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Comparing Different Dictionary-Based Classifiers for the Classification of Volatile Compounds Measured with an E-nose 1

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proceedings of the 15th international conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022

作者： Alves, Rita Rodrigues, Joao Ramou, Efthymia Palma, Susana I. C. J. Roque, Ana C. A. Gamboa, Hugo Faculdade de Ciencias e Tecnologia Universidade Nova de Lisboa Caparica Portugal Associate Laboratory i4HB- Institute for Health and Bioeconomy School of Science and Technology NOVA University Lisbon Caparica2829-516 Portugal UCIBIO - Applied Molecular Biosciences Unit Department of Chemistry School of Science and Technology NOVA University Lisbon Caparica2829-516 Portugal

ISBN: (数字)9783031388545

ISBN: (纸本)9783031388538

Electronic noses (e-noses) are devices that mimic the biological sense of olfaction to recognize gaseous samples in a very fast and accurate manner, being applicable in a multitude of scenarios. E-noses are composed of an array of gas sensors, a signal acquisition unit and a pattern recognition unit including automatic classifiers based on machine learning. In a previous work, a text-based approach was developed to classify volatile organic compounds (VOCs) using as input signals from an in-house developed e-nose. this text-based algorithm was compared with a 1-nearest neighbor classifier with euclidean distance (1-NN ED). In this work we studied other text-based approaches that relied in the Bag of Words model and compared it with the previous approach that relied in the term frequency-inverse document frequency (TF-IDF) model and other traditional text-mining classifiers, namely the naive bayes and linear Support Vector machines (SVM). the results show that the TF-IDF model is more robust overall when compared with the Bag of Words (BoW) model. An average F1-score of 0.84 and 0.70 was achieved for the TF-IDF model with a linear SVM for two distinct gas sensor formulations (5CB and 8CB, respectively), while an F1-score of 0.66 and 0.71 was achieved for the BoW model for the same formulations. the text-based approaches appeared to be less reliable than the traditional 1-NN ED method. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Volatile organic compounds

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Reexamining Disposition in AI Models Impact in Building Fiction based Characters

Reexamining Disposition in AI Models Impact in Building Fict...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Ramkishan Baburao Bhise Ranjeet Puyed Smita Kiran Rane Swati Ramkishan Bhise Deparment.of Humanities and Applied Sciences SIES Graduate School Of Technology Navi Mumbai India Department of Sciences and Humanities Dwarkadas J. Sanghvi College of Engineering and Technology Mumbai India Department - Applied Sciences and Humanities College - Dwarkadas J. Sanghvi College of Engineering Mumbai Amity University Maharashtra Mumbai India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

As artificial intelligence (AI) continues to revolutionize various facets of human existence, its influence in creative domains, particularly literature, has increasingly come to be pronounced. the following paper will handle the ways that character development is changing in modern fiction. It investigates the ways in which AI technologies are outlining the way the creation, representation, and perception of characters in fiction are being developed. that means AI is advancing in character creation in ways that open up a whole new set of doors for writers: the use of powerful tools to generate, field, analyze, and refine their characters in ways previously unthinkable. Using the algorithms of natural language processing (NLP), these days, AI is used to help write dialogue, create characters with their traits, and sometimes even develop the entire character’s arc. this union of human creativity and computational powers doesn’t just make the process of writing faster but, rather, opens doors for new possibilities in innovative storytelling. AI-driven analytics help authors to pick up the insight into reader preferences and trends that tend to help them develop character traits and the storyline, most appealing to the readers. the application of machine-learning algorithms to vast repositories of literature seems to derive character dynamics and tropes that are so archetyped, but their renderings are much more nuanced and impactful. At the same time, this AI character raises very basic questions about authorship, creativity, and the nature of the story. As more and more writers adopt AI for character generation, they do raise some question about authenticity and originality.

关键词： Ethics Sensitivity Heuristic algorithms machine learning Writing Rendering (computer graphics) Market research

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LARW: Network Representation learning Algorithm Based On Long Anonymous Random Walks 22

LARW: Network Representation Learning Algorithm Based On Lon...

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proceedings of the 4th international conference on Advanced Information Science and System

作者： Wei Liu Xin Du School of Computer Science and Technology Heilongjiang University China Graduate Division Harbin Conservatory of Music China

ISBN: (纸本)9781450397933

Network Representation learning (NRL) plays an important role in network analysis and aims to represent complex networks more concisely by transforming nodes into low-dimensional vectors. Network representation learning has become the focus of increasing research interest in academia and industry. Much of the work in one direction is based on learning network features based on random walk derived models. However, it is difficult to extract effective features in the face of long wandering sequences. therefore, we propose a dynamic network characterization method based on Long Anonymous Random Walks(LARW). LARW incorporates the latest long series prediction method Informer, which allows more feature information to be retained. the model parameters are optimized in the process of comparing with the actual results, thus making the node embedding more predictive and causal. In our experiments, we compare our model with the existing NRL model on four real-world datasets. the experimental results show that LARW achieves superior results in tasks such as node classification and link prediction.

