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

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Iterative Algorithm for the Generalized Segmental Continuous Cutting Problem with Optimization Time Constraint

Iterative Algorithm for the Generalized Segmental Continuous...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Petunin, Aleksandr Ukolov, Stanislav Ural Fed Univ 19 Mira St Ekaterinburg 620002 Russia IMM UB Ras 16 S Kovalevskaya St Ekaterinburg 620108 Russia

ISBN: (纸本)9783031671944;9783031671951

The paper proposes a new algorithm for solving one class of the tool path problems for CNC sheet cutting machines (the generalized segmental continuous cutting problem, GSCCP) with an additional parameter limited the calculation time for finding an optimal solution. As input data in the GSCCP, 2D layout of nested parts and a finite set of subtasks for optimizing the cutting path are used. Each subtask contains a set of so-called basic cutting segments, which determine the trajectories of tool movement between the points for material piercing and the points for switching tool off. Each subtask can be solved independently within the specified calculation time. The best solution found for the all subtasks is a solution to the GSCCP problem. The proposed iterative algorithm involves quantizing the total computation time. Moreover, within each time quant, all subtasks are also solved sequentially by calculation the upper and lower bounds. Initial upper bounds can, for example, be obtained using any fast heuristic based on effective combinatorial optimization methods. Next, for each of them, the procedure for searching for the lower boundary is launched. The next iteration of the solution process is performed taking into account the adjusted bounds. The solution procedures are interrupted when the specified time quant is reached, or upon obtaining a guaranteed exact solution. The effectiveness of the algorithm is illustrated by practical examples. It is also shown that for generate a set of finite set of subtasks for GSCCP, it is advisable to use neural networks.

关键词： Cutting path Tool Path Problem Segment Cutting Optimization time Iterative algorithm Time Quantizing Lower and upper bounds Classification Problem

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Sentiment Analysis of Product Reviews Using Deep Learning and Transformer Models: A Comparative Study 1

引用

International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Kusal, Sheetal Patil, Shruti Gupta, Aashna Saple, Harsh Jaiswal, Devashish Deshpande, Vaishnavi Kotecha, Ketan Symbiosis Int Deemed Univ Symbiosis Inst Technol Pune 412115 Maharashtra India Symbiosis Int Deemed Univ Symbiosis Ctr Appl Artificial Intelligence SCAAI Symbiosis Inst Technol Pune 412115 Maharashtra India

ISBN: (数字)9789819984763

ISBN: (纸本)9789819984756;9789819984763

In recent times, the WWW has changed the way people interact with one another, express their opinions, provide feedback on websites, and share feelings on social media on different platforms. In the age of AI, it becomes necessary to understand the sentiments from peoples' opinions, feedback, and interactions. Sentiment analysis has recently drawn a lot of attention. Sentiment analysis has been extensively used and applied in a range of domains, including business and e-commerce. Mainly in the e-commerce sector, where product reviews and service reviews are one of the most important criteria for improving products and services, so analyzing the sentiments behind customer reviews or feedback becomes crucial. On the other hand, sentiment analysis helps customers in decision-making. The authors explored the Amazon reviews dataset, using state-of-the-art natural language-based transformer models to analyze product review sentiment. This paper presents a comparative investigation of different techniques used for sentiment analysis in product reviews to discover which AI-based technique works pre-eminent for review datasets.

关键词： Transformer Pre-trained models Deep learning E-commerce Sentiment analysis Natural language processing Amazon review dataset

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PharmaSys: Toward Preventing Prescription Misuse Using a HIPAA-Compliant Blockchain Protocol 9th

PharmaSys: Toward Preventing Prescription Misuse Using a HIP...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Nguyen, Aaron H. Lewtschuk, Alex Durham, James Lucas Lundquist, Peyton Dagher, Gaby G. San Jose State Univ San Jose CA 95192 USA Marshall Univ Huntington WV 25755 USA Boise State Univ Boise ID 83725 USA

ISBN: (纸本)9789819732883;9789819732890

Prescription drug misuse has become a major health crisis across the United States with prescription drug abuse skyrocketing during the twenty-first century resulting in classification as an epidemic. The rise in prescription misuse has resulted in negative consequences including heightened instances of overdose deaths, increased crime rate, and escalated healthcare expenditures. Although illegal sales contribute to the issue, a notable portion of prescription drugs stems from licensed medical professionals who engage in over-prescribing medications. To help combat this issue we propose PharmaSys, a novel multi-party blockchain system with a quorum-based consensus protocol that aims to prevent prescription misuse. PharmaSys achieves this by introducing and establishing a checks and balances system for prescription transactions, all while maintaining compliance with HIPAA regulations. PharmaSys consists of three distinct node types: prescribers, pharmacists, and patients;each of which executes unique functions within the system. We have implemented PharmaSys to illustrate our system's scalability and security.

