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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

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Smart Prosthetics in Surgery: AI-Driven Tactile Feedback Using Piezoelectric Sensors 1

Smart Prosthetics in Surgery: AI-Driven Tactile Feedback Usi...

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1st international workshop on Bioinformatics and Applied Information Technologies, BAIT 2024

作者： Tymkiv, Pavlo Klos-Witkowska, Aleksandra Bahrii-Zaiats, Oksana Kovalyk, Serhii Ternopil Ivan Puluj National Technical University Ruska str.56 Ternopil46001 Ukraine University of Bielsko-Biala Willowa St. 2 Bielsko-Biala43-300 Poland I. Horbachevsky Ternopil National Medical University Maidan Voli St. 1 Ternopil46002 Ukraine

This paper explores the innovative application of piezoelectric sensors in robotic prosthetics for surgical systems, emphasizing their potential to enhance tactile feedback during delicate procedures. Piezoelectric sensors can efficiently convert mechanical pressure and vibrations into electrical signals, offering a crucial means for surgeons to feel and interpret force, texture, and other surface characteristics in real time. The ability to generate and transmit tactile feedback through cloud-based systems allows for the creation of a database of tactile patterns, enabling automated recognition of specific tactile interactions during surgery. The integration of artificial intelligence (AI) further enhances the system by learning from collected data, predicting future interactions, and optimizing pattern recognition. Additionally, combining piezoelectric sensors with other types of sensory inputs, such as temperature and strain gauges, allows for a multi-dimensional feedback system. This results in an immersive experience, granting surgeons precise control over their robotic tools. The continuous improvement of these systems through AI and data collection holds vast potential for future developments in robotic surgery, leading to more accurate, safer procedures and better patient outcomes. This research underscores the transformative impact of AI-driven, multi-sensory feedback systems in enhancing the capabilities of robotic-assisted surgeries. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Robotic surgery

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A study of English Neologisms Through Large-Scale Probabilistic Indexing of Bentham’s Manuscripts 2nd

A Study of English Neologisms Through Large-Scale Probabilis...

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

作者： Toselli, Alejandro H. Romero, Verónica Vidal, Enrique Sánchez, Joan Andreu Seaward, Louise Schofield, Philip PRHLT Universitat Politècnica de València Camí de Vera s/n València46022 Spain Bentham Project UCL Laws 4-8 Endsleigh Gardens LondonWC1H 0EG United Kingdom

ISBN: (纸本)9783030307530

Probabilistic indexes (PI) are obtained from untranscribed handwritten text images by means of recently introduced lexicon-free, query-by-string, probabilistic keyword spotting techniques. PIs have proven to be a powerful tool that allow efficient, free textual searching in very large collections of handwritten historical documents. PIs convey uncertain information about the textual contents of the document images. However, text uncertainty is accurately modeled by the associated lexical probability distributions, which can be conveniently exploited in many applications. As an example of these applications, here we study the dating of a number of English neologisms in the large collection of Bentham’s manuscripts, which encompass images. The statistical techniques used for neologism dating are theoretically motivated and experiments on this collection are reported. Among other interesting contributions of this study, it provides sound evidence that some commonly assumed neologism introduction dates need to be revised. © 2019, Springer Nature Switzerland AG.

关键词： Probability distributions

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Proceedings of the international Conference on Document Analysis and recognition, ICDAR

Proceedings of the International Conference on Document Anal...

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1st international workshop on Human-Document Interaction, HDI 2017

ISBN: (纸本)9781538635865

The proceedings contain 314 papers. The topics discussed include: pyramidal stochastic graphlet embedding for document pattern classification;image operator learning coupled with CNN classification and its application to staff line removal;a complete scheme of spatially categorized glyph recognition for the transliteration of Balinese palm leaf manuscripts;early recognition of handwritten gestures based on multi-classifier reject option;an efficient combinatorial algorithm for optimal compression of a polyline with segments and arcs;the graphic narrative corpus (GNC): design, annotation, and analysis for the digital humanities;segmentation-free speech text recognition for comic books;an overview of comics research in computer science;Transkribus - a service platform for transcription, recognition and retrieval of historical documents;DAE-NG: a shareable and open document image annotation data framework;guiding text image keypoints extraction through layout analysis;click-free, video-based document capture - methodology and evaluation;human-assisted signature recognition based on comparative attributes;cognitive state measurement on learning materials by utilizing eye tracker and thermal camera;deep convolutional recurrent network for segmentation-free offline handwritten Japanese text recognition;KHATT: a deep learning benchmark on Arabic script;multilevel context representation for improving object recognition;semantic text encoding for text classification using convolutional neural networks;a case study of the relationship between local pen action and three dimensional shapes of handwritten strokes;and robustness of character recognition techniques to double print-and-scan process.

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Proceedings of the international Conference on Document Analysis and recognition, ICDAR

Proceedings of the International Conference on Document Anal...

