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Electrical Power Enterprise Training System Based on Interactive 3D Virtual Reality 3

Electrical Power Enterprise Training System Based on Interac...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Li, Qin Electric Power of Henan Zhengzhou China

ISBN: (纸本)9798331508456

In recent years, the electrical power enterprises require skilled personnel to operate and maintain the complex systems. The trainees need effective training for the systems to ensure safety, efficiency and reliability. The existing electrical power system faced several challenges due to complex systems and high voltage risks with limited hands-on experience. To overcome these issues, interactive 3D Virtual Reality (3D-VR) is proposed for maintaining realistic experience for the trainees. Initially, the Building Information Modelling (BIM) is utilized to generate 115 kV substation data for an accurate 3D model of electric power training system. Further, the BIM model is integrated with VR simulator, where it enables immerse experience for trainees in the substation environment. Then, the interactive 3D simulator is employed to create a realistic environment for enterprise training system to the trainers. Finally, Convolutional Neural Network (CNN) model is incorporated to evaluate the performance with complex pattern recognition. At last, the CNN model detects the unusual behaviors of trainees based on probability threshold and provides personalized feedback. From the results, the proposed interactive 3D-VR attained better results in terms usability (68.23%) and Learnability (45.32%) of when compared to existing VR. © 2025 IEEE.

关键词： Convolutional neural networks

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Analysis of Vocal Music Teaching in University Based on Artificial intelligence 3

Analysis of Vocal Music Teaching in University Based on Arti...

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3rd international conference on Artificial intelligence and Education, ICAIE 2024

作者： Han, Jinshan Army Logistics Academy Of Pla Chongqing China

ISBN: (纸本)9798400712692

With the increasing development of electronic computer information technology, concepts such as intelligent networking and big data have been introduced one after another. In this study, With the help of python tools, this paper explores optimization strategies for vocal music teaching based on machine learning, such as using convolutional neural network (CNN) and long short-term memory network (LSTM) to process and analyze audio data, and classifying and sentiment analysis of students' feedback through natural language processing technology. This article introduces the concept of artificial intelligence and its relationship with vocal music teaching. From the perspectives of intelligent teaching equipment, archival textbook materials, and auxiliary training methods, it aims to provide new ideas and development directions for the integration of artificial intelligence into vocal music teaching in universities. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Artificial intelligence Teaching Analysis Vocal teaching

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Leveraging Neural Networks with BiLSTM and CNN Models for Enhanced HR Management Practices 3

Leveraging Neural Networks with BiLSTM and CNN Models for En...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kalva, Yamini Parasar, Anuradha Pujar, Rajlaxmi Pramod Balasubramanian, K. Pasha, Zameer Ramya, S. Sri Ramachandra Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India School of Liberal Education Galgotias University Dankaur India Indira School of Business Studies Pgdm Department of Marketing Pune India Kalasalingam Academy of Research and Education-Deemed to be University Department of Computer Applications Krishnankoil India Department of Management Bengaluru India M.Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (纸本)9798331508456

The most valuable asset of any business is its employees. Even with technological advances, organizations rely heavily on human resources for system administration. An increasingly competitive sector makes managing and retaining talented workers difficult. An organization's ability to develop, survive, and compete depends on its employees' skills. These challenges can be overcome using preprocessing, feature extraction, and model training. Data preparation for machine learning is necessary yet time-consuming. Features selection is critical since it enhances accuracy and reduces overfitting. CNN-BiLSTM trained the model. The CNN- BiLSTM model outperformed BiLSTM and CNN. The model's 90.62 percent accuracy showed it could handle HR tasks. For effective human resource management, this study recommends sophisticated machine learning models. Today's businesses need reliable tools for managing and keeping people, and the CNN-BiLSTM model helps. © 2025 IEEE.

关键词： Personnel selection

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Bangla License Plate Detection and recognition Approach Based on Computer Vision for Authentic Vehicle Identification 3rd

Bangla License Plate Detection and Recognition Approach Base...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Dhar, Rajib Kumar Khanom, Fahmida Uddin, Khandaker Mohammad Mohi Imam, Md. Hasan Mostafiz, Rafid Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Mathematics American International University—Bangladesh 408/1 Kuratoli Khilkhet Dhaka1229 Bangladesh Department of Computer Science and Engineering Southeast University Dhaka1208 Bangladesh

ISBN: (纸本)9789819739363

Vehicle license plates (LPs) detection and recognition is a fundamental technique in computer vision that plays a crucial role to extract, localize, and authenticate the information of a vehicle. This paper presents an efficient method for Bangla LPs recognition and vehicle authentication using edge analysis and pattern matching algorithm in real-time nature. This method performs four modules as: preprocessing, license plate identification, character recognition, vehicle authentication. A multi-step preprocessing technique has been applied to extract and understand the potential information in case of any uneven images. To identify edge connectivity and uniformity, Canny edge detector provides more promising in contour analysis and character localization. The segmented characters are converted into texts and verified using the pattern matching algorithm by a predefined database. For this research, a database consisting of 3000 automobile license plate photos captured under different climatic circumstances has been compiled. Additionally, we have curated two additional databases consisting of templates for recognition and registered automobiles for the purpose of authentication. The proposed model can achieve accuracy of 96% in LPs detection, 99% in character recognition, and 100% in vehicle authentication by extracted texts from LPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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A Novel Methodology to Predict Flood and Landslide Disasters using Learning Assisted Artificial intelligence (AI) Logic 3

