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Design and Implementation of Immersive and Interactive System Based on Unreal Engine 2021

Design and Implementation of Immersive and Interactive Syste...

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5th international conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Ren Ke Liu Huaqun Zhang Mingyu Qiu Xiaofeng Yan Huimin Zhang Jinbo Beijing Inst Graph Commun Sch New Media Beijing Peoples R China

ISBN: (纸本)9781450384148

the interactive experience of virtual scenes is a computer-generated virtual scene. this virtual scene allows users to temporarily detach from reality, immerse themselves in it and interact with the objects in the scene. this is conducive to mobilizing the user's enthusiasm to use, while adding a touch of comfort to the traveler in the tired journey, will experience a different fun from the real life. At the same times, this scenario will also allow the user to experience it in a way that will also inspire or connect with the mind. the two scenes selected for this project are "porthole" and "listening, moving in the sky". the first scene does not change the interaction between the user and the screen, and enables the user to experience the scenic spots of chinese distinctive cultural heritage and traditional culture, and in the age of the internet, it reflects chinese friendliness to people all over the world, and at the same time better spreads and protects chinese traditional culture. the second landscape allows children to experience the feeling of stepping into the sky in the game, reducing the anxiety brought to children by long waiting time, and the interaction of the connection between the speaker and the horn can effectively promote the communication between children.

关键词： Beijing Daxing international Airport Virtual Reality Interactive System Unreal Engine 4

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A Comparative Study of Intelligents algorithms for Optimizing Lithium-ion Battery thermal Management Systems

A Comparative Study of Intelligents Algorithms for Optimizin...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Hajar El Yassini Kenza Oufaska Khalid El Yassini Rachid Bannari Engineering Sciences Laboratory National School of Applied Sciences Ibn Tofail University Kenitra Morocco College of Engineering and Architecture LERMA Lab International University of Rabat Sala Al Jadida Morocco IA Laboratory Faculty of Sciences Moulay Ismail University Meknes Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Controlling the temperature of lithium-ion batteries, also known as BTMS (Battery thermal Management System), has become a crucial factor in guaranteeing their smooth operation, safety and lifespan, particularly in demanding fields such as electric vehicles and energy storage devices. To meet the thermal challenges posed by these batteries, several avenues have been explored. On the one hand, there are classic methods based on thermal modelling and analytical optimization, and on the other, more modern approaches involving intelligent algorithms. the smart algorithms, often supported by artificial intelligence and advanced optimization techniques, open up new prospects for improving the efficiency and precision of thermal control systems. It is interesting to note that in 2023, research work in this field saw a sharp increase, with growing interest in solutions incorporating AI. this study proposes a comparative analysis of these intelligent approaches applied to BTMS, based on data from the Web of Science database.

关键词： Lithium-ion batteries Machine learning algorithms Temperature thermal engineering Machine learning thermal management thermal analysis Temperature control Safety Optimization

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

5th International Conference on Recent Trends in Machine Lea...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

ISBN: (纸本)9789819788644

the proceedings contain 41 papers. the special focus in this conference is on Recent Trends in Machine Learning. the topics include: Video Enhancement by Progressive Use of Denoising and Super-Resolution;Integrating GPS Technology and Smart Payment Systems for Enhanced Public Transportation Efficiency in India;a Machine Learning-Driven Approach for Early Leaf Disease Detection and Classification: A Survey;software Defect Code Analyzer Using Cosine Similarity;unleashing Agricultural Precision: A Deep Learning Paradigm for Papaya (Carica Papaya L.) Variety Discrimination and Yield Optimization;Fish Farm Monitoring and Controlled System Using LoRaWAN Network;Deep Neuro-Fuzzy Imaging: Enhanced 3D Brain Reconstruction from MRI Scans Using ANFIS and Deep Learning;analysis of Different Machine Learning Techniques in Troll Data Detection;comparative Analysis of Machine Learning algorithms for Prostate Cancer;breaking Barriers in Communication Using Long Short-Term Memory Networks for Sign Language Recognition;data Transmission and Reception with Error Detection Circuitry;An Amphibious Surface Cleaning Robot with GVM Wireless Control and Automatic Waste Segregation;Automatic Speech Recognition (ASR) Elevator;integrated Smart Water Management System for Urban Sustainability;a Simplified Deep Learning Approach for Diabetic Macular Edema Detection;innovative Rumor Detection on Social Media Text: A Comprehensive Study of Dual Co-Attention Ensemble Based Voting Approach;artificial Intelligence for Glaucoma and Diabetic Retinopathy: A Review;query-Based Text Summarization: A Comparative Investigation and TextRank Implementation;An Acoustic Method for Inspection of CNC Cutting Operations;Enhancing Question Answer Generation from PDFs: A Fusion of BERT, RAKE, T5 and DistilBERT with RQUGE Evaluation;Detecting fundus Lesions for Diabetic Retinopathy (DR) Analysis.

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Suicidal Ideation Detection Using Semi-Supervised Learning Technique: Self-Training Classifier

Suicidal Ideation Detection Using Semi-Supervised Learning T...

