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Light-Weight Gated Recurrent Unit for Electroencephalogram Emotion Classification

Light-Weight Gated Recurrent Unit for Electroencephalogram E...

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International Electrical engineering Congress (iEECON)

作者： Suchada Sitjongsataporn Wenzhang Du Pipat Sakarin Dept. of Electronic Engineering Mahanakorn Institute of Innovation (MII) Faculty of Engineering and Technology Mahanakorn University of Technology Bangkok Thailand Dept. of Computer Engineering Mahanakorn University of Technology International College (MUTIC) Faculty of Engineering and Technology Mahanakorn University of Technology

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This experiment investigates the impact of reducing GRU units and dense layer neurons in a lightweight GRU architecture (LW-GRU-RU) compared to a baseline GRU model for electroencephalogram (EEG) emotion classification. A baseline GRU model is used as a reference, with an optimized GRU variant utilizing feature selection through a Random Forest-based algorithm. Experiments are conducted on a labeled emotion dataset, comparing accuracy and training efficiency across five trials. Results highlight the trade-off between model complexity, accuracy, and computational efficiency, providing insights for practical applications. Both models are evaluated on an emotion dataset for accuracy and training efficiency. The lightweight model achieves a competitive accuracy of 97.486% while reducing the average training time to 0.19 seconds per epoch, showcasing its potential for efficient real-world applications.

关键词： Training Hands Accuracy Computational modeling Neurons computer architecture Brain modeling Feature extraction Electroencephalography Computational efficiency

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A Comprehensive Review of Leap Motion Controller-Based Hand Gesture Datasets

A Comprehensive Review of Leap Motion Controller-Based Hand ...

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Next Generation Electronics (NEleX), International Conference on

作者： Bharatesh Chakravarthi Prabhu Prasad B M Raju Imandi Pavan Kumar B N School of Computing and Augmented Intelligence Arizona State University Tempe Arizona USA Dept. of Computer Science and Engineering Indian Institute of Information Technology Dharwad India Dept. of Computer Science and Engineering Indian Institute of Information Technology Sri City Chittoor India

This paper comprehensively reviews hand gesture datasets based on Ultraleap's leap motion controller, a popular device for capturing and tracking hand gestures in real-time. The aim is to offer researchers and practitioners a valuable resource for developing and evaluating gesture recognition algorithms. The review compares various datasets found in the literature, considering factors such as target domain, dataset size, gesture diversity, subject numbers, and data modality. The strengths and limitations of each dataset are discussed, along with the applications and research areas in which they have been utilized. An experimental evaluation of the leap motion controller 2 device is conducted to assess its capabilities in generating gesture data for various applications, specifically focusing on touchless interactive systems and virtual reality. This review serves as a roadmap for researchers, aiding them in selecting appropriate datasets for their specific gesture recognition tasks and advancing the field of hand gesture recognition using leap motion controller technology.

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Analysis of Sentiment on Amazon Product Reviews

Analysis of Sentiment on Amazon Product Reviews

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International Conference on Secure Cyber Computing and Communication (ICSCCC)

作者： Najam Ul Saaqib Gunika Harsh Kumar Verma Dept. of Computer Science & Engg. National Institute of Technology Jalandhar India

Sentiment Analysis is an essential process in the field of NLP (Natural Language Processing) that includes identifying the sentiment or emotion behind a text. Natural Language Processing (NLP) has a rapidly growing subfield called sentiment analysis that aims to determine the sentiment or emotion underlying a given text. Using the TF/IDF (Term Frequency-Inverse Document Frequency) and Logical Regression techniques, In this study, we did a sentiment analysis on user reviews of products on Amazon. This study aims to assess the tone of Amazon customer reviews and give significant information into how customers see the items. A customer review dataset was acquired from kaggle and preprocessed to remove noise and extraneous information. Utilizing the TF/IDF technique, features were extracted from the preprocessed reviews. Then, these characteristics were utilised to train a Logistic Regression classifier to predict the reviews' sentiment. Standard performance indicators such as accuracy, In order to evaluate the performance of the classifier of Logistic Regression, precision, recall, & F1 score have been used.

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GenAI-Based News Article Analysis Chatbot 4

GenAI-Based News Article Analysis Chatbot

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Dubey, Akanksha Sharma, Rahul Diwakar, Manoj Singh, Prabhishek Mishra, Amit Kumar Lamba, Sumita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India D. Y. Patil International University Pune India Graphic Era Deemed to Be University Department of Cse Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Graphic Era Hill University Department of Cse Dehradun India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350364729

In today's era of digital landscape, data analysis plays a vital role in informed decision making and strategy formulation. Research analysts, tasked with extracting insights from vast datasets, face challenges of information overload and time-consuming manual analysis. Conventional analysis systems limited in handling large data sets,, prone to human error and analysis is based on individuals analysis expertise. Our proposed paper introduces a revolutionary research analyst tool that integrates Generative AI and a Large Language Model(LLM) within a chatbot framework. Utilizing Generative AI enhances the chatbot's communication capabilities, while LLM serves as the backbone for processing diverse linguistic structures in news articles. Our proposed architecture is detailed, outlining the methodology and efficiency of the implemented analyst tool. Utilizing language models for question-answering and a FAISS vector index for data retrieval, the tool enables seamless access to relevant information, empowering researchers, and analysis. © 2024 IEEE.

