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International Conference on Trends in Electronics and Informatics (ICEI)

作者： G Shivaji Rao Devasenan S Gana Shree C Rathakrishnan M Swathi S Department of Computer Science and Design Karpagam College of Engineering Coimbatore

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Traditional city parking infrastructures cannot adapt to the demands of modern motorists, resulting in congestion, delays, and increased emissions. This project aims to optimize parking space utilization and user experience by employing machine learning algorithms. Based on historical data as well as real-time user-updated information, the system predicts parking availability and enables optimal parking management without extensive-scale IoT infrastructure. The app utilizes Firebase for a scalable and secure database and authentication mechanism, and Google Maps integration provides real-time navigation assistance for better parking directions. Flutter ensures cross-platform compatibility, making it possible for both Android and iOS users to have an intuitive and smooth UI/UX. The primary features include advance booking of parking lots, the possibility of time extension, and slot availability based on the hour, granting flexibility to users. A secure reservation system prevents double booking, and GPS-based navigation simplifies finding a parking space in an easy and convenient way. By preventing the use of costly hardware installations, this cost-effective, scalable, and eco-friendly solution enables sustainable urban mobility by reducing emissions and improving the overall parking experience.

关键词： Photography Machine learning algorithms Web services Navigation Operating systems Urban areas Market research Real-time systems User experience Satellite images

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Prediction Analysis of Greeting Gestures based on Data Augmentation and LSTM 23

Prediction Analysis of Greeting Gestures based on Data Augme...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wibow, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of System Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798331528041

The field of Human Activity Recognition (HAR) is growing significantly in several areas but little research focuses on cultural behavior. How machine learning can explain human activity as a promotional tool in understanding cultural differences in a region is very challenging. Studies using Recurrent Neural Network (RNN), especially Long Short-Term Memory (LSTM), require large data when used to research human activity recognition. Using small data is a challenge when using LSTM. This study aims to determine which data augmentation is most appropriate when used using LSTM to predict Japanese greeting gestures, namely eshaku, keirei, saikeirei, waving hands, and Indonesian greeting gestures, namely the movement of bringing both hands together in front of the chest with a slight bow of the head. This study proposes to compare several data augmentations, namely jittering, scaling, permutation, cropping, reverse, time shifting, frequency domain, and synthetic data generation. We evaluate model performance using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) metrics. The experimental results show that Time Shifting has the best MSE, RMSE, and MAE values compared to others and Frequency Domain has the best R2 values compared to others. This study concludes that Time Shifting and Frequency Domain are the best data augmentation methods when using LSTM in predicting greeting gestures using small data. These findings provide valuable insights into the effectiveness of various data augmentation methods in motion prediction tasks with limited data sets, as well as the importance of selecting appropriate data augmentation methods in human motion analysis. Future work could use data from two or more people using model explanations generated by machine learning and developing a new method for cultural behavior. © 2024 IEEE.

关键词： Mean square error

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Direct ink writing of gradient shear-stiffening elastomer for enhanced toughness and impact resistance

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Journal of Industrial and engineering Chemistry 2025年 149卷 593-603页

作者： Gong, Liping Zhao, Chunyu Lu, Hongda Zhang, Qingtian Chen, Zexin Yang, Junjie Yang, Jinyu Sun, Shuaishuai Du, Haiping Li, Weihua School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong Wollongong2522 Australia CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Modern Mechanics University of Science and Technology of China Hefei230027 China School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong2522 Australia

Shear-stiffening materials have garnered significant attention due to their inherent ability to rapidly respond to changes in strain rate. However, conventional studies on these materials exhibit limited mechanical performance and insufficient structural design, hindering applications in material science and smart mechatronics. Inspired by the gradient structure of a squid beak, this study uses a direct-ink-writing (DIW) technology to develop a novel gradient shear-stiffening elastomer (GSSE). Comprehensive characterizations were conducted to optimize the printability of the shear-stiffening elastomer under various testing conditions, including rheology, compression, and tension. Additionally, the toughness and impact resistance of the elastomer were systematically investigated. It is demonstrated that the biomimetic GSSE exhibits enhanced flexibility, efficient energy dissipation, and outstanding impact resistance. Consequently, this work advances the field of shear-stiffening elastomers for diverse applications by leveraging the gradient structure design strategy to achieve superior mechanical performance under complex loading conditions. © 2025 The Author(s)

关键词： Strain rate

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Shoes-ACOSI: A Dataset for Aspect-Based Sentiment Analysis with Implicit Opinion Extraction

Shoes-ACOSI: A Dataset for Aspect-Based Sentiment Analysis w...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Peper, Joseph J. Qiu, Wenzhao Bruggeman, Ryan Han, Yi Chehade, Estefania Ciliotta Wang, Lu Computer Science and Engineering University of Michigan Ann ArborMI United States College of Arts Media and Design Northeastern University BostonMA United States

