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10th International Multidisciplinary Engineering Research Conference on Moratuwa Engineering Research, MERCon 2024

作者： Ayesha, R.L.A. Sanduni Vijayarathna, L.B. Hasini M. Vithanage, N.P.A. Thayasivam, Uthayasanker Alahakoon, Damminda Adikari, Achini Pallewela, Nuwan University of Moratuwa Department of Computer Science and Engineering Katubedda Sri Lanka La Trobe University Research Centre for Data Analytics and Cognition Australia

ISBN: (纸本)9798331529048

Chatbots have become a trending topic with emerging platforms like ChatGPT, Gemini, and Copilot, for conversation assistance. Current chatbots mainly focus on the general public assuming a natural flow of conversation. However, there is a need for a chatbot that supports people with various communication disabilities. This research fills this gap by offering a novel technique for a chatbot that assists people with Aphasia, a condition characterised by difficulties with language. We propose a multi-modal chatbot that is customised and designed to assist users with communication disabilities in navigating a website. Unlike typical chatbots, which rely on one form of communication, our architecture combines multiple modalities to improve comprehension and promote effective communication for people with Aphasia. We focus on gathering multimodal inputs by recognising and combining user intents from diverse sources. The use of Txtai, an all-in-one embeddings database for semantic search improves our chatbot's capacity to process various inputs efficiently. We leverage specialised models like Whisper for audio transcription and MediaPipe Gesture Recognizer for gesture detection to enhance user interactions. Additionally, Rasa Core integration improves conversational experiences for users. We propose that this new approach will make communication more accessible and inclusive for individuals with Aphasia. © 2024 IEEE.

关键词： Gesture recognition

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Research and Application of Music Personalized Recommendation System Based on Random Forest Algorithm 6

Research and Application of Music Personalized Recommendatio...

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6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Shu, Ziyan Shen, Qing Zeng, Tianlun Wuhan Business University Department of Information Engineering Hubei China Major in Data Science and Big Data Technology China Major in Internet of Things Engineering China

ISBN: (纸本)9781665460033

In recent years, with the advent of the era of big data and the rise of data mining technology, people used to waste a lot of time looking for advantageous information, which was very inefficient. Music is an indispensable part of everyone's daily life, and major music platforms are gradually emerging. Finding music that users like is the key to attracting users. At present, there are relatively few studies on personalized music recommendation using data mining algorithms. In this paper, the random forest algorithm is used to pre-process the collected data, and then the data is modeled, the model parameters and fitting coefficients are adjusted, the appropriate index is selected as the learning parameter, and the learning curve is obtained at the same time. Finally, the prediction is carried out through examples, and it is found that the prediction accuracy is high, and the WeChat applet is used for development and application. The approach in this article is simple and constructive, and it is dedicated to recommending personalized and exclusive music for particular users, so that you can spend every day happily. © 2023 IEEE.

关键词： Curve fitting

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Explaining Learned Patterns in Deep Learning by Association Rules Mining 1

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Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Jiménez-Navarro, M.J. Martínez-Ballesteros, M. Martínez-Álvarez, F. Asencio-Cortés, G. Department of Computer Languages and Systems University of Seville Seville Spain Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (数字)9783031425363

ISBN: (纸本)9783031425356

This paper proposes a novel approach that combines an association rule algorithm with a deep learning model to enhance the interpretability of prediction outcomes. The study aims to gain insights into the patterns that were learned correctly or incorrectly by the model. To identify these scenarios, an association rule algorithm is applied to extract the patterns learned by the deep learning model. The rules are then analyzed and classified based on specific metrics to draw conclusions about the behavior of the model. We applied this approach to a well-known dataset in various scenarios, such as underfitting and overfitting. The results demonstrate that the combination of the two techniques is highly effective in identifying the patterns learned by the model and analyzing its performance in different scenarios, through error analysis. We suggest that this methodology can enhance the transparency and interpretability of black-box models, thus improving their reliability for real-world applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Association rules

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Nonparametric Regression Estimator of Multivariable Truncated Spline For Categorical data

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IAENG International Journal of Applied Mathematics 2025年第5期55卷 1357-1368页

作者： Suriaslan, Afiqah Saffa Budiantara, I Nyoman Ratnasari, Vita Statistics Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Statistics Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