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1st international workshop on Open Services and Tools for Document Analysis, ICDAR-Ost 2017

ISBN: (纸本)9781538635865

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1st international workshop on Biologically Inspired Approaches to Advanced Information Technology, BioADIT 2004

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1st international workshop on Biologically Inspired Approaches to Advanced Information Technology, BioADIT 2004

ISBN: (纸本)3540233393

The proceedings contain 36 papers. The special focus in this conference is on Biosystems for IT Evolution, Bio-inspired Software Systems, Hardware Systems and Robotics. The topics include: Object-oriented specification of complex bio-computing processes;analysis of responses of complex bionetworks to changes in environmental conditions;experimental molecular evolution showing flexibility of fitness leading to coexistence and diversification in biological system;learning Bayesian networks by Lamarckian genetic algorithm and its application to yeast cell-cycle gene network reconstruction from time-series microarray data;biologically inspired reinforcement learning;biologically plausible speech recognition with LstM neural nets;spatial tangible user interfaces for cognitive assessment and training;biologically inspired computer virus detection system;explaining low-level brightness-contrast illusions using disinhibition;autonomous acquisition of the meaning of sensory states through sensory-invariance driven action;embryonic machines that divide and differentiate;a hardware implementation of a network of functional spiking neurons with hebbian learning;a study on designing robot controllers by using reinforcement learning with evolutionary state recruitment strategy;movement generation and control with generic neural microcircuits;anatomy and physiology of an artificial vision matrix;an adaptive mechanism for epidemic communication;distributed central pattern generator model for robotics application based on phase sensitivity analysis;ant-based approach to mobile agent traversal;media streaming on p2p networks with bio-inspired cache replacement algorithm and scalable and robust scheme for data gathering in sensor networks.

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Proceedings of the international Conference on Document Analysis and recognition, ICDAR

Proceedings of the International Conference on Document Anal...

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1st international workshop on Computational Document Forensics, IWCDF 2017

ISBN: (纸本)9781538635865

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Unraveling Negation in Modern Greek Using machine learning: A Comprehensive Analysis and Detection Framework 13th

Unraveling Negation in Modern Greek Using Machine Learning:...

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13th mining Humanistic data workshop, MHDW 2024, 9th workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Adam, styliani Anastasiou, Kleitos Karastathis, Panagiotis Kioseoglou, Avraam Tsivelekidis, Giorgos Kermanidis, Katia Lida Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9783031632266

Negation detection, a crucial component of natural language processing (NLP), plays a vital role in deciphering sentiment, context, and meaning in textual data. This study delves into the complexity of negation detection in the Greek language, providing a thorough examination of linguistic challenges that set Greek apart from other languages. Greek presents distinctive challenges for negation detection owing to its intricate morphology, complex syntax, and nuanced expressions of negation. In contrast to languages with rigid word order, Greek relies on inflections and particles, demanding NLP models to adeptly navigate diverse linguistic structures. This research reviews prevailing methodologies and tools for negation detection in Greek, encompassing both rule-based and machine learning-based approaches. Through the utilization of linguistic resources and annotated corpora specific to the Greek language, including a X (Twitter) corpus, we assess the performance of these methods, deliberating on their effectiveness in capturing the subtle nuances of negation. It is the first time that negation is being detected in Greek tweets and the annotation process was performed manually. In conclusion, this study offers valuable insights into the inherent challenges of negation detection in the Greek language. It provides a roadmap for researchers and practitioners in the NLP field to craft more precise and context-aware models tailored to the unique linguistic characteristics of Greek. © IFIP international Federation for Information Processing 2024.

关键词： machine learning

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A Novel Ensemble K-Nearest Neighbours Classifier with Attribute Bagging 1st

A Novel Ensemble K-Nearest Neighbours Classifier with Attri...

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1st international Conference on Intelligent Systems and data Science, ISDS 2023

作者： Islam, Niful Noor, Humaira Farid, Dewan Md. Department of Computer Science and Engineering United International University United City Madani Avenue Badda Dhaka1212 Bangladesh

ISBN: (纸本)9789819976652

Classification of supervised learning in machine learning is a challenging task. K-Nearest Neighbours (kNN) is one of the simple classifiers that is commonly used in many real-life pattern recognition applications. The kNN is a lazy learner as it does not build any decision line from historical training data. In this paper, we have proposed a novel ensemble based K-Nearest Neighbours classifier with attribute bagging technique. The proposed method is designed to reduce the time complexity of the traditional kNN classifier, which also ameliorates the classification accuracy. It employs the attribute bagging technique and selects a sorted sub-set of instances from the original training data to apply kNN classifier. We have applied binary search technique to effectively reduce the search space to find the nearest neighbours. The proposed approach reduces the time consumption of kNN classifier from linear to logarithmic making the algorithm feasible for Big data. We have tested the performance of the proposed algorithm with traditional machine learning classifiers e.g. kNN, Random Forest, AdaBoost on 10 benchmark datasets. The datasets are taken from UCI machine learning Repository. The results show that the proposed method outperformed the basic kNN and AdaBoost classifiers, and is compatible with Random Forest classifier. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Color texture-based object detection: An application to license plate localization 1st

Color texture-based object detection: An application to lice...

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1st international workshop on pattern recognition with Support Vector machines, SVM 2002

作者： In Kim, Kwang Jung, Keechul Kim Hyung, Jin Artificial Intelligence Lab CS Department Korea Advanced Institute of Science and Technology Taejon305-701 Korea Republic of Pattern Recognition and Image Processing Lab CS and Engineering Department Michigan State University East LansingMI48824-1226 United States

ISBN: (纸本)354044016X

This paper presents a novel color texture-based method for object detection in images. To demonstrate our technique, a vehicle license plate (LP) localization system is developed. A support vector machine (SVM) is used to analyze the color textural properties of LPs. No external feature extraction module is used, rather the color values of the raw pixels that make up the color textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, LP regions are identified by applying a continuously adaptive meanshift algorithm (CAMShift) to the results of the color texture analysis. The combination of CAMShift and SVMs produces not only robust and but also efficient LP detection as time-consuming color texture analyses for less relevant pixels are restricted, leaving only a small part of the input image to be analyzed. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Object detection

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Accidental Face recognition and Detection Using machine learning 1

Accidental Face Recognition and Detection Using Machine Lear...

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1st international Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Sharma, Ashish Agrawal, Piyansh Krishan Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications UP Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The data collected from all the states and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms. © 2023 IEEE.

关键词： Face recognition

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