ISBN: (纸本)9798891761681

We explore implicit opinion extraction as a new component of aspect-based sentiment analysis (ABSA) systems. Prior work in ABSA has investigated opinion extraction as an important subtask, however, these works only label concise, explicitly-stated opinion spans. In this work, we present Shoes-ACOSI, a new and challenging ABSA dataset in the ecommerce domain with implicit opinion span annotations, the first of its kind. Shoes-ACOSI builds upon the existing Aspect-Category-Opinion-Sentiment (ACOS) quadruple extraction task, extending the task to quintuple extraction-now localizing and differentiating both implicit and explicit opinion. In addition to the new annotation schema, our dataset contains paragraph-length inputs which, importantly, present complex challenges through increased input length, increased number of sentiment expressions, and more mixed-sentiment-polarity examples when compared with existing benchmarks. We quantify the difficulty of our new dataset by evaluating with state-of-the-art fully-supervised and prompted-LLM baselines. We find our dataset presents significant challenges for both supervised models and LLMs, particularly from the new implicit opinion extraction component of the ACOSI task, highlighting the need for continued research into implicit opinion understanding. © 2024 Association for Computational Linguistics.

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A Surrogate-Assisted Evolutionary Framework for Expensive Multitask Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Tan, Shenglian Wang, Yong Sun, Guangyong Pang, Tong Tang, Ke School of Automation Central South University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

This paper proposes a surrogate-assisted evolutionary framework (called SELF) to solve expensive multitask optimization problems (ExMTOPs). SELF consists of two main phases: global knowledge transfer phase and local knowledge transfer phase. In the former, a multitask Gaussian process model (MTGP) is established by fusing previously evaluated solutions of multiple optimization tasks. MTGP can capture task-relevant information and the knowledge of landscapes. Then, differential evolution assisted with MTGP is proposed to preselect high-quality candidates. During the preselection, the knowledge of landscapes is transferred among multiple optimization tasks for locating promising regions quickly. In the latter, for each optimization task, Bayesian optimization is adopted to improve the quality of the best individual in the population. Moreover, the improved best individuals in the populations of multiple optimization tasks are adaptively transferred based on a transfer probability, which is computed through the task-relevant information provided by MTGP. By combining these two phases, SELF not only achieves the tradeoff between exploration and exploitation, but also utilizes the global and local knowledge transfer to improve the efficiency for solving ExMTOPs. We test SELF on seven benchmark test problems in the IEEE CEC2017 evolutionary multitask optimization competition. The results demonstrate that the performance of SELF is better than that of other seven advanced methods. In addition, we also apply SELF to deal with two real-world ExMTOPs. The designs provided by SELF exhibit the best performance among all the compared methods, verifying the potential of SELF in practical engineering applications. IEEE

关键词： Linear programming

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Numerical Investigation of Hybrid Savonius-Darrieus Vertical Axis Wind Turbine at Low Wind Speeds 18

Numerical Investigation of Hybrid Savonius-Darrieus Vertical...

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18th International engineering Research Conference 2022, EURECA 2022

作者： Eftekhari, Hesam Al-Obaidi, Abdulkareem Sh Mahdi Srirangam, Sucharita School of Computer Science and Engineering Taylor's University Malaysia School of Architecture Taylor's University Building & Design Malaysia

The need for electricity on the Earth is increasing day by day, and this requires building more electric power plants where most of the power plants cause CO2 emission which would increase the global warming. Using wind energy as one type of renewable energies, would contribute to reduce the global warming. In countries such as Malaysia where the average annual wind speed is 2-2.5 m/s using wind energy is a challenge because of the low wind speed. However, using a hybrid Savonius-Darrieus vertical axis wind turbine in low wind speed areas, the performance of the wind turbine may increase, and the starting rotation speed will decrease. This research aims at a comprehensive study on a hybrid Savonius-Darrieus wind turbine with helical Savonius rotor combined with helical and straight bladed Darrieus rotor. In this research, a base model with the name of Model Base was generated and two models with new configurations were generated in SOLIDWORKS. A numerical studies and simulation were conducted on these three designs using Ansys Fluent to identify the more suitable combination for low wind speed areas. Based on the obtained result hybrid Savonius-Darrieus with helical Savonius rotor and helical Darrieus rotor were found to be the most suitable combination because of their stable aerodynamic performance throughout a full rotation. Best performance for these models was found to be at 0.45 TRS with maximum torque and power coefficients of 0.68 and 0.306 respectively. © Published under licence by IOP Publishing Ltd.