关键词： Temporal Networks Network Embedding Sequence Forecasting Network Representation learning

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Efficient Method for Tomato Leaf Disease Detection and Classification based on Hybrid Model of CNN and Extreme learning machine

Efficient Method for Tomato Leaf Disease Detection and Class...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Abhay Chaturvedi Sirivella Yagnasree Gunjan Chhabra P. Chidambara Rajan Amit Chauhan Ramya Maranan Department of Electronics and Communication Engineering GLA University Mathura Uttar Pradesh India Department of Electronics & Communication Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Biotechnology Dr. N. G. P. Arts and Science College Coimbatore Tamilnadu India Department of Life Sciences CHRIST (Deemed to be University) Bengaluru Karnataka India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

through India, most people make a living through agriculture or a related industry. Crops and other agricultural output suffer significant quality and quantity losses when plant diseases are present. the solution to preventing losses in the harvest and quantity of agricultural products is the detection of these illnesses. Improving classification accuracy while decreasing computational time is the primary focus of the suggested method for identifying leaf disease in tomato plant. Pests and illnesses wipe off thousands of tons of tomatoes in India’s harvest every year. the agricultural industry is in danger from tomato leaf disease, which generates substantial losses for producers. Scientists and engineers can improve their models for detecting tomato leaf diseases if they have a better understanding of how algorithms learn to identify them. this proposed approaches a unique method for detecting diseases on tomato leaves using a five-step procedure that begins with image preprocessing and ends with feature extraction, feature selection, and model classification. Preprocessing is done to improve image quality. that improved K-Means picture segmentation technique proposes segmentation as a key intermediate step. the GLCM feature extraction approach is then used to extract relevant features from the segmented image. Relief feature selection is used to get rid of the categorization results. finally, classification techniques such as CNN and ELM are used to categorize infected leaves. the proposed approach to outperforms other two models such as CNN and ELM.

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Risk assessment of Highway Safety Based on Artificial Intelligence: A New Method of Highway Risk Assessment 23

Risk assessment of Highway Safety Based on Artificial Intell...

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proceedings of the 2023 4th international conference on Artificial Intelligence in Electronics Engineering

作者： Chunyang Li Research Institute of Highway Ministry of Transport China

ISBN: (纸本)9781450399517

A new risk assessment method is proposed which combines deep learning, computer vision and traffic safety. the method consists of several models. the highway risk factors identification model, extracts risk elements from video images. the vehicle velocity speculation model is built for the driving speeds calculation of different vehicle types. the Road alignment computational model is built for calculating highway alignment according to GPS. the highway risk management model for evaluating highway risk grades and factors is given. the new method greatly accelerates the speed of highway risk assessment and reduces the human cost. For the overall risk assessment, its accuracy depends on the results of each module. through experiments and engineering applications, the new method can achieve the same or even better effect as the expert manpower.

关键词： Insert comma delimited author-supplied keyword list Keyword number 2 Keyword number 3 Keyword number 4

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A study based on machine learning: Categorizing and predicting models of American Twitter users' ideology 22

A study based on machine learning: Categorizing and predicti...

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proceedings of the 2022 4th international conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Caikun Cheng School of International Relations and Public Affairs Shanghai International Studies University China

ISBN: (纸本)9781450398343

the division of political groups on Twitter has always been one of the hot topics in political communication, but the research in this area is often difficult to reproduce and ignores the shielding of the Internet. I will use the method of decision tree classification in machine learning to improve this research. Two decision tree classification models are constructed to research American Twitter users in this study. the first decision tree classification model has three dependent variable categories (Republican, Democrat, Independent or else), and the F1 of the model test set is 0.5. the second decision tree classification model has two dependent variable categories (Republican, Democrat), and the F1 of this model's test set is 0.856. the two decision tree classification models are a complete set of hierarchical models, and the internal comparison of two models also reveals a more distinct classification of American adult Twitter users who have a preference for "Republican" or "Democrat", and American adult Twitter users who prefer "Independent or else" are indistinguishable. the characteristic importance of this model also explains "the perception that Twitter is banning users' use, the impact of Twitter on American democracy, the frequency of searches for issues related to politics and society" is particularly important for Twitter's ideological categorization. the integrated use of the two decision classification tree models is also practical and can provide a reference for later researchers.

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4th international conference on Smart Computing and Informatics, SCI 2020

4th International Conference on Smart Computing and Informat...

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4th international conference on Smart Computing and Informatics, SCI 2020

