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8th International Conference on Information technology, InCIT 2024

作者： Hnoohom, Narit Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Faculty of Engineering Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand University of Phayao Department of Computer Engineering School of Information and Communication Technology Phayao Thailand King Mongkut's University of Technology Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics North Bangkok Thailand

ISBN: (纸本)9798350366303

Motorcyclist behavior recognition (MBR) is a significant factor in improving road safety and preventing accidents. Inertial sensors, such as accelerometers, gyroscopes, and magnetometers, have shown promising potential for capturing the dynamic behavior of motorcyclists. However, existing approaches often require complex models and significant computational resources, limiting their deployment in real-world scenarios. This study presents a MotoNeXt architecture, which is a light deep residual network for correctly identifying the behavior of motorcyclists using data from inertial sensors. Our approach leverages the power of deep learning while maintaining a compact model size, making it suitable for resource-constrained environments. The proposed network incorporates multi-kernel residual blocks to facilitate the learning of hierarchical features and improve gradient flow during training. We evaluate our method on a publicly available benchmark dataset, MB-IMU, collected from multiple riders under various riding conditions. Experimentation has shown that our lightweight model achieves cutting-edge performance, outperforming a variety of existing techniques. In Scenario I, using accelerometer, gyroscope, and magnetometer data, MotoNeXt achieved an accuracy result of 78.90088% along with an F1-score of 81.56738%. According to Scenario II, which included additional sensor data such as VelInc, OriInc, and Euler angles, MotoNeXt further improved its performance, achieving an accuracy result of 83.29735% along with an F1-score of 84.95352%. The proposed approach offers a practical solution for real-time MBR, enabling the development of intelligent transportation systems and rider assistance technologies. Our experiments highlight the effectiveness of lightweight deep learning models in extracting meaningful patterns from inertial sensor data. This opens up opportunities for further investigation in this field. © 2024 IEEE.

关键词： Accelerometers

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A review of federated learning in renewable energy applications:Potential,challenges,and future directions

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Energy and AI 2024年第3期17卷 444-457页

作者： Albin Grataloup Stefan Jonas Angela Meyer Bern University of Applied Sciences School of Engineering and Computer ScienceQuellgasse 21Biel2501Switzerland Universitàdella Svizzera italiana Faculty of InformaticsVia la Santa 1Lugano-Viganello6962Switzerland Delft University of Technology Department of Geoscience and Remote SensingStevinweg 1Delft2628The Netherlands

Federated learning has recently emerged as a privacy-preserving distributed machine learning *** learning enables collaborative training of multiple clients and entire fleets without sharing the involved training *** preserving data privacy,federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation,research and *** paper provides an overview of federated learning in renewable energy *** discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and *** also evaluate the potential and the challenges associated with federated learning applied in power and energy ***,we outline promising future research directions in federated learning for applications in renewable energy.

关键词： Federated learning Renewable energy Dis tributed learning Data heterogeneity Privacy Data sharing Wind energy Solar energy Buildings Review

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PrAViC: Probabilistic Adaptation Framework for Real-Time Video Classification

arXiv

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arXiv 2024年

作者： Trędowicz, Magdalena Struski, Lukasz Mazur, Marcin Janusz, Szymon Lewicki, Arkadiusz Tabor, Jacek Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland Faculty of Applied Computer Science University of Information Technology and Management Rzeszów Poland

Video processing is generally divided into two main categories: processing of the entire video, which typically yields optimal classification outcomes, and real-time processing, where the objective is to make a decision as promptly as possible. The latter is often driven by the need to identify rapidly potential critical or dangerous situations. These could include machine failure, traffic accidents, heart problems, or dangerous behavior. Although the models dedicated to the processing of entire videos are typically well-defined and clearly presented in the literature, this is not the case for online processing, where a plethora of hand-devised methods exist. To address this, we present PrAViC, a novel, unified, and theoretically-based adaptation framework for dealing with the online classification problem for video data. The initial phase of our study is to establish a robust mathematical foundation for the theory of classification of sequential data, with the potential to make a decision at an early stage. This allows us to construct a natural function that encourages the model to return an outcome much faster. The subsequent phase is to demonstrate a straightforward and readily implementable method for adapting offline models to online and recurrent operations. Finally, by comparing the proposed approach to the non-online state-of-the-art baseline, it is demonstrated that the use of PrAViC encourages the network to make earlier classification decisions without compromising accuracy. © 2024, CC BY.

