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6th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022 - proceedings

6th International Conference on I-SMAC (IoT in Social, Mobil...

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4th international conference on Cybernetics, Cognition and machine learning applications, ICCCMLA 2022

ISBN: (纸本)9781665462464

the proceedings contain 95 papers. the topics discussed include: forensic evidence collection from windows host using python based tool;recent advancements in finger vein biometrics: a review;advancements in simulator technologies for power plant operator training;advancement in public distribution system;real time gesture detection using convolutional neural network;K-nearest neighbor (KNN) algorithm based cooperative spectrum sensing in cognitive radio networks;improving effectiveness of parallel processing with smart cache utilization for energy saving and temperature reduction in chip multiprocessors;an overview of machine learning techniques for evaluation of pavement condition;automated defense surveillance using tranquilizer;face recognition using deep neural network and support based anchor boxes methods in video frames;a simple user-friendly android application for oxygen and blood pressure measurement;calculating and comparing the weighted and unweighted water quality indices for Cauvery River banks based on accuracy;and prediction of cardiovascular diseases using neural networks and machine learning.

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Enhancing Industrial Load Scheduling through Demand Side Management and Advanced Optimization Techniques Using Neuro-Fuzzy and Deep learning Approaches 4

Enhancing Industrial Load Scheduling through Demand Side Man...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Agarwal, Shivani Sharma, Ritu Tamilselvi, M. Sharma, Harsh Mohan Sahu, Dinesh Prasad Singh, Jagendra Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Gyan Ganga Institute of Technology and Science Dept. of Electrical and Electronics Engineering Jabalpur India Roever Engineering College Perambalur Deartment of Computer Science and Engineering Perambalur India Raj Kumar Goel Institute of Technology Department of Management Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350306118

this study aims to investigate whether physical factors like temperature, humidity, and pressure interact with current in Micro-electromechanical systems (MEMS). To study these interactions, a straightforward system with four spin-operated engines was employed. the pressure, humidity, and temperature may all change as a result of changing the motor's output current. the results are evaluated and predicted using LSTM and Neuro-Fuzzy models. Sensors are used to keep an eye on these circumstances. Neurofuzzy models employ fuzzy logic and artificial intelligence to capture interconnections, as opposed to LSTM models, which use their capacity to comprehend data connections to capture long-Term effects. the results demonstrate that the body's capacity to exert energy is enhanced by the effect of body energy on temperature, humidity, and pressure. the research could lead to improvements in temperature control, energy efficiency, and the functionality and design of massive industrial machinery. A straightforward design is used to launch controlled experiments, and the data collected is then used to train and assess the model. By offering helpful suggestions for the improvement of the motor and the operating electric economy, this study contributes to our understanding of the evolution of the electric motor. © 2023 IEEE.

关键词： Electric loads

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Sentiment Analysis of Tweets on Social Issues Using machine learning Approach 4

Sentiment Analysis of Tweets on Social Issues Using Machine ...

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4th international conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2023

作者： Bellar, Oumaima Baina, Amine Bellafkih, Mostafa National Institute of Posts and Telecommunications Inpt Raiss Team Strs Lab Rabat Morocco

ISBN: (纸本)9798350311112

Since the arrival of Web 2.0, there has been a growing interest in knowing the opinions of Internet users who express themselves spontaneously and in real time. this mass of opinion data is accessible with web mining tools, with a constantly renewed collection of information. Sites have specialized in collecting these opinions in certain fields (movie reviews, for example), and Internet users have become accustomed to consulting the opinions and ratings submitted by others as soon as they have to make a purchasing decision for a technical product, or even for a hotel reservation. Opinions are therefore of interest to Internet users and have given rise to multiple applications and services, which creates a virtuous circle of encouragement to give one's opinion and even to be recognized as giving relevant opinions and followed by others. But this data is also of interest to brands and research firms who are trying to understand this 'aggregated crowd sentiment'. Often sensitive to the 'your reputation can be destroyed because of a blog comment' fantasy, brands are concerned about their online identity but also seek to better understand the expectations and criticisms that Internet users have of them. Hence the growing development of techniques to capture these evaluations from Internet users, ranging from the simple counting of positive or negative comments to a more detailed analysis of the content of these comments. the purpose of this document is to provide detailed steps in the process of analyzing sentiments on a data using machine learning. © 2023 IEEE.

关键词： Sentiment analysis

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Towards Evaluating Image Recommendations in Digital News and Media Ecosystem 5

Towards Evaluating Image Recommendations in Digital News and...

