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检索条件"丛书名=Communications in Computer and Information Science"

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Comparative Analysis of Statistical Machine Learning and Deep Learning Approaches for Forecasting News Popularity on Social Media Platforms 8th

Comparative Analysis of Statistical Machine Learning and Dee...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Maddukuri, Sai Krishna Kaur, Devinder Univ Toledo Elect Engn & Comp Sci Toledo OH 43606 USA

ISBN: (纸本)9783031856273;9783031856280

Forecasting news popularity on social media platforms is a challenging task that requires accurate prediction. The news popularity progression needs to be monitored at an interval of specified time steps. The research was carried out using experiments on a real-world dataset of news articles. This problem was modeled as Multivariate Time Series prediction. The Popularity Index is monitored from three social media sources, Google+, LinkedIn and Facebook at an interval of 2 h statistical and Deep Machine Learning Models were used to predict the News Popularity Index after 48 h based on the progression of News every 2 h. For Statistical Regression Linear Regression Model was used. For Deep Learning Long Short-Term Memory based LSTM and BiLSTM models were used. The models were compared using the metric of root-mean-square error (RMSE) for the accurate prediction of News Popularity. LSTM and Bi-LSTM outperform regression techniques in terms of prediction accuracy. Additionally, the performance of LSTM and Bi-LSTM were compared. Bi-LSTM slightly outperforms LSTM. Our findings suggest that deep learning models, specifically LSTM and BiLSTM, are well-suited for forecasting news popularity on social media platforms. Our research provides insights into the strengths and weaknesses of these models and offers practical implications for media outlets and social media platforms in their efforts to increase the reach and impact of news content.

关键词： Multivariate Time Series Recurrent Neural Networks Long short term Memory (LSTM) Bidirectional Long short term Memory (Bi-LSTM) Variance Inflation Factor (VIF)

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Design and Development of Wearable Knee Rehabilitation System Based on Motor Imagery Brain computer Interface 5th

Design and Development of Wearable Knee Rehabilitation Syste...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Deng, Ruirui Zheng, Xu Wang, Yanping Wang, Kaifa Gao, Nuo Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

In view of the existing problems in rehabilitation training in China and the great potential of rehabilitation training robots based on Motor Imagery Brain computer Interface, this paper designs and develops a wearable knee rehabilitation training system based on Motor Imagery Brain computer Interface from the perspective of portability and low cost. The proposed knee rehabilitation training system can extract the patient's active rehabilitation intention through EEG signal, and complete the active and passive rehabilitation training of the knee joint through the knee exoskeleton, which can effectively overcome the problems of the traditional rehabilitation training system, such as high training cost, limited number of professional rehabilitation therapist, and low willingness of the patient's active training. A series of offline and online experiments verify the feasibility and effectiveness of the proposed rehabilitation system. The development and application of this system is expected to provide a rehabilitation system with autonomous control, active rehabilitation and convenient wearing for patients with knee joint dysfunction, and effectively alleviate the problems existing in the current motor function rehabilitation training.

关键词： Motor Imagery Brain-computer interface Wearable knee rehabilitation system electroencephalogram Introduction

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BladeWorks: A Novel Manufacturing-as-a-Service Platform for the Development of Customized Knives 5th

BladeWorks: A Novel Manufacturing-as-a-Service Platform for ...

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5th 5th Olympus - International Conference on Supply Chain

作者： Kladovasilakis, Nikolaos Charalampous, Paschalis Pelekoudas, Athanasios Panagiotis Kostavelis, Ioannis Ioannidis, Dimosthenis Tzovaras, Dimitrios Ctr Res & Technol Hellas Informat Technol Inst 6th Km Charilaou Thermi Rd Thessaloniki 57001 Greece Int Hellen Univ Sch Econ & Business Adm Dept Supply Chain Management Katerini 60100 Greece