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Sustainable Technologies for Industry 4.0 (STI), international conference on

作者： Rukaiya Sharmeen Saima Khan Tasnia Sanjana Zinnia Sultana Dept of Computer Science and Engineering International Islamic University Chittagong

Suicide is now a major cause of death on a global scale. Reportedly, globally almost 800,000 people commit suicide every year. Currently, different types of methods are being used for suicidal ideation detection. As people share their daily activities on social media, they share their depression, stress, anxiety, success, happiness, and all. So, by utilizing these social sites it is possible to detect suicidal texts. therefore, ideation of suicidal thoughts may significantly reduce unwanted deaths. this paper proposes a semi-supervised learning technique which is a self-training classifier to detect suicidal thoughts. Which utilizes both labeled and unlabeled data to enhance the performance of machine learning models. In this paper, we adapted eight different supervised learning algorithms to work in a semi-supervised manner using a self-training classifier to see which one is best suited for detection. Among all the algorithms Logistic Regression (LR), SVM, and SGD showed the best accuracy and KNN showed the lowest. the early detection of suicidal intention is highly significant to reduce the deaths and our paper proposes the best method of detection.

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

5th International Conference on Recent Trends in Machine Lea...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

ISBN: (纸本)9789819788606

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5th international conference on Applied Informatics, ICAI 2022

5th International Conference on Applied Informatics, ICAI 20...

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5th international conference on Applied Informatics, ICAI 2022

ISBN: (纸本)9783031196461

the proceedings contain 32 papers. the special focus in this conference is on Applied Informatics. the topics include: Statistical Characterization of Image Intensities in Regions Inside Arteries to Facilitate the Extraction of Center Lines in Atherosclerosis Frameworks;Text Encryption with Advanced Encryption Standard (AES) for Near Field Communication (NFC) Using Huffman Compression;a Systematic Review on Phishing Detection: A Perspective Beyond a High Accuracy in Phishing Detection;application of Duality Properties of Renyi Entropy for Parameter Tuning in an Unsupervised Machine Learning Task;preliminary Study for Impact of Social Media Networks on Traffic Prediction;website Phishing Detection Using Machine Learning Classification algorithms;AESRSA: A New Cryptography Key for Electronic Health Record Security;Detection of COVID-19 Using Denoising Autoencoders and Gabor Filters;development of a Mobile Application to Save Lives on Catastrophic Situations;COVID-19 Article Classification Using Word-Embedding and Different Variants of Deep-Learning Approach;internet of things with Wearable Devices and Artificial Intelligence for Elderly Uninterrupted Healthcare Monitoring Systems;a Wayuunaiki Translator to Promote Ethno-Tourism Using Deep Learning Approaches;rating the Acquisition of Pre-writing Skills in Children: An Analysis Based on Computer Vision and Data Mining Techniques in the Ecuadorian Context;study Techniques: Procedure to Promote the Comprehension of Texts in University Students;TPACK and Toondoo Digital Storytelling Tool Transform Teaching and Learning;Fully Automated Lumen Segmentation Method and BVS Stent Struts Detection in OCT Images;predictive Modeling Toward the Design of a Forensic Decision Support System Using Cheiloscopy for Identification from Lip Prints;Automotive Industry Applications Based on Industrial Internet of things (IIoT). A Review;implementation of an IoT Architecture for Automated Garbage Segregation;architecture on Knowledge Managem

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5th international conference on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

5th International Conference on Emerging Technologies in Com...

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5th international conference on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

ISBN: (纸本)9783031070112

the proceedings contain 60 papers presendted at a virtual meeting. the special focus in this conference is on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent. the topics include: Machine Learning Based Rumor Detection on Twitter Data;performance Analysis of Machine Learning algorithms in Intrusion Detection and Classification;fruit Classification Using Deep Convolutional Neural Network and Transfer Learning;assessment of Network Intrusion Detection System Based on Shallow and Deep Learning Approaches;an Efficient Classifier Model for Opinion Mining to Analyze Drugs Satisfaction Among Patients;a Case Study on Machine Learning Techniques for Plant Disease Identification;detection of Network Intrusion Using Machine Learning Technique;scrutinization of Urdu Handwritten Text Recognition with Machine Learning Approach;a Machine Learning Approach for Multiclass Sentiment Analysis of Twitter Data: A Review;efficacy of Online Event Detection with Contextual and Structural Biases;analysis of Text Classification Using Machine Learning and Deep Learning;evaluation of Fairness in Recommender Systems: A Review;detection of Liver Disease Using Machine Learning Techniques: A Systematic Survey;Association Rule Chains (ARC): A Novel Data Mining Technique for Profiling and Analysis of Terrorist Attacks;classification of Homogeneous and Non Homogeneous Single Image Dehazing Techniques;comparative Study to Analyse the Effect of Speaker’s Voice on the Compressive Sensing Based Speech Compression;Employing an Improved Loss Sensitivity Factor Approach for Optimal DG Allocation at Different Penetration Level Using ETAP;a Method for Data Compression and Personal Information Suppressing in Columnar Databases;the Impact and Challenges of Covid-19 Pandemic on E-Learning;identification of Dysgraphia: A Comparative Review;blockchain Framework for Agro Financing of Farmers in South India.

