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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Suicide Ideation Prediction Through Deep Learning: An Integration of CNN and Bidirectional LSTM with Word Embeddings

Suicide Ideation Prediction Through Deep Learning: An Integr...

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Computing Conference

作者： Oyewale, Christianah T. Ibitoye, Ayodeji O. J. Akinyemi, Joseph D. Onifade, Olufade F. W. Mohammed VI Polytech Univ Rabat Morocco Univ Greenwich London England Univ York York YO10 5GH N Yorkshire England Univ Ibadan Ibadan Nigeria

ISBN: (纸本)9783031622793;9783031622779;9783031622762

Suicide ideation prediction received attention in public health because it affects not only the victim but the general public. Identifying suicide ideation can be traditionally tricky and time-consuming, and the victim might have harmed himself or herself before detection. Easy access to social media provided textual data for Deep Learning (DL) models like Convolutional Neural networks (CNN) and Long Short-Term Memory (LSTM) for the identification of suicide ideation. These models are more promising at identifying suicide ideation than traditional Machine Learning models because they can be applied to every form of datasets. The main challenge is combining the DL models and word embeddings. This research employed the DL model by combining CNN with bidirectional LSTM and two-word embedding techniques, FastText and GloVe (global vectors for word representation). The research questions are as follows: Will there be a difference in F1 score between twoword embeddings when combined with CNN-BiLSTM? And will the overfitting issue in the existing study be addressed? Using an existing study as a baseline, experimentation was done using a secondary Reddit dataset of 232,074 posts, achieving a 94% F1-score for FastText and GloVe. It was observed that GloVe could do better without approximating the F1-score and FastText when the running time is considered.

关键词： Suicide ideation FastText GloVe Word embedding Public health CNN-BiLSTM

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Intelligent Digitalization and Immersive Experience in Cross-Border e-Commerce Environment (I): The Formation Pathway and Underlying "Mediator" of Consumer Brand Attachment

Intelligent Digitalization and Immersive Experience in Cross...

引用

Intelligent systems Conference

作者： Xi, Zhexu Liu, Bing Univ Bristol Bristol Ctr Funct Nanomat Bristol BS8 1TL Avon England Kings Coll London Dept War Studies London WC2R 2LS England

ISBN: (纸本)9783031664304;9783031664311

The exponential growth of cross-border e-commerce (CBEC) has notably emphasized the significance of strengthening consumer-brand relationships and enhancing brand equity in the digital age. Particularly, user-generated content (UGC) has emerged as a pivotal element in shaping brand perceptions and fostering emotional connections between consumers and brands. This study aims to dissect the influence of UGC quality on the formation of brand attachment (BA) and examine the mediating role of brand involvement (BI) in this relationship. By adopting a mixed-method approach that integrates theoretical induction with empirical analysis, our research scrutinizes the pathway through which UGC quality impacts BA, with a special focus on the mediating influence of BI. The findings illuminate that UGC quality significantly affects BA directly and indirectly through BI, highlighting the critical role of consumer engagement in the digital branding strategy. The implications of this study offer profound insights into consumer behavior dynamics and strategic guidelines for enhancing brand management practices in the global marketplace. Our investigation contributes to the nascent body of literature on consumer-brand relationships within CBEC settings by uncovering the bottom-up mechanisms of BA from a consumer-centric perspective.

关键词： Cross-border e-commerce User-generated content UGC quality Brand attachment Brand involvement Consumer-brand relationship

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Tank Road 1929: Design and Evaluation of Immersive Social Environments for Context-Based Learning

Tank Road 1929: Design and Evaluation of Immersive Social En...