关键词： Blockchain Prescription tracking Healthcare HIPAA compliance Consensus protocol Drug abuse Proof of sharding

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Inverse Dynamics for Feedforward Control of an Underactuated 6-DOF Gantry Crane 1

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3rd International Symposium on Industrial Engineering and Automation

作者： Bettega, Jason Fabris, Francesco Richiedei, Dario Tamellin, Iacopo Trevisani, Alberto Univ Padua Dept Management & Engn DTG Stradella San Nicola 3 I-36100 Vicenza Italy

ISBN: (数字)9783031704659

ISBN: (纸本)9783031704642;9783031704659

Inverse dynamics in underactuated, non-minimum phase multibody systems is addressed in this paper by means of the challenging example of a gantry crane driving a spatial double pendulum. The latter consists of a pendulum plus a spatial rigid body attached to the pendulum terminal mass. The reference to be tracked is imposed to the two planar Cartesian coordinates of the rigid-body tip, while the actuated coordinates are those of the trolley. Because of underactuation and non-flat property of the system, inverse dynamics relies on a mixed algebraic and differential scheme. Additionally, this input-output configuration leads to a system with an unstable internal dynamic, whose integration cannot be done unless stabilization is performed. By exploiting the concept of output redefinition, i.e., by assuming a fictitious output in the internal dynamics equations, stabilization is performed, while ensuring excellent tracking performances. Numerical simulations proof the method effectiveness.

关键词： Inverse dynamics Feedforward control Underactuated multibody systems Internal dynamics Gantry crane

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Work-in-Progress: Technology-Driven Introductory Phase in Engineering Sciences for Sustainable Individual Support of Students' Academic Success 21st

Work-in-Progress: Technology-Driven Introductory Phase in En...

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21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Block, Brit-Maren Dethmann, Jannis Haus, Benedikt Leuphana Univ Luneburg Inst Prod Technol & Syst D-21335 Luneburg Germany

ISBN: (纸本)9783031618901;9783031618918

The work-in-progress (WiP) paper comprises the conception, implementation, and evaluation project of an integrated introductory phase in engineering sciences with the aim of providing personalized support to students before commencing their studies and during their initial two semesters. The didactic innovation lies in designing a digitally supported comprehensive introductory phase that effectively accompanies the transition from school to university and significantly contributes to enhancing students' academic abilities during their initial semesters. As a specific contribution to the ongoing research discussion on learning with and about technologies in engineering education, this paper aims to provide theoretical and practical insights into the following aspects: how to design an enhanced introductory phase based on theoretical foundations, how to agilely address forthcoming developments and assist freshmen using technologies in engineering educational environments, and how to conduct evaluations for quality assurance and validation processes.

关键词： Engineering Education of the Future Virtual Environments Learning Analytics Undergraduate Mobile and Micro Learning

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Continuous Action Recognition in Manufacturing Contexts by Deep Graph Convolutional networks 1

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Intelligent systems Conference (IntelliSys)

作者： Maselli, M. V. Marani, R. Cicirelli, G. D'Orazio, T. CNR Inst Intelligent Ind Syst & Technol Adv Mfg STIIM Bari Italy

ISBN: (数字)9783031477188

ISBN: (纸本)9783031477171;9783031477188

Human action recognition is an active topic of research in computer vision and machine learning. Its application in the industrial domain is even more challenging since workers can handle multiple objects and follow different assembly sequences, and only a few datasets are target-oriented. However, the availability of low-cost cameras capable of extracting high-level information about human posture and movement opens up new possibilities. This work compares four state-of-the-art graph neural networks working with skeletal data to recognize the actions in the HA4M dataset, where subjects perform an assembly task. Videos are divided into clips of consecutive frames that form the input skeletal graphs of the networks. Then, an algorithm for action segmentation is proposed to assess each action's exact starting and ending instants. Results show that the best performance is achieved by a twostream Adaptive Graph Convolutional Network trained with input clips 77 frames long.

关键词： Smart manufacturing Action recognition Skeleton data Graph convolutional network

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Quantum Annealing as a Chemical Kinetics Solver for Pollutant Transport Models 16th

Quantum Annealing as a Chemical Kinetics Solver for Pollutan...

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Rodriguez Garrido, Juan de Dios Pujante Perez, Alejandro Illueca Fernandez, Eduardo Jara Valera, Antonio J. Libelium Lab SL Murcia 30006 Spain

ISBN: (纸本)9783031775703;9783031775710

In the past years, quantum computing has been proved to have a huge potential in a wide range of applications, such as cryptography, finance and material science, although its applicability in the world of ambient intelligence and smart cities is still under study. This paper aims to shed light on this, specifically, regarding its application in the current models for air quality and dispersion of contaminants used in pollution monitoring, which typically imply large computational costs. Among all the procedures and techniques in the field, the quantum annealing paradigm is the one that has shown a more mature state of development and seems to be the most promising solution for industrial applications at the moment, particularly excelling in optimization problems. For this reason, quantum annealing was chosen to test its capability in solving chemical kinetic problems, which usually consist in sets of coupled differential equations whose linearity depends on the complexity of the system. A simple chemical system was proposed and solved by a quantum annealer, comparing its solution to one obtained by classical methods in terms of precision, time and scalability. The study shows that these problems can be already solved by quantum computers to a certain degree of accuracy but they lack some key features to be a viable substitute for classical methods at the moment. Quantum annealing is thus positioned as a promising tool due to its scalability, only awaiting suitable hardware that enables the proper treatment of these problems.