A Novel Methodology to Predict Flood and Landslide Disasters...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Raj, G. Bhupal Patan, Muzeeb Khan Gupta, Ashwani Kumar Lakshmi, C. Gopinath, D. Yadav, Ajay Singh Sr University School of Agriculture Warangal India University of Technology and Applied Sciences College of Engineering and Technology Department of Eee Muscat Oman Chandigarh University Faculty of Business and Commerce Uttar Pradesh Unnao India Saveetha Engineering College Chennai India Department of Computer Science Karnataka Bengaluru India Srm Institute of Science and Technology Department of Mathematics Delhi-NCR Campus U.P Ghaziabad India

ISBN: (纸本)9798331509675

Comprehending the dynamics of flood and landslide prediction in current and adjacent regions is crucial for the formulation and advancement of effective predictive models, conservation, and management techniques. Conventional forecasting techniques, typically dependent on statistical models and empirical data, may prove inadequate because of the intrinsic complexities and variability of environmental elements. The complex interaction of climatic conditions, hydrological processes, geological attributes, and topographical aspects complicates precise forecasting. The constraints of traditional methods highlight the pressing necessity for more advanced and efficient predictive instruments. Artificial intelligence, especially machine learning (ML) and deep learning (DL), presents a robust paradigm that utilizes sophisticated algorithms to examine extensive datasets, discern intricate patterns, and produce more precise and prompt predictions. This proposed ViT-DL model prediction ability can greatly boost catastrophe preparedness, mitigation strategies, and ultimately, preserve lives and reduce economic damages. This study will comprehensively examine contemporary research trends, emphasizing the methodology used, model efficacy, related problems, and potential future research avenues in AI-based flood and landslide prediction. The experimental results of our proposed model produces 96.6% of Accuracy and 96.31% of Precision, comparision shows our model outperforms the overall accuracy to conventions model techniques. © 2025 IEEE.

关键词： Landslides

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Deep Learning for Automated Image Captioning: A CNN and Transformer Model Analysis 3

Deep Learning for Automated Image Captioning: A CNN and Tran...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saarika, Kathula Varsha, Vasaraju Sri Harsha, P. Sheikh, Anjum Nabi Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology India

ISBN: (纸本)9798331527549

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and IEEEXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning. © 2025 IEEE.

关键词： Convolutional neural networks

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Stock Price Prediction Using Gradient Boosting machine with Technical Indicators 3

Stock Price Prediction Using Gradient Boosting Machine with ...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Jayanthi, G. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India Saveetha Nagar Tamil Nadu Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

This research endeavors to design a predictive model to forecast stock prices using a Gradient Boosting machine (GBM) regressor, specifically emphasizing the adjusted closing price to mitigate the autocorrelation issues intrinsic to time series data. The investigation encompasses a 12-month duration of historical stock data, integrating two technical indicators, namely Exponential Moving Averages (EMA-5 and EMA-10), to augment predictive precision. Gradient Boosting, recognized for its proficiency in managing intricate and non-linear data patterns, was employed to forecast forthcoming stock prices. Critical performance indicators, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2), played a role in evaluating model accuracy, attaining an R2 score of 0.99, thereby signifying an almost perfect correlation between actual and predicted prices. Beyond numerical assessments, visual examinations through scatter plots, residual plots, and prediction error distribution further elucidated the model's efficiency. The findings reveal that the amalgamation of Gradient Boosting with Technical Indicators such as EMA-5 and EMA-10, coupled with an emphasis on the adjusted closing price, proves exceptionally effective for stock price forecasting. This methodology achieved an accuracy rate nearing 99%, illustrating its substantial promise for stock market prediction and its proficiency in alleviating autocorrelation challenges within time series datasets. © 2025 IEEE.

关键词： Autocorrelation

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3rd international conference on Human and Artificial Rationalities, HAR 2024

3rd International Conference on Human and Artificial Rationa...