引用

Intelligent systems Conference

作者： Fang, Dexter Zong Rui Sathikh, Peer Hodgkinson, Gray Nanyang Technol Univ Sch Art Design & Media 50 Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9783031664304;9783031664311

This paper examines the potential of immersive social environments conducted through Virtual Reality (VR). This enhances audience engagement in order to deepen contextual understanding of unfamiliar learning content. This is done through introducing and evaluating propositions for designing meaningful interactions in VR grounded in site exploration, affordances to inform participation, and collaborative information exchange. An experiment, Tank Road 1929, was developed based off these propositions and implemented to assess the impact on social learning. Set in 1920s Singapore, participants explored a virtual recreation of the former Tank Road railway station and its vicinity in solo or collaborative sessions. Quantitative and qualitative analyses evaluated four learning outcomes regarding the site's geographical, social, lifestyle, and economic elements. Results demonstrated substantive learning across all participants, but collaboration showed clearer, more accurate comprehension of intricate details like goods, businesses, transportation, architecture, and inhabitant roles. They also displayed greater engagement through shared discoveries, focused discussions, and reinforced understanding. Moreover, collaboration precisely distinguished observations, unlike solo participants who sometimes conflated details. Hence, interactivity within Tank Road 1929 aided concentration on relevant specifics and the broader historical context. In summary, social interaction meaningfully improved recollection and grasping of factual elements, underscoring its effectiveness over individual exploration for context-based learning. The study hence provides a framework for designing immersive social environments that leverage interactions to increase understanding.

关键词： Interactions in virtual reality Contextual understanding Collaborative information exchange Immersive social environment Context-based learning

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Digital Business Transformation for SMEs: Maturity Model for Systematic Roadmap 11th

Digital Business Transformation for SMEs: Maturity Model for...

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Medina, Maximiliano Jeanneret De Santo, Alessio Oswald, Philippe Sokhn, Maria Univ Appl Sci Western Switzerland HES SO HEG Arc Neuchatel Switzerland

ISBN: (纸本)9783031456473;9783031456480

Digital maturity models (DMM) aim to measure the current level of a company's digitalization, as well as providing a path towards digital maturity. While many DMMs exist, several challenges surround their application for small and medium-sized enterprises (SME) that would ben the process on their own. The aim of this paper is to describe the development and the evaluation of a tool that assist SMEs with their digital transformation, from the maturity assessment to the setup of a systematic digital transformation roadmap. Following a Design Science Research approach, we elucidated the needs of SME managers regarding digital maturity through interviews (n=10). Then, we developed and evaluated a DMM integrated within an online tool through focus groups (n=3). We finally evaluated our tool in the context of six SME digital transformation initiatives. Our findings highlight that SME managers need prescriptions about digital maturity rather than a sole description of their organization' maturity stage. Evaluations show good results in terms of utility and validity. Moreover, a need for a more complete and specialized recommendation service was highlighted which pave the way for future research.

关键词： digital transformation maturity level digital business digital maturity model digital transformation roadmap

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Comparison of Bucket Brigades and Working Balance Types of Lean Cellular Production 10th

Comparison of Bucket Brigades and Working Balance Types of L...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Bajric, Hadis Ferizbegovic, Faris Derakovic, Faris Pasic, Mirza Univ Sarajevo Fac Mech Engn Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662676;9783031662683

The cellular production organization enables the elimination of seven types of Lean process wastes: transport, inventory, unnecessary movements, waiting, overproduction, overprocessing, and defects. This paper presents the basic principles of lean production, focusing on different types of cellular production organization models. Bucket Brigades (BB) and Working Balance (WB) types of cellular production are most commonly used in the assembly processes. Simulations and tests for BB and WB types are performed while monitoring efficiency for different numbers of operators. The goal was to demonstrate and test the hypothesis of the BBmodel's superiority in conditions of uncertainty and frequent changes due to the fluctuation of the workforce and required capacities. To establish BB and WB types of cellular organization, the time standardization of each operator by operation was carried out. The simulations were preceded by operator training to eliminate the effect of the learning curve on the results. Starting differences in the times of individual operators were up to 156%.The results showed that Bucket Brigades is a superior and more robust model that provides higher productivity. Even in some specific cases, productivity is higher up to 89,5% compared to the Working Balance concept. In addition, social loafing described by the Ringelmann effect was noticed and presented in the work.