Recent years have witnessed significant interest in truncated spline estimators for nonparametric regression with quantitative data. However, the applicability of these estimators is limited by the frequent occurrence of categorical response variables in real-world applications. A paucity of nonparametric estimators exists for handling categorical response data. This necessitates a method capable of modeling relationships between variables exhibiting pattern shifts across sub-intervals with categorical outcomes. This article thus introduces a novel multivariate truncated spline nonparametric regression estimator for categorical data, developed through a synthesis of literature and theoretical research. The developed method was applied to Indonesia's 2023 poverty depth index data and East Java's 2020 gender development index data. A comparative analysis of the Truncated Spline nonparametric regression model and the binary logistic regression model for estimating categorical data revealed that the Truncated Spline approach yielded superior estimations. Some of the highlights of the proposed method are: 1) This study employs truncated spline nonparametric regression to model categorical response data, 2) Optimal knot placement is determined using the Akaike Information Criterion (AIC), 3) The method's overall performance is demonstrated through its application to two established datasets, and 4) This study compares truncated spline nonparametric regression and logistic regression. © (2025), (International Association of Engineers). All Rights Reserved.

关键词： Maximum likelihood estimation

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Changes in frailty and incident cardiovascular disease in three prospective cohorts (vol 45, pg 1048, 2024)

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EUROPEAN HEART JOURNAL 2024年第19期45卷 1768-1768页

作者： He, Di Wang, Zhaoping Li, Jun Yu, Kaixin He, Yusa He, Xinyue Liu, Yuanjiao Li, Yuhao Fu, Ruiyi Zhou, Dan Zhu, Yimin Department of Epidemiology and Biostatistics and Department of Respiratory Disease Sir Run Run Shaw Hospital Zhejiang University School of Medicine 866 Yuhangtang Rd Hangzhou 310058 Zhejiang China Department of Big Data in Health Science School of Public Health and the Second Affiliated Hospital Zhejiang University School of Medicine 866 Yuhangtang Rd Hangzhou 310058 Zhejiang China Center of Clinical Big Data and Analytics of the Second Affiliated Hospital Zhejiang University School of Medicine 866 Yuhangtang Rd Hangzhou 310058 Zhejiang China Cancer Center Zhejiang University 866 Yuhangtang Rd Hangzhou 310058 Zhejiang China

Background and Aims Previous studies found that frailty was an important risk factor for cardiovascular disease (CVD). However, previous studies only focused on baseline frailty status, not taking into consideration the changes in frailty status during follow-up. The aim of this study was to investigate the associations of changes in frailty status with incident *** This study used data of three prospective cohorts: China Health and Retirement Longitudinal Study (CHARLS), English Longitudinal Study of Ageing (ELSA), and Health and Retirement Study (HRS). Frailty status was evaluated by the Rockwood frailty index and classified as robust, pre-frail, or frail. Changes in frailty status were assessed by frailty status at baseline and the second survey which was two years after the baseline. Cardiovascular disease was ascertained by self-reported physician-diagnosed heart disease (including angina, heart attack, congestive heart failure, and other heart problems) or stroke. Cox proportional hazard models were used to calculate the hazard ratio (HR) and 95% confidence interval (95% CI) after adjusting for potential *** A total of 7116 participants from CHARLS (female: 48.6%, mean age: 57.4 years), 5303 from ELSA (female: 57.7%, mean age: 63.7 years), and 7266 from HRS (female: 64.9%, mean age: 65.1 years) were included according to inclusion and exclusion criteria. The median follow-up periods were 5.0 years in the CHARLS, 10.7 years in the ELSA, and 9.5 years in the HRS. Compared with stable robust participants, robust participants who progressed to pre-frail or frail status had increased risks of incident CVD (CHARLS, HR = 1.84, 95% CI: 1.54–2.21; ELSA, HR = 1.53, 95% CI: 1.25–1.86; HRS, HR = 1.59, 95% CI: 1.31–1.92). In contrast, frail participants who recovered to robust or pre-frail status presented decreased risks of incident CVD (CHARLS, HR = 0.62, 95% CI: 0.47–0.81; ELSA, HR = 0.49, 95% CI: 0.34–0.69; HRS, HR = 0.70, 95% CI: 0.55–0.89) whe

关键词： Frailty Cardiovascular disease Dynamic nature Epidemiology

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EXAMINING THE ROLE AND FUTURE POTENTIAL OF DESIGN FOR DISASSEMBLY METHODS TO SUPPORT CIRCULAR PRODUCT DESIGN 24

EXAMINING THE ROLE AND FUTURE POTENTIAL OF DESIGN FOR DISASS...