ISBN: (纸本)9783031693434;9783031693441

In recent years, the business model of Manufacturing-as-a-Service (MaaS) has gained increased interest in Industry 4.0 activities paving the way for Industry 5.0. MaaS emphasizes on-demand and scalable manufacturing services allowing common people to have access to the development and fabrication processes of their products. More specifically, MaaS enables industries to have access to several manufacturing procedures without the need for extensive in-house production facilities. In this context, the BladeWorks platform aims to offer a groundbreaking approach to MaaS in the knife-manufacturing industry. BladeWorks is a web-based tool, where the users can produce the desired blade design employing Artificial Intelligence (AI) and numerical methodologies. In the first approach, a novel AI-retrieval module was developed, where the input of the software is a simple blade picture, and BladeWorks proposes a design that best fits the query image. In the second tool, the desired design is generated in real-time considering the actual geometrical characteristics of the input picture. Then, the users can validate the produced design via an online finite element analyses (FEA) tool and modify it based on the simulated results. Finally, the user can choose the manufacturing process considering a list of available fabrication methods derived from the platform's connectivity with workshops and receive an instant cost estimation along with an estimated time of arrival for their product. In the future, this platform could expand to numerous other applications, such as mechanical component crafting, minimizing the intermediary steps and revolutionizing the current manufacturing and supply chain processes.

关键词： Manufacturing-as-a-Service Customized Blades Machine learning Finite Element Analysis

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Predicting Solar Radiation in Manabi: A Machine Learning Approach 4th

Predicting Solar Radiation in Manabi: A Machine Learning App...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Arteaga-Subiaga, Daniel Parraga-Alava, Jorge Rivadeneira, Lucia Univ Tecn Manabi Maestria Ingn Software Fac Posgrad Ave Jose Maria Urbina 130105 Portoviejo Ecuador Univ Tecn Manabi Dept Sistemas Computac Fac Ciencias Informat Ave Jose Maria Urbina 130105 Portoviejo Ecuador

ISBN: (纸本)9783031832093;9783031832109

Solar radiation prediction has been studied within the academic context in several geographical locations to determine the variables that affect this phenomenon. However, there are no specific studies that have been carried out in the province of Manabi in Ecuador. To fill this gap in knowledge, this study develops a predictive model using machine learning algorithms to estimate solar radiation. A quantitative methodology is used with data collected from the TJ-JUNAYA weather station located in the Playa Prieta area of Manabi. The results indicate that the XGBoost algorithm is the most effective for predicting solar radiation based on RMSE, MSE, and R-2 metrics, with the UV index being the most influential variable. This study is important for two main reasons. First, it helps people in make better decisions by giving them tools to predict sunlight, which can make solar energy systems work better. Second, it uses weather details that were not used before to make better prediction models.

关键词： Solar radiation Machine learning Predictive Model Random Forest XGBoost Manabi

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Embracing Residuality Theory in Software Architecture to Address Uncertainty: Key Challenges and Strategies 33rd

Embracing Residuality Theory in Software Architecture to Add...

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33rd International Conference on Software and Data Engineering

作者： Fellah, Aziz Northwest Missouri State Univ Sch Comp Sci & Informat Syst Maryville MO 64468 USA

ISBN: (纸本)9783031752001;9783031752018

The source of uncertainty in software architecture isn't impossible to predict, but it is certainly challenging given its inherent complexity and the dynamic environments of technology, external factors and events that can potentially impact the system's operation and stability. Residuality theory, in particular, provides a new ideology that challenges conventional approaches to software design. In this paper, we propose a Residual Dynamic Management (RDM) framework for software architecture to manage residual components and stressors that constitute a residual system. RDM ensures that the system not only remains robust and capable of thriving but also flourishes in the face of uncertainty, dynamic changes, and unpredictable conditions. Furthermore, we propose a model called Residual Finite State Machines (R-FSM) to incorporate residuality complexity into software architecture, enhancing the overall system's ability to manage unforeseen changes and effectively benefit from them through the concept of antifragility.

关键词： Residuality theory Software architecture Uncertainty Antifragility

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PoliticPA 2024: Author Profiling Gender and Political Ideology of Politicians in Panama 10th

PoliticPA 2024: Author Profiling Gender and Political Ideolo...