关键词： Working Balance Bucket Brigades Lean cellular manufacturing productivity hands-on simulation Ringelmann effect

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Design of a Dynamic Absorber for a Vibrating Frame with Three Degrees of Freedom 10th

Design of a Dynamic Absorber for a Vibrating Frame with Thre...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Donadio, Angela Isola, Giuseppe Berardi, Valentino Paolo Pappalardo, Carmine Maria MEID4 Via Giovanni Paolo II 132 I-84084 Fisciano Italy Univ Salerno Dept Ind Engn Via Giovanni Paolo II 132 I-84084 Fisciano Italy

ISBN: (纸本)9783031662676;9783031662683

The study of the mechanical vibrations of a system is important to ensure the safety of a structure. In the field of civil engineering, it is becoming increasingly common to use auxiliary systems called dynamic absorbers, also called Tuned Mass Dampers (TMDs), which are capable of decreasing the vibrations of the main system. They are characterized by mass-spring systems and eventually by a damper. In virtue of the presence of such systems, the operating frequency band of the main structure can be expanded. They are used in civil engineering works to counteract both the action of earthquakes, which cause excitation at the base of the structure, and wind action, which must be taken into account especially in the case of high-rise buildings. In this paper, we will focus on the optimal design of the parameters of a TMD for a vibrating frame with three degrees of freedom. The purpose of this paper is to demonstrate that the Den Hartog method can be generalized for a Multiple Degrees of Freedom (MDF) system. To this end, we will consider the structure subjected to a displacement imposed at the base by an earthquake that occurred in Loma Pietra in 1989. The dynamic analysis of the system was carried out by using MATLAB, while for the realization of the virtual 3D structure we used INVENTOR.

关键词： absorber multiple degrees of freedom (MDF) optimal design vibrations TMD DVA

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A Privacy-Preserving Federated Learning Framework for Financial Crime 16th

A Privacy-Preserving Federated Learning Framework for Financ...

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Haseeb, Abdul Ekerete, Idongesit Moore, Samuel Ulster Univ Belfast Sch Comp Belfast BT15 1ED Antrim North Ireland

ISBN: (纸本)9783031775703;9783031775710

This paper presents a Federated Learning (FL) framework tailored for decentralized data environments, focusing on preserving data privacy while training models across multiple branches or regions. Synthetic credit card transaction datasets are utilized to mimic real-world scenarios, where sensitive data is distributed across branches. The proposed framework employs encrypted data training at the local level, ensuring privacy through additive encryption techniques. This work introduces a privacy-preserving FL framework using additive encrypted data training technique as additive encryption method. This ensures data privacy while enabling collaborative model training across decentralized environments. The study compares global test accuracy (GTA) of variousMLmodels on a synthetic credit card transaction dataset, demonstrating the superior performance of the Multi-Layer Perceptron (MLP). Additionally, it reveals high local training accuracies (LTA) of 90% to 98% on encrypted data, showcasing the effectiveness of local training. The paper also highlights the importance of model selection for optimal performance and provides practical insights into classifying fraudulent credit card transactions using the proposed FL framework. These contributions enhance the understanding of effective FL implementation while ensuring data privacy and robust model performance.

关键词： Machine Learning models Federated Learning decentralized Data privacy Financial Crime

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Aiding from Deep Learning Applications in the Classification of Medical Images

Aiding from Deep Learning Applications in the Classification...

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Intelligent systems Conference (IntelliSys)

作者： Behery, G. M. Farouk, R. M. Ahmed, Elham Ali, Abd Elmounem Damietta Univ Comp Informat Fac Comp Informat Syst Dumyat Egypt Zagazig Univ Math Dept Fac Sci Zagazig Egypt

ISBN: (纸本)9783031477171;9783031477188

One of the important fields in computer science is called computer vision. Computer vision technology can be used to solve many problems in various fields. Computer vision plays a major role in image classification, recognition, and analysis. For example, in healthcare, computer vision algorithms could help with an automatic task, such as detecting cancer in the skin image, classifying breast cancer, and identifying Covid-19 images. Deep learning (DL) is responsible for developing computer vision. Deep learning is a subset of the world of Artificial Intelligence (AI). The aim of this paper is to classify medical images by deep learning algorithms. Several models of deep learning like VGG16, VGG19, ResNet 50, inception v3, etc. are used to improve image classification. Three different data are used in this paper. Data on skin cancer, breast cancer, and Covid-19 were used due to the recent spread of these diseases, as they are among the lethal diseases. The concept of transfer learning is used to reduce the cost of learning and to overcome the problem of overfitting. When models are hybrid with transfer learning, good results appear. Accuracy is 98.03% of classified skin images, 99.35% of Covid, and 97.77% of breast images. These results will be useful to doctors in making medical classification images more accurate.