关键词： Quantum Annealing Chemical Kinetics Air Quality Modelling Numerical Methods

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A New Model for the Arabic Language Based on the Semantic Representation of Words and Sentences 1

引用

Intelligent systems Conference (IntelliSys)

作者： Awajan, Arafat A. Princess Sumaya Univ Technol King Hussein Sch Comp Sci Amman Jordan

ISBN: (数字)9783031477188

ISBN: (纸本)9783031477171;9783031477188

We addressed, in this work, the problem of developing a new computerized model for the Arabic language. Language modeling is crucial in modern NLP applications as natural language modeling could help a machine to grasp a high understanding of human language. The proposed model is based on the contextual and semantic representation of words in this language. It is a new and robust version of Word2Vec that aims to feed NLP applications with rich word representation, which is done using three main additions to the basic Wrod2Vec model: it is built on a labeled dataset instead of using unlabeled text data, named entities are detected and then represented as independent entities from their constituents, and the context window is defined to include only the other words in the same sentence that are linked with the target word by dependency relations regardless their position in the sentence. The proposed model was tested and evaluated by assessing the similarity of semantics between the words using a dataset collected and annotated by a homemade system developed for this purpose.

关键词： Natural language modeling Semantic representation Arabic language processing and understanding

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Review of the Deep Learning Models for Anomaly Detection-Based Video Scrutiny System 1

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4th Congress on Intelligent systems (CIS)

作者： Kukade, Jyoti Panse, Prashant Medi Caps Univ Indore India

ISBN: (数字)9789819990405

ISBN: (纸本)9789819990399;9789819990405

Video anomaly detection is a fast-growing computer vision field. Alertness and asset protection explain this. It takes a long time and may yield incorrect results to search surveillance film for illicit activity like a brawl, robbery, or wrong U-turn. High-tech, computer-controlled video surveillance systems are needed for safety. Surveillance cameras regularly capture inexplicable events. This study examines oddities using typical and uncommon video examples. We recommend using the fusion deep learning model to detect abnormalities in weakly tagged training films. Thus, training labels (anomalous or normal) are video-level, not clip-level. This saves people's time from manually marking training video oddities. We use multiple instances learning to automatically develop a deep anomaly ranking model that predicts high anomaly scores for strange video segments. Our method learns a deep anomaly ranking model that predicts high anomaly scores for video segments that deviate from the norm (fusion deep learning model). To help identify anomalies, we add sparsity and temporal smoothness constraints to the ranking loss function during training. We did this to improve data ranking. Their method can be applied in a near-real-time context. Our methodology works for anomaly detection-based video surveillance.

关键词： Deep learning Anomaly detection Video surveillance system

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Comparative Analysis of Neural networks and Data Processing Techniques for Parkinson's Gait Classification

Comparative Analysis of Neural Networks and Data Processing ...

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Intelligent systems Conference

作者： Reyes, Israel Andaluz, Francis Troya, Kerly Zhinin-Vera, Luis Almeida-Galarraga, Diego Cadena-Morejon, Carolina Tirado-Espin, Andres Villalba-Meneses, Santiago Guevara, Cesar Yachay Tech Univ Sch Biol Sci & Engn Urcuqui 100119 Ecuador Univ Castilla La Mancha LoUISE Res Grp Albacete 02071 Spain Yachay Tech Univ Sch Math & Computat Sci Urcuqui 100650 Ecuador Univ Int La Rioja Sch Engn & Technol Logrono 26006 Spain Univ Tecnol Indoamer Ctr Invest Mecatron & Sistemas Interact MIST Quito 170103 Ecuador MIND Res Grp Model Intelligent Networks Dev Urcuqui Ecuador

ISBN: (纸本)9783031663352;9783031663369

Parkinson's disease (PD) is an advancing neurodegenerative condition characterized by motor symptoms, including disturbances in gait and varying degrees of severity, typically assessed using the Hoehn and Yahr stages. Precise classification of PD gait patterns and severity levels is of paramount importance for efficient diagnosis and continuous treatment monitoring. This research article presents a comprehensive assessment of the performance of three distinct Artificial Neural Network (ANN) models integrated with diverse data processing techniques, encompassing segmentation, filtration, and noise reduction, in the context of classifying PD severity. The classification is based on the vertical ground reaction force (VGRF) measurements obtained from both healthy individuals and those afflicted by Parkinson's disease, sourced from a well-established database (GaitPDB, Physio Net). The study provides a comparative analysis of the efficacy of these models in accurately discriminating between various gait patterns and stages of disease severity, underscoring their potential to enhance clinical decision-making and patient care. Additionally, the study offers valuable insights into the impact of data processing methodologies on classification performance.

关键词： Parkinson's disease Gait analysis Artificial Neural Network Classification Hoehn Yahr

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