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10th International Conference on Technologies and Innovation

作者： Cedeno-Moreno, Denis Pan, Ronghao Vargas-Lombardo, Miguel Garcia-Diaz, Jose Antonio Garcia-Sanchez, Francisco Univ Tecnol Panama Panama City Panama Univ Murcia Fac Informat Campus Espinardo Murcia 30100 Spain

ISBN: (纸本)9783031757013;9783031757020

Author profiling in computational linguistics and Natural Language Processing identifies traits such as age, gender, education level, and personality from written text, with applications in marketing, cybersecurity, forensics, and academia. Psychographic traits, such as political ideology, enhance the understanding of behavior. Recent initiatives such as PoliticES and PoliticIT have demonstrated the extraction of political ideology and demographic traits from text clusters. Here we present the first version of the PoliticPA 2024 dataset, designed for author profiling tasks on Panamanian politicians. This dataset allows the extraction of gender and political ideology from both binary and multi-class perspectives. We evaluate this dataset using a baseline based on Bag of Words features and logistic regression, as well as fine-tuned versions of several Spanish and multilingual Large Language Models and an approach based on mBART. Preliminary results indicate that lightweight models generally outperform others, except for multiclass political ideology. However, the current corpus is limited compared to other political author profiling datasets, making comprehensive comparisons difficult. Future work will include compiling additional tweets and users to expand the dataset, aiming for at least 500 clusters while exploring zero and few-shot learning models.

关键词： Authorship Analysis Author Profiling Natural Language Processing

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PMoET: Going Wider Than Deeper Using the Parallel Mixture of Experts Transformer for 3D Hand Gesture Recognition 4th

PMoET: Going Wider Than Deeper Using the Parallel Mixture of...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Alboody, Ahed Slama, Rim CESI CESI LINEACT Lab Equipe Accueil UR 7527 Paris France

ISBN: (纸本)9783031821554;9783031821561

Mistral AI (Artificial Intelligence) Startup has released Mixtral-Transformers based on a Mixture of Experts layer (MoE). MoE architectures have gained prominence in both Large Language Modeling (LLM) and computer Vision due to their ability to scale efficiently by dynamically selecting an ensemble of specialized sub-models (a group of experts) for different inputs rather than using the entire model for every input to optimize the overall performance with a constant computational cost. Following Mixtral Transformers, GSHard, GLaM, and Switch Transformer, we propose the "Parallel Mixture-of-Experts Transformer (PMoET)" that coupled with Spatial Linear Layer (SLL) and use parallel Mixture of Experts (MoE) layers for going wider instead of deeper in traditional Transformer. PMoET model could be scaled along width using parallel MoE layers instead of a single Feed-Forward Network (FFN) layer in traditional Transformer. Using MoE architecture for graph learning of 3D dynamic skeleton-data for 3D hand gesture recognition can provide significant benefits due to the model's ability to handle complex and varied 3D skeleton data efficiently. For this purpose, Decoupling spatial and temporal graph learning using a PMoET model by integrating parallel MoE layers into parallel configuration is an advanced approach for recognizing 3D hand gestures. By focusing on both the spatial configuration of the hand and the temporal dynamics of gestures, the PMoET architecture provides a robust and efficient solution for complex gesture recognition tasks. Finally, to evaluate the PMoET performance, conducting extensive experiments on SHREC'17 dataset provides strong evidence of PMoET effectiveness. PMoET improves the overall performance, the training stability and reduces the computational cost. Experiments show the efficiency and superiority of the PMoET model variants, particularly PMoET Model 4, which surpasses the state-of-the-art (SOTA) performance achieved by DG-STA method.

关键词： Mixture-of-Experts (MoE) Parallel MoE Transformer Accelerating Training 3D Hand Gesture Recognition Human-computer Interaction

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Integrating Digitalization and Sustainability: An Innovative Approach to Assess Digitainability in Manufacturing 5th

Integrating Digitalization and Sustainability: An Innovative...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Miciaccia, Pietro Andrea Devito, Fabrizia Semeraro, Concetta Dassisti, Michele Polytech Univ Bari Dept Mech Math & Management DMMM Bari Italy Scuola Univ Super IUSS Pavia Dept Sci Technol & Soc I-27100 Pavia Italy Univ Sharjah Dept Ind & Management Engn Sharjah 27272 U Arab Emirates SKF Ind Spa Bari Italy