关键词： Global warming

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Greeting Gesture Analysis Using Boosting Algorithms and SHAP 13

Greeting Gesture Analysis Using Boosting Algorithms and SHAP

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Wibowo, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of Systems Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350373332

Recently, studies related to human activity recognition have developed which have been applied in various fields. In that field Machine learning and deep learning techniques have been widely used for several classification and prediction tasks. However, the first problem is that there is rarely any research on human activities that reflect the culture of a region, such as greeting gestures. Second, there are few studies that do not provide models resulting from the classification or prediction process using machine learning, which can result in a lack of understanding of how a model from machine learning is generated. This study aims to predict Japanese greeting gestures (eshaku, keirei, saikeirei, waving hand) and Indonesian (placing both hands in front of the chest) using Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting. Then, represent the model of the learning machine using SHapley Additive exPlanations (SHAP) to find out how the learning machine obtain a model. Our findings show the efficacy of this method and show that the model not only predicts greeting gestures with high accuracy but also offers a transparent understanding of the factors influencing its predictions, thus enhancing the interpretability and applicability of machine learning in recognizing culturally significant human activities. In the future, we plan to use machine learning and other Explainable Artificial Intelligence (XAI) methods to study human activities with cultural concepts such as greeting gestures in different styles, create more greeting gesture-based human activity datasets, and examine the ethical implications or considerations of using machine learning in cultural contexts to respect cultural differences. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Heart Murmur Detection: A Comparative Study of Machine Learning Models Utilizing Digital Stethoscopes 3

Enhancing Heart Murmur Detection: A Comparative Study of Mac...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Mastracci, Nathaniel Derakhshan, Farnaz Sykes, Edward R. Abdullah, Saad Oakville Canada University of Guelph School of Computer Science Guelph Canada Mälardalen University School of Innovation Design and Engineering Västerås Sweden

ISBN: (纸本)9798350384598

Machine learning has proven to be a powerful tool across many domains including healthcare. Heart sound classification using machine learning can revolutionize cardiac care by improving diagnostic accuracy, enabling early intervention, and facilitating personalized treatment strategies. However, obtaining labeled data for classification model training can be difficult, especially for rare or complex conditions. Furthermore, classifying heart sounds accurately can be challenging due to the variability in sound patterns and the presence of noise which requires preprocessing. In this paper, two machine learning models were trained and evaluated using DenseNet architecture on the CirCor DigiScope Phonocardiagram dataset and an ensemble of Support Vector Machine (SVM) and Decision Tree (DT) algorithms on a custom dataset curated by our partner, Tech4Life. The F1 score of the CirCor trained model was 75%. This is our effort to advance the application of machine learning in heart sound classification. © 2024 IEEE.

关键词： Contrastive Learning

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Hyborg Agency: Cultivating Conversational AI Creatures through Community Connections 23

Hyborg Agency: Cultivating Conversational AI Creatures throu...

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Special Interest Group on computer Graphics and Interactive Techniques Conference - Art Gallery, SIGGRAPH 2023

作者： Sun, Yuqian Lee, Chang Hee Asadipour, Ali Computer Science Research Centre Royal College of Art United Kingdom Design Department College of Engineering KAIST Korea Republic of

ISBN: (纸本)9798400701573

Hyborg Agency, an interactive art installation, integrates a digital forest with a human discord chat channel, enabling communication with AI deer or 'hyborgs'. The forest metaphorically represents human society, where AI agents use the large language model (LLM) ChatGPT to learn and grow by summarising conversations and updating their knowledge base, assimilating community thoughts as enriching nutrients. © 2023 Owner/Author.

关键词： Knowledge based systems

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Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm

Improving Breast Cancer Classification Using an Adaptive Vot...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Mohammed Ansar Ali Aswin D Midhunraj S Rajesh B Sukuna D Department of Computer Science and Design Karpagam College of Engineering Coimbatore

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Breast cancer is still one of the leading and most life-threatening diseases in the world for women. Diagnosis of early disease is necessary for improved treatment chances and survival. This paper presents a novel approach to classifying breast cancer based on an adaptive ensemble learning algorithm, which is used to improve prediction accuracy. The proposed methodology combines the strengths of the Decision Tree and Random Forest classifiers to create a more robust and accurate classification model. It uses a weighted voting mechanism that dynamically adjusts the influence of each classifier according to its performance, prioritizing the models that are most accurate for the final prediction. The main objective of this framework is to formulate an accurate and reliable breast cancer classification system to increase diagnostic precision, enable earlier detection, and optimize treatment planning to improve patient outcomes.

关键词： Adaptation models Accuracy Prediction algorithms Breast cancer Classification algorithms Ensemble learning Decision trees Reliability Random forests Diseases

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