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5th international conference on computer and applications, ICCA 2023

作者： Raptis, George E. theodorou, Vasilis Katsini, Christina Human Opsis Patras Greece

ISBN: (纸本)9798350303254

the News and Media landscape has undergone significant transformations in recent years, driven by the rise of new technologies and the widespread use of social media. this evolution introduces unique challenges for professionals working within this environment (e.g., journalists, content creators, and news authors), with a major one being the efficient sourcing of images that complement article content. In response to this challenge, we developed VIREO, a tool that recommends images based on textual content. In this paper, we make a step towards the practical effectiveness of VIREO's core models in recommending images for real-world articles, with a specific focus on image recommendation efficiency. Our results indicate that VIREO offers a promising solution for professionals seeking to meet the evolving demands of the News and Media landscape while maintaining content quality and engagement. © 2023 IEEE.

关键词： machine learning

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Multimodal Data Fusion: Combining Big Data and Deep learning for Enhanced Predictive Models 1

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Sreelakshmi, K.R. Abraham, Sajimon School of Computer Sciences Mahatma Gandhi University Kerala Kottayam686560 India School of Management and Business Studies Mahatma Gandhi University Kerala Kottayam686560 India

ISBN: (数字)9789819752003

ISBN: (纸本)9789819751990

In recent years, the integration of multimodal data and the application of advanced machine learning techniques have revolutionized the domain of predictive modeling. this research investigates the potential of multimodal data fusion, specifically combining textual and image-based information, using convolutional neural networks (CNNs) to predict house prices accurately. this research paper presents an advanced hybrid convolutional neural network (CNN) architecture designed to predict house prices based on house attributes, including numerical attributes, textual attributes, spatial information, and images. this defines the multimodal attribute of the data used in the study. the proposed model incorporates residual and skip connections to capture complicated and detailed spatial patterns in the input images and improve overall prediction performance. this helps to resolve the vanishing gradient problem that occurs with the deep neural networks. the study demonstrates the effectiveness of the proposed model on a real-world housing dataset, achieving accurate price predictions. the model is evaluated using mean squared error (MSE) as the loss function, and the Adam optimizer is utilized for efficient convergence during training. the research emphasizes the importance of integrating both numerical and visual information for better housing price estimation. Experimental results highlight the capability of advanced CNN to effectively learn features from images, leading to enhanced prediction accuracy in real estate valuation. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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4th international conference on Artificial Intelligence in China, AIC 2022

4th International Conference on Artificial Intelligence in C...

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4th international conference on Artificial Intelligence in China, AIC 2022

ISBN: (纸本)9789819912551

the proceedings contain 49 papers. the special focus in this conference is on Artificial Intelligence in China. the topics include: Research on Helmet Detection Algorithm Based on Improved YOLOv5s;label Embedding Based Scoring Method for Secondary School Essays;Research on Wheat Ears Detection Method Based on Improved YOLOv5;Aircraft Target Detection Algorithm Based on Improved YOLOv5s;a Survey in Virtual Image Generation Based on Generative Adversarial Networks;research on the Service for the Disabled in Interior Design and Product Design Based on Artificial Intelligence;application and Prospect of Artificial Intelligence Image Analysis Technology in Natural Resources Survey;application and Technical Analysis of computer Vision Technology in Natural Resource Survey;research on Natural Language Processing Technology in Construction Project Management;review of Generative Adversarial Networks in Object Detection;a Review of the application of Convolutional Neural Networks in Object Detection;optimal Deployment of Large-Scale Wireless Sensor Network Based on Topological Potential Adaptive Graph Clustering;INSL: Text2SQL Generation Based on Inverse Normalized Schema Linking;the Research on Prediction for Financial Distress in Car Company Listed Combining Financial Indicators and Text Data;frame Interpolation for Weather Radar Data;a Hybrid Neural Network for Music Generation Using Frequency Domain Data;Prediction Model for Inclusive Finance Development Considering the Impact of COVID-19: the Case of China;certifiable Optimal Visual Pose Estimation for Space applications;Automotive Manufacturing Revenue Prediction Using Financial and Comment Sentiment Data Based on CNN Model;exploring the application of SketchUp and Twinmotion in Building Design Planning;Deep learning-Enhanced ICA Algorithm for Sub-Gaussian Blind Source Separation.