ISBN: (纸本)9789811615016

the proceedings contain 82 papers. the special focus in this conference is on Smart Computing and Informatics. the topics include: Log-Based Authentication for Cloud Environments;development of a Wireless Sensor Node for Electrical Power Distribution Monitoring System;CommonKADS and Ontology Reasoner: Bulky-Baggage Case Study;accelerated Sorting of Apples Based on machine learning;execution of Smart Contacts Concurrently Using Fine-Grain Locking and Asynchronous Functions;artificial Intelligence-Based Automatic Evaluation Engine;SmartRPL: Secure RPL Routing Protocol for Internet of things Using Smart Contracts;customized Music Classification and Recommendation System Based on Classifiers of Neural Networks and Sensor Embedded on Smart Devices;recognizing the Faces from Surveillance Video;localization of Optic Cup-Disc in Retinal Images Using Morphological Filters for Glaucoma Detection;Region Labeling Based Brain Tumor Segmentation from MR Images;a Comparative Evaluation of Decomposition Methods Based on Pitch Estimation of Piano Notes;application of Bat Optimization Algorithm for Power System Loss Reduction Using DSSSC;preface;pad Vending machine with Cashless Payment;malaria Cell Detection Model;design and Analysis of Fractal Monopole Antennas for Multiband Wireless applications;fake News Detection Using Text Analytics;an Extreme learning machine-Based Model for Cryptocurrencies Prediction;an Ensemble Approach for Intrusion Detection in Collaborative Attack Environment;a Decision Based Asymmetrically Trimmed Modified Geometric Mean Algorithm for the Removal of High Density Salt and Pepper Noise in Images and Videos;anti-poaching System Using Wireless Sensor Networking;performance Issues and Monitoring Mechanisms for Serverless IoT applications—An Exploratory Study;detection of Pemphigus Vulgaris in Development Stage of Skin Erosion;fingerprint Genuinity Classification.

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DancÆR: Efficient and Accurate Dance Choreography learning by Feedback through Pose Classification 24th

DancÆR: Efficient and Accurate Dance Choreography Learning ...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Bas, Iremsu Alp, Demir Ergenc, Lara Ceren Kocak, Andy Emre Yalcin, Sedat Hisar Sch Comp Sci IdeaLab Istanbul Turkiye

ISBN: (纸本)9783031363351;9783031363368

Educational philosophies have slowly focused on differentiated and self-learning systems that respectively emphasize tailoring instruction to meet individual needs and gathering, processing, and retaining knowledge without the help of another person in recent years. In this regard, creating opportunities to further self-learning resources has become increasingly important. Some people started to prefer these over traditional learning practices for various reasons, such as the difficulty of transportation in metropolises or fitting their timetables to in-person lessons. the creation of such platforms or opportunities for physical education, however, proves to be more difficult as individuals require continuous and precise feedback regarding the usage of their bodies. Accordingly, we have developed an augmented reality application that presents a platform for dance that focuses on differentiated and self-learning principles with accurate feedback. We built the augmented reality (AR) app prototype using the Swift programming language and used the MoveNet pose detection model along with our own neural network to capture the body position. Our proposal could prove a valuable addition to learning physical activities assisted by AI systems since the application of AI technologies to dance and physical education could be improved by further investigation and research.

关键词： machine learning AI-Assisted Tutoring Motivational Diagnosis and Feedback AR

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Real-Time Human Pose Estimation Using Media-Pipe an Artificial Intelligence applications in Health and Fitness

Real-Time Human Pose Estimation Using Media-Pipe an Artifici...

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Artificial Intelligence and Signal Processing (AISP), INDIA, international conference on

作者： Ketan Totlani Shiva S. Dhavala S Sandeep Kumar Vijayarao Yagnesh Challagundla B Roy Eugenia R. Zhuo School of Computer and Systems Sciences Jaipur National University Rajasthan India School of Information Science and Engg. Rashtreeya Vidyalaya College of Engineering Bengaluru India Dept. of Computer Science and Engineering RM.K Engineering College Kavaraipettai Tamil Nadu India School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh India College of Information and Computing Sciences University of Santo Tomas Manila Philippines

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Significant progress has been achieved in the fields of Artificial Intelligence (AI), machine learning (ML), and Deep learning (DL) in recent years. Nevertheless, a significant gap continues to exist in the effective surveillance of human actions, activities, and the recognition of human movements or poses. machines and computers are fundamentally incapable of comprehending human activities without user intervention. Consequently, we now depend on technology to record and archive human actions through live broadcast data, videos, or images. the field of pose estimation, which is a critical area of research with applications in fields such as Augmented Reality, Animation, Gaming, Healthcare, and Sports, is expanding due to the rapid advancement of computer vision technology. this project implements innovative methods to train models and deter- mine poses, utilizing random inputs from internet images, videos, and live streaming. In particular, models that have been trained with artificial intelligence have been created for daily physical activities that necessitate intricate pose estimation. the initial step in the project is to utilize MediaPipe to estimate the poses of the human body. Subsequently, the angles between the body joints are calculated to create the desired models. Additionally, a web-based user interface (WEB-UI) was developed to enable access from any location. the methodology involves the extraction of data from critical body locations, the framing of models, and the utilization of the MediaPipe model due to its superior performance and features in comparison to other models. the objective of this method is to develop a web application for end-users, train AI models for daily physical activities, and establish an efficient system for single human pose estimation. this approach provides a cost-effective alternative to physical trainers and enables the monitoring of health and sports activities to be enhanced.

关键词： Deep learning Computational modeling Pose estimation Data visualization Feature extraction Data models Artificial intelligence Recommender systems Videos Sports

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4th international conference on Smart Computing and Informatics, SCI 2020

4th International Conference on Smart Computing and Informat...

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4th international conference on Smart Computing and Informatics, SCI 2020

ISBN: (纸本)9789811608773

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