关键词： Image classification Medical image Deep learning Models Automatic task

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Federated Interoperable Digital Twins for Collaborative Learning Factories 14th

Federated Interoperable Digital Twins for Collaborative Lear...

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14th International Conference on Learning Factories (CLF)

作者： Iyer, Suveg, V Sangwan, Kuldip Singh Dhiraj Birla Inst Technol & Sci Pilani Pilani Campus Pilani 333031 Rajasthan India CSIR Cent Elect Engn Res Inst Pilani 333031 Rajasthan India

ISBN: (纸本)9783031654107;9783031654114

Globalization has made the production systems accessible across the globe by removing the geographical boundaries. The original equipment manufacturers outsource many of the parts which are not their core competencies to leverage the globalized production for reduced cost. However, the complexity associated with the supply chain and the manufacturing process have increased and have become critical. The modular learning factories designed to understand, develop, and train industry 4.0 based manufacturing solutions should be able to oversee these complexities of globalized or de-centralized production with different quality standards. Digital twins of manufacturing applications mimic the real time working and has the potential to be developed as a platform - Digital twin as a Service - for collaborative learning factories. This paper proposes a federated digital twin based collaborative learning factory framework to develop manufacturing solutions for global scenarios. The network of learning factories provides a globalized field to train and test solutions. The proposed framework is elaborated with a use case on the development of federated digital twins for production, assembly, and inspection systems. The proposed solution is expected to help in improving the productivity and better planning for disruptions in production. The collaborative manufacturing framework can be enhanced by developing digital twins for manufacturing partners in the supply chain.

关键词： Interoperable digital twins Collaborative learning factory Industry 4.0

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Application of Supervised Machine Learning Models with Inter Simple Sequence Repeat Markers for Prediction of Grass Species

Application of Supervised Machine Learning Models with Inter...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Mohammadi, Reza Panahi, Bahman Amiri, Saleh Agr Biotechnol Res Inst Iran ABRII Dept Genom Branch Northwest & West Reg Agr Res Educ & Extens Org AREEO Tabriz Iran

ISBN: (纸本)9783031671913;9783031671920

Recently, machine learning is used to make accurate predictions using experimental data in biology, and the outcomes of these predictions could be effective for managing future research. In this study, supervised machine learning models in combination with ISSR (inter-simple sequence repeat) markers were used to categorize and predict the grass species and genera. The collected plant materials comprise 46 genotypes representing nine distinct grass species sourced from various regions across Iran. To characterize these accessions, a set of 16 ISSR primers was employed for profiling. The clusters were created by the UPGMA (unweighted pair group method with arithmetic average) clustering algorithm. In order to dimensionality reduction and define the most indicative predictor alleles, weighting algorithms were applied on processed data set. Tree Induction and Naive Bayes were also employed to predict and classify grass species. Performance evaluation of constructed models was performed by cross-validation with 10 times repeats. Based on the results, interspecies genetic diversity was higher than intra-species genetic diversity. In terms of the correlation between grouping patterns and the regions of collection, disparate findings were observed across different genera. The consequences suggested that Naive Bayes and Tree Induction models exhibited effectiveness in predicting grass species. In the Tree Induction models, the highest accuracy of 90.10% was achieved when Decision Tree and Random Forest were run with the information gain ratio criterion on Cdb. However, Bayesian models demonstrated even greater efficiency in predicting grass genera and species, achieving a 100% accuracy rate. This research has successfully developed effective, rapid and inexpensive analytical tools that can be helpful in future grass germplasm management and breeding programs.

关键词： Supervised Machine Learning ISSR Grass Species

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