dynamic systems and introduce ModerCMarkov, a novel web application designed in R Studio using Shiny packages. This application leverages Markov chains for forecasting outcomes from varied database typologies. Our comprehensive evaluation of ModerCMarkov assesses its processing speed and predictive accuracy across multiple databases, varying in scope and complexity. The results highlight the application's robustness, evidenced by its rapid processing capabilities and precise predictions. Furthermore, our research utilizes the Markov chain approach to identify critical nodes within key variables, enhancing our understanding of these systems. ModerCMarkov emerges as a powerful tool for intricate analysis and modeling of complex variable databases, offering significant contributions to multidisciplinary research endeavors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Markov chains

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Intelligent waste classification approach based on improved multi-layered convolutional neural network

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Multimedia Tools and Applications 2024年第36期83卷 84095-84120页

作者： Chhabra, Megha Sharan, Bhagwati Elbarachi, May Kumar, Manoj Department of Computer Science & amp Engineering School of Engineering & ampTechnology Sharda University UP Gr. Noida India School of Engineering and Sciences Department of Computer Science and Engineering SRM University-AP Andhra Pradesh Amaravati India Head of School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates MEU Research Unit Middle East University Amman11831 Jordan

This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%. © The Author(s) 2024.

关键词： Convolutional neural networks

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