关键词： Data handling

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Implementing Newly Proposed Dynamic Tree Structure: Development, Integration, and Optimization

Implementing Newly Proposed Dynamic Tree Structure: Developm...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Pallab Banerjee Sweety Kumari Biresh Kumar Kanika Thakur Kumar Gaurav Purushottam Kumar Dept. of Computer Science Engineering Amity University Jharkhand Ranchi Jharkhand India Dept. of Computer Science Engineering Galgotia University Greater Noida campus Amity School of Information Technology Amity University Jharkhand Ranchi Jharkhand India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

An innovative data structure called a “dynamic tree” has important uses in algorithmic time complexity optimization. It creates well-organized trees by modifying the properties of several single-node trees, therefore preserving the forest structure. This structure is essential to improving the effectiveness of many algorithms. In this work, I particularly address the “Maximum Flow method,” a crucial subtopic in the larger field of dynamic trees. One basic method for solving network flow issues is the Maximum Flow Algorithm, but it frequently runs into issues with time complexity. My research attempts to overcome these obstacles by using dynamic trees to enhance the efficiency of the algorithm. This work reduces the time complexity of network flow problems, leading to more effective solutions. The findings have potential implications in a variety of computing and real-world scenarios.

关键词： Runtime Heuristic algorithms Signal processing algorithms Forestry Signal processing Data structures Prediction algorithms Time complexity Optimization Periodic structures

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Video key concept extraction using Convolution Neural Network

Video key concept extraction using Convolution Neural Networ...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Tanvir H Sardar Ruhul Amin Hazarika Bishwajeet Pandey Guru Prasad M S Sk Mahmudul Hassan Radhakrishna Dodmane Hardik Gohel Dept. of Computer Science & Engg School of Technology (GST) GITAM University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru India Department of Intelligent System and Cyber Security Astana IT University Kazakhstan Dept. of Computer Science & Engg. Graphic Era (Deemed to be University) Dehradun India School of Computer Science and Engineering VIT-AP India Department of Computer Science & Engg. NMAM Institute of Technology Nitte India Department of CSE University of Houston Victoria USA

Objectives: This work aims to develop an automated video summarising methodology and timestamping that uses natural language processing (NLP) tools to extract significant video ***: The methodology comprises extracting the audio from the video, splitting it into chunks by the size of the pauses, and transcribing the audio using Google's speech recognition. The transcribed text is tokenised to create a summary, sentence and word frequencies are calculated, and the most relevant sentences are selected. The summary quality is assessed using ROUGE criteria, and the most important keywords are extracted from the transcript using ***: Our proposed method successfully extracts key points from video lectures and creates text summaries. Timestamping these key points provides valuable context and facilitates navigation within the lecture. Our method combines video-to-text conversion and text summarisation with timestamping key concepts, offering a novel approach to video lecture analysis. Existing video analysis methods focus on keyword extraction or summarisation, while our method offers a more comprehensive approach. Our timestamped key points provide a unique feature compared to other methods. Our method enhances existing video reports by (i) providing concise summaries of key concepts and (ii) enabling quick access to specific information through timestamps. (iii) Facilitating information retrieval through a searchable index. Further research directions: (i) Improve the accuracy of the multi-stage processing pipeline. (ii) Develop techniques to handle diverse accents and pronunciations. (iii) Explore applications of the proposed method to other video genres and ***/Improvements: This approach is practical in giving accurate video summaries, saving viewers time and effort when comprehending the main concepts presented in a video.

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Multi-level Otsu thresholding and Morphological operations for Fundus Image Segmentation for Diabetic Retinopathy identification

Multi-level Otsu thresholding and Morphological operations f...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Anju Mishra Mrinal Pandey Laxman Singh Dept. of Computer Science and Technology Manav Rachna University Faridabad India Dept. of Computer Science and Engineering (AI-ML) KIET Group of Institutions Ghaziabad INDIA

Diabetic Retinopathy (DR) disease is found in eyes of diabetic patients, and become the primary cause of vision loss. Regular screening of retinal images can save vision and avoid the sightlessness situation. Although DR is not reversible disease, but if DR identified in early stages and then treatment can lower down the possibility of vision loss. Ophthalmologists can assess the disease from Optical Coherence Tomography (OCT) or Fundus Images. Assessing DR from fundus images is tedious task or may be inaccurate. computer assisted diagnosis of DR can provide sustainable assistance to ophthalmologists. Image segmentation has been accomplished by the most authoritative thresholding technique. Retinal blood vessel segmentation is the tedious task using fundus images. This paper includes two main parts, in which the author applied the Multiple preprocessing techniques, then in second part Multi-level Otsu thresholding and Morphological operations for Fundus Image Segmentation has performed and the resultant images are shared in each stage.