ISBN: (纸本)9783031807749;9783031807756

Manufacturing faces significant sustainability challenges due to its intensive consumption of natural resources. The paradigm of sustainable manufacturing stood out in recent years, prompting the development of various tools and methodologies to assess the sustainability performance of manufacturing activities. These tools often evaluate environmental, economic, and social impacts based on the Triple Bottom Line (TBL) framework. However, their practical application remains limited due to scalability issues (with few tools addressing entire manufacturing systems), sector-specific designs, and challenges in the comparability of results. The adoption of the Best Available Techniques (BAT) framework has also set new benchmarks for assessing sustainability performance. In the context of Industry 4.0 and 5.0, there is a growing need to rethink sustainability assessment as a tool that not only guides the digitalization process but also ensures that sustainable practices are integrated effectively. This paper introduces the concept of "digitainability", representing the convergence of sustainability and digital transformation, shaping future manufacturing paradigms. The DigiSustain Manufacturing Assessment Model (DSMAM) provides a comprehensive framework for evaluating sustainability performance by addressing both the environmental impacts and the digital transformation of manufacturing systems. The model focuses on reducing resource consumption and enhancing operational efficiency through digital innovation, offering strategies for redesigning and reengineering manufacturing processes. It also offers insights for redesigning and reengineering manufacturing processes, identifying opportunities for sustainable digital advancements that drive the development of the factory of the future.

关键词： Sustainable processes Digital manufacturing Sustainability assessment

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Determining the Minimum Operational Period of the Refinery Production Operation Plan 2nd

Determining the Minimum Operational Period of the Refinery P...

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2nd International Conference on information Technologies and Their Applications

作者： Saveliev, Mikhail Zykina, Anna LLC Digital Ind Platform Off 1-HKropotkina 1A St Petersburg Russia Omsk State Tech Univ 11 Mira Prospect Omsk Russia

ISBN: (纸本)9783031734168;9783031734175

In creating integrated automated production control systems, there is an open question of simulation fidelity distribution between MES- and APC-systems according to the requirement for speed and decision-making horizon at each level. Operational planning forms a set of discrete operations organized by time and indicators. The period of the operations includes the transient and steady-state mode of the manufacturing process, but does not affect the values of transient mode indicators. Transient mode is the "noise" for operational production planning. The period of scheduled operations should be determined for petroleum refineries so that the level of the "noise" does not exceed the required production planning accuracy. The paper presents a method of calculating the transient mode parameters and the analysis results for refinery process time series, enabling us to determine the minimum operation period for the required operational production planning accuracy in the MES-system. Presents response time, average control action period and obtained estimates of transient speed on the example unit for each group of real refinery processes.

关键词： Petroleum refinery operation Integration systems Measurement data discretization Data compression algorithm Algorithm for local trend detection Regression analysis Transient speed

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A Deep Learning-Based Novel RNN-BiLSTM Architecture for Efficient Threat Detection in IoT Ecosystem 4th

A Deep Learning-Based Novel RNN-BiLSTM Architecture for Effi...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Chintale, Pradeep Naruka, Davinder Khanna, Anirudh Mandala, Vishwanadham Desaboyina, Gopi Sure, Tharun Anand Reddy SEI Investment Co Downingtown PA 19335 USA 2230 133rd PL SE Mill Creek WA 98012 USA 900 Acad Dr Plano TX 75023 USA Cummins Inc Columbus IN USA ServiceNow 933 Heartleaf Dr Leander TX USA

ISBN: (纸本)9783031734762;9783031734779

Internet of Things (IoT) security is essential because of exponential growth in interconnected devices that enhance the surface attack for cyber threats. The occurrence of IoT threats like data breaches and unauthorized access have severe consequences which compromise privacy and disruption of critical infrastructure. Hence, effective IoT security is significant to protect from these vulnerabilities and ensure interconnected systems reliability. However, detecting threats in IoT is challenging because of a huge number of interconnected devices, and diverse networks. This research proposes, the hybrid Recurrent Neural Network - Bidirectional Long Short-Term Memory (RNN-BiLSTM) to effectively detect the threats in IoT. RNN effectively capture temporal dependencies and BiLSTM analyze data from both backward and forward directions which increase threat detection accuracy by understanding the sequence patterns. Dataset like CICIDS-2017, N-BaIoT and IoT-23 are utilized to evaluate the proposed approach performance. These data are preprocessed by cleaning dataset labels, removal of redundant and min-max normalization leading to normalization of the features. Then, synthetic minority over-sampling technique (SMOTE) is used to balance data. To estimate model performance accuracy, precision, recall and f1-score are utilized as parameters. The proposed RNN-BiLSTM accomplished better accuracy of 99.45%, 99.51%, and 99.38% for CICIDS-2017, N-BaIoT, and IoT-23 datasets correspondingly.

关键词： Bi-LSTM DL techniques Deep Neural Network Internet of Things Threat Detection

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