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Moth Flame Optimization Algorithm including Renewable Energy for Minimization of Generation & Emission Costs in Optimal Power Flow 5

Moth Flame Optimization Algorithm including Renewable Energy...

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5th Asia conference on Energy and Electrical Engineering, ACEEE 2022

作者： Alam, Mohammad Khurshed Sulaiman, Mohd Herwan Ferdowsi, Asma Sayem, Md. Shaoran Bin Khair, Nazmus Sakib Phang Pekan26600 Malaysia Faculty of Electrical & Electronics Engineering Technology American International University-Bangladesh Dhaka1229 Bangladesh Department of Pharmacology Sir Salimullah Medical College Hospital Dhaka1206 Bangladesh

ISBN: (数字)9781665499330

ISBN: (纸本)9781665499330

Optimal power flow is an approach for enhancing power system performance, scheduling, and energy management. Because of its adaptability in a variety of settings, optimum power flow is becoming increasingly vital. the demand for optimization is driven by the need for cost-effective, efficient, and optimum solutions. Optimization is useful in a variety of fields, including science, economics, and engineering. this problem must be overcome to achieve the goals while keeping the system stable. Moth Flame Optimization (MFO), a recently developed metaheuristic algorithm, will be used to solve objective functions of the OPF issue for combined cost and emission reduction in IEEE 57-bus systems with thermal and stochastic wind-solar-small hydropower producing systems. According to the data, the MFO generated the best results across all simulated research conditions. MFO, for example, offers a total cost and emission of power generation of 248.4547 /h for IEEE 57-bus systems, providing a 1.5 percent cost savings per hour above the worst values obtained when comparing approaches. According to the statistics, MFO beats the other algorithms and is a viable solution to the OPF problem. © 2022 IEEE.

关键词： Optimization

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Comparative Analysis of Optimization algorithms for Energy Consumption Minimization in HVAC Systems 23

Comparative Analysis of Optimization Algorithms for Energy C...

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Proceedings of the 5th international conference on Information Management & Machine Intelligence

作者： Shruti Maheshwari Dhanraj Shivendrakumar Dubey Vikas Gulia Nitin Solke Mechanical Engineering Department Symbiosis Institute of Technology Symbiosis International (Deemed) University India Symbiosis Institute of Technology Symbiosis International (Deemed) University India

ISBN: (纸本)9798400709418

Energy optimization is essential in the pursuit of a more sustainable future. In this research paper we describe implementation and comparison of optimization algorithms for reducing energy consumption in HVAC (Heating, Ventilation and Air Conditioning) Systems. We explore two categories of algorithms: swarm-based and physics-based. the swarm-based algorithms involve Particle Swarm Optimization, Ant Colony Optimization, Firefly Algorithm, and Cuckoo Search algorithm whereas the physics-based algorithms consist of Simulated Annealing and Gravitational Search algorithms. We apply these algorithms to optimize damper settings of an HVAC system based on real-life energy consumption data for reducing energy consumptions. Comparing the performances of these diverse algorithms, our study shows that their energy consumption is lowered by almost half. Firefly Algorithm resulted in better energy optimization when compared to other algorithms. the energy before optimization is around 508003 kWh and after optimization the energy consumption is reduced to a range of 20000-25500 kWh. Our research covers methodology used, experiences gained, and results presented as well as what those mean in context. Findings from this research reveal how optimization algorithms are capable of promoting sustainable energy practices.

关键词： HVAC System

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Opinion Mining of Customer Reviews Using Supervised Learning algorithms 5

Opinion Mining of Customer Reviews Using Supervised Learning...

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5th international conference on Electrical Information and Communication Technology, EICT 2021

作者： Arif, Shibbir Ahmed Binte Hossain, Taslima Port City International University Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9781665409063

Nowadays, a lot of people express their opinions on various topics using social networking sites. Twitter has become a famous social networking site where people can express their opinions to the point and so it has become a great source for opinion mining. In this research, the goal was to train and build a model that can automatically and accurately categorize the opinion of customer tweet reviews about popular cell phone brands. We have used python TextBlob library for getting the polarity values of all the tweet reviews of the dataset. We have also used Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree and Random Forest algorithms along with Bag of Words and TF-IDF vectorizers separately to train and build the model that will categorize the customer tweet reviews into five opinion categories: Strongly Negative, Weakly Negative, Neutral, Weakly Positive and Strongly Positive. We have observed that SVM and Logistic Regression algorithms have outperformed other algorithms with 88% accuracy using Bag of Words vectorizer while SVM algorithm has outperformed other algorithms with 87% accuracy using TF-IDF vectorizer. © 2021 IEEE.

关键词： Sentiment analysis

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