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Applying machine learning to Improve Weather Forecasting in a Mediterranean Area: Preliminary Experiments 1

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Yousaf, Umair De Rango, Alessio Furnari, Luca D’Ambrosio, Donato Senatore, Alfonso Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (数字)9783031812446

ISBN: (纸本)9783031812439

We investigate the effectiveness of machine learning (ML) as a complementary tool in enhancing the accuracy of precipitation forecasting in the Calabria region (southern Italy), a Mediterranean area frequently affected by extreme rainfall events, with significant material damage and potential loss of human lives. the operational weather forecasting system developed at the University of Calabria, based on the Weather Research and Forecasting (WRF) model, is considered to this end. We test two ML models, namely an artificial neural network and a random forest, to improve the accuracy of WRF forecasting. We provide several WRF output variables to the ML models. After a proper calibration (learning and test phases) over a two-year-long extended dataset, we assess the results to the original WRF outcomes (validation phase). Preliminary experiments resulted in a significant improvement by the WRF-ML coupled systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Defect Detection Algorithm for Cigarette Outer Packaging Based on Deep learning 4

Defect Detection Algorithm for Cigarette Outer Packaging Bas...

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4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Lin, Yizhen Wang, Yong Song, Jinhua Shu, Meng Chen, Haitao Xu, Zhihao Ningbo Cigarette Factory China Tobacco Zhejiang Industry Co. Ltd. Zhejiang Hangzhou310024 China

ISBN: (纸本)9781510672505

In the cigarette production process, detecting defects in the outer packaging is a crucial step. To address the issue of high false recognition rates in factory environments, we propose an improved YOLOv7-tiny target detection algorithm. Our algorithm is based on the YOLOv7-tiny network structure and enhances feature extraction and location by adding the SA module at the end of the Backbone. Additionally, we integrate the GSConv module into the network Neck part to improve information flow and feature fusion ability. Our experimental results show that adding the SA module and the GSConv module to the network increases average accuracy by 2.2% and 3.5%, respectively. After fusing the two, the average detection accuracy is 94.6%. Compared to the original YOLOv7-tiny, our improvements result in a 5.4% increase in accuracy. © 2023 SPIE.

关键词： Defects

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Implementation of machine learning Methods on Data to Analyze Emotional Health 4th

Implementation of Machine Learning Methods on Data to Analyz...

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4th international conference on Recent Trends in Advanced Computing - computer Vision and machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

作者： Jadala, Vijaya Chandra Pasupuleti, Sai Kiran Hrushikesava Raju, S. Gole, Srihari Babu Ravinder, N. Sreedhar, B. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Greenfields Vaddeswaram Guntur522502 India

ISBN: (纸本)9789811971686

the design and implementation of the intelligent model based on machine learning methodologies on datasets to analyze emotional and mental health, majorly concentrating to measure, cognize, simulate, and react to human emotions. the data captured by the apparatus is always available to the user and provides contact to interact with patients in case of Emergency. It stores data for future analysis and for pattern identification, Experts will be able to use Artificial Intelligent based machines to augment the human capacity to detect, diagnose, and treat mental health issues. machines have a significantly higher computational capacity than humans, creating assessments from large pools of patient’s data that will eventually make informed clinical decisions. this article will examine AI applications on emotional and mental health, It also highlights the implications of using emotional AI in identifying, diagnosing, and improving mental health. We focused on the conceptual and simulation model for result analysis where data stored in the cloud is retrieved as per the requirement and analyzed. Different machine learning algorithms are implemented on the dataset and results are identified through graphical analysis. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Reconstructing Atmospheric Parameters of Exoplanets Using Deep learning 17

Reconstructing Atmospheric Parameters of Exoplanets Using De...

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17th IEEE international conference on application of Information and Communication Technologies, AICT 2023

作者： Giobergia, Flavio Koudounas, Alkis Baralis, Elena Politecnico di Torino Turin Italy

ISBN: (纸本)9798350303568

Exploring exoplanets has transformed our understanding of the universe by revealing many planetary systems that defy our current understanding. To study their atmospheres, spectroscopic observations are used to infer essential atmospheric properties that are not directly measurable. Estimating atmospheric parameters that best fit the observed spectrum within a specified atmospheric model is a complex problem that is difficult to model. In this paper, we present a multi-target probabilistic regression approach that combines deep learning and inverse modeling techniques within a multimodal architecture to extract atmospheric parameters from exoplanets. Our methodology overcomes computational limitations and outperforms previous approaches, enabling efficient analysis of exoplanetary atmospheres. this research contributes to advancements in the field of exoplanet research and offers valuable insights for future studies. © 2023 IEEE.

关键词： Deep learning

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