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3rd international conference on Human and Artificial Rationalities, HAR 2024

ISBN: (纸本)9783031845949

The proceedings contain 24 papers. The special focus in this conference is on Human and Artificial Rationalities. The topics include: Data Science and Environmental Sustainability: Mastering the Information and Knowledge;multi-agent Simulation of Violence Emergence in Protests;the Social Exchange Heuristic Operation During a One-Shot Prisoner’s Dilemma Game;development of Intention-Based Moral Judgement in Children and Adolescents with and Without Intellectual Disability;artificialization of intelligence and Embodied Cognition;Consciousness in AI: A Return to Fetishism or Technological Progress?;agent Rationality Under Partially Resolving Uncertainty and the Hurwicz Criterion;expressing Rational Agency Through Pragmatic Meta-vocabularies;the Effect of Initial Letters in Word recognition: A Phonological or Positional Effect ?;coherence as Logical Consistency;degrees of Intuition: Coherence in the New Dual Process Theory;a Tribute to Kahneman and Tversky in the Context of Mathematics Education;effect of Brief Mindfulness Training on Cognitive Rigidity;research Trends in Information Accessibility to Web Content for Adults with Developmental Disabilities: A Literature Review;NAO Robot to Help People with Alzheimer's Manage the Recall of Activities in Prospective Memory;Does Replacing the Experimenter with an Ignorant Student Robot Improve the Success of Children with ASD in the False Belief Task?;enhancing Pharmacology Education: Investigating the Efficacy of Serious Game-Based Learning Through Virtual Simulation;video-Based Analysis of the Mechanical Design Process in a Student Dyadic Activity;critical Thinking and Psychorhetoric for Promoting Environmental Awareness;the Effect of Screens on Children’s Development: Concrete Action Taken in Schools, Closer to Families;can We Really Learn in the Metaverse? A Discussion on Learning Through Immersive Technology;emotion-Enhanced Pain Assessment Protocol.

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Multimodal Facial Expression recognition using Long Short-Term Memory with Position Attention Mechanism 3

Multimodal Facial Expression Recognition using Long Short-Te...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Jyoti Vishwanath, P. Alabdeli, Haideer Sundaram, S. Meenakshi Kodati, Sarangam Sir M Visvesvaraya College of Engineering Raichur India Visvesvaraya Technological University Belagavi India Hke Society's Sir M Visvesvaraya College of Engineering Department of Electronics and Communication Engineering Raichur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Cvr College of Engineering Department of Information Technology Hyderabad India

ISBN: (纸本)9798331508456

Multimodal Facial Expression recognition (FER) integrates multiple modalities such as audio, video and text data to enhance the accuracy through capturing various aspects of human expressions. However, accurate recognition of facial expressions among audio, video and text modalities remains challenging because of a variation in expression representation. Therefore, this research proposes a Long Short-Term Memory with Position Attention Mechanism (LSTM-PAM) for multimodal FER. Initially, the Surrey Audio-Visual Expressed Emotion (SAVEE) and Multimodal Corpus of Sentiment Intensity (CMU-MOSI) datasets are used which has audio, video and text modalities. Then, the wiener filtering is applied for audio data, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is used for video data and lemmatization for text data which enhances the FER performance through reducing noise, improving contrast and standardizing textual data. The Mel-Frequency Cepstral Coefficients (MFCC) is applied for audio data, Gray-Level Co-Occurrence Matrix (GLCM) is used for video data and Word2Vector for text data which captures spectral, texture and semantic features for better accuracy in FER. Then, these extracted multimodality features are combined into a single vector. At last, the LSTM-PAM is applied for FER which provides the network to allocate various spatial positions in face images thereby enhancing the accurate recognition. The LSTM-PAM achieved accuracy of 98.25% and 95.78% for SAVEE and CMU-MOSI datasets which is better than existing approaches. © 2025 IEEE.

关键词： Image enhancement

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Intelligent Hotel Recommendation System Using Dynamic Graph Learning 3

Intelligent Hotel Recommendation System Using Dynamic Graph ...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kiruthiga, G. Kamalesh, S. Jegadeesan, S. Manojkumar, S. Kavinesh, K.K. Ruban, A.S. Karpagam College of Engineering Department of Artificial Intelligence And Data Science Coimbatore India Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Inherently, the framework of Inn Suggestion presents an entirely hybrid approach to making hotel recommendations. The framework encompasses content-based and collaborative filtering techniques into a single system by analyzing user behaviors, including past bookings and reviews, besides features, which are generally associated with hotels, such as location and price, to get relevant suggestions. At the heart of the framework lies the Adaptive Meta-Learning Hybrid algorithm that coordinates several machine learning techniques, like RNN, CNN, KNN, and Node2Vec. This meta-learning layer can recalculate the selection of algorithms dynamically based on performance in targeted subsets of users. The feature affords rapid adaptation to shifts in the user behavior and underlying data distributions, thus significantly enhancing both accuracy and diversity of recommendations over time. Key features include real-time updates, an intuitive user interface, and a robust feedback system. The technologies which architecture is based on including ***, Python, ***, and cloud services, with which scalability and security are the essence of their execution. Introducing these methodologies with AMLH, the Brilliantly Inn Suggestion Framework aims to change the entire experience of booking hotels. Which means a more dynamic personalized experience through booking in respect of each user's needs and overall satisfaction toward travel planning. © 2025 IEEE.

关键词： Collaborative filtering

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