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An LSTM-Based Model for Stock Price Prediction

An LSTM-Based Model for Stock Price Prediction

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IEEE International Conference on engineering Education: Innovative Practices and Future Trends (AICERA)

作者： V. Sarika G. V. Siva Kamal S. V. Pratham N. V. S. S. Deepak T. Veneela Dept. of Computer Science B V Raju Institute of Technology Narsapur India

The endeavor to predict the future price of an organization's stock is referred to as stock market prediction. It is challenging to predict future trends accurately because the stock market is a dynamic system continually evolving. An impressive profit might be made by correctly predicting the price of a stock in the future. Understanding a company's stock price pattern and forecasting its future development and financial growth will be quite advantageous. The use of machine learning algorithms to forecast stock values has gained popularity in recent years. The objective of this research endeavor is to develop an artificially intelligent model capable of estimating a certain company's stock prices. The application of a sort of machine learning technique known as Long Short Term Memory, which is based on recurrent neural networks (RNNs), is the main topic of this study.

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Reliability of Electronic Connections in Fast Chargers of Electric Vehicle 1

Reliability of Electronic Connections in Fast Chargers of El...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Semwal, Priyanka Raturi Ekatpure, Shubham Rajendra Sharma, Merlyn Singh, Neelima Kumar, Anil Pandey, Bhumika Tula's Institute Dehradun Department of Electrical & Electronics Engineering Dehradun India Global Supply Chain in Semiconductor Manufacturing Kulicke and Soffa PA United States IIMT College of Engineering Department of ASH UP Grater Noida India Uttaranchal University Department of Computer Science and Engineering Dehradun India Physics Bharati Vidyapeeth's College of Engineering Dept. of Applied Science New Delhi India Bipin Tripathi Kumaon Institute of Technology Department of ECE Almora Dwarahat India

ISBN: (纸本)9798331518578

Global mobility is undergoing a dramatic change as cars fuelled by fossil fuels give way to those with zero or extremely low tailpipe emissions. Every one of the parts in big battery packs normally don't have similar properties and may corrupt essentially in view of assembling fluctuation and different elements. The performance and economics of the entire battery pack are impacted by the increased power waste from degraded battery modules. Additionally, the influence changes depending on the trip pattern. A variety of important factors, such as the age of the car, the internal resistance of the lithium-ion battery pack, awareness of grid energy pricing, and the distance driven, affect how much an electric vehicle (EV) costs to drive every day. It has used the grid's energy price data to optimise the cost of the typical driving cycle. Accurately forecasting battery behaviour reliability for the daily driving cycle is the aim of this project. The driver will be able to determine the best charging schedules, driving energy consumption, and excess battery energy with the aid of the reliability battery behaviour forecast. The driver can reduce the cost of travel both daily and over the battery's lifespan by having a previous understanding of the battery's behaviour, the grid's energy pricing, and the trip's duration. © 2025 IEEE.

关键词： Lithium-ion batteries

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SE-Resnet152 Model: Early Corn Leaf Disease Identification and Classification using Feature Based Transfer Learning Technique

SE-Resnet152 Model: Early Corn Leaf Disease Identification a...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kalpana, Ponugoti Chanti, Yerrolla Ravi, G. Regan, D. Pareek, Piyush Kumar Avn Institute of Engineering & Technology Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Sona College of Technology Dept of Ece Salem India Siddartha Institute of Science and Technology Department of Ece Puttur India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Various corn leaf diseases reduce the quantity and quality of corn crop production, so early detection and classification are important for preventing crop yield. However, the detection and classification of corn leaf disease are more difficult due to the regions of the leaf blur and noise effect. To solve the above-mentioned problems, a feature-based transfer learning approach called the Convolutional Deep Learning (CDL) model is proposed. First, the images of corn leaves taken from Plant Village are preprocessed with Green Channel Conversion (GCC) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms to enhance low-contrast images. Then the important features are extracted through ResNet152, which contains two fully connected layers with a sigmoid function. Next, squeeze and excitation are combined with ResNet152 to gain better performance in detection and classification. The proposed SE-ResNet152 model outperforms with a Precision 98.71%, Recall 98.14%, F1-score 96.87% and accuracy 99.02% respectively compared to existing model like U-Net and ResNet50 models. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Green Channel Extraction Low-Contrast Images ResNet152

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