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24th International Conference on Engineering Design, ICED 2023

作者： Formentini, Giovanni Ramanujan, Devarajan Department of Mechanical and Production Engineering - Design and Manufacturing Aarhus University Katrinebjergvej 89 G-F Aarhus N8200 Denmark Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus8200 Denmark

Design for disassembly (DfD) approaches are crucial in supporting the industrial circular economy transition. In literature, a great amount of DfD methodologies is available, however, it is still not clear how they can be used to improve product circularity. To address this gap, our work proposed a systematic literature review of DfD methodologies applied in the field of product design with the aim to provide an overview of the topic in the last decade (i.e., from 2012 to 2022) in terms of methods applicability (i.e., design phase), required parameters and integration capability with circularity assessment. As a result, the paper shows that DfD methods are mainly used in the later design phase to improve product sustainability performances, but a method that simultaneously considers DfD and CE is currently missing. Based on the obtained results, we outlined the requirements that a new DfX method would need to consider both DfD and CE simultaneously. Finally, we proposed a modified version of the butterfly diagram in which DfD parameters are linked to CE indicators to help visualize the connection between the two areas. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Product design

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Seismology and Climatology: A Study of Seismological Impacts of Climate Change in Indonesia

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Atmospheric and Climate sciences 2024年第2期14卷 210-220页

作者： Lukundo Mtambo Xingxiang Tao The Department of Big Data Science Zhejiang University of Science and Technology Hangzhou China

Climate change has been a matter of discourse for the last several decades. Much research has been conducted regarding the causes and impacts of climate change around the world. The current research contributes to the knowledge of the influence of climate change on our environment, with emphasis on earthquake occurrences in the region of Indonesia. Using global temperature anomaly as a measure of climate change, and earthquake data in Indonesia for the period 1900-2022, the paper seeks to find a relationship (if any) between the two variables. Statistical methods used include normal distribution analysis, linear regression and correlation test. The results show peculiar patterns in the progression of earthquake occurrences as well as global temperature anomaly occurring in the same time periods. The findings also indicated that the magnitudes of earthquakes remained unaffected by global temperature anomalies over the years. Nonetheless, there appears to be a potential correlation between temperature anomalies and the frequency of earthquake occurrences. As per the results, an increase in temperature anomaly is associated with a higher frequency of earthquakes.

关键词： Earthquakes Climatology Climate Change Seismology Correlation Linear Regression Indonesia

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Towards Simulation-Based Circular Product Design 30

Towards Simulation-Based Circular Product Design

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30th CIRP Life Cycle Engineering Conference, LCE 2023

作者： Aher, Gaurav Boudjadar, Jalil Ramanujan, Devarajan Department of Mechanical and Production Engineering Aarhus University Aarhus8200 Denmark Department of Electrical and Computer Engineering Aarhus University Aarhus8200 Denmark Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus8200 Denmark

Assessing the circular economy (CE) performance of products has become an essential consideration in product design processes. While a growing body of literature is exploring the use of CE indicators to guide design processes, they typically assume broad design changes (e.g., material changes, end-of-life treatment) that may not accurately capture trade-offs in CE indicators, technical performance, and design feasibility. This paper aims to address the above gaps with the goal of incorporating CE assessment into simulation-based design. To this end, this paper defines a novel framework for using simulation-based design to analyze the effects of parametric changes in product design attributes on CE performance. First, the proposed framework describes the lifecycle information needed for computing resource-based, product-level CE indicators at the design stage. Depending on the set of selected CE indicators, corresponding simulation models are used to compute the effects of changing design parameters, and setting design/performance constraints, on the product's CE performance. Thus, the proposed framework enables the selection and optimization of product design attributes for achieving a specific level of CE performance, given specific technical performance requirements and other design constraints. The application of this framework is demonstrated using a case study conducted on a flywheel. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Product design

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Object Detection with YOLO Version 3 for big data 1

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5th International Conference on Information Systems and Management science, ISMS 2022

作者： Saxena, Rajat Dubey, Shatendra Karna, Vishma Kumar Big Data Lab Department of Computer Science and Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeth Vishwavidyalaya Indore India Big Data Lab Department of Information Technology NRI Institute of Information Science and Technology Bhopal India

ISBN: (数字)9783031311536

ISBN: (纸本)9783031311529

Object detection is the most vital task for the application of computer vision. The You only Look Once (YOLO) version3 is the most promising technique used for deep learning-based object detection. It is the k-means cluster method, which estimates initial weight and height of the predicting bounding boxes that are sensitive to the initial cluster centers. This paper focuses on study of approaches for efficient object detection techniques in the field of computer vision. The improvement in the accuracy had been studied based on the deep learning techniques. It aims to develop an online tool for a better study about the object detection techniques as a platform to review the object detection in both online and offline images. A popular state of art object detection algorithm called YOLOv3 which is trained on COCO and ImageNet. The result obtained in the developed online tool has shown a positive result with a possibility in detecting the most common objects available in this real world. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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