关键词： Classification (of information)

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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Automated Fracture Detection and Classification Using Siamese Neural Networks

Automated Fracture Detection and Classification Using Siames...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Anderson, K.L. Engelbrecht, A.P. Birkholtz, F.F. Stellenbosch University Department of Computer Science Stellenbosch South Africa Stellenbosch University Computer Science Division Department of Industrial Engineering Stellenbosch South Africa Gulf University of Science and Technology GUST Engineering and Applied Innovation Research Centre Kuwait Institute of Orthopaedics and Rheumatology Mediclinic Winelands Orthopaedic Hospital Stellenbosch University Stellenbosch Division of Orthopaedic Surgery Department of Surgical Sciences Stellenbosch South Africa

ISBN: (纸本)9798350369106

This study aims to address the critical need for accurate and efficient fracture detection and classification in medical radiography, by leveraging recent developments in deep learning techniques. A two-stage pipeline is proposed combining a cutting-edge convolutional neural network (CNN), You only look once (YOLOv9), for wrist fracture detection using a Siamese neural network (SNN) for fracture type classification. The proposed methods aim to overcome the limitations of a single-stage approach by optimizing detection and classification stages independently. The results obtained demonstrate the efficacy of the two-stage pipeline in improving accuracy and reducing class imbalance issues, highlighting the potential of artificial intelligence (AI) driven solutions in enhancing patient care and reducing diagnosis time. © 2024 IEEE.

关键词： Convolutional neural networks

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Robust Detection of Tables in Documents Using Scores from Table Cell Cores

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SN computer science 2022年第2期3卷 161页

作者： Ajij, Md. Pratihar, Sanjoy Roy, Diptendu Sinha Hanne, Thomas Department of Computer Science and Engineering National Institute of Technology Meghalaya Shillong India Department of Computer Science and Engineering Indian Institute of Information Technology Kalyani India FHNW University of Applied Sciences and Arts Northwestern Switzerland School of Business Riggenbachstrasse 16 Olten 4600 Switzerland

Table detection is an essential step in many document analysis systems. Tabular data are a pivotal form of information representation that can organize data in a conventional structure for comfortable and quick information retrieval and comparison. Detection of table structures in PDF files or images is a challenging task because of the variability of table layouts, and sometimes the tabular structures’ similarities with non-tabular elements like charts, plots, etc. In this work, we have presented a table detection method using a geometric analysis of the table cell cores that represents the table cell texts. The proposed method works by analyzing the text gap information, and hence it can detect the table cell cores, irrespective of the presence of the table boundary lines and cell-separating rule-lines. Experimentations have been done on various document images of complex structures from well-known datasets. The detection accuracies obtained by us corroborate the usefulness of the proposed method. © The Author(s) 2022.

关键词： Document analysis Information retrieval Table recognition

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Phytochemical screening, antioxidant properties, and photocytotoxicity of Clinacanthus nutans leaf extracts

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Vegetos 2024年第4期37卷 1652-1661页

作者： Chen, Chee-Shien Loke, Chui-Fung Poh, Tze-Ven Tan, Siow-Ping Department of Physical Science Faculty of Applied Sciences Tunku Abdul Rahman University of Management and Technology Kuala Lumpur 53300 Malaysia Research & Development and Commercialization Tunku Abdul Rahman University Management and Technology Kuala Lumpur 53300 Malaysia Department of Computer Science and Embedded Systems Faculty of Computing and Information Technology Tunku Abdul Rahman University Management and Technology Kuala Lumpur 53300 Malaysia

Clinacanthus nutans (Burm. f.) Lindau (C. nutans) is renowned in many tropical countries for its wide range of traditional uses and medicinal properties. This study qualitatively screened ethanol, hexane, and ethyl acetate (EA) leaf extracts of C. nutans for phytochemicals and evaluated their antioxidant activities using 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging and ferric reducing antioxidant power (FRAP) assays. Furthermore, the photocytotoxicity of the extracts against the A431 human skin squamous carcinoma cells was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Qualitative phytochemicals screening revealed the presence of terpenes, alkaloids, phenols, quinones, and chlorophyll, whereas flavonoids and anthraquinones were absent in all crude extracts. In the DPPH assay, the EA crude extract exhibited the lowest EC₅₀ value of 31 ± 2 μg/mL, whereas the same extract demonstrated the highest FRAP value of 7.42 ± 1.87 mmol Fe2+/g DW. After photodynamic therapy (PDT) activation, EA crude extract demonstrated the strongest photocytotoxic activity against A431 cells with an IC₅₀ value of 8 ± 1 μg/mL, followed by the hexane crude extract (IC₅₀ = 16 ± 2 μg/mL) and the ethanol crude extract (IC₅₀ = 20 ± 1 μg/mL). However, all three crude extracts showed negligible activity against the A431 cells in the absence of PDT treatment, even at concentrations as high as 200 μg/mL after 24 h of dark incubation. These findings suggest that C. nutans could serve as a potential source of natural antioxidants and photosensitizers for photodynamic therapy in cancer treatment. © 2023, The Author(s) under exclusive licence to Society for Plant Research.

关键词： Antioxidant activity Clinacanthus nutans PDT treatment Photocytotoxicity Phytochemical screening

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A Physical Framework for Testing and Evaluating of a Mechatronic Shunting System

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International Journal Bioautomation 2023年第4期27卷 181-192页

作者： Alkharabsheh, Abdelrahman Momani, Lina Qawaqzeh, Yousef Alsakarneh, Amjad Computer Science Department British Applied College Umm Al Quwain United Arab Emirates Mechanical Engineering Department Higher Colleges of Technology Fujairah United Arab Emirates Information Technology Department Fujairah University Fujairah United Arab Emirates Department of Mechanical Engineering Yarmouk University Irbid Jordan

Shunt is a flexible tube called a catheter implanted inside the brain where cerebrospinal fluid (CSF) is produced. Existing treatments rely on passive implantable shunts with differential pressure valves. The authors defined, designed, and programmed an intelligent wireless hydrocephalus shunting system. The shunting system was designed, simulated, and tested to carry out many tasks such as regulating the mechatronic valve, collecting Injection Control Pressure (ICP) readings, analyzing these readings, responding to all emergency cases, and diagnosing the whole shunting system. Nowadays, many challenges face clinical trials for new medical devices. Clinical trials are complex and require following several rules and regulations to ensure compliance with different standards. Due to the difficulty of using medical trials, an urgent need for a physical framework for implementing a virtual model of intracranial pressure and cerebrospinal fluid dynamics in hydrocephalus mechatronic shunt testing. Such a physical framework will play a vital role in assessing the functioning of the whole mechatronic shunting system. A framework of a mechatronic shunting system (implanted and external) is illustrated and integrated with embedded management and diagnosis software. Such a framework will help assess, test, and evaluate the main functions of the proposed shunting system. © 2023 by the authors. Licensee Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of sciences. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://***/licenses/by/4.0/). All Rights Reserved.

关键词： Cerebrospinal fluid

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Integrating Renewable Energy Sources into Smart Grids with an Aggregator-Based Energy Management System for Efficiency and Resilience 7

Integrating Renewable Energy Sources into Smart Grids with a...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kumar, B. Santhosh Devi, Anusuya Sharma, Smita Bhavana, Munugapati Babu, B. Sankara Singh, Navdeep Institute of Aeronautical Engineering Department of Cse Ai and Ml Telangana Hyderabad India New Horizon College of Engineering Department of Applied Science Karnataka Bangalore India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350350067

Integrating renewable energy sources (RESs) into smart grids (SGs) is challenging due to the intermittent and unpredictable nature of the sources, which are often the sun or wind. Additionally, it is important to think about how to get customers involved in the electricity sector so that we can lower energy prices globally. According to this article, an energy management system (EMS) may help the SG run more efficiently. The EMS aggregates distributed energy resources to make SG market participation easier. Integrating demand-side management (DSM) with active management strategies (AMS) has financial and environmental benefits as it increases the use of renewable energy sources while decreasing consumer energy costs. By incorporating their preferences and costs into market processes, energy supply/demand and distribution system operators (DSOs) may respond to real-time price signals. This has the potential to make the grid more adaptable and resilient. To verify the effectiveness of the proposed EMS, a 23-bus, 11-kV transmission network is used. © 2024 IEEE.

关键词： Clean energy

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Can metacognition predict your success in solving problems? An exploratory case study in programming

arXiv

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arXiv 2024年

作者： Bubnic, Bostjan Kovačević, Željko Kosar, Tomaž Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Computer Science and Information Technology Zagreb University of Applied Sciences Zagreb Croatia

Metacognition has been recognized as an essential skill for academic success and for performance in solving problems. During learning or problem-solving, metacognitive skills facilitate a range of cognitive and affective processes, leading collectively to improved performance. This study explores the predictive potential of metacognition in the second introductory programming course. A two-dimensional model has been proposed, consisting of metacognitive awareness and metacognitive behavior. To evaluate the predictive capacity of metacognition empirically, an exploratory case study with 194 participants from two institutions was conducted in the second introductory programming course. A latent approach was employed to examine the associations between metacognition and performance in object-oriented programming. Our findings indicate that both metacognitive dimensions have a positive effect on programming. Likewise, the results of the structural equation modeling show that 27% of variance in programming performance is explained by metacognitive behavior. Following the results, metacognition has the potential to be considered as one of the important predictors of performance in introductory programming. © 2024, CC